%0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e54753 %T Sociodemographic Differences in Logins and Engagement With the Electronic Health Coach Messaging Feature of a Mobile App to Support Opioid and Stimulant Use Recovery: Results From a 1-Month Observational Study %A Filiatreau,Lindsey M %A Szlyk,Hannah %A Ramsey,Alex T %A Kasson,Erin %A Li,Xiao %A Zhang,Zhuoran %A Cavazos-Rehg,Patricia %+ Division of Infectious Diseases, School of Medicine, Washington University in St Louis, 620 S Taylor Ave, St Louis, MO, 63110, United States, 1 3142737579, flindsey@wustl.edu %K substance misuse %K substance use recovery %K opioid use disorder %K stimulant use disorder %K uptake %K engagement %K mHealth %K digital health intervention %K sociodemographic %K mobile app %K stimulant use %K observational study %K mobile health %K smartphone %K St. Louis %K eCoach messaging %K Wilcoxon rank-sum tests %K Pearson chi-square %K recovery %K app %D 2025 %7 10.4.2025 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health apps can serve as a critical tool in supporting the overall health of uninsured and underinsured individuals and groups who have been historically marginalized by the medical community and may be hesitant to seek health care. However, data on uptake and engagement with specific app features (eg, in-app messaging) are often lacking, limiting our ability to understand nuanced patterns of app use. Objective: This study aims to characterize sociodemographic differences in uptake and engagement with a smartphone app (uMAT-R) to support recovery efforts in a sample of individuals with opioid and stimulant use disorders in the Greater St. Louis area. Methods: We enrolled individuals into the uMAT-R service program from facilities providing recovery support in the Greater St. Louis area between January 2020 and April 2022. Study participants were recruited from service project enrollees. We describe the number of logins and electronic health coach (eCoach) messages participants sent in the first 30 days following enrollment using medians and IQRs and counts and proportions of those who ever (vs never) logged in and sent their eCoach a message. We compare estimates across sociodemographic subgroups, by insurance status, and for those who did and did not participate in the research component of the project using Wilcoxon rank-sum tests and Pearson chi-square tests. Results: Of all 695 participants, 446 (64.2%) logged into uMAT-R at least once during the 30 days following enrollment (median 2, IQR 0-8 logins). Approximately half of those who logged in (227/446) used the eCoach messaging feature (median 1, IQR 0-3 messages). Research participants (n=498), who could receive incentives for app engagement, were more likely to log in and use the eCoach messaging feature compared to others (n=197). Younger individuals, those with higher educational attainment, and White, non-Hispanic individuals were more likely to log in at least once compared to their counterparts. The median number of logins was higher among women, and those who were younger, employed, and not on Medicaid compared to their counterparts. Among those who logged in at least once, younger individuals and those with lower educational attainment were more likely to send at least one eCoach message compared to others. Conclusions: Mobile apps are a viable tool for supporting individuals in recovery from opioid and stimulant use disorders. However, older individuals, racial and ethnic minorities, and those with lower educational attainment may need additional login support, or benefit from alternative mechanisms of recovery support. In addition, apps may need to be tailored to achieve sustained engagement (ie, repeat logins) among men, and individuals who are older, unemployed, or on Medicaid. Older individuals and those with higher educational attainment who may be less likely to use eCoach messaging features could benefit from features tailored to their preferences. %M 40210205 %R 10.2196/54753 %U https://mhealth.jmir.org/2025/1/e54753 %U https://doi.org/10.2196/54753 %U http://www.ncbi.nlm.nih.gov/pubmed/40210205 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e57018 %T Understanding the Relationship Between Ecological Momentary Assessment Methods, Sensed Behavior, and Responsiveness: Cross-Study Analysis %A Cook,Diane %A Walker,Aiden %A Minor,Bryan %A Luna,Catherine %A Tomaszewski Farias,Sarah %A Wiese,Lisa %A Weaver,Raven %A Schmitter-Edgecombe,Maureen %K ecological momentary assessment %K smart home %K smartwatch %K cognitive assessment %K well-being %K monitoring %K monitoring behavior %K machine learning %K artificial intelligence %K app %K wearables %K sensor %K effectiveness %K accuracy %D 2025 %7 10.4.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Ecological momentary assessment (EMA) offers an effective method to collect frequent, real-time data on an individual’s well-being. However, challenges exist in response consistency, completeness, and accuracy. Objective: This study examines EMA response patterns and their relationship with sensed behavior for data collected from diverse studies. We hypothesize that EMA response rate (RR) will vary with prompt time of day, number of questions, and behavior context. In addition, we postulate that response quality will decrease over the study duration and that relationships will exist between EMA responses, participant demographics, behavior context, and study purpose. Methods: Data from 454 participants in 9 clinical studies were analyzed, comprising 146,753 EMA mobile prompts over study durations ranging from 2 weeks to 16 months. Concurrently, sensor data were collected using smartwatch or smart home sensors. Digital markers, such as activity level, time spent at home, and proximity to activity transitions (change points), were extracted to provide context for the EMA responses. All studies used the same data collection software and EMA interface but varied in participant groups, study length, and the number of EMA questions and tasks. We analyzed RR, completeness, quality, alignment with sensor-observed behavior, impact of study design, and ability to model the series of responses. Results: The average RR was 79.95%. Of those prompts that received a response, the proportion of fully completed response and task sessions was 88.37%. Participants were most responsive in the evening (82.31%) and on weekdays (80.43%), although results varied by study demographics. While overall RRs were similar for weekday and weekend prompts, older adults were more responsive during the week (an increase of 0.27), whereas younger adults responded less during the week (a decrease of 3.25). RR was negatively correlated with the number of EMA questions (r=−0.433, P<.001). Additional correlations were observed between RR and sensor-detected activity level (r=0.045, P<.001), time spent at home (r=0.174, P<.001), and proximity to change points (r=0.124, P<.001). Response quality showed a decline over time, with careless responses increasing by 0.022 (P<.001) and response variance decreasing by 0.363 (P<.001). The within-study dynamic time warping distance between response sequences averaged 14.141 (SD 11.957), compared with the 33.246 (SD 4.971) between-study average distance. ARIMA (Autoregressive Integrated Moving Average) models fit the aggregated time series with high log-likelihood values, indicating strong model fit with low complexity. Conclusions: EMA response patterns are significantly influenced by participant demographics and study parameters. Tailoring EMA prompt strategies to specific participant characteristics can improve RRs and quality. Findings from this analysis suggest that timing EMA prompts close to detected activity transitions and minimizing the duration of EMA interactions may improve RR. Similarly, strategies such as gamification may be introduced to maintain participant engagement and retain response variance. %R 10.2196/57018 %U https://mhealth.jmir.org/2025/1/e57018 %U https://doi.org/10.2196/57018 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e62962 %T Adolescent Self-Reflection Process Through Self-Recording on Multiple Health Metrics: Qualitative Study %A Cho,Minseo %A Park,Doeun %A Choo,Myounglee %A Han,Doug Hyun %A Kim,Jinwoo %+ Business Administration, School of Business, Yonsei University, Building 212, 5th Fl., 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 10 6307 2528, jinwoo@haii.io %K self-recording %K self-tracking %K self-regulation %K personal informatics %K digital health %K qualitative study %K grounded theory %K adolescents %K teenagers %K adolescent health %K self-reflection %K health metrics %K behavior change %K self-awareness %K decision-making %K mental health %K behavioral health %K health management %K semi-structured interview %D 2025 %7 9.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Self-recording is an effective behavior change technology that has long been used in diverse health contexts. Recent technological advancements have broadened its applications. While previous studies have explored its role and benefits in enhancing self-awareness and informed decision-making, relatively little attention has been given to its potential to address the multidimensional nature of health with various health metrics. Objective: This study investigates the process of self-recording in adolescent health, recognizing the connections between lifestyle behaviors and mental health. Specifically, we aim to incorporate both behavioral and emotional health metrics into the self-recording process. Grounded in self-regulation theory, we explore how adolescents record lifestyle behaviors and emotions, and how they inform and implement health management strategies. Methods: We conducted a qualitative study using the grounded theory methodology. Data were collected through individual semistructured interviews with 17 adolescents, who recorded their emotions and behaviors over 4 weeks using a prototype application. Analysis followed iterative phases of coding, constant comparison, and theme saturation. This process revealed how adolescents engage in self-recording for behaviors and emotions, as well as their failures and potential system support strategies. We further examined the relevance of the identified themes to theoretical constructs in self-regulation theory. Results: Under self-regulation theory, we gained insights into how adolescents manage their health through self-recording. The findings suggested variability in the self-recording process, in relation to specific health metrics of lifestyle behaviors and emotions. Adolescents focused on evaluating behaviors for management purposes while exploring the causes underlying emotional experiences. Throughout the health management, which involved modifying behavior or distancing from triggering factors, they monitored progress and outcomes, demonstrating a self-experimental approach. Uncertainty emerged as a barrier throughout the self-regulation process, suggesting that self-recording systems for adolescents should prioritize strategies to address these uncertainties. In addition, the self-recording system demonstrated interventional effects in aiding future planning and fostering a sense of relatedness among users. Conclusions: This study offers a theoretical framework for adolescents’ self-recording process on diverse health metrics. By integrating self-regulation theory, we suggest a stepwise process from recording lifestyle behaviors and emotions to health management behaviors. Through exploring potential features and health-supportive effects, our findings contribute to the development of digital self-recording systems that address various health metrics in adolescent health. %M 40202781 %R 10.2196/62962 %U https://www.jmir.org/2025/1/e62962 %U https://doi.org/10.2196/62962 %U http://www.ncbi.nlm.nih.gov/pubmed/40202781 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63064 %T Moving Standard Deviation of Trunk Acceleration as a Quantification Index for Physical Activities: Validation Study %A Suzuki,Takuya %A Kono,Yuji %A Ogasawara,Takayuki %A Mukaino,Masahiko %A Aoshima,Yasushi %A Furuzawa,Shotaro %A Fujita,Yurie %A Matsuura,Hirotaka %A Yamaguchi,Masumi %A Tsukada,Shingo %A Otaka,Yohei %K smart clothing %K step count %K moving standard deviation of acceleration %K MSDA %K wheelchair %K activity quantification %K physical activities %K validation study %K accelerometer %K regular gait patterns %K older people %K aging %K motor impairments %K step detection %K stroke %K hemiparesis %K measurement system %K walking %K mobility %K rehabilitation %D 2025 %7 8.4.2025 %9 %J JMIR Form Res %G English %X Background: Step count is used to quantify activity in individuals using accelerometers. However, challenges such as difficulty in detecting steps during slow or irregular gait patterns and the inability to apply this method to wheelchair (WC) users limit the broader utility of accelerometers. Alternative device-specific measures of physical activity exist, but their specificity limits cross-applicability between different device sensors. Moving standard deviation of acceleration (MSDA), obtained from truncal acceleration measurements, is proposed as another alternative variable to quantify physical activity in patients. Objective: This study aimed to evaluate the validity and feasibility of MSDA for quantifying physical activity in patients with stroke-induced hemiparesis by comparing it with the traditional step count. Methods: We enrolled 197 consecutive patients with stroke hemiparesis admitted to a convalescent rehabilitation ward. Using the hitoe system, a smart clothing–based physical activity measurement system, we measured the MSDA of trunk movement and step count. The correlation between MSDA and step count was examined in all participants. Based on their daily living mobility levels, measured using the Functional Independence Measure (FIM), participants were categorized into 6 subgroups: FIM1-4, FIM5 (WC), FIM5 (walking), FIM6 (WC), FIM6 (walking), and FIM7 (walking). Intersubgroup differences in MSDA were analyzed. Results: A strong correlation was observed between MSDA and step count (r=0.78; P<.001), with a stronger correlation in the walking group (r=0.79; P<.001) compared with the WC group (r=0.55; P<.001). The Shapiro-Wilk test indicated no significant results for MSDA across all subgroups, supporting a normal distribution within these groups. In contrast, the step count data for the WC subgroups showed significant results, indicating a deviation from a normal distribution. Additionally, 10.2% (20/197) of participants recorded zero steps, demonstrating a floor effect in the step count data. The median MSDA values for the 6 subgroups (FIM1-4, FIM5 WC, FIM5 walking, FIM6 WC, FIM6 walking, and FIM7) were 0.006, 0.007, 0.010, 0.011, 0.011, and 0.014, respectively, reflecting their levels of independence based on the FIM mobility scores. The median step counts for these subgroups were 68, 233, 1386, 367, 2835, and 4462, respectively. FIM5 participants who walked had higher step counts than FIM6 participants using WCs, though the difference was marginally but not statistically significant (P=.07), highlighting the impact of mobility type (walking vs WC). Conclusions: The results suggest the validity of MSDA as a variable for physical activity in patients with stroke, applicable to patients with stroke irrespective of their mobility measures. This finding highlights the potential of MSDA for use in individuals with motor impairments, including WC users, underscoring its broad utility in rehabilitation clinical practice. %R 10.2196/63064 %U https://formative.jmir.org/2025/1/e63064 %U https://doi.org/10.2196/63064 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e60484 %T Opportunities and Challenges Surrounding the Use of Wearable Sensor Bracelets for Infectious Disease Detection During Hajj: Qualitative Interview Study %A Maddah,Noha %A Verma,Arpana %A Ainsworth,John %+ Division of Informatics Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Centre for Health Informatics, The University of Manchester, Vaughn House, Manchester, M13 9GB, United Kingdom, 44 1612757675, noha.maddah@postgrad.manchester.ac.uk %K wearable sensor %K unified theory of acceptance and use of technology %K task-technology fit %K hajj %K presymptomatic detection %K infectious diseases %K artificial intelligence %D 2025 %7 8.4.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Wearable sensor bracelets have gained interest for their ability to detect symptomatic and presymptomatic infections through alterations in physiological indicators. Nevertheless, the use of these devices for public health surveillance among attendees of large-scale events such as hajj, the Islamic religious mass gathering held in Saudi Arabia, is currently in a nascent phase. Objective: This study aimed to explore hajj stakeholders’ perspectives on the use of wearable sensor bracelets for disease detection. Methods: We conducted a qualitative, theoretically informed, interview-based study from March 2022 to October 2023 involving a diverse sample of hajj stakeholders, including technology experts, health care providers, and hajj service providers. The study was guided by the task-technology fit model and the unified theory of acceptance and use of technology to provide a comprehensive understanding of the factors influencing the acceptance and use of the technology. Semistructured in-depth interviews were used to capture perspectives on using wearable sensor bracelets for infectious disease detection during hajj. Thematic analysis of interview transcripts was conducted. Results: A total of 14 individuals were interviewed. In total, 4 main themes and 13 subthemes emerged from the study, highlighting crucial challenges, considerations, recommendations, and opportunities in the use of wearable sensor bracelets for the presymptomatic detection of infectious diseases during hajj. Implementing wearable sensor bracelets for disease detection during hajj faces obstacles from multiple perspectives, encompassing users, implementing stakeholders, and technological factors. Hajj stakeholders were concerned about the substantial financial and operational barriers. The motivation of implementing stakeholders and users is essential for the acceptance and uptake of devices during hajj. Successful integration of wearables into the hajj surveillance system depends on several factors, including infrastructure, device features, suitable use cases, training, and a smooth organizational integration process. Conclusions: This study provides valuable insights into the potential opportunities and challenges of adopting wearable sensor bracelets for disease detection during hajj. It offers essential factors to consider and important suggestions to enhance comprehension and ensure the effective implementation of this technology. %M 40198912 %R 10.2196/60484 %U https://formative.jmir.org/2025/1/e60484 %U https://doi.org/10.2196/60484 %U http://www.ncbi.nlm.nih.gov/pubmed/40198912 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e65629 %T Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study %A Lim,Ming De %A Connie,Tee %A Goh,Michael Kah Ong %A Saedon,Nor ‘Izzati %+ Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka, 75450, Malaysia, 60 62523592, tee.connie@mmu.edu.my %K model-based features %K gait analysis %K Parkinson disease %K computer vision %K support vector machine %D 2025 %7 8.4.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that affects motor coordination, leading to gait abnormalities. Early detection of PD is crucial for effective management and treatment. Traditional diagnostic methods often require invasive procedures or are performed when the disease has significantly progressed. Therefore, there is a need for noninvasive techniques that can identify early motor symptoms, particularly those related to gait. Objective: The study aimed to develop a noninvasive approach for the early detection of PD by analyzing model-based gait features. The primary focus is on identifying subtle gait abnormalities associated with PD using kinematic characteristics. Methods: Data were collected through controlled video recordings of participants performing the timed up and go (TUG) assessment, with particular emphasis on the turning phase. The kinematic features analyzed include shoulder distance, step length, stride length, knee and hip angles, leg and arm symmetry, and trunk angles. These features were processed using advanced filtering techniques and analyzed through machine learning methods to distinguish between normal and PD-affected gait patterns. Results: The analysis of kinematic features during the turning phase of the TUG assessment revealed that individuals with PD exhibited subtle gait abnormalities, such as freezing of gait, reduced step length, and asymmetrical movements. The model-based features proved effective in differentiating between normal and PD-affected gait, demonstrating the potential of this approach in early detection. Conclusions: This study presents a promising noninvasive method for the early detection of PD by analyzing specific gait features during the turning phase of the TUG assessment. The findings suggest that this approach could serve as a sensitive and accurate tool for diagnosing and monitoring PD, potentially leading to earlier intervention and improved patient outcomes. %M 40198116 %R 10.2196/65629 %U https://aging.jmir.org/2025/1/e65629 %U https://doi.org/10.2196/65629 %U http://www.ncbi.nlm.nih.gov/pubmed/40198116 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67294 %T Detecting Sleep/Wake Rhythm Disruption Related to Cognition in Older Adults With and Without Mild Cognitive Impairment Using the myRhythmWatch Platform: Feasibility and Correlation Study %A Jones,Caleb D %A Wasilko,Rachel %A Zhang,Gehui %A Stone,Katie L %A Gujral,Swathi %A Rodakowski,Juleen %A Smagula,Stephen F %K sleep %K sleep/wake %K circadian %K activity pattern %K dementia %K cognition %K mobile sensing %K actigraphy %K accelerometer %D 2025 %7 7.4.2025 %9 %J JMIR Aging %G English %X Background: Consumer wearable devices could, in theory, provide sufficient accelerometer data for measuring the 24-hour sleep/wake risk factors for dementia that have been identified in prior research. To our knowledge, no prior study in older adults has demonstrated the feasibility and acceptability of accessing sufficient consumer wearable accelerometer data to compute 24-hour sleep/wake rhythm measures. Objective: We aimed to establish the feasibility of characterizing 24-hour sleep/wake rhythm measures using accelerometer data gathered from the Apple Watch in older adults with and without mild cognitive impairment (MCI), and to examine correlations of these sleep/wake rhythm measures with neuropsychological test performance. Methods: Of the 40 adults enrolled (mean [SD] age 67.2 [8.4] years; 72.5% female), 19 had MCI and 21 had no cognitive disorder (NCD). Participants were provided devices, oriented to the study software (myRhythmWatch or myRW), and asked to use the system for a week. The primary feasibility outcome was whether participants collected enough data to assess 24-hour sleep/wake rhythm measures (ie, ≥3 valid continuous days). We extracted standard nonparametric and extended-cosine based sleep/wake rhythm metrics. Neuropsychological tests gauged immediate and delayed memory (Hopkins Verbal Learning Test) as well as processing speed and set-shifting (Oral Trails Parts A and B). Results: All participants meet the primary feasibility outcome of providing sufficient data (≥3 valid days) for sleep/wake rhythm measures. The mean (SD) recording length was somewhat shorter in the MCI group at 6.6 (1.2) days compared with the NCD group at 7.2 (0.6) days. Later activity onset times were associated with worse delayed memory performance (β=−.28). More fragmented rhythms were associated with worse processing speed (β=.40). Conclusions: Using the Apple Watch-based myRW system to gather raw accelerometer data is feasible in older adults with and without MCI. Sleep/wake rhythms variables generated from this system correlated with cognitive function, suggesting future studies can use this approach to evaluate novel, scalable, risk factor characterization and targeted therapy approaches. %R 10.2196/67294 %U https://aging.jmir.org/2025/1/e67294 %U https://doi.org/10.2196/67294 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e65668 %T Participant Adherence in Repeated-Dose Clinical Studies Using Video-Based Observation: Retrospective Data Analysis %A Han,Seunghoon %A Song,Jihong %A Han,Sungpil %A Choi,Suein %A Lim,Jonghyuk %A Oh,Byeong Yeob %A Shin,Dongoh %+ Department of Clinical Pharmacology and Therapeutics, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea, 82 10 9484 2415, waystolove@catholic.ac.kr %K adherence %K mobile health %K self-administration %K repeated-dose clinical trials %K video-based monitoring %K mobile phone %D 2025 %7 7.4.2025 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Maintaining accurate medication records in clinical trials is essential to ensure data validity. Traditional methods such as direct observation, self-reporting, and pill counts have shown limitations that make them inaccurate or impractical. Video-based monitoring systems, available as commercial or proprietary mobile applications for smartphones and tablets, offer a promising solution to these traditional limitations. In Korea, a system applicable to the clinical trial context has been developed and used. Objective: This study aimed to evaluate the usefulness of an asynchronous video-based self-administration of the investigational medicinal product (SAI) monitoring system (VSMS) in ensuring accurate dosing and validating participant adherence to planned dosing times in repeated-dose clinical trials. Methods: A retrospective analysis was conducted using data from 17,619 SAI events in repeated-dose clinical trials using the VSMS between February 2020 and March 2023. The SAI events were classified into four categories: (1) Verified on-time dosing, (2) Verified deviated dosing, (3) Unverified dosing, and (4) Missed dosing. Analysis methods included calculating the success rate for verified SAI events and analyzing trends in difference between planned and actual dosing times (PADEV) over the dosing period and by push notification type. The mean PADEV for each subsequent dosing period was compared with the initial period using either a paired t test or a Wilcoxon signed-rank test to assess any differences. Results: A comprehensive analysis of 17,619 scheduled SAI events across 14 cohorts demonstrated a high success rate of 97% (17,151/17,619), with only 3% (468/17,619) unsuccessful due to issues like unclear video recordings or technical difficulties. Of the successful events, 99% (16,975/17,151) were verified as on-time dosing, confirming that the dosing occurred within the designated SAI time window with appropriate recorded behavior. In addition, over 90% (367/407) of participants consistently reported dosing videos on all analyzed SAI days, with most days showing over 90% objective dosing data, underscoring the system’s effectiveness in supporting accurate SAI. There were cohort differences in the tendency to dose earlier or later, but no associated cohort characteristics were identified. The initial SAI behaviors were generally sustained during the whole period of participation, with only 16% (13/79) of study days showing significant shifts in actual dosing times. Earlier deviations in SAI times were observed when only dosing notifications were used, compared with using reminders together or no notifications. Conclusions: VSMS has proven to be an effective tool for obtaining dosing information with accuracy comparable to direct observation, even in remote settings. The use of various alarm features and appropriate intervention by the investigator or observer was identified as a way to minimize adherence deterioration. It is expected that the usage and usefulness of VSMS will be continuously improved through the accumulation of experience in various medical fields. %M 40194283 %R 10.2196/65668 %U https://mhealth.jmir.org/2025/1/e65668 %U https://doi.org/10.2196/65668 %U http://www.ncbi.nlm.nih.gov/pubmed/40194283 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e56092 %T Urologists’ Estimation of Online Support Group Utilization Behavior of Their Patients With Newly Diagnosed Nonmetastatic Prostate Cancer in Germany: Predefined Secondary Analysis of a Randomized Controlled Trial %A Karschuck,Philipp %A Groeben,Christer %A Koch,Rainer %A Krones,Tanja %A Neisius,Andreas %A von Ahn,Sven %A Klopf,Christian Peter %A Weikert,Steffen %A Siebels,Michael %A Haseke,Nicolas %A Weissflog,Christian %A Baunacke,Martin %A Thomas,Christian %A Liske,Peter %A Tosev,Georgi %A Benusch,Thomas %A Schostak,Martin %A Stein,Joachim %A Spiegelhalder,Philipp %A Ihrig,Andreas %A Huber,Johannes %+ Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 420, Heidelberg, 69120, Germany, 49 6221 56 364, philipp.karschuck@med.uni-heidelberg.de %K peer support %K prostate cancer %K online support %K health services research %K randomized controlled trial %K decision aid %D 2025 %7 7.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Due to its high incidence, prostate cancer (PC) imposes a burden on Western societies. Individualized treatment decision for nonmetastatic PC (eg, surgery, radiation, focal therapy, active surveillance, watchful waiting) is challenging. The range of options might make affected persons seek peer-to-peer counseling. Besides traditional face-to-face support groups (F2FGs), online support groups (OSGs) became important, especially during COVID-19. Objective: This study aims to investigate utilization behavior and physician advice concerning F2FGs and OSGs for patients with newly diagnosed PC. We hypothesized greater importance of OSGs to support treatment decisions. We assumed that this form of peer-to-peer support is underestimated by the treating physicians. We also considered the effects of the COVID-19 pandemic. Methods: This was a secondary analysis of data from a randomized controlled trial comparing an online decision aid versus a printed brochure for patients with nonmetastatic PC. We investigated 687 patients from 116 urological practices throughout Germany before primary treatment. Of these, 308 were included before and 379 during the COVID-19 pandemic. At the 1-year follow-up visit, patients filled an online questionnaire about their use of traditional or online self-help, including consultation behaviors or attitudes concerning initial treatment decisions. We measured secondary outcomes with validated questionnaires such as Distress Thermometer and the Patient Health Questionnaire-4 items to assess distress, anxiety, and depression. Physicians were asked in a paper-based questionnaire whether patients had accessed peer-to-peer support. Group comparisons were made using chi-square or McNemar tests for nominal variables and 2-sided t tests for ordinally scaled data. Results: Before COVID-19, 2.3% (7/308) of the patients attended an F2FG versus none thereafter. The frequency of OSG use did not change significantly: OSGs were used by 24.7% (76/308) and 23.5% (89/308) of the patients before and during COVID-19, respectively. OSG users had higher levels of anxiety and depression; 38% (46/121) reported OSG as helpful for decision-making. Although 4% (19/477) of OSG nonusers regretted treatment decisions, only 0.7% (1/153) of OSG users did (P=.03). More users than nonusers reported that OSGs were mentioned by physicians (P<.001). Patients and physicians agreed that F2FGs and OSGs were not mentioned in conversations or visited by patients. For 86% (6/7) of the patients, the physician was not aware of F2FG attendance. Physicians underestimated OSG usage by 2.6% (18/687) versus 24% (165/687) of actual use (P<.001). Conclusions: Physicians are more aware of F2FGs than OSGs. Before COVID-19, F2FGs played a minor role. One out of 4 patients used OSGs. One-third considered them helpful for treatment decision-making. OSG use rarely affects the final treatment decision. Urologists significantly underestimate OSG use by their patients. Peer-to-peer support is more likely to be received by patients with anxiety and depression. Comparative interventional trials are needed to recommend peer-to-peer interventions for suitable patients. Trial Registration: German Clinical Trials Register DRKS-ID DRKS00014627; https://drks.de/search/en/trial/DRKS00014627 %M 40194272 %R 10.2196/56092 %U https://www.jmir.org/2025/1/e56092 %U https://doi.org/10.2196/56092 %U http://www.ncbi.nlm.nih.gov/pubmed/40194272 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e70149 %T Digital Health Platform for Maternal Health: Design, Recruitment Strategies, and Lessons Learned From the PowerMom Observational Cohort Study %A Ajayi,Toluwalase %A Kueper,Jacqueline %A Ariniello,Lauren %A Ho,Diana %A Delgado,Felipe %A Beal,Matthew %A Waalen,Jill %A Baca Motes,Katie %A Ramos,Edward %+ Jacobs Center for Health Innovation, Department of Medicine and Pediatrics, University of California, San Diego, 9300 Campus Point Drive, MC 7196, La Jolla, CA, 92037, United States, 1 785 218 1643, tajayi@health.ucsd.edu %K maternal health research %K digital health platforms %K pregnancy monitoring %K decentralized clinical trials %K participant engagement %K health disparities %D 2025 %7 7.4.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Maternal health research faces challenges in participant recruitment, retention, and data collection, particularly among underrepresented populations. Digital health platforms like PowerMom (Scripps Research) offer scalable solutions, enabling decentralized, real-world data collection. Using innovative recruitment and multimodal techniques, PowerMom engages diverse cohorts to gather longitudinal and episodic data during pregnancy and post partum. Objective: This study aimed to evaluate the design, implementation, and outcomes of the PowerMom research platform, with a focus on participant recruitment, engagement, and data collection across diverse populations. Secondary objectives included identifying challenges encountered during implementation and deriving lessons to inform future digital maternal health studies. Methods: Participants were recruited via digital advertisements, pregnancy apps, and the PowerMom Consortium of more than 15 local and national organizations. Data collection included self-reported surveys, wearable devices, and electronic health records. Anomaly detection measures were implemented to address fraudulent enrollment activity. Recruitment trends and descriptive statistics from survey data were analyzed to summarize participant characteristics, assess engagement metrics, and quantify missing data to identify gaps. Results: Overall, 5617 participants were enrolled from 2021 to 2024, with 69.8% (n=3922) providing demographic data. Of these, 48.5% (2723/5617) were younger than 35 years, 14% (788/5617) identified as Hispanic or Latina, and 13.7% (770/5617) identified as Black or African American. Geographic representation spanned all 50 US states, Puerto Rico, and Guam, with 58.3% (3276/5617) residing in areas with moderate access to maternity care and 16.4% (919/5617) in highly disadvantaged neighborhoods based on the Area Deprivation Index. Enrollment rates increased substantially over the study period, from 55 participants in late 2021 to 3310 in 2024, averaging 99.4 enrollments per week in 2024. Participants completed a total of 17,123 surveys, with 71.8% (4033/5617) completing the Intake Survey and 12.4% (697/5617) completing the Postpartum Survey. Wearable device data were shared by 1168 participants, providing more than 378,000 daily biometric measurements, including activity levels, sleep, and heart rate. Additionally, 96 participants connected their electronic health records, contributing 276 data points such as diagnoses, medications, and laboratory results. Among pregnancy-related characteristics, 28.1% (1578/5617) enrolled during the first trimester, while 15.1% (849/5617) reported information about the completion of their pregnancies during the study period. Among the 913 participants who shared delivery information, 56.1% (n=512) had spontaneous vaginal deliveries and 17.9% (n=163) underwent unplanned cesarean sections. Conclusions: The PowerMom platform demonstrates the feasibility of using digital tools to recruit and engage diverse populations in maternal health research. Its ability to integrate multimodal data sources showcases its potential to provide comprehensive maternal-fetal health insights. Challenges with data completeness and survey attrition underscore the need for sustained participant engagement strategies. These findings offer valuable lessons for scaling digital health platforms and addressing disparities in maternal health research. Trial Registration: ClinicalTrials.gov NCT03085875; https://clinicaltrials.gov/study/NCT03085875 %M 40194282 %R 10.2196/70149 %U https://formative.jmir.org/2025/1/e70149 %U https://doi.org/10.2196/70149 %U http://www.ncbi.nlm.nih.gov/pubmed/40194282 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e63105 %T Modernizing the Staging of Parkinson Disease Using Digital Health Technology %A Templeton,John Michael %A Poellabauer,Christian %A Schneider,Sandra %A Rahimi,Morteza %A Braimoh,Taofeek %A Tadamarry,Fhaheem %A Margolesky,Jason %A Burke,Shanna %A Al Masry,Zeina %+ , Bellini College of Artificial Intelligence, Cybersecurity, and Computing, University of South Florida, 4202 E Fowler Ave, Tampa, FL, 33620, United States, 1 813 396 0962, jtemplet@usf.edu %K digital health %K Parkinson disease %K disease classification %K wearables %K personalized medicine %K neurocognition %K artificial intelligence %K AI %D 2025 %7 4.4.2025 %9 Viewpoint %J J Med Internet Res %G English %X Due to the complicated nature of Parkinson disease (PD), a number of subjective considerations (eg, staging schemes, clinical assessment tools, or questionnaires) on how best to assess clinical deficits and monitor clinical progression have been published; however, none of these considerations include a comprehensive, objective assessment of all functional areas of neurocognition affected by PD (eg, motor, memory, speech, language, executive function, autonomic function, sensory function, behavior, and sleep). This paper highlights the increasing use of digital health technology (eg, smartphones, tablets, and wearable devices) for the classification, staging, and monitoring of PD. Furthermore, this Viewpoint proposes a foundation for a new staging schema that builds from multiple clinically implemented scales (eg, Hoehn and Yahr Scale and Berg Balance Scale) for ease and homogeneity, while also implementing digital health technology to expand current staging protocols. This proposed staging system foundation aims to provide an objective, symptom-specific assessment of all functional areas of neurocognition via inherent device capabilities (eg, device sensors and human-device interactions). As individuals with PD may manifest different symptoms at different times across the spectrum of neurocognition, the modernization of assessments that include objective, symptom-specific monitoring is imperative for providing personalized medicine and maintaining individual quality of life. %M 40184612 %R 10.2196/63105 %U https://www.jmir.org/2025/1/e63105 %U https://doi.org/10.2196/63105 %U http://www.ncbi.nlm.nih.gov/pubmed/40184612 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e63090 %T Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study %A Kriara,Lito %A Dondelinger,Frank %A Capezzuto,Luca %A Bernasconi,Corrado %A Lipsmeier,Florian %A Galati,Adriano %A Lindemann,Michael %+ , F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, CH-4070, Switzerland, 41 61 687 10 20, lito.kriara@roche.com %K Floodlight Open %K multiple sclerosis %K smartphone %K sensors %K mobile phone %K wearable electronic devices %K digital health %K equivalence %K device equivalence %K cognition %K gait %K upper extremity function %K hand motor function %K balance %K digital biomarker %K variability %K mHealth %K mobile health %K autoimmune disease %K motor %K digital assessment %D 2025 %7 3.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Floodlight Open is a global, open-access, fully remote, digital-only study designed to understand the drivers and barriers in deployment and persistence of use of a smartphone app for measuring functional impairment in a naturalistic setting and broad study population. Objective: This study aims to assess measurement equivalence properties of the Floodlight Open app across operating system (OS) platforms, OS versions, and smartphone device models. Methods: Floodlight Open enrolled adult participants with and without self-declared multiple sclerosis (MS). The study used the Floodlight Open app, a “bring-your-own-device” (BYOD) solution that remotely measured MS-related functional ability via smartphone sensor–based active tests. Measurement equivalence was assessed in all evaluable participants by comparing the performance on the 6 active tests (ie, tests requiring active input from the user) included in the app across OS platforms (iOS vs Android), OS versions (iOS versions 11-15 and separately Android versions 8-10; comparing each OS version with the other OS versions pooled together), and device models (comparing each device model with all remaining device models pooled together). The tests in scope were Information Processing Speed, Information Processing Speed Digit-Digit (measuring reaction speed), Pinching Test (PT), Static Balance Test, U-Turn Test, and 2-Minute Walk Test. Group differences were assessed by permutation test for the mean difference after adjusting for age, sex, and self-declared MS disease status. Results: Overall, 1976 participants using 206 different device models were included in the analysis. Differences in test performance between subgroups were very small or small, with percent differences generally being ≤5% on the Information Processing Speed, Information Processing Speed Digit-Digit, U-Turn Test, and 2-Minute Walk Test; <20% on the PT; and <30% on the Static Balance Test. No statistically significant differences were observed between OS platforms other than on the PT (P<.001). Similarly, differences across iOS or Android versions were nonsignificant after correcting for multiple comparisons using false discovery rate correction (all adjusted P>.05). Comparing the different device models revealed a statistically significant difference only on the PT for 4 out of 17 models (adjusted P≤.001-.03). Conclusions: Consistent with the hypothesis that smartphone sensor–based measurements obtained with different devices are equivalent, this study showed no evidence of a systematic lack of measurement equivalence across OS platforms, OS versions, and device models on 6 active tests included in the Floodlight Open app. These results are compatible with the use of smartphone-based tests in a bring-your-own-device setting, but more formal tests of equivalence would be needed. %M 40179369 %R 10.2196/63090 %U https://www.jmir.org/2025/1/e63090 %U https://doi.org/10.2196/63090 %U http://www.ncbi.nlm.nih.gov/pubmed/40179369 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59878 %T Validation of Ecological Momentary Assessment With Reference to Accelerometer Data: Repeated-Measures Panel Study With Multilevel Modeling %A Noh,Jung Min %A Im,SongHyun %A Park,JooYong %A Kim,Jae Myung %A Lee,Miyoung %A Choi,Ji-Yeob %+ Department of Biomedical Sciences, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 02 740 8922, jiyeob.choi@gmail.com %K telemedicine %K wearable electronic devices %K physical activity %K mobile phone %K wearables %K smartphones %K ecological momentary assessment %K EMA %K global physical activity questionnaire %K GPAQ %K Bouchard’s physical activity %K multilevel modeling %K females %K women %K males %K men %K sensors %K evaluation %K comparative %K South Korea %D 2025 %7 1.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: There is growing interest in the real-time assessment of physical activity (PA) and physiological variables. Acceleration, particularly those collected through wearable sensors, has been increasingly adopted as an objective measure of physical activity. However, sensor-based measures often pose challenges for large-scale studies due to their associated costs, inability to capture contextual information, and restricted user populations. Smartphone-delivered ecological momentary assessment (EMA) offers an unobtrusive and undemanding means to measure PA to address these limitations. Objective: This study aimed to evaluate the usability of EMA by comparing its measurement outcomes with 2 self-report assessments of PA: Global Physical Activity Questionnaire (GPAQ) and a modified version of Bouchard Physical Activity Record (BAR). Methods: A total of 235 participants (137 female, 98 male, and 94 repeated) participated in one or more 7-day studies. Waist-worn sensors provided by ActiGraph captured accelerometer data while participants completed 3 self-report measures of PA. The multilevel modeling method was used with EMA, GPAQ, and BAR as separate measures, with 6 subdomains of physiological activity (overall PA, overall excluding occupational, transport, exercise, occupational, and sedentary) to model accelerometer data. In addition, EMA and GPAQ were further compared with 6 domains of PA from the BAR as outcome measures. Results: Among the 3 self-reporting instruments, EMA and BAR exhibited better overall performance in modeling the accelerometer data compared to GPAQ (eg EMA daily: β=.387, P<.001; BAR daily: β=.394, P<.001; GPAQ: β=.281, P<.001, based on repeated-only participants with step counts from accelerometer as dependent variables). Conclusions: Multilevel modeling on 3 self-report assessments of PA indicates that smartphone-delivered EMA is a valid and efficient method for assessing PA. %M 40168069 %R 10.2196/59878 %U https://www.jmir.org/2025/1/e59878 %U https://doi.org/10.2196/59878 %U http://www.ncbi.nlm.nih.gov/pubmed/40168069 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e57599 %T Accelerometry-Assessed Physical Activity and Circadian Rhythm to Detect Clinical Disability Status in Multiple Sclerosis: Cross-Sectional Study %A Bou Rjeily,Nicole %A Sanjayan,Muraleetharan %A Guha Niyogi,Pratim %A Dewey,Blake E %A Zambriczki Lee,Alexandra %A Hulett,Christy %A Dagher,Gabriella %A Hu,Chen %A Mazur,Rafal D %A Kenney,Elena M %A Brennan,Erin %A DuVal,Anna %A Calabresi,Peter A %A Zipunnikov,Vadim %A Fitzgerald,Kathryn C %A Mowry,Ellen M %K multiple sclerosis %K disability %K progressive %K physical activity %K circadian rhythm %K accelerometer %K ActiGraph %K accelerometry %D 2025 %7 31.3.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Tools for measuring clinical disability status in people with multiple sclerosis (MS) are limited. Accelerometry objectively assesses physical activity and circadian rhythmicity profiles in the real-world environment and may potentially distinguish levels of disability in MS. Objective: This study aims to determine if accelerometry can detect differences in physical activity and circadian rhythms between relapsing-remitting multiple sclerosis (RRMS) and progressive multiple sclerosis (PMS) and to assess the interplay within person between the 2 domains of physical activity (PA) and circadian rhythm (CR) in relation to MS type. Methods: This study represents an analysis of the baseline data from the prospective HEAL-MS (home-based evaluation of actigraphy to predict longitudinal function in multiple sclerosis) study. Participants were divided into 3 groups based on the Expanded Disability Status Scale (EDSS) criteria for sustained disability progression: RRMS-Stable, RRMS-Suspected progression, and PMS. Baseline visits occurred between January 2021 and March 2023. Clinical outcome measures were collected by masked examiners. Participants wore the GT9X Link ActiGraph on their nondominant wrists for 2 weeks. After adjusting for age, sex, and BMI, a logistic regression model was fitted to evaluate the association of each accelerometry metric with odds of PMS versus RRMS. We also evaluated the association of accelerometry metrics in differentiating the 2 RRMS subtypes. The Joint and Individual Variation Explained (JIVE) model was used to assess the codependencies between the PA and CR domains and their joint and individual association with MS subtype. Results: A total of 253 participants were included: 86 with RRMS-Stable, 82 with RRMS-Suspected progression, and 85 with PMS. Compared to RRMS, participants with PMS had lower total activity counts (β=−0.32, 95% CI −0.61 to −0.03), lower time spent in moderate to vigorous physical activity (β=−0.01, 95% CI −0.02 to −0.004), higher active-to-sedentary transition probability (β=5.68, 95% CI 1.86-9.5), lower amplitude (β=−0.0004, 95% CI −0.0008 to −0.0001), higher intradaily variability (β=4.64, 95% CI 1.45-7.84), and lower interdaily stability (β=−4.43, 95% CI −8.77 to −0.10). Using the JIVE model for PA and CR domains, PMS had higher first joint component (β=0.367, 95% CI 0.088-0.656), lower PA-1 component (β=−0.441, 95% CI −0.740 to −0.159), and lower PA-2 component (β=−0.415, 95% CI −0.717 to −0.126) compared to RRMS. No significant differences were detected between the 2 RRMS subtypes except for lower relative amplitude in those with suspected progression (β=−5.26, 95% CI −10.80 to −0.20). Conclusions: Accelerometry detected differences in physical activity patterns between RRMS and PMS. More advanced analytic techniques may help discern differences between the 2 RRMS subgroups. Longitudinal follow-up is underway to assess the potential for accelerometry to detect or predict disability progression. %R 10.2196/57599 %U https://mhealth.jmir.org/2025/1/e57599 %U https://doi.org/10.2196/57599 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e69001 %T Convergent and Known-Groups Validity and Sensitivity to Change of the Virtual Performance Measure in Patients With Hip and Knee Osteoarthritis: Longitudinal Study %A Razmjou,Helen %A Denis,Suzanne %A Robarts,Susan %A Wainwright,Amy %A Dickson,Patricia %A Murnaghan,John %+ Sunnybrook Health Science Centre, University of Toronto, 43 Wellesley Street East, Toronto, ON, M4Y 1H1, Canada, helen.razmjou@sunnybrook.ca %K virtual %K video-based outcome %K longitudinal validity %K sensitivity to change %K osteoarthritis %D 2025 %7 28.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Subsequent to the COVID-19 pandemic in 2020, a different approach to health care utilization was required to improve safety and efficiency. In the postpandemic era, virtual care and remote assessment of musculoskeletal conditions has become more common, and examining the accuracy of these remote encounters remains vital. In 2023, an innovative, video-based tool—the Virtual Performance Measure (VPM)—was introduced to assess the functional difficulties of patients with osteoarthritis of the knee joint. Further validation of this tool is warranted to expand its application longitudinally and in more diverse populations. Objective: This study examined the longitudinal validity of the VPM, a digitally based outcome tool, in patients with osteoarthritis of the hip and knee joints who had undergone arthroplasty. Methods: Patients completed a web-based survey after watching 40 videos that demonstrated 10 functional tasks with increasing difficulty, prior to and at approximately 3-5 months following surgery. The Lower Extremity Functional Scale (LEFS) was used as the reference measure. Longitudinal convergent and known-groups validity as well as sensitivity to change were assessed. Results: The data of 120 patients (n=80, 67% female; mean age 67, SD 9 years; n=58, 48% with hip osteoarthritis and n=62, 52% with knee osteoarthritis) were examined. There was a statistically significant improvement in both LEFS (t119=16.04, P<.001) and VPM total scores (t119=13.92, P<.001) over time. The correlation between the postoperative LEFS and VPM scores was higher (r=0.66; P<.001) than the correlation between the change scores of these measures (r=0.51; P<.001). The area under the curve value for the VPM’s ability to differentiate between urgent and nonurgent candidates for surgery was 0.71 (95% CI 0.57-0.84). Sensitivity to change as measured by the standardized response mean was 1.27 (95% CI 1.09-1.45), indicating good ability to detect change over time. Conclusions: The VPM demonstrated sufficient longitudinal convergent and known-groups validity as well as sensitivity to change in patients with hip and knee osteoarthritis following arthroplasty. This tool has a potential to improve the delivery of care by increasing access, reducing the frequency of in-person visits, and improving the overall efficiency of the health care system following a major surgery. %M 40153784 %R 10.2196/69001 %U https://formative.jmir.org/2025/1/e69001 %U https://doi.org/10.2196/69001 %U http://www.ncbi.nlm.nih.gov/pubmed/40153784 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e67861 %T Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation %A Brown,Jeffrey %A Mitchell,Zachary %A Jiang,Yu Albert %A Archdeacon,Ryan %K snore detection %K snore tracking %K machine learning %K SleepWatch %K Bodymatter %K neural net %K mobile device %K smartphone %K smartphone application %K mobile health %K sleep monitoring %K sleep tracking %K sleep apnea %D 2025 %7 28.3.2025 %9 %J JMIR Form Res %G English %X Background: High-quality sleep is essential for both physical and mental well-being. Insufficient or poor-quality sleep is linked to numerous health issues, including cardiometabolic diseases, mental health disorders, and increased mortality. Snoring—a prevalent condition—can disrupt sleep and is associated with disease states, including coronary artery disease and obstructive sleep apnea. Objective: The SleepWatch smartphone app (Bodymatter, Inc) aims to monitor and improve sleep quality and has snore detection capabilities that were built through a machine-learning process trained on over 60,000 acoustic events. This study evaluated the accuracy of the SleepWatch snore detection algorithm in a simulated real-world setting. Methods: The snore detection algorithm was tested by using 36 simulated snoring audio files derived from 18 participants. Each file simulated a snoring index between 30 and 600 snores per hour. Additionally, 9 files with nonsnoring sounds were tested to evaluate the algorithm’s capacity to avoid false positives. Sensitivity, specificity, and accuracy were calculated for each test, and results were compared by using Bland-Altman plots and Spearman correlation to assess the statistical association between detected and actual snores. Results: The SleepWatch algorithm showed an average sensitivity of 86.3% (SD 16.6%), an average specificity of 99.5% (SD 10.8%), and an average accuracy of 95.2% (SD 5.6%) across the snoring tests. The positive predictive value and negative predictive value were 98.9% (SD 2.6%) and 93.8% (SD 14.4%) respectively. The algorithm performed exceptionally well in avoiding false positives, with a specificity of 97.1% (SD 3.5%) for nonsnoring files. Inclusive of all snoring and nonsnore tests, the aggregated accuracy for all trials in this bench study was 95.6% (SD 5.3%). The Bland-Altman analysis indicated a mean bias of −29.8 (SD 41.7) snores per hour, and the Spearman correlation analysis revealed a strong positive correlation (rs=0.974; P<.001) between detected and actual snore rates. Conclusions: The SleepWatch snore detection algorithm demonstrates high accuracy and compares favorably with other snore detection apps. Aside from its broader use in sleep monitoring, SleepWatch demonstrates potential as a tool for identifying individuals at risk for sleep-disordered breathing, including obstructive sleep apnea, on the basis of the snoring index. %R 10.2196/67861 %U https://formative.jmir.org/2025/1/e67861 %U https://doi.org/10.2196/67861 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66123 %T Toward Unsupervised Capacity Assessments for Gait in Neurorehabilitation: Validation Study %A Naef,Aileen C %A Duarte,Guichande %A Neumann,Saskia %A Shala,Migjen %A Branscheidt,Meret %A Easthope Awai,Chris %+ Data Analytics & Rehabilitation Technology (DART), Lake Lucerne Institute, Rubistrasse 9, Vitznau, Switzerland, 41 77 466 81 35, chris.awai@llui.org %K gait analysis %K gait rehabilitation %K 10-meter walk test %K stroke %K unsupervised assessments %K supervised assessments %K sensors %K motivation %K capacity %K monitoring %K wearables %K stroke survivors %K quality of life %D 2025 %7 26.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Gait impairments are common in stroke survivors, negatively impacting their overall quality of life. Therefore, gait rehabilitation is often targeted during in-clinic rehabilitation. While standardized assessments are available for inpatient evaluation, the literature often reports variable results when these assessments are conducted in a home environment. Several factors, such as the presence of an observer, the environment itself, or the technology used, may contribute to these differing results. Therefore, it is relevant to establish unsupervised capacity assessments for both in-clinic use and across the continuum of care. Objective: This study aimed to investigate the effect of supervision on the outcomes of a sensor-based 10-meter walk test conducted in a clinical setting, maintaining a controlled environment and setup. Methods: In total, 21 stroke survivors (10 female, 11 male; age: mean 63.9, SD 15.5 years) were assigned alternately to one of two data collection sequences and tested over 4 consecutive days, alternating between supervised test (ST) and unsupervised test (UST) assessments. For both assessments, participants were required to walk a set distance of 10 meters as fast as possible while data were collected using a single wearable sensor (Physilog 5) attached to each shoe. After each walking assessment, the participants completed the Intrinsic Motivation Inventory. Statistical analyses were conducted to examine the mean speed, stride length, and cadence, across repeated measurements and between assessment conditions. Results: The intraclass correlation coefficient indicated good to excellent reliability for speed (ST: κ=0.93, P<.001; UST: κ=0.93, P<.001), stride length (ST: κ=0.92, P<.001; UST: κ=0.88, P<.001), and cadence (ST: κ=0.91, P<.001; UST: κ=0.95, P<.001) across repeated measurements for both ST and UST assessments. There was no significant effect of testing order (ie, sequence A vs B). Comparing ST and UST, there were no significant differences in speed (t39=–0.735, P=.47, 95% CI 0.06-0.03), stride length (z=0.835, P=.80), or cadence (t39=–0.501, P=.62, 95% CI 3.38-2.04) between the 2 assessments. The overall motivation did not show any significant differences between the ST and UST conditions (P>.05). However, the self-reported perceived competence increased during the unsupervised assessment from the first to the second measurement. Conclusions: Unsupervised gait capacity assessments offer a reliable alternative to supervised assessments in a clinical environment, showing comparable results for gait speed, stride length, and cadence, with no differences in overall motivation between the two. Future work should build upon these findings to extend unsupervised assessment of both capacity and performance in home environments. Such assessments could allow improved and more specific tracking of rehabilitation progress across the continuum of care. %M 40138688 %R 10.2196/66123 %U https://www.jmir.org/2025/1/e66123 %U https://doi.org/10.2196/66123 %U http://www.ncbi.nlm.nih.gov/pubmed/40138688 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e69506 %T Physiological Sensors Equipped in Wearable Devices for Management of Long COVID Persisting Symptoms: Scoping Review %A Kukreti,Shikha %A Lu,Meng-Ting %A Yeh,Chun-Yin %A Ko,Nai-Ying %+ Department of Nursing, College of Medicine, National Cheng Kung University, No.1, Dasyue Rd, East District, Tainan City, 701, Taiwan, 886 062353535, konaiying@gmail.com %K wearable devices %K long COVID %K physiological sensors %K review %K COVID %K COVID-19 %D 2025 %7 26.3.2025 %9 Review %J J Med Internet Res %G English %X Background: Wearable technology has evolved in managing COVID-19, offering early monitoring of key physiological parameters. However, the role of wearables in tracking and managing long COVID is less understood and requires further exploration of their potential. Objective: This review assessed the application and effectiveness of wearable devices in managing long COVID symptoms, focusing on commonly used sensors and their potential for improving long-term patient care. Methods: A literature search was conducted across databases including PubMed, Embase, Web of Science, and Cochrane Central, adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guidelines. The search was updated regularly throughout 2024. Abstract and full-text screening and selection were facilitated using Rayyan software developed by Qatar Computing Research Institute. Quality appraisal was conducted using the Joanna Briggs Institute (JBI) critical appraisal tool to ensure the methodological rigor of the included studies. Data were extracted on study characteristics, wearable devices, sensors used, and monitored physiological parameters, and the results were synthesized in a narrative format. Results: A total of 1186 articles were identified, and after duplicate removal and screening, 15 studies were initially included, with 11 studies meeting the criteria for final data synthesis. The included studies varied in design, ranging from observational to interventional trials, and involved sample sizes from 3 to 17,667 participants across different countries. In total, 10 different wearable devices were used to monitor long COVID symptoms, capturing key metrics such as heart rate variability, body temperature, sleep, and physical activity. Smartwatches were the most used wearable devices and fitness trackers with electrocardiography and photoplethysmography sensors were used to monitor heart rate, oxygen saturation, and respiratory rate. Of the 10 devices, 4 were Food and Drug Administration–approved, emphasizing the reliability and validation of the physiological data collected. Studies were primarily conducted in the United States and Europe, reflecting significant regional research interest in wearable technology for long COVID management. Conclusions: This review highlights the potential of wearable technology in providing continuous and personalized monitoring for long COVID patients. Although wearables show promise in tracking persistent symptoms, further research is needed to improve usability, validate long-term efficacy, and enhance patient engagement. %M 40137051 %R 10.2196/69506 %U https://www.jmir.org/2025/1/e69506 %U https://doi.org/10.2196/69506 %U http://www.ncbi.nlm.nih.gov/pubmed/40137051 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e52650 %T The Utility of a Smartphone-Based Retinal Imaging Device as a Screening Tool in an Outpatient Clinic Setting: Protocol for an Observational Study %A Mittal,Ajay %A Sanchez,Victor %A Azad,Navjot Singh %A Zuyev,Yaroslav %A Robles,Rafael %A Sherwood,Mark %+ University of Florida College of Medicine, 1600 SW Archer Rd, Gainesville, FL, 32610, United States, 1 3526158883, ajaymittal2400@gmail.com %K digital health %K digital ophthalmoscope %K ophthalmology %K smartphone-based %K mobile health %K applications %K screening tool %K retinal imaging device %K glaucoma %K eye disease %K visual problems %K ophthalmoscope %K ocular disease %K cost-effective %K mobile phone %D 2025 %7 25.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Glaucoma, a disease leading to the degeneration of retinal ganglion cells, results in changes to the optic nerve head that are often diagnosed late when visual problems arise. With the prevalence of glaucoma surpassing 76 million adults worldwide and with glaucoma being the leading cause of irreversible blindness in the world, the early detection and management of glaucoma is imperative. Digital ophthalmoscopes, such as the D-EYE (D-EYE, Srl), have emerged as a technology that uses smartphone cameras with an attachment on the lens to visualize the retina and optic nerve head without the need for dilation. The purpose of this pilot study is to examine the acceptability and feasibility of a D-EYE digital ophthalmoscope to screen for ocular pathology involving the optic nerve, particularly glaucoma. Objective: This study aimed to demonstrate the effect of a smartphone-based ophthalmoscope as a potential vision screening tool for optic nerve head pathology in participants enrolled in this study. The first specific aim was to determine the ability of the D-EYE smartphone ophthalmoscope to gather high-quality imaging to be used for grading the fundus into low- and high-risk categories for eye pathology. The second specific aim was to determine the difference in the quality of data capture between still retinal images and 30-second retinal video recordings produced by D-EYE smartphone ophthalmoscopes. Methods: This observational pilot study enrolled 110 patients receiving routine eye care at the University of Florida Health from February 2019 to February 2022 to assess the use of the D-EYE device in capturing still images and 30-second videos of the bilateral retina and optic nerves of each participant. Study participants completed a survey to gather demographics and past medical history data with a particular focus on previous eye health history. Images were reviewed by 5 ophthalmology residents with interrater reliability analysis performed to assess findings. Results: Ophthalmology resident review indicated greater visualizability and clarity of the bilateral retina and optic nerves with 30-second videos of retinal imaging compared with still-image ophthalmic capture. Furthermore, an increase in visualizability and clarity allowed for a more accurate measurement of the cup-to-disc ratio, a diagnostic marker for glaucoma. In addition, the likelihood of referral of the glaucomatous and healthy sample groups to ophthalmologists indicated a greater sensitivity of digital ophthalmoscopes in being able to detect retinal abnormalities requiring early intervention and management, supporting the technology’s use as a screening tool. Conclusions: This investigation suggests that the use of smartphone-based digital ophthalmoscopes can be more effectively applied as a screening tool by capturing 30-second videos compared with still images alone. This novel assessment of an emerging technology in the field of ophthalmology may better equip further research as smartphone camera technology advances. International Registered Report Identifier (IRRID): DERR1-10.2196/52650 %M 40132180 %R 10.2196/52650 %U https://www.researchprotocols.org/2025/1/e52650 %U https://doi.org/10.2196/52650 %U http://www.ncbi.nlm.nih.gov/pubmed/40132180 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 4 %N %P e59094 %T Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review %A Dawadi,Research %A Inoue,Mai %A Tay,Jie Ting %A Martin-Morales,Agustin %A Vu,Thien %A Araki,Michihiro %+ Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17, Senrioka-Shinmachi, Settsu, Osaka, 566-0002, Japan, 81 661701069 ext 31234, dawadi-research@nibiohn.go.jp %K literature review %K machine learning %K smartphone %K health diagnosis %D 2025 %7 25.3.2025 %9 Review %J JMIR AI %G English %X Background: The application of machine learning methods to data generated by ubiquitous devices like smartphones presents an opportunity to enhance the quality of health care and diagnostics. Smartphones are ideal for gathering data easily, providing quick feedback on diagnoses, and proposing interventions for health improvement. Objective: We reviewed the existing literature to gather studies that have used machine learning models with smartphone-derived data for the prediction and diagnosis of health anomalies. We divided the studies into those that used machine learning models by conducting experiments to retrieve data and predict diseases, and those that used machine learning models on publicly available databases. The details of databases, experiments, and machine learning models are intended to help researchers working in the fields of machine learning and artificial intelligence in the health care domain. Researchers can use the information to design their experiments or determine the databases they could analyze. Methods: A comprehensive search of the PubMed and IEEE Xplore databases was conducted, and an in-house keyword screening method was used to filter the articles based on the content of their titles and abstracts. Subsequently, studies related to the 3 areas of voice, skin, and eye were selected and analyzed based on how data for machine learning models were extracted (ie, the use of publicly available databases or through experiments). The machine learning methods used in each study were also noted. Results: A total of 49 studies were identified as being relevant to the topic of interest, and among these studies, there were 31 different databases and 24 different machine learning methods. Conclusions: The results provide a better understanding of how smartphone data are collected for predicting different diseases and what kinds of machine learning methods are used on these data. Similarly, publicly available databases having smartphone-based data that can be used for the diagnosis of various diseases have been presented. Our screening method could be used or improved in future studies, and our findings could be used as a reference to conduct similar studies, experiments, or statistical analyses. %M 40132187 %R 10.2196/59094 %U https://ai.jmir.org/2025/1/e59094 %U https://doi.org/10.2196/59094 %U http://www.ncbi.nlm.nih.gov/pubmed/40132187 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63184 %T Smartwatch-Based Ecological Momentary Assessment for High-Temporal-Density, Longitudinal Measurement of Alcohol Use (AlcoWatch): Feasibility Evaluation %A Stone,Chris %A Adams,Sally %A Wootton,Robyn E %A Skinner,Andy %+ School of Psychological Science, University of Bristol, 12a Priory Road, Bristol, BS8 1TU, United Kingdom, 44 07983 317748, cstone2@btinternet.com %K smartwatch %K ecological momentary assessment %K μEMA %K alcohol %K ALSPAC %D 2025 %7 25.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Ecological momentary assessment methods have recently been adapted for use on smartwatches. One particular class of these methods, developed to minimize participant burden and maximize engagement and compliance, is referred to as microinteraction-based ecological momentary assessment (μEMA). Objective: This study explores the feasibility of using these smartwatch-based μEMA methods to capture longitudinal, high-temporal-density self-report data about alcohol consumption in a nonclinical population selected to represent high- and low-socioeconomic position (SEP) groups. Methods: A total of 32 participants from the Avon Longitudinal Study of Parents and Children (13 high and 19 low SEP) wore a smartwatch running a custom-developed μEMA app for 3 months between October 2019 and June 2020. Every day over a 12-week period, participants were asked 5 times a day about any alcoholic drinks they had consumed in the previous 2 hours, and the context in which they were consumed. They were also asked if they had missed recording any alcoholic drinks the day before. As a comparison, participants also completed fortnightly online diaries of alcohol consumed using the Timeline Followback (TLFB) method. At the end of the study, participants completed a semistructured interview about their experiences. Results: The compliance rate for all participants who started the study for the smartwatch μEMA method decreased from around 70% in week 1 to 45% in week 12, compared with the online TLFB method which was flatter at around 50% over the 12 weeks. The compliance for all participants still active for the smartwatch μEMA method was much flatter, around 70% for the whole 12 weeks, while for the online TLFB method, it varied between 50% and 80% over the same period. The completion rate for the smartwatch μEMA method varied around 80% across the 12 weeks. Within high- and low-SEP groups there was considerable variation in compliance and completion at each week of the study for both methods. However, almost all point estimates for both smartwatch μEMA and online TLFB indicated lower levels of engagement for low-SEP participants. All participants scored “experiences of using” the 2 methods equally highly, with “willingness to use again” slightly higher for smartwatch μEMA. Conclusions: Our findings demonstrate the acceptability and potential utility of smartwatch μEMA methods for capturing data on alcohol consumption. These methods have the benefits of capturing higher-temporal-density longitudinal data on alcohol consumption, promoting greater participant engagement with less missing data, and potentially being less susceptible to recall errors than established methods such as TLFB. Future studies should explore the factors impacting participant attrition (the biggest reason for reduced engagement), latency issues, and the validity of alcohol data captured with these methods. The consistent pattern of lower engagement among low-SEP participants than high-SEP participants indicates that further work is warranted to explore the impact and causes of these differences. %M 40131326 %R 10.2196/63184 %U https://formative.jmir.org/2025/1/e63184 %U https://doi.org/10.2196/63184 %U http://www.ncbi.nlm.nih.gov/pubmed/40131326 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 17 %N %P e57084 %T Real-World Data on Alcohol Consumption Behavior Among Smartphone Health Care App Users in Japan: Retrospective Study %A Eguchi,Kana %A Kubota,Takeaki %A Koyanagi,Tomoyoshi %A Muto,Manabu %+ Department of Informatics, Graduate School of Informatics, Kyoto University, Yoshida-Hommachi, Sakyo-ku,, Kyoto, 606-8501, Japan, 81 75 753 3369, kana.eguchi@ieee.org %K alcohol consumption %K individual behavior %K mobile health %K mobile health app %K mobile health care app log-based survey %K real-world data %K RWD %K RWD analysis %K smartphone health care app %K surveillance system %K health care app %D 2025 %7 25.3.2025 %9 Original Paper %J Online J Public Health Inform %G English %X Background: Although many studies have used smartphone apps to examine alcohol consumption, none have clearly delineated long-term (>1 year) consumption among the general population. Objective: The objective of our study is to elucidate in detail the alcohol consumption behavior of alcohol drinkers in Japan using individual real-world data. During the state of emergency associated with the COVID-19 outbreak, the government requested that people restrict social gatherings and stay at home, so we hypothesize that alcohol consumption among Japanese working people decreased during this period due to the decrease in occasions for alcohol consumption. This analysis was only possible with individual real-world data. We also aimed to clarify the effects of digital interventions based on notifications about daily alcohol consumption. Methods: We conducted a retrospective study targeting 5-year log data from January 1, 2018, to December 31, 2022, obtained from a commercial smartphone health care app (CALO mama Plus). First, to investigate the possible size of the real-world data, we investigated the rate of active users of this commercial smartphone app. Second, to validate the individual real-world data recorded in the app, we compared individual real-world data from 9991 randomly selected users with government-provided open data on the number of daily confirmed COVID-19 cases in Japan and with nationwide alcohol consumption data. To clarify the effects of digital interventions, we investigated the relationship between 2 types of notification records (ie, “good” and “bad”) and a 3-day daily alcohol consumption log following the notification. The protocol of this retrospective study was approved by the Ethics Committee of the Kyoto University Graduate School and Faculty of Medicine (R4699). %M 40131328 %R 10.2196/57084 %U https://ojphi.jmir.org/2025/1/e57084 %U https://doi.org/10.2196/57084 %U http://www.ncbi.nlm.nih.gov/pubmed/40131328 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59209 %T Embedding a Choice Experiment in an Online Decision Aid or Tool: Scoping Review %A Wickramasekera,Nyantara %A Shackley,Phil %A Rowen,Donna %+ , Sheffield Centre for Health and Related Research (SCHARR), The University of Sheffield, 30 Regent St, Sheffield, S14DA, United Kingdom, 44 01142224348, N.Wickramasekera@sheffield.ac.uk %K decision aid %K decision tool %K discrete choice experiment %K conjoint analysis %K value clarification %K scoping review %K choice experiment %K database %K study %K article %K data charting %K narrative synthesis %D 2025 %7 21.3.2025 %9 Review %J J Med Internet Res %G English %X Background: Decision aids empower patients to understand how treatment options match their preferences. Choice experiments, a method to clarify values used within decision aids, present patients with hypothetical scenarios to reveal their preferences for treatment characteristics. Given the rise in research embedding choice experiments in decision tools and the emergence of novel developments in embedding methodology, a scoping review is warranted. Objective: This scoping review examines how choice experiments are embedded into decision tools and how these tools are evaluated, to identify best practices. Methods: This scoping review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Searches were conducted on MEDLINE, PsycInfo, and Web of Science. The methodology, development and evaluation details of decision aids were extracted and summarized using narrative synthesis. Results: Overall, 33 papers reporting 22 tools were included in the scoping review. These tools were developed for various health conditions, including musculoskeletal (7/22, 32%), oncological (8/22, 36%), and chronic conditions (7/22, 32%). Most decision tools (17/22, 77%) were developed in the United States, with the remaining tools originating in the Netherlands, United Kingdom, Canada, and Australia. The number of publications increased, with 73% (16/22) published since 2015, peaking at 4 publications in 2019. The primary purpose of these tools (20/22, 91%) was to help patients compare or choose treatments. Adaptive conjoint analysis was the most frequently used design type (10/22, 45%), followed by conjoint analysis and discrete choice experiments (DCEs; both 4/22, 18%), modified adaptive conjoint analysis (3/22, 14%), and adaptive best-worst conjoint analysis (1/22, 5%). The number of tasks varied depending on the design (6-12 for DCEs and adaptive conjoint vs 16-20 for conjoint analysis designs). Sawtooth software was commonly used (14/22, 64%) to embed choice tasks. Four proof-of-concept embedding methods were identified: scenario analysis, known preference phenotypes, Bayesian collaborative filtering, and penalized multinomial logit model. After completing the choice tasks patients received tailored information, 73% (16/22) of tools provided attribute importance scores, and 23% (5/22) presented a “best match” treatment ranking. To convey probabilistic attributes, most tools (13/22, 59%) used a combination of approaches, including percentages, natural frequencies, icon arrays, narratives, and videos. The tools were evaluated across diverse study designs (randomized controlled trials, mixed methods, and cohort studies), with sample sizes ranging from 23 to 743 participants. Over 40 different outcomes were included in the evaluations, with the decisional conflict scale being the most frequently used in 6 tools. Conclusions: This scoping review provides an overview of how choice experiments are embedded into decision tools. It highlights the lack of established best practices for embedding methods, with only 4 proof-of-concept methods identified. Furthermore, the review reveals a lack of consensus on outcome measures, emphasizing the need for standardized outcome selection for future evaluations. %M 40117570 %R 10.2196/59209 %U https://www.jmir.org/2025/1/e59209 %U https://doi.org/10.2196/59209 %U http://www.ncbi.nlm.nih.gov/pubmed/40117570 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e46149 %T Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: Case Study Using Fitbit Data %A Baroudi,Loubna %A Zernicke,Ronald Fredrick %A Tewari,Muneesh %A Carlozzi,Noelle E %A Choi,Sung Won %A Cain,Stephen M %K wear time %K wearables %K smartwatch %K mobile health %K physical activity %K engagement %K walking %K dataset %K wearable devices %K reliability %K behavior %K caregiver %K students %K Fitbit %K users %D 2025 %7 21.3.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users’ engagement remains a challenge and can lead to a decrease in compliance (eg, wear time in the context of wearable sensors) over time (eg, “wearables’ abandonment”). Objective: In this work, we analyzed datasets from diverse populations (eg, caregivers for various health issues, college students, and pediatric oncology patients) to quantify the impact that wear time requirements can have on study results. We found evidence that emphasizes the need to account for participants’ wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrate the sensitivity of parameter estimates to different data processing methods with respect to wear time. In Aim 2, we demonstrate that not all research questions necessitate the same wear time requirements; some parameter estimates are not sensitive to wear time. Methods: We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy population samples. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population sample and individual levels to different methods of defining “valid” days using wear time. For Aim 2, we evaluated whether some research questions can be answered with data from lower compliance population samples. We explored (1) the estimation of the average daily step count and (2) the estimation of the average heart rate while walking. Results: For Aim 1, we found that the changes in the population sample average daily step count could reach 2000 steps for different methods of analysis and were dependent on the wear time compliance of the sample. As expected, population samples with a low daily wear time (less than 15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1000 steps for 4 of the 6 population samples analyzed when using different data processing methods. Those individual differences were higher than 3000 steps for close to 5% of individuals across all population samples. Average daily heart rate appeared to be robust to changes in wear time. For Aim 2, we found that, for 5 population samples out of 6, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count. Conclusions: We leveraged datasets from diverse populations to demonstrate the direct relationship between parameter estimates from consumer-grade wearable devices and participants’ wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings. %R 10.2196/46149 %U https://mhealth.jmir.org/2025/1/e46149 %U https://doi.org/10.2196/46149 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67322 %T A Smartphone-Based Timed Up and Go Test Self-Assessment for Older Adults: Validity and Reliability Study %A Böttinger,Melissa Johanna %A Mellone,Sabato %A Klenk,Jochen %A Jansen,Carl-Philipp %A Stefanakis,Marios %A Litz,Elena %A Bredenbrock,Anastasia %A Fischer,Jan-Philipp %A Bauer,Jürgen M %A Becker,Clemens %A Gordt-Oesterwind,Katharina %K timed up and go test %K self-assessment %K instrumented assessment %K technology-based assess-ment %K physical capacity %K mobility %K aged %K mobile applications %K smartphone %K diagnostic self evaluation %D 2025 %7 21.3.2025 %9 %J JMIR Aging %G English %X Background: The Timed Up and Go test (TUG) is recommended as an evidence-based tool for measuring physical capacity. Instrumented TUG (iTUG) approaches expand classical supervised clinical applications offering the potential of self-assessment for older adults. Objective: This study aimed to evaluate the concurrent validity and test-retest reliability of a smartphone-based TUG self-assessment “up&go app.” Methods: A total of 52 community-dwelling older adults (>67 years old) were recruited. A validated and medically certified system attached with a belt at the lower back was used as a reference system to validate the “up&go app” algorithm. The participants repeated the TUG 5 times wearing, a smartphone with the “up&go app” in their front trouser pocket and an inertial sensor to test the concurrent validity. A subsample of 37 participants repeated the “up&go app” measurement 2 weeks later to examine the test-retest reliability. Results: The correlation between the “up&go app” and the reference measurement was r=0.99 for the total test duration and r=0.97 for the 5 single repetitions. Agreement between the 5 repetitions was intraclass correlation coefficient (ICC)=0.9 (0.84‐0.94). Leaving out the first repetition, the agreement was ICC=0.95 (0.92‐0.97). Test-retest agreement had an ICC=0.79 (0.53‐0.9). Conclusions: The duration of 5 repetitions of the TUG test, measured with the pocket-worn “up&go app,” was very consistent with the results of a lower-back sensor system, indicating excellent concurrent validity. Participants walked slower in the first round than in the other 4 repetitions within a test run. Test-retest reliability was also excellent. The “up&go app” provides a useful smartphone-based approach to measure 5 repetitions of the TUG. The app could be used by older adults as a self-screening and monitoring tool of physical capacity at home and thereby help to early identify functional limitations and take interventions when necessary. %R 10.2196/67322 %U https://aging.jmir.org/2025/1/e67322 %U https://doi.org/10.2196/67322 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e64965 %T Investigating the Magnitude and Persistence of COVID-19–Related Impacts on Affect and GPS-Derived Daily Mobility Patterns in Adolescence and Emerging Adulthood: Insights From a Smartphone-Based Intensive Longitudinal Study of Colorado-Based Youths From June 2016 to April 2022 %A Alexander,Jordan D %A Duffy,Kelly A %A Freis,Samantha M %A Chow,Sy-Miin %A Friedman,Naomi P %A Vrieze,Scott I %+ Department of Psychology, University of Minnesota, 75 East River Parkway, Minneapolis, MN, 55455, United States, 1 6126252818, alexa877@umn.edu %K adolescence %K emerging adulthood %K intensive longitudinal assessment %K COVID-19 %K affect %K GPS %K mobility patterns %K smartphone data %K respiratory %K infectious %K pulmonary %K pandemic %K adolescents %K teens %K teenagers %K mobility %K apps %K smartphones %K intensive longitudinal panel studies %K emotional well-being %K well-being %K daily routines %K affect survey %D 2025 %7 17.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: The onset of the COVID-19 pandemic in early 2020 introduced unprecedented disruptions impacting the emotional well-being and daily routines of US youths. However, the patterns and persistence of these impacts over the pandemic’s multiyear course remain less well understood. Objective: This study examined longitudinal changes in affect and daily mobility patterns observed in adolescence and young adulthood from June 2016 to April 2022. The study aimed to quantify changes in youths’ mood and daily routines following the pandemic’s onset and in response to local COVID-19 case rates as well as the persistence of these effects over the pandemic’s multiyear course. Methods: Colorado-based adolescent and young adult twins (N=887; n=479, 54% female; meanage 19.2, SDage 1.5 years on January 01, 2020) participating in the CoTwins study between June 2016 and April 2022 were followed via a smartphone app, which recorded persistent GPS location data and, beginning in February 2019, administered an abbreviated Positive and Negative Affect Schedule every 2 weeks. Nonlinear trajectories in affect and daily mobility over time and in response to local COVID-19 counts were modeled via generalized additive mixed models, while the magnitude and persistence of pandemic-related changes were quantified via linear mixed effects regressions. Results: Between January and April 2020, participants experienced a 28.6% decline in daily locations visited (from 3.5 to 2.5; SD 0.9) and a 60% reduction in daily travel distance (from 20.0 to 8.0 km; SD 9.4). Mean positive affect similarly declined by 0.3 SD (from 3.0 to 2.79; SD 0.6), while, correspondingly, mean negative affect increased by 0.3 SD (from 1.85 to 2.10; SD 0.6). Though mobility levels partially recovered beginning in the summer of 2020, daily locations visited remained slightly below 2019 levels through the study’s conclusion in April 2022 (standardized β=–0.10; P<.001). Average positive affect similarly remained slightly below (standardized β=–0.20; P<.001) and negative affect slightly above (standardized β=0.14; P=.04) 2019 levels through April 2022. Weekly county-level COVID-19 transmission rates were negatively associated with mobility and positive affect and positively with negative affect, though these effects were greatly weakened later in the pandemic (eg, early 2022) or when transmission rates were high (eg, >200 new cases per 100,000 people per week). Conclusions: Findings demonstrate large initial declines in daily mobility, a moderate decline in positive affect, and a moderate increase in negative affect following the pandemic’s onset in 2020. Though most effects attenuated over time, affect and mobility levels had not recovered to prepandemic levels by April 2022. Findings support theories of hedonic adaptation and resiliency while also identifying lingering emotional and behavioral consequences. The study highlights both youth’s resiliency in adapting to major stressors while also underscoring the need for continued support for youth mental health and psychosocial functioning in the pandemic’s aftermath. %M 40096681 %R 10.2196/64965 %U https://www.jmir.org/2025/1/e64965 %U https://doi.org/10.2196/64965 %U http://www.ncbi.nlm.nih.gov/pubmed/40096681 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e63636 %T Measuring Mental Health in 2 Brazilian University Centers: Protocol for a Cohort Survey %A Di Santi,Talita %A Nascimento,Ariana Gomes %A Fukuti,Pedro %A Marchisio,Vinnie %A Araujo do Amaral,Gian Carlo %A Vaz,Camille Figueiredo Peternella %A Carrijo,Luiz David Finotti %A Oliveira,Lilian Cristie de %A Costa,Luiz Octávio da %A Mancini Marion Konieczniak,Elisângela %A Zuppi Garcia,Luana Aparecida %A Cabrelon Jusevicius,Vanessa Cristina %A Humes,Eduardo de Castro %A Rossi Menezes,Paulo %A Miguel,Euripedes %A Caye,Arthur %+ Department of Psychiatry, Faculty of Medicine, University of São Paulo, Ovidio Pires St Sao Paulo, São Paulo, 05403-903, Brazil, 55 11995580667, tadisanti@gmail.com %K study design %K university students %K mental health screening %K longitudinal survey %K college students %D 2025 %7 14.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Global concern for the mental well-being of university students is on the rise. Recent studies estimate that around 30% of students experience mental health disorders, and nearly 80% of these individuals do not receive adequate treatment. Brazil, home to around eight million university students, lacks sufficient research addressing their mental health. To address this gap, we aim to conduct a longitudinal mental health survey at 2 Brazilian universities. Objective: This paper outlines the research protocol for a web-based mental health survey designed to assess the well-being of Brazilian university students. Methods: The survey targets undergraduate students (N=8028) from 2 institutions: UniFAJ (Centro Universitário de Jaguariúna) and UniMAX (Centro Universitário Max Planck). Students will be invited to respond to self-reported questionnaires, including theSMILE-U (lifestyle and quality of life), the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) self-rated level 1 cross-cutting symptom measure, and a brief version of the Adult Self-Report Scale for attention-deficit/hyperactivity disorder. Students who exceed thresholds for conditions such as depression, anxiety, and attention-deficit/hyperactivity disorder will receive additional diagnostic instruments. The survey will be conducted annually, tracking individual and group trajectories and enrolling new cohorts each year. Data will be analyzed using cross-sectional and longitudinal methods, focusing on descriptive, associative, and trajectory analyses. Results: The first wave of data collection began in February 2024 and is expected to conclude in December 2024. As of October 2024, a total of 2034 of 7455 (27.2 in 100) eligible students had completed the questionnaire. Cross-sectional statistical analysis is planned to commence immediately after data collection and is expected to be completed by June 2025. Conclusions: This survey uses a scalable, cost-effective design to evaluate mental health conditions among Brazilian university students. The longitudinal framework facilitates the monitoring of mental health trends, supports the development of targeted interventions, and informs policy initiatives in higher education. Trial Registration: OSF Registries OSF.IO/AM5WS; https://doi.org/10.17605/OSF.IO/AM5WS International Registered Report Identifier (IRRID): DERR1-10.2196/63636 %M 40085140 %R 10.2196/63636 %U https://www.researchprotocols.org/2025/1/e63636 %U https://doi.org/10.2196/63636 %U http://www.ncbi.nlm.nih.gov/pubmed/40085140 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 4 %N %P e67239 %T Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation %A Tzeng,Jing-Tong %A Li,Jeng-Lin %A Chen,Huan-Yu %A Huang,Chu-Hsiang %A Chen,Chi-Hsin %A Fan,Cheng-Yi %A Huang,Edward Pei-Chuan %A Lee,Chi-Chun %+ Department of Electrical Engineering, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 300, Taiwan, 886 35162439, cclee@ee.nthu.edu.tw %K respiratory sound %K lung sound %K audio enhancement %K noise robustness %K clinical applicability %K artificial intelligence %K AI %D 2025 %7 13.3.2025 %9 Original Paper %J JMIR AI %G English %X Background: Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. In addition, predicting signals with only background noise could undermine user trust in the system. Objective: This study aimed to investigate the feasibility and effectiveness of incorporating a deep learning–based audio enhancement preprocessing step into automatic respiratory sound classification systems to improve robustness and clinical applicability. Methods: We conducted extensive experiments using various audio enhancement model architectures, including time-domain and time-frequency–domain approaches, in combination with multiple classification models to evaluate the effectiveness of the audio enhancement module in an automatic respiratory sound classification system. The classification performance was compared against the baseline noise injection data augmentation method. These experiments were carried out on 2 datasets: the International Conference in Biomedical and Health Informatics (ICBHI) respiratory sound dataset, which contains 5.5 hours of recordings, and the Formosa Archive of Breath Sound dataset, which comprises 14.6 hours of recordings. Furthermore, a physician validation study involving 7 senior physicians was conducted to assess the clinical utility of the system. Results: The integration of the audio enhancement module resulted in a 21.88% increase with P<.001 in the ICBHI classification score on the ICBHI dataset and a 4.1% improvement with P<.001 on the Formosa Archive of Breath Sound dataset in multi-class noisy scenarios. Quantitative analysis from the physician validation study revealed improvements in efficiency, diagnostic confidence, and trust during model-assisted diagnosis, with workflows that integrated enhanced audio leading to an 11.61% increase in diagnostic sensitivity and facilitating high-confidence diagnoses. Conclusions: Incorporating an audio enhancement algorithm significantly enhances the robustness and clinical utility of automatic respiratory sound classification systems, improving performance in noisy environments and fostering greater trust among medical professionals. %M 40080816 %R 10.2196/67239 %U https://ai.jmir.org/2025/1/e67239 %U https://doi.org/10.2196/67239 %U http://www.ncbi.nlm.nih.gov/pubmed/40080816 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 12 %N %P e60649 %T Exploring the Views of Young People, Including Those With a History of Self-Harm, on the Use of Their Routinely Generated Data for Mental Health Research: Web-Based Cross-Sectional Survey Study %A Dekel,Dana %A Marchant,Amanda %A Del Pozo Banos,Marcos %A Mhereeg,Mohamed %A Lee,Sze Chim %A John,Ann %+ Swansea University Medical School, Swansea University, 3rd Floor, Data Science Building, Singleton Park, Swansea, SA2 8PP, United Kingdom, 44 1792 602568, a.john@swansea.ac.uk %K self-harm %K mental health %K big data %K survey %K youth %D 2025 %7 12.3.2025 %9 Original Paper %J JMIR Ment Health %G English %X Background: Secondary use of routinely collected health care data has great potential benefits in epidemiological studies primarily due to the large scale of preexisting data. Objective: This study aimed to engage respondents with and without a history of self-harm, gain insight into their views on the use of their data for research, and determine whether there were any differences in opinions between the 2 groups. Methods: We examined young people’s views on the use of their routinely collected data for mental health research through a web-based survey, evaluating any differences between those with and without a history of self-harm. Results: A total of 1765 respondents aged 16 to 24 years were included. Respondents’ views were mostly positive toward the use and linkage of their data for research purposes for public benefit, particularly with regard to the use of health care data (mental health or otherwise), and generally echoed existing evidence on the opinions of older age groups. Individuals who reported a history of self-harm and subsequently contacted health services more often reported being “extremely likely” or “likely” to share mental health data (contacted: 209/609, 34.3%; 95% CI 28.0-41.2; not contacted: 169/782, 21.6%; 95% CI 15.8-28.7) and physical health data (contacted: 117/609, 19.2%; 95% CI 12.7-27.8; not contacted: 96/782, 12.3%; 95% CI 6.7-20.9) compared with those who had not contacted services. Respondents were overall less likely to want to share their social media data, which they considered to be more personal compared to their health care data. Respondents stressed the importance of anonymity and the need for an appropriate ethical framework. Conclusions: Young people are aware, and they care about how their data are being used and for what purposes, irrespective of having a history of self-harm. They are largely positive about the use of health care data (mental or physical) for research and generally echo the opinions of older age groups raising issues around data security and the use of data for the public interest. %M 40073393 %R 10.2196/60649 %U https://mental.jmir.org/2025/1/e60649 %U https://doi.org/10.2196/60649 %U http://www.ncbi.nlm.nih.gov/pubmed/40073393 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59955 %T Assessment of Fraud Deterrence and Detection Procedures Used in a Web-Based Survey Study With Adult Black Cisgender Women: Description of Lessons Learned and Recommendations %A Sophus,Amber I %A Mitchell,Jason W %+ Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health & Social Work, Florida International University, 11200 S.W. 8th Street, Miami, FL, 33199, United States, asophus@fiu.edu %K Black women %K HIV %K fraud deterrence %K fraud detection %K web-based research %K online research %K data integrity %K data collection %K survey %D 2025 %7 12.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Online research studies enable engagement with more Black cisgender women in health-related research. However, fraudulent data collection responses in online studies raise important concerns about data integrity, particularly when incentives are involved. Objective: The purpose of this study was to assess the strengths and limitations of fraud deterrence and detection procedures implemented in an incentivized, cross-sectional, online study about HIV prevention and sexual health with Black cisgender women living in Texas. Methods: Data for this study came from a cross-sectional web-based survey that examined factors associated with potential pre-exposure prophylaxis use among a convenience sample of adult Black cisgender women from 3 metropolitan areas in Texas. Each eligibility screener and associated survey entry was evaluated using 4 fraud deterrence features and 7 fraud detection benchmarks with corresponding decision rules. Results: A total of 5862 respondents provided consent and initiated the eligibility screener, of whom 2150 (36.68%) were ineligible for not meeting the inclusion criteria, and 131 (2.23%) completed less than 80% of the survey and were removed from further consideration. Other entries were removed for not passing level 1 fraud deterrent safeguards: duplicate entries with the same IP address (388/5862, 6.62%), same telephone number (69/5862, 1.18%), same email address (114/5862, 1.94%), and same telephone number and email address (17/5862, 0.29%). Of the remaining 2993 entries, 1652 entries were removed for not passing the first 2 items of the level 2 fraud detection benchmarks: screeners and surveys with latitude and longitude coordinates outside of the United States (347/2993, 11.59%) and survey completion time of less than 10 minutes (1305/2993, 43.6%). Of the remaining 1341 entries, 130 (9.69%) passed all 5 of the remaining level 2 data validation benchmarks, and 763 (56.89%) entries were removed due to passing less than 3. An additional 33.4% (423/1341) entries were removed after passing 4 of the 5 remaining validation benchmarks, being contacted to verify survey information, and not providing legitimate contact information or being unable to confirm personal information. The final enrolled sample in this online study consisted of 155 respondents who provided consent, were deemed eligible, and passed fraud deterrence features and fraud detection benchmarks. In this paper, we discuss the lessons learned and provide recommendations for leveraging available features in survey software programs to help deter bots and enhance fraud detection procedures beyond relying on survey software options. Conclusions: Effectively identifying fraudulent responses in online surveys is an ongoing challenge. The data validation approach used in this study establishes a robust protocol for identifying genuine participants, thereby contributing to the removal of false data from study findings. By sharing experiences and implementing thorough fraud deterrence and detection protocols, researchers can maintain data validity and contribute to best practices in web-based research. %M 40073396 %R 10.2196/59955 %U https://formative.jmir.org/2025/1/e59955 %U https://doi.org/10.2196/59955 %U http://www.ncbi.nlm.nih.gov/pubmed/40073396 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e51804 %T Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations %A Portela,Diana %A Freitas,Alberto %A Costa,Elísio %A Giovannini,Mattia %A Bousquet,Jean %A Almeida Fonseca,João %A Sousa-Pinto,Bernardo %+ Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, R. Dr. Plácido da Costa, Porto, 4200-450, Portugal, 351 22 551 3622, bernardosousapinto@protonmail.com %K infodemiology %K asthma %K administrative databases %K multimorbidity %K co-morbidity %K respiratory %K pulmonary %K Google Trends %K correlation %K hospitalization %K admissions %K autoregressive %K information seeking %K searching %K searches %K forecasting %D 2025 %7 10.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Google Trends (GT) data have shown promising results as a complementary tool to classical surveillance approaches. However, GT data are not necessarily provided by a representative sample of patients and may be skewed toward demographic and clinical groups that are more likely to use the internet to search for their health. Objective: In this study, we aimed to assess whether GT-based models perform differently in distinct population subgroups. To assess that, we analyzed a case study on asthma hospitalizations. Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 3 different countries (Portugal, Spain, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold for the same countries and time period were retrieved from GT. We estimated the correlation between GT data and the weekly occurrence of asthma hospitalizations (considering separate asthma admissions data according to patients’ age, sex, ethnicity, and presence of comorbidities). In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations (for the different aforementioned subgroups) for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results: Overall, correlation coefficients between GT on the pseudo-influenza syndrome topic and asthma hospitalizations ranged between 0.33 (in Portugal for admissions with at least one Charlson comorbidity group) and 0.86 (for admissions in women and in White people in Brazil). In the 3 assessed countries, forecasted hospitalizations for 2015-2016 correlated more strongly with observed admissions of older versus younger individuals (Portugal: Spearman ρ=0.70 vs ρ=0.56; Spain: ρ=0.88 vs ρ=0.76; Brazil: ρ=0.83 vs ρ=0.82). In Portugal and Spain, forecasted hospitalizations had a stronger correlation with admissions occurring for women than men (Portugal: ρ=0.75 vs ρ=0.52; Spain: ρ=0.83 vs ρ=0.51). In Brazil, stronger correlations were observed for admissions of White than of Black or Brown individuals (ρ=0.92 vs ρ=0.87). In Portugal, stronger correlations were observed for admissions of individuals without any comorbidity compared with admissions of individuals with comorbidities (ρ=0.68 vs ρ=0.66). Conclusions: We observed that the models based on GT data may perform differently in demographic and clinical subgroups of participants, possibly reflecting differences in the composition of internet users’ health-seeking behaviors. %M 40063932 %R 10.2196/51804 %U https://www.jmir.org/2025/1/e51804 %U https://doi.org/10.2196/51804 %U http://www.ncbi.nlm.nih.gov/pubmed/40063932 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e51975 %T FRAILSURVEY—an mHealth App for Self-Assessment of Frailty Based on the Portuguese Version of the Groningen Frailty Indicator: Validation and Reliability Study %A Midao,Luis %A Duarte,Mafalda %A Sampaio,Rute %A Almada,Marta %A Dias,Cláudia Camila %A Paúl,Constança %A Costa,Elísio %+ RISE-Health, Biochemistry Lab, Faculty of Pharmacy, University of Porto, R. Jorge de Viterbo Ferreira 228, Porto, 4050-313, Portugal, 351 22 042 8500, luismidao@gmail.com %K frailty %K mHealth %K assessment %K validation %K GFI %K reliability %K self-assessment %K Groningen Frailty Indicator %K FRAILSURVEY %K mobile phone %D 2025 %7 7.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Portugal is facing the challenge of population ageing, with a notable increase in the proportion of older individuals. This has positioned the country among those in Europe with a high prevalence of frailty. Frailty, a geriatric syndrome characterized by diminished physiological reserve and heightened vulnerability to stressors, imposes a substantial burden on public health. Objective: This study seeks to address two primary objectives: (1) translation and psychometric evaluation of the European Portuguese version of the Groningen Frailty Indicator (GFI); and (2) development and evaluation of the FRAILSURVEY app, a novel assessment tool for frailty based on the GFI. By achieving these objectives, the study aims to enhance the accuracy and reliability of frailty assessment in the Portuguese context, ultimately contributing to improved health care outcomes for older individuals in the region. Methods: To accomplish the objectives of the study, a comprehensive research methodology was used. The study comprised 2 major phases: the initial translation and validation of the GFI into European Portuguese and the development of the FRAILSURVEY app. Following this, an extensive examination of the app’s validity and reliability was conducted compared with the conventional paper version of the GFI. A randomized repeated crossover design was used to ensure rigorous evaluation of both assessment methods, using both the paper form of the GFI and the smartphone-based app FRAILSURVEY. Results: The findings of the study revealed promising outcomes in line with the research objectives. The meticulous translation process yielded a final version of the GFI with robust psychometric properties, ensuring clarity and comprehensibility for participants. The study included 522 participants, predominantly women (367/522, 70.3%), with a mean age of 73.7 (SD 6.7) years. Psychometric evaluation of the European Portuguese GFI in paper form demonstrates good reliability (internal consistency: Cronbach a value of 0.759; temporal stability: intraclass correlation coefficient=0.974) and construct validity (revealing a 4D structure explaining 56% of variance). Evaluation of the app-based European Portuguese GFI indicates good reliability (interinstrument reliability: Cohen k=0.790; temporal stability: intraclass correlation coefficient=0.800) and concurrent validity (r=0.694; P<.001). Conclusions: Both the smartphone-based app and the paper version of the GFI were feasible and acceptable for use. The findings supported that FRAILSURVEY exhibited comparable validity and reliability to its paper counterpart. FRAILSURVEY uses a standardized and validated assessment tool, offering objective and consistent measurements while eliminating subjective biases, enhancing accuracy, and ensuring reliability. This app holds promising potential for aiding health care professionals in identifying frailty in older individuals, enabling early intervention, and improving the management of adverse health outcomes associated with this syndrome. Its integration with electronic health records and other data may lead to personalized interventions, improving frailty management and health outcomes for at-risk individuals. %M 40053720 %R 10.2196/51975 %U https://formative.jmir.org/2025/1/e51975 %U https://doi.org/10.2196/51975 %U http://www.ncbi.nlm.nih.gov/pubmed/40053720 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e64016 %T MetaAnalysisOnline.com: Web-Based Tool for the Rapid Meta-Analysis of Clinical and Epidemiological Studies %A Fekete,János Tibor %A Győrffy,Balázs %+ Department of Bioinformatics, Semmelweis University, Tuzolto u 7-9, Budapest, H-1095, Hungary, 36 305142822, gyorffy.balazs@yahoo.com %K statistics %K pharmacology %K treatment %K epidemiology %K fixed effect model %K random effect model %K hazard rate %K response rate %K clinical trial %K funnel plot %K z score plot %D 2025 %7 6.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: A meta-analysis is a quantitative, formal study design in epidemiology and clinical medicine that systematically integrates and quantitatively synthesizes findings from multiple independent studies. This approach not only enhances statistical power but also enables the exploration of effects across diverse populations and helps resolve controversies arising from conflicting studies. Objective: This study aims to develop and implement a user-friendly tool for conducting meta-analyses, addressing the need for an accessible platform that simplifies the complex statistical procedures required for evidence synthesis while maintaining methodological rigor. Methods: The platform available at MetaAnalysisOnline.com enables comprehensive meta-analyses through an intuitive web interface, requiring no programming expertise or command-line operations. The system accommodates diverse data types including binary (total and event numbers), continuous (mean and SD), and time-to-event data (hazard rates with CIs), while implementing both fixed-effect and random-effect models using established statistical approaches such as DerSimonian-Laird, Mantel-Haenszel, and inverse variance methods for effect size estimation and heterogeneity assessment. Results: In addition to statistical tests, graphical representations including the forest plot, the funnel plot, and the z score plot can be drawn. A forest plot is highly effective in illustrating heterogeneity and pooled results. The risk of publication bias can be revealed by a funnel plot. A z score plot provides a visual assessment of whether more research is needed to establish a reliable conclusion. All the discussed models and visualization options are integrated into the registration-free web-based portal. Leveraging MetaAnalysisOnline.com's capabilities, we examined treatment-related adverse events in patients with cancer receiving perioperative anti–PD-1 immunotherapy through a systematic review encompassing 10 studies with 8099 total participants. Meta-analysis revealed that anti–PD-1 therapy doubled the risk of adverse events (risk ratio 2.15, 95% CI 1.39-3.32), with significant between-study heterogeneity (I2=95%) and publication bias detected through the Egger test (P=.02). While these findings suggest increased toxicity associated with anti–PD-1 treatment, the z score analysis indicated that additional studies are needed for definitive conclusions. Conclusions: In summary, the web-based tool aims to bridge the void for clinical and life science researchers by offering a user-friendly alternative for the swift and reproducible meta-analysis of clinical and epidemiological trials. %M 39928123 %R 10.2196/64016 %U https://www.jmir.org/2025/1/e64016 %U https://doi.org/10.2196/64016 %U http://www.ncbi.nlm.nih.gov/pubmed/39928123 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e59660 %T Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review %A Woll,Simon %A Birkenmaier,Dennis %A Biri,Gergely %A Nissen,Rebecca %A Lutz,Luisa %A Schroth,Marc %A Ebner-Priemer,Ulrich W %A Giurgiu,Marco %+ Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Hertzstrasse 16, Karlsruhe, 76187, Germany, 49 721 608 ext 41974, simon.woll@kit.edu %K machine learning %K mental health %K wearables %K physical behavior %K artificial intelligence %K mobile phone %K smartphone %D 2025 %7 6.3.2025 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Wearable technology is used by consumers worldwide for continuous activity monitoring in daily life but more recently also for classifying or predicting mental health parameters like stress or depression levels. Previous studies identified, based on traditional approaches, that physical activity is a relevant factor in the prevention or management of mental health. However, upcoming artificial intelligence methods have not yet been fully established in the research field of physical activity and mental health. Objective: This systematic review aims to provide a comprehensive overview of studies that integrated passive monitoring of physical activity data measured via wearable technology in machine learning algorithms for the detection, prediction, or classification of mental health states and traits. Methods: We conducted a review of studies processing wearable data to gain insights into mental health parameters. Eligibility criteria were (1) the study uses wearables or smartphones to acquire physical behavior and optionally other sensor measurement data, (2) the study must use machine learning to process the acquired data, and (3) the study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in 5 electronic databases. Results: Of 11,057 unique search results, 49 published papers between 2016 and 2023 were included. Most studies examined the connection between wearable sensor data and stress (n=15, 31%) or depression (n=14, 29%). In total, 71% (n=35) of the studies had less than 100 participants, and 47% (n=23) had less than 14 days of data recording. More than half of the studies (n=27, 55%) used step count as movement measurement, and 44% (n=21) used raw accelerometer values. The quality of the studies was assessed, scoring between 0 and 18 points in 9 categories (maximum 2 points per category). On average, studies were rated 6.47 (SD 3.1) points. Conclusions: The use of wearable technology for the detection, prediction, or classification of mental health states and traits is promising and offers a variety of applications across different settings and target groups. However, based on the current state of literature, the application of artificial intelligence cannot realize its full potential mostly due to a lack of methodological shortcomings and data availability. Future research endeavors may focus on the following suggestions to improve the quality of new applications in this context: first, by using raw data instead of already preprocessed data. Second, by using only relevant data based on empirical evidence. In particular, crafting optimal feature sets rather than using many individual detached features and consultation with in-field professionals. Third, by validating and replicating the existing approaches (ie, applying the model to unseen data). Fourth, depending on the research aim (ie, generalization vs personalization) maximizing the sample size or the duration over which data are collected. %M 40053765 %R 10.2196/59660 %U https://mhealth.jmir.org/2025/1/e59660 %U https://doi.org/10.2196/59660 %U http://www.ncbi.nlm.nih.gov/pubmed/40053765 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e63805 %T An Actor-Partner Interdependence Mediation Model for Assessing the Association Between Health Literacy and mHealth Use Intention in Dyads of Patients With Chronic Heart Failure and Their Caregivers: Cross-Sectional Study %A Jin,Xiaorong %A Zhang,Yimei %A Zhou,Min %A Mei,Qian %A Bai,Yangjuan %A Hu,Qiulan %A Wei,Wei %A Zhang,Xiong %A Ma,Fang %K chronic heart failure %K caregivers %K health literacy %K mHealth %K actor-partner interdependence mediation model %K mobile health %D 2025 %7 6.3.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Chronic heart failure (CHF) has become a serious threat to the health of the global population. Self-management is the key to treating CHF, and the emergence of mobile health (mHealth) has provided new ideas for the self-management of CHF. Despite the many potential benefits of mHealth, public utilization of mHealth apps is low, and poor health literacy (HL) is a key barrier to mHealth use. However, the mechanism of the influence is unclear. Objective: The aim of this study is to explore the dyadic associations between HL and mHealth usage intentions in dyads of patients with CHF and their caregivers, and the mediating role of mHealth perceived usefulness and perceived ease of use in these associations. Methods: This study had a cross-sectional research design, with a sample of 312 dyads of patients with CHF who had been hospitalized in the cardiology departments of 2 tertiary care hospitals in China from March to October 2023 and their caregivers. A general information questionnaire, the Chinese version of the Heart Failure-Specific Health Literacy Scale, and the mHealth Intention to Use Scale were used to conduct the survey; the data were analyzed using the actor-partner interdependence mediation model. Results: The results of the actor-partner interdependent mediation analysis of HL, perceived usefulness of mHealth, and mHealth use intention among patients with CHF and their caregivers showed that all of the model’s actor effects were valid (β=.26‐0.45; P<.001), the partner effects were partially valid (β=.08‐0.20; P<.05), and the mediation effects were valid (β=.002‐0.242, 95% CI 0.003‐0.321; P<.05). Actor-partner interdependent mediation analyses of HL, perceived ease of use of mHealth, and mHealth use intention among patients with CHF and caregivers showed that the model’s actor effect partially held (β=.17‐0.71; P<.01), the partner effect partially held (β=.15; P<.01), and the mediation effect partially held (β=.355‐0.584, 95% CI 0.234‐0.764; P<.001). Conclusions: Our study proposes that the HL of patients with CHF and their caregivers positively contributes to their own intention to use mHealth, suggesting that the use of mHealth by patients with CHF can be promoted by improving the HL of patients and caregivers. Our findings also suggest that the perceived usefulness of patients with CHF and caregivers affects patients’ mHealth use intention, and therefore patients with CHF and their caregivers should be involved throughout the mHealth development process to improve the usability of mHealth for both patients and caregivers. This study emphasizes the key role of patients’ perception that mHealth is easy to use in facilitating their use of mHealth. Therefore, it is recommended that the development of mHealth should focus on simplifying operational procedures and providing relevant operational training according to the needs of the patients when necessary. %R 10.2196/63805 %U https://mhealth.jmir.org/2025/1/e63805 %U https://doi.org/10.2196/63805 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e64721 %T Emerging Domains for Measuring Health Care Delivery With Electronic Health Record Metadata %A Tawfik,Daniel %A Rule,Adam %A Alexanian,Aram %A Cross,Dori %A Holmgren,A Jay %A Lou,Sunny S %A McPeek Hinz,Eugenia %A Rose,Christian %A Viswanadham,Ratnalekha V N %A Mishuris,Rebecca G %A Rodríguez-Fernández,Jorge M %A Ford,Eric W %A Florig,Sarah T %A Sinsky,Christine A %A Apathy,Nate C %+ Department of Pediatrics, Stanford University School of Medicine, 770 Welch Road, Suite 435, Palo Alto, CA, 94304, United States, 1 6507239902, dtawfik@stanford.edu %K metadata %K health services research %K audit logs %K event logs %K electronic health record data %K health care delivery %K patient care %K healthcare teams %K clinician-patient relationship %K cognitive environment %D 2025 %7 6.3.2025 %9 Viewpoint %J J Med Internet Res %G English %X This article aims to introduce emerging measurement domains made feasible through the electronic health record (EHR) use metadata, to inform the changing landscape of health care delivery. We reviewed emerging domains in which EHR metadata may be used to measure health care delivery, outlining a framework for evaluating measures based on desirability, feasibility, and viability. We argue that EHR use metadata may be leveraged to develop and operationalize novel measures in the domains of team structure and dynamics, workflows, and cognitive environment to provide a clearer understanding of modern health care delivery. Examples of measures feasible using metadata include quantification of teamwork and collaboration, patient continuity measures, workflow conformity measures, and attention switching. By enabling measures that can be used to inform the next generation of health care delivery, EHR metadata may be used to improve the quality of patient care and support clinician well-being. Careful attention is needed to ensure that these measures are desirable, feasible, and viable. %M 40053814 %R 10.2196/64721 %U https://www.jmir.org/2025/1/e64721 %U https://doi.org/10.2196/64721 %U http://www.ncbi.nlm.nih.gov/pubmed/40053814 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e60486 %T Feasibility and Links Between Emotions, Physical States, and Eating Behavior in Patients After Metabolic Bariatric Surgery: Experience Sampling Study %A Kuipers,Ellen A M %A Timmerman,Josien G %A van Det,Marc J %A Vollenbroek-Hutten,Miriam M R %+ Department of Surgery, Hospital Group Twente, Zilvermeeuw 1, Almelo, 7609 PP, The Netherlands, 31 620024496, el.kuipers@zgt.nl %K feasibility %K experience sampling methodology %K metabolic bariatric surgery %K eating behavior %K positive and negative affect %K physical states %K contextual factors %K mobile phone %D 2025 %7 5.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Lifestyle modification is essential to achieve and maintain successful outcomes after metabolic bariatric surgery (MBS). Emotions, physical states, and contextual factors are considered important determinants of maladaptive eating behavior, emphasizing their significance in understanding and addressing weight management. In this context, experience sampling methodology (ESM) offers promise for measuring lifestyle and behavior in the patient’s natural environment. Nevertheless, there is limited research on its feasibility and association among emotions and problematic eating behavior within the population after MBS. Objective: This study aimed to examine the feasibility of ESM in the population after MBS regarding emotions, physical states, contextual factors, and problematic eating behavior, and to explore the temporal association among these variables. Methods: An experience sampling study was conducted in which participants rated their current affect (positive and negative), physical states (disgust, boredom, fatigue, and hunger), contextual factors (where, with whom, and doing what), and problematic eating behavior (ie, grazing, dietary relapse, craving, and binge eating) via smartphone-based ESM questionnaires at 6 semirandom times daily for 14 consecutive days. Feasibility was operationalized as the study’s participation rate and completion rate, compliance in answering ESM questionnaires, and response rates per day. At the end of the study period, patients reflected on the feasibility of ESM in semistructured interviews. Generalized estimation equations were conducted to examine the temporal association between emotions, physical states, contextual factors, and problematic eating behavior. Results: In total, 25 out of 242 participants consented to participate, resulting in a study participation rate of 10.3%. The completion rate was 83%. Overall compliance was 57.4% (1072/1868), varying from 13% (11/84) to 89% (75/84) per participant. Total response rates per day decreased from 65% (90/138) to 52% (67/130) over the 14-day study period. According to the interviews, ESM was considered feasible and of added value. Temporal associations were found for hunger and craving (odds ratio 1.04, 95% CI 1.00-1.07; P=.03), and for positive affect and grazing (odds ratio 1.61, 95% CI 1.03-2.51; P=.04). Conclusions: In this exploratory study, patients after MBS were not amenable to participate. Only a small number of patients were willing to participate. However, those who participated found it feasible and expressed satisfaction with it. Temporal associations were identified between hunger and craving, as well as between positive affect and grazing. However, no clear patterns were observed among emotions, physical states, context, and problematic eating behaviors. %M 40053719 %R 10.2196/60486 %U https://formative.jmir.org/2025/1/e60486 %U https://doi.org/10.2196/60486 %U http://www.ncbi.nlm.nih.gov/pubmed/40053719 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65343 %T Association of Screen Content With Early Development Among Preschoolers in Shanghai: 7-Day Monitoring Study With Auto Intelligent Technology %A Chen,Hao %A Sun,Yi %A Luo,Sha %A Ma,Yingyan %A Li,Chenshu %A Xiao,Yingcheng %A Zhang,Yimeng %A Lin,Senlin %A Jia,Yingnan %+ Preventive Medicine and Health Education Department, School of Public Health, Fudan University, No 138 Yixueyuan Road, Shanghai, 200032, China, 86 13764665540, jyn@fudan.edu.cn %K types of screen content %K screen time %K intelligent technology %K early development %K preschool %D 2025 %7 5.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: It is unclear how exposure to different types of screen content is associated with early development among preschool children. Objective: This study aims to precisely evaluate the screen exposure time across different content types and to explore the associations with the Ages and Stages Questionnaire, Third Edition (ASQ-3) score and 5 capacity domains in children aged 34.5-66 months. Methods: This monitoring study used intelligent technology to collect data on the 7-day screen time and the time spent viewing each content type. The participants were 2 groups of Shanghai kindergarten kids. The data were collected between March 2023 and July 2023. Screen exposure data (total daily time and time for each type of content) were collected from children aged between 34.5 and 66 months. A self-designed questionnaire and the Healthy Screen Viewing for Children intelligent technology app were used to assess screen exposure to all media and tablets. The ASQ-3 was used to assess early development in children aged 34.5-66 months. Results: In the 535-child sample, the results of linear regression analysis indicated that both screen time of more than 60 minutes and exposure to smartphones and tablets were negatively associated with ASQ-3 score. Among 365 participants with data collected by the Healthy Screen Viewing for Children app, median regression showed that the median total ASQ-3 score was negatively associated with screen time for noneducational content (β=–.055; 95% CI –0.148 to –0.006; P=.03), screen time for both educational and noneducational content (β=–.042; 95% CI –0.081 to –0.007; P=.001), and fast-paced content (β=–.034; 95% CI –0.062 to –0.011; P=.049). The median gross motor score was negatively associated with screen time for parental guidance-13–rated content (β=–.015; 95% CI –0.022 to 0.009; P=.03), educational and noneducational content (β=–.018, 95% CI –0.038 to –0.001; P=.02), static content (β=–.022; 95% CI –0.050 to 0.007; P=.02). This study also revealed that the median fine motor score was negatively associated with screen time for guidance–rated content (β=–.032, 95% CI –0.057 to –0.003; P=.006), parental guidance (PG) rated content (β=–.020; 95% CI –0.036 to –0.007; P=.004), noneducational content (β=–.026; 95% CI –0.067 to –0.003; P=.01), both educational and noneducational content (β=–.020; 95% CI –0.034 to –0.001; P<.001), fast-paced content (β=–.022; 95% CI –0.033 to –0.014; P<.001), static content (β=–.034; 95% CI –0.050 to 0.018; P<.001), animated content (β=–.038; 95% CI –0.069 to –0.001; P=.004), and screen use during the daytime (β=–.026; 95% CI –0.043 to 0.005; P=.005). Conclusions: The results indicated that the time spent viewing noneducational, static, fast-paced, and animated content was negatively associated with early development among preschool children. Limiting screen time in relevant aspects is recommended. %M 40053802 %R 10.2196/65343 %U https://www.jmir.org/2025/1/e65343 %U https://doi.org/10.2196/65343 %U http://www.ncbi.nlm.nih.gov/pubmed/40053802 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59217 %T Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative %A Mandel,Hannah L %A Shah,Shruti N %A Bailey,L Charles %A Carton,Thomas %A Chen,Yu %A Esquenazi-Karonika,Shari %A Haendel,Melissa %A Hornig,Mady %A Kaushal,Rainu %A Oliveira,Carlos R %A Perlowski,Alice A %A Pfaff,Emily %A Rao,Suchitra %A Razzaghi,Hanieh %A Seibert,Elle %A Thomas,Gelise L %A Weiner,Mark G %A Thorpe,Lorna E %A Divers,Jasmin %A , %+ Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, United States, 1 732 314 1595, Hannah.Mandel@nyulangone.org %K COVID-19 %K SARS-CoV-2 %K Long COVID, post-acute COVID-19 syndrome %K electronic health records %K machine learning %K public health surveillance %K post-infection syndrome %K medical informatics %K electronic medical record %K electronic health record network %K electronic health record data %K clinical research network %K clinical data research network %K common data model %K digital health %K infection %K respiratory %K infectious %K epidemiological %K pandemic %D 2025 %7 5.3.2025 %9 Viewpoint %J J Med Internet Res %G English %X The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)–funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research. %M 40053748 %R 10.2196/59217 %U https://www.jmir.org/2025/1/e59217 %U https://doi.org/10.2196/59217 %U http://www.ncbi.nlm.nih.gov/pubmed/40053748 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59954 %T Supplementing Consent for a Prospective Longitudinal Cohort Study of Infants With Antenatal Opioid Exposure: Development and Assessment of a Digital Tool %A Newman,Jamie E %A Clarke,Leslie %A Athimuthu,Pranav %A Dhawan,Megan %A Owen,Sharon %A Beiersdorfer,Traci %A Parlberg,Lindsay M %A Bangdiwala,Ananta %A McMillan,Taya %A DeMauro,Sara B %A Lorch,Scott %A Peralta-Carcelen,Myriam %A Wilson-Costello,Deanne %A Ambalavanan,Namasivayam %A Merhar,Stephanie L %A Poindexter,Brenda %A Limperopoulos,Catherine %A Davis,Jonathan M %A Walsh,Michele %A Bann,Carla M %K informed consent digital tool %K avatars %K video-assisted consent %K MRI %K antenatal opioid exposure %K infant %K antenatal %K opioid exposure %K caregiver %K survey %K magnetic resonance imaging %K Outcomes of Babies With Opioid Exposure %D 2025 %7 4.3.2025 %9 %J JMIR Form Res %G English %X Background: The Outcomes of Babies With Opioid Exposure (OBOE) study is an observational cohort study examining the impact of antenatal opioid exposure on outcomes from birth to 2 years of age. COVID-19 social distancing measures presented challenges to research coordinators discussing the study at length with potential participants during the birth hospitalization, which impacted recruitment, particularly among caregivers of unexposed (control) infants. In response, the OBOE study developed a digital tool (consenter video) to supplement the informed consent process, make it more engaging, and foster greater identification with the research procedures among potential participants. Objective: We aim to examine knowledge of the study, experiences with the consent process, and perceptions of the consenter video among potential participants of the OBOE study. Methods: Analyses included 129 caregivers who were given the option to view the consenter video as a supplement to the consent process. Participants selected from 3 racially and ethnically diverse avatars to guide them through the 11-minute video with recorded voice-overs. After viewing the consenter video, participants completed a short survey to assess their knowledge of the study, experiences with the consent process, and perceptions of the tool, regardless of their decision to enroll in the main study. Chi-square tests were used to assess differences between caregivers of opioid-exposed and unexposed infants in survey responses and whether caregivers who selected avatars consistent with their racial or ethnic background were more likely to enroll in the study than those who selected avatars that were not consistent with their background. Results: Participants demonstrated good understanding of the information presented, with 95% (n=123) correctly identifying the study purpose and 88% (n=112) correctly indicating that their infant would not be exposed to radiation during the magnetic resonance imaging. Nearly all indicated they were provided “just the right amount of information” (n=123, 98%) and that they understood the consent information well enough to decide whether to enroll (n=125, 97%). Survey responses were similar between caregivers of opioid-exposed infants and unexposed infants on all items except the decision to enroll. Those in the opioid-exposed group were more likely to enroll in the main study compared to the unexposed group (n=49, 89% vs n=38, 51%; P<.001). Of 81 caregivers with known race or ethnicity, 35 (43%) chose avatars to guide them through the video that matched their background. Caregivers selecting avatars consistent with their racial or ethnic background were more likely to enroll in the main study (n=29, 83% vs n=43, 57%; P=.01). Conclusions: This interactive digital tool was helpful in informing prospective participants about the study. The consenter tool enhanced the informed consent process, reinforced why caregivers of unexposed infants were being approached, and was particularly helpful as a resource for families to understand magnetic resonance imaging procedures. Trial Registration: ClinicalTrials.gov NCT04149509; https://clinicaltrials.gov/study/NCT04149509 %R 10.2196/59954 %U https://formative.jmir.org/2025/1/e59954 %U https://doi.org/10.2196/59954 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e70015 %T Monitoring Nystagmus in a Patient With Vertigo Using a Commercial Mini-Infrared Camera and 3D Printer: Cost-Effectiveness Evaluation and Case Report %A Sakazaki,Hiroyuki %A Noda,Masao %A Dobashi,Yumi %A Kuroda,Tatsuaki %A Tsunoda,Reiko %A Fushiki,Hiroaki %K dizziness %K vertigo %K smartphone %K BPPV %K telemedicine %K 3D-printer %D 2025 %7 27.2.2025 %9 %J JMIR Form Res %G English %X Background: Observing eye movements during episodic vertigo attacks is crucial for accurately diagnosing vestibular disorders. In clinical practice, many cases lack observable symptoms or clear findings during outpatient examinations, leading to diagnostic challenges. An accurate diagnosis is essential for timely treatment, as conditions such as benign paroxysmal positional vertigo (BPPV), Ménière’s disease, and vestibular migraine require different therapeutic approaches. Objective: This study aimed to develop and evaluate a cost-effective diagnostic tool that integrates a mini-infrared camera with 3D-printed goggles, enabling at-home recording of nystagmus during vertigo attacks. Methods: A commercially available mini-infrared camera (US $25) was combined with 3D-printed goggles (US $13) to create a system for recording eye movements in dark conditions. A case study was conducted on a male patient in his 40s who experienced recurrent episodic vertigo. Results: Initial outpatient evaluations, including oculomotor and vestibular tests using infrared Frenzel glasses, revealed no spontaneous or positional nystagmus. However, with the proposed system, the patient successfully recorded geotropic direction-changing positional nystagmus during a vertigo attack at home. The nystagmus was beating distinctly stronger on the left side down with 2.0 beats/second than the right side down with 1.2 beats/second. Based on the recorded videos, a diagnosis of lateral semicircular canal-type BPPV was made. Treatment with the Gufoni maneuver effectively alleviated the patient’s symptoms, confirming the diagnosis. The affordability and practicality of the device make it particularly suitable for telemedicine and emergency care applications, enabling patients in remote or underserved areas to receive accurate diagnoses. Conclusions: The proposed system demonstrates the feasibility and utility of using affordable, accessible technology for diagnosing vestibular disorders outside of clinical settings. By addressing key challenges, such as the absence of symptoms during clinical visits and the high costs associated with traditional diagnostic tools, this device offers a practical solution for real-time monitoring and accurate diagnosis. Its potential applications extend to telemedicine, emergency settings, and resource-limited environments. Future iterations that incorporate higher-resolution imaging and automated analysis could further enhance its diagnostic capabilities and usability across diverse patient populations. %R 10.2196/70015 %U https://formative.jmir.org/2025/1/e70015 %U https://doi.org/10.2196/70015 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59875 %T Person-Specific Analyses of Smartphone Use and Mental Health: Intensive Longitudinal Study %A Cerit,Merve %A Lee,Angela Y %A Hancock,Jeffrey %A Miner,Adam %A Cho,Mu-Jung %A Muise,Daniel %A Garròn Torres,Anna-Angelina %A Haber,Nick %A Ram,Nilam %A Robinson,Thomas N %A Reeves,Byron %+ Graduate School of Education, Stanford University, 520 Galvez Mall, Stanford, CA, 94305, United States, 1 650 723 21 46, mervecer@stanford.edu %K media use %K mental health %K mHealth %K uHealth %K digital health %K precision mental health %K idiographic analysis %K person-specific modeling %K p-technique %K longitudinal study %K precision interventions %K smartphones %K idiosyncrasy %K psychological well-being %K canonical correlation analysis %K United States %D 2025 %7 26.2.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly been found null or have small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health. Objective: This exploratory empirical demonstration aims to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of 1 year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders. Methods: The activity on individuals’ smartphone screens was recorded every 5 seconds when devices were in use over 1 year, resulting in a dataset of 6,744,013 screenshots and 123 fortnightly surveys from 5 adult participants. Each participant contributed between 0.8 and 2.7 million screens. Six media use metrics were derived from smartphone metadata. Fortnightly surveys captured symptoms of depression, attention-deficit/hyperactivity disorder, state anxiety, and positive affect. Idiographic filter models (p-technique canonical correlation analyses) were applied to explore person-specific associations. Results: Canonical correlations revealed substantial person-specific associations between media use and mental health, ranging from r=0.82 (P=.008) to r=0.92 (P=.03). The specific combinations of media use metrics and mental health dimensions were different for each person, reflecting significant individual variability. For instance, the media use canonical variate for 1 participant was characterized by higher loadings for app-switching, which, in combination with other behaviors, correlated strongly with a mental health variate emphasizing anxiety symptoms. For another, prolonged screen time, alongside other media use behaviors, contributed to a mental health variate weighted more heavily toward depression symptoms. These within-person correlations are among the strongest reported in this literature. Conclusions: Results suggest that the relationships between media use and mental health are highly individualized, with implications for the development of personalized models and precision smartphone-informed interventions in mental health. We discuss how our approach can be extended generally, while still emphasizing the importance of idiographic approaches. This study highlights the potential for granular, longitudinal data to reveal person-specific patterns that can inform theory development, personalized screening, diagnosis, and interventions in mental health. %M 39808832 %R 10.2196/59875 %U https://formative.jmir.org/2025/1/e59875 %U https://doi.org/10.2196/59875 %U http://www.ncbi.nlm.nih.gov/pubmed/39808832 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 5 %N %P e65610 %T Characterizing Experiences With Hikikomori Syndrome on Twitter Among Japanese-Language Users: Qualitative Infodemiology Content Analysis %A Uchiyama,Misa Ashley %A Bekki,Hirofumi %A McMann,Tiana %A Li,Zhuoran %A Mackey,Tim %+ Global Health Program, Department of Anthropology, University of California San Diego, 9500 Gilman Drive, MC 0505, La Jolla, CA, 92093, United States, 1 (951) 491 4161, tmackey@ucsd.edu %K hikikomori %K social withdrawal %K hikikomori syndrome %K mental health %K social isolation %D 2025 %7 24.2.2025 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Hikikomori syndrome is a form of severe social withdrawal prevalent in Japan but is also a worldwide psychiatric issue. Twitter (subsequently rebranded X) offers valuable insights into personal experiences with mental health conditions, particularly among isolated individuals or hard-to-reach populations. Objective: This study aimed to examine trends in firsthand and secondhand experiences reported on Twitter between 2021 and 2023 in the Japanese language. Methods: Tweets were collected using the Twitter academic research application programming interface filtered for the following keywords: “#引きこもり,” “#ひきこもり,” “#hikikomori,” “#ニート,” “#脱ひきこもり,” “#不登校,” and “#自宅警備員.” The Bidirectional Encoder Representations From Transformers language model was used to analyze all Japanese-language posts collected. Themes and subthemes were then inductively coded for in-depth exploration of topic clusters relevant to first- and secondhand experiences with hikikomori syndrome. Results: We collected 2,018,822 tweets, which were narrowed down to 379,265 (18.79%) tweets in Japanese from January 2021 to January 2023. After examining the topic clusters output by the Bidirectional Encoder Representations From Transformers model, 4 topics were determined to be relevant to the study aims. A total of 400 of the most highly interacted with tweets from these topic clusters were manually annotated for inclusion and exclusion, of which 148 (37%) tweets from 89 unique users were identified as relevant to hikikomori experiences. Of these 148 relevant tweets, 71 (48%) were identified as firsthand accounts, and 77 (52%) were identified as secondhand accounts. Within firsthand reports, the themes identified included seeking social support, personal anecdotes, debunking misconceptions, and emotional ranting. Within secondhand reports, themes included seeking social support, personal anecdotes, seeking and giving advice, and advocacy against the negative stigma of hikikomori. Conclusions: This study provides new insights into experiences reported by web-based users regarding hikikomori syndrome specific to Japanese-speaking populations. Although not yet found in diagnostic manuals classifying mental disorders, the rise of web-based lifestyles as a consequence of the COVID-19 pandemic has increased the importance of discussions regarding hikikomori syndrome in web-based spaces. The results indicate that social media platforms may represent a web-based space for those experiencing hikikomori syndrome to engage in social interaction, advocacy against stigmatization, and participation in a community that can be maintained through a web-based barrier and minimized sense of social anxiety. %M 39993295 %R 10.2196/65610 %U https://infodemiology.jmir.org/2025/1/e65610 %U https://doi.org/10.2196/65610 %U http://www.ncbi.nlm.nih.gov/pubmed/39993295 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59101 %T Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches %A Lin,Bing %A Liu,Jiaxiu %A Li,Kangjie %A Zhong,Xiaoni %+ School of Public Health, Chongqing Medical University, No.1 Medical College Road, Yuzhong District, Chongqing, 400016, China, 86 13527545050, zhongxiaoni@cqmu.edu.cn %K HIV %K sexually transmitted diseases %K men who have sex with men %K machine learning %K web application %K risk stratification %D 2025 %7 20.2.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Men who have sex with men (MSM) are at high risk for HIV infection and sexually transmitted diseases (STDs). However, there is a lack of accurate and convenient tools to assess this risk. Objective: This study aimed to develop machine learning models and tools to predict and assess the risk of HIV infection and STDs among MSM. Methods: We conducted a cross-sectional study that collected individual characteristics of 1999 MSM with negative or unknown HIV serostatus in Western China from 2013 to 2023. MSM self-reported their STD history and were tested for HIV. We compared the accuracy of 6 machine learning methods in predicting the risk of HIV infection and STDs using 7 parameters for a comprehensive assessment, ranking the methods according to their performance in each parameter. We selected data from the Sichuan MSM for external validation. Results: Of the 1999 MSM, 72 (3.6%) tested positive for HIV and 146 (7.3%) self-reported a history of previous STD infection. After taking the results of the intersection of the 3 feature screening methods, a total of 7 and 5 predictors were screened for predicting HIV infection and STDs, respectively, and multiple machine learning prediction models were constructed. Extreme gradient boost models performed optimally in predicting the risk of HIV infection and STDs, with area under the curve values of 0.777 (95% CI 0.639-0.915) and 0.637 (95% CI 0.541-0.732), respectively, demonstrating stable performance in both internal and external validation. The highest combined predictive performance scores of HIV and STD models were 33 and 39, respectively. Interpretability analysis showed that nonadherence to condom use, low HIV knowledge, multiple male partners, and internet dating were risk factors for HIV infection. Low degree of education, internet dating, and multiple male and female partners were risk factors for STDs. The risk stratification analysis showed that the optimal model effectively distinguished between high- and low-risk MSM. MSM were classified into HIV (predicted risk score <0.506 and ≥0.506) and STD (predicted risk score <0.479 and ≥0.479) risk groups. In total, 22.8% (114/500) were in the HIV high-risk group, and 43% (215/500) were in the STD high-risk group. HIV infection and STDs were significantly higher in the high-risk groups (P<.001 and P=.05, respectively), with higher predicted probabilities (P<.001 for both). The prediction results of the optimal model were displayed in web applications for probability estimation and interactive computation. Conclusions: Machine learning methods have demonstrated strengths in predicting the risk of HIV infection and STDs among MSM. Risk stratification models and web applications can facilitate clinicians in accurately assessing the risk of infection in individuals with high risk, especially MSM with concealed behaviors, and help them to self-monitor their risk for targeted, timely diagnosis and interventions to reduce new infections. %M 39977856 %R 10.2196/59101 %U https://www.jmir.org/2025/1/e59101 %U https://doi.org/10.2196/59101 %U http://www.ncbi.nlm.nih.gov/pubmed/39977856 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66910 %T Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study %A Seinen,Tom M %A Kors,Jan A %A van Mulligen,Erik M %A Rijnbeek,Peter R %+ Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 40, Rotterdam, 3015 GD, Netherlands, 31 010 7044122, t.seinen@erasmusmc.nl %K natural language processing %K named entity recognition %K clinical concept extraction %K machine learning %K electronic health records %K EHR %K word embeddings %K clinical concept similarity %K text mining %K code %K free-text %K information %K electronic record %K data %K patient records %K framework %K structured data %K unstructured data %D 2025 %7 13.2.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic health records (EHRs) consist of both structured data (eg, diagnostic codes) and unstructured data (eg, clinical notes). It is commonly believed that unstructured clinical narratives provide more comprehensive information. However, this assumption lacks large-scale validation and direct validation methods. Objective: This study aims to quantitatively compare the information in structured and unstructured EHR data and directly validate whether unstructured data offers more extensive information across a patient population. Methods: We analyzed both structured and unstructured data from patient records and visits in a large Dutch primary care EHR database between January 2021 and January 2024. Clinical concepts were identified from free-text notes using an extraction framework tailored for Dutch and compared with concepts from structured data. Concept embeddings were generated to measure semantic similarity between structured and extracted concepts through cosine similarity. A similarity threshold was systematically determined via annotated matches and minimized weighted Gini impurity. We then quantified the concept overlap between structured and unstructured data across various concept domains and patient populations. Results: In a population of 1.8 million patients, only 13% of extracted concepts from patient records and 7% from individual visits had similar structured counterparts. Conversely, 42% of structured concepts in records and 25% in visits had similar matches in unstructured data. Condition concepts had the highest overlap, followed by measurements and drug concepts. Subpopulation visits, such as those with chronic conditions or psychological disorders, showed different proportions of data overlap, indicating varied reliance on structured versus unstructured data across clinical contexts. Conclusions: Our study demonstrates the feasibility of quantifying the information difference between structured and unstructured data, showing that the unstructured data provides important additional information in the studied database and populations. The annotated concept matches are made publicly available for the clinical natural language processing community. Despite some limitations, our proposed methodology proves versatile, and its application can lead to more robust and insightful observational clinical research. %M 39946687 %R 10.2196/66910 %U https://www.jmir.org/2025/1/e66910 %U https://doi.org/10.2196/66910 %U http://www.ncbi.nlm.nih.gov/pubmed/39946687 %0 Journal Article %@ 2817-092X %I JMIR Publications %V 4 %N %P e64624 %T Exploring Speech Biosignatures for Traumatic Brain Injury and Neurodegeneration: Pilot Machine Learning Study %A Rubaiat,Rahmina %A Templeton,John Michael %A Schneider,Sandra L %A De Silva,Upeka %A Madanian,Samaneh %A Poellabauer,Christian %K speech biosignatures %K speech feature analysis %K amyotrophic lateral sclerosis %K ALS %K neurodegenerative disease %K Parkinson's disease %K detection %K speech %K neurological %K traumatic brain injury %K concussion %K mobile device %K digital health %K machine learning %K mobile health %K diagnosis %K mobile phone %D 2025 %7 12.2.2025 %9 %J JMIR Neurotech %G English %X Background: Speech features are increasingly linked to neurodegenerative and mental health conditions, offering the potential for early detection and differentiation between disorders. As interest in speech analysis grows, distinguishing between conditions becomes critical for reliable diagnosis and assessment. Objective: This pilot study explores speech biosignatures in two distinct neurodegenerative conditions: (1) mild traumatic brain injuries (eg, concussions) and (2) Parkinson disease (PD) as the neurodegenerative condition. Methods: The study included speech samples from 235 participants (97 concussed and 94 age-matched healthy controls, 29 PD and 15 healthy controls) for the PaTaKa test and 239 participants (91 concussed and 104 healthy controls, 29 PD and 15 healthy controls) for the Sustained Vowel (/ah/) test. Age-matched healthy controls were used. Young age-matched controls were used for concussion and respective age-matched controls for neurodegenerative participants (15 healthy samples for both tests). Data augmentation with noise was applied to balance small datasets for neurodegenerative and healthy controls. Machine learning models (support vector machine, decision tree, random forest, and Extreme Gradient Boosting) were employed using 37 temporal and spectral speech features. A 5-fold stratified cross-validation was used to evaluate classification performance. Results: For the PaTaKa test, classifiers performed well, achieving F1-scores above 0.9 for concussed versus healthy and concussed versus neurodegenerative classifications across all models. Initial tests using the original dataset for neurodegenerative versus healthy classification yielded very poor results, with F1-scores below 0.2 and accuracy under 30% (eg, below 12 out of 44 correctly classified samples) across all models. This underscored the need for data augmentation, which significantly improved performance to 60%‐70% (eg, 26‐31 out of 44 samples) accuracy. In contrast, the Sustained Vowel test showed mixed results; F1-scores remained high (more than 0.85 across all models) for concussed versus neurodegenerative classifications but were significantly lower for concussed versus healthy (0.59‐0.62) and neurodegenerative versus healthy (0.33‐0.77), depending on the model. Conclusions: This study highlights the potential of speech features as biomarkers for neurodegenerative conditions. The PaTaKa test exhibited strong discriminative ability, especially for concussed versus neurodegenerative and concussed versus healthy tasks, whereas challenges remain for neurodegenerative versus healthy classification. These findings emphasize the need for further exploration of speech-based tools for differential diagnosis and early identification in neurodegenerative health. %R 10.2196/64624 %U https://neuro.jmir.org/2025/1/e64624 %U https://doi.org/10.2196/64624 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e56533 %T Assessment of Environmental, Sociocultural, and Physiological Influences on Women’s Toileting Decisions and Behaviors Using “Where I Go”: Pilot Study of a Mobile App %A Smith,Abigail R %A Mueller,Elizabeth R %A Lewis,Cora E %A Markland,Alayne %A Smerdon,Caroline %A Smith,Ariana L %A Sutcliffe,Siobhan %A Wyman,Jean F %A Low,Lisa Kane %A Miller,Janis M %A , %K ecological momentary assessment %K time location factors %K voiding diary %K voiding behaviors %K population studies %K mobile application %K app %K bladder health %K data collection tool %K decision support %D 2025 %7 12.2.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Little is known about women’s decisions around toileting for urination and how those decisions influence moment-to-moment behaviors to manage bladder needs. The new smartphone app “Where I Go” captures such nuanced and granular data in real-world environments. Objective: This study aims to describe participant engagement with “Where I Go”, variation in novel parameters collected, and readiness for the data collection tool’s use in population-based studies. Methods: “Where I Go” has three components: (1) real-time data, (2) short look-back periods (3‐4 h), and (3) event location (GPS recorded at each interaction). The sample size was 44 women. Recording of real-time toileting events and responding to look-back questions was measured over 2 days of data collection. The participant’s self-entered location descriptions and the automatic GPS recordings were compared. Results: A total of 44 women with an average age of 44 (range 21-85) years interacted with the app. Real-time reporting of at least 1 toileting event per day was high (38/44, 86%, on day 1 and 40/44, 91%, on day 2) with a median of 5 (IQR 3-7 on day 1 and IQR 3-8 on day 2) toileting events recorded each day. Toileting most commonly occurred at home (85/140, 61%, on day 1 and 129/171, 75%, on day 2) due to a need to go (114/140, 66%, on day 1 and 153/171, 74%, on day 2). The most common reasons for delaying toileting were “work duties” (33/140, 21%, on day 1 and 21/171, 11%, on day 2) and “errands or traveling” (19/140, 12%, on day 1 and 19/171, 10%, on day 2). Response to at least 1 look-back notification was similarly high (41/44, 93%, on day 1 and 42/44, 95%, on day 2), with number of responses higher on average on day 2 compared with day 1 (mean on day 1=3.2, 95% CI 3.0-3.5; mean on day 2=4.3, 95% CI 3.9-4.7; P<.001). Median additional toileting events reported on the look-back survey were 1 (IQR 1-2) and 2 (IQR 1-2) on days 1 and 2, respectively. Overall concordance between self-reported location recording and GPS was 76% (188/247). Participants reported lower urge ratings when at home versus away when reporting real-time toileting (median rating 61, IQR 41-84 vs 72, IQR 56-98), and daily fluid intake showed a small to medium positive correlation with toileting frequency (day 1 r=0.3, day 2 r=0.24). Toileting frequency reported in “Where I Go” showed a small positive correlation with the frequency item from the International Consultation on Incontinence Questionnaire (r=0.31 with day 1 toileting frequency and r=0.21 with day 2 toileting frequency). Conclusions: “Where I Go” has potential to increase the understanding of factors that affect women’s toileting decisions and long-term bladder health. We anticipate its use as a data collection tool in population-based studies. International Registered Report Identifier (IRRID): RR2-10.2196/54046 %R 10.2196/56533 %U https://mhealth.jmir.org/2025/1/e56533 %U https://doi.org/10.2196/56533 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e52887 %T Participant Compliance With Ecological Momentary Assessment in Movement Behavior Research Among Adolescents and Emerging Adults: Systematic Review %A Wang,Shirlene %A Yang,Chih-Hsiang %A Brown,Denver %A Cheng,Alan %A Kwan,Matthew Y W %+ Department of Population and Public Health Sciences, University of Southern California, 1875 N Soto Street, Los Angeles, CA, 90032, United States, 1 3125327663, shirlene@northwestern.edu %K compliance %K ecological momentary assessment %K mobile health %K adolescents %K emerging adults %K physical activity %K movement behavior %K systematic review %K cognitive %K social %K development %K youth %K literature search %K inclusion %K data quality %K mobile phone %D 2025 %7 11.2.2025 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Adolescence through emerging adulthood represents a critical period associated with changes in lifestyle behaviors. Understanding the dynamic relationships between cognitive, social, and environmental contexts is informative for the development of interventions aiming to help youth sustain physical activity and limit sedentary time during this life stage. Ecological momentary assessment (EMA) is an innovative method involving real-time assessment of individuals’ experiences and behaviors in their naturalistic or everyday environments; however, EMA compliance can be problematic due to high participant burdens. Objective: This systematic review synthesized existing evidence pertaining to compliance in EMA studies that investigated wake-time movement behaviors among adolescent and emerging adult populations. Differences in EMA delivery scheme or protocol, EMA platforms, prompting schedules, and compensation methods—all of which can affect participant compliance and overall study quality—were examined. Methods: An electronic literature search was conducted in PubMed, PsycINFO, and Web of Science databases to select relevant papers that assessed movement behaviors among the population using EMA and reported compliance information for inclusion (n=52) in October 2022. Study quality was assessed using a modified version of the Checklist for Reporting of EMA Studies (CREMAS). Results: Synthesizing the existing evidence revealed several factors that influence compliance. The platform used for EMA studies could affect compliance and data quality in that studies using smartphones or apps might lessen additional burdens associated with delivering EMAs, yet most studies used web-based formats (n=18, 35%). Study length was not found to affect EMA compliance rates, but the timing and frequency of prompts may be critical factors associated with missingness. For example, studies that only prompted participants once per day had higher compliance (91% vs 77%), but more frequent prompts provided more comprehensive data for researchers at the expense of increased participant burden. Similarly, studies with frequent prompting within the day may provide more representative data but may also be perceived as more burdensome and result in lower compliance. Compensation type did not significantly affect compliance, but additional motivational strategies could be applied to encourage participant response. Conclusions: Ultimately, researchers should consider the best strategies to limit burdens, balanced against requirements to answer the research question or phenomena being studied. Findings also highlight the need for greater consistency in reporting and more specificity when explaining procedures to understand how EMA compliance could be optimized in studies examining physical activity and sedentary time among youth. Trial Registration: PROSPERO CRD42021282093; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=282093 %M 39933165 %R 10.2196/52887 %U https://mhealth.jmir.org/2025/1/e52887 %U https://doi.org/10.2196/52887 %U http://www.ncbi.nlm.nih.gov/pubmed/39933165 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e66127 %T Investigation of Information Overload in Electronic Health Records: Protocol for Usability Study %A Khairat,Saif %A Morelli,Jennifer %A Boynton,Marcella H %A Bice,Thomas %A Gold,Jeffrey A %A Carson,Shannon S %+ School of Nursing, University of North Carolina at Chapel Hill, Carrington Hall, CB #7460, Chapel Hill, NC, 27514, United States, 1 919 843 5413, saif@unc.edu %K electronic health records %K information overload %K eye-tracking %K EHR usability %K EHR interface %D 2025 %7 11.2.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Electronic health records (EHRs) have been associated with information overload, causing providers to miss critical information, make errors, and delay care. Information overload can be especially prevalent in medical intensive care units (ICUs) where patients are often critically ill and their charts contain large amounts of data points such as vitals, test and laboratory results, medications, and notes. Objective: We propose to study the relationship between information overload and EHR use among medical ICU providers in 4 major United States medical centers. In this study, we examined 2 prominent EHR systems in the United States to generate reproducible and generalizable findings. Methods: Our study collected physiological and objective data through the use of a screen-mounted eye-tracker. We aim to characterize information overload in the EHR by examining ICU providers’ decision-making and EHR usability. We also surveyed providers on their institution’s EHR to better understand how they rate the system’s task load and usability using the NASA (National Aeronautics and Space Administration) Task Load Index and Computer System Usability Questionnaire. Primary outcomes include the number of eye fixations during each case, the number of correct decisions, the time to complete each case, and number of screens visited. Secondary outcomes include case complexity performance, frequency of mouse clicks, and EHR task load and usability using provided surveys. Results: This EHR usability study was funded in 2021. The study was initiated in 2022 with a completion date of 2025. Data collection for this study was completed in December 2023 and data analysis is ongoing with a total of 81 provider sessions recorded. Conclusions: Our study aims to characterize information overload in the EHR among medical ICU providers. By conducting a multisite, cross-sectional usability assessment of information overload in 2 leading EHRs, we hope to reveal mechanisms that explain information overload. The insights gained from this study may lead to potential improvements in EHR usability and interface design, which could improve health care delivery and patient safety. International Registered Report Identifier (IRRID): DERR1-10.2196/66127 %M 39932774 %R 10.2196/66127 %U https://www.researchprotocols.org/2025/1/e66127 %U https://doi.org/10.2196/66127 %U http://www.ncbi.nlm.nih.gov/pubmed/39932774 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 12 %N %P e62776 %T NutriDiary, a Smartphone-Based Dietary Record App: Description and Usability Evaluation %A Klasen,Linda %A Koch,Stefanie Anna Julia %A Benz,Maike Elena %A Conrad,Johanna %A Alexy,Ute %A Blaszkiewicz,Konrad %A Andone,Ionut %A Nöthlings,Ute %K dietary assessment %K food record %K barcode scanning %K app %K mobile phone %D 2025 %7 10.2.2025 %9 %J JMIR Hum Factors %G English %X Background: Repeated applications of short-term dietary assessment instruments are recommended for estimating usual dietary intake. For this purpose, NutriDiary, a smartphone app for collecting weighed dietary records (WDRs) in the German population, was developed. Objective: We aim to describe NutriDiary and evaluate its usability and acceptability. Methods: NutriDiary was developed as a WDR, allowing users to enter food items via text search, barcode scanning, or free text entry. The sample for the evaluation study included 74 participants (n=51, 69% female, aged 18‐64 years), including 27 (37.5%) experts and 47 (63.5%) laypersons (including n=22, 30%, nutrition students). Participants completed a 1-day WDR and entered a predefined sample meal (n=17 foods) the following day by using NutriDiary. An evaluation questionnaire was answered from which the system usability scale (SUS) score (0‐100) was calculated. A backward selection procedure (PROC REG in SAS; SAS Institute) was used to identify potential predictors for the SUS score (age, sex, status [expert or laypersons], and operating system [iOS or Android]). Results: The median SUS score of 75 (IQR 63‐88) indicated good usability. Age was the only characteristic identified as a potential predictor for a lower SUS score (P<.001). The median completion time for an individual WDR was 35 (IQR 19‐52) minutes. Older participants took longer to enter the data than younger ones (18‐30 y: median 1.5, IQR 1.1‐2.0 min/item vs 45‐64 y: median 1.8, IQR 1.3‐2.3 min/item). Most participants expressed a preference for NutriDiary over the traditional paper-based method. Conclusions: Good usability and acceptability make NutriDiary promising for use in epidemiological studies. %R 10.2196/62776 %U https://humanfactors.jmir.org/2025/1/e62776 %U https://doi.org/10.2196/62776 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59161 %T Exploring the Relationship Between Smartphone GPS Patterns and Quality of Life in Patients With Advanced Cancer and Their Family Caregivers: Longitudinal Study %A Lee,Kyungmi %A Azuero,Andres %A Engler,Sally %A Kumar,Sidharth %A Puga,Frank %A Wright,Alexi A %A Kamal,Arif %A Ritchie,Christine S %A Demiris,George %A Bakitas,Marie A %A Odom,J Nicholas %K cancer %K digital phenotyping %K global positioning system %K quality of life %K smartphone %K mobile phone %K family caregiver %D 2025 %7 7.2.2025 %9 %J JMIR Form Res %G English %X Background: Patients with advanced cancer and their family caregivers often experience poor quality of life (QOL). Self-report measures are commonly used to quantify QOL of family caregivers but may have limitations such as recall bias and social desirability bias. Variables derived from passively obtained smartphone GPS data are a novel approach to measuring QOL that may overcome these limitations and enable detection of early signs of mental and physical health (PH) deterioration. Objective: This study explored the feasibility of a digital phenotyping approach by assessing participant adherence and examining correlations between smartphone GPS data and QOL levels among family caregivers and patients with advanced cancer. Methods: This was a secondary analysis involving 7 family caregivers and 4 patients with advanced cancer that assessed correlations between GPS sensor data captured by a personally owned smartphone and QOL self-report measures over 12 weeks through linear correlation coefficients. QOL as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health 10 was collected at baseline, 6, and 12 weeks. Using a Beiwe smartphone app, GPS data were collected and processed into variables including total distance, time spent at home, transition time, and number of significant locations. Results: The study identified relevant temporal correlations between QOL and smartphone GPS data across specific time periods. For instance, in terms of PH, associations were observed with the total distance traveled (12 and 13 wk, with r ranging 0.37 to 0.38), time spent at home (−4 to −2 wk, with r ranging from −0.41 to −0.49), and transition time (−4 to −2 wk, with r ranging −0.38 to −0.47). Conclusions: This research offers insights into using passively obtained smartphone GPS data as a novel approach for assessing and monitoring QOL among family caregivers and patients with advanced cancer, presenting potential advantages over traditional self-report measures. The observed correlations underscore the potential of this method to detect early signs of deteriorating mental health and PH, providing opportunities for timely intervention and support. %R 10.2196/59161 %U https://formative.jmir.org/2025/1/e59161 %U https://doi.org/10.2196/59161 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e58956 %T A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies %A Bakker,Jessie P %A McClenahan,Samantha J %A Fromy,Piper %A Turner,Simon %A Peterson,Barry T %A Vandendriessche,Benjamin %A Goldsack,Jennifer C %+ Digital Medicine Society, 90 Canal Street, Boston, MA, 02114, United States, 1 7652343463, benjamin.vandendriessche@dimesociety.org %K digital health technologies %K analytical validation %K digital medicine %K reference measures %K fit-for-purpose digital clinical measures %D 2025 %7 7.2.2025 %9 Viewpoint %J J Med Internet Res %G English %X Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs—evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable—is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development. %M 39918870 %R 10.2196/58956 %U https://www.jmir.org/2025/1/e58956 %U https://doi.org/10.2196/58956 %U http://www.ncbi.nlm.nih.gov/pubmed/39918870 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e55541 %T Smartphone-Based Intervention Targeting Norms and Risk Perception Among University Students with Unhealthy Alcohol Use: Secondary Mediation Analysis of a Randomized Controlled Trial %A Studer,Joseph %A Cunningham,John A %A Schmutz,Elodie %A Gaume,Jacques %A Adam,Angéline %A Daeppen,Jean-Bernard %A Bertholet,Nicolas %+ Addiction Medicine, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 23A, Lausanne, 1011, Switzerland, 41 213149033, joseph.studer@chuv.ch %K brief intervention %K alcohol use %K mechanism of action %K mediation analysis %K personalized feedback %K smartphone app %K students %K Switzerland %K mobile phone %K mediation %K feedback %K student %K health risk %K drinking %K drinker %K support %K feedback intervention %D 2025 %7 6.2.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Many digital interventions for unhealthy alcohol use are based on personalized normative feedback (PNF) and personalized feedback on risks for health (PFR). The hypothesis is that PNF and PFR affect drinkers’ perceptions of drinking norms and risks, resulting in changes in drinking behaviors. This study is a follow-up mediation analysis of the primary and secondary outcomes of a randomized controlled trial testing the effect of a smartphone-based intervention to reduce alcohol use. Objective: This study aimed to investigate whether perceptions of drinking norms and risks mediated the effects of a smartphone-based intervention to reduce alcohol use. Methods: A total of 1770 students from 4 higher education institutions in Switzerland (mean age 22.35, SD 3.07 years) who screened positive for unhealthy alcohol use were randomized to receive access to a smartphone app or to the no-intervention control condition. The smartphone app provided PNF and PFR. Outcomes were drinking volume (DV) in standard drinks per week and the number of heavy drinking days (HDDs) assessed at baseline and 6 months. Mediators were perceived drinking norms and perceived risks for health measured at baseline and 3 months. Parallel mediation analyses and moderated mediation analyses were conducted to test whether (1) the intervention effect was indirectly related to lower DV and HDDs at 6 months (adjusting for baseline values) through perceived drinking norms and perceived risks for health at 3 months (adjusting for baseline values) and (2) the indirect effects through perceived drinking norms differed between participants who overestimated or who did not overestimate other people’s drinking at baseline. Results: The intervention’s total effects were significant (DV: b=–0.85, 95% bootstrap CI –1.49 to –0.25; HDD: b=–0.44, 95% bootstrap CI –0.72 to –0.16), indicating less drinking at 6 months in the intervention group than in the control group. The direct effects (ie, controlling for mediators) were significant though smaller (DV: b=–0.73, 95% bootstrap CI –1.33 to –0.16; HDD: b=–0.39, 95% bootstrap CI –0.66 to –0.12). For DV, the indirect effect was significant through perceived drinking norms (b=–0.12, 95% bootstrap CI –0.25 to –0.03). The indirect effects through perceived risk (for DV and HDD) and perceived drinking norms (for HDD) were not significant. Results of moderated mediation analyses showed that the indirect effects through perceived drinking norms were significant among participants overestimating other people’s drinking (DV: b=–0.17, 95% bootstrap CI –0.32 to –0.05; HDD: b=–0.08, 95% bootstrap CI –0.15 to –0.01) but not significant among those not overestimating. Conclusions: Perceived drinking norms, but not perceived risks, partially mediated the intervention’s effect on alcohol use, confirming one of its hypothesized mechanisms of action. These findings lend support to using normative feedback interventions to discourage unhealthy alcohol use. Trial Registration: ISRCTN Registry 10007691; https://doi.org/10.1186/ISRCTN10007691 %M 39914807 %R 10.2196/55541 %U https://www.jmir.org/2025/1/e55541 %U https://doi.org/10.2196/55541 %U http://www.ncbi.nlm.nih.gov/pubmed/39914807 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e56185 %T Utility of Digital Phenotyping Based on Wrist Wearables and Smartphones in Psychosis: Observational Study %A Yang,Zixu %A Heaukulani,Creighton %A Sim,Amelia %A Buddhika,Thisum %A Abdul Rashid,Nur Amirah %A Wang,Xuancong %A Zheng,Shushan %A Quek,Yue Feng %A Basu,Sutapa %A Lee,Kok Wei %A Tang,Charmaine %A Verma,Swapna %A Morris,Robert J T %A Lee,Jimmy %K schizophrenia %K psychosis %K digital phenotyping %K wrist wearables %K mobile phone %D 2025 %7 5.2.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Digital phenotyping provides insights into an individual’s digital behaviors and has potential clinical utility. Objective: In this observational study, we explored digital biomarkers collected from wrist-wearable devices and smartphones and their associations with clinical symptoms and functioning in patients with schizophrenia. Methods: We recruited 100 outpatients with schizophrenia spectrum disorder, and we collected various digital data from commercially available wrist wearables and smartphones over a 6-month period. In this report, we analyzed the first week of digital data on heart rate, sleep, and physical activity from the wrist wearables and travel distance, sociability, touchscreen tapping speed, and screen time from the smartphones. We analyzed the relationships between these digital measures and patient baseline measurements of clinical symptoms assessed with the Positive and Negative Syndrome Scale, Brief Negative Symptoms Scale, and Calgary Depression Scale for Schizophrenia, as well as functioning as assessed with the Social and Occupational Functioning Assessment Scale. Linear regression was performed for each digital and clinical measure independently, with the digital measures being treated as predictors. Results: Digital data were successfully collected from both the wearables and smartphones throughout the study, with 91% of the total possible data successfully collected from the wearables and 82% from the smartphones during the first week of the trial—the period under analysis in this report. Among the clinical outcomes, negative symptoms were associated with the greatest number of digital measures (10 of the 12 studied here), followed by overall measures of psychopathology symptoms, functioning, and positive symptoms, which were each associated with at least 3 digital measures. Cognition and cognitive/disorganization symptoms were each associated with 1 or 2 digital measures. Conclusions: We found significant associations between nearly all digital measures and a wide range of symptoms and functioning in a community sample of individuals with schizophrenia. These findings provide insights into the digital behaviors of individuals with schizophrenia and highlight the potential of using commercially available wrist wearables and smartphones for passive monitoring in schizophrenia. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-046552 %R 10.2196/56185 %U https://mhealth.jmir.org/2025/1/e56185 %U https://doi.org/10.2196/56185 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e63864 %T Colorectal Cancer Racial Equity Post Volume, Content, and Exposure: Observational Study Using Twitter Data %A Tong,Chau %A Margolin,Drew %A Niederdeppe,Jeff %A Chunara,Rumi %A Liu,Jiawei %A Jih-Vieira,Lea %A King,Andy J %+ School of Journalism, University of Missouri, 140B Walter Williams, Columbia, MO, 65203, United States, 1 573 882 7875, ctong@missouri.edu %K racial equity information %K information exposure %K health disparities %K colorectal cancer %K cancer communication %K Twitter %K X %D 2025 %7 3.2.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Racial inequity in health outcomes, particularly in colorectal cancer (CRC), remains one of the most pressing issues in cancer communication and public health. Social media platforms like Twitter (now X) provide opportunities to disseminate health equity information widely, yet little is known about the availability, content, and reach of racial health equity information related to CRC on these platforms. Addressing this gap is essential to leveraging social media for equitable health communication. Objective: This study aims to analyze the volume, content, and exposure of CRC racial health equity tweets from identified CRC equity disseminator accounts on Twitter. These accounts were defined as those actively sharing information related to racial equity in CRC outcomes. By examining the behavior and impact of these disseminators, this study provides insights into how health equity content is shared and received on social media. Methods: We identified accounts that posted CRC-related content on Twitter between 2019 and 2021. Accounts were classified as CRC equity disseminators (n=798) if they followed at least 2 CRC racial equity organization accounts. We analyzed the volume and content of racial equity–related CRC tweets (n=1134) from these accounts and categorized them by account type (experts vs nonexperts). Additionally, we evaluated exposure by analyzing follower reach (n=6,266,269) and the role of broker accounts—accounts serving as unique sources of CRC racial equity information to their followers. Results: Among 19,559 tweets posted by 798 CRC equity disseminators, only 5.8% (n=1134) mentioned racially and ethnically minoritized groups. Most of these tweets (641/1134, 57%) addressed disparities in outcomes, while fewer emphasized actionable content, such as symptoms (11/1134, 1%) or screening procedures (159/1134, 14%). Expert accounts (n=479; 716 tweets) were more likely to post CRC equity tweets compared with nonexpert accounts (n=319; 418 tweets). Broker accounts (n=500), or those with a substantial portion of followers relying on them for equity-related information, demonstrated the highest capacity for exposing followers to CRC equity content, thereby extending the reach of these critical messages to underserved communities. Conclusions: This study emphasizes the critical roles played by expert and broker accounts in disseminating CRC racial equity information on social media. Despite the limited volume of equity-focused content, broker accounts were crucial in reaching otherwise unexposed audiences. Public health practitioners should focus on encouraging equity disseminators to share more actionable information, such as symptoms and screening benefits, and implement measures to amplify the reach of such content on social media. Strengthening these efforts could help bridge disparities in cancer outcomes among racially minoritized groups. %M 39899839 %R 10.2196/63864 %U https://www.jmir.org/2025/1/e63864 %U https://doi.org/10.2196/63864 %U http://www.ncbi.nlm.nih.gov/pubmed/39899839 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e64749 %T Relationship Among Macronutrients, Dietary Components, and Objective Sleep Variables Measured by Smartphone Apps: Real-World Cross-Sectional Study %A Seol,Jaehoon %A Iwagami,Masao %A Kayamare,Megane Christiane Tawylum %A Yanagisawa,Masashi %+ International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan, 81 29 853 5857, yanagisawa.masa.fu@u.tsukuba.ac.jp %K sleep quality %K dietary health %K unsaturated fatty acids %K dietary fiber intake %K sodium-to-potassium ratio %K compositional data analysis %K sleep %K smartphone %K application %D 2025 %7 30.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Few studies have explored the relationship between macronutrient intake and sleep outcomes using daily data from mobile apps. Objective: This cross-sectional study aimed to examine the associations between macronutrients, dietary components, and sleep parameters, considering their interdependencies. Methods: We analyzed data from 4825 users of the Pokémon Sleep and Asken smartphone apps, each used for at least 7 days to record objective sleep parameters and dietary components, respectively. Multivariable regression explored the associations between quartiles of macronutrients (protein; carbohydrate; and total fat, including saturated, monounsaturated, and polyunsaturated fats), dietary components (sodium, potassium, dietary fiber, and sodium-to-potassium ratio), and sleep variables (total sleep time [TST], sleep latency [SL], and percentage of wakefulness after sleep onset [%WASO]). The lowest intake group was the reference. Compositional data analysis accounted for macronutrient interdependencies. Models were adjusted for age, sex, and BMI. Results: Greater protein intake was associated with longer TST in the third (+0.17, 95% CI 0.09-0.26 h) and fourth (+0.18, 95% CI 0.09-0.27 h) quartiles. In contrast, greater fat intake was linked to shorter TST in the third (–0.11, 95% CI –0.20 to –0.27 h) and fourth (–0.16, 95% CI –0.25 to –0.07 h) quartiles. Greater carbohydrate intake was associated with shorter %WASO in the third (–0.82%, 95% CI –1.37% to –0.26%) and fourth (–0.57%, 95% CI –1.13% to –0.01%) quartiles, while greater fat intake was linked to longer %WASO in the fourth quartile (+0.62%, 95% CI 0.06%-1.18%). Dietary fiber intake correlated with longer TST and shorter SL. A greater sodium-to-potassium ratio was associated with shorter TST in the third (–0.11, 95% CI –0.20 to –0.02 h) and fourth (–0.19, 95% CI –0.28 to –0.10 h) quartiles; longer SL in the second (+1.03, 95% CI 0.08-1.98 min) and fourth (+1.50, 95% CI 0.53-2.47 min) quartiles; and longer %WASO in the fourth quartile (0.71%, 95% CI 0.15%-1.28%). Compositional data analysis, involving 6% changes in macronutrient proportions, showed that greater protein intake was associated with an elevated TST (+0.27, 95% CI 0.18-0.35 h), while greater monounsaturated fat intake was associated with a longer SL (+4.6, 95% CI 1.93-7.34 min) and a larger %WASO (+2.2%, 95% CI 0.63%-3.78%). In contrast, greater polyunsaturated fat intake was associated with a reduced TST (–0.22, 95% CI –0.39 to –0.05 h), a shorter SL (–4.7, 95% CI to 6.58 to –2.86 min), and a shorter %WASO (+2.0%, 95% CI –3.08% to –0.92%). Conclusions: Greater protein and fiber intake were associated with longer TST, while greater fat intake and sodium-to-potassium ratios were linked to shorter TST and longer WASO. Increasing protein intake in place of other nutrients was associated with longer TST, while higher polyunsaturated fat intake improved SL and reduced WASO. %M 39883933 %R 10.2196/64749 %U https://www.jmir.org/2025/1/e64749 %U https://doi.org/10.2196/64749 %U http://www.ncbi.nlm.nih.gov/pubmed/39883933 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e55308 %T Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study %A Terhorst,Yannik %A Messner,Eva-Maria %A Opoku Asare,Kennedy %A Montag,Christian %A Kannen,Christopher %A Baumeister,Harald %+ Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Lise-Meitner-Str. 16, Ulm, 89081, Germany, 49 8921805057, yannik.terhorst@psy.lmu.de %K smart sensing %K digital phenotyping %K depression %K observation study %K smartphone %K mHealth %K mobile health %K app %K mental health %K symptoms %K assessments %D 2025 %7 30.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process, called smart sensing, allows a fine-grained assessment of various features (eg, time spent at home based on the GPS sensor). Based on its prevalence and impact, depression is a promising target for smart sensing. However, currently, it is unclear which sensor-based features should be used in depression severity prediction and if they hold an incremental benefit over established fine-grained assessments like the ecological momentary assessment (EMA). Objective: The aim of this study was to investigate various features based on the smartphone screen, app usage, and call sensor alongside EMA to infer depression severity. Bivariate, cluster-wise, and cluster-combined analyses were conducted to determine the incremental benefit of smart sensing features compared to each other and EMA in parsimonious regression models for depression severity. Methods: In this exploratory observational study, participants were recruited from the general population. Participants needed to be 18 years of age, provide written informed consent, and own an Android-based smartphone. Sensor data and EMA were collected via the INSIGHTS app. Depression severity was assessed using the 8-item Patient Health Questionnaire. Missing data were handled by multiple imputations. Correlation analyses were conducted for bivariate associations; stepwise linear regression analyses were used to find the best prediction models for depression severity. Models were compared by adjusted R2. All analyses were pooled across the imputed datasets according to Rubin’s rule. Results: A total of 107 participants were included in the study. Ages ranged from 18 to 56 (mean 22.81, SD 7.32) years, and 78% of the participants identified as female. Depression severity was subclinical on average (mean 5.82, SD 4.44; Patient Health Questionnaire score ≥10: 18.7%). Small to medium correlations were found for depression severity and EMA (eg, valence: r=–0.55, 95% CI –0.67 to –0.41), and there were small correlations with sensing features (eg, screen duration: r=0.37, 95% CI 0.20 to 0.53). EMA features could explain 35.28% (95% CI 20.73% to 49.64%) of variance and sensing features (adjusted R2=20.45%, 95% CI 7.81% to 35.59%). The best regression model contained EMA and sensing features (R2=45.15%, 95% CI 30.39% to 58.53%). Conclusions: Our findings underline the potential of smart sensing and EMA to infer depression severity as isolated paradigms and when combined. Although these could become important parts of clinical decision support systems for depression diagnostics and treatment in the future, confirmatory studies are needed before they can be applied to routine care. Furthermore, privacy, ethical, and acceptance issues need to be addressed. %M 39883512 %R 10.2196/55308 %U https://www.jmir.org/2025/1/e55308 %U https://doi.org/10.2196/55308 %U http://www.ncbi.nlm.nih.gov/pubmed/39883512 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e54601 %T Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study %A Trevena,William %A Zhong,Xiang %A Alvarado,Michelle %A Semenov,Alexander %A Oktay,Alp %A Devlin,Devin %A Gohil,Aarya Yogesh %A Chittimouju,Sai Harsha %+ Department of Industrial and Systems Engineering, The University of Florida, PO BOX 115002, GAINESVILLE, FL, 32611-5002, United States, 1 3523922477, xiang.zhong@ise.ufl.edu %K natural language processing %K large language models %K ChatGPT %K drug discontinuation events %K zero-shot classification %K artificial intelligence %K AI %D 2025 %7 30.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored. Identifying DDEs is crucial for understanding medication adherence and patient outcomes. Objective: The aim of this study is to provide a flexible framework for investigating various clinical research questions in data-sparse environments. We provide an example of the utility of this framework by identifying DDEs and their root causes in an open-source web-based forum, MedHelp, and by releasing the first open-source DDE datasets to aid further research in this domain. Methods: We used several LLMs, including GPT-4 Turbo, GPT-4o, DeBERTa (Decoding-Enhanced Bidirectional Encoder Representations from Transformer with Disentangled Attention), and BART, among others, to detect and determine the root causes of DDEs in user comments posted on MedHelp. Our study design included the use of zero-shot classification, which allows these models to make predictions without task-specific training. We split user comments into sentences and applied different classification strategies to assess the performance of these models in identifying DDEs and their root causes. Results: Among the selected models, GPT-4o performed the best at determining the root causes of DDEs, predicting only 12.9% of root causes incorrectly (hamming loss). Among the open-source models tested, BART demonstrated the best performance in detecting DDEs, achieving an F1-score of 0.86, a false positive rate of 2.8%, and a false negative rate of 6.5%, all without any fine-tuning. The dataset included 10.7% (107/1000) DDEs, emphasizing the models’ robustness in an imbalanced data context. Conclusions: This study demonstrated the effectiveness of open- and closed-source LLMs, such as GPT-4o and BART, for detecting DDEs and their root causes from publicly accessible data through zero-shot classification. The robust and scalable framework we propose can aid researchers in addressing data-sparse clinical research questions. The launch of open-access DDE datasets has the potential to stimulate further research and novel discoveries in this field. %M 39883487 %R 10.2196/54601 %U https://www.jmir.org/2025/1/e54601 %U https://doi.org/10.2196/54601 %U http://www.ncbi.nlm.nih.gov/pubmed/39883487 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e60521 %T The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review %A Smits Serena,Ricardo %A Hinterwimmer,Florian %A Burgkart,Rainer %A von Eisenhart-Rothe,Rudiger %A Rueckert,Daniel %+ Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, Munich, 81675, Germany, 49 8941402271, ricardo.smits@tum.de %K artificial intelligence %K accelerometer %K gyroscope %K IMUs %K time series data %K wearable %K systematic review %K patient care %K machine learning %K data collection %D 2025 %7 29.1.2025 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Artificial intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with wearable inertial measurement units (IMUs), AI could transform health care again by opening new opportunities in patient care and medical research. Objective: This systematic review aims to evaluate the integration of AI models with wearable IMUs in health care, identifying current applications, challenges, and future opportunities. The focus will be on the types of models used, the characteristics of the datasets, and the potential for expanding and enhancing the use of this technology to improve patient care and advance medical research. Methods: This study examines this synergy of AI models and IMU data by using a systematic methodology, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, to explore 3 core questions: (1) Which medical fields are most actively researching AI and IMU data? (2) Which models are being used in the analysis of IMU data within these medical fields? (3) What are the characteristics of the datasets used for in this fields? Results: The median dataset size is of 50 participants, which poses significant limitations for AI models given their dependency on large datasets for effective training and generalization. Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. Impressively, 93% of the studies used supervised learning, revealing an underuse of unsupervised learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. In addition, there was a preference for conducting studies in clinical settings (77%), rather than in real-life scenarios, a choice that, along with the underapplication of the full potential of wearable IMUs, is recognized as a limitation in terms of practical applicability. Furthermore, the focus of 65% of the studies on neurological issues suggests an opportunity to broaden research scope to other clinical areas such as musculoskeletal applications, where AI could have significant impacts. Conclusions: In conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field toward the adoption of more complex deep learning models, and the expansion of the application of AI models on IMU data methodologies across various medical fields. This approach aims to enhance the reliability, generalizability, and clinical applicability of research findings, ultimately improving patient outcomes and advancing medical research. %M 39880389 %R 10.2196/60521 %U https://mhealth.jmir.org/2025/1/e60521 %U https://doi.org/10.2196/60521 %U http://www.ncbi.nlm.nih.gov/pubmed/39880389 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e58628 %T Technological Adjuncts to Streamline Patient Recruitment, Informed Consent, and Data Management Processes in Clinical Research: Observational Study %A Koh,Jodie %A Caron,Stacey %A Watters,Amber N %A Vaidyanathan,Mahesh %A Melnick,David %A Santi,Alyssa %A Hudson,Kenneth %A Arguelles,Catherine %A Mathur,Priyanka %A Etemadi,Mozziyar %+ Kellogg School of Management, Northwestern University, 2211 Campus Drive, Evanston, IL, 60208, United States, 1 847 491 3300, jodie.koh@kellogg.northwestern.edu %K digital health %K patient recruitment %K consent %K technological adjuncts %K data management %K clinical research processes %K automation %K digital platforms %K data warehouse %K patient data %K imaging data %K pregnancy %K clinical research methods %D 2025 %7 29.1.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies. Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration. Methods: Using one clinical research study as an example, we highlighted the use of technological adjuncts to automate and streamline research processes across various digital platforms, including a centralized database of electronic medical records (enterprise data warehouse [EDW]); a clinical research data management tool (REDCap [Research Electronic Data Capture]); and a locally managed, Health Insurance Portability and Accountability Act–compliant server. Eligible participants were identified through automated queries in the EDW, after which they received personalized email invitations with digital consent forms. After digital consent, patient data were transferred to a single Health Insurance Portability and Accountability Act–compliant server where each participant was assigned a unique QR code to facilitate data collection and integration. After the research study visit, data obtained were associated with existing electronic medical record data for each participant via a QR code system that collated participant consent, imaging data, and associated clinical data according to a unique examination ID. Results: Over a 19-month period, automated EDW queries identified 20,988 eligible patients, and 10,582 patients received personalized email invitations. In total, 1000 (9.45%) patients signed consents to participate in the study. Of the consented patients, 549 unique patients completed 779 study visits; some patients consented to the study at more than 1 time period during their pregnancy. Conclusions: Technological adjuncts in clinical research decrease human labor while increasing participant reach and minimizing disruptions to clinic operations. Automating portions of the clinical research process benefits clinical research efforts by expanding and optimizing participant reach while reducing the limitations of labor and time in completing research studies. %M 39879093 %R 10.2196/58628 %U https://formative.jmir.org/2025/1/e58628 %U https://doi.org/10.2196/58628 %U http://www.ncbi.nlm.nih.gov/pubmed/39879093 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e60708 %T The Effects of Self-Monitoring Using a Smartwatch and Smartphone App on Stress Awareness, Self-Efficacy, and Well-Being–Related Outcomes in Police Officers: Longitudinal Mixed Design Study %A de Vries,Herman Jaap %A Delahaij,Roos %A van Zwieten,Marianne %A Verhoef,Helen %A Kamphuis,Wim %K wearable electronic devices %K ecological momentary assessment %K psychological stress %K psychological well-being %K awareness %K self-efficacy %K occupational medicine %K emergency responders %K well-being %K psychological %K efficacy %K stress %K wearables %K wearable device %K smartwatch %K smartphone app %K app %K sensor %K sensor technology %K police officers %K questionnaire %K stress awareness %K stress management %D 2025 %7 28.1.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Wearable sensor technologies, often referred to as “wearables,” have seen a rapid rise in consumer interest in recent years. Initially often seen as “activity trackers,” wearables have gradually expanded to also estimate sleep, stress, and physiological recovery. In occupational settings, there is a growing interest in applying this technology to promote health and well-being, especially in professions with highly demanding working conditions such as first responders. However, it is not clear to what extent self-monitoring with wearables can positively influence stress- and well-being–related outcomes in real-life conditions and how wearable-based interventions should be designed for high-risk professionals. Objective: The aim of this study was to investigate (1) whether offering a 5-week wearable-based intervention improves stress- and well-being–related outcomes in police officers and (2) whether extending a basic “off-the-shelf” wearable-based intervention with ecological momentary assessment (EMA) questionnaires, weekly personalized feedback reports, and peer support groups improves its effectiveness. Methods: A total of 95 police officers from 5 offices participated in the study. The data of 79 participants were included for analysis. During the first 5 weeks, participants used no self-monitoring technology (control period). During the following 5 weeks (intervention period), 41 participants used a Garmin Forerunner 255 smartwatch with a custom-built app (comparable to that of the consumer-available wearable), whereas the other 38 participants used the same system, but complemented by daily EMA questionnaires, weekly personalized feedback reports, and access to peer support groups. At baseline (T0) and after the control (T1) and intervention (T2) periods, questionnaires were administered to measure 15 outcomes relating to stress awareness, stress management self-efficacy, and outcomes related to stress and general well-being. Linear mixed models that accounted for repeated measures within subjects, the control and intervention periods, and between-group differences were used to address both research questions. Results: The results of the first analysis showed that the intervention had a small (absolute Hedges g=0.25‐0.46) but consistent effect on 8 of 15 of the stress- and well-being–related outcomes in comparison to the control group. The second analysis provided mixed results; the extended intervention was more effective than the basic intervention at improving recovery after work but less effective at improving self-efficacy in behavior change and sleep issues, and similarly effective in the remaining 12 outcomes. Conclusions: Offering a 5-week wearable-based intervention to police officers can positively contribute to optimizing their stress-related, self-efficacy, and well-being–related outcomes. Complementing the basic “off-the-shelf” wearable-based intervention with additional EMA questionnaires, weekly personalized feedback reports, and peer support groups did not appear to improve the effectiveness of the intervention. Future work is needed to investigate how different aspects of these interventions can be tailored to specific characteristics and needs of employees to optimize these effects. %R 10.2196/60708 %U https://mhealth.jmir.org/2025/1/e60708 %U https://doi.org/10.2196/60708 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e51689 %T Cross-Platform Ecological Momentary Assessment App (JTrack-EMA+): Development and Usability Study %A Sahandi Far,Mehran %A Fischer,Jona M %A Senge,Svea %A Rathmakers,Robin %A Meissner,Thomas %A Schneble,Dominik %A Narava,Mamaka %A Eickhoff,Simon B %A Dukart,Juergen %+ Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Wilhelm-Johnen-Straße, Jülich, 52428, Germany, 49 17636977109, m.sahandi.far@fz-juelich.de %K digital biomarkers %K mobile health %K remote monitoring %K smartphone %K mobile phone %K monitoring %K biomarker %K ecological momentary assessment %K application %K costly %K user experience %K data management %K mobility %D 2025 %7 28.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Traditional in-clinic methods of collecting self-reported information are costly, time-consuming, subjective, and often limited in the quality and quantity of observation. However, smartphone-based ecological momentary assessments (EMAs) provide complementary information to in-clinic visits by collecting real-time, frequent, and longitudinal data that are ecologically valid. While these methods are promising, they are often prone to various technical obstacles. However, despite the potential of smartphone-based EMAs, they face technical obstacles that impact adaptability, availability, and interoperability across devices and operating systems. Deficiencies in these areas can contribute to selection bias by excluding participants with unsupported devices or limited digital literacy, increase development and maintenance costs, and extend deployment timelines. Moreover, these limitations not only impede the configurability of existing solutions but also hinder their adoption for addressing diverse clinical challenges. Objective: The primary aim of this research was to develop a cross-platform EMA app that ensures a uniform user experience and core features across various operating systems. Emphasis was placed on maximizing the integration and adaptability to various study designs, all while maintaining strict adherence to security and privacy protocols. JTrack-EMA+ was designed and implemented per the FAIR (findable, accessible, interpretable, and reusable) principles in both its architecture and data management layers, thereby reducing the burden of integration for clinicians and researchers. Methods: JTrack-EMA+ was built using the Flutter framework, enabling it to run seamlessly across different platforms. This platform comprises two main components. JDash (Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour [INM-7]) is an online management tool created using Python (Python Software Foundation) with the Django (Django Software Foundation) framework. This online dashboard offers comprehensive study management tools, including assessment design, user administration, data quality control, and a reminder casting center. The JTrack-EMA+ app supports a wide range of question types, allowing flexibility in assessment design. It also has configurable assessment logic and the ability to include supplementary materials for a richer user experience. It strongly commits to security and privacy and complies with the General Data Protection Regulations to safeguard user data and ensure confidentiality. Results: We investigated our platform in a pilot study with 480 days of follow-up to assess participants’ compliance. The 6-month average compliance was 49.3%, significantly declining (P=.004) from 66.7% in the first month to 42% in the sixth month. Conclusions: The JTrack-EMA+ platform prioritizes platform-independent architecture, providing an easy entry point for clinical researchers to deploy EMA in their respective clinical studies. Remote and home-based assessments of EMA using this platform can provide valuable insights into patients’ daily lives, particularly in a population with limited mobility or inconsistent access to health care services. %M 39874571 %R 10.2196/51689 %U https://www.jmir.org/2025/1/e51689 %U https://doi.org/10.2196/51689 %U http://www.ncbi.nlm.nih.gov/pubmed/39874571 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 14 %N %P e66694 %T The Evolution of Uroflowmetry and Bladder Diary and the Emerging Trend of Using Home Devices From Hospital to Home %A Li,Ming-wei %A Tsai,Yao-Chou %A Yang,Stephen Shei-Dei %A Pong,Yuan-Hung %A Tsai,Yu-Ting %A Tsai,Vincent Fang-Sheng %+ Division of Urology, Department of Surgery, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, 16F, No 289, Jianguo Road, New Taipei City, 231016, Taiwan, 886 952 691807, ntubala@gmail.com %K lower urinary tract symptoms %K uroflowmetry %K bladder diary %K home devices %K bladder %K noninvasive %K evaluations %K viewpoint %K diagnostic %K mobile health %D 2025 %7 28.1.2025 %9 Viewpoint %J Interact J Med Res %G English %X Although uroflowmetry and bladder diaries are widely used for noninvasive evaluation of lower urinary tract symptoms, they still have limitations in diagnostic capability and users’ convenience. The aim of this paper is to discuss potential solutions by reviewing (1) the evolution and current clinical use of uroflowmetry and bladder diary, including clinical guidelines, daily practice applications, and their historical development; (2) a growing trend toward using home devices with various technologies; and (3) a comprehensive comparison of the strengths and weaknesses of these home devices. In our opinion, the following points can be highlighted: (1) the emerging trend of using home devices can enhance diagnostic capabilities through repeated measurements and the convenience of at-home testing and (2) home devices, which provide both frequency-volume and uroflowmetry information, have the potential to transform the management of lower urinary tract symptoms. %M 39874564 %R 10.2196/66694 %U https://www.i-jmr.org/2025/1/e66694 %U https://doi.org/10.2196/66694 %U http://www.ncbi.nlm.nih.gov/pubmed/39874564 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e63634 %T Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study %A Rojas,Natalia K %A Martin,Sam %A Cortina-Borja,Mario %A Shafran,Roz %A Fox-Smith,Lana %A Stephenson,Terence %A Ching,Brian C F %A d'Oelsnitz,Anaïs %A Norris,Tom %A Xu,Yue %A McOwat,Kelsey %A Dalrymple,Emma %A Heyman,Isobel %A Ford,Tamsin %A Chalder,Trudie %A Simmons,Ruth %A , %A Pinto Pereira,Snehal M %+ Division of Surgery & Interventional Science, Faculty of Medical Sciences, University College London, 43-45 Foley St, W1W 7TY, London, United Kingdom, 44 (0) 20 7679 200, n.rojas@ucl.ac.uk %K children and young people %K text mining %K free-text responses %K experiences %K COVID-19 %K long COVID %K InfraNodus %K sentiment analysis %K discourse analysis %K AI %K artificial intelligence %D 2025 %7 28.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development. Objective: This study aims to analyze free-text responses from children and young people participating in the Children and Young People With Long COVID study to get a clearer understanding of how young people were feeling during the pandemic. Methods: A total of 8224 free-text responses from children and young people were analyzed using InfraNodus, an artificial intelligence–powered text network analysis tool, to determine the most prevalent topics. A random subsample of 411 (5%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England. Results: Experiences fell into 6 main overlapping topical clusters: school, examination stress, mental health, emotional impact of the pandemic, social and family support, and physical health (including COVID-19 symptoms). Sentiment analysis showed that statements were largely negative (314/411, 76.4%), with a small proportion being positive (57/411, 13.9%). Those reporting negative sentiment were mostly female (227/314, 72.3%), while those reporting positive sentiment were mostly older (170/314, 54.1%). There were significant observed associations between sentiment and COVID-19 status as well as sex (P=.001 and P<.001, respectively) such that the majority of the responses, regardless of COVID-19 status or sex, were negative; for example, 84.1% (227/270) of the responses from female individuals and 61.7% (87/141) of those from male individuals were negative. There were no observed associations between sentiment and all other examined demographics. The results were broadly similar when reweighted to the general population of children and young people in England: 78.52% (negative), 13.23% (positive), and 8.24% (neutral). Conclusions: We used InfraNodus to analyze free-text responses from a large sample of children and young people. The majority of responses (314/411, 76.4%) were negative, and many of the children and young people reported experiencing distress across a range of domains related to school, social situations, and mental health. Our findings add to the literature, highlighting the importance of specific considerations for children and young people when responding to national emergencies. %M 39874576 %R 10.2196/63634 %U https://www.jmir.org/2025/1/e63634 %U https://doi.org/10.2196/63634 %U http://www.ncbi.nlm.nih.gov/pubmed/39874576 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 12 %N %P e67478 %T Exploring the Psychological and Physiological Insights Through Digital Phenotyping by Analyzing the Discrepancies Between Subjective Insomnia Severity and Activity-Based Objective Sleep Measures: Observational Cohort Study %A Yeom,Ji Won %A Kim,Hyungju %A Pack,Seung Pil %A Lee,Heon-Jeong %A Cheong,Taesu %A Cho,Chul-Hyun %+ , Department of Psychiatry, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea, 82 029205505, david0203@gmail.com %K insomnia %K wearable devices %K sleep quality %K subjective assessment %K digital phenotyping %K psychological factors %K mobile phone %D 2025 %7 27.1.2025 %9 Original Paper %J JMIR Ment Health %G English %X Background: Insomnia is a prevalent sleep disorder affecting millions worldwide, with significant impacts on daily functioning and quality of life. While traditionally assessed through subjective measures such as the Insomnia Severity Index (ISI), the advent of wearable technology has enabled continuous, objective sleep monitoring in natural environments. However, the relationship between subjective insomnia severity and objective sleep parameters remains unclear. Objective: This study aims to (1) explore the relationship between subjective insomnia severity, as measured by ISI scores, and activity-based objective sleep parameters obtained through wearable devices; (2) determine whether subjective perceptions of insomnia align with objective measures of sleep; and (3) identify key psychological and physiological factors contributing to the severity of subjective insomnia complaints. Methods: A total of 250 participants, including both individuals with and without insomnia aged 19-70 years, were recruited from March 2023 to November 2023. Participants were grouped based on ISI scores: no insomnia, mild, moderate, and severe insomnia. Data collection involved subjective assessments through self-reported questionnaires and objective measurements using wearable devices (Fitbit Inspire 3) that monitored sleep parameters, physical activity, and heart rate. The participants also used a smartphone app for ecological momentary assessment, recording daily alcohol consumption, caffeine intake, exercise, and stress. Statistical analyses were used to compare groups on subjective and objective measures. Results: Results indicated no significant differences in general sleep structure (eg, total sleep time, rapid eye movement sleep time, and light sleep time) among the insomnia groups (mild, moderate, and severe) as classified by ISI scores (all P>.05). Interestingly, the no insomnia group had longer total awake times and lower sleep quality compared with the insomnia groups. Among the insomnia groups, no significant differences were observed regarding sleep structure (all P>.05), suggesting similar sleep patterns regardless of subjective insomnia severity. There were significant differences among the insomnia groups in stress levels, dysfunctional beliefs about sleep, and symptoms of restless leg syndrome (all P≤.001), with higher severity associated with higher scores in these factors. Contrary to expectations, no significant differences were observed in caffeine intake (P=.42) and alcohol consumption (P=.07) between the groups. Conclusions: The findings demonstrate a discrepancy between subjective perceptions of insomnia severity and activity-based objective sleep parameters, suggesting that factors beyond sleep duration and quality may contribute to subjective sleep complaints. Psychological factors, such as stress, dysfunctional sleep beliefs, and symptoms of restless legs syndrome, appear to play significant roles in the perception of insomnia severity. These results highlight the importance of considering both subjective and objective assessments in the evaluation and treatment of insomnia and suggest potential avenues for personalized treatment strategies that address both psychological and physiological aspects of sleep disturbances. Trial Registration: Clinical Research Information Service KCT0009175; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=26133 %M 39869900 %R 10.2196/67478 %U https://mental.jmir.org/2025/1/e67478 %U https://doi.org/10.2196/67478 %U http://www.ncbi.nlm.nih.gov/pubmed/39869900 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e54133 %T Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study %A Yang,Doris %A Zhou,Doudou %A Cai,Steven %A Gan,Ziming %A Pencina,Michael %A Avillach,Paul %A Cai,Tianxi %A Hong,Chuan %K ensemble learning %K semantic learning %K distribution learning %K variable harmonization %K machine learning %K cardiovascular health study %K intracohort comparison %K intercohort comparison %K gold standard labels %D 2025 %7 22.1.2025 %9 %J JMIR Med Inform %G English %X Background: Cohort studies contain rich clinical data across large and diverse patient populations and are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alternative is to harmonize data from existing cohorts through multicohort studies. However, given differences in variable encoding, accurate variable harmonization is difficult. Objective: We propose SONAR (Semantic and Distribution-Based Harmonization) as a method for harmonizing variables across cohort studies to facilitate multicohort studies. Methods: SONAR used semantic learning from variable descriptions and distribution learning from study participant data. Our method learned an embedding vector for each variable and used pairwise cosine similarity to score the similarity between variables. This approach was built off 3 National Institutes of Health cohorts, including the Cardiovascular Health Study, the Multi-Ethnic Study of Atherosclerosis, and the Women’s Health Initiative. We also used gold standard labels to further refine the embeddings in a supervised manner. Results: The method was evaluated using manually curated gold standard labels from the 3 National Institutes of Health cohorts. We evaluated both the intracohort and intercohort variable harmonization performance. The supervised SONAR method outperformed existing benchmark methods for almost all intracohort and intercohort comparisons using area under the curve and top-k accuracy metrics. Notably, SONAR was able to significantly improve harmonization of concepts that were difficult for existing semantic methods to harmonize. Conclusions: SONAR achieves accurate variable harmonization within and between cohort studies by harnessing the complementary strengths of semantic learning and variable distribution learning. %R 10.2196/54133 %U https://medinform.jmir.org/2025/1/e54133 %U https://doi.org/10.2196/54133 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e51955 %T Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review %A Song,Shanshan %A Ashton,Micaela %A Yoo,Rebecca Hahn %A Lkhagvajav,Zoljargal %A Wright,Robert %A Mathews,Debra J H %A Taylor,Casey Overby %+ Biomedical Informatics & Data Science Section, The Johns Hopkins University School of Medicine, 2024 East Monument St. S 1-200, Baltimore, MD, 21205, United States, 1 6174170316, ssong41@jhmi.edu %K scoping review %K person-generated health data %K PGHD %K mHealth %K mobile device %K smartphone %K mobile phone %K wearable %K fitness tracker %K smartwatch %K BYOD %K crowdsourcing %K reporting deficiency %D 2025 %7 20.1.2025 %9 Review %J J Med Internet Res %G English %X Background: Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results. Objective: The primary objective of this scoping review was to characterize publications’ reporting practices for research that collects PGHD using mobile devices. Methods: We comprehensively searched PubMed and screened the results. Qualifying publications were classified according to 6 dimensions—1 covering key bibliographic details (for all articles) and 5 covering reporting criteria considered necessary for reproducible and responsible research (ie, “participant,” “data,” “device,” “study,” and “ethics,” for original research). For each of the 5 reporting dimensions, we also assessed reporting completeness. Results: Out of 3602 publications screened, 100 were included in this review. We observed a rapid increase in all publications from 2016 to 2021, with the largest contribution from US authors, with 1 exception, review articles. Few original research publications used crowdsourcing platforms (7%, 3/45). Among the original research publications that reported device ownership, most (75%, 21/28) reported using participant-owned devices for data collection (ie, a Bring-Your-Own-Device [BYOD] strategy). A significant deficiency in reporting completeness was observed for the “data” and “ethics” dimensions (5 reporting factors were missing in over half of the research publications). Reporting completeness for data ownership and participants’ access to data after contribution worsened over time. Conclusions: Our work depicts the reporting practices in publications about research involving PGHD from mobile devices. We found that very few papers reported crowdsourcing platforms for data collection. BYOD strategies are increasingly popular; this creates an opportunity for improved mechanisms to transfer data from device owners to researchers on crowdsourcing platforms. Given substantial reporting deficiencies, we recommend reaching a consensus on best practices for research collecting PGHD from mobile devices. Drawing from the 5 reporting dimensions in this scoping review, we share our recommendations and justifications for 9 items. These items require improved reporting to enhance data representativeness and quality and empower participants. %M 39832140 %R 10.2196/51955 %U https://www.jmir.org/2025/1/e51955 %U https://doi.org/10.2196/51955 %U http://www.ncbi.nlm.nih.gov/pubmed/39832140 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e60413 %T Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial %A Zheng,Wu Yi %A Shvetcov,Artur %A Slade,Aimy %A Jenkins,Zoe %A Hoon,Leonard %A Whitton,Alexis %A Logothetis,Rena %A Ravindra,Smrithi %A Kurniawan,Stefanus %A Gupta,Sunil %A Huckvale,Kit %A Stech,Eileen %A Agarwal,Akash %A Funke Kupper,Joost %A Cameron,Stuart %A Rosenberg,Jodie %A Manoglou,Nicholas %A Senadeera,Manisha %A Venkatesh,Svetha %A Mouzakis,Kon %A Vasa,Rajesh %A Christensen,Helen %A Newby,Jill M %+ Black Dog Institute, University of New South Wales, Hospital Road, Randwick, Sydney, 2031, Australia, 61 0422510718, wuyi.zheng@blackdog.org.au %K recruitment %K Facebook %K retention, COVID-19 %K artificial intelligence %D 2025 %7 14.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence–driven adaptive trial—Vibe Up. Objective: We evaluated the effectiveness of recruitment via Facebook and Instagram compared to traditional methods for a treatment trial and compared different recruitment methods’ retention rates. With recruitment coinciding with COVID-19 lockdowns across Australia, we also compared the cost-effectiveness of social media recruitment during and after lockdowns. Methods: Recruitment was completed for 2 pilot trials and 6 minitrials from June 2021 to May 2022. To recruit participants, paid social media advertising on Facebook and Instagram was used, alongside mailing lists of university networks and student organizations or services, media releases, announcements during classes and events, study posters or flyers on university campuses, and health professional networks. Recruitment data, including engagement metrics collected by Meta (Facebook and Instagram), advertising costs, and Qualtrics data on recruitment methods and survey completion rates, were analyzed using RStudio with R (version 3.6.3; R Foundation for Statistical Computing). Results: In total, 1314 eligible participants (aged 22.79, SD 4.71 years; 1079, 82.1% female) were recruited to 2 pilot trials and 6 minitrials. The vast majority were recruited via Facebook and Instagram advertising (n=1203; 92%). Pairwise comparisons revealed that the lead institution’s website was more effective in recruiting eligible participants than Facebook (z=3.47; P=.003) and Instagram (z=4.23; P<.001). No differences were found between recruitment methods in retaining participants at baseline, at midpoint, and at study completion. Wilcoxon tests found significant differences between lockdown (pilot 1 and pilot 2) and postlockdown (minitrials 1-6) on costs incurred per link click (lockdown: median Aus $0.35 [US $0.22], IQR Aus $0.27-$0.47 [US $0.17-$0.29]; postlockdown: median Aus $1.00 [US $0.62], IQR Aus $0.70-$1.47 [US $0.44-$0.92]; W=9087; P<.001) and the amount spent per hour to reach the target sample size (lockdown: median Aus $4.75 [US $2.95], IQR Aus $1.94-6.34 [US $1.22-$3.97]; postlockdown: median Aus $13.29 [US $8.26], IQR Aus $4.70-25.31 [US $2.95-$15.87]; W=16044; P<.001). Conclusions: Social media advertising via Facebook and Instagram was the most successful strategy for recruiting distressed tertiary students into this artificial intelligence–driven adaptive trial, providing evidence for the use of this recruitment method for this type of trial in digital mental health research. No recruitment method stood out in terms of participant retention. Perhaps a reflection of the added distress experienced by young people, social media recruitment during the COVID-19 lockdown period was more cost-effective. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621001092886; https://tinyurl.com/39f2pdmd; Australian New Zealand Clinical Trials Registry ACTRN12621001223820; https://tinyurl.com/bdhkvucv %M 39808785 %R 10.2196/60413 %U https://www.jmir.org/2025/1/e60413 %U https://doi.org/10.2196/60413 %U http://www.ncbi.nlm.nih.gov/pubmed/39808785 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e60520 %T Usefulness of Automatic Speech Recognition Assessment of Children With Speech Sound Disorders: Validation Study %A Kim,Do Hyung %A Jeong,Joo Won %A Kang,Dayoung %A Ahn,Taekyung %A Hong,Yeonjung %A Im,Younggon %A Kim,Jaewon %A Kim,Min Jung %A Jang,Dae-Hyun %+ Department of Rehabilitation Medicine, Incheon St Mary’s Hospital, College of Medicine, The Catholic University of Korea, 22 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea, 82 0322806601, dhjangmd@naver.com %K speech sound disorder %K speech recognition software %K speech articulation tests %K speech-language pathology %K child %D 2025 %7 14.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Speech sound disorders (SSDs) are common communication challenges in children, typically assessed by speech-language pathologists (SLPs) using standardized tools. However, traditional evaluation methods are time-intensive and prone to variability, raising concerns about reliability. Objective: This study aimed to compare the evaluation outcomes of SLPs and an automatic speech recognition (ASR) model using two standardized SSD assessments in South Korea, evaluating the ASR model’s performance. Methods: A fine-tuned wav2vec 2.0 XLS-R model, pretrained on 436,000 hours of adult voice data spanning 128 languages, was used. The model was further trained on 93.6 minutes of children’s voices with articulation errors to improve error detection. Participants included children referred to the Department of Rehabilitation Medicine at a general hospital in Incheon, South Korea, from August 19, 2022, to June 14, 2023. Two standardized assessments—the Assessment of Phonology and Articulation for Children (APAC) and the Urimal Test of Articulation and Phonology (U-TAP)—were used, with ASR transcriptions compared to SLP transcriptions. Results: This study included 30 children aged 3-7 years who were suspected of having SSDs. The phoneme error rates for the APAC and U-TAP were 8.42% (457/5430) and 8.91% (402/4514), respectively, indicating discrepancies between the ASR model and SLP transcriptions across all phonemes. Consonant error rates were 10.58% (327/3090) and 11.86% (331/2790) for the APAC and U-TAP, respectively. On average, there were 2.60 (SD 1.54) and 3.07 (SD 1.39) discrepancies per child for correctly produced phonemes, and 7.87 (SD 3.66) and 7.57 (SD 4.85) discrepancies per child for incorrectly produced phonemes, based on the APAC and U-TAP, respectively. The correlation between SLPs and the ASR model in terms of the percentage of consonants correct was excellent, with an intraclass correlation coefficient of 0.984 (95% CI 0.953-0.994) and 0.978 (95% CI 0.941-0.990) for the APAC and UTAP, respectively. The z scores between SLPs and ASR showed more pronounced differences with the APAC than the U-TAP, with 8 individuals showing discrepancies in the APAC compared to 2 in the U-TAP. Conclusions: The results demonstrate the potential of the ASR model in assessing children with SSDs. However, its performance varied based on phoneme or word characteristics, highlighting areas for refinement. Future research should include more diverse speech samples, clinical settings, and speech data to strengthen the model’s refinement and ensure broader clinical applicability. %M 39576242 %R 10.2196/60520 %U https://www.jmir.org/2025/1/e60520 %U https://doi.org/10.2196/60520 %U http://www.ncbi.nlm.nih.gov/pubmed/39576242 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59937 %T Unified Mobile App for Streamlining Verbal Autopsy and Cause of Death Assignment in India: Design and Development Study %A Kaur,Harleen %A Tripathi,Stuti %A Chalga,Manjeet Singh %A Benara,Sudhir K %A Dhiman,Amit %A Gupta,Shefali %A Nair,Saritha %A Menon,Geetha %A Gulati,B K %A Sharma,Sandeep %A Sharma,Saurabh %K verbal autopsy %K cause of death %K mortality %K mHealth %K public health %K India %K mobile health %D 2025 %7 10.1.2025 %9 %J JMIR Form Res %G English %X Background: Verbal autopsy (VA) has been a crucial tool in ascertaining population-level cause of death (COD) estimates, specifically in countries where medical certification of COD is relatively limited. The World Health Organization has released an updated instrument (Verbal Autopsy Instrument 2022) that supports electronic data collection methods along with analytical software for assigning COD. This questionnaire encompasses the primary signs and symptoms associated with prevalent diseases across all age groups. Traditional methods have primarily involved paper-based questionnaires and physician-coded approaches for COD assignment, which is time-consuming and resource-intensive. Although computer-coded algorithms have advanced the COD assignment process, data collection in densely populated countries like India remains a logistical challenge. Objective: This study aimed to develop an Android-based mobile app specifically tailored for streamlining VA data collection by leveraging the existing Indian public health workforce. The app has been designed to integrate real-time data collection by frontline health workers and seamless data transmission and digital reporting of COD by physicians. This process aimed to enhance the efficiency and accuracy of COD assignment through VA. Methods: The app was developed using Android Studio, the primary integrated development environment for developing Android apps using Java. The front-end interface was developed using XML, while SQLite and MySQL were employed to streamline complete data storage on the local and server databases, respectively. The communication between the app and the server was facilitated through a PHP application programming interface to synchronize data from the local to the server database. The complete prototype was specifically built to reduce manual intervention and automate VA data collection. Results: The app was developed to align with the current Indian public health system for district-level COD estimation. By leveraging this mobile app, the average duration required for VA data collection to ascertainment of COD, which typically ranges from 6 to 8 months, is expected to decrease by approximately 80%, reducing it to about 1‐2 months. Based on annual caseload projections, the smallest administrative public health unit, health and wellness centers, is anticipated to handle 35‐40 VA cases annually, while medical officers at primary health centers are projected to manage 150‐200 physician-certified VAs each year. The app’s data collection and transmission efficiency were further improved based on feedback from user and subject area experts. Conclusions: The development of a unified mobile app could streamline the VA process, enabling the generation of accurate national and subnational COD estimates. This mobile app can be further piloted and scaled to different regions to integrate the automated VA model into the existing public health system for generating comprehensive mortality statistics in India. %R 10.2196/59937 %U https://formative.jmir.org/2025/1/e59937 %U https://doi.org/10.2196/59937 %0 Journal Article %@ 2817-092X %I JMIR Publications %V 4 %N %P e56679 %T Exploring Remote Monitoring of Poststroke Mood With Digital Sensors by Assessment of Depression Phenotypes and Accelerometer Data in UK Biobank: Cross-Sectional Analysis %A Zawada,Stephanie J %A Ganjizadeh,Ali %A Conte,Gian Marco %A Demaerschalk,Bart M %A Erickson,Bradley J %K depression %K cerebrovascular disease %K remote monitoring %K stroke %K accelerometers %K mobile phone %D 2025 %7 10.1.2025 %9 %J JMIR Neurotech %G English %X Background: Interest in using digital sensors to monitor patients with prior stroke for depression, a risk factor for poor outcomes, has grown rapidly; however, little is known about behavioral phenotypes related to future mood symptoms and if patients with and without previously diagnosed depression experience similar phenotypes. Objective: This study aimed to assess the feasibility of using digital sensors to monitor mood in patients with prior stroke with a prestroke depression diagnosis (DD) and controls. We examined relationships between physical activity behaviors and self-reported depression frequency. Methods: In the UK Biobank wearable accelerometer cohort, we retrospectively identified patients who had previously suffered a stroke (N=1603) and conducted cross-sectional analyses with those who completed a subsequent depression survey follow-up. Sensitivity analyses assessed a general population cohort excluding previous stroke participants and 2 incident cohorts: incident stroke (IS) and incident cerebrovascular disease (IC). Results: In controls, the odds of being in a higher depressed mood frequency category decreased by 23% for each minute spent in moderate‐to‐vigorous physical activity (odds ratio 0.77, 95% CI 0.69‐0.87; P<.001). This association persisted in both general cohorts and in the IC control cohort. Conclusions: Although moderate‐to‐vigorous physical activity was linked with less frequent depressed mood in patients with prior stroke without DD, this finding did not persist in DDs. Thus, accelerometer-mood monitoring may provide clinically useful insights about future mood in patients with prior stroke without DDs. Considering the finding in the IC cohort and the lack of findings in the IS cohorts, accelerometer-mood monitoring may also be appropriately applied to observing broader cerebrovascular disease pathogenesis. %R 10.2196/56679 %U https://neuro.jmir.org/2025/1/e56679 %U https://doi.org/10.2196/56679 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e65448 %T Longitudinal Changes in Pitch-Related Acoustic Characteristics of the Voice Throughout the Menstrual Cycle: Observational Study %A Kaufman,Jaycee %A Jeon,Jouhyun %A Oreskovic,Jessica %A Thommandram,Anirudh %A Fossat,Yan %K menstrual cycle %K women's health %K voice %K acoustic analysis %K longitudinal observational study %K fertility tracking %K fertility %K reproductive health %K feasibility %K voice recording %K vocal pitch %K follicular %K luteal phase %K fertility status %K mobile phone %D 2025 %7 9.1.2025 %9 %J JMIR Form Res %G English %X Background: Identifying subtle changes in the menstrual cycle is crucial for effective fertility tracking and understanding reproductive health. Objective: The aim of the study is to explore how fundamental frequency features vary between menstrual phases using daily voice recordings. Methods: This study analyzed smartphone-collected voice recordings from 16 naturally cycling female participants, collected every day for 1 full menstrual cycle. Fundamental frequency features (mean, SD, 5th percentile, and 95th percentile) were extracted from each voice recording. Ovulation was estimated using luteinizing hormone urine tests taken every morning. The analysis included comparisons of these features between the follicular and luteal phases and the application of changepoint detection algorithms to assess changes and pinpoint the day in which the shifts in vocal pitch occur. Results: The fundamental frequency SD was 9.0% (SD 2.9%) lower in the luteal phase compared to the follicular phase (95% CI 3.4%‐14.7%; P=.002), and the 5th percentile of the fundamental frequency was 8.8% (SD 3.6%) higher (95% CI 1.7%‐16.0%; P=.01). No significant differences were found between phases in mean fundamental frequency or the 95th percentile of the fundamental frequency (P=.65 and P=.07). Changepoint detection, applied separately to each feature, identified the point in time when vocal frequency behaviors shifted. For the fundamental frequency SD and 5th percentile, 81% (n=13) of participants exhibited shifts within the fertile window (P=.03). In comparison, only 63% (n=10; P=.24) and 50% (n=8; P=.50) of participants had shifts in the fertile window for the mean and 95th percentile of the fundamental frequency, respectively. Conclusions: These findings indicate that subtle variations in vocal pitch may reflect changes associated with the menstrual cycle, suggesting the potential for developing a noninvasive and convenient method for monitoring reproductive health. Changepoint detection may provide a promising avenue for future work in longitudinal fertility analysis. %R 10.2196/65448 %U https://formative.jmir.org/2025/1/e65448 %U https://doi.org/10.2196/65448 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e65139 %T Comparative Evaluation of Consumer Wearable Devices for Atrial Fibrillation Detection: Validation Study %A Wouters,Femke %A Gruwez,Henri %A Smeets,Christophe %A Pijalovic,Anessa %A Wilms,Wouter %A Vranken,Julie %A Pieters,Zoë %A Van Herendael,Hugo %A Nuyens,Dieter %A Rivero-Ayerza,Maximo %A Vandervoort,Pieter %A Haemers,Peter %A Pison,Laurent %K atrial fibrillation %K AF %K mobile health %K photoplethysmography %K electrocardiography %K smartphone %K consumer wearable device %K wearable devices %K detection %K electrocardiogram %K ECG %K mHealth %D 2025 %7 9.1.2025 %9 %J JMIR Form Res %G English %X Background: Consumer-oriented wearable devices (CWDs) such as smartphones and smartwatches have gained prominence for their ability to detect atrial fibrillation (AF) through proprietary algorithms using electrocardiography or photoplethysmography (PPG)–based digital recordings. Despite numerous individual validation studies, a direct comparison of interdevice performance is lacking. Objective: This study aimed to evaluate and compare the ability of CWDs to distinguish between sinus rhythm and AF. Methods: Patients exhibiting sinus rhythm or AF were enrolled through a cardiology outpatient clinic. The participants were instructed to perform heart rhythm measurements using a handheld 6-lead electrocardiogram (ECG) device (KardiaMobile 6L), a smartwatch-derived single-lead ECG (Apple Watch), and two PPG-based smartphone apps (FibriCheck and Preventicus) in a random sequence, with simultaneous 12-lead reference ECG as the gold standard. Results: A total of 122 participants were included in the study: median age 69 (IQR 61-77) years, 63.9% (n=78) men, 25% (n=30) with AF, 9.8% (n=12) without prior smartphone experience, and 73% (n=89) without experience in using a smartwatch. The sensitivity to detect AF was 100% for all devices. The specificity to detect sinus rhythm was 96.4% (95% CI 89.5%-98.8%) for KardiaMobile 6L, 97.8% (95% CI 91.6%‐99.5%) for Apple Watch, 98.9% (95% CI 92.5%‐99.8%) for FibriCheck, and 97.8% (95% CI 91.5%‐99.4%) for Preventicus (P=.50). Insufficient quality measurements were observed in 10.7% (95% CI 6.3%-17.5%) of cases for both KardiaMobile 6L and Apple Watch, 7.4% (95% CI 3.9%‐13.6%) for FibriCheck, and 14.8% (95% CI 9.5%‐22.2%) for Preventicus (P=.21). Participants preferred Apple Watch over the other devices to monitor their heart rhythm. Conclusions: In this study population, the discrimination between sinus rhythm and AF using CWDs based on ECG or PPG was highly accurate, with no significant variations in performance across the examined devices. Trial Registration: ClinicalTrials.gov NCT06023290; https://clinicaltrials.gov/study/NCT06023290 %R 10.2196/65139 %U https://formative.jmir.org/2025/1/e65139 %U https://doi.org/10.2196/65139 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e54871 %T Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study %A Ceugniez,Maxime %A Devanne,Hervé %A Hermand,Eric %K photoplethysmography %K physical activity %K ecological conditions %K accuracy %K reliability %K Fitbit Charge 4 %K Fitbit %K exercise %K ecological %K wrist-worn device %K device %K sensor %K wearables %K usefulness %K variability %K sensitivity %K heart rate %K heart rate sensor %D 2025 %7 8.1.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Wrist-worn photoplethysmography (PPG) sensors allow for continuous heart rate (HR) measurement without the inconveniences of wearing a chest belt. Although green light PPG technology reduces HR measurement motion artifacts, only a limited number of studies have investigated the reliability and accuracy of wearables in non–laboratory-controlled conditions with actual specific and various physical activity movements. Objective: The purpose of this study was to (1) assess the reliability and accuracy of the PPG-based HR sensor of the Fitbit Charge 4 (FC4) in ecological conditions and (2) quantify the potential variability caused by the nature of activities. Methods: We collected HR data from participants who performed badminton, tennis, orienteering running, running, cycling, and soccer while simultaneously wearing the FC4 and the Polar H10 chest belt (criterion sensor). Skin tone was assessed with the Fitzpatrick Skin Scale. Once data from the FC4 and criterion data were synchronized, accuracy and reliability analyses were performed, using intraclass correlation coefficients (ICCs), Lin concordance correlation coefficients (CCCs), mean absolute percentage errors (MAPEs), and Bland-Altman tests. A linear univariate model was also used to evaluate the effect of skin tone on bias. All analyses were stratified by activity and pooled activity types (racket sports and running sports). Results: A total of 77.5 hours of HR recordings from 26 participants (age: mean 21.1, SD 5.8 years) were analyzed. The highest reliability was found for running sports, with ICCs and CCCs of 0.90 and 0.99 for running and 0.80 and 0.93 for orienteering running, respectively, whereas the ICCs and CCCs were 0.37 and 0.78, 0.42 and 0.88, 0.65 and 0.97, and 0.49 and 0.81 for badminton, tennis, cycling, and soccer, respectively. We found the highest accuracy for running (bias: 0.1 beats per minute [bpm]; MAPE 1.2%, SD 4.6%) and the lowest for badminton (bias: −16.5 bpm; MAPE 16.2%, SD 14.4%) and soccer (bias: −16.5 bpm; MAPE 17.5%, SD 20.8%). Limit of agreement (LOA) width and artifact rate followed the same trend. No effect of skin tone was observed on bias. Conclusions: LOA width, bias, and MAPE results found for racket sports and soccer suggest a high sensitivity to motion artifacts for activities that involve “sharp” and random arm movements. In this study, we did not measure arm motion, which limits our results. However, whereas individuals might benefit from using the FC4 for casual training in aerobic sports, we cannot recommend the use of the FC4 for specific purposes requiring high reliability and accuracy, such as research purposes. %R 10.2196/54871 %U https://mhealth.jmir.org/2025/1/e54871 %U https://doi.org/10.2196/54871 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e60324 %T Urban-Suburban Differences in Public Perspectives on Digitalizing Pediatric Research: Cross-Sectional Survey Study %A Fang,Heping %A Xian,Ruoling %A Li,Juan %A Li,Yingcun %A Liu,Enmei %A Zhao,Yan %A Hu,Yan %+ Department of Child Health Care, Children’s Hospital of Chongqing Medical University, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, No. 20 Jinyu Avenue, Liangjiang New Area, Chongqing, 401122, China, 86 02368370551, hy420@126.com %K pediatrics %K pediatric research %K digital health %K public opinion %K research %K patient participation %K urban %K rural %K caregiver attitudes %K social media %K mobile phone %D 2025 %7 7.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Recruiting and retaining participants in pediatric research has always been challenging, particularly in healthy populations and remote areas, leading to selection bias and increased health disparities. In the digital age, medical research has been transformed by digital tools, offering new opportunities to enhance engagement in clinical research. However, public perspectives on digitalizing pediatric research and potential differences between urban and suburban areas remain unclear. Objective: This study aimed to investigate public perspectives on digitalizing pediatric research and compare differences between urban and suburban areas to help diversify participants and address health disparities. Methods: A cross-sectional web-based survey targeting caregivers of kindergarten children (aged 2-7 years) in Chongqing was conducted between June and December 2023. A total of 4231 valid questionnaires were analyzed, with 25.1% (n=1064) of the children residing in urban areas and 74.9% (n=3167) in suburban areas. Descriptive statistics and intergroup comparisons were used for data analysis. Results: Approximately 59.8% (n=2531) of the caregivers had first impressions of pediatric research, with 36.9% (n=1561) being positive and 22.9% (n=970) being negative. A total of 38.3% (n=1621) of caregivers recognized the growing popularity of digital tools, and 36.7% (n=1552) supported their use in pediatric research, but only 25.2% (n=1068) favored online-only research methods. The main concerns regarding the use of software in pediatric research were privacy issues (n=3273, 77.4%) and potential addiction (n=2457, 58.1%). Public accounts of research institutions (n=3400, 80.4%) were the most favored for online recruitment. Telephones (1916/3076, 62.3%) and social media apps (1801/3076, 58.6%) were the most popular for regular contact. Intergroup comparisons revealed that suburban caregivers had more positive first impressions of pediatric research (38.6% vs 32%; P<.001; adjusted odds ratio [aOR] 1.27, 95% CI 1.09-1.47) and faced fewer participation barriers: “worry about being an experimental subject” (70.9% vs 76.6%; P<.001; aOR 0.79, 95% CI 0.67-0.93), “pose a risk to children’s health” (58.6% vs 67.8%; P<.001; aOR 0.71, 95% CI 0.61-0.83), “do not have enough background information” (55.2% vs 61.6%; P<.001; aOR 0.78, 95% CI 0.67-0.89), and “worry about recommending other products” (48.2% vs 55%; P<.001; aOR 0.78, 95% CI 0.67-0.89). They also showed greater support for online-only research methods (26% vs 22.9%; P=.045; aOR 1.19, 95% CI 1.01-1.41) and greater openness to unofficial online recruitment sources (social media friends: 24.7% vs 18.9%; P<.001; aOR 1.33, 95% CI 1.11-1.59; moments on social media: 15.5% vs 11.1%; P<.001; aOR 1.35, 95% CI 1.09-1.67). Conclusions: In the digital age, enhancing recruitment and retention in pediatric research can be achieved by integrating both official and unofficial social media strategies, implementing a hybrid online-offline follow-up approach, and addressing privacy concerns. %M 39773676 %R 10.2196/60324 %U https://www.jmir.org/2025/1/e60324 %U https://doi.org/10.2196/60324 %U http://www.ncbi.nlm.nih.gov/pubmed/39773676 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 4 %N %P e52270 %T Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study %A Bae,Sang Won %A Chung,Tammy %A Zhang,Tongze %A Dey,Anind K %A Islam,Rahul %+ Human-Computer Interaction and Human-Centered AI Systems Lab, AI for Healthcare Lab, Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030-5906, United States, 1 4122658616, sbae4@stevens.edu %K digital phenotyping %K smart devices %K intoxication %K smartphone-based sensors %K wearables %K mHealth %K marijuana %K cannabis %K data collection %K passive sensing %K Fitbit %K machine learning %K eXtreme Gradient Boosting Machine classifier %K XGBoost %K algorithmic decision-making process %K explainable artificial intelligence %K XAI %K artificial intelligence %K JITAI %K decision support %K just-in-time adaptive interventions %K experience sampling %D 2025 %7 2.1.2025 %9 Original Paper %J JMIR AI %G English %X Background: Acute marijuana intoxication can impair motor skills and cognitive functions such as attention and information processing. However, traditional tests, like blood, urine, and saliva, fail to accurately detect acute marijuana intoxication in real time. Objective: This study aims to explore whether integrating smartphone-based sensors with readily accessible wearable activity trackers, like Fitbit, can enhance the detection of acute marijuana intoxication in naturalistic settings. No previous research has investigated the effectiveness of passive sensing technologies for enhancing algorithm accuracy or enhancing the interpretability of digital phenotyping through explainable artificial intelligence in real-life scenarios. This approach aims to provide insights into how individuals interact with digital devices during algorithmic decision-making, particularly for detecting moderate to intensive marijuana intoxication in real-world contexts. Methods: Sensor data from smartphones and Fitbits, along with self-reported marijuana use, were collected from 33 young adults over a 30-day period using the experience sampling method. Participants rated their level of intoxication on a scale from 1 to 10 within 15 minutes of consuming marijuana and during 3 daily semirandom prompts. The ratings were categorized as not intoxicated (0), low (1-3), and moderate to intense intoxication (4-10). The study analyzed the performance of models using mobile phone data only, Fitbit data only, and a combination of both (MobiFit) in detecting acute marijuana intoxication. Results: The eXtreme Gradient Boosting Machine classifier showed that the MobiFit model, which combines mobile phone and wearable device data, achieved 99% accuracy (area under the curve=0.99; F1-score=0.85) in detecting acute marijuana intoxication in natural environments. The F1-score indicated significant improvements in sensitivity and specificity for the combined MobiFit model compared to using mobile or Fitbit data alone. Explainable artificial intelligence revealed that moderate to intense self-reported marijuana intoxication was associated with specific smartphone and Fitbit metrics, including elevated minimum heart rate, reduced macromovement, and increased noise energy around participants. Conclusions: This study demonstrates the potential of using smartphone sensors and wearable devices for interpretable, transparent, and unobtrusive monitoring of acute marijuana intoxication in daily life. Advanced algorithmic decision-making provides valuable insight into behavioral, physiological, and environmental factors that could support timely interventions to reduce marijuana-related harm. Future real-world applications of these algorithms should be evaluated in collaboration with clinical experts to enhance their practicality and effectiveness. %M 39746202 %R 10.2196/52270 %U https://ai.jmir.org/2025/1/e52270 %U https://doi.org/10.2196/52270 %U http://www.ncbi.nlm.nih.gov/pubmed/39746202 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e64636 %T Baseline Smartphone App Survey Return in the Electronic Framingham Heart Study Offspring and Omni 1 Study: eCohort Study %A Rong,Jian %A Pathiravasan,Chathurangi H %A Zhang,Yuankai %A Faro,Jamie M %A Wang,Xuzhi %A Schramm,Eric %A Borrelli,Belinda %A Benjamin,Emelia J %A Liu,Chunyu %A Murabito,Joanne M %K mHealth %K mobile health %K mobile application %K smartphone %K digital health %K digital technology %K digital intervention %K gerontology %K geriatric %K older adult %K aging %K eFHS %K eCohort %K smartphone app %K baseline app surveys %K Framingham Heart Study %K health information %K information collection %K mobile phone %D 2024 %7 31.12.2024 %9 %J JMIR Aging %G English %X Background: Smartphone apps can be used to monitor chronic conditions and offer opportunities for self-assessment conveniently at home. However, few digital studies include older adults. Objective: We aim to describe a new electronic cohort of older adults embedded in the Framingham Heart Study including baseline smartphone survey return rates and survey completion rates by smartphone type (iPhone [Apple Inc] and Android [Google LLC] users). We also aim to report survey results for selected baseline surveys and participant experience with this study’s app. Methods: Framingham Heart Study Offspring and Omni (multiethnic cohort) participants who owned a smartphone were invited to download this study’s app that contained a range of survey types to report on different aspects of health including self-reported measures from the Patient-Reported Outcomes Measurement Information System (PROMIS). iPhone users also completed 4 tasks including 2 cognitive and 2 physical function testing tasks. Baseline survey return and completion rates were calculated for 12 surveys and compared between iPhone and Android users. We calculated standardized scores for the PROMIS surveys. The Mobile App Rating Scale (MARS) was deployed 30 days after enrollment to obtain participant feedback on app functionality and aesthetics. Results: We enrolled 611 smartphone users (average age 73.6, SD 6.3 y; n=346, 56.6% women; n=88, 14.4% Omni participants; 478, 78.2% iPhone users) and 596 (97.5%) returned at least 1 baseline survey. iPhone users had higher app survey return rates than Android users for each survey (range 85.5% to 98.3% vs 73.8% to 95.2%, respectively), but survey completion rates did not differ in the 2 smartphone groups. The return rate for the 4 iPhone tasks ranged from 80.9% (380/470) for the gait task to 88.9% (418/470) for the Trail Making Test task. The Electronic Framingham Heart Study participants had better standardized t scores in 6 of 7 PROMIS surveys compared to the general population mean (t score=50) including higher cognitive function (n=55.6) and lower fatigue (n=45.5). Among 469 participants who returned the MARS survey, app functionality and aesthetics was rated high (total MARS score=8.6 on a 1‐10 scale). Conclusions: We effectively engaged community-dwelling older adults to use a smartphone app designed to collect health information relevant to older adults. High app survey return rates and very high app survey completion rates were observed along with high participant rating of this study’s app. %R 10.2196/64636 %U https://aging.jmir.org/2024/1/e64636 %U https://doi.org/10.2196/64636 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e57824 %T Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review %A Dimitsaki,Stella %A Natsiavas,Pantelis %A Jaulent,Marie-Christine %+ Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, 15 Rue de l'École de Médecine, Paris, 75006, France, 33 767968072, Stella.Dimitsaki@etu.sorbonne-universite.fr %K pharmacovigilance %K drug safety %K artificial intelligence %K machine learning %K real-world data %K scoping review %D 2024 %7 30.12.2024 %9 Review %J J Med Internet Res %G English %X Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology. Objective: This scoping review depicts the emerging use of AI on structured RWD for pharmacovigilance purposes to identify relevant trends and potential research gaps. Methods: The scoping review methodology is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We queried the MEDLINE database through the PubMed search engine. Relevant scientific manuscripts published from January 2010 to January 2024 were retrieved. The included studies were “mapped” against a set of evaluation criteria, including applied AI approaches, code availability, description of the data preprocessing pipeline, clinical validation of AI models, and implementation of trustworthy AI criteria following the guidelines of the FUTURE (Fairness, Universality, Traceability, Usability, Robustness, and Explainability)-AI initiative. Results: The scoping review ultimately yielded 36 studies. There has been a significant increase in relevant studies after 2019. Most of the articles focused on adverse drug reaction detection procedures (23/36, 64%) for specific adverse effects. Furthermore, a substantial number of studies (34/36, 94%) used nonsymbolic AI approaches, emphasizing classification tasks. Random forest was the most popular machine learning approach identified in this review (17/36, 47%). The most common RWD sources used were electronic health care records (28/36, 78%). Typically, these data were not available in a widely acknowledged data model to facilitate interoperability, and they came from proprietary databases, limiting their availability for reproducing results. On the basis of the evaluation criteria classification, 10% (4/36) of the studies published their code in public registries, 16% (6/36) tested their AI models in clinical environments, and 36% (13/36) provided information about the data preprocessing pipeline. In addition, in terms of trustworthy AI, 89% (32/36) of the studies followed at least half of the trustworthy AI initiative guidelines. Finally, selection and confounding biases were the most common biases in the included studies. Conclusions: AI, along with structured RWD, constitutes a promising line of work for drug safety and pharmacovigilance. However, in terms of AI, some approaches have not been examined extensively in this field (such as explainable AI and causal AI). Moreover, it would be helpful to have a data preprocessing protocol for RWD to support pharmacovigilance processes. Finally, because of personal data sensitivity, evaluation procedures have to be investigated further. %M 39753222 %R 10.2196/57824 %U https://www.jmir.org/2024/1/e57824 %U https://doi.org/10.2196/57824 %U http://www.ncbi.nlm.nih.gov/pubmed/39753222 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e53613 %T Using Active and Passive Smartphone Data to Enhance Adolescents’ Emotional Awareness in Forensic Outpatient Setting: A Qualitative Feasibility and Usability Study %A Leijse,Merel M L %A van Dam,Levi %A Jambroes,Tijs %A Timmerman,Amber %A Popma,Arne %+ Child and Adolescent Psychiatry & Psychosocial Care, Amsterdam UMC location Vrije Universiteit Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, Netherlands, 31 020 8901000, m.m.l.leijse@amsterdamumc.nl %K emotion regulation %K emotion awareness %K smartphone data %K forensic outpatient youth care %K treatment motivation %K treatment alliance %K emotion %K behavioral %K interview %K mHealth %K app %K forensic %K usability %K feasibility %K delinquent %K pediatrics %K youth %K adolescent %K teenager %K experience %K attitude %K opinion %K perception %K perspective %K acceptance %K emoji %K behavioral data %K mobile phone %D 2024 %7 30.12.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Delinquent behavior in adolescence is a prevalent issue, often associated with difficulties across multiple life domains, which in turn perpetuates negative life outcomes. While current treatment programs show partial success in improving behavioral changes and reducing recidivism, comprehensive conclusions regarding the overall efficacy of these interventions have yet to be established. In forensic outpatient settings, the discrepancy between adolescents’ limited emotional awareness and the predominant emphasis on cognitive reflection, combined with low treatment adherence, may be factors that undermine treatment efficacy. New technologies, such as smartphone apps, may offer a solution by integrating real-life data into treatment to improve emotional and behavioral patterns. The low-threshold use of smartphone data can be useful in addressing these treatment challenges. Objective: This study aimed to explore the feasibility and usability of Feelee (Garage2020), a smartphone app that integrates active emoji and passive behavioral data, as a potential addition to treatment for adolescents in a forensic outpatient setting. Methods: We conducted a prepilot study with adolescents (n=4) who used the Feelee app over a 2-week period. App usage included completing a brief emoji survey 3 times a day (active data) and allowing Feelee to track the call logs, Bluetooth devices in proximity, cell tower IDs, app usage, and phone status (passive data). During treatment sessions, both adolescents and clinicians reviewed and discussed the active and passive data. Semistructured interviews were conducted with adolescents and clinicians (n=7) to gather experiences and feedback on the feasibility and usability of incorporating smartphone data into treatment. Results: The study showed that adolescents (n=3) succeeded in using Feelee for the full 2 weeks, and data were available for discussion in at least 1 session per participant. Both adolescents and clinicians (n=7) stated that Feelee was valuable for viewing, discussing, and gaining insight into their emotions, which facilitated targeted actions based on the Feelee data. However, neither adolescents nor clinicians reported increased engagement in treatment as a result of using Feelee. Despite technical issues, overall feedback on the Feelee app, in addition to treatment, was positive (n=7). However, further improvements are needed to address the high battery consumption and the inaccuracies in the accelerometer. Conclusions: This qualitative study provides an in-depth understanding of the potential benefits of integrating active and passive smartphone data for adolescents in a forensic outpatient setting. Feelee appears to contribute to a better understanding of emotions and behaviors, suggesting its potential value in enhancing emotional awareness in treatment. Further research is needed to assess Feelee’s clinical effectiveness and explore how it enhances emotional awareness. Recommendations from adolescents and clinicians emphasize the need for prepilot studies to address user issues, guiding technical improvements and future research in forensic outpatient settings. %M 39753211 %R 10.2196/53613 %U https://formative.jmir.org/2024/1/e53613 %U https://doi.org/10.2196/53613 %U http://www.ncbi.nlm.nih.gov/pubmed/39753211 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 9 %N %P e62770 %T Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design, Development, and Evaluation %A Wu,Sixuan %A Song,Kefan %A Cobb,Jason %A Adams,Alexander T %+ School of Interactive Computing, Georgia Institute of Technology, Technology Square Research Building, Atlanta, GA, 30332, United States, 1 404 894 2000, swu469@gatech.edu %K mobile health %K mHealth %K ubiquitous health %K smartphone %K chip %K microscope %K microfluidics %K cells counting, body fluid analysis, blood test, urinalysis, computer vision, machine learning %K fluid %K cell %K cellular %K concentration %D 2024 %7 23.12.2024 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms. Objective: The objective was to design and fabricate microfluidic chips, coupled with clinical utilities, for cell counting and concentration analysis. We answered the following research questions (RQs): RQ1, Can gravity drive the flow within the microfluidic chips, eliminating the need for external pumps? RQ2, How does the microfluidic chip design impact video quality for cell analysis? RQ3, Can smartphone-captured videos be used to estimate cell count and concentration in microfluidic chips? Methods: To answer the 3 RQs, 2 experiments were conducted. In the cell flow velocity experiment, diluted sheep blood flowed through the microfluidic chips with and without a bottleneck design to answer RQ1 and RQ2, respectively. In the cell concentration analysis experiment, sheep blood diluted into 13 concentrations flowed through the microfluidic chips while videos were recorded by smartphones for the concentration measurement. Results: In the cell flow velocity experiment, we designed and fabricated 2 versions of microfluidic chips. The ANOVA test (Straight: F6, 99=6144.45, P<.001; Bottleneck: F6, 99=3475.78, P<.001) showed the height difference had a significant impact on the cell velocity, which implied gravity could drive the flow. The video sharpness analysis demonstrated that video quality followed an exponential decay with increasing height differences (video quality=100e–k×Height) and a bottleneck design could effectively preserve video quality (Straight: R2=0.95, k=4.33; Bottleneck: R2=0.91, k=0.59). Samples from the 13 cell concentrations were used for cell counting and cell concentration estimation analysis. The accuracy of cell counting (n=35, 60-second samples, R2=0.96, mean absolute error=1.10, mean squared error=2.24, root mean squared error=1.50) and cell concentration regression (n=39, 150-second samples, R2=0.99, mean absolute error=0.24, mean squared error=0.11, root mean squared error=0.33 on a logarithmic scale, mean average percentage error=0.25) were evaluated using 5-fold cross-validation by comparing the algorithmic estimation to ground truth. Conclusions: In conclusion, we demonstrated the importance of the flow velocity in a microfluidic system, and we proposed SmartFlow, a low-cost system for computer vision–based cellular analysis. The proposed system could count the cells and estimate cell concentrations in the samples. %M 39715548 %R 10.2196/62770 %U https://biomedeng.jmir.org/2024/1/e62770 %U https://doi.org/10.2196/62770 %U http://www.ncbi.nlm.nih.gov/pubmed/39715548 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60493 %T Resting Heart Rate and Associations With Clinical Measures From the Project Baseline Health Study: Observational Study %A Feng,Kent Y %A Short,Sarah A %A Saeb,Sohrab %A Carroll,Megan K %A Olivier,Christoph B %A Simard,Edgar P %A Swope,Susan %A Williams,Donna %A Eckstrand,Julie %A Pagidipati,Neha %A Shah,Svati H %A Hernandez,Adrian F %A Mahaffey,Kenneth W %+ Verily Life Sciences, 269 E Grand Ave, South San Francisco, CA, 94080, United States, 1 650 495 7100, sarahshort@verily.com %K resting heart rate %K wearable devices %K remote monitoring %K physiology %K PBHS %K Project Baseline Health Study %K Verily Study Watch %K heart rate %K observational study %K cohort study %K wearables %K electrocardiogram %K regression analyses %K socioeconomic status %K medical condition %K vital signs %K laboratory assessments %K physical function %K electronic health %K eHealth %D 2024 %7 20.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Though widely used, resting heart rate (RHR), as measured by a wearable device, has not been previously evaluated in a large cohort against a variety of important baseline characteristics. Objective: This study aimed to assess the validity of the RHR measured by a wearable device compared against the gold standard of ECG (electrocardiography), and assess the relationships between device-measured RHR and a broad range of clinical characteristics. Methods: The Project Baseline Health Study (PHBS) captured detailed demographic, occupational, social, lifestyle, and clinical data to generate a deeply phenotyped cohort. We selected an analysis cohort within it, which included participants who had RHR determined by both ECG and the Verily Study Watch (VSW). We examined the correlation between these simultaneous RHR measures and assessed the relationship between VSW RHR and a range of baseline characteristics, including demographic, clinical, laboratory, and functional assessments. Results: From the overall PBHS cohort (N=2502), 875 (35%) participants entered the analysis cohort (mean age 50.9, SD 16.5 years; n=519, 59% female and n=356, 41% male). The mean and SD of VSW RHR was 66.6 (SD 11.2) beats per minute (bpm) for female participants and 64.4 (SD 12.3) bpm for male participants. There was excellent reliability between the two measures of RHR (ECG and VSW) with an intraclass correlation coefficient of 0.946. On univariate analyses, female and male participants had similar baseline characteristics that trended with higher VSW RHR: lack of health care insurance (both P<.05), higher BMI (both P<.001), higher C-reactive protein (both P<.001), presence of type 2 diabetes mellitus (both P<.001) and higher World Health Organization Disability Assessment Schedule (WHODAS) 2.0 score (both P<.001) were associated with higher RHR. On regression analyses, within each domain of baseline characteristics (demographics and socioeconomic status, medical conditions, vitals, physical function, laboratory assessments, and patient-reported outcomes), different characteristics were associated with VSW RHR in female and male participants. Conclusions: RHR determined by the VSW had an excellent correlation with that determined by ECG. Participants with higher VSW RHR had similar trends in socioeconomic status, medical conditions, vitals, laboratory assessments, physical function, and patient-reported outcomes irrespective of sex. However, within each domain of baseline characteristics, different characteristics were most associated with VSW RHR in female and male participants. Trial Registration: ClinicalTrials.gov NCT03154346; https://clinicaltrials.gov/study/NCT03154346 %M 39705694 %R 10.2196/60493 %U https://www.jmir.org/2024/1/e60493 %U https://doi.org/10.2196/60493 %U http://www.ncbi.nlm.nih.gov/pubmed/39705694 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58637 %T Decades in the Making: The Evolution of Digital Health Research Infrastructure Through Synthetic Data, Common Data Models, and Federated Learning %A Austin,Jodie A %A Lobo,Elton H %A Samadbeik,Mahnaz %A Engstrom,Teyl %A Philip,Reji %A Pole,Jason D %A Sullivan,Clair M %+ Queensland Digital Health Centre, Centre for Health Services Research, The University of Queensland, Level 5, UQ Health Sciences Building, Fig Tree Cres, Brisbane, 4029, Australia, 61 7 3176 5530, j.austin1@uq.edu.au %K real-world data %K digital health research %K synthetic data %K common data models %K federated learning %K university-industry collaboration %D 2024 %7 20.12.2024 %9 Viewpoint %J J Med Internet Res %G English %X Traditionally, medical research is based on randomized controlled trials (RCTs) for interventions such as drugs and operative procedures. However, increasingly, there is a need for health research to evolve. RCTs are expensive to run, are generally formulated with a single research question in mind, and analyze a limited dataset for a restricted period. Progressively, health decision makers are focusing on real-world data (RWD) to deliver large-scale longitudinal insights that are actionable. RWD are collected as part of routine care in real time using digital health infrastructure. For example, understanding the effectiveness of an intervention could be enhanced by combining evidence from RCTs with RWD, providing insights into long-term outcomes in real-life situations. Clinicians and researchers struggle in the digital era to harness RWD for digital health research in an efficient and ethically and morally appropriate manner. This struggle encompasses challenges such as ensuring data quality, integrating diverse sources, establishing governance policies, ensuring regulatory compliance, developing analytical capabilities, and translating insights into actionable strategies. The same way that drug trials require infrastructure to support their conduct, digital health also necessitates new and disruptive research data infrastructure. Novel methods such as common data models, federated learning, and synthetic data generation are emerging to enhance the utility of research using RWD, which are often siloed across health systems. A continued focus on data privacy and ethical compliance remains. The past 25 years have seen a notable shift from an emphasis on RCTs as the only source of practice-guiding clinical evidence to the inclusion of modern-day methods harnessing RWD. This paper describes the evolution of synthetic data, common data models, and federated learning supported by strong cross-sector collaboration to support digital health research. Lessons learned are offered as a model for other jurisdictions with similar RWD infrastructure requirements. %M 39705072 %R 10.2196/58637 %U https://www.jmir.org/2024/1/e58637 %U https://doi.org/10.2196/58637 %U http://www.ncbi.nlm.nih.gov/pubmed/39705072 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 3 %N %P e64362 %T Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study %A Dahu,Butros M %A Khan,Solaiman %A Toubal,Imad Eddine %A Alshehri,Mariam %A Martinez-Villar,Carlos I %A Ogundele,Olabode B %A Sheets,Lincoln R %A Scott,Grant J %+ University of Missouri, Institute for Data Science and Informatics, Columbia, MO, United States, 1 8325124825, peterdahu@gmail.com %K geospatial modeling %K deep convolutional neural network %K DCNN %K Residual Network-50 %K ResNet-50 %K satellite imagery %K Moran I %K local indicators of spatial association %K LISA %K spatial lag model %K obesity rate %K artificial intelligence %K AI %D 2024 %7 17.12.2024 %9 Original Paper %J JMIR AI %G English %X Background: The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri. Objective: This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri. Methods: Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates. Results: Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R2 of 0.93 and a spatial pseudo R2 of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps. Conclusions: This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model’s high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research. %M 39688897 %R 10.2196/64362 %U https://ai.jmir.org/2024/1/e64362 %U https://doi.org/10.2196/64362 %U http://www.ncbi.nlm.nih.gov/pubmed/39688897 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60184 %T From Doubt to Confidence—Overcoming Fraudulent Submissions by Bots and Other Takers of a Web-Based Survey %A Hardesty,Jeffrey J %A Crespi,Elizabeth %A Sinamo,Joshua K %A Nian,Qinghua %A Breland,Alison %A Eissenberg,Thomas %A Kennedy,Ryan David %A Cohen,Joanna E %+ Institute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Fourth Floor, 2213 McElderry St, Baltimore, MD, 21205, United States, 1 410 641 4537, jhardesty@jhu.edu %K fake data %K recruitment %K online survey %K internet survey %K challenges %K data integrity %K data quality %K e-cigs %K tobacco control %K longitudinal survey %K web-based survey %K e-cigarette %K vaping %K smoking %K smoke %K cessation %K prevalence %K data collection %K United States %K US %K adult %K VAPER %K Vaping and Patterns of E-cigarette Use Research %D 2024 %7 16.12.2024 %9 Viewpoint %J J Med Internet Res %G English %X In 2019, we launched a web-based longitudinal survey of adults who frequently use e-cigarettes, called the Vaping and Patterns of E-cigarette Use Research (VAPER) Study. The initial attempt to collect survey data failed due to fraudulent survey submissions, likely submitted by survey bots and other survey takers. This paper chronicles the journey from that setback to the successful completion of 5 waves of data collection. The section “Naïve Beginnings” examines the study preparation phase, identifying the events, decisions, and assumptions that contributed to the failure (eg, allowing anonymous survey takers to submit surveys and overreliance on a third-party’s proprietary fraud detection tool to identify participants attempting to submit multiple surveys). “A 5-Alarm Fire and Subsequent Investigation” summarizes the warning signs that suggested fraudulent survey submissions had compromised the data integrity after the initial survey launched (eg, an unanticipated acceleration in recruitment and a voicemail alleging fraudulent receipt of multiple gift codes). This section also covers the investigation process, along with conclusions regarding how the methodology was exploited (eg, clearing cookies and using virtual private networks) and the extent of the issue (ie, only 363/1624, 22.4% of the survey completions were likely valid). “Building More Resilient Methodology” details the vulnerabilities and threats that likely compromised the initial survey attempt (eg, anonymity and survey bots); the corresponding mitigation strategies and their benefits and limitations (eg, personal record verification platforms, IP address matching, virtual private network detection services, and CAPTCHA [Completely Automated Public Turing test to tell Computers and Humans Apart]); and the array of strategies that were implemented in future survey attempts. “Staying Vigilant” recounts the identification and management of an additional threat that emerged despite the implementation of an array of mitigation strategies, underscoring the need for ongoing vigilance and adaptability. While the precise nature of the threat remains unknown, the evidence suggested multiple fraudulent surveys were submitted by a single or connected entities, who likely did not possess e-cigarettes. To mitigate the chance of reoccurrence, participants were required to submit an authentic photo of their most used e-cigarette. Finally, in “Reflection 4 Years Later,” we share insights after completing 5 waves of data collection without additional threats or vulnerabilities uncovered that necessitated the application of further mitigation strategies. Reflections include reasons for confidence in the data’s integrity, the scalability and cost-effectiveness of the study protocols, and the potential introduction of sampling bias through recruitment and mitigation strategies. By sharing our journey, we aim to provide valuable insights for researchers facing similar challenges with web-based surveys and those seeking to minimize such challenges a priori. Our experiences highlight the importance of proactive measures, continuous monitoring, and adaptive problem-solving to ensure the integrity of data collected from participants recruited from web-based platforms. %M 39680887 %R 10.2196/60184 %U https://www.jmir.org/2024/1/e60184 %U https://doi.org/10.2196/60184 %U http://www.ncbi.nlm.nih.gov/pubmed/39680887 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e63476 %T Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study %A Ahn,Seong-Ho %A Yim,Kwangil %A Won,Hyun-Sik %A Kim,Kang-Min %A Jeong,Dong-Hwa %+ Department of Artificial Intelligence, The Catholic University of Korea, Jibong-Ro 43 3-1, Bucheon-Si, Republic of Korea, 82 2 2164 5564, kangmin89@catholic.ac.kr %K COVID-19 %K confirmed case prediction %K search engine queries %K query expansion %K word embedding %K public health %K case prediction %K South Korea %K search engine %K infodemiology %K infodemiology study %K policy %K lifestyle %K machine learning %K machine learning techniques %K utilization %K temporal variation %K novel framework %K temporal %K web-based search %K temporal semantics %K prediction model %K model %D 2024 %7 16.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The number of confirmed COVID-19 cases is a crucial indicator of policies and lifestyles. Previous studies have attempted to forecast cases using machine learning techniques that use a previous number of case counts and search engine queries predetermined by experts. However, they have limitations in reflecting temporal variations in queries associated with pandemic dynamics. Objective: This study aims to propose a novel framework to extract keywords highly associated with COVID-19, considering their temporal occurrence. We aim to extract relevant keywords based on pandemic variations using query expansion. Additionally, we examine time-delayed web-based search behavior related to public interest in COVID-19 and adjust for better prediction performance. Methods: To capture temporal semantics regarding COVID-19, word embedding models were trained on a news corpus, and the top 100 words related to “Corona” were extracted over 4-month windows. Time-lagged cross-correlation was applied to select optimal time lags correlated to confirmed cases from the expanded queries. Subsequently, ElasticNet regression models were trained after reducing the feature dimensions using principal component analysis of the time-lagged features to predict future daily case counts. Results: Our approach successfully extracted relevant keywords depending on the pandemic phase, encompassing keywords directly related to COVID-19, such as its symptoms, and its societal impact. Specifically, during the first outbreak, keywords directly linked to COVID-19 and past infectious disease outbreaks similar to those of COVID-19 exhibited a high positive correlation. In the second phase of the pandemic, as community infections emerged, keywords related to the government’s pandemic control policies were frequently observed with a high positive correlation. In the third phase of the pandemic, during the delta variant outbreak, keywords such as “economic crisis” and “anxiety” appeared, reflecting public fatigue. Consequently, prediction models trained by the extracted queries over 4-month windows outperformed previous methods for most predictions 1-14 days ahead. Notably, our approach showed significantly higher Pearson correlation coefficients than models based solely on the number of past cases for predictions 9-11 days ahead (P=.02, P<.01, and P<.01), in contrast to heuristic- and symptom-based query sets. Conclusions: This study proposes a novel COVID-19 case-prediction model that automatically extracts relevant queries over time using word embedding. The model outperformed previous methods that relied on static symptom-based or heuristic queries, even without prior expert knowledge. The results demonstrate the capability of our approach to track temporal shifts in public interest regarding changes in the pandemic. %M 39680913 %R 10.2196/63476 %U https://www.jmir.org/2024/1/e63476 %U https://doi.org/10.2196/63476 %U http://www.ncbi.nlm.nih.gov/pubmed/39680913 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e66049 %T Using Video Cameras to Assess Physical Activity and Other Well-Being Behaviors in Urban Environments: Feasibility, Reliability, and Participant Reactivity Studies %A Benton,Jack S %A Evans,James %A Anderson,Jamie %A French,David P %+ Manchester Centre for Health Psychology, Division of Psychology and Mental Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 01613066000, jack.benton@manchester.ac.uk %K unobtrusive observation %K video cameras %K measurement %K physical activity %K well-being %K urban environments %D 2024 %7 16.12.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Unobtrusive observation is a promising method for assessing physical activity and other well-being behaviors (eg, social interactions) in urban environments, without participant burden and biases associated with self-report. However, current methods require multiple in-person observers. Using video cameras instead could allow for more accurate observations at lower cost and with greater flexibility in scheduling. Objective: This research aimed to test the feasibility of using stationary wireless video cameras to observe physical activity and other well-being behaviors, and to assess its reliability and potential participant reactivity. Methods: Across 3 cross-sectional studies, 148 hours of video recordings were collected from 6 outdoor public spaces in Manchester, United Kingdom. The videos were coded by 3 researchers using MOHAWk (Method for Observing Physical Activity and Wellbeing)—a validated in-person observation tool for assessing physical activity, social interactions, and people taking notice of the environment. Inter- and intrarater reliabilities were assessed using intraclass correlation coefficients (ICCs). Intercept surveys were conducted to assess public awareness of the cameras and whether they altered their behavior due to the presence of cameras. Results: The 148 hours of video recordings were coded in 85 hours. Interrater reliability between independent coders was mostly “excellent” (ICCs>0.90; n=36), with a small number of “good” (ICCs>0.75; n=2), “moderate” (ICCs=0.5-0.75; n=3), or “poor” (ICCs<0.5; n=1) ICC values. Reliability decreased at night, particularly for coding ethnic group and social interactions, but remained mostly “excellent” or “good.” Intrarater reliability within a single coder after a 2-week interval was “excellent” for all but 1 code, with 1 “good” ICC value for assessing vigorous physical activity, indicating that the coder could reproduce similar results over time. Intrarater reliability was generally similar during the day and night, apart from ICC values for coding ethnic group, which reduced from “excellent” to “good” at night. Intercept surveys with 86 public space users found that only 5 (5.8%) participants noticed the cameras used for this study. Importantly, all 5 said that they did not alter their behavior as a result of noticing these cameras, therefore, indicating no evidence of reactivity. Conclusions: Camera-based observation methods are more reliable than in-person observations and do not produce participant reactivity often associated with self-report methods. This method requires less time for data collection and coding, while allowing for safe nighttime observation without the risk to research staff. This research is a significant first step in demonstrating the potential for camera-based methods to improve natural experimental studies of real-world environmental interventions. It also provides a rigorous foundation for developing more scalable automated computer vision algorithms for assessing human behaviors. %M 39680427 %R 10.2196/66049 %U https://publichealth.jmir.org/2024/1/e66049 %U https://doi.org/10.2196/66049 %U http://www.ncbi.nlm.nih.gov/pubmed/39680427 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e63457 %T Whole-Body and Segmental Phase Angles and Cognitive Function in the Older Korean Population: Cross-Sectional Analysis %A Chen,Jiaren %A Park,Jong-Hwan %A Lin,Chien-Yu %A Lai,Ting-Fu %A Kim,Du-Ri %A Shin,Myung-Jun %A Moon,Eunsoo %A Kang,Jung Mo %A Lee,Jong Won %A Cho,Yoon Jae %A Liao,Yung %A Goh,Tae Sik %A Lee,Jung Sub %K bioelectrical impedance analysis %K oxidative stress %K cellular health %K cognitive function %K older adults %K BIA %K phase angle %K PhA %D 2024 %7 16.12.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Recently, the phase angle (PhA) has emerged as an essential indicator of cellular health. Most studies have examined its association with physiological conditions, such as sarcopenia, frailty, and physical function, in older populations. Simultaneously, growing attention is being paid to the clinical relevance of segmental PhAs for future applications. However, few studies have explored the relationship between PhAs, especially segmental PhAs, and the psychological aspects of health, particularly cognitive function. Objective: We aimed to investigate the association between whole-body and segmental PhAs and cognitive function in older adults. Methods: Individuals aged 65 years and above were recruited from adult community groups residing in Busan, South Korea, through the 2022 Bus-based Screening and Assessment Network (BUSAN) study of Pusan National University Hospital. Participants’ whole-body and segmental PhAs were measured using a bioelectrical impedance analyzer (BWA 2.0 Body Water Analyzer, InBody), and cognitive functions (overall and subdomains, including memory, orientation, attention and calculation, and language) were self-reported using the Korean version of the Mini-Mental State Examination. Multiple linear regression analyses were performed to examine these associations. Results: This study included 625 older adults aged 65‐96 years (women: n=444, 71%; men: n=191, 29%). A positive association was observed between whole-body PhA and cognitive function (b=0.62, 95% CI 0.16‐1.08; P<.01). We observed significant positive associations between the PhA of the lower limbs (b=0.72, 95% CI 0.38‐1.06; P<.001) and cognitive function. Analysis of the Mini-Mental State Examination subdomains revealed that whole-body PhA was significantly related to memory (b=0.11, 95% CI 0.00‐0.22; P=.04); the PhA of the upper limbs was significantly related to orientation (b=0.29, 95% CI 0.09‐0.49; P=.01); and the PhA of the lower limbs was significantly related to orientation (b=0.24, 95% CI 0.10‐0.38; P<.001), attention and calculation (b=0.21, 95% CI 0.06‐0.37; P=.01), memory (b=0.14, 95% CI 0.05‐0.22; P=.001), and language functions (b=0.07, 95% CI 0.01‐0.12; P=.01). However, trunk PhA showed no significant association. Conclusions: Our findings bolster the emerging evidence of a significant positive correlation between whole-body PhA and cognitive function in our sample, with nuanced relationships observed across different segmental PhAs and cognitive subdomains. Therefore, this study revealed that PhAs could be a useful tool for screening or preventing cognitive decline in the general older population, offering substantial evidence for future interventional studies. Further research should delve into the mechanisms and assess targeted interventions that enhance regional physical function to support cognitive health in older adults. Further long-term investigation on these associations is warranted. %R 10.2196/63457 %U https://publichealth.jmir.org/2024/1/e63457 %U https://doi.org/10.2196/63457 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e63311 %T Discussions of Cannabis Over Patient Portal Secure Messaging: Content Analysis %A Shetty,Vishal A %A Gregor,Christina M %A Tusing,Lorraine D %A Pradhan,Apoorva M %A Romagnoli,Katrina M %A Piper,Brian J %A Wright,Eric A %+ Department of Health Promotion and Policy, University of Massachusetts, 715 North Pleasant St., Amherst, MA, 01003, United States, 1 413 230 4015, vashetty@geisinger.edu %K patient portal %K secure message %K marijuana %K patient-provider communication %K message content %K content analysis %K United States %K pain %K anxiety %K depression %K insomnia %K electronic messaging %K electronic health record %K EHR %K cannabis %D 2024 %7 12.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient portal secure messaging allows patients to describe health-related behaviors in ways that may not be sufficiently captured in standard electronic health record (EHR) documentation, but little is known about how cannabis is discussed on this platform. Objective: This study aimed to identify patient and provider secure messages that discussed cannabis and contextualize these discussions over periods before and after its legalization for medical purposes in Pennsylvania. Methods: We examined 382,982 secure messages sent by 15,340 patients and 6101 providers from an integrated health delivery system in Pennsylvania, United States, from January 2012 to June 2022. We used an unsupervised natural language processing approach to construct a lexicon that identified messages explicitly discussing cannabis. We then conducted a qualitative content analysis on a random sample of identified messages to understand the medical reasons behind patients’ use, the primary purposes of the cannabis-related discussions, and changes in these purposes over time. Results: We identified 1782 messages sent by 1098 patients (7.2% of total patients in the study) and 800 messages sent by 430 providers (7% of total providers in the study) as explicitly discussing cannabis. The most common medical reasons for use stated by patients in 190 sampled messages included pain or a pain-related condition (50.5% of messages), anxiety (13.7% of messages), and sleep (11.1% of messages). We coded 56 different purposes behind the mentions of cannabis in patient messages and 33 purposes in 100 sampled provider messages. In years before the legalization (2012-2016), patient and provider messages (n=20 for both) were primarily driven by discussions about cannabis screening results (38.9% and 76.5% of messages, respectively). In the years following legalization (2017-2022), patient messages (n=170) primarily involved seeking assistance to facilitate medical use (35.2% of messages) and reporting current use (25.3% of messages). Provider messages (n=80) were driven by giving assistance with medical marijuana access (27.5% of messages) and stating that they were unable to refer, prescribe or recommend medical marijuana (26.3% of messages). Conclusions: Patients showed a willingness to discuss cannabis use over patient portal secure messages and expressed interest in use after the legalization of medical marijuana. Some providers responded to patient inquiries with assistance in obtaining access to medical marijuana, while others cautioned patients on the risks of use. Insight into cannabis-related discussions through secure messages can help health systems determine opportunities to improve care processes around patients’ cannabis use, and providers should be supported to communicate accurate and consistent information. %M 39666375 %R 10.2196/63311 %U https://www.jmir.org/2024/1/e63311 %U https://doi.org/10.2196/63311 %U http://www.ncbi.nlm.nih.gov/pubmed/39666375 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54321 %T Combining Topic Modeling, Sentiment Analysis, and Corpus Linguistics to Analyze Unstructured Web-Based Patient Experience Data: Case Study of Modafinil Experiences %A Walsh,Julia %A Cave,Jonathan %A Griffiths,Frances %+ Warwick Medical School, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, United Kingdom, 44 02476528009, julia.walsh@warwick.ac.uk %K unstructured text %K natural language processing %K NLP %K topic modeling %K sentiment analysis %K corpus linguistics %K social media data %K patient experience %K unsupervised %K modafinil %D 2024 %7 11.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient experience data from social media offer patient-centered perspectives on disease, treatments, and health service delivery. Current guidelines typically rely on systematic reviews, while qualitative health studies are often seen as anecdotal and nongeneralizable. This study explores combining personal health experiences from multiple sources to create generalizable evidence. Objective: The study aims to (1) investigate how combining unsupervised natural language processing (NLP) and corpus linguistics can explore patient perspectives from a large unstructured dataset of modafinil experiences, (2) compare findings with Cochrane meta-analyses on modafinil’s effectiveness, and (3) develop a methodology for analyzing such data. Methods: Using 69,022 posts from 790 sources, we used a variety of NLP and corpus techniques to analyze the data, including data cleaning techniques to maximize post context, Python for NLP techniques, and Sketch Engine for linguistic analysis. We used multiple topic mining approaches, such as latent Dirichlet allocation, nonnegative matrix factorization, and word-embedding methods. Sentiment analysis used TextBlob and Valence Aware Dictionary and Sentiment Reasoner, while corpus methods including collocation, concordance, and n-gram generation. Previous work had mapped topic mining to themes, such as health conditions, reasons for taking modafinil, symptom impacts, dosage, side effects, effectiveness, and treatment comparisons. Results: Key findings of the study included modafinil use across 166 health conditions, most frequently narcolepsy, multiple sclerosis, attention-deficit disorder, anxiety, sleep apnea, depression, bipolar disorder, chronic fatigue syndrome, fibromyalgia, and chronic disease. Word-embedding topic modeling mapped 70% of posts to predefined themes, while sentiment analysis revealed 65% positive responses, 6% neutral responses, and 28% negative responses. Notably, the perceived effectiveness of modafinil for various conditions strongly contrasts with the findings of existing randomized controlled trials and systematic reviews, which conclude insufficient or low-quality evidence of effectiveness. Conclusions: This study demonstrated the value of combining NLP with linguistic techniques for analyzing large unstructured text datasets. Despite varying opinions, findings were methodologically consistent and challenged existing clinical evidence. This suggests that patient-generated data could potentially provide valuable insights into treatment outcomes, potentially improving clinical understanding and patient care. %M 39662896 %R 10.2196/54321 %U https://www.jmir.org/2024/1/e54321 %U https://doi.org/10.2196/54321 %U http://www.ncbi.nlm.nih.gov/pubmed/39662896 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e56848 %T The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study %A Khusial,Rishi Jayant %A Sont,Jacob K %A Usmani,Omar S %A Bonini,Matteo %A Chung,Kian Fan %A Fowler,Stephen James %A Honkoop,Persijn J %+ Department of Biomedical Data Sciences, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, Netherlands, 31 715261319, r.j.khusial@lumc.nl %K asthma %K mHealth %K side effects %K beta-2 agonists %K inhaler medication %K heart rate %K sensor %K observational study %K asthma management %K cardiac cells %K monitoring %K Fitbit %K inhaler %D 2024 %7 11.12.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Beta-2 agonists play an important role in the management of asthma. Inhaled long-acting beta-2 agonists (LABAs) and short-acting beta-2 agonists (SABAs) cause bronchodilation by stimulating adrenoceptors. These receptors are also present in cardiac cells and, as a side effect, could also be stimulated by inhaled beta-2 agonists. Objective: This study aims to assess the effect of beta-2 agonists on heart rate (HR). Methods: The data were retrieved from an observational study, the myAirCoach Quantification Campaign. Beta-2 agonist use was registered by self-reported monthly questionnaires and by smart inhalers. HR was monitored continuously with the Fitbit Charge HR tracker (Fitbit Inc). Patients (aged 18 years and older) were recruited if they had uncontrolled asthma and used inhalation medication. Our primary outcome was the difference in HR between LABA and non-LABA users. Secondary outcomes were the difference in HR on days SABAs were used compared to days SABAs were not used and an assessment of the timing of inhaler use during the day. Results: Patients using LABA did not have a clinically relevant higher HR (average 0.8 beats per minute difference) during the day. Around the moment of SABA inhalation itself, the HR does increase steeply, and it takes 138 minutes before it returns to the normal range. Conclusions: This study indicates that LABAs do not have a clinically relevant effect on HR. SABAs are instead associated with a short-term HR increase. Trial Registration: ClinicalTrials.gov NCT02774772; https://clinicaltrials.gov/study/NCT02774772 %M 39661964 %R 10.2196/56848 %U https://cardio.jmir.org/2024/1/e56848 %U https://doi.org/10.2196/56848 %U http://www.ncbi.nlm.nih.gov/pubmed/39661964 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e65095 %T Analysis of Physical Activity Using Wearable Health Technology in US Adults Enrolled in the All of Us Research Program: Multiyear Observational Study %A Singh,Rujul %A Tetrick,Macy K %A Fisher,James L %A Washington,Peter %A Yu,Jane %A Paskett,Electra D %A Penedo,Frank J %A Clinton,Steven K %A Benzo,Roberto M %+ Division of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Suite 200, 3650 Olentangy River Road, Columbus, OH, 43124, United States, 1 614 293 3675, roberto.benzo@osumc.edu %K Physical Activity Guidelines for Americans %K accelerometry %K All of Us Research Program %K wearable activity monitors %K health equity %K multiyear activity tracking %K activity intensity estimation %K US adult population %K sociodemographic determinants of physical activity %K physical activity %K wearables %K United States %K older adults %K observational studies %K longitudinal setting %K sociodemographic determinants %K physical activity data %K Fitbit data %K step-based method %K adherence %D 2024 %7 10.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: To date, no studies have examined adherence to the 2018 Physical Activity Guidelines for Americans (PAGA) in real-world longitudinal settings using objectively measured activity monitoring data. This study addresses this gap by using commercial activity monitoring (Fitbit) data from the All of Us dataset. Objective: The primary objectives were to describe the prevalence of adherence to the 2018 PAGA and identify associated sociodemographic determinants. Additionally, we compared 3 distinct methods of processing physical activity (PA) data to estimate adherence to the 2008 PAGA. Methods: We used the National Institutes of Health’s All of Us dataset, which contains minute-level Fitbit data for 13,947 US adults over a 7-year time span (2015-2022), to estimate adherence to PAGA. A published step-based method was used to estimate metabolic equivalents and assess adherence to the 2018 PAGA (ie, ≥150 minutes of moderate- to vigorous-intensity PA per week). We compared the step-based method, the heart rate–based method, and the proprietary Fitbit-developed algorithm to estimate adherence to the 2008 PAGA. Results: The average overall adherence to the 2018 PAGA was 21.6% (3006/13,947; SE 0.4%). Factors associated with lower adherence in multivariate logistic regression analysis included female sex (relative to male sex; adjusted odds ratio [AOR] 0.66, 95% CI 0.60-0.72; P<.001); BMI of 25.0-29.9 kg/m2 (AOR 0.53, 95% CI 0.46-0.60; P<.001), 30-34.9 kg/m2 (AOR 0.30, 95% CI 0.25-0.36; P<.001), or ≥35 kg/m2 (AOR 0.13, 95% CI 0.10-0.16; P<.001; relative to a BMI of 18.5-24.9 kg/m2); being aged 30-39 years (AOR 0.66, 95% CI 0.56-0.77; P<.001), 40-49 years (AOR 0.79, 95% CI 0.68-0.93; P=.005), or ≥70 years (AOR 0.74, 95% CI 0.62-0.87; P<.001; relative to being 18-29 years); and non-Hispanic Black race or ethnicity (AOR 0.63, 95% CI 0.50-0.79; P<.001; relative to non-Hispanic White race or ethnicity). The Fitbit algorithm estimated that a larger percentage of the sample (10,307/13,947, 73.9%; 95% CI 71.2-76.6) adhered to the 2008 PAGA compared to the heart rate method estimate (4740/13,947, 34%; 95% CI 32.8-35.2) and the step-based method (1401/13,947, 10%; 95% CI 9.4-10.6). Conclusions: Our results show significant sociodemographic differences in PAGA adherence and notably different estimates of adherence depending on the algorithm used. These findings warrant the need to account for these disparities when implementing PA interventions and the need to establish an accurate and reliable method of using commercial accelerometers to examine PA, particularly as health care systems begin integrating wearable device data into patient health records. %M 39658010 %R 10.2196/65095 %U https://www.jmir.org/2024/1/e65095 %U https://doi.org/10.2196/65095 %U http://www.ncbi.nlm.nih.gov/pubmed/39658010 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e53401 %T An Ecological Momentary Assessment Approach of Environmental Triggers in the Role of Daily Affect, Rumination, and Movement Patterns in Early Alcohol Use Among Healthy Adolescents: Exploratory Study %A Prignitz,Maren %A Guldner,Stella %A Lehmler,Stephan Johann %A Aggensteiner,Pascal-M %A Nees,Frauke %A , %+ Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J 5, Mannheim, 68159, Germany, 49 62117036313, maren.prignitz@zi-mannheim.de %K alcohol use %K adolescence %K affect %K rumination %K ecological momentary assessment %K geospatial measures %D 2024 %7 10.12.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Adolescence is a period characterized by an increased susceptibility to developing risky alcohol consumption habits. This susceptibility can be influenced by social and situational factors encountered in daily life, which, in conjunction with emotions and thoughts, contribute to behavioral patterns related to alcohol use even in the early stages of alcohol experimentation, when initial experiences with alcohol are formed, and regular consumption is still evolving. Objective: This study aimed to examine the association between detailed behavioral and movement patterns, along with emotional and cognitive factors, and the early onset of alcohol use in the everyday lives of adolescents. Methods: A total of 65 healthy adolescents (33 male, twenty-nine 14-year-olds, and thirty-six 16-year-olds) underwent mobile-based ecological momentary assessments on alcohol (once a day at 9 AM, assessing alcohol use the day before), positive and negative affect, craving, rumination, and social context (6 prompts/day at 9 AM, 11 AM, 2 PM, 4 PM, 6 PM and 8 PM), type of day (weekdays or weekends, with weekend including Fridays, Saturdays, and Sundays), and using geospatial measures (specifically roaming entropy and number and type of trigger points for alcohol use met) over 14 days. After adjusting for a compliance rate of at least 50%, 52 participants (26 male and twenty-four 14-year-olds) were included in the analyses. Results: Generalized linear multilevel models revealed that higher positive affect (b=0.685, P=.007), higher rumination (b=0.586, P=.02), and a larger movement radius (roaming entropy) (b=8.126, P=.02) were positively associated with alcohol use on the same day. However, social context (b=–0.076, P=.90), negative affect (b=–0.077, P=.80), or potential trigger points (all P>.05) did not show significant associations. Alcohol use varied depending on the type of day, with more alcohol use on weekends (b=1.082, P<.001) and age (t50=–2.910, P=.005), with 16-year-olds (mean 1.61, SD 1.66) reporting more days of alcohol consumption than 14-year-olds (mean 0.548, SD 0.72). Conclusions: Our findings support previously identified factors as significant contributors to very early and low levels of alcohol consumption through fine-grained analysis of daily behaviors. These factors include positive affect, rumination, weekend days, and age. In addition, we emphasize that exploratory environmental movement behavior (roaming entropy) is also significantly associated with adolescent alcohol use, highlighting its importance as an additional factor. %M 39657181 %R 10.2196/53401 %U https://mhealth.jmir.org/2024/1/e53401 %U https://doi.org/10.2196/53401 %U http://www.ncbi.nlm.nih.gov/pubmed/39657181 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55712 %T Ecological Momentary Assessment of Mental Health Problems Among University Students: Data Quality Evaluation Study %A Portillo-Van Diest,Ana %A Mortier,Philippe %A Ballester,Laura %A Amigo,Franco %A Carrasco,Paula %A Falcó,Raquel %A Gili,Margalida %A Kiekens,Glenn %A H Machancoses,Francisco %A Piqueras,Jose A %A Rebagliato,Marisa %A Roca,Miquel %A Rodríguez-Jiménez,Tíscar %A Alonso,Jordi %A Vilagut,Gemma %+ Hospital del Mar Research Institute, Carrer Doctor Aiguader, 88, Barcelona, 08023, Spain, 34 93 316 07 60, pmortier@researchmar.net %K experience sampling method %K ecological momentary assessment %K mental health %K university students %K participation %K compliance %K reliability %K sensitivity analysis %K mobile phone %D 2024 %7 10.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of ecological momentary assessment (EMA) designs has been on the rise in mental health epidemiology. However, there is a lack of knowledge of the determinants of participation in and compliance with EMA studies, reliability of measures, and underreporting of methodological details and data quality indicators. Objective: This study aims to evaluate the quality of EMA data in a large sample of university students by estimating participation rate and mean compliance, identifying predictors of individual-level participation and compliance, evaluating between- and within-person reliability of measures of negative and positive affect, and identifying potential careless responding. Methods: A total of 1259 university students were invited to participate in a 15-day EMA study on mental health problems. Logistic and Poisson regressions were used to investigate the associations between sociodemographic factors, lifetime adverse experiences, stressful events in the previous 12 months, and mental disorder screens and EMA participation and compliance. Multilevel reliability and intraclass correlation coefficients were obtained for positive and negative affect measures. Careless responders were identified based on low compliance or individual reliability coefficients. Results: Of those invited, 62.1% (782/1259) participated in the EMA study, with a mean compliance of 76.9% (SD 27.7%). Participation was higher among female individuals (odds ratio [OR] 1.41, 95% CI 1.06-1.87) and lower among those aged ≥30 years (OR 0.20, 95% CI 0.08-0.43 vs those aged 18-21 years) and those who had experienced the death of a friend or family member in the previous 12 months (OR 0.73, 95% CI 0.57-0.94) or had a suicide attempt in the previous 12 months (OR 0.26, 95% CI 0.10-0.64). Compliance was particularly low among those exposed to sexual abuse before the age of 18 years (exponential of β=0.87) or to sexual assault or rape in the previous year (exponential of β=0.80) and among those with 12-month positive alcohol use disorder screens (exponential of β=0.89). Between-person reliability of negative and positive affect was strong (RkRn>0.97), whereas within-person reliability was fair to moderate (Rcn>0.43). Of all answered assessments, 0.86% (291/33,626) were flagged as careless responses because the response time per item was <1 second or the participants gave the same response to all items. Of the participants, 17.5% (137/782) could be considered careless responders due to low compliance (<25/56, 45%) or very low to null individual reliability (raw Cronbach α<0.11) for either negative or positive affect. Conclusions: Data quality assessments should be carried out in EMA studies in a standardized manner to provide robust conclusions to advance the field. Future EMA research should implement strategies to mitigate nonresponse bias as well as conduct sensitivity analyses to assess possible exclusion of careless responders. %M 39657180 %R 10.2196/55712 %U https://www.jmir.org/2024/1/e55712 %U https://doi.org/10.2196/55712 %U http://www.ncbi.nlm.nih.gov/pubmed/39657180 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55635 %T Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study %A Lee,Ting-Yi %A Chen,Ching-Hsuan %A Chen,I-Ming %A Chen,Hsi-Chung %A Liu,Chih-Min %A Wu,Shu-I %A Hsiao,Chuhsing Kate %A Kuo,Po-Hsiu %+ Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 521, No 17, Xuzhou Road, Taipei, 10055, Taiwan, 886 2 33668015, phkuo@ntu.edu.tw %K ecological momentary assessment %K digital phenotyping %K GPS mobility %K bipolar disorder %K major depressive disorder %K GPS %K global positioning system %K mood disorders %K assessment %K depression %K anxiety %K digital phenotype %K smartphone app %K technology %K behavioral changes %K patient %K monitoring %D 2024 %7 6.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Although significant research has explored the digital phenotype in mood disorders, the time-lagged and bidirectional relationship between mood and global positioning system (GPS) mobility remains relatively unexplored. Leveraging the widespread use of smartphones, we examined correlations between mood and behavioral changes, which could inform future scalable interventions and personalized mental health monitoring. Objective: This study aims to investigate the bidirectional time lag relationships between passive GPS data and active ecological momentary assessment (EMA) data collected via smartphone app technology. Methods: Between March 2020 and May 2022, we recruited 45 participants (mean age 42.3 years, SD 12.1 years) who were followed up for 6 months: 35 individuals diagnosed with mood disorders referred by psychiatrists and 10 healthy control participants. This resulted in a total of 5248 person-days of data. Over 6 months, we collected 2 types of smartphone data: passive data on movement patterns with nearly 100,000 GPS data points per individual and active data through EMA capturing daily mood levels, including fatigue, irritability, depressed, and manic mood. Our study is limited to Android users due to operating system constraints. Results: Our findings revealed a significant negative correlation between normalized entropy (r=–0.353; P=.04) and weekly depressed mood as well as between location variance (r=–0.364; P=.03) and depressed mood. In participants with mood disorders, we observed bidirectional time-lagged associations. Specifically, changes in homestay were positively associated with fatigue (β=0.256; P=.03), depressed mood (β=0.235; P=.01), and irritability (β=0.149; P=.03). A decrease in location variance was significantly associated with higher depressed mood the following day (β=–0.015; P=.009). Conversely, an increase in depressed mood was significantly associated with reduced location variance the next day (β=–0.869; P<.001). These findings suggest a dynamic interplay between mood symptoms and mobility patterns. Conclusions: This study demonstrates the potential of utilizing active EMA data to assess mood levels and passive GPS data to analyze mobility behaviors, with implications for managing disease progression in patients. Monitoring location variance and homestay can provide valuable insights into this process. The daily use of smartphones has proven to be a convenient method for monitoring patients’ conditions. Interventions should prioritize promoting physical movement while discouraging prolonged periods of staying at home. %M 39642364 %R 10.2196/55635 %U https://www.jmir.org/2024/1/e55635 %U https://doi.org/10.2196/55635 %U http://www.ncbi.nlm.nih.gov/pubmed/39642364 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e58076 %T Web-Based Respondent-Driven Sampling to Assess Biobehavioral Factors Among Men Who Have Sex With Men in Thailand: Cross-Sectional Study %A Srinor,Watcharapol %A Tanpradech,Suvimon %A Thiengtham,Panupit %A Karuchit,Samart %A Naksuk,Charif %A Yingyong,Thitipong %A Naiwatanakul,Thananda %A Northbrook,Sanny %A Hladik,Wolfgang %K online respondent-driven sampling %K HIV %K men who have sex with men %K MSM %K Bangkok %K health clinic %K public health %K testing %K stigma %K online testing %K HIV prevention %K research data collection %D 2024 %7 6.12.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Respondent-driven sampling (RDS) is the current standard for sampling key populations at risk for HIV infections but is usually limited to local implementation in single towns or cities. Web-based sampling eliminates this spatial constraint but often relies on self-selected convenience samples. We piloted a web-based RDS survey with biomarker collection among men who have sex with men (MSM) in Thailand. Objective: This study aimed to evaluate and demonstrate the feasibility of implementing a web-based RDS survey as a routine surveillance system in Thailand. The goal was to enhance surveillance efforts targeting hard-to-reach populations in the country. Methods: We developed a website to fully function like a conventional RDS survey office, including coupon verification, eligibility screening, consenting, interviewing (self-administered), peer recruitment training, coupon issuance, compensation, and recruitment tracking. All functions were automated; data managers monitored recruitment, data collection, and payment and could be contacted by recruits as needed. Eligible participants were male, older than 15 years, resided in Thailand, and had anal sex with a man in the past 6 months. Recruits who resided in Bangkok were additionally invited to physically attend a participating health clinic of their choice for an HIV-related blood draw. Data were weighted to account for the complex sampling design. Results: The survey was implemented from February to June 2022; seeds (21 at start, 14 added later) were identified mostly through targeted web-based banner ads; coupon uptake was 45.1%. Of 2578 candidate recruits screened for eligibility, 2151 (83.4%) were eligible and 2142 (83.1%) enrolled. Almost all (2067/2578, 80.2%) completed the questionnaire; however, 318 survey records were removed from analysis as fraudulent enrollments. The final sample size was 1749, the maximum number of waves achieved was 191, and sampling covered all 6 geographic regions and 75 of 77 (97.4%) provinces; convergence was reached for several salient variables. The mean age was 20.5 (SD 4.0) years, and most (69.8%) had never tested for HIV before, with fear of stigma as the biggest reason (97.1%) for not having tested. Most (76.9%) had visited gay-focused physical venues several times a week. A condom was used in 97.6% of the last sex acts, 11.0% had purchased sex from other men (past 12 mo), 4.5% had sold sex to men (past 12 mo), and 95.3% had 3+ male sex partners (last 3 mo). No participant in Bangkok presented for a blood draw. Conclusions: We successfully conducted a web-based RDS survey among MSM in Thailand, covering nearly the entire country, although, as in physical RDS surveys, sampling was dominated by younger MSM. The survey also failed to collect biomarkers in Bangkok. Public health interventions should aim at increasing testing and addressing (the perception of) stigma. %R 10.2196/58076 %U https://publichealth.jmir.org/2024/1/e58076 %U https://doi.org/10.2196/58076 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e60193 %T Impact of Providing a Personalized Data Dashboard on Ecological Momentary Assessment Compliance Among College Students Who Use Substances: Pilot Microrandomized Trial %A Linden-Carmichael,Ashley %A Stull,Samuel W %A Wang,Danny %A Bhandari,Sandesh %A Lanza,Stephanie T %+ The Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, 320E Biobehavioral Health Building, University Park, PA, 16802, United States, 1 541 346 1978, AshleyLC@uoregon.edu %K ecological momentary assessment %K data dashboard %K study compliance %K substance use %K substance use behavior %K college student %K alcohol %K cannabis %K cannabis use %K personalized data dashboard %K EMA protocol %K EMA %K health behaviors %K survey %K compliance %K self-reported %D 2024 %7 5.12.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The landscape of substance use behavior among young adults has observed rapid changes over time. Intensive longitudinal designs are ideal for examining and intervening in substance use behavior in real time but rely on high participant compliance in the study protocol, representing a significant challenge for researchers. Objective: This study aimed to evaluate the effect of including a personalized data dashboard (DD) in a text-based survey prompt on study compliance outcomes among college students participating in a 21-day ecological momentary assessment (EMA) study. Methods: Participants (N=91; 61/91, 67% female and 84/91, 92% White) were college students who engaged in recent alcohol and cannabis use. Participants were randomized to either complete a 21-day EMA protocol with 4 prompts/d (EMA Group) or complete the same EMA protocol with 1 personalized message and a DD indicating multiple metrics of progress in the study, delivered at 1 randomly selected prompt/d (EMA+DD Group) via a microrandomized design. Study compliance, completion time, self-reported protocol experiences, and qualitative responses were assessed for both groups. Results: Levels of compliance were similar across groups. Participants in the EMA+DD Group had overall faster completion times, with significant week-level differences in weeks 2 and 3 of the study (P=.047 and P=.03, respectively). Although nonsignificant, small-to-medium effect sizes were observed when comparing the groups in terms of compensation level (P=.08; Cohen w=0.19) and perceived burden (P=.09; Cohen d=-0.36). Qualitative findings revealed that EMA+DD participants perceived that seeing their progress facilitated engagement. Within the EMA+DD Group, providing a DD at the moment level did not significantly impact participants’ likelihood of completing the EMA or completion time at that particular prompt (all P>.05), with the exception of the first prompt of the day (P=.01 and P<.001). Conclusions: Providing a DD may be useful to increase engagement, particularly for researchers aiming to assess health behaviors shortly after a survey prompt is deployed to participants’ mobile devices. International Registered Report Identifier (IRRID): RR2-10.2196/57664 %M 39637378 %R 10.2196/60193 %U https://formative.jmir.org/2024/1/e60193 %U https://doi.org/10.2196/60193 %U http://www.ncbi.nlm.nih.gov/pubmed/39637378 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e59045 %T Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study %A Chen,Hongbo %A Alfred,Myrtede %A Brown,Andrew D %A Atinga,Angela %A Cohen,Eldan %+ Department of Mechanical and Industrial Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, Canada, 1 416 978 4184, ecohen@mie.utoronto.ca %K explainable artificial intelligence %K deep learning %K chest x-ray %K thoracic pathology %K fairness %K interpretability %D 2024 %7 5.12.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: While deep learning classifiers have shown remarkable results in detecting chest X-ray (CXR) pathologies, their adoption in clinical settings is often hampered by the lack of transparency. To bridge this gap, this study introduces the neural prototype tree (NPT), an interpretable image classifier that combines the diagnostic capability of deep learning models and the interpretability of the decision tree for CXR pathology detection. Objective: This study aimed to investigate the utility of the NPT classifier in 3 dimensions, including performance, interpretability, and fairness, and subsequently examined the complex interaction between these dimensions. We highlight both local and global explanations of the NPT classifier and discuss its potential utility in clinical settings. Methods: This study used CXRs from the publicly available Chest X-ray 14, CheXpert, and MIMIC-CXR datasets. We trained 6 separate classifiers for each CXR pathology in all datasets, 1 baseline residual neural network (ResNet)–152, and 5 NPT classifiers with varying levels of interpretability. Performance, interpretability, and fairness were measured using the area under the receiver operating characteristic curve (ROC AUC), interpretation complexity (IC), and mean true positive rate (TPR) disparity, respectively. Linear regression analyses were performed to investigate the relationship between IC and ROC AUC, as well as between IC and mean TPR disparity. Results: The performance of the NPT classifier improved as the IC level increased, surpassing that of ResNet-152 at IC level 15 for the Chest X-ray 14 dataset and IC level 31 for the CheXpert and MIMIC-CXR datasets. The NPT classifier at IC level 1 exhibited the highest degree of unfairness, as indicated by the mean TPR disparity. The magnitude of unfairness, as measured by the mean TPR disparity, was more pronounced in groups differentiated by age (chest X-ray 14 0.112, SD 0.015; CheXpert 0.097, SD 0.010; MIMIC 0.093, SD 0.017) compared to sex (chest X-ray 14 0.054 SD 0.012; CheXpert 0.062, SD 0.008; MIMIC 0.066, SD 0.013). A significant positive relationship between interpretability (ie, IC level) and performance (ie, ROC AUC) was observed across all CXR pathologies (P<.001). Furthermore, linear regression analysis revealed a significant negative relationship between interpretability and fairness (ie, mean TPR disparity) across age and sex subgroups (P<.001). Conclusions: By illuminating the intricate relationship between performance, interpretability, and fairness of the NPT classifier, this research offers insightful perspectives that could guide future developments in effective, interpretable, and equitable deep learning classifiers for CXR pathology detection. %M 39636692 %R 10.2196/59045 %U https://formative.jmir.org/2024/1/e59045 %U https://doi.org/10.2196/59045 %U http://www.ncbi.nlm.nih.gov/pubmed/39636692 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 7 %N %P e64669 %T Parental Assessment of Postsurgical Pain in Infants at Home Using Artificial Intelligence–Enabled and Observer-Based Tools: Construct Validity and Clinical Utility Evaluation Study %A Sada,Fatos %A Chivers,Paola %A Cecelia,Sokol %A Statovci,Sejdi %A Ukperaj,Kujtim %A Hughes,Jeffery %A Hoti,Kreshnik %+ Faculty of Medicine, University of Prishtina, 31 George Bush St, Prishtina, 10000, Kosovo, 383 44945173, kreshnik.hoti@uni-pr.edu %K PainChek Infant %K Observer-Administered Visual Analog Scale %K parents %K infant pain %K pain assessment %K circumcision %K infant home assessment %K clinical utility %K construct validity %K artificial intelligence %D 2024 %7 3.12.2024 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: Pain assessment in the infant population is challenging owing to their inability to verbalize and hence self-report pain. Currently, there is a paucity of data on how parents identify and manage this pain at home using standardized pain assessment tools. Objective: This study aimed to explore parents’ assessment and intervention of pain in their infants at home following same-day surgery, using standardized pain assessment tools. Methods: This prospective study initially recruited 109 infant boys undergoing circumcision (same-day surgery). To assess pain at home over 3 days after surgery, parents using iOS devices were assigned to use the PainChek Infant tool, which is a point-of-care artificial intelligence–enabled tool, while parents using Android devices were assigned to use the Observer-Administered Visual Analog Scale (ObsVAS) tool. Chi-square analysis compared the intervention undertaken and pain presence. Generalized estimating equations were used to evaluate outcomes related to construct validity and clinical utility. Receiver operating characteristic analysis assessed pain score cutoffs in relation to the intervention used. Results: A total of 69 parents completed postsurgery pain assessments at home and returned their pain diaries. Of these 69 parents, 24 used ObsVAS and 45 used PainChek Infant. Feeding alone and feeding with medication were the most common pain interventions. Pain presence over time reduced. In the presence of pain, an intervention was likely to be administered (χ22=21.4; P<.001), with a medicinal intervention being 12.6 (95% CI 4.3-37.0; P<.001) times more likely and a nonmedicinal intervention being 5.2 (95% CI 1.8-14.6; P=.002) times more likely than no intervention. In the presence of intervention, score cutoff values were ≥2 for PainChek Infant and ≥20 for ObsVAS. A significant effect between the use of the pain instrument (χ21=7.2, P=.007) and intervention (χ22=43.4, P<.001) was found, supporting the construct validity of both instruments. Standardized pain scores were the highest when a medicinal intervention was undertaken (estimated marginal mean [EMM]=34.2%), followed by a nonmedicinal intervention (EMM=23.5%) and no intervention (EMM=11.2%). Similar trends were seen for both pain instruments. Pain was reduced in 94.5% (224/237) of assessments where parents undertook an intervention. In 75.1% (178/237) of assessments indicative of pain, the score changed from pain to no pain, with PainChek Infant assessments more likely to report this change (odds ratio 4.1, 95% CI 1.4-12.3) compared with ObsVAS assessments. Conclusions: The use of standardized pain assessment instruments by parents at home to assess pain in their infants can inform their decision-making regarding pain identification and management, including determining the effectiveness of the chosen intervention. In addition to the construct validity and clinical utility of PainChek Infant and ObsVAS in this setting, feeding alone and a combination of feeding with medication use were the key pain intervention strategies used by parents. %M 39626240 %R 10.2196/64669 %U https://pediatrics.jmir.org/2024/1/e64669 %U https://doi.org/10.2196/64669 %U http://www.ncbi.nlm.nih.gov/pubmed/39626240 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60892 %T Accuracy, Reproducibility, and Responsiveness to Treatment of Home Spirometry in Cystic Fibrosis: Multicenter, Retrospective, Observational Study %A Oppelaar,Martinus C %A van Helvoort,Hanneke AC %A Bannier,Michiel AGE %A Reijers,Monique HE %A van der Vaart,Hester %A van der Meer,Renske %A Altenburg,Josje %A Conemans,Lennart %A Rottier,Bart L %A Nuijsink,Marianne %A van den Wijngaart,Lara S %A Merkus,Peter JFM %A Roukema,Jolt %+ Department of Pediatric Pulmonology, Amalia Children's Hospital, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6500 HB, Netherlands, +31 243614430, marc.oppelaar@radboudumc.nl %K telemonitoring %K digital health %K telespirometry %K remote monitoring %K cystic fibrosis %K pediatrics %K reliability %K mobile phone %K hereditary %K chronic pulmonary inflammation %K pulmonary infections %K morbidity %K mortality %K chronic respiratory disease %D 2024 %7 3.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Portable spirometers are increasingly used to measure lung function at home, but doubts about the accuracy of these devices persist. These doubts stand in the way of the digital transition of chronic respiratory disease care, hence there is a need to address the accuracy of home spirometry in routine care across multiple settings and ages. Objective: This study aimed to assess the accuracy, reproducibility, and responsiveness to the treatment of home spirometry in long-term pediatric and adult cystic fibrosis care. Methods: This retrospective observational study was carried out in 5 Dutch cystic fibrosis centers. Home spirometry outcomes (forced expiratory volume in one second [FEV1], and forced vital capacity [FVC]) for 601 anonymized users were collected during 3 years. For 81 users, data on clinic spirometry and elexacaftor/tezacaftor/ivacaftor (ETI) use were available. Accuracy was assessed using Bland-Altman plots for paired clinic-home measurements on the same day and within 7 days of each other (nearest neighbor). Intratest reproducibility was assessed using the American Thoracic Society/European Respiratory Society repeatability criteria, the coefficient of variation, and spirometry quality grades. Responsiveness was measured by the percentage change in home spirometry outcomes after the start of ETI. Results: Bland-Altman analysis was performed for 86 same-day clinic-home spirometry pairs and for 263 nearest neighbor clinic-home spirometry pairs (n=81). For both sets and for both FEV1 and FVC, no heteroscedasticity was present and hence the mean bias was expressed as an absolute value. Overall, home spirometry was significantly lower than clinic spirometry (mean ΔFEV1clinic-home 0.13 L, 95% CI 0.10 to 0.19; mean ΔFVCclinic-home 0.20 L, 95% CI 0.14 to 0.25) and remained lower than clinic spirometry independent of age and experience. One-way ANOVA with post hoc comparisons showed significantly lower differences in clinic-home spirometry in adults than in children (Δmean 0.11, 95% CI –0.20 to –0.01) and teenagers (Δmean 0.14, 95% CI –0.25 to –0.02). For reproducibility analyses, 2669 unique measurement days of 311 individuals were included. Overall, 87.3% (2331/2669) of FEV1 measurements and 74.3% (1985/2669) of FVC measurements met reproducibility criteria. Kruskal-Wallis with pairwise comparison demonstrated that for both FVC and FEV1, coefficient of variation was significantly lower in adults than in children and teenagers. A total of 5104 unique home measurements were graded. Grade E was given to 2435 tests as only one home measurement was performed. Of the remaining 2669 tests, 43.8% (1168/2669) and 43.6% (1163/2669) received grade A and B, respectively. The median percentage change in FEV1 from baseline after initiation of ETI was 19.2% after 7-14 days and remained stable thereafter (n=33). Conclusions: Home spirometry is feasible but not equal to clinic spirometry. Home spirometry can confirm whether lung functions remain stable, but the context of measurement and personal trends are more relevant than absolute outcomes. %M 39626236 %R 10.2196/60892 %U https://www.jmir.org/2024/1/e60892 %U https://doi.org/10.2196/60892 %U http://www.ncbi.nlm.nih.gov/pubmed/39626236 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e56874 %T Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study %A Ikäheimonen,Arsi %A Luong,Nguyen %A Baryshnikov,Ilya %A Darst,Richard %A Heikkilä,Roope %A Holmen,Joel %A Martikkala,Annasofia %A Riihimäki,Kirsi %A Saleva,Outi %A Isometsä,Erkki %A Aledavood,Talayeh %+ Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland, 358 449750110, arsi.ikaheimonen@aalto.fi %K data analysis %K digital phenotyping %K digital behavioral data %K depression symptoms %K depression monitoring %K mHealth %K mobile health %K smartphone %K mobile phone %D 2024 %7 3.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Clinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression. Objective: This paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time. Methods: In a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time. Results: Our correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, battery charge levels, communication patterns, app usage, and location data. Similarly, for predicting changes in depression state, the most important features were related to location, battery level, screen, and accelerometer data patterns. Conclusions: The use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms. %M 39626241 %R 10.2196/56874 %U https://www.jmir.org/2024/1/e56874 %U https://doi.org/10.2196/56874 %U http://www.ncbi.nlm.nih.gov/pubmed/39626241 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e53430 %T Personalized Smartphone-Enabled Assessment of Blood Pressure and Its Treatment During the SARS-CoV-2 COVID-19 Pandemic in Patients From the CURE-19 Study: Longitudinal Observational Study %A Richardson,Leanne %A Noori,Nihal %A Fantham,Jack %A Timlin,Gregor %A Siddle,James %A Godec,Thomas %A Taylor,Mike %A Baum,Charles %+ Encore Health, 1901 S Calumet Ave, Chicago, IL, 60616, United States, 44 20 3023 0644, baum73@gmail.com %K digital diary %K hypertension %K blood pressure %K remote monitoring %K smartphone app %K mobile phone %K app %K monitoring %K COVID-19 %K SARS-CoV-2 %K digital intervention %K management %K observational study %K deployment %K feasibility %K use %K safety %K medication %K symptoms %K community %K systolic %K diastolic %K utilization %D 2024 %7 3.12.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of digital interventions by patients for remote monitoring and management of health and disease is increasing. This observational study examined the feasibility, use, and safety of a digital smartphone app for routine monitoring of blood pressure (BP), medication, and symptoms of COVID-19 during the COVID-19 pandemic. Objective: The objective of this study was to deploy and test electronic data recording using a smartphone app developed for routine monitoring of BP in patients with primary hypertension. We tested the app for ease of data entry in BP management and tracking symptoms of new-onset COVID-19 to determine if participants found this app approach useful and sustainable. Methods: This remote, decentralized, 12-week, prospective, observational study was conducted in a community setting within the United States. Participants were approached and recruited from affiliated sites where they were enrolled in an ongoing remote decentralized study (CURE-19) of participants experiencing the COVID-19 pandemic. Potential participants were asked to complete a digital screener to determine eligibility and given informed consent forms to read and consent to using the Curebase digital platform. Following enrollment, participants downloaded the digital app to their smartphones for all data collection. Participants recorded daily BP, associated medication use, and emergent symptoms associated with SARS-CoV-2 infection. In addition, usability (adherence, acceptability, and user experience) was assessed using standard survey questions. Adverse events were collected based on participant self-report. Compliance and engagement were determined from user data entry rates. Feasibility and participant feedback were assessed upon study completion using the User Experience Questionnaire. Results: Of the 389 participants who enrolled in and completed the study, 380 (98%) participants downloaded and entered BP routines in week 1. App engagement remained high; 239 (62.9%) of the 380 participants remained in the study for the full 12-week observation period, and 201 (84.1%) of the 239 participants entered full BP routines into the digital app 80% or more of the time. The smartphone app scored an overall positive evaluation as assessed by the User Experience Questionnaire and was benchmarked as “excellent” for domains of perspicuity, efficiency, and dependability and “above average” for domains of attractiveness and stimulation. Highly adherent participants with hypertension demonstrated well-controlled BP, with no significant changes in average systolic or diastolic BP between week 1 and week 12 (all P>.05). Participants were able to record BP medications and symptoms of SARS-CoV-2 infection. No adverse events attributable to the use of the smartphone app were reported during the observational period. Conclusions: The high retention, engagement and acceptability and positive feedback in this study demonstrates that routine monitoring of BP and medications using a smartphone app is feasible for patients with hypertension in a community setting. Remote monitoring of BP and data collection could be coupled with hypertensive medication in a combination product (drug+digital) for precision management of hypertension. %M 39626222 %R 10.2196/53430 %U https://mhealth.jmir.org/2024/1/e53430 %U https://doi.org/10.2196/53430 %U http://www.ncbi.nlm.nih.gov/pubmed/39626222 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e57708 %T Effectiveness and Acceptability of Asynchronous Digital Health in Asthma Care: Mixed Methods Systematic Review %A Uzzaman,Nazim %A Hammersley,Victoria %A McClatchey,Kirstie %A Sheringham,Jessica %A Singh,Diksha %A Habib,GM Monsur %A Pinnock,Hilary %+ Usher Institute, The University of Edinburgh, 5-7 Little France Road, The Usher Building, Edinburgh, EH16 4UX, United Kingdom, 44 01316517869, hilary.pinnock@ed.ac.uk %K digital health %K asthma %K asynchronous %K asthma care %K effectiveness %K acceptability %K mixed-methods review %K systematic review %K barrier %K remote synchronous %K chronic respiratory disease %K self-management %K digital technology %K asynchronous consultation %K caregiver %K PRISMA %D 2024 %7 3.12.2024 %9 Review %J J Med Internet Res %G English %X Background: Asynchronous digital health (eg, web-based portal, text, and email communication) can overcome practical barriers associated with in-person and remote synchronous (real-time) consultations. However, little is known about the effectiveness and acceptability of asynchronous digital health to support care for individuals with asthma (eg, asthma reviews). Objective: We aimed to systematically review the qualitative and quantitative evidence on the role of asynchronous digital health for asthma care. Methods: Following Cochrane methodology, we searched 6 databases (January 2001-July 2022; search update: September 2023) for quantitative, qualitative, or mixed methods studies supporting asthma care using asynchronous digital health. Screening and data extraction were duplicated. We assessed the risk of bias in the clinical outcomes of randomized controlled trials included in the meta-analysis using the revised Cochrane risk of bias tool. For the remaining studies, we evaluated the methodological quality using the Downs and Black checklist, critical appraisal skills program, and mixed methods appraisal tool for quantitative, qualitative, and mixed methods studies, respectively. We determined the confidence in the evidence using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) criteria. We conducted a meta-analysis of trial data and a thematic analysis of qualitative data. Results: We included 30 studies (20 quantitative, 6 qualitative, and 4 mixed methods) conducted in 9 countries involving individuals with asthma, their caregivers, and health care professionals. Asynchronous digital consultations linked with other functionalities, compared to usual care, improved asthma control (standardized mean difference 0.32, 95% CI 0.02-0.63; P=.04) and reduced hospitalizations (risk ratio 0.36; 95% CI 0.14-0.94; P=.04). However, there were no significant differences in quality of life (standardized mean difference 0.16; 95% CI –0.12 to 0.43; P=.26) or emergency department visits (risk ratio 0.83; 95% CI 0.33-2.09; P=.69). Patients appreciated the convenience of asynchronous digital health, though health care professionals expressed concerns. Successful implementation necessitated an organizational approach. Integrative synthesis underscored the ease of asking questions, monitoring logs, and medication reminders as key digital functionalities. Conclusions: Despite low confidence in evidence, asynchronous consultation supported by digital functionalities is an effective and convenient option for nonemergency asthma care. This type of consultation, well accepted by individuals with asthma and their caregivers, offers opportunities for those facing challenges with traditional synchronous consultations due to lifestyle or geographic constraints. However, efficient organizational strategies are needed to manage the associated workload. Trial Registration: PROSPERO CRD42022344224; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=344224 International Registered Report Identifier (IRRID): RR2-10.1371/journal.pone.0281538 %M 39626243 %R 10.2196/57708 %U https://www.jmir.org/2024/1/e57708 %U https://doi.org/10.2196/57708 %U http://www.ncbi.nlm.nih.gov/pubmed/39626243 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 9 %N %P e62831 %T Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis %A Miyakoshi,Takashi %A Ito,Yoichi M %+ Department of Health Data Science, Hokkaido University Graduate School of Medicine, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan, 81 11 706 7923, mi_taka_1112@huhp.hokudai.ac.jp %K PhysioNet %K Empatica %K Dexcom %K acceleration %K heart rate %K temperature %K electrodermal activity %D 2024 %7 3.12.2024 %9 Original Paper %J JMIR Diabetes %G English %X Background: Wearable devices can simultaneously collect data on multiple items in real time and are used for disease detection, prediction, diagnosis, and treatment decision-making. Several factors, such as diet and exercise, influence blood glucose levels; however, the relationship between blood glucose and these factors has yet to be evaluated in real practice. Objective: This study aims to investigate the association of blood glucose data with various physiological index and nutritional values using wearable devices and dietary survey data from PhysioNet, a public database. Methods: Three analytical methods were used. First, the correlation of each physiological index was calculated and examined to determine whether their mean values or SDs affected the mean value or SD of blood glucose. To investigate the impact of each physiological indicator on blood glucose before and after the time of collection of blood glucose data, lag data were collected, and the correlation coefficient between blood glucose and each physiological indicator was calculated for each physiological index. Second, to examine the relationship between postprandial blood glucose rise and fall and physiological and dietary nutritional assessment indices, multiple regression analysis was performed on the relationship between the slope before and after the peak in postprandial glucose over time and physiological and dietary nutritional indices. Finally, as a supplementary analysis to the multiple regression analysis, a 1-way ANOVA was performed to compare the relationship between the upward and downward slopes of blood glucose and the groups above and below the median for each indicator. Results: The analysis revealed several indicators of interest: First, the correlation analysis of blood glucose and physiological indices indicated meaningful relationships: acceleration SD (r=–0.190 for lag data at –15-minute values), heart rate SD (r=–0.121 for lag data at –15-minute values), skin temperature SD (r=–0.121), and electrodermal activity SD (r=–0.237) for lag data at –15-minute values. Second, in multiple regression analysis, physiological indices (temperature mean: t=2.52, P=.01; acceleration SD: t=–2.06, P=.04; heart rate_30 SD: t=–2.12, P=.04; electrodermal activity_90 SD: t=1.97, P=.049) and nutritional indices (mean carbohydrate: t=6.53, P<.001; mean dietary fiber: t=–2.51, P=.01; mean sugar: t=–3.72, P<.001) were significant predictors. Finally, the results of the 1-way ANOVA corroborated the findings from the multiple regression analysis. Conclusions: Similar results were obtained from the 3 analyses, consistent with previous findings, and the relationship between blood glucose, diet, and physiological indices in the real world was examined. Data sharing facilitates the accessibility of wearable data and enables statistical analyses from various angles. This type of research is expected to be more common in the future. %M 39626230 %R 10.2196/62831 %U https://diabetes.jmir.org/2024/1/e62831 %U https://doi.org/10.2196/62831 %U http://www.ncbi.nlm.nih.gov/pubmed/39626230 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e53248 %T Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study %A Edler,Johanna-Sophie %A Winter,Michael %A Steinmetz,Holger %A Cohrdes,Caroline %A Baumeister,Harald %A Pryss,Rüdiger %+ Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, PO Box 650261, Berlin, 12101, Germany, 49 1723842979, johannasophie.edler@gmail.com %K depression %K COVID-19 %K mobile phone %K geographic information systems %K GPS-based data %K mobile applications %K mental health %D 2024 %7 3.12.2024 %9 Original Paper %J Interact J Med Res %G English %X Background: Numerous studies have been conducted to predict depressive symptoms using passive smartphone data, mostly integrating the GPS signal as a measure of mobility. Environmental factors have been identified as correlated with depressive symptoms in specialized studies both before and during the pandemic. Objective: This study combined a data-based approach using passive smartphone data to predict self-reported depressive symptoms with a wide range of GPS-based environmental factors as predictors. Methods: The CORONA HEALTH app was developed for the purpose of data collection, and this app enabled the collection of both survey and passive data via smartphone. After obtaining informed consent, we gathered GPS signals at the time of study participation and evaluated depressive symptoms in 249 Android users with the Patient Health Questionnaire-9. The only GPS-based data collected were the participants’ location at the time of the questionnaire, which was used to assign participants to the nearest district for linking regional sociodemographic data. Data collection took place from July 2020 to February 2021, coinciding with the COVID-19 pandemic. Using GPS data, each dataset was linked to a wide variety of data on regional sociodemographic, geographic, and economic characteristics describing the respondent’s environment, which were derived from a publicly accessible database from official German statistical offices. Moreover, pandemic-specific predictors such as the current pandemic phase or the number of new regional infections were matched via GPS. For the prediction of individual depressive symptoms, we compared 3 models (ie, ridge, lasso, and elastic net regression) and evaluated the models using 10-fold cross-validation. Results: The final elastic net regression model showed the highest explained variance (R2=0.06) and reduced the dataset from 121 to 9 variables, the 3 main predictors being current COVID-19 infections in the respective district, the number of places in nursing homes, and the proportion of fathers receiving parental benefits. The number of places in nursing homes refers to the availability of care facilities for the elderly, which may indicate regional population characteristics that influence mental health. The proportion of fathers receiving parental benefits reflects family structure and work-life balance, which could impact stress and mental well-being during the pandemic. Conclusions: Passive data describing the environment contributed to the prediction of individual depressive symptoms and revealed regional risk and protective factors that may be of interest without their inclusion in routine assessments being costly. %M 39625745 %R 10.2196/53248 %U https://www.i-jmr.org/2024/1/e53248 %U https://doi.org/10.2196/53248 %U http://www.ncbi.nlm.nih.gov/pubmed/39625745 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e51877 %T Challenges and Approaches to Recruitment for and Retention in a Dyad-Focused eHealth Intervention During COVID-19: Randomized Controlled Trial %A Ma,Chunxuan %A Adler,Rachel H %A Neidre,Daria B %A Chen,Ronald C %A Northouse,Laurel L %A Rini,Christine %A Tan,Xianming %A Song,Lixin %+ School of Nursing, Mays Cancer Center, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, United States, 1 210 450 8561, songl2@uthscsa.edu %K randomized controlled trials %K RCT %K prostate cancer %K accrual %K retention %K COVID-19 pandemic %K family-based research %D 2024 %7 3.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Family-based randomized controlled trials (RCTs) encounter recruitment and retention challenges. Cancer-focused RCTs typically recruit convenience samples from local cancer centers and hospitals. Objective: This study aimed to examine the recruitment and retention of a population-based, patient-partner dyad cohort in an RCT testing a dyadic eHealth intervention to improve the quality of life in patients with prostate cancer and their partners. Methods: In this 2-arm, parallel-group RCT, men who recently completed treatment for localized prostate cancer statewide were recruited through North Carolina Central Cancer Registry rapid case ascertainment between April 2018 and April 2021, coinciding with the COVID-19 pandemic. Patient-partner dyads underwent baseline assessments and were randomly assigned to either the intervention or control groups. Follow-up surveys were conducted at 4, 8, and 12 months after baseline. Descriptive and logistic regression analyses were used to achieve the study’s aims. Results: Of the 3078 patients referred from rapid case ascertainment, 2899 were screened. A total of 357 partners were approached after obtaining the eligible patients’ permission, 280 dyads completed baseline assessments and were randomized (dyad enrollment rate: 85.11%, 95% CI 81.3%-88.9%), and 221 dyads completed the 12-month follow-up (retention rate: 78.93%, 95% CI 74.2%-83.7%). Regarding the factors associated with retention, compared with White participants, people self-reporting as “other races” (including American Indian, Asian, and multiracial) were more likely to drop out of the study (odds ratio 2.78, 95% CI 1.10-7.04), and older participants were less likely to withdraw (odds ratio 0.96, 95% CI 0.92-0.99). Conclusions: Despite the negative impact of the pandemic, we successfully recruited enough patient-partner dyads to test our RCT hypotheses. Our recruitment and retention rates were equivalent to or higher than those in most dyadic intervention studies. A well-functioning research team and specific strategies (eg, eHealth intervention, internet phone, and online surveys) facilitated the recruitment and retention of patients with prostate cancer and their partners during the unprecedented pandemic. Trial Registration: ClinicalTrials.gov NCT03489057; https://clinicaltrials.gov/study/NCT03489057 International Registered Report Identifier (IRRID): RR2-https://doi.org/10.1186/s13063-021-05948-5 %M 39625741 %R 10.2196/51877 %U https://www.jmir.org/2024/1/e51877 %U https://doi.org/10.2196/51877 %U http://www.ncbi.nlm.nih.gov/pubmed/39625741 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e60759 %T Relationship Between Heart Rate and Perceived Stress in Intensive Care Unit Residents: Exploratory Analysis Using Fitbit Data %A Wang,Ruijing %A Rezaeian,Olya %A Asan,Onur %A Zhang,Linghan %A Liao,Ting %+ Department of Systems and Enterprises, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, United States, 1 2012168643, tliao@stevens.edu %K stress %K perceived stress %K heart rate %K Fitbit %K wearable %K provider %K occupational health %K resident %K trainee %K physician %K health care worker %K intensive care unit %K secondary data analysis %K mental health %K self-reported %D 2024 %7 27.11.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Intensive care unit (ICU) residents are exposed to high stress levels due to the intense nature of their work, which can impact their mental health and job performance. Heart rate measured through wearable devices has the potential to provide insights into residents’ self-reported stress and aid in developing targeted interventions. Objective: This exploratory study aims to analyze continuous heart rate data and self-reported stress levels and stressors in ICU residents to examine correlations between physiological responses, stress levels, and daily stressors reported. Methods: A secondary data analysis was conducted on heart rate measurements and stress assessments collected from 57 ICU residents over a 3-week period using Fitbit Charge 3 devices. These devices captured continuous physiological data alongside daily surveys that assessed stress levels and identified stressors. The study used Spearman rank correlation, point-biserial correlation analysis, 2-tailed paired t tests, and mixed-effect models to analyze the relationship between heart rate features and stress indicators. Results: The findings reveal complex interactions between stress levels and heart rate patterns. The correlation analysis between stress levels and median heart rate values across different percentile ranges showed that lower percentile heart rates (bottom 5%, 10%, 25%, and 50%) had modest correlations with stress, whereas higher percentiles (top 50%, 25%, 10%, and 5%) did not correlate significantly (all P>.05). The 2-tailed paired t test indicated significant differences in stress levels reported in midday versus end-of-day surveys (P<.001), although these changes in stress levels were not consistently reflected in heart rate patterns. Additionally, we explored and found that stressors related to “other health” issues had the highest positive correlation with stress level changes from midday to end-of-day surveys. However, the weak effect of these stressors on peak heart rate suggests that their impact on physiological measures like heart rate is not yet clear. According to our mixed-effects model, stress levels significantly influenced heart rate variations when hierarchical data were taken into account (P=.03), meaning that as the stress level increased, there was a significant increase in mean heart rate. Conclusions: This study highlights the complexity of using heart rate as an indicator of stress, particularly in high-stress environments like the ICU. Our findings suggest that while heart rate is found to correlate with self-reported stress in the mixed-effect model, its impact is modest, and it should be combined with other physiological and psychological measures to obtain a more accurate and comprehensive assessment of residents’ stress levels. %M 39602805 %R 10.2196/60759 %U https://formative.jmir.org/2024/1/e60759 %U https://doi.org/10.2196/60759 %U http://www.ncbi.nlm.nih.gov/pubmed/39602805 %0 Journal Article %@ 1947-2579 %I JMIR Publications %V 16 %N %P e60261 %T Population Digital Health: Continuous Health Monitoring and Profiling at Scale %A Hossein Motlagh,Naser %A Zuniga,Agustin %A Thi Nguyen,Ngoc %A Flores,Huber %A Wang,Jiangtao %A Tarkoma,Sasu %A Prosperi,Mattia %A Helal,Sumi %A Nurmi,Petteri %K digital health %K population health %K modeling, health data %K health monitoring %K monitoring %K wearable devices %K wearables %K machine learning %K networking infrastructure %K cost-effectiveness %K device %K sensor %K PDH %K equity %D 2024 %7 20.11.2024 %9 %J Online J Public Health Inform %G English %X This paper introduces population digital health (PDH)—the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling—as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices’ sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions. %R 10.2196/60261 %U https://ojphi.jmir.org/2024/1/e60261 %U https://doi.org/10.2196/60261 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55694 %T Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study %A Slade,Christopher %A Benzo,Roberto M %A Washington,Peter %+ Computer Science Department, Brigham Young University—Hawaii, 55-220 Kulanui Street #1919, Laie, HI, 96762, United States, 1 8086753471, christopher.slade@byuh.edu %K mobile health sensing %K mHealth %K active data collection %K passive data collection %K ecological momentary assessment %K mobile data %K mobile phone %K machine learning %K real-world setting %K mixed method %K college %K student %K user data %K data consistency %D 2024 %7 18.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist. Objective: We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions? Methods: We developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection. Results: For RQ1, setup and contextual prompting yielded no significant difference (ANOVA F1,144=0.0227; P=.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (χ21=4.4; P=.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks. Conclusions: We developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible. We found that contextual prompting is more efficient for authorizing background events. We therefore recommend using contextual prompting for passive sensing. Finally, we conclude that developing a persistent reminder and requiring participants to install it provides an additional way to poll for sensor and user data and could improve data collection to support adaptive interventions powered by machine learning. %R 10.2196/55694 %U https://www.jmir.org/2024/1/e55694 %U https://doi.org/10.2196/55694 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e51432 %T Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review %A Chotwanvirat,Phawinpon %A Prachansuwan,Aree %A Sridonpai,Pimnapanut %A Kriengsinyos,Wantanee %+ Human Nutrition Unit, Food and Nutrition Academic and Research Cluster, Institute of Nutrition, Mahidol University, 999 Phutthamonthon 4 Rd., Salaya, Nakhon Pathom, 73170, Thailand, 66 2 800 2380, wantanee.krieng@mahidol.ac.th %K image-assisted dietary assessment %K artificial intelligence %K dietary assessment %K mobile phone %K food intake %K image recognition %K portion size %D 2024 %7 15.11.2024 %9 Review %J J Med Internet Res %G English %X Background: To accurately capture an individual’s food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client’s memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely. Objective: This review aims to fill the gap by providing a current overview of AI’s integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems’ strengths and weaknesses and propose enhancements to improve IADA’s accuracy and adoption in the nutrition community. Methods: This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021. Results: A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance. Conclusions: This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field. %M 39546777 %R 10.2196/51432 %U https://www.jmir.org/2024/1/e51432 %U https://doi.org/10.2196/51432 %U http://www.ncbi.nlm.nih.gov/pubmed/39546777 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 7 %N %P e58663 %T Impact of Consumer Wearables Data on Pediatric Surgery Clinicians’ Management: Multi-Institutional Scenario-Based Usability Study %A Carter,Michela %A Linton,Samuel C %A Zeineddin,Suhail %A Pitt,J Benjamin %A De Boer,Christopher %A Figueroa,Angie %A Gosain,Ankush %A Lanning,David %A Lesher,Aaron %A Islam,Saleem %A Sathya,Chethan %A Holl,Jane L %A Ghomrawi,Hassan MK %A Abdullah,Fizan %+ Division of Pediatric Surgery, Department of Surgery, Ann and Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, 225 East Chicago Avenue, Box 63, Chicago, IL, 60611, United States, 1 312 227 4210, Fabdullah@luriechildrens.org %K postoperative care %K telehealth %K consultation %K remote %K appendectomy %K pediatric hospital %K children %K wearable device %K minimally invasive surgery %K pediatric surgery %K remote simulation study %D 2024 %7 12.11.2024 %9 Original Paper %J JMIR Perioper Med %G English %X Background: At present, parents lack objective methods to evaluate their child’s postoperative recovery following discharge from the hospital. As a result, clinicians are dependent upon a parent’s subjective assessment of the child’s health status and the child’s ability to communicate their symptoms. This subjective nature of home monitoring contributes to unnecessary emergency department (ED) use as well as delays in treatment. However, the integration of data remotely collected using a consumer wearable device has the potential to provide clinicians with objective metrics for postoperative patients to facilitate informed longitudinal, remote assessment. Objective: This multi-institutional study aimed to evaluate the impact of adding actual and simulated objective recovery data that were collected remotely using a consumer wearable device to simulated postoperative telephone encounters on clinicians’ management. Methods: In total, 3 simulated telephone scenarios of patients after an appendectomy were presented to clinicians at 5 children’s hospitals. Each scenario was then supplemented with wearable data concerning or reassuring against a postoperative complication. Clinicians rated their likelihood of ED referral before and after the addition of wearable data to evaluate if it changed their recommendation. Clinicians reported confidence in their decision-making. Results: In total, 34 clinicians participated. Compared with the scenario alone, the addition of reassuring wearable data resulted in a decreased likelihood of ED referral for all 3 scenarios (P<.01). When presented with concerning wearable data, there was an increased likelihood of ED referral for 1 of 3 scenarios (P=.72, P=.17, and P<.001). At the institutional level, there was no difference between the 5 institutions in how the wearable data changed the likelihood of ED referral for all 3 scenarios. With the addition of wearable data, 76% (19/25) to 88% (21/24 and 22/25) of clinicians reported increased confidence in their recommendations. Conclusions: The addition of wearable data to simulated telephone scenarios for postdischarge patients who underwent pediatric surgery impacted clinicians’ remote patient management at 5 pediatric institutions and increased clinician confidence. Wearable devices are capable of providing real-time measures of recovery, which can be used as a postoperative monitoring tool to reduce delays in care and avoidable health care use. %M 39531288 %R 10.2196/58663 %U https://periop.jmir.org/2024/1/e58663 %U https://doi.org/10.2196/58663 %U http://www.ncbi.nlm.nih.gov/pubmed/39531288 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e57664 %T A Personalized Data Dashboard to Improve Compliance with Ecological Momentary Assessments in College Students: Protocol for a Microrandomized Trial %A Lanza,Stephanie T %A Linden-Carmichael,Ashley N %A Wang,Danny %A Bhandari,Sandesh %A Stull,Samuel W %+ Department of Biobehavioral Health, Pennsylvania State University, 129 Biobehavioral Health Building, University Park, PA, 16802, United States, 1 814 865 7095, SLanza@psu.edu %K ecological momentary assessments %K data dashboard %K study compliance %K microrandomized trial %K intensive longitudinal data %K EMA %K adolescents %K substance use %K wearables %D 2024 %7 11.11.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Ecological momentary assessments (EMA) are ideal for capturing the dynamic nature of young adult substance use behavior in daily life and identifying contextual risk factors that signal higher-risk episodes. These methods could provide a signal to trigger real-time intervention delivery. Study compliance and engagement are common barriers to participation but may be improved by personalizing messages. This study compares compliance outcomes between one group of young adults receiving standard (generic) prompts at each assessment and another group that received additional personalization and an updated data dashboard (DD) showing study progress to date at 1 randomly selected prompt per day. Objective: The primary objectives are to (1) develop a real-time DD for giving participants personalized updates on their progress in the study and (2) examine its preliminary overall effects on study compliance and experiences. Secondary objectives are to identify person-, day-, and moment-level characteristics associated with study compliance and person-level characteristics associated with perceived usefulness of the DD. Methods: This is a protocol for Project ENGAGE, a 2-arm randomized controlled trial. Arm 1 (EMA group) is engaged in a standard EMA protocol, and arm 2 (EMA+DD group) is engaged in the same study but with additional personalization and feedback. Inclusion criteria are (1) previous participation in a recent college student survey about health behavior and mental health who indicated willingness to participate in future research studies and (2) indicated past-month alcohol use; lifetime marijuana, hashish, or Delta-8-tetrahydrocannabinol (THC) use; or some combination of these on that survey. All participants in this study completed a baseline survey; EMA at 11 AM, 2 PM, 5 PM, and 8 PM each day for 21 days; and an exit survey. Participants in arm 2 engaged in a microrandomized trial, receiving a personalized DD at 1 randomly selected prompt per day. Primary outcomes include whether a survey was completed, time to complete a survey, and subjective experiences in the study. Primary analyses will compare groups on overall study compliance and, for arm 2, use marginal models to assess the momentary effect of receiving 1 updated DD per day. Results: Approval was granted by the university’s institutional review board on February 8, 2023. Recruitment via direct email occurred on March 30 and April 6, 2023; data collection was completed by April 29, 2023. A total of 91 individuals participated in the study. Results have been accepted for publication in JMIR Formative Research. Conclusions: Results from the evaluation of this study will indicate whether providing (at randomly selected prompts) real-time, personalized feedback on a participant’s progress in an EMA study improves study compliance. Overall, this study will inform whether a simple, automated DD presenting study compliance and incentives earned to date may improve young adults’ compliance and engagement in intensive longitudinal studies. International Registered Report Identifier (IRRID): DERR1-10.2196/57664 %M 39527809 %R 10.2196/57664 %U https://www.researchprotocols.org/2024/1/e57664 %U https://doi.org/10.2196/57664 %U http://www.ncbi.nlm.nih.gov/pubmed/39527809 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e60673 %T Sensor-Derived Measures of Motor and Cognitive Functions in People With Multiple Sclerosis Using Unsupervised Smartphone-Based Assessments: Proof-of-Concept Study %A Scaramozza,Matthew %A Ruet,Aurélie %A Chiesa,Patrizia A %A Ahamada,Laïtissia %A Bartholomé,Emmanuel %A Carment,Loïc %A Charre-Morin,Julie %A Cosne,Gautier %A Diouf,Léa %A Guo,Christine C %A Juraver,Adrien %A Kanzler,Christoph M %A Karatsidis,Angelos %A Mazzà,Claudia %A Penalver-Andres,Joaquin %A Ruiz,Marta %A Saubusse,Aurore %A Simoneau,Gabrielle %A Scotland,Alf %A Sun,Zhaonan %A Tang,Minao %A van Beek,Johan %A Zajac,Lauren %A Belachew,Shibeshih %A Brochet,Bruno %A Campbell,Nolan %+ Biogen, 225 Binney St, Cambridge, MA, 02142, United States, 1 781 464 2000, matt.scaramozza@biogen.com %K multiple sclerosis %K sensor-derived measure %K smartphone %K cognitive function %K motor function %K digital biomarkers %K mobile phone %D 2024 %7 8.11.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Smartphones and wearables are revolutionizing the assessment of cognitive and motor function in neurological disorders, allowing for objective, frequent, and remote data collection. However, these assessments typically provide a plethora of sensor-derived measures (SDMs), and selecting the most suitable measure for a given context of use is a challenging, often overlooked problem. Objective: This analysis aims to develop and apply an SDM selection framework, including automated data quality checks and the evaluation of statistical properties, to identify robust SDMs that describe the cognitive and motor function of people with multiple sclerosis (MS). Methods: The proposed framework was applied to data from a cross-sectional study involving 85 people with MS and 68 healthy participants who underwent in-clinic supervised and remote unsupervised smartphone-based assessments. The assessment provided high-quality recordings from cognitive, manual dexterity, and mobility tests, from which 47 SDMs, based on established literature, were extracted using previously developed and publicly available algorithms. These SDMs were first separately and then jointly screened for bias and normality by 2 expert assessors. Selected SDMs were then analyzed to establish their reliability, using an intraclass correlation coefficient and minimal detectable change at 95% CI. The convergence of selected SDMs with in-clinic MS functional measures and patient-reported outcomes was also evaluated. Results: A total of 16 (34%) of the 47 SDMs passed the selection framework. All selected SDMs demonstrated moderate-to-good reliability in remote settings (intraclass correlation coefficient 0.5-0.85; minimal detectable change at 95% CI 19%-35%). Selected SDMs extracted from the smartphone-based cognitive test demonstrated good-to-excellent correlation (Spearman correlation coefficient, |ρ|>0.75) with the in-clinic Symbol Digit Modalities Test and fair correlation with Expanded Disability Status Scale (EDSS) scores (0.25≤|ρ|<0.5). SDMs extracted from the manual dexterity tests showed either fair correlation (0.25≤|ρ|<0.5) or were not correlated (|ρ|<0.25) with the in-clinic 9-hole peg test and EDSS scores. Most selected SDMs from mobility tests showed fair correlation with the in-clinic timed 25-foot walk test and fair to moderate-to-good correlation (0.5<|ρ|≤0.75) with EDSS scores. SDM correlations with relevant patient-reported outcomes varied by functional domain, ranging from not correlated (cognitive test SDMs) to good-to-excellent correlation (|ρ|>0.75) for mobility test SDMs. Overall, correlations were similar when smartphone-based tests were performed in a clinic or remotely. Conclusions: Reported results highlight that smartphone-based assessments are suitable tools to remotely obtain high-quality SDMs of cognitive and motor function in people with MS. The presented SDM selection framework promises to increase the interpretability and standardization of smartphone-based SDMs in people with MS, paving the way for their future use in interventional trials. %M 39515815 %R 10.2196/60673 %U https://formative.jmir.org/2024/1/e60673 %U https://doi.org/10.2196/60673 %U http://www.ncbi.nlm.nih.gov/pubmed/39515815 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e59155 %T Collecting Real-Time Patient-Reported Outcome Data During Latent Labor: Feasibility Study of the MyCap Mobile App in Prospective Person-Centered Research %A Kissler,Katherine %A Phillippi,Julia C %A Erickson,Elise %A Holmes,Leah %A Tilden,Ellen %+ College of Nursing, Anschutz Medical Campus, University of Colorado, 13120 E 19th Ave., Mailstop C288, Aurora, CO, 80045, United States, 1 3037244769, katherine.kissler@cuanschutz.edu %K patient-reported outcomes %K survey methods %K smartphone %K labor onset %K prodromal symptoms %K prospective studies %D 2024 %7 8.11.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The growing emphasis on patient experience in medical research has increased the focus on patient-reported outcomes and symptom measures. However, patient-reported outcomes data are subject to recall bias, limiting reliability. Patient-reported data are most valid when reported by patients in real time; however, this type of data is difficult to collect from patients experiencing acute health events such as labor. Mobile technologies such as the MyCap app, integrated with the REDCap (Research Electronic Data Capture) platform, have emerged as tools for collecting patient-generated health data in real time offering potential improvements in data quality and relevance. Objective: This study aimed to evaluate the feasibility of using MyCap for real-time, patient-reported data collection during latent labor. The objective was to assess the usability of MyCap in characterizing patient experiences during this acute health event and to identify any challenges in data collection that could inform future research. Methods: In this descriptive cohort study, we quantified and characterized data collected prospectively through MyCap and the extent to which participants engaged with the app as a research tool for collecting patient-reported data in real time. Longitudinal quantitative and qualitative surveys were sent to (N=18) enrolled patients with term pregnancies planning vaginal birth at Oregon Health Sciences University. Participants were trained in app use prenatally. Then participants were invited to initiate the research survey on their personal smartphone via MyCap when they experienced labor symptoms and were asked to return to MyCap every 3 hours to provide additional longitudinal symptom data. Results: Out of 18 enrolled participants, 17 completed the study. During latent labor, 13 (76.5%) participants (all those who labored at home and two-thirds of those who were induced) recorded at least 1 symptom report during latent labor. A total of 191 quantitative symptom reports (mean of 10 per participant) were recorded. The most commonly reported symptoms were fatigue, contractions, and pain, with nausea and diarrhea being less frequent but more intense. Four participants recorded qualitative data during labor and 14 responded to qualitative prompts in the postpartum period. The study demonstrated that MyCap could effectively capture real-time patient-reported data during latent labor, although qualitative data collection during active symptoms was less robust. Conclusions: MyCap is a feasible tool for collecting prospective data on patient-reported symptoms during latent labor. Participants engaged actively with quantitative symptom reporting, though qualitative data collection was more challenging. The use of MyCap appears to reduce recall bias and facilitate more accurate data collection for patient-reported symptoms during acute health events outside of health care settings. Future research should explore strategies to enhance qualitative data collection and assess the tool’s usability across more diverse populations and disease states. %M 39515816 %R 10.2196/59155 %U https://formative.jmir.org/2024/1/e59155 %U https://doi.org/10.2196/59155 %U http://www.ncbi.nlm.nih.gov/pubmed/39515816 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e56027 %T Validity of a Consumer-Based Wearable to Measure Clinical Parameters in Patients With Chronic Obstructive Pulmonary Disease and Healthy Controls: Observational Study %A Hermans,Fien %A Arents,Eva %A Blondeel,Astrid %A Janssens,Wim %A Cardinaels,Nina %A Calders,Patrick %A Troosters,Thierry %A Derom,Eric %A Demeyer,Heleen %K chronic obstructive pulmonary disease %K COPD %K wearable %K Fitbit %K clinical parameters %K physical activity %K validity %K observational study %K wrist-worn wearable %K heart rate %K heart rate variability %K respiratory rate %K oxygen saturation %K devices %K monitoring %D 2024 %7 6.11.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: Consumer-based wearables are becoming more popular and provide opportunities to track individual’s clinical parameters remotely. However, literature about their criterion and known-groups validity is scarce. Objective: This study aimed to assess the validity of the Fitbit Charge 4, a wrist-worn consumer-based wearable, to measure clinical parameters (ie, daily step count, resting heart rate [RHR], heart rate variability [HRV], respiratory rate [RR], and oxygen saturation) in patients with chronic obstructive pulmonary disease (COPD) and healthy controls in free-living conditions in Belgium by comparing it with medical-grade devices. Methods: Participants wore the Fitbit Charge 4 along with three medical-grade devices: (1) Dynaport MoveMonitor for 7 days, retrieving daily step count; (2) Polar H10 for 5 days, retrieving RHR, HRV, and RR; and (3) Nonin WristOX2 3150 for 4 nights, retrieving oxygen saturation. Criterion validity was assessed by investigating the agreement between day-by-day measures of the Fitbit Charge 4 and the corresponding reference devices. Known-groups validity was assessed by comparing patients with COPD and healthy controls. Results: Data of 30 patients with COPD and 25 age- and gender-matched healthy controls resulted in good agreement between the Fitbit Charge 4 and the corresponding reference device for measuring daily step count (intraclass correlation coefficient [ICC2,1]=0.79 and ICC2,1=0.85, respectively), RHR (ICC2,1=0.80 and ICC2,1=0.79, respectively), and RR (ICC2,1=0.84 and ICC2,1=0.77, respectively). The agreement for HRV was moderate (healthy controls: ICC2,1=0.69) to strong (COPD: ICC2,1=0.87). The agreement in measuring oxygen saturation in patients with COPD was poor (ICC2,1=0.32). The Fitbit device overestimated the daily step count and underestimated HRV in both groups. While RHR and RR were overestimated in healthy controls, no difference was observed in patients with COPD. Oxygen saturation was overestimated in patients with COPD. The Fitbit Charge 4 detected significant differences in daily step count, RHR, and RR between patients with COPD and healthy controls, similar to those identified by the reference devices, supporting known-groups validity. Conclusions: Although the Fitbit Charge 4 shows mainly moderate to good agreement, measures of clinical parameters deviated from the reference devices, indicating that monitoring patients remotely and interpreting parameters requires caution. Differences in clinical parameters between patients with COPD and healthy controls that were measured by the reference devices were all detected by the Fitbit Charge 4. %R 10.2196/56027 %U https://mhealth.jmir.org/2024/1/e56027 %U https://doi.org/10.2196/56027 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e54735 %T Using a Quality-Controlled Dataset From ViSi Mobile Monitoring for Analyzing Posture Patterns of Hospitalized Patients: Retrospective Observational Study %A Huang,Emily J %A Chen,Yuexin %A Clark,Clancy J %K posture monitoring %K ViSi mobile %K wearable device %K inpatient %K quality control %K observational study %K monitoring data %K inpatient monitoring %K wearables %K posture %D 2024 %7 6.11.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: ViSi Mobile has the capability of monitoring a patient’s posture continuously during hospitalization. Analysis of ViSi telemetry data enables researchers and health care providers to quantify an individual patient’s movement and investigate collective patterns of many patients. However, erroneous values can exist in routinely collected ViSi telemetry data. Data must be scrutinized to remove erroneous records before statistical analysis. Objective: The objectives of this study were to (1) develop a data cleaning procedure for a 1-year inpatient ViSi posture dataset, (2) consolidate posture codes into categories, (3) derive concise summary statistics from the continuous monitoring data, and (4) study types of patient posture habits using summary statistics of posture duration and transition frequency. Methods: This study examined the 2019 inpatient ViSi posture records from Atrium Health Wake Forest Baptist Medical Center. First, 2 types of errors, record overlap and time inconsistency, were identified. An automated procedure was designed to search all records for these errors. A data cleaning procedure removed erroneous records. Second, data preprocessing was conducted. Each patient’s categorical time series was simplified by consolidating the 185 ViSi codes into 5 categories (Lying, Reclined, Upright, Unknown, User-defined). A majority vote process was applied to remove bursts of short duration. Third, statistical analysis was conducted. For each patient, summary statistics were generated to measure average time duration of each posture and rate of posture transitions during the whole day and separately during daytime and nighttime. A k-means clustering analysis was performed to divide the patients into subgroups objectively. Results: The analysis used a sample of 690 patients, with a median of 3 days of extensive ViSi monitoring per patient. The median of posture durations was 10.2 hours/day for Lying, 8.0 hours/day for Reclined, and 2.5 hours/day for Upright. Lying had similar percentages of patients in low and high durations. Reclined showed a decrease in patients for higher durations. Upright had its peak at 0‐2 hours, with a decrease for higher durations. Scatter plots showed that patients could be divided into several subgroups with different posture habits. This was reinforced by the k-means analysis, which identified an active subgroup and two sedentary ones with different resting styles. Conclusions: Using a 1-year ViSi dataset from routine inpatient monitoring, we derived summary statistics of posture duration and posture transitions for each patient and analyzed the summary statistics to identify patterns in the patient population. This analysis revealed several types of patient posture habits. Before analysis, we also developed methodology to clean and preprocess routinely collected inpatient ViSi monitoring data, which is a major contribution of this study. The procedure developed for data cleaning and preprocessing can have broad application to other monitoring systems used in hospitals. %R 10.2196/54735 %U https://mhealth.jmir.org/2024/1/e54735 %U https://doi.org/10.2196/54735 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e52383 %T Sensors for Smoking Detection in Epidemiological Research: Scoping Review %A Favara,Giuliana %A Barchitta,Martina %A Maugeri,Andrea %A Magnano San Lio,Roberta %A Agodi,Antonella %+ Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, University of Catania, Via Santa Sofia 87, Catania, 95123, Italy, 39 0953782183, agodia@unict.it %K smoking %K tobacco smoke %K smoke exposure %K cigarette smoking %K wearable sensor %K public health %D 2024 %7 30.10.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The use of wearable sensors is being explored as a challenging way to accurately identify smoking behaviors by measuring physiological and environmental factors in real-life settings. Although they hold potential benefits for aiding smoking cessation, no single wearable device currently achieves high accuracy in detecting smoking events. Furthermore, it is crucial to emphasize that this area of study is dynamic and requires ongoing updates. Objective: This scoping review aims to map the scientific literature for identifying the main sensors developed or used for tobacco smoke detection, with a specific focus on wearable sensors, as well as describe their key features and categorize them by type. Methods: According to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol, an electronic search was conducted on the PubMed, MEDLINE, and Web of Science databases, using the following keywords: (“biosensors” OR “biosensor” OR “sensors” OR “sensor” OR “wearable”) AND (“smoking” OR “smoke”). Results: Among a total of 37 studies included in this scoping review published between 2012 and March 2024, 16 described sensors based on wearable bands, 15 described multisensory systems, and 6 described other strategies to detect tobacco smoke exposure. Included studies provided details about the design or application of wearable sensors based on an elastic band to detect different aspects of tobacco smoke exposure (eg, arm, wrist, and finger movements, and lighting events). Some studies proposed a system composed of different sensor modalities (eg, Personal Automatic Cigarette Tracker [PACT], PACT 2.0, and AutoSense). Conclusions: Our scoping review has revealed both the obstacles and opportunities linked to wearable devices, offering valuable insights for future research initiatives. Tackling the recognized challenges and delving into potential avenues for enhancement could elevate wearable devices into even more effective tools for aiding smoking cessation. In this context, continuous research is essential to fine-tune and optimize these devices, guaranteeing their practicality and reliability in real-world applications. %M 39476379 %R 10.2196/52383 %U https://mhealth.jmir.org/2024/1/e52383 %U https://doi.org/10.2196/52383 %U http://www.ncbi.nlm.nih.gov/pubmed/39476379 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e59623 %T Development and Initial Evaluation of a Digital Phenotype Collection System for Adolescents: Proof-of-Concept Study %A Cho,Minseo %A Park,Doeun %A Choo,Myounglee %A Kim,Jinwoo %A Han,Doug Hyun %+ College of Medicine, Chung-Ang University, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea, 82 1050430876, hduk70@gmail.com %K adolescents %K adolescent mental health %K smartphone apps %K self-monitoring %K qualitative research %K phenotypes %K proof of concept %K digital phenotyping %K phenotype data %K ecological momentary assessment %D 2024 %7 24.10.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects. Objective: This study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process. Methods: In a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses. Results: The interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents’ lives, with surveys scheduled at convenient times and supportive messages tailored to their daily routines. The high compliance rates observed and positive feedback from participants underscore the potential of our approach in addressing the challenges of collecting digital phenotypes among adolescents. Conclusions: Integrating observations of adolescents’ recording behavior into the design process proved to be beneficial for developing an effective and highly compliant digital phenotype collection system. %M 39446465 %R 10.2196/59623 %U https://formative.jmir.org/2024/1/e59623 %U https://doi.org/10.2196/59623 %U http://www.ncbi.nlm.nih.gov/pubmed/39446465 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e51259 %T Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform %A Rashid,Zulqarnain %A Folarin,Amos A %A Zhang,Yuezhou %A Ranjan,Yatharth %A Conde,Pauline %A Sankesara,Heet %A Sun,Shaoxiong %A Stewart,Callum %A Laiou,Petroula %A Dobson,Richard J B %K digital biomarkers %K mHealth %K mobile apps %K Internet of Things %K remote data collection %K wearables %K real-time monitoring %K platform %K biomarkers %K wearable %K smartphone %K data collection %K open-source platform %K RADAR-base %K phenotyping %K mobile phone %K IoT %D 2024 %7 23.10.2024 %9 %J JMIR Ment Health %G English %X Background: The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient’s condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Objective: Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. Methods: We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent’s Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. Results: The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. Conclusions: RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management. %R 10.2196/51259 %U https://mental.jmir.org/2024/1/e51259 %U https://doi.org/10.2196/51259 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e58991 %T A Texting- and Internet-Based Self-Reporting System for Enhanced Vaccine Safety Surveillance With Insights From a Large Integrated Health Care System in the United States: Prospective Cohort Study %A Malden,Debbie E %A Gee,Julianne %A Glenn,Sungching %A Li,Zhuoxin %A Ryan,Denison S %A Gu,Zheng %A Bezi,Cassandra %A Kim,Sunhea %A Jazwa,Amelia %A McNeil,Michael M %A Weintraub,Eric S %A Tartof,Sara Y %+ Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles Ave, Pasadena, CA, 91101, United States, 1 310 456 4324, d.malden.11@aberdeen.ac.uk %K digital health %K survey participation %K vaccine safety monitoring %K COVID-19 vaccines %K vaccine %K vaccine safety %K vaccine monitoring %K text %K text message %K USA %K US %K surveillance %K internet based %K survey %K monitoring %K cohort study %K self-reporting %K vaccination %K COVID-19 vaccination %K medical records %K text-based surveys %K survey %K surveys %K surveillance system %K data collection %K disparity %K vulnerable %K EHR %K electronic health records %K mobile phone %D 2024 %7 11.10.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: SMS text messaging- and internet-based self-reporting systems can supplement existing vaccine safety surveillance systems, but real-world participation patterns have not been assessed at scale. Objective: This study aimed to describe the participation rates of a new SMS text messaging- and internet-based self-reporting system called the Kaiser Permanente Side Effect Monitor (KPSEM) within a large integrated health care system. Methods: We conducted a prospective cohort study of Kaiser Permanente Southern California (KPSC) patients receiving a COVID-19 vaccination from April 23, 2021, to July 31, 2023. Patients received invitations through flyers, SMS text messages, emails, or patient health care portals. After consenting, patients received regular surveys to assess adverse events up to 5 weeks after each dose. Linkage with medical records provided demographic and clinical data. In this study, we describe KPSEM participation rates, defined as providing consent and completing at least 1 survey within 35 days of COVID-19 vaccination. Results: Approximately, 8% (164,636/2,091,975) of all vaccinated patients provided consent and completed at least 1 survey within 35 days. The lowest participation rates were observed for parents of children aged 12-17 years (1349/152,928, 0.9% participation rate), and the highest participation was observed among older adults aged 61-70 years (39,844/329,487, 12.1%). Persons of non-Hispanic White race were more likely to participate compared with other races and ethnicities (13.1% vs 3.9%-7.5%, respectively; P<.001). In addition, patients residing in areas with a higher neighborhood deprivation index were less likely to participate (5.1%, 16,503/323,122 vs 10.8%, 38,084/352,939 in the highest vs lowest deprivation quintiles, respectively; P<.001). Invitations through the individual's Kaiser Permanente health care portal account and by SMS text message were associated with the highest participation rate (19.2%, 70,248/366,377 and 10.5%, 96,169/914,793, respectively), followed by email (19,464/396,912, 4.9%) and then QR codes on flyers (25,882/2,091,975, 1.2%). SMS text messaging–based surveys demonstrated the highest sustained daily response rates compared with internet-based surveys. Conclusions: This real-world prospective study demonstrated that a novel digital vaccine safety self-reporting system implemented through an integrated health care system can achieve high participation rates. Linkage with participants’ electronic health records is another unique benefit of this surveillance system. We also identified lower participation among selected vulnerable populations, which may have implications when interpreting data collected from similar digital systems. %M 39393058 %R 10.2196/58991 %U https://mhealth.jmir.org/2024/1/e58991 %U https://doi.org/10.2196/58991 %U http://www.ncbi.nlm.nih.gov/pubmed/39393058 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e53557 %T User-Centered Design for Designing and Evaluating a Prototype of a Data Collection Tool to Submit Information About Incidents of Violence Against Sex Workers: Multiple Methods Approach %A Ditmore,Melissa H %A Florez-Arango,Jose Fernando %K mobile health %K sex worker %K user-centered design methods %K usability %K heuristic analysis %K cognitive walkthrough %K aggression %K abuse %K occupational health %K reporting %K prototype %K heuristics %K human-centered design %K implementation %K barriers %K enablers %K data collection %K digital health %K underreporting %D 2024 %7 9.10.2024 %9 %J JMIR Hum Factors %G English %X Background: Sex workers face an epidemic of violence in the United States. However, violence against sex workers in the United States is underreported. Sex workers hesitate to report it to the police because they are frequently punished themselves; therefore, an alternative for reporting is needed. Objective: We aim to apply human-centered design methods to create and evaluate the usability of the prototype interface for ReportVASW (violence against sex worker, VASW) and identify opportunities for improvement. Methods: This study explores ways to improve the prototype of ReportVASW, with particular attention to ways to improve the data collection tool. Evaluation methods included cognitive walkthrough, system usability scale, and heuristic evaluation. Results: End users were enthusiastic about the idea of a website to document violence against sex workers. ReportVASW scored 90 on the system usability scale. The tool scored neutral on consistency, and all other responses were positive toward the app, with most being strong. Conclusions: Many opportunities to improve the interface were identified. Multiple methods identified multiple issues to address. Most changes are not overly complex, and the majority were aesthetic or minor. Further development of the ReportVASW data collection tool is worth pursuing. %R 10.2196/53557 %U https://humanfactors.jmir.org/2024/1/e53557 %U https://doi.org/10.2196/53557 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e59587 %T Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care %A Ortiz,Bengie L %A Gupta,Vibhuti %A Kumar,Rajnish %A Jalin,Aditya %A Cao,Xiao %A Ziegenbein,Charles %A Singhal,Ashutosh %A Tewari,Muneesh %A Choi,Sung Won %+ School of Applied Computational Sciences, Meharry Medical College, 3401 West End Ave #260, Nashville, TN, 37208, United States, 1 (615) 327 567, vgupta@mmc.edu %K machine learning %K artificial intelligence %K preprocessing %K wearables %K mobile phone %K cancer care %D 2024 %7 27.9.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. Objective: This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. Methods: We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. Results: The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. Conclusions: While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care. %M 38626290 %R 10.2196/59587 %U https://mhealth.jmir.org/2024/1/e59587 %U https://doi.org/10.2196/59587 %U http://www.ncbi.nlm.nih.gov/pubmed/38626290 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e57158 %T Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors %A Hibbing,Paul R %A Khan,Maryam Misal %+ Department of Kinesiology and Nutrition, University of Illinois Chicago, 1919 W Taylor St, Rm 650, Mail Code 517, Chicago, IL, 60612, United States, 1 312 355 1088, phibbing@uic.edu %K measurement %K optical sensors %K sensor fusion %K wearable electronic devices %K accelerometry %K photoplethysmography %K digital health %K exercise %K sedentary behavior %D 2024 %7 27.9.2024 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years. %M 39331461 %R 10.2196/57158 %U https://mhealth.jmir.org/2024/1/e57158 %U https://doi.org/10.2196/57158 %U http://www.ncbi.nlm.nih.gov/pubmed/39331461 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e63306 %T Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study %A Albrecht,Urs-Vito %A Mielitz,Annabelle %A Rahman,Kazi Mohammad Abidur %A Kulau,Ulf %+ Department of Digital Medicine, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany, 49 521 106 ext 86714, urs-vito.albrecht@uni-bielefeld.de %K ballistocardiography %K seismocardiography %K acceleration %K artifact %K weightlessness %K gravity %K observational study %K heartbeat %K blood flow %K intrinsic sensor %K hypotheses %K assessment %K heart-induced %K sensor %K gyroscopes %K cardiovascular %K diagnostic %D 2024 %7 26.9.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Modern ballistocardiography (BCG) and seismocardiography (SCG) use acceleration sensors to measure oscillating recoil movements of the body caused by the heartbeat and blood flow, which are transmitted to the body surface. Acceleration artifacts occur through intrinsic sensor roll, pitch, and yaw movements, assessed by the angular velocities of the respective sensor, during measurements that bias the signal interpretation. Objective: This observational study aims to generate hypotheses on the detection and elimination of acceleration artifacts due to the intrinsic rotation of accelerometers and their differentiation from heart-induced sensor accelerations. Methods: Multimodal data from 4 healthy participants (3 male and 1 female) using BCG-SCG and an electrocardiogram will be collected and serve as a basis for signal characterization, model modulation, and location vector derivation under parabolic flight conditions from µg to 1.8g. The data will be obtained during a parabolic flight campaign (3 times 30 parabolas) between September 24 and July 25 (depending on the flight schedule). To detect the described acceleration artifacts, accelerometers and gyroscopes (6-degree-of-freedom sensors) will be used for measuring acceleration and angular velocities attributed to intrinsic sensor rotation. Changes in acceleration and angular velocities will be explored by conducting descriptive data analysis of resting participants sitting upright in varying gravitational states. Results: A multimodal data set will serve as a basis for research into a noninvasive and gentle method of BCG-SCG with the aid of low-noise and synchronous 3D gyroscopes and 3D acceleration sensors. Hypotheses will be generated related to detecting and eliminating acceleration artifacts due to the intrinsic rotation of accelerometers and gyroscopes (6-degree-of-freedom sensors) and their differentiation from heart-induced sensor accelerations. Data will be collected entirely and exclusively during the parabolic flights, taking place between September 2024 and July 2025. Thus, as of June 2024, no data have been collected yet. The data will be analyzed until December 2025. The results are expected to be published by June 2026. Conclusions: The study will contribute to understanding artificial acceleration bias to signal readings. It will be a first approach for a detection and elimination method. Trial Registration: Deutsches Register Klinische Studien DRKS00034402; https://drks.de/search/en/trial/DRKS00034402 International Registered Report Identifier (IRRID): PRR1-10.2196/63306 %M 39326041 %R 10.2196/63306 %U https://www.researchprotocols.org/2024/1/e63306 %U https://doi.org/10.2196/63306 %U http://www.ncbi.nlm.nih.gov/pubmed/39326041 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e59469 %T Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study %A Stankoski,Simon %A Kiprijanovska,Ivana %A Gjoreski,Martin %A Panchevski,Filip %A Sazdov,Borjan %A Sofronievski,Bojan %A Cleal,Andrew %A Fatoorechi,Mohsen %A Nduka,Charles %A Gjoreski,Hristijan %+ Emteq Ltd., Science Park Square, Brighton, BN1 9SB, United Kingdom, 44 1273 769251, simon.stankoski@emteqlabs.com %K chewing detection %K eating detection %K smart glasses %K automatic dietary monitoring %K eating behavior %D 2024 %7 26.9.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its strong connection to chronic diseases such as diabetes, cancer, and cardiovascular conditions. Monitoring dietary behavior is crucial for designing effective interventions that help decrease obesity prevalence and promote healthy lifestyles. However, traditional dietary tracking methods are limited by participant burden and recall bias. Exploring microlevel eating activities, such as meal duration and chewing frequency, in addition to eating episodes, is crucial due to their substantial relation to obesity and disease risk. Objective: The primary objective of the study was to develop an accurate and noninvasive system for automatically monitoring eating and chewing activities using sensor-equipped smart glasses. The system distinguishes chewing from other facial activities, such as speaking and teeth clenching. The secondary objective was to evaluate the system’s performance on unseen test users using a combination of laboratory-controlled and real-life user studies. Unlike state-of-the-art studies that focus on detecting full eating episodes, our approach provides a more granular analysis by specifically detecting chewing segments within each eating episode. Methods: The study uses OCO optical sensors embedded in smart glasses to monitor facial muscle activations related to eating and chewing activities. The sensors measure relative movements on the skin’s surface in 2 dimensions (X and Y). Data from these sensors are analyzed using deep learning (DL) to distinguish chewing from other facial activities. To address the temporal dependence between chewing events in real life, we integrate a hidden Markov model as an additional component that analyzes the output from the DL model. Results: Statistical tests of mean sensor activations revealed statistically significant differences across all 6 comparison pairs (P<.001) involving 2 sensors (cheeks and temple) and 3 facial activities (eating, clenching, and speaking). These results demonstrate the sensitivity of the sensor data. Furthermore, the convolutional long short-term memory model, which is a combination of convolutional and long short-term memory neural networks, emerged as the best-performing DL model for chewing detection. In controlled laboratory settings, the model achieved an F1-score of 0.91, demonstrating robust performance. In real-life scenarios, the system demonstrated high precision (0.95) and recall (0.82) for detecting eating segments. The chewing rates and the number of chews evaluated in the real-life study showed consistency with expected real-life eating behaviors. Conclusions: The study represents a substantial advancement in dietary monitoring and health technology. By providing a reliable and noninvasive method for tracking eating behavior, it has the potential to revolutionize how dietary data are collected and used. This could lead to more effective health interventions and a better understanding of the factors influencing eating habits and their health implications. %M 39325528 %R 10.2196/59469 %U https://mhealth.jmir.org/2024/1/e59469 %U https://doi.org/10.2196/59469 %U http://www.ncbi.nlm.nih.gov/pubmed/39325528 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58578 %T Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study %A Chen,David %A Cao,Christian %A Kloosterman,Robert %A Parsa,Rod %A Raman,Srinivas %+ Department of Radiation Oncology, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada, 1 416 946 4501 ext 2320, srinivas.raman@uhn.ca %K artificial intelligence %K clinical trial %K completion %K AI %K cross-sectional study %K application %K intervention %K trial design %K logistic regression %K Europe %K clinical %K trials testing %K health care %K informatics %K health information %D 2024 %7 23.9.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial failure are unknown. Objective: This study aims to compare trial design factors of complete and incomplete clinical trials testing AI tools. We conducted a case-control study of complete (n=485) and incomplete (n=51) clinical trials that evaluated AI as an intervention of ClinicalTrials.gov. Methods: Trial design factors, including area of clinical application, intended use population, and intended role of AI, were extracted. Trials that did not evaluate AI as an intervention and active trials were excluded. The assessed trial design factors related to AI interventions included the domain of clinical application related to organ systems; intended use population for patients or health care providers; and the role of AI for different applications in patient-facing clinical workflows, such as diagnosis, screening, and treatment. In addition, we also assessed general trial design factors including study type, allocation, intervention model, masking, age, sex, funder, continent, length of time, sample size, number of enrollment sites, and study start year. The main outcome was the completion of the clinical trial. Odds ratio (OR) and 95% CI values were calculated for all trial design factors using propensity-matched, multivariable logistic regression. Results: We queried ClinicalTrials.gov on December 23, 2023, using AI keywords to identify complete and incomplete trials testing AI technologies as a primary intervention, yielding 485 complete and 51 incomplete trials for inclusion in this study. Our nested propensity-matched, case-control results suggest that trials conducted in Europe were significantly associated with trial completion when compared with North American trials (OR 2.85, 95% CI 1.14-7.10; P=.03), and the trial sample size was positively associated with trial completion (OR 1.00, 95% CI 1.00-1.00; P=.02). Conclusions: Our case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure. We observed that trial design factors positively associated with trial completion include trials conducted in Europe and sample size. Given the promising clinical use of AI tools in health care, our results suggest that future translational research should prioritize addressing the design factors of AI clinical trials associated with trial incompletion and common reasons for study failure. %M 39312296 %R 10.2196/58578 %U https://www.jmir.org/2024/1/e58578 %U https://doi.org/10.2196/58578 %U http://www.ncbi.nlm.nih.gov/pubmed/39312296 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54617 %T Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study %A Shin,Daun %A Kim,Hyoseung %A Lee,Seunghwan %A Cho,Younhee %A Jung,Whanbo %+ Department of Psychiatry, Anam Hospital, Korea University, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea, 82 1093649735, rune1018@gmail.com %K depression %K screening %K artificial intelligence %K digital health technology %K text data %D 2024 %7 18.9.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT. Objective: We aimed to detect depression based on user-generated diary text through an emotional diary writing app using a large language model (LLM). We aimed to validate the value of the semistructured diary text data as an EMA data source. Methods: Participants were assessed for depression using the Patient Health Questionnaire and suicide risk was evaluated using the Beck Scale for Suicide Ideation before starting and after completing the 2-week diary writing period. The text data from the daily diaries were also used in the analysis. The performance of leading LLMs, such as ChatGPT with GPT-3.5 and GPT-4, was assessed with and without GPT-3.5 fine-tuning on the training data set. The model performance comparison involved the use of chain-of-thought and zero-shot prompting to analyze the text structure and content. Results: We used 428 diaries from 91 participants; GPT-3.5 fine-tuning demonstrated superior performance in depression detection, achieving an accuracy of 0.902 and a specificity of 0.955. However, the balanced accuracy was the highest (0.844) for GPT-3.5 without fine-tuning and prompt techniques; it displayed a recall of 0.929. Conclusions: Both GPT-3.5 and GPT-4.0 demonstrated relatively reasonable performance in recognizing the risk of depression based on diaries. Our findings highlight the potential clinical usefulness of user-generated text data for detecting depression. In addition to measurable indicators, such as step count and physical activity, future research should increasingly emphasize qualitative digital expression. %M 39292502 %R 10.2196/54617 %U https://www.jmir.org/2024/1/e54617 %U https://doi.org/10.2196/54617 %U http://www.ncbi.nlm.nih.gov/pubmed/39292502 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e56784 %T Assessing and Enhancing Movement Quality Using Wearables and Consumer Technologies: Thematic Analysis of Expert Perspectives %A Swain,T Alexander %A McNarry,Melitta A %A Mackintosh,Kelly A %+ Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, Swansea University, A110 Engineering East, Bay Campus, Fabian Way, Swansea, SA1 8EN, United Kingdom, 44 1792295075, k.mackintosh@swansea.ac.uk %K physical activity %K exercise %K wellness %K qualitative %K sensors %K motor skill %K motor learning %K movement skills %K skill development %K movement assessment %D 2024 %7 13.9.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Improvements in movement quality (ie, how well an individual moves) facilitate increases in movement quantity, subsequently improving general health and quality of life. Wearable technology offers a convenient, affordable means of measuring and assessing movement quality for the general population, while technology more broadly can provide constructive feedback through various modalities. Considering the perspectives of professionals involved in the development and implementation of technology helps translate user needs into effective strategies for the optimal application of consumer technologies to enhance movement quality. Objective: This study aimed to obtain the opinions of wearable technology experts regarding the use of wearable devices to measure movement quality and provide feedback. A secondary objective was to determine potential strategies for integrating preferred assessment and feedback characteristics into a technology-based movement quality intervention for the general, recreationally active population. Methods: Semistructured interviews were conducted with 12 participants (age: mean 42, SD 9 years; 5 males) between August and September 2022 using a predetermined interview schedule. Participants were categorized based on their professional roles: commercial (n=4) and research and development (R&D; n=8). All participants had experience in the development or application of wearable technology for sports, exercise, and wellness. The verbatim interview transcripts were analyzed using reflexive thematic analysis in QSR NVivo (release 1.7), resulting in the identification of overarching themes and subthemes. Results: Three main themes were generated as follows: (1) “Grab and Go,” (2) “Adjust and Adapt,” and (3) “Visualize and Feedback.” Participants emphasized the importance of convenience to enhance user engagement when using wearables to collect movement data. However, it was suggested that users would tolerate minor inconveniences if the benefits were perceived as valuable. Simple, easily interpretable feedback was recommended to accommodate diverse audiences and aid understanding of their movement quality, while avoiding excessive detail was advised to prevent overload, which could deter users. Adaptability was endorsed to accommodate progressions in user movement quality, and customizable systems were advocated to offer variety, thereby increasing user interest and engagement. The findings indicate that visual feedback representative of the user (ie, an avatar) should be used, supplemented with concise text or audible instructions to form a comprehensive, multimodal feedback system. Conclusions: The study provides insights from wearable technology experts on the use of consumer technologies for enhancing movement quality. The findings recommend the prioritization of user convenience and simplistic, multimodal feedback centered around visualizations, and an adaptable system suitable for a diverse audience. Emphasizing individualized feedback and user-centric design, this study provides valuable findings around the use of wearables and other consumer technologies to enhance movement quality among the general population. These findings, in conjunction with those of future research into user perspectives, should be applied in practical settings to evaluate their effectiveness in enhancing movement quality. %M 39269744 %R 10.2196/56784 %U https://formative.jmir.org/2024/1/e56784 %U https://doi.org/10.2196/56784 %U http://www.ncbi.nlm.nih.gov/pubmed/39269744 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e49719 %T Leveraging mHealth Technologies for Public Health %A Velmovitsky,Pedro Elkind %A Kirolos,Merna %A Alencar,Paulo %A Leatherdale,Scott %A Cowan,Donald %A Morita,Plinio Pelegrini %+ School of Public Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567, plinio.morita@uwaterloo.ca %K mobile health %K mHealth %K smart technology %K wearables %K public health %K population health %K apps %K surveys %K self-report %K surveillance %K digital public health %K mobile phone %D 2024 %7 12.9.2024 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Traditional public health surveillance efforts are generally based on self-reported data. Although well validated, these methods may nevertheless be subjected to limitations such as biases, delays, and costs or logistical challenges. An alternative is the use of smart technologies (eg, smartphones and smartwatches) to complement self-report indicators. Having embedded sensors that provide zero-effort, passive, and continuous monitoring of health variables, these devices generate data that could be leveraged for cases in which the data are related to the same self-report metric of interest. However, some challenges must be considered when discussing the use of mobile health technologies for public health to ensure digital health equity, privacy, and best practices. This paper provides, through a review of major Canadian surveys and mobile health studies, an overview of research involving mobile data for public health, including a mapping of variables currently collected by public health surveys that could be complemented with self-report, challenges to technology adoption, and considerations on digital health equity, with a specific focus on the Canadian context. Population characteristics from major smart technology brands—Apple, Fitbit, and Samsung—and demographic barriers to the use of technology are provided. We conclude with public health implications and present our view that public health agencies and researchers should leverage mobile health data while being mindful of the current barriers and limitations to device use and access. In this manner, data ecosystems that leverage personal smart devices for public health can be put in place as appropriate, as we move toward a future in which barriers to technology adoption are decreasing. %M 39265164 %R 10.2196/49719 %U https://publichealth.jmir.org/2024/1/e49719 %U https://doi.org/10.2196/49719 %U http://www.ncbi.nlm.nih.gov/pubmed/39265164 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e55354 %T Using Text Messaging Surveys in General Practice Research to Engage With People From Low-Income Groups: Multi-Methods Study %A Sturgiss,Elizabeth %A Advocat,Jenny %A Barton,Christopher %A Walker,Emma N %A Nielsen,Suzanne %A Wright,Annemarie %A Lam,Tina %A Gunatillaka,Nilakshi %A Oad,Symrin %A Wood,Christopher %+ School of Primary and Allied Health Care, Monash University, Peninsula Campus, Moorooduc Highway, Frankston, 3199, Australia, 61 412233119, liz.sturgiss@monash.edu %K SMS %K data collection %K research methods %K disadvantaged population %K priority populations %K message %K messages %K messaging %K disadvantaged %K underserved %K survey %K surveys %K digital divide %K marginalized %K access %K accessibility %K barrier %K barriers %K smartphone %K smartphones %K digital health %K underrepresented %K data collection %K mobile phone %K short message service %D 2024 %7 5.9.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: SMS text messages through mobile phones are a common means of interpersonal communication. SMS text message surveys are gaining traction in health care and research due to their feasibility and patient acceptability. However, challenges arise in implementing SMS text message surveys, especially when targeting marginalized populations, because of barriers to accessing phones and data as well as communication difficulties. In primary care, traditional surveys (paper-based and online) often face low response rates that are particularly pronounced among disadvantaged groups due to financial limitations, language barriers, and time constraints. Objective: This study aimed to investigate the potential of SMS text message–based patient recruitment and surveys within general practices situated in lower socioeconomic areas. This study was nested within the Reducing Alcohol-Harm in General Practice project that aimed to reduce alcohol-related harm through screening in Australian general practice. Methods: This study follows a 2-step SMS text message data collection process. An initial SMS text message with an online survey link was sent to patients, followed by subsequent surveys every 3 months for consenting participants. Interviews were conducted with the local primary health network organization staff, the participating practice staff, and the clinicians. The qualitative data were analyzed using constructs from the Consolidated Framework for Implementation Research. Results: Out of 6 general practices, 4 were able to send SMS text messages to their patients. The initial SMS text message was sent to 8333 patients and 702 responses (8.2%) were received, most of which were not from a low-income group. This low initial response was in contrast to the improved response rate to the ongoing 3-month SMS text message surveys (55/107, 51.4% at 3 months; 29/67, 43.3% at 6 months; and 44/102, 43.1% at 9 months). We interviewed 4 general practitioners, 4 nurses, and 4 administrative staff from 5 of the different practices. Qualitative data uncovered barriers to engaging marginalized groups including limited smartphone access, limited financial capacity (telephone, internet, and Wi-Fi credit), language barriers, literacy issues, mental health conditions, and physical limitations such as manual dexterity and vision issues. Practice managers and clinicians suggested strategies to overcome these barriers, including using paper-based surveys in trusted spaces, offering assistance during survey completion, and offering honoraria to support participation. Conclusions: While SMS text message surveys for primary care research may be useful for the broader population, additional efforts are required to ensure the representation and involvement of marginalized groups. More intensive methods such as in-person data collection may be more appropriate to capture the voice of low-income groups in primary care research. International Registered Report Identifier (IRRID): RR2-10.3399/BJGPO.2021.0037 %M 39235843 %R 10.2196/55354 %U https://mhealth.jmir.org/2024/1/e55354 %U https://doi.org/10.2196/55354 %U http://www.ncbi.nlm.nih.gov/pubmed/39235843 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e58259 %T Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis %A Zhang,Zhenwen %A Zhu,Jianghong %A Guo,Zhihua %A Zhang,Yu %A Li,Zepeng %A Hu,Bin %K depression %K social media %K natural language processing %K deep learning %K mental health %K statistical analysis %K linguistic analysis %K Sina Weibo %K risk prediction %K mood analysis %D 2024 %7 4.9.2024 %9 %J JMIR Ment Health %G English %X Background: Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for depression continue to face significant barriers to timely diagnosis and effective treatment, thereby exacerbating a burgeoning social health crisis. Objective: This study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo. Methods: First, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical transformer network for learning user-level semantic representations, which consists of 3 primary components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder learns semantic embeddings from individual posts, while the post-level encoder explores features in user post sequences. The semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation that can be classified as depressed or nondepressed. Next, a classifier is employed to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the post content from individuals with and without depression using the Chinese Linguistic Inquiry and Word Count. Results: We divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each validation and test set comprised 600 users, with 300 individuals from both cohorts (depression and nondepression). Our method achieved an accuracy of 84.62%, precision of 84.43%, recall of 84.50%, and F1-score of 84.32% on the test set without employing sampling techniques. However, by applying our proposed retrieval-based sampling strategy, we observed significant improvements in performance: an accuracy of 95.46%, precision of 95.30%, recall of 95.70%, and F1-score of 95.43%. These outstanding results clearly demonstrate the effectiveness and superiority of our proposed depression risk detection model and retrieval-based sampling technique. This breakthrough provides new insights for large-scale depression detection through social media. Through language behavior analysis, we discovered that individuals with depression are more likely to use negation words (the value of “swear” is 0.001253). This may indicate the presence of negative emotions, rejection, doubt, disagreement, or aversion in individuals with depression. Additionally, our analysis revealed that individuals with depression tend to use negative emotional vocabulary in their expressions (“NegEmo”: 0.022306; “Anx”: 0.003829; “Anger”: 0.004327; “Sad”: 0.005740), which may reflect their internal negative emotions and psychological state. This frequent use of negative vocabulary could be a way for individuals with depression to express negative feelings toward life, themselves, or their surrounding environment. Conclusions: The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and noninvasive prediction of depression among online social media users. %R 10.2196/58259 %U https://mental.jmir.org/2024/1/e58259 %U https://doi.org/10.2196/58259 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e54509 %T Reliability Issues of Mobile Nutrition Apps for Cardiovascular Disease Prevention: Comparative Study %A Ho,Dang Khanh Ngan %A Chiu,Wan-Chun %A Kao,Jing-Wen %A Tseng,Hsiang-Tung %A Lin,Cheng-Yu %A Huang,Pin-Hsiang %A Fang,Yu-Ren %A Chen,Kuei-Hung %A Su,Ting-Ying %A Yang,Chia-Hui %A Yao,Chih-Yuan %A Su,Hsiu-Yueh %A Wei,Pin-Hui %A Chang,Jung-Su %K mobile apps %K mHealth %K dietary assessment %K validity %K cardiovascular disease prevention %K app %K apps %K applications %K application %K nutrition %K cardiovascular %K nutrients %K fitness %K diet %K mobile health %D 2024 %7 4.9.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: Controlling saturated fat and cholesterol intake is important for the prevention of cardiovascular diseases. Although the use of mobile diet-tracking apps has been increasing, the reliability of nutrition apps in tracking saturated fats and cholesterol across different nations remains underexplored. Objective: This study aimed to examine the reliability and consistency of nutrition apps focusing on saturated fat and cholesterol intake across different national contexts. The study focused on 3 key concerns: data omission, inconsistency (variability) of saturated fat and cholesterol values within an app, and the reliability of commercial apps across different national contexts. Methods: Nutrient data from 4 consumer-grade apps (COFIT, MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!) and an academic app (Formosa FoodApp) were compared against 2 national reference databases (US Department of Agriculture [USDA]–Food and Nutrient Database for Dietary Studies [FNDDS] and Taiwan Food Composition Database [FCD]). Percentages of missing nutrients were recorded, and coefficients of variation were used to compute data inconsistencies. One-way ANOVAs were used to examine differences among apps, and paired 2-tailed t tests were used to compare the apps to national reference data. The reliability across different national contexts was investigated by comparing the Chinese and English versions of MyFitnessPal with the USDA-FNDDS and Taiwan FCD. Results: Across the 5 apps, 836 food codes from 42 items were analyzed. Four apps, including COFIT, MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!, significantly underestimated saturated fats, with errors ranging from −13.8% to −40.3% (all P<.05). All apps underestimated cholesterol, with errors ranging from −26.3% to −60.3% (all P<.05). COFIT omitted 47% of saturated fat data, and MyFitnessPal-Chinese missed 62% of cholesterol data. The coefficients of variation of beef, chicken, and seafood ranged from 78% to 145%, from 74% to 112%, and from 97% to 124% across MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!, respectively, indicating a high variability in saturated fats across different food groups. Similarly, cholesterol variability was consistently high in dairy (71%-118%) and prepackaged foods (84%-118%) across all selected apps. When examining the reliability of MyFitnessPal across different national contexts, errors in MyFitnessPal were consistent across different national FCDs (USDA-FNDSS and Taiwan FCD). Regardless of the FCDs used as a reference, these errors persisted to be statistically significant, indicating that the app’s core database is the source of the problems rather than just mismatches or variances in external FCDs. Conclusions: The findings reveal substantial inaccuracies and inconsistencies in diet-tracking apps’ reporting of saturated fats and cholesterol. These issues raise concerns for the effectiveness of using consumer-grade nutrition apps in cardiovascular disease prevention across different national contexts and within the apps themselves. %R 10.2196/54509 %U https://mhealth.jmir.org/2024/1/e54509 %U https://doi.org/10.2196/54509 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e53389 %T Sleep During the COVID-19 Pandemic: Longitudinal Observational Study Combining Multisensor Data With Questionnaires %A Luong,Nguyen %A Mark,Gloria %A Kulshrestha,Juhi %A Aledavood,Talayeh %+ Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland, 358 0442404485, nguyen.luong@aalto.fi %K computational social science %K digital health %K COVID-19 %K sleep %K longitudinal %K wearables %K surveys %K observational study %K isolation %K sleep patterns %K sleep pattern %K questionnaires %K Finland %K fitness trackers %K fitness tracker %K wearable %K sleeping habits %K sleeping habit %K work from home %D 2024 %7 3.9.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The COVID-19 pandemic prompted various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people’s sleep routines. While previous studies have highlighted the negative impacts of these restrictions on sleep, they often lack a comprehensive perspective that considers other factors, such as seasonal variations and physical activity (PA), which can also influence sleep. Objective: This study aims to longitudinally examine the detailed changes in sleep patterns among working adults during the COVID-19 pandemic using a combination of repeated questionnaires and high-resolution passive measurements from wearable sensors. We investigate the association between sleep and 5 sets of variables: (1) demographics; (2) sleep-related habits; (3) PA behaviors; and external factors, including (4) pandemic-specific constraints and (5) seasonal variations during the study period. Methods: We recruited working adults in Finland for a 1-year study (June 2021-June 2022) conducted during the late stage of the COVID-19 pandemic. We collected multisensor data from fitness trackers worn by participants, as well as work and sleep-related measures through monthly questionnaires. Additionally, we used the Stringency Index for Finland at various points in time to estimate the degree of pandemic-related lockdown restrictions during the study period. We applied linear mixed models to examine changes in sleep patterns during this late stage of the pandemic and their association with the 5 sets of variables. Results: The sleep patterns of 27,350 nights from 112 working adults were analyzed. Stricter pandemic measures were associated with an increase in total sleep time (TST) (β=.003, 95% CI 0.001-0.005; P<.001) and a delay in midsleep (MS) (β=.02, 95% CI 0.02-0.03; P<.001). Individuals who tend to snooze exhibited greater variability in both TST (β=.15, 95% CI 0.05-0.27; P=.006) and MS (β=.17, 95% CI 0.03-0.31; P=.01). Occupational differences in sleep pattern were observed, with service staff experiencing longer TST (β=.37, 95% CI 0.14-0.61; P=.004) and lower variability in TST (β=–.15, 95% CI –0.27 to –0.05; P<.001). Engaging in PA later in the day was associated with longer TST (β=.03, 95% CI 0.02-0.04; P<.001) and less variability in TST (β=–.01, 95% CI –0.02 to 0.00; P=.02). Higher intradaily variability in rest activity rhythm was associated with shorter TST (β=–.26, 95% CI –0.29 to –0.23; P<.001), earlier MS (β=–.29, 95% CI –0.33 to –0.26; P<.001), and reduced variability in TST (β=–.16, 95% CI –0.23 to –0.09; P<.001). Conclusions: Our study provided a comprehensive view of the factors affecting sleep patterns during the late stage of the pandemic. As we navigate the future of work after the pandemic, understanding how work arrangements, lifestyle choices, and sleep quality interact will be crucial for optimizing well-being and performance in the workforce. %M 39226100 %R 10.2196/53389 %U https://mhealth.jmir.org/2024/1/e53389 %U https://doi.org/10.2196/53389 %U http://www.ncbi.nlm.nih.gov/pubmed/39226100 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e60052 %T Ecological Momentary Assessment of Alcohol Marketing Exposure, Alcohol Use, and Purchases Among University Students: Prospective Cohort Study %A Zhang,Min Jin %A Luk,Tzu Tsun %A Ho,Sai Yin %A Wang,Man Ping %A Lam,Tai Hing %A Cheung,Yee Tak Derek %+ School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 5/F, Academic Building 3 Sassoon Road, Pokfulam, Hong Kong, China (Hong Kong), 852 39176652, derekcheung@hku.hk %K alcohol marketing %K drinking %K ecological momentary assessment %K health behaviors %K young adults %K mobile phone %D 2024 %7 3.9.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The relationships between alcohol marketing exposure, alcohol use, and purchase have been widely studied. However, prospective studies examining the causal relationships in real-world settings using mobile health tools are limited. Objective: We used ecological momentary assessment (EMA) to examine both the within-person– and between-person–level effects of alcohol marketing exposure on any alcohol use, amount of alcohol use, any alcohol purchase, and frequency of alcohol purchase among university students. Methods: From January to June 2020, we conducted a prospective cohort study via EMA among university students in Hong Kong who reported current drinking. Over 14 consecutive days, each participant completed 5 fixed-interval, signal-contingent EMAs daily via a smartphone app. Each EMA asked about the number and types of alcohol marketing exposures, the amount and types of alcohol used, and whether any alcohol was purchased, all within the past 3 hours. We used 2-part models, including multilevel logistic regressions and multilevel gamma regressions, to examine if the number of alcohol marketing exposure was associated with subsequent alcohol use and alcohol purchase. Results: A total of 49 students participated, with 33% (16/49) being male. The mean age was 22.6 (SD 2.6) years. They completed 2360 EMAs (completion rate: 2360/3430, 68.8%). Participants reported exposure to alcohol marketing in 5.9% (140/2360), alcohol use in 6.1% (145/2360), and alcohol purchase in 2.4% (56/2360) of all the EMAs. At the between-person level, exposure to more alcohol marketing predicted a higher likelihood of alcohol use (adjusted odd ratio [AOR]=3.51, 95% CI 1.29-9.54) and a higher likelihood of alcohol purchase (AOR=4.59, 95% CI 1.46-14.49) the following day. Exposure to more alcohol marketing did not increase the amount of alcohol use or frequency of alcohol purchases the following day in participants who used or purchased alcohol. At the within-person level, exposure to more alcohol marketing was not associated with a higher likelihood of alcohol use, amount of alcohol use, higher likelihood of alcohol purchase, or frequency of alcohol purchases the following day (all Ps>.05). Each additional exposure to alcohol marketing within 1 week predicted an increase of 0.85 alcoholic drinks consumed in the following week (adjusted B=0.85, 95% CI 0.09-1.61). On days of reporting alcohol use, the 3 measures for alcohol marketing receptivity were not associated with more alcohol use or purchase (all Ps>.05). Conclusions: By using EMA, we provided the first evidence for the effect of alcohol marketing exposure on initiating alcohol use and purchase in current-drinking university students. Our findings provide evidence of the regulation of alcohol marketing for the reduction of alcohol use and purchase among young adults. %M 39226102 %R 10.2196/60052 %U https://mhealth.jmir.org/2024/1/e60052 %U https://doi.org/10.2196/60052 %U http://www.ncbi.nlm.nih.gov/pubmed/39226102 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e47246 %T Clinician-Prioritized Measures to Use in a Remote Concussion Assessment: Delphi Study %A Barnes,Keely %A Sveistrup,Heidi %A Bayley,Mark %A Egan,Mary %A Bilodeau,Martin %A Rathbone,Michel %A Taljaard,Monica %A Marshall,Shawn %+ School of Rehabilitation Sciences, Faculty of Health Sciences, University of Ottawa, 75 Laurier Avenue East, Ottawa, ON, K1N 6N5, Canada, 1 6136126127, kbarn076@uottawa.ca %K telehealth %K remote care %K concussion %K mTBI %K mild traumatic brain injury %K assessment %K examination %K telemedicine %K remote care %K TBI %K traumatic brain injury %K brain injury %K Delphi %K measure %K measures %K measurement %K mobile phone %D 2024 %7 2.9.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: There is little guidance available, and no uniform assessment battery is used in either in-person or remote evaluations of people who are experiencing persistent physical symptoms post concussion. Selecting the most appropriate measures for both in-person and remote physical assessments is challenging because of the lack of expert consensus and guidance. Objective: This study used expert consensus processes to identify clinical measures currently used to assess 5 physical domains affected by concussion (neurological examination, cervical spine, vestibular, oculomotor, or effort) and determine the feasibility of applying the identified measures virtually. Methods: The Delphi approach was used. In the first round, experienced clinicians were surveyed regarding using measures in concussion assessment. In the second round, clinicians reviewed information regarding the psychometric properties of all measures identified in the first round by at least 15% (9/58) of participants. In the second round, experts rank-ordered the measures from most relevant to least relevant based on their clinical experience and documented psychometric properties. A working group of 4 expert clinicians then determined the feasibility of virtually administering the final set of measures. Results: In total, 59 clinicians completed survey round 1 listing all measures they used to assess the physical domains affected by a concussion. The frequency counts of the 146 different measures identified were determined. Further, 33 clinicians completed the second-round survey and rank-ordered 22 measures that met the 15% cutoff criterion retained from round 1. Measures ranked first were coordination, range of motion, vestibular ocular motor screening, and smooth pursuits. These measures were feasible to administer virtually by the working group members; however, modifications for remote administration were recommended, such as adjusting the measurement method. Conclusions: Clinicians ranked assessment of coordination (finger-to-nose test and rapid alternating movement test), cervical spine range of motion, vestibular ocular motor screening, and smooth pursuits as the most relevant measures under their respective domains. Based on expert opinion, these clinical measures are considered feasible to administer for concussion physical examinations in the remote context, with modifications; however, the psychometric properties have yet to be explored. International Registered Report Identifier (IRRID): RR2-10.2196/40446 %M 39222352 %R 10.2196/47246 %U https://formative.jmir.org/2024/1/e47246 %U https://doi.org/10.2196/47246 %U http://www.ncbi.nlm.nih.gov/pubmed/39222352 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e53119 %T Automated Pain Spots Recognition Algorithm Provided by a Web Service–Based Platform: Instrument Validation Study %A Cescon,Corrado %A Landolfi,Giuseppe %A Bonomi,Niko %A Derboni,Marco %A Giuffrida,Vincenzo %A Rizzoli,Andrea Emilio %A Maino,Paolo %A Koetsier,Eva %A Barbero,Marco %K pain drawing %K image processing %K body charts %K scan %K pain %K draw %K drawing %K scanner %K scanners %K app %K apps %K applications %K device %K devices %K image %K images %K smartphone %K smartphones %K scale %K musculoskeletal %K body chart %K accuracy %K reliability %K accurate %K reliable %K picture %K pictures %K mobile phone %D 2024 %7 27.8.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: Understanding the causes and mechanisms underlying musculoskeletal pain is crucial for developing effective treatments and improving patient outcomes. Self-report measures, such as the Pain Drawing Scale, involve individuals rating their level of pain on a scale. In this technique, individuals color the area where they experience pain, and the resulting picture is rated based on the depicted pain intensity. Analyzing pain drawings (PDs) typically involves measuring the size of the pain region. There are several studies focusing on assessing the clinical use of PDs, and now, with the introduction of digital PDs, the usability and reliability of these platforms need validation. Comparative studies between traditional and digital PDs have shown good agreement and reliability. The evolution of PD acquisition over the last 2 decades mirrors the commercialization of digital technologies. However, the pen-on-paper approach seems to be more accepted by patients, but there is currently no standardized method for scanning PDs. Objective: The objective of this study was to evaluate the accuracy of PD analysis performed by a web platform using various digital scanners. The primary goal was to demonstrate that simple and affordable mobile devices can be used to acquire PDs without losing important information. Methods: Two sets of PDs were generated: one with the addition of 216 colored circles and another composed of various red shapes distributed randomly on a frontal view body chart of an adult male. These drawings were then printed in color on A4 sheets, including QR codes at the corners in order to allow automatic alignment, and subsequently scanned using different devices and apps. The scanners used were flatbed scanners of different sizes and prices (professional, portable flatbed, and home printer or scanner), smartphones with varying price ranges, and 6 virtual scanner apps. The acquisitions were made under normal light conditions by the same operator. Results: High-saturation colors, such as red, cyan, magenta, and yellow, were accurately identified by all devices. The percentage error for small, medium, and large pain spots was consistently below 20% for all devices, with smaller values associated with larger areas. In addition, a significant negative correlation was observed between the percentage of error and spot size (R=−0.237; P=.04). The proposed platform proved to be robust and reliable for acquiring paper PDs via a wide range of scanning devices. Conclusions: This study demonstrates that a web platform can accurately analyze PDs acquired through various digital scanners. The findings support the use of simple and cost-effective mobile devices for PD acquisition without compromising the quality of data. Standardizing the scanning process using the proposed platform can contribute to more efficient and consistent PD analysis in clinical and research settings. %R 10.2196/53119 %U https://mhealth.jmir.org/2024/1/e53119 %U https://doi.org/10.2196/53119 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e53643 %T Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study %A G Ravindran,Kiran K %A della Monica,Ciro %A Atzori,Giuseppe %A Lambert,Damion %A Hassanin,Hana %A Revell,Victoria %A Dijk,Derk-Jan %+ Surrey Sleep Research Centre, University of Surrey, Guildford, GU2 7XP, United Kingdom, 44 01483 68 3709, k.guruswamyravindran@surrey.ac.uk %K Withings Sleep Analyzer %K Emfit %K Somnofy %K contactless technologies %K vital signs %K evaluation %K apnea-hypopnea index %K wearables %K nearables %D 2024 %7 27.8.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. Objective: We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. Methods: Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. Results: All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). Conclusions: Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. International Registered Report Identifier (IRRID): RR2-10.3390/clockssleep6010010 %M 39190477 %R 10.2196/53643 %U https://mhealth.jmir.org/2024/1/e53643 %U https://doi.org/10.2196/53643 %U http://www.ncbi.nlm.nih.gov/pubmed/39190477 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e58330 %T Current Status of Outcomes Reported by Patients With Stroke and an Analysis of Influencing Factors: Cross-Sectional Questionnaire Study %A Sun,Jia %A Ma,Liang %A Miao,Xiao %A Sun,Hui %A Zhu,SuSu %A Zhang,Ran %A Fan,LeLe %A Hu,TingTing %+ Nursing Department, The Affiliated Lianyungang Hospital of Xuzhou Medical University, No.6 Zhenhua East Road, Haizhou District, Lianyungang, 222061, China, 86 18961322211, 18961322211@189.cn %K stroke %K patient-reported outcomes %K blood lipids %K influence factor %K correlation analysis %K nursing care %D 2024 %7 26.8.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Stroke is the leading cause of acquired disability and the second leading cause of death worldwide. Its rate of incidence, disability, mortality, and recurrence is high, and the patients experience various symptoms of discomfort, which not only affect their rehabilitation function but also reduce their ability to perform daily activities and their quality of life. Nowadays, with the improvement of China’s medical standards, patients are increasingly attentive to their quality of life and health status. However, diagnostic techniques and effective treatments for patients with stroke are still limited but urgently required. Objective: This study aimed to evaluate the quality of life during hospitalization using a stroke patient-reported outcomes (PROs) scale and additionally to recognize potential factors and risk indicators that may impact recurrent events, facilitating early intervention measures. Methods: This is a registry-based, retrospective observational cross-sectional study on patients with stroke. A convenient sampling method was used to select various indicators of patients. The Stroke-PRO scale was then used to assess patients’ conditions across physical, psychological, social, and therapeutic domains. Multiple linear regression analysis was applied to identify factors influencing stroke PROs, while correlation analysis was conducted to explore the relationship between these outcomes and blood lipid levels. Results: The mean Stroke-PRO score in this study was 4.09 (SD 0.29) points. By multiple linear regression analysis, residence, occupation, physical exercise, Barthel index, Braden scale, National Institutes of Health Stroke Scale scores at admission, and stroke type were the risk factors for reported outcomes of patients with stroke (P<.05). Correlation analysis showed that serum triglyceride, total cholesterol, and low-density lipoprotein were negatively correlated with Stroke-PRO scores in patients with stroke (P<.05), while high-density lipoprotein was positively correlated with patients with stroke (P<.05). The 95% CI was –0.31 to –0.03 for triglyceride, 0.17-0.44 for high-density lipoprotein, –0.29 to –0.01 for cholesterol, –0.30 to –0.02 for low-density lipoprotein, and –0.12 to 0.16 for blood glucose. Conclusions: Patients with stroke have a low level of health, and their reported outcomes need to be improved. Accordingly, nursing staff should pay attention to the quality of life and blood lipid indexes of patients with stroke, actively assess their actual health status, and take early intervention measures to promote their recovery. %M 39186763 %R 10.2196/58330 %U https://formative.jmir.org/2024/1/e58330 %U https://doi.org/10.2196/58330 %U http://www.ncbi.nlm.nih.gov/pubmed/39186763 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58502 %T Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial %A Burns,James %A Chen,Kelly %A Flathers,Matthew %A Currey,Danielle %A Macrynikola,Natalia %A Vaidyam,Aditya %A Langholm,Carsten %A Barnett,Ian %A Byun,Andrew (Jin Soo) %A Lane,Erlend %A Torous,John %+ Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, United States, 1 6176676700, jtorous@bidmc.harvard.edu %K digital phenotyping %K mental health %K data visualization %K data analysis %K smartphones %K smartphone %K Cortex %K open-source %K data processing %K mindLAMP %K app %K apps %K data set %K clinical %K real world %K methodology %K mobile phone %D 2024 %7 23.8.2024 %9 Tutorial %J J Med Internet Res %G English %X As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology. %M 39178032 %R 10.2196/58502 %U https://www.jmir.org/2024/1/e58502 %U https://doi.org/10.2196/58502 %U http://www.ncbi.nlm.nih.gov/pubmed/39178032 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e57615 %T Data Quality–Driven Improvement in Health Care: Systematic Literature Review %A Lighterness,Anthony %A Adcock,Michael %A Scanlon,Lauren Abigail %A Price,Gareth %+ Clinical Outcomes and Data Unit, The Christie NHS Foundation Trust, 550 Wilmslow Road, Manchester, M20 4BX, United Kingdom, 44 7305054646, anthony.lighterness@nhs.net %K real-world data %K data quality %K quality improvement %K systematic literature review %K PRISMA %D 2024 %7 22.8.2024 %9 Review %J J Med Internet Res %G English %X Background: The promise of real-world evidence and the learning health care system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), best practices for its assessment and improvement are unknown. Objective: This review aims to investigate how existing research studies define, assess, and improve the quality of structured real-world health care data. Methods: A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardized DQ concepts according to the Data Management Association (DAMA) DQ framework to enable comparison between studies. After screening and filtering by 2 independent authors, we identified 39 relevant articles reporting DQ improvement initiatives. Results: The studies were characterized by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains. DQ assessment methods were largely manual and targeted completeness and 1 other DQ dimension. Use of DQ frameworks was limited to the Weiskopf and Weng (3/6, 50%) or Kahn harmonized model (3/6, 50%). Use of standardized methodologies to design and implement quality improvement was lacking, but mainly included plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles. Most studies reported DQ improvements using multiple interventions, which included either DQ reporting and personalized feedback (24/39, 61%), IT-related solutions (21/39, 54%), training (17/39, 44%), improvements in workflows (5/39, 13%), or data cleaning (3/39, 8%). Most studies reported improvements in DQ through a combination of these interventions. Statistical methods were used to determine significance of treatment effect (22/39, 56% times), but only 1 study implemented a randomized controlled study design. Variability in study designs, approaches to delivering interventions, and reporting DQ changes hindered a robust meta-analysis of treatment effects. Conclusions: There is an urgent need for standardized guidelines in DQ improvement research to enable comparison and effective synthesis of lessons learned. Frameworks such as PDSA learning cycles and the DAMA DQ framework can facilitate this unmet need. In addition, DQ improvement studies can also benefit from prioritizing root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long-term, sustainable improvement. Despite the rise in DQ improvement studies in the last decade, significant heterogeneity in methodologies and reporting remains a challenge. Adopting standardized frameworks for DQ assessment, analysis, and improvement can enhance the effectiveness, comparability, and generalizability of DQ improvement initiatives. %M 39173155 %R 10.2196/57615 %U https://www.jmir.org/2024/1/e57615 %U https://doi.org/10.2196/57615 %U http://www.ncbi.nlm.nih.gov/pubmed/39173155 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e38189 %T Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up %A Milliken,Tabitha %A Beiler,Donielle %A Hoffman,Samantha %A Olenginski,Ashlee %A Troiani,Vanessa %+ Research Institute, Geisinger, 100 N. Academy Ave, Danville, PA, 17821, United States, 1 215 681 1733, vtroiani@geisinger.edu %K telecommunication %K enrollment rate %K Northern Appalachia %K web-based %K aging %K recruitment %K rural %D 2024 %7 22.8.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Participant recruitment in rural and hard-to-reach (HTR) populations can present unique challenges. These challenges are further exacerbated by the need for low-cost recruiting, which often leads to use of web-based recruitment methods (eg, email, social media). Despite these challenges, recruitment strategy statistics that support effective enrollment strategies for underserved and HTR populations are underreported. This study highlights how a recruitment strategy that uses email in combination with follow-up, mostly phone calls and email reminders, produced a higher-than-expected enrollment rate that includes a diversity of participants from rural, Appalachian populations in older age brackets and reports recruitment and demographic statistics within a subset of HTR populations. Objective: This study aims to provide evidence that a recruitment strategy that uses a combination of email, telephonic, and follow-up recruitment strategies increases recruitment rates in various HTR populations, specifically in rural, older, and Appalachian populations. Methods: We evaluated the overall enrollment rate of 1 recruitment arm of a larger study that aims to understand the relationship between genetics and substance use disorders. We evaluated the enrolled population’s characteristics to determine recruitment success of a combined email and follow-up recruitment strategy, and the enrollment rate of HTR populations. These characteristics included (1) enrollment rate before versus after follow-up; (2) zip code and county of enrollee to determine rural or urban and Appalachian status; (3) age to verify recruitment in all eligible age brackets; and (4) sex distribution among age brackets and rural or urban status. Results: The email and follow-up arm of the study had a 17.4% enrollment rate. Of the enrolled participants, 76.3% (4602/6030) lived in rural counties and 23.7% (1428/6030) lived in urban counties in Pennsylvania. In addition, of patients enrolled, 98.7% (5956/6030) were from Appalachian counties and 1.3% (76/6030) were from non-Appalachian counties. Patients from rural Appalachia made up 76.2% (4603/6030) of the total rural population. Enrolled patients represented all eligible age brackets from ages 20 to 75 years, with the 60-70 years age bracket having the most enrollees. Females made up 72.5% (4371/6030) of the enrolled population and males made up 27.5% (1659/6030) of the population. Conclusions: Results indicate that a web-based recruitment method with participant follow-up, such as a phone call and email follow-up, increases enrollment numbers more than web-based methods alone for rural, Appalachian, and older populations. Adding a humanizing component, such as a live person phone call, may be a key element needed to establish trust and encourage patients from underserved and rural areas to enroll in studies via web-based recruitment methods. Supporting statistics on this recruitment strategy should help researchers identify whether this strategy may be useful in future studies and HTR populations. %M 39173153 %R 10.2196/38189 %U https://formative.jmir.org/2024/1/e38189 %U https://doi.org/10.2196/38189 %U http://www.ncbi.nlm.nih.gov/pubmed/39173153 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e57344 %T Acceptability, Perceptions, and Experiences Regarding Electronic Patient-Reported Outcomes After Laparoscopic Cholecystectomy: Protocol for a Mixed Methods Feasibility Study %A Choucair,Kareem %A Corrigan,Mark %A O'Sullivan,Adrian %A Barber,Sean %A Stankiewicz,Lucja %A Henn,Patrick %A Dennehy,Oscar %A Kayyal,Mohd Yasser %A Tan,Yong Yu %A Fadahunsi,Kayode Philip %A O'Donoghue,John %+ Department of Primary Care and Public Health, School of Public Health, Imperial College London, Exhibition Rd, South Kensington, London, SW7 2BX, United Kingdom, 44 2075895111, k.fadahunsi14@imperial.ac.uk %K patient-reported outcomes %K digital technology %K hepatobiliary surgery %K surgery %K laparoscopic cholecystectomy %K electronic patient %K general surgeon %K mixed methods %K prospective study %K quantitative %K qualitative %K Qualtrics %K interview %K Microsoft Teams %K data collection %K patient care %K patient-centric %K patient-doctor communication %K eHealth %D 2024 %7 19.8.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Patient-reported outcomes (PROs) can be defined as any report of a patient’s health taken directly from the patient. Routine collection of PRO data has been shown to offer potential benefits to patient-doctor communication. Electronic forms of PRO measures (PROMs) could be more beneficial in comparison to traditional PROMs in obtaining PROs from patients. However, it is currently unclear whether the routine collection of electronic PRO data could result in better outcomes for patients undergoing laparoscopic cholecystectomy (LC). Objective: This study aims to explore the perspectives of patients and surgeons on the use of electronic PROMs. Based on prior research, technical skill and experience level of the surgeon, long-term quality of life, patient involvement in decision-making, communication skills of the surgeon, cleanliness of the ward environment, and standards of nursing care are identified to be the most important factors for the patients. Methods: This is a mixed methods prospective study that will collect both quantitative (survey) and qualitative (interview) data. The study has two components. The first involves the distribution of an electronic presurvey to patients who received elective LC within 48 hours of their surgery (n=80). This survey will explore the perspective of patients regarding the procedure, hospital experience, long-term outcomes, and the perceived value of using PROMs. These patients will then be followed up after 1 year and given another survey. The second component involves the distribution of the same survey and the completion of structured interviews with general surgeons (n=10). The survey will ascertain what PROs from the participants are most useful for the surgeons and the interviews will focus on how the surgeons view routine PRO collection. A convenience sampling approach will be used. Surveys will be distributed through Qualtrics and interviews will be completed on Microsoft Teams. Results: Data collection began on February 14, 2023. As of February 12, 2024, 71 of 80 recruited patients have been given the presurvey. The follow-up with the patients and the general surgeon components of the study have not begun. The expected completion date of this study is in April 2025. Conclusions: Overall, this study will investigate the potential of electronic PRO collection to offer value for patients and general surgeons. This approach will ensure that patient care is investigated in a multifaceted way, offering patient-centric guidance to surgeons in their approach to care. International Registered Report Identifier (IRRID): DERR1-10.2196/57344 %M 39159444 %R 10.2196/57344 %U https://www.researchprotocols.org/2024/1/e57344 %U https://doi.org/10.2196/57344 %U http://www.ncbi.nlm.nih.gov/pubmed/39159444 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e49542 %T Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study %A Fruchart,Mathilde %A Quindroit,Paul %A Jacquemont,Chloé %A Beuscart,Jean-Baptiste %A Calafiore,Matthieu %A Lamer,Antoine %K data reuse %K Observational Medical Outcomes Partnership %K common data model %K data warehouse %K reproducible research %K primary care %K dashboard %K electronic health record %K patient tracking system %K patient monitoring %K EHR %K primary care data %D 2024 %7 13.8.2024 %9 %J JMIR Med Inform %G English %X Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research. Objective: This study aimed to transform primary care data into the OMOP CDM format. Methods: We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard. Results: Data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data. Conclusions: Primary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice. %R 10.2196/49542 %U https://medinform.jmir.org/2024/1/e49542 %U https://doi.org/10.2196/49542 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e52166 %T A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study %A Lee,Pei-An %A Yu,Wanting %A Zhou,Junhong %A Tsai,Timothy %A Manor,Brad %A Lo,On-Yee %K smartphone app %K gait speed %K dual-task walking %K validity %K reliability %K mobile phone %D 2024 %7 13.8.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: Gait speed is a valuable biomarker for mobility and overall health assessment. Existing methods to measure gait speed require expensive equipment or personnel assistance, limiting their use in unsupervised, daily-life conditions. The availability of smartphones equipped with a single inertial measurement unit (IMU) presents a viable and convenient method for measuring gait speed outside of laboratory and clinical settings. Previous works have used the inverted pendulum model to estimate gait speed using a non–smartphone-based IMU attached to the trunk. However, it is unclear whether and how this approach can estimate gait speed using the IMU embedded in a smartphone while being carried in a pants pocket during walking, especially under various walking conditions. Objective: This study aimed to validate and test the reliability of a smartphone IMU–based gait speed measurement placed in the user’s front pants pocket in both healthy young and older adults while walking quietly (ie, normal walking) and walking while conducting a cognitive task (ie, dual-task walking). Methods: A custom-developed smartphone application (app) was used to record gait data from 12 young adults and 12 older adults during normal and dual-task walking. The validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. The magnitude of error (ie, bias and limits of agreement) between the gait data from the smartphone and the GAITRite mat was calculated for each stride. The Passing-Bablok orthogonal regression model was used to provide agreement (ie, slopes and intercepts) between the smartphone and the GAITRite mat. Results: The gait speed measured by the smartphone was valid when compared to the GAITRite mat. The original limits of agreement were 0.50 m/s (an ideal value of 0 m/s), and the orthogonal regression analysis indicated a slope of 1.68 (an ideal value of 1) and an intercept of −0.70 (an ideal value of 0). After adjustment, the accuracy of the smartphone-derived gait speed estimation improved, with limits of agreement reduced to 0.34 m/s. The adjusted slope improved to 1.00, with an intercept of 0.03. The test-retest reliability of smartphone-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds. Conclusions: The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pockets of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific data sets. %R 10.2196/52166 %U https://mhealth.jmir.org/2024/1/e52166 %U https://doi.org/10.2196/52166 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50275 %T Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment %A Businelle,Michael S %A Hébert,Emily T %A Shi,Dingjing %A Benson,Lizbeth %A Kezbers,Krista M %A Tonkin,Sarah %A Piper,Megan E %A Qian,Tianchen %+ TSET Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, 655 Research Parkway, Suite 400, Oklahoma City, OK, 73104, United States, 1 405 271 8001 ext 50460, michael-businelle@ouhsc.edu %K ecological momentary assessment %K mobile health %K smartphone %K compliance %K ambulatory assessment %K adherence %K experience sampling %K mobile phone %K mHealth %K real-time data %K behavior %K dynamic behavioral processes %K self-report %K factorial design %D 2024 %7 12.8.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Ecological momentary assessment (EMA) is a measurement methodology that involves the repeated collection of real-time data on participants’ behavior and experience in their natural environment. While EMA allows researchers to gain valuable insights into dynamic behavioral processes, the need for frequent self-reporting can be burdensome and disruptive. Compliance with EMA protocols is important for accurate, unbiased sampling; yet, there is no “gold standard” for EMA study design to promote compliance. Objective: The purpose of this study was to use a factorial design to identify optimal study design factors, or combinations of factors, for achieving the highest completion rates for smartphone-based EMAs. Methods: Participants recruited from across the United States were randomized to 1 of 2 levels on each of 5 design factors in a 2×2×2×2×2 design (32 conditions): factor 1—number of questions per EMA survey (15 vs 25); factor 2—number of EMAs per day (2 vs 4); factor 3—EMA prompting schedule (random vs fixed times); factor 4—payment type (US $1 paid per EMA vs payment based on the percentage of EMAs completed); and factor 5—EMA response scale type (ie, slider-type response scale vs Likert-type response scale; this is the only within-person factor; each participant was randomized to complete slider- or Likert-type questions for the first 14 days or second 14 days of the study period). All participants were asked to complete prompted EMAs for 28 days. The effect of each factor on EMA completion was examined, as well as the effects of factor interactions on EMA completion. Finally, relations between demographic and socioenvironmental factors and EMA completion were examined. Results: Participants (N=411) were aged 48.4 (SD 12.1) years; 75.7% (311/411) were female, 72.5% (298/411) were White, 18.0% (74/411) were Black or African American, 2.7% (11/411) were Asian, 1.5% (6/411) were American Indian or Alaska Native, 5.4% (22/411) belonged to more than one race, and 9.6% (38/396) were Hispanic/Latino. On average, participants completed 83.8% (28,948/34,552) of scheduled EMAs, and 96.6% (397/411) of participants completed the follow-up survey. Results indicated that there were no significant main effects of the design factors on compliance and no significant interactions. Analyses also indicated that older adults, those without a history of substance use problems, and those without current depression tended to complete more EMAs than their counterparts. No other demographic or socioenvironmental factors were related to EMA completion rates. Finally, the app was well liked (ie, system usability scale score=82.7), and there was a statistically significant positive association between liking the app and EMA compliance. Conclusions: Study results have broad implications for developing best practices guidelines for future studies that use EMA methodologies. Trial Registration: ClinicalTrials.gov number NCT05194228; https://clinicaltrials.gov/study/NCT05194228 %M 39133915 %R 10.2196/50275 %U https://www.jmir.org/2024/1/e50275 %U https://doi.org/10.2196/50275 %U http://www.ncbi.nlm.nih.gov/pubmed/39133915 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e50043 %T Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture %A Pulantara,I Wayan %A Wang,Yuhan %A Burke,Lora E %A Sereika,Susan M %A Bizhanova,Zhadyra %A Kariuki,Jacob K %A Cheng,Jessica %A Beatrice,Britney %A Loar,India %A Cedillo,Maribel %A Conroy,Molly B %A Parmanto,Bambang %K integrated system %K IoT integration %K wearable %K mHealth Fitbit %K Nokia %K clinical trial management %K research study management %K study tracking %K remote assessment %K tracking %K Fitbit %K wearable devices %K device %K management %K data analysis %K behavioral %K data collection %K Internet of Things %K IoT %K mHealth %K mobile health %D 2024 %7 7.8.2024 %9 %J JMIR Mhealth Uhealth %G English %X The integration of health and activity data from various wearable devices into research studies presents technical and operational challenges. The Awesome Data Acquisition Method (ADAM) is a versatile, web-based system that was designed for integrating data from various sources and managing a large-scale multiphase research study. As a data collecting system, ADAM allows real-time data collection from wearable devices through the device’s application programmable interface and the mobile app’s adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a behavioral weight-loss intervention study (SMARTER trial) as a test case to evaluate the ADAM system. SMARTER was a randomized controlled trial that screened 1741 participants and enrolled 502 adults. As a result, the ADAM system was efficiently and successfully deployed to organize and manage the SMARTER trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments and tracking that are performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contact. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER trial. The success of SMARTER proved the comprehensiveness and efficiency of the ADAM system. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, redevelopment, and customization. The ADAM system can benefit various behavioral interventions and different populations. %R 10.2196/50043 %U https://mhealth.jmir.org/2024/1/e50043 %U https://doi.org/10.2196/50043 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e53427 %T Pediatric Sedation Assessment and Management System (PSAMS) for Pediatric Sedation in China: Development and Implementation Report %A Zhu,Ziyu %A Liu,Lan %A Du,Min %A Ye,Mao %A Xu,Ximing %A Xu,Ying %K electronic data capture %K information systems %K pediatric sedation %K sedation management %K workflow optimization %D 2024 %7 7.8.2024 %9 %J JMIR Med Inform %G English %X Background: Recently, the growing demand for pediatric sedation services outside the operating room has imposed a heavy burden on pediatric centers in China. There is an urgent need to develop a novel system for improved sedation services. Objective: This study aimed to develop and implement a computerized system, the Pediatric Sedation Assessment and Management System (PSAMS), to streamline pediatric sedation services at a major children’s hospital in Southwest China. Methods: PSAMS was designed to reflect the actual workflow of pediatric sedation. It consists of 3 main components: server-hosted software; client applications on tablets and computers; and specialized devices like gun-type scanners, desktop label printers, and pulse oximeters. With the participation of a multidisciplinary team, PSAMS was developed and refined during its application in the sedation process. This study analyzed data from the first 2 years after the system’s deployment. Implementation (Results): From January 2020 to December 2021, a total of 127,325 sedations were performed on 85,281 patients using the PSAMS database. Besides basic variables imported from Hospital Information Systems (HIS), the PSAMS database currently contains 33 additional variables that capture comprehensive information from presedation assessment to postprocedural recovery. The recorded data from PSAMS indicates a one-time sedation success rate of 97.1% (50,752/52,282) in 2020 and 97.5% (73,184/75,043) in 2021. The observed adverse events rate was 3.5% (95% CI 3.4%‐3.7%) in 2020 and 2.8% (95% CI 2.7%-2.9%) in 2021. Conclusions: PSAMS streamlined the entire sedation workflow, reduced the burden of data collection, and laid a foundation for future cooperation of multiple pediatric health care centers. %R 10.2196/53427 %U https://medinform.jmir.org/2024/1/e53427 %U https://doi.org/10.2196/53427 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e49576 %T Using Wearables to Study Biopsychosocial Dynamics in Couples Who Cope With a Chronic Health Condition: Ambulatory Assessment Study %A Pauly,Theresa %A Lüscher,Janina %A Wilhelm,Lea Olivia %A Amrein,Melanie Alexandra %A Boateng,George %A Kowatsch,Tobias %A Fleisch,Elgar %A Bodenmann,Guy %A Scholz,Urte %+ Department of Gerontology, Simon Fraser University, 515 West Hastings Street, Vancouver, BC, V6B 5K3, Canada, 1 778 782 7834, theresa_pauly@sfu.ca %K couples %K wearables %K type II diabetes %K heart rate %K biopsychosocial dynamics %K physiological linkage %K mobile health %K technology %K social support %K chronic disease %K usability %K utility %K mHealth %D 2024 %7 5.8.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Technology has become an integral part of our everyday life, and its use to manage and study health is no exception. Romantic partners play a critical role in managing chronic health conditions as they tend to be a primary source of support. Objective: This study tests the feasibility of using commercial wearables to monitor couples’ unique way of communicating and supporting each other and documents the physiological correlates of interpersonal dynamics (ie, heart rate linkage). Methods: We analyzed 617 audio recordings of 5-minute duration (384 with concurrent heart rate data) and 527 brief self-reports collected from 11 couples in which 1 partner had type II diabetes during the course of their typical daily lives. Audio data were coded by trained raters for social support. The extent to which heart rate fluctuations were linked among couples was quantified using cross-correlations. Random-intercept multilevel models explored whether cross-correlations might differ by social contexts and exchanges. Results: Sixty percent of audio recordings captured speech between partners and partners reported personal contact with each other in 75% of self-reports. Based on the coding, social support was found in 6% of recordings, whereas at least 1 partner self-reported social support about half the time (53%). Couples, on average, showed small to moderate interconnections in their heart rate fluctuations (r=0.04-0.22). Couples also varied in the extent to which there was lagged linkage, that is, meaning that changes in one partner’s heart rate tended to precede changes in the other partner’s heart rate. Exploratory analyses showed that heart rate linkage was stronger (1) in rater-coded partner conversations (vs moments of no rater-coded partner conversations: rdiff=0.13; P=.03), (2) when partners self-reported interpersonal contact (vs moments of no self-reported interpersonal contact: rdiff=0.20; P<.001), and (3) when partners self-reported social support exchanges (vs moments of no self-reported social support exchange: rdiff=0.15; P=.004). Conclusions: Our study provides initial evidence for the utility of using wearables to collect biopsychosocial data in couples managing a chronic health condition in daily life. Specifically, heart rate linkage might play a role in fostering chronic disease management as a couple. Insights from collecting such data could inform future technology interventions to promote healthy lifestyle engagement and adaptive chronic disease management. International Registered Report Identifier (IRRID): RR2-10.2196/13685 %M 39102683 %R 10.2196/49576 %U https://mhealth.jmir.org/2024/1/e49576 %U https://doi.org/10.2196/49576 %U http://www.ncbi.nlm.nih.gov/pubmed/39102683 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e46070 %T Factors Associated With the Spatial Distribution of Severe Fever With Thrombocytopenia Syndrome in Zhejiang Province, China: Risk Analysis Based on Maximum Entropy %A Tao,Mingyong %A Liu,Ying %A Ling,Feng %A Ren,Jiangping %A Zhang,Rong %A Shi,Xuguang %A Guo,Song %A Jiang,Jianmin %A Sun,Jimin %K severe fever with thrombocytopenia syndrome %K MaxEnt %K maximum entropy %K tick density %K spatial distribution %K risk factor %K China %D 2024 %7 2.8.2024 %9 %J JMIR Public Health Surveill %G English %X Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that was first identified in mainland China in 2009 and has been reported in Zhejiang Province, China, since 2011. However, few studies have focused on the association between ticks, host animals, and SFTS. Objective: In this study, we analyzed the influence of meteorological and environmental factors as well as the influence of ticks and host animals on SFTS. This can serve as a foundational basis for the development of strategic policies aimed at the prevention and control of SFTS. Methods: Data on SFTS incidence, tick density, cattle density, and meteorological and environmental factors were collected and analyzed using a maximum entropy–based model. Results: As of December 2019, 463 laboratory-confirmed SFTS cases were reported in Zhejiang Province. We found that the density of ticks, precipitation in the wettest month, average temperature, elevation, and the normalized difference vegetation index were significantly associated with SFTS spatial distribution. The niche model fitted accurately with good performance in predicting the potential risk areas of SFTS (the average test area under the receiver operating characteristic curve for the replicate runs was 0.803 and the SD was 0.013). The risk of SFTS occurrence increased with an increase in tick density, and the response curve indicated that the risk was greater than 0.5 when tick density exceeded 1.4. The risk of SFTS occurrence decreased with increased precipitation in the wettest month, and the risk was less than 0.5 when precipitation exceeded 224.4 mm. The relationship between elevation and SFTS occurrence showed a reverse V shape, and the risk peaked at approximately 400 m. Conclusions: Tick density, precipitation, and elevation were dominant influencing factors for SFTS, and comprehensive intervention measures should be adjusted according to these factors to reduce SFTS incidence in Zhejiang Province. %R 10.2196/46070 %U https://publichealth.jmir.org/2024/1/e46070 %U https://doi.org/10.2196/46070 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e52985 %T Using a Mobile Messenger Service as a Digital Diary to Capture Patients’ Experiences Along Their Interorganizational Treatment Path in Gynecologic Oncology: Lessons Learned %A Baum,Eleonore %A Thiel,Christian %A Kobleder,Andrea %A Bernhardsgrütter,Daniela %A Engst,Ramona %A Maurer,Carola %A Koller,Antje %+ Institute of Applied Nursing Science, School of Health, Eastern Switzerland University of Applied Sciences, Neumarkt 3, Vadianstrasse 29, St.Gallen, 9000, Switzerland, 41 58 257 12 13, antje.koller@ost.ch %K mobile apps %K computer security %K confidentiality %K data collection %K oncology %K breast neoplasms %K mobile phone %D 2024 %7 29.7.2024 %9 Viewpoint %J JMIR Cancer %G English %X A digital diary in the form of a mobile messenger service offers a novel method for data collection in cancer research. Little is known about the things to consider when using this data collection method in clinical research for patients with cancer. In this Viewpoint paper, we discuss the lessons we learned from using a qualitative digital diary method via a mobile messenger service for data collection in oncology care. The lessons learned focus on three main topics: (1) data quality, (2) practical aspects, and (3) data protection. We hope to provide useful information to other researchers who consider this method for their research with patients. First, in this paper, we argue that the interactive nature of a digital diary via a messenger service is very well suited for the phenomenological approach and produces high-quality data. Second, we discuss practical issues of data collection with a mobile messenger service, including participant and researcher interaction. Third, we highlight corresponding aspects around technicalities, particularly those regarding data security. Our views on data privacy and information security are summarized in a comprehensive checklist to inform fellow researchers on the selection of a suitable messenger service for different scenarios. In our opinion, a digital diary via a mobile messenger service can provide high-quality data almost in real time and from participants’ daily lives. However, some considerations must be made to ensure that patient data are sufficiently protected. The lessons we learned can guide future qualitative research using this relatively novel method for data collection in cancer research. %M 39073852 %R 10.2196/52985 %U https://cancer.jmir.org/2024/1/e52985 %U https://doi.org/10.2196/52985 %U http://www.ncbi.nlm.nih.gov/pubmed/39073852 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e56504 %T Patterns of Ownership and Usage of Wearable Devices in the United States, 2020-2022: Survey Study %A Nagappan,Ashwini %A Krasniansky,Adriana %A Knowles,Madelyn %+ Department of Health Policy and Management, University of California, Los Angeles, 650 Charles E Young Dr S, 31-269 CHS, Los Angeles, CA, 90095, United States, 1 3108252594, ashwininagappan@ucla.edu %K digital health %K health equity %K adoption %K usage patterns %K wearable devices %K United States %K adoption %K technology %K sociodemographic %K survey %K health insurance %K public health %D 2024 %7 26.7.2024 %9 Short Paper %J J Med Internet Res %G English %X Background: Although wearable technology has become increasingly common, comprehensive studies examining its ownership across different sociodemographic groups are limited.  Objective: The aims of this study were to (1) measure wearable device ownership by sociodemographic characteristics in a cohort of US consumers and (2) investigate how these devices are acquired and used for health-related purposes. Methods: Data from the Rock Health Digital Health Consumer Adoption Survey collected from 2020 to 2022 with 23,974 US participants were analyzed. The sample was US Census–matched for demographics, including age, race/ethnicity, gender, and income. The relationship between sociodemographic factors and wearable ownership was explored using descriptive analysis and multivariate logistic regression. Results: Of the 23,974 respondents, 10,679 (44.5%) owned wearables. Ownership was higher among younger individuals, those with higher incomes and education levels, and respondents living in urban areas. Compared to those aged 18-24 years, respondents 65 years and older had significantly lower odds of wearable ownership (odds ratio [OR] 0.18, 95% CI 0.16-0.21). Higher annual income (≥US $200,000; OR 2.27, 95% CI 2.01-2.57) and advanced degrees (OR 2.23, 95% CI 2.01-2.48) were strong predictors of ownership. Living in rural areas reduced ownership odds (OR 0.65, 95% CI 0.60-0.72). There was a notable difference in ownership based on gender and health insurance status. Women had slightly higher ownership odds than men (OR 1.10, 95% CI 1.04-1.17). Private insurance increased ownership odds (OR 1.28, 95% CI 1.17-1.40), whereas being uninsured (OR 0.41, 95% CI 0.36-0.47) or on Medicaid (OR 0.75, 95% CI 0.68-0.82) decreased the odds of ownership. Interestingly, minority groups such as non-Hispanic Black (OR 1.14, 95% CI 1.03-1.25) and Hispanic/Latine (OR 1.20, 95% CI 1.10-1.31) respondents showed slightly higher ownership odds than other racial/ethnic groups. Conclusions: Our findings suggest that despite overall growth in wearable ownership, sociodemographic divides persist. The data indicate a need for equitable access strategies as wearables become integral to clinical and public health domains. %M 39058548 %R 10.2196/56504 %U https://www.jmir.org/2024/1/e56504 %U https://doi.org/10.2196/56504 %U http://www.ncbi.nlm.nih.gov/pubmed/39058548 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 9 %N %P e54631 %T Agreement Between Apple Watch and Actical Step Counts in a Community Setting: Cross-Sectional Investigation From the Framingham Heart Study %A Spartano,Nicole L %A Zhang,Yuankai %A Liu,Chunyu %A Chernofsky,Ariel %A Lin,Honghuang %A Trinquart,Ludovic %A Borrelli,Belinda %A Pathiravasan,Chathurangi H %A Kheterpal,Vik %A Nowak,Christopher %A Vasan,Ramachandran S %A Benjamin,Emelia J %A McManus,David D %A Murabito,Joanne M %+ Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine, 72 E Concord St, Suite 301 Collamore, Boston, MA, 02118-2371, United States, 1 3154152040, spartano@bu.edu %K accelerometer %K mobile health %K mHealth %K wearable device %K fitness tracker %K physical activity %K mobile phone %K Apple Watch %K step counts %K Framingham Heart Study %K Actical %K digital health %K tracker %K wearable %K wearables %D 2024 %7 24.7.2024 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings. Objective: The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting. Methods: Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices. Results: We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity. Conclusions: Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices. %M 39047284 %R 10.2196/54631 %U https://biomedeng.jmir.org/2024/1/e54631 %U https://doi.org/10.2196/54631 %U http://www.ncbi.nlm.nih.gov/pubmed/39047284 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e48582 %T Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis %A Little,Claire L %A Schultz,David M %A House,Thomas %A Dixon,William G %A McBeth,John %+ School of Primary Care, Population Sciences and Medical Education, University of Southamptom, University Road, Southampton, SO17 1BJ, United Kingdom, 1 02380595000, john.mcbeth@southampton.ac.uk %K mobile health %K mHealth %K pain %K cluster %K trajectory %K k-medoids %K transition %K forecast %K mobile phone %D 2024 %7 19.7.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity. Objective: This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model. Methods: Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters. Results: Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster. Conclusions: The clusters of pain severity identified in this study provide a parsimonious description of the weekly experiences of people with chronic pain. These clusters could be used for future study of between-cluster movement and within-cluster variability to develop accurate and stakeholder-informed pain-forecasting tools. %M 39028557 %R 10.2196/48582 %U https://mhealth.jmir.org/2024/1/e48582 %U https://doi.org/10.2196/48582 %U http://www.ncbi.nlm.nih.gov/pubmed/39028557 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e55094 %T Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study %A Corponi,Filippo %A Li,Bryan M %A Anmella,Gerard %A Valenzuela-Pascual,Clàudia %A Mas,Ariadna %A Pacchiarotti,Isabella %A Valentí,Marc %A Grande,Iria %A Benabarre,Antoni %A Garriga,Marina %A Vieta,Eduard %A Young,Allan H %A Lawrie,Stephen M %A Whalley,Heather C %A Hidalgo-Mazzei,Diego %A Vergari,Antonio %+ School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton St, Newington, Edinburgh, EH89AB, United Kingdom, 44 131 651 5661, filippo.corponi@ed.ac.uk %K mood disorder %K time-series classification %K wearable %K personal sensing %K deep learning %K self-supervised learning %K transformer %D 2024 %7 17.7.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. Objective: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables’ data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. Methods: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks—from emotion recognition in Super Mario players to stress detection in undergraduates—and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre–deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. Results: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. Conclusions: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing. %M 39018100 %R 10.2196/55094 %U https://mhealth.jmir.org/2024/1/e55094 %U https://doi.org/10.2196/55094 %U http://www.ncbi.nlm.nih.gov/pubmed/39018100 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e56749 %T Rationale, Design, and Baseline Characteristics of Participants in the Health@NUS mHealth Augmented Cohort Study Examining Student-to-Work Life Transition: Protocol for a Prospective Cohort Study %A Chua,Xin Hui %A Edney,Sarah Martine %A Müller,Andre Matthias %A Petrunoff,Nicholas A %A Whitton,Clare %A Tay,Zoey %A Goh,Claire Marie Jie Lin %A Chen,Bozhi %A Park,Su Hyun %A Rebello,Salome A %A Low,Alicia %A Chia,Janelle %A Koek,Daphne %A Cheong,Karen %A van Dam,Rob M %A Müller-Riemenschneider,Falk %+ Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building (MD1), 12 Science Drive 2, #10-01, Singapore, 117549, Singapore, 65 6601 3122, falk.m-r@nus.edu.sg %K wearable %K wearables %K movement behaviors %K university students %K mHealth %K cohort study %K data collection %K well-being %K young adults %K health behaviors %K physical health %K Singapore %K biometric assessment %K questionnaire %K Fitbit %K smartwatch %K smartphone app %K app %K application %K sleep %K dietary data %K diet %K dietary %K psychological distress %K distress %K mobile phone %D 2024 %7 17.7.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Integration of mobile health data collection methods into cohort studies enables the collection of intensive longitudinal information, which gives deeper insights into individuals’ health and lifestyle behavioral patterns over time, as compared to traditional cohort methods with less frequent data collection. These findings can then fill the gaps that remain in understanding how various lifestyle behaviors interact as students graduate from university and seek employment (student-to-work life transition), where the inability to adapt quickly to a changing environment greatly affects the mental well-being of young adults. Objective: This paper aims to provide an overview of the study methodology and baseline characteristics of participants in Health@NUS, a longitudinal study leveraging mobile health to examine the trajectories of health behaviors, physical health, and well-being, and their diverse determinants, for young adults during the student-to-work life transition. Methods: University students were recruited between August 2020 and June 2022 in Singapore. Participants would complete biometric assessments and questionnaires at 3 time points (baseline, 12-, and 24-month follow-up visits) and use a Fitbit smartwatch and smartphone app to continuously collect physical activity, sedentary behavior, sleep, and dietary data over the 2 years. Additionally, up to 12 two-week-long bursts of app-based ecological momentary surveys capturing lifestyle behaviors and well-being would be sent out among the 3 time points. Results: Interested participants (n=1556) were screened for eligibility, and 776 participants were enrolled in the study between August 2020 and June 2022. Participants were mostly female (441/776, 56.8%), of Chinese ethnicity (741/776, 92%), undergraduate students (759/776, 97.8%), and had a mean BMI of 21.9 (SD 3.3) kg/m2, and a mean age of 22.7 (SD 1.7) years. A substantial proportion were overweight (202/776, 26.1%) or obese (42/776, 5.4%), had indicated poor mental well-being (World Health Organization-5 Well-Being Index ≤50; 291/776, 37.7%), or were at higher risk for psychological distress (Kessler Psychological Distress Scale ≥13; 109/776, 14.1%). Conclusions: The findings from this study will provide detailed insights into the determinants and trajectories of health behaviors, health, and well-being during the student-to-work life transition experienced by young adults. Trial Registration: ClinicalTrials.gov NCT05154227; https://clinicaltrials.gov/study/NCT05154227 International Registered Report Identifier (IRRID): DERR1-10.2196/56749 %M 39018103 %R 10.2196/56749 %U https://www.researchprotocols.org/2024/1/e56749 %U https://doi.org/10.2196/56749 %U http://www.ncbi.nlm.nih.gov/pubmed/39018103 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54867 %T Comparison of the Response to an Electronic Versus a Traditional Informed Consent Procedure in Terms of Clinical Patient Characteristics: Observational Study %A Zondag,Anna G M %A Hollestelle,Marieke J %A van der Graaf,Rieke %A Nathoe,Hendrik M %A van Solinge,Wouter W %A Bots,Michiel L %A Vernooij,Robin W M %A Haitjema,Saskia %A , %+ Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, Netherlands, 31 631117922, a.g.m.zondag@umcutrecht.nl %K informed consent %K learning health care system %K e-consent %K cardiovascular risk management %K digital health %K research ethics %D 2024 %7 11.7.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic informed consent (eIC) is increasingly used in clinical research due to several benefits including increased enrollment and improved efficiency. Within a learning health care system, a pilot was conducted with an eIC for linking data from electronic health records with national registries, general practitioners, and other hospitals. Objective: We evaluated the eIC pilot by comparing the response to the eIC with the former traditional paper-based informed consent (IC). We assessed whether the use of eIC resulted in a different study population by comparing the clinical patient characteristics between the response categories of the eIC and former face-to-face IC procedure. Methods: All patients with increased cardiovascular risk visiting the University Medical Center Utrecht, the Netherlands, were eligible for the learning health care system. From November 2021 to August 2022, an eIC was piloted at the cardiology outpatient clinic. Prior to the pilot, a traditional face-to-face paper-based IC approach was used. Responses (ie, consent, no consent, or nonresponse) were assessed and compared between the eIC and face-to-face IC cohorts. Clinical characteristics of consenting and nonresponding patients were compared between and within the eIC and the face-to-face cohorts using multivariable regression analyses. Results: A total of 2254 patients were included in the face-to-face IC cohort and 885 patients in the eIC cohort. Full consent was more often obtained in the eIC than in the face-to-face cohort (415/885, 46.9% vs 876/2254, 38.9%, respectively). Apart from lower mean hemoglobin in the full consent group of the eIC cohort (8.5 vs 8.8; P=.0021), the characteristics of the full consenting patients did not differ between the eIC and face-to-face IC cohorts. In the eIC cohort, only age differed between the full consent and the nonresponse group (median 60 vs 56; P=.0002, respectively), whereas in the face-to-face IC cohort, the full consent group seemed healthier (ie, higher hemoglobin, lower glycated hemoglobin [HbA1c], lower C-reactive protein levels) than the nonresponse group. Conclusions: More patients provided full consent using an eIC. In addition, the study population remained broadly similar. The face-to-face IC approach seemed to result in a healthier study population (ie, full consenting patients) than the patients without IC, while in the eIC cohort, the characteristics between consent groups were comparable. Thus, an eIC may lead to a better representation of the target population, increasing the generalizability of results. %M 38990640 %R 10.2196/54867 %U https://www.jmir.org/2024/1/e54867 %U https://doi.org/10.2196/54867 %U http://www.ncbi.nlm.nih.gov/pubmed/38990640 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54407 %T A Simple and Systematic Approach to Qualitative Data Extraction From Social Media for Novice Health Care Researchers: Tutorial %A Pretorius,Kelly %+ School of Health Sciences, St. Edward's University, 3001 South Congress Avenue, Austin, TX, 78704, United States, 1 (512) 448 8500, kpretori@stedwards.edu %K social media analysis %K data extraction %K health care research %K extraction tutorial %K Facebook extraction %K Facebook analysis %K safe sleep %K sudden unexpected infant death %K social media %K analysis %K systematic approach %K qualitative data %K data extraction %K Facebook %K health-related %K maternal perspective %K maternal perspectives %K sudden infant death syndrome %K mother %K mothers %K women %K United States %K SIDS %K SUID %K post %K posts %D 2024 %7 9.7.2024 %9 Tutorial %J JMIR Form Res %G English %X Social media analyses have become increasingly popular among health care researchers. Social media continues to grow its user base and, when analyzed, offers unique insight into health problems. The process of obtaining data for social media analyses varies greatly and involves ethical considerations. Data extraction is often facilitated by software tools, some of which are open source, while others are costly and therefore not accessible to all researchers. The use of software for data extraction is accompanied by additional challenges related to the uniqueness of social media data. Thus, this paper serves as a tutorial for a simple method of extracting social media data that is accessible to novice health care researchers and public health professionals who are interested in pursuing social media research. The discussed methods were used to extract data from Facebook for a study of maternal perspectives on sudden unexpected infant death. %M 38980712 %R 10.2196/54407 %U https://formative.jmir.org/2024/1/e54407 %U https://doi.org/10.2196/54407 %U http://www.ncbi.nlm.nih.gov/pubmed/38980712 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55834 %T Novel Methodology for Identifying the Occurrence of Ovulation by Estimating Core Body Temperature During Sleeping: Validity and Effectiveness Study %A Sato,Daisuke %A Ikarashi,Koyuki %A Nakajima,Fumiko %A Fujimoto,Tomomi %+ Sports Physiology Laboratory, Department of Health and Sports, Niigata University of Health and Welfare, 1398 Shimami-cho, Kita-ku, Niigata, 950-3198, Japan, 81 25 257 4624, daisuke@nuhw.ac.jp %K menstrual cycle %K ovulation %K biphasic temperature shift %K estimation method %K women %D 2024 %7 5.7.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Body temperature is the most-used noninvasive biomarker to determine menstrual cycle and ovulation. However, issues related to its low accuracy are still under discussion. Objective: This study aimed to improve the accuracy of identifying the presence or absence of ovulation within a menstrual cycle. We investigated whether core body temperature (CBT) estimation can improve the accuracy of temperature biphasic shift discrimination in the menstrual cycle. The study consisted of 2 parts: experiment 1 assessed the validity of the CBT estimation method, while experiment 2 focused on the effectiveness of the method in discriminating biphasic temperature shifts. Methods: In experiment 1, healthy women aged between 18 and 40 years had their true CBT measured using an ingestible thermometer and their CBT estimated from skin temperature and ambient temperature measured during sleep in both the follicular and luteal phases of their menstrual cycles. This study analyzed the differences between these 2 measurements, the variations in temperature between the 2 phases, and the repeated measures correlation between the true and estimated CBT. Experiment 2 followed a similar methodology, but focused on evaluating the diagnostic accuracy of these 2 temperature measurement approaches (estimated CBT and traditional oral basal body temperature [BBT]) for identifying ovulatory cycles. This was performed using urine luteinizing hormone (LH) as the reference standard. Menstrual cycles were categorized based on the results of the LH tests, and a temperature shift was identified using a specific criterion called the “three-over-six rule.” This rule and the nested design of the study facilitated the assessment of diagnostic measures, such as sensitivity and specificity. Results: The main findings showed that CBT estimated from skin temperature and ambient temperature during sleep was consistently lower than directly measured CBT in both the follicular and luteal phases of the menstrual cycle. Despite this, the pattern of temperature variation between these phases was comparable for both the estimated and true CBT measurements, suggesting that the estimated CBT accurately reflected the cyclical variations in the true CBT. Significantly, the CBT estimation method showed higher sensitivity and specificity for detecting the occurrence of ovulation than traditional oral BBT measurements, highlighting its potential as an effective tool for reproductive health monitoring. The current method for estimating the CBT provides a practical and noninvasive method for monitoring CBT, which is essential for identifying biphasic shifts in the BBT throughout the menstrual cycle. Conclusions: This study demonstrated that the estimated CBT derived from skin temperature and ambient temperature during sleep accurately captures variations in true CBT and is more accurate in determining the presence or absence of ovulation than traditional oral BBT measurements. This method holds promise for improving reproductive health monitoring and understanding of menstrual cycle dynamics. %M 38967967 %R 10.2196/55834 %U https://formative.jmir.org/2024/1/e55834 %U https://doi.org/10.2196/55834 %U http://www.ncbi.nlm.nih.gov/pubmed/38967967 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e50240 %T The Impact of Incentives on Data Collection for Online Surveys: Social Media Recruitment Study %A Sobolewski,Jessica %A Rothschild,Allie %A Freeman,Andrew %+ RTI International, 3040 E Cornwallis Road, Research Triangle Park, NC, 27709, United States, 1 203 770 3115, jsobolewski@rti.org %K social media %K online survey recruitment %K incentive %K experiment %K online surveys %K Facebook %K Instagram %K data collection %K users %K cost %K social media recruitment %K survey %D 2024 %7 4.7.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The use of targeted advertisements on social media platforms (eg, Facebook and Instagram) has become increasingly popular for recruiting participants for online survey research. Many of these surveys offer monetary incentives for survey completion in the form of gift cards; however, little is known about whether the incentive amount impacts the cost, speed, and quality of data collection. Objective: This experiment addresses this gap in the literature by examining how different incentives in paid advertising campaigns on Instagram for completing a 10-minute online survey influence the response rate, recruitment advertising cost, data quality, and length of data collection. Methods: This experiment tested three incentive conditions using three Instagram campaigns that were each allocated a US $1400 budget to spend over a maximum of 4 days; ads targeted users aged 15-24 years in three nonadjacent designated market areas of similar size to avoid overlapping audiences. Four ad creatives were designed for each campaign; all ads featured the same images and text, but the incentive amount varied: no incentive, US $5 gift card, and US $15 gift card. All ads had a clickable link that directed users to an eligibility screener and a 10-minute online survey, if eligible. Each campaign ran for either the full allotted time (4 days) or until there were 150 total survey completes, prior to data quality checks for fraud. Results: The US $15 incentive condition resulted in the quickest and cheapest data collection, requiring 17 hours and ad spending of US $338.64 to achieve 142 survey completes. The US $5 condition took more than twice as long (39 hours) and cost US $864.33 in ad spending to achieve 148 survey completes. The no-incentive condition ran for 60 hours, spending nearly the full budget (US $1398.23), and achieved only 24 survey completes. The US $15 and US $5 incentive conditions had similar levels of fraudulent respondents, whereas the no-incentive condition had no fraudulent respondents. The completion rate for the US $15 and US $5 incentive conditions were 93.4% (155/166) and 89.8% (149/166), respectively, while the completion rate for the no-incentive condition was 43.6% (24/55). Conclusions: Overall, we found that a higher incentive resulted in quicker data collection, less money spent on ads, and higher response rates, despite some fraudulent cases that had to be dropped from the sample. However, when considering the total incentive amounts in addition to the ad spending, a US $5 incentive appeared to be the most cost-effective data collection option. Other costs associated with running a campaign for a longer period should also be considered. A longer experiment is warranted to determine whether fraud varies over time across conditions. %M 38963924 %R 10.2196/50240 %U https://formative.jmir.org/2024/1/e50240 %U https://doi.org/10.2196/50240 %U http://www.ncbi.nlm.nih.gov/pubmed/38963924 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55342 %T A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study %A Chun,Minki %A Yu,Ha-Jin %A Jung,Hyunggu %+ Department of Computer Science and Engineering, University of Seoul, Information and Technology Building, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul, 02504, Republic of Korea, 82 2 6490 2455, hjung@uos.ac.kr %K digital health %K mobile health %K mHealth %K app %K apps %K application %K applications %K smartphone %K smartphones %K classification %K digital sensor %K deep learning %K artificial intelligence %K machine learning %K food %K foods %K fruit %K fruits %K experience %K experiences %K attitude %K attitudes %K opinion %K opinions %K perception %K perceptions %K perspective %K perspectives %K acceptance %K adoption %K usability %K gerontology %K geriatric %K geriatrics %K older adult %K older adults %K elder %K elderly %K older person %K older people %K ageing %K aging %K aged %K camera %K image %K imaging %K photo %K photos %K photograph %K photographs %K recognition %K picture %K pictures %K sensor %K sensors %K develop %K development %K design %D 2024 %7 3.7.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items. Objective: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app. Methods: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants’ perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses. Results: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants’ perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory. Conclusions: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains. %M 38959501 %R 10.2196/55342 %U https://formative.jmir.org/2024/1/e55342 %U https://doi.org/10.2196/55342 %U http://www.ncbi.nlm.nih.gov/pubmed/38959501 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55302 %T Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis %A Zhang,Yuezhou %A Folarin,Amos A %A Sun,Shaoxiong %A Cummins,Nicholas %A Ranjan,Yatharth %A Rashid,Zulqarnain %A Stewart,Callum %A Conde,Pauline %A Sankesara,Heet %A Laiou,Petroula %A Matcham,Faith %A White,Katie M %A Oetzmann,Carolin %A Lamers,Femke %A Siddi,Sara %A Simblett,Sara %A Vairavan,Srinivasan %A Myin-Germeys,Inez %A Mohr,David C %A Wykes,Til %A Haro,Josep Maria %A Annas,Peter %A Penninx,Brenda WJH %A Narayan,Vaibhav A %A Hotopf,Matthew %A Dobson,Richard JB %A , %+ Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SGDP Centre, De Crespigny Park, Denmark Hill, London, SE5 8AF, United Kingdom, 44 7579856617, yuezhou.zhang@kcl.ac.uk %K circadian rhythm %K biological rhythms %K mental health %K major depressive disorder %K MDD %K wearable %K mHealth %K mobile health %K digital health %K monitoring %D 2024 %7 28.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. Objective: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. Methods: Data were sourced from a large longitudinal mHealth study, wherein participants’ depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants’ behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. Results: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=–93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = –31.51, P=.002) and summer (β of PHQ-8 × summer = –42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. Conclusions: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression. %M 38941600 %R 10.2196/55302 %U https://www.jmir.org/2024/1/e55302 %U https://doi.org/10.2196/55302 %U http://www.ncbi.nlm.nih.gov/pubmed/38941600 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55361 %T The Accuracy of Pulse Oxygen Saturation, Heart Rate, Blood Pressure, and Respiratory Rate Raised by a Contactless Telehealth Portal: Validation Study %A Gerald Dcruz,Julian %A Yeh,Paichang %+ Docsun Biomedical Holdings, Inc, 6763 32ND Ave N, Saint Petersburg, FL, 33710, United States, 1 (813) 4380045, jan.yeh@docsun.health %K medical devices %K mHealth %K vital signs %K measurements validity %K validation %K validity %K device %K devices %K vital %K vitals %K accuracy %K pulse %K oxygen %K saturation %K heart rate %K blood pressure %K respiration %K respiratory %K telehealth %K telemedicine %K eHealth %K e-health %K self-check %K self-checker %K breathing %K portal %K portals %K self-checking %K self-monitor %K self-monitoring %D 2024 %7 28.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The traditional measurement of heart rate (HR), oxygen saturation (SpO2), blood pressure (BP), and respiratory rate (RR) via physical examination can be challenging, and the recent pandemic has accelerated trends toward telehealth and remote monitoring. Instead of going to the physician to check these vital signs, measuring them at home would be more convenient. Vital sign monitors, also known as physiological parameter monitors, are electronic devices that measure and display biological information about patients under constant monitoring. Objective: The purpose of this study was to validate the accuracy of the pulse SpO2, HR, BP, and RR raised by Docsun Telehealth Portal by comparing it with approved medical devices. Methods: This is a noninvasive, self-check, system-based study conducted to validate the detection of vital signs (SpO2, HR, BP, and RR) raised by Docsun Telehealth Portal. The input for software processing involves facial screening without any accessories on the face, scanning directly through the software application portal. The participant’s facial features are detected and screened for the extraction of necessary readings. Results: For the validation of HR, SpO2, BP, and RR measurements, the main outcomes were the mean of the absolute difference between the respective investigational devices and the reference values as well as the absolute percentage difference between the respective investigational devices and the reference values. If the HR was within ±10% of the reference standard or 5 beats per minute, it was considered acceptable for clinical purposes. The average absolute difference between the Docsun Telehealth Portal and the reference values was 1.41 (SD 1.14) beats per minute. The mean absolute percentage difference was 1.69% (SD 1.37). Therefore, the Docsun Telehealth Portal met the predefined accuracy cutoff for HR measurements. If the RR was within ±10% of the reference standard or 3 breaths per minute, it was considered acceptable for clinical purposes. The average absolute difference between the Docsun Telehealth Portal and the reference values was 0.86 breaths per minute. The mean absolute percentage difference was 4.72%. Therefore, the Docsun Telehealth Portal met the predefined accuracy cutoff for RR measurements. SpO2 levels were considered acceptable if the average absolute difference between the Docsun Telehealth Portal and the reference values was ±3%. The mean absolute percentage difference was 0.59%. Therefore, the Docsun Telehealth Portal met the predefined accuracy cutoff for SpO2 measurements. The Docsun Telehealth Portal predicted systolic BP with an accuracy of 94.81% and diastolic BP with an accuracy of 95.71%. Conclusions: The results of the study show that the accuracy of the HR, BP, SpO2, and RR values raised by the Docsun Telehealth Portal, compared against the clinically approved medical devices, proved to be accurate by meeting predefined accuracy guidelines. %M 38598698 %R 10.2196/55361 %U https://formative.jmir.org/2024/1/e55361 %U https://doi.org/10.2196/55361 %U http://www.ncbi.nlm.nih.gov/pubmed/38598698 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55013 %T Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation %A Liu,Chuchu %A Holme,Petter %A Lehmann,Sune %A Yang,Wenchuan %A Lu,Xin %+ College of Systems Engineering, National University of Defense Technology, No 137 Yanwachi Street, Changsha, 410073, China, 86 18627561577, xin.lu.lab@outlook.com %K human mobility %K data representativeness %K population composition %K COVID-19 %K epidemiological modeling %D 2024 %7 28.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near–real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic’s importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. Objective: The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies. Methods: To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission. Results: We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days. Conclusions: The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting. %M 38941609 %R 10.2196/55013 %U https://formative.jmir.org/2024/1/e55013 %U https://doi.org/10.2196/55013 %U http://www.ncbi.nlm.nih.gov/pubmed/38941609 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e57111 %T Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study %A Huecker,Martin %A Schutzman,Craig %A French,Joshua %A El-Kersh,Karim %A Ghafghazi,Shahab %A Desai,Ravi %A Frick,Daniel %A Thomas,Jarred Jeremy %+ Department of Emergency Medicine, University of Louisville, 530 South Jackson St., Louisville, KY, 40202, United States, 1 5028525689, martin.huecker@louisville.edu %K ejection fraction %K stroke volume %K auscultation %K digital health %K telehealth %K acoustic recording %K acoustic recordings %K acoustic %K mHealth %K mobile health %K mobile phone %K mobile phones %K heart failure %K heart %K cardiac %K cardiology %K health care costs %K audio %K echocardiographic %K echocardiogram %K ultrasonography %K echocardiography %K accuracy %K monitoring %K telemonitoring %K recording %K recordings %K ejection %K machine learning %K algorithm %K algorithms %D 2024 %7 26.6.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients. Objective: This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone. Methods: This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness. Results: Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion. Conclusions: This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist. %M 38924781 %R 10.2196/57111 %U https://cardio.jmir.org/2024/1/e57111 %U https://doi.org/10.2196/57111 %U http://www.ncbi.nlm.nih.gov/pubmed/38924781 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e48777 %T Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study %A Li,Aoyu %A Li,Jingwen %A Chai,Jiali %A Wu,Wei %A Chaudhary,Suamn %A Zhao,Juanjuan %A Qiang,Yan %+ College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, No. 209, University Street, Yuji District, Shanxi Province, Jinzhong, 030024, China, 86 18635168680, qiangyan@tyut.edu.cn %K mild cognitive impairment %K movement kinetics %K digital cognitive test %K dual task %K mobile phone %D 2024 %7 26.6.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary. Objective: In this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based “drawing and dragging” tasks. Methods: We iteratively designed “drawing and dragging” tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded. Results: The “drawing and dragging” tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception. Conclusions: The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients’ executive function and visual perceptual abilities as the disease advances. %M 38924786 %R 10.2196/48777 %U https://mhealth.jmir.org/2024/1/e48777 %U https://doi.org/10.2196/48777 %U http://www.ncbi.nlm.nih.gov/pubmed/38924786 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e49785 %T User Preferences and Needs for Health Data Collection Using Research Electronic Data Capture: Survey Study %A Soni,Hiral %A Ivanova,Julia %A Wilczewski,Hattie %A Ong,Triton %A Ross,J Nalubega %A Bailey,Alexandra %A Cummins,Mollie %A Barrera,Janelle %A Bunnell,Brian %A Welch,Brandon %+ Doxy.me Research, Doxy.me Inc, 18 Broad Street, 3rd Floor, Suite 6 and 7, Charleston, SC, 29401, United States, 1 8444369963, sonihiralc@gmail.com %K Research Electronic Data Capture %K REDCap %K user experience %K electronic data collection %K health data %K personal health information %K clinical research %K mobile phone %D 2024 %7 25.6.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Self-administered web-based questionnaires are widely used to collect health data from patients and clinical research participants. REDCap (Research Electronic Data Capture; Vanderbilt University) is a global, secure web application for building and managing electronic data capture. Unfortunately, stakeholder needs and preferences of electronic data collection via REDCap have rarely been studied. Objective: This study aims to survey REDCap researchers and administrators to assess their experience with REDCap, especially their perspectives on the advantages, challenges, and suggestions for the enhancement of REDCap as a data collection tool. Methods: We conducted a web-based survey with representatives of REDCap member organizations in the United States. The survey captured information on respondent demographics, quality of patient-reported data collected via REDCap, patient experience of data collection with REDCap, and open-ended questions focusing on the advantages, challenges, and suggestions to enhance REDCap’s data collection experience. Descriptive and inferential analysis measures were used to analyze quantitative data. Thematic analysis was used to analyze open-ended responses focusing on the advantages, disadvantages, and enhancements in data collection experience. Results: A total of 207 respondents completed the survey. Respondents strongly agreed or agreed that the data collected via REDCap are accurate (188/207, 90.8%), reliable (182/207, 87.9%), and complete (166/207, 80.2%). More than half of respondents strongly agreed or agreed that patients find REDCap easy to use (165/207, 79.7%), could successfully complete tasks without help (151/207, 72.9%), and could do so in a timely manner (163/207, 78.7%). Thematic analysis of open-ended responses yielded 8 major themes: survey development, user experience, survey distribution, survey results, training and support, technology, security, and platform features. The user experience category included more than half of the advantage codes (307/594, 51.7% of codes); meanwhile, respondents reported higher challenges in survey development (169/516, 32.8% of codes), also suggesting the highest enhancement suggestions for the category (162/439, 36.9% of codes). Conclusions: Respondents indicated that REDCap is a valued, low-cost, secure resource for clinical research data collection. REDCap’s data collection experience was generally positive among clinical research and care staff members and patients. However, with the advancements in data collection technologies and the availability of modern, intuitive, and mobile-friendly data collection interfaces, there is a critical opportunity to enhance the REDCap experience to meet the needs of researchers and patients. %M 38917448 %R 10.2196/49785 %U https://medinform.jmir.org/2024/1/e49785 %U https://doi.org/10.2196/49785 %U http://www.ncbi.nlm.nih.gov/pubmed/38917448 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50253 %T Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review %A Barac,Milica %A Scaletty,Samantha %A Hassett,Leslie C %A Stillwell,Ashley %A Croarkin,Paul E %A Chauhan,Mohit %A Chesak,Sherry %A Bobo,William V %A Athreya,Arjun P %A Dyrbye,Liselotte N %+ Department of Medicine, University of Colorado School of Medicine, Mail Stop C290, Fitzsimons Bldg, 13001 E 17th Pl. Rm #E1347, Aurora, CO, 80045, United States, 1 303 724 4982, Liselotte.dyrbye@cuanschutz.edu %K wearable %K healthcare professionals %K burnout %K digital health %K mental health %D 2024 %7 25.6.2024 %9 Review %J J Med Internet Res %G English %X Background: The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers. Objective: This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs). Methods: A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies. Results: The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements. Conclusions: With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation. %M 38916948 %R 10.2196/50253 %U https://www.jmir.org/2024/1/e50253 %U https://doi.org/10.2196/50253 %U http://www.ncbi.nlm.nih.gov/pubmed/38916948 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e56144 %T Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities %A Chen,Hung-Hsun %A Lin,Chen %A Chang,Hsiang-Chih %A Chang,Jen-Ho %A Chuang,Hai-Hua %A Lin,Yu-Hsuan %+ Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, Taiwan, 886 37 206 166 ext 36383, yuhsuanmed@gmail.com %K digital phenotyping %K human-smartphone interaction %K labor or leisure %K machine learning %K mental activity %K physical activity %D 2024 %7 17.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms. Objective: This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA). Methods: A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the “Staff Hours” app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data’s nested structure. Results: Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P<.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P<.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night’s midpoint of sleep (β=–0.263, P<.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA’s activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night. Conclusions: This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies. %M 38885499 %R 10.2196/56144 %U https://www.jmir.org/2024/1/e56144 %U https://doi.org/10.2196/56144 %U http://www.ncbi.nlm.nih.gov/pubmed/38885499 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e56676 %T Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study %A Zhang,Yuankai %A Wang,Xuzhi %A Pathiravasan,Chathurangi H %A Spartano,Nicole L %A Lin,Honghuang %A Borrelli,Belinda %A Benjamin,Emelia J %A McManus,David D %A Larson,Martin G %A Vasan,Ramachandran S %A Shah,Ravi V %A Lewis,Gregory D %A Liu,Chunyu %A Murabito,Joanne M %A Nayor,Matthew %+ Sections of Cardiology and Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E Concord St, Suite L-516, Boston, MA, 02118, United States, 1 617 638 8771, mnayor@bu.edu %K mobile health %K smartwatch %K heart rate %K physical activity %K cardiorespiratory fitness %K cardiopulmonary exercise testing %D 2024 %7 13.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. Objective: This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. Methods: Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. Results: We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO2), % predicted peak VO2, and VO2 at the ventilatory anaerobic threshold, with false discovery rate (FDR)–adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO2. In addition, ventilatory efficiency (VE/VCO2; FDR-adjusted P=.009), % predicted maximum HR (FDR-adjusted P<.001), and systolic blood pressure-to-workload slope (FDR-adjusted P=.01) were associated with nonactive HR but not associated with daily steps. Conclusions: Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO2), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels. %M 38870519 %R 10.2196/56676 %U https://www.jmir.org/2024/1/e56676 %U https://doi.org/10.2196/56676 %U http://www.ncbi.nlm.nih.gov/pubmed/38870519 %0 Journal Article %@ 2817-092X %I JMIR Publications %V 3 %N %P e58398 %T Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study %A Maxin,Anthony J %A Lim,Do H %A Kush,Sophie %A Carpenter,Jack %A Shaibani,Rami %A Gulek,Bernice G %A Harmon,Kimberly G %A Mariakakis,Alex %A McGrath,Lynn B %A Levitt,Michael R %+ Department of Neurological Surgery, University of Washington, 325 9th Avenue, Seattle, WA, 98104, United States, 1 2067449305, mlevitt@uw.edu %K smartphone pupillometry %K pupillary light reflex %K biomarkers %K digital health %K mild traumatic brain injury %K concussion %K machine learning %K artificial intelligence %K AI %K pupillary %K pilot study %K brain %K brain injury %K injury %K diagnostic %K pupillometer %K neuroimaging %K diagnosis %K artificial %K mobile phone %D 2024 %7 13.6.2024 %9 Original Paper %J JMIR Neurotech %G English %X Background: Quantitative pupillometry is used in mild traumatic brain injury (mTBI) with changes in pupil reactivity noted after blast injury, chronic mTBI, and sports-related concussion. Objective: We evaluated the diagnostic capabilities of a smartphone-based digital pupillometer to differentiate patients with mTBI in the emergency department from controls. Methods: Adult patients diagnosed with acute mTBI with normal neuroimaging were evaluated in an emergency department within 36 hours of injury (control group: healthy adults). The PupilScreen smartphone pupillometer was used to measure the pupillary light reflex (PLR), and quantitative curve morphological parameters of the PLR were compared between mTBI and healthy controls. To address the class imbalance in our sample, a synthetic minority oversampling technique was applied. All possible combinations of PLR parameters produced by the smartphone pupillometer were then applied as features to 4 binary classification machine learning algorithms: random forest, k-nearest neighbors, support vector machine, and logistic regression. A 10-fold cross-validation technique stratified by cohort was used to produce accuracy, sensitivity, specificity, area under the curve, and F1-score metrics for the classification of mTBI versus healthy participants. Results: Of 12 patients with acute mTBI, 33% (4/12) were female (mean age 54.1, SD 22.2 years), and 58% (7/12) were White with a median Glasgow Coma Scale (GCS) of 15. Of the 132 healthy patients, 67% (88/132) were female, with a mean age of 36 (SD 10.2) years and 64% (84/132) were White with a median GCS of 15. Significant differences were observed in PLR recordings between healthy controls and patients with acute mTBI in the PLR parameters, that are (1) percent change (mean 34%, SD 8.3% vs mean 26%, SD 7.9%; P<.001), (2) minimum pupillary diameter (mean 34.8, SD 6.1 pixels vs mean 29.7, SD 6.1 pixels; P=.004), (3) maximum pupillary diameter (mean 53.6, SD 12.4 pixels vs mean 40.9, SD 11.9 pixels; P<.001), and (4) mean constriction velocity (mean 11.5, SD 5.0 pixels/second vs mean 6.8, SD 3.0 pixels/second; P<.001) between cohorts. After the synthetic minority oversampling technique, both cohorts had a sample size of 132 recordings. The best-performing binary classification model was a random forest model using the PLR parameters of latency, percent change, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity as features. This model produced an overall accuracy of 93.5%, sensitivity of 96.2%, specificity of 90.9%, area under the curve of 0.936, and F1-score of 93.7% for differentiating between pupillary changes in mTBI and healthy participants. The absolute values are unable to be provided for the performance percentages reported here due to the mechanism of 10-fold cross validation that was used to obtain them. Conclusions: In this pilot study, quantitative smartphone pupillometry demonstrates the potential to be a useful tool in the future diagnosis of acute mTBI. %R 10.2196/58398 %U https://neuro.jmir.org/2024/1/e58398 %U https://doi.org/10.2196/58398 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e54579 %T Mobile Health App and Web Platform (eDOL) for Medical Follow-Up of Patients With Chronic Pain: Cohort Study Involving the French eDOL National Cohort After 1 Year %A Delage,Noémie %A Cantagrel,Nathalie %A Soriot-Thomas,Sandrine %A Frost,Marie %A Deleens,Rodrigue %A Ginies,Patrick %A Eschalier,Alain %A Corteval,Alice %A Laveyssière,Alicia %A Phalip,Jules %A Bertin,Célian %A Pereira,Bruno %A Chenaf,Chouki %A Doreau,Bastien %A Authier,Nicolas %A , %A Kerckhove,Nicolas %+ Service de pharmacologie médicale, CHU Clermont-Ferrand, 58 rue Montalembert, Clermont-Ferrand, 63000, France, 33 473754833, nkerckhove@chu-clermontferrand.fr %K mHealth %K mobile health %K eHealth %K self-monitoring %K chronic pain %K observational study %D 2024 %7 12.6.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Chronic pain affects approximately 30% of the general population, severely degrades quality of life and professional life, and leads to additional health care costs. Moreover, the medical follow-up of patients with chronic pain remains complex and provides only fragmentary data on painful daily experiences. This situation makes the management of patients with chronic pain less than optimal and may partly explain the lack of effectiveness of current therapies. Real-life monitoring of subjective and objective markers of chronic pain using mobile health (mHealth) programs could better characterize patients, chronic pain, pain medications, and daily impact to help medical management. Objective: This cohort study aimed to assess the ability of our mHealth tool (eDOL) to collect extensive real-life medical data from chronic pain patients after 1 year of use. The data collected in this way would provide new epidemiological and pathophysiological data on chronic pain. Methods: A French national cohort of patients with chronic pain treated at 18 pain clinics has been established and followed up using mHealth tools. This cohort makes it possible to collect the determinants and repercussions of chronic pain and their evolutions in a real-life context, taking into account all environmental events likely to influence chronic pain. The patients were asked to complete several questionnaires, body schemes, and weekly meters, and were able to interact with a chatbot and use educational modules on chronic pain. Physicians could monitor their patients’ progress in real time via an online platform. Results: The cohort study included 1427 patients and analyzed 1178 patients. The eDOL tool was able to collect various sociodemographic data; specific data for characterizing pain disorders, including body scheme; data on comorbidities related to chronic pain and its psychological and overall impact on patients’ quality of life; data on drug and nondrug therapeutics and their benefit-to-risk ratio; and medical or treatment history. Among the patients completing weekly meters, 49.4% (497/1007) continued to complete them after 3 months of follow-up, and the proportion stabilized at 39.3% (108/275) after 12 months of follow-up. Overall, despite a fairly high attrition rate over the follow-up period, the eDOL tool collected extensive data. This amount of data will increase over time and provide a significant volume of health data of interest for future research involving the epidemiology, care pathways, trajectories, medical management, sociodemographic characteristics, and other aspects of patients with chronic pain. Conclusions: This work demonstrates that the mHealth tool eDOL is able to generate a considerable volume of data concerning the determinants and repercussions of chronic pain and their evolutions in a real-life context. The eDOL tool can incorporate numerous parameters to ensure the detailed characterization of patients with chronic pain for future research and pain management. Trial Registration: ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096 %M 38865173 %R 10.2196/54579 %U https://mhealth.jmir.org/2024/1/e54579 %U https://doi.org/10.2196/54579 %U http://www.ncbi.nlm.nih.gov/pubmed/38865173 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e50653 %T Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks %A Levin-Rector,Alison %A Kulldorff,Martin %A Peterson,Eric R %A Hostovich,Scott %A Greene,Sharon K %+ Bureau of Communicable Disease, New York City Department of Health and Mental Hygiene, 42-09 28th St, Long Island City, NY, 11101, United States, 1 347 396 2600, alevinrector@health.nyc.gov %K communicable diseases %K disease outbreaks %K disease surveillance %K epidemiology %K infectious disease %K outbreak detection %K public health practice %K SaTScan %K spatiotemporal %K urban health %D 2024 %7 11.6.2024 %9 Tutorial %J JMIR Public Health Surveill %G English %X Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease’s systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve. %M 38861711 %R 10.2196/50653 %U https://publichealth.jmir.org/2024/1/e50653 %U https://doi.org/10.2196/50653 %U http://www.ncbi.nlm.nih.gov/pubmed/38861711 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54207 %T Leveraging Ecological Momentary Assessment Data to Characterize Individual Mobility: Exploratory Pilot Study in Rural Uganda %A Khalifa,Aleya %A Beres,Laura K %A Anok,Aggrey %A Mbabali,Ismail %A Katabalwa,Charles %A Mulamba,Jeremiah %A Thomas,Alvin G %A Bugos,Eva %A Nakigozi,Gertrude %A Chang,Larry W %A Grabowski,M Kate %+ Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY, 10032, United States, 1 212 305 2862, ak4598@cumc.columbia.edu %K ecological momentary assessment %K spatial analysis %K geographic mobility %K global positioning system %K health behaviors %K Uganda %K mobility %K pilot study %K smartphone %K alcohol %K cigarette %K smoking %K promoting %K promotion %K alcohol use %K cigarette smoking %K mobile phone %D 2024 %7 10.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The geographical environments within which individuals conduct their daily activities may influence health behaviors, yet little is known about individual-level geographic mobility and specific, linked behaviors in rural low- and middle-income settings. Objective: Nested in a 3-month ecological momentary assessment intervention pilot trial, this study aims to leverage mobile health app user GPS data to examine activity space through individual spatial mobility and locations of reported health behaviors in relation to their homes. Methods: Pilot trial participants were recruited from the Rakai Community Cohort Study—an ongoing population-based cohort study in rural south-central Uganda. Participants used a smartphone app that logged their GPS coordinates every 1-2 hours for approximately 90 days. They also reported specific health behaviors (alcohol use, cigarette smoking, and having condomless sex with a non–long-term partner) via the app that were both location and time stamped. In this substudy, we characterized participant mobility using 3 measures: average distance (kilometers) traveled per week, number of unique locations visited (deduplicated points within 25 m of one another), and the percentage of GPS points recorded away from home. The latter measure was calculated using home buffer regions of 100 m, 400 m, and 800 m. We also evaluated the number of unique locations visited for each specific health behavior, and whether those locations were within or outside the home buffer regions. Sociodemographic information, mobility measures, and locations of health behaviors were summarized across the sample using descriptive statistics. Results: Of the 46 participants with complete GPS data, 24 (52%) participants were men, 30 (65%) participants were younger than 35 years, and 33 (72%) participants were in the top 2 socioeconomic status quartiles. On median, participants traveled 303 (IQR 152-585) km per week. Over the study period, participants on median recorded 1292 (IQR 963-2137) GPS points—76% (IQR 58%-86%) of which were outside their 400-m home buffer regions. Of the participants reporting drinking alcohol, cigarette smoking, and engaging in condomless sex, respectively, 19 (83%), 8 (89%), and 12 (86%) reported that behavior at least once outside their 400-m home neighborhood and across a median of 3.0 (IQR 1.5-5.5), 3.0 (IQR 1.0-3.0), and 3.5 (IQR 1.0-7.0) unique locations, respectively. Conclusions: Among residents in rural Uganda, an ecological momentary assessment app successfully captured high mobility and health-related behaviors across multiple locations. Our findings suggest that future mobile health interventions in similar settings can benefit from integrating spatial data collection using the GPS technology in mobile phones. Leveraging such individual-level GPS data can inform place-based strategies within these interventions for promoting healthy behavior change. %M 38857493 %R 10.2196/54207 %U https://formative.jmir.org/2024/1/e54207 %U https://doi.org/10.2196/54207 %U http://www.ncbi.nlm.nih.gov/pubmed/38857493 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50049 %T Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping %A Stellmach,Caroline %A Hopff,Sina Marie %A Jaenisch,Thomas %A Nunes de Miranda,Susana Marina %A Rinaldi,Eugenia %A , %+ Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str 2, Berlin, 10178, Germany, 49 15752614677, caroline.stellmach@charite.de %K core data element %K CDE %K case report form %K CRF %K interoperability %K semantic standards %K infectious disease %K diagnostic test %K covid19 %K COVID-19 %K mpox %K ZIKV %K patient data %K data model %K syntactic interoperability %K clinical data %K FHIR %K SNOMED CT %K LOINC %K virus infection %K common element %D 2024 %7 10.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets. Objective: This study’s objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials. Methods: We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs. Results: Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date. Conclusions: The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element’s (variable’s) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by “wrapping” them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium’s Clinical Data Acquisition Standards Harmonization Model. %M 38857066 %R 10.2196/50049 %U https://www.jmir.org/2024/1/e50049 %U https://doi.org/10.2196/50049 %U http://www.ncbi.nlm.nih.gov/pubmed/38857066 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e53806 %T Defining Activity Thresholds Triggering a “Stand Hour” for Apple Watch Users: Cross-Sectional Study %A Lyons,Katy %A Hei Man,Alison Hau %A Booth,David %A Rena,Graham %+ Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Jacqui Wood Centre, James Arrott Drive, Dundee, DD1 9SY, United Kingdom, 44 660111, k.m.lyons@dundee.ac.uk %K stand hour %K Apple Watch %K sedentary behavior %K light physical activity %K cardiovascular disease %K type 2 diabetes %K data collection %K wearable %K wearables %K watch %K smartwatch %K stand %K standing %K sedentary %K physical activity %K exercise %K movement %K algorithm %K algorithms %K predict %K predictive %K predictor %K predictors %K prediction %K machine learning %D 2024 %7 10.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Sedentary behavior (SB) is one of the largest contributing factors increasing the risk of developing noncommunicable diseases, including cardiovascular disease and type 2 diabetes. Guidelines from the World Health Organization for physical activity suggest the substitution of SB with light physical activity. The Apple Watch contains a health metric known as the stand hour (SH). The SH is intended to record standing with movement for at least 1 minute per hour; however, the activity measured during the determination of the SH is unclear. Objective: In this cross-sectional study, we analyzed the algorithm used to determine time spent standing per hour. To do this, we investigated activity measurements also recorded on Apple Watches that influence the recording of an SH. We also aimed to estimate the values of any significant SH predictors in the recording of a SH. Methods: The cross-sectional study used anonymized data obtained in August 2022 from 20 healthy individuals gathered via convenience sampling. Apple Watch data were extracted from the Apple Health app through the use of a third-party app. Appropriate statistical models were fitted to analyze SH predictors. Results: Our findings show that active energy (AE) and step count (SC) measurements influence the recording of an SH. Comparing when an SH is recorded with when an SH is not recorded, we found a significant difference in the mean and median AE and SC. Above a threshold of 97.5 steps or 100 kJ of energy, it became much more likely that an SH would be recorded when each predictor was analyzed as a separate entity. Conclusions: The findings of this study reveal the pivotal role of AE and SC measurements in the algorithm underlying the SH recording; however, our findings also suggest that a recording of an SH is influenced by more than one factor. Irrespective of the internal validity of the SH metric, it is representative of light physical activity and might, therefore, have use in encouraging individuals through various means, for example, notifications, to reduce their levels of SB. %M 38857078 %R 10.2196/53806 %U https://formative.jmir.org/2024/1/e53806 %U https://doi.org/10.2196/53806 %U http://www.ncbi.nlm.nih.gov/pubmed/38857078 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e56003 %T Smartphone-Based Survey and Message Compliance in Adults Initially Unready to Quit Smoking: Secondary Analysis of a Randomized Controlled Trial %A Ulm,Clayton %A Chen,Sixia %A Fleshman,Brianna %A Benson,Lizbeth %A Kendzor,Darla E %A Frank-Pearce,Summer %A Neil,Jordan M %A Vidrine,Damon %A De La Torre,Irene %A Businelle,Michael S %+ TSET Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, 655 Research Parkway, Suite 400, Oklahoma City, OK, 73104, United States, 1 405 271 8001, michael-businelle@ouhsc.edu %K just-in-time adaptive intervention %K tailored messaging %K smoking cessation %K mobile health %K survey compliance %K phase-based model %K smoking %K smoker %K survey %K smokers %K messaging %K smartphone %K efficacy %K pilot randomized controlled trial %K adult smokers %K linear regression %K age %K intervention engagement %K engagement %D 2024 %7 7.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Efficacy of smartphone-based interventions depends on intervention content quality and level of exposure to that content. Smartphone-based survey completion rates tend to decline over time; however, few studies have identified variables that predict this decline over longer-term interventions (eg, 26 weeks). Objective: This study aims to identify predictors of survey completion and message viewing over time within a 26-week smoking cessation trial. Methods: This study examined data from a 3-group pilot randomized controlled trial of adults who smoke (N=152) and were not ready to quit smoking within the next 30 days. For 182 days, two intervention groups received smartphone-based morning and evening messages based on current readiness to quit smoking. The control group received 2 daily messages unrelated to smoking. All participants were prompted to complete 26 weekly smartphone-based surveys that assessed smoking behavior, quit attempts, and readiness to quit. Compliance was operationalized as percentages of weekly surveys completed and daily messages viewed. Linear regression and mixed-effects models were used to identify predictors (eg, intervention group, age, and sex) of weekly survey completion and daily message viewing and decline in compliance over time. Results: The sample (mean age 50, SD 12.5, range 19-75 years; mean years of education 13.3, SD 1.6, range 10-20 years) was 67.8% (n=103) female, 74.3% (n=113) White, 77% (n=117) urban, and 52.6% (n=80) unemployed, and 61.2% (n=93) had mental health diagnoses. On average, participants completed 18.3 (71.8%) out of 25.5 prompted weekly surveys and viewed 207.3 (60.6%) out of 345.1 presented messages (31,503/52,460 total). Age was positively associated with overall weekly survey completion (P=.003) and daily message viewing (P=.02). Mixed-effects models indicated a decline in survey completion from 77% (114/148) in the first week of the intervention to 56% (84/150) in the last week of the intervention (P<.001), which was significantly moderated by age, sex, ethnicity, municipality (ie, rural/urban), and employment status. Similarly, message viewing declined from 72.3% (1533/2120) in the first week of the intervention to 44.6% (868/1946) in the last week of the intervention (P<.001). This decline in message viewing was significantly moderated by age, sex, municipality, employment status, and education. Conclusions: This study demonstrated the feasibility of a 26-week smartphone-based smoking cessation intervention. Study results identified subgroups that displayed accelerated rates in the decline of survey completion and message viewing. Future research should identify ways to maintain high levels of interaction with mobile health interventions that span long intervention periods, especially among subgroups that have demonstrated declining rates of intervention engagement over time. Trial Registration: ClinicalTrials.gov NCT03405129; https://clinicaltrials.gov/ct2/show/NCT03405129 %M 38848557 %R 10.2196/56003 %U https://formative.jmir.org/2024/1/e56003 %U https://doi.org/10.2196/56003 %U http://www.ncbi.nlm.nih.gov/pubmed/38848557 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e48491 %T News Coverage of the COVID-19 Pandemic on Social Media and the Public’s Negative Emotions: Computational Study %A Wang,Hanjing %A Li,Yupeng %A Ning,Xuan %+ Department of Interactive Media, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, 000000, China (Hong Kong), 852 3411 8263, ivanypli@gmail.com %K web news coverage %K emotions %K social media %K Facebook %K COVID-19 %D 2024 %7 6.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Social media has become an increasingly popular and critical tool for users to digest diverse information and express their perceptions and attitudes. While most studies endeavor to delineate the emotional responses of social media users, there is limited research exploring the factors associated with the emergence of emotions, particularly negative ones, during news consumption. Objective: We aim to first depict the web coverage by news organizations on social media and then explore the crucial elements of news coverage that trigger the public’s negative emotions. Our findings can act as a reference for responsible parties and news organizations in times of crisis. Methods: We collected 23,705 Facebook posts with 1,019,317 comments from the public pages of representative news organizations in Hong Kong. We used text mining techniques, such as topic models and Bidirectional Encoder Representations from Transformers, to analyze news components and public reactions. Beyond descriptive analysis, we used regression models to shed light on how news coverage on social media is associated with the public’s negative emotional responses. Results: Our results suggest that occurrences of issues regarding pandemic situations, antipandemic measures, and supportive actions are likely to reduce the public’s negative emotions, while comments on the posts mentioning the central government and the Government of Hong Kong reveal more negativeness. Negative and neutral media tones can alleviate the rage and interact with the subjects and issues in the news to affect users’ negative emotions. Post length is found to have a curvilinear relationship with users’ negative emotions. Conclusions: This study sheds light on the impacts of various components of news coverage (issues, subjects, media tone, and length) on social media on the public’s negative emotions (anger, fear, and sadness). Our comprehensive analysis provides a reference framework for efficient crisis communication for similar pandemics at present or in the future. This research, although first extending the analysis between the components of news coverage and negative user emotions to the scenario of social media, echoes previous studies drawn from traditional media and its derivatives, such as web newspapers. Although the era of COVID-19 pandemic gradually brings down the curtain, the commonality of this research and previous studies also contributes to establishing a clearer territory in the field of health crises. %M 38843521 %R 10.2196/48491 %U https://www.jmir.org/2024/1/e48491 %U https://doi.org/10.2196/48491 %U http://www.ncbi.nlm.nih.gov/pubmed/38843521 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e50650 %T Detecting and Understanding Social Influence During Drinking Situations: Protocol for a Bluetooth-Based Sensor Feasibility and Acceptability Study %A Jackson,Kristina %A Meisel,Matthew %A Sokolovsky,Alexander %A Chen,Katie %A Barnett,Nancy %+ Center for Alcohol and Addiction Studies, Department of Behavioral and Social Sciences, Brown University, Box G-S121-4, Providence, RI, 02912, United States, 1 (401) 863 6617, kristina_jackson@brown.edu %K Bluetooth technology %K passive sensing %K social influence %K alcohol use %K ecological momentary assessment %K social network %K feasibility %K acceptability %K mobile phone %D 2024 %7 6.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: High-risk alcohol consumption among young adults frequently occurs in the presence of peers who are also drinking. A high-risk drinking situation may consist of particular social network members who have a primary association with drinking. Fine-grained approaches such as ecological momentary assessment (EMA) are growing in popularity for studying real-time social influence, but studies using these approaches exclusively rely on participant self-report. Passive indicators of peer presence using Bluetooth-based technology to detect real-time interactions have the potential to assist in the development of just-in-time interventions. Objective: This study seeks to examine the feasibility and acceptability of using a Bluetooth-based sensor and smartphone app to measure social contact in real-world drinking situations. Methods: Young adults (N=20) who drink heavily and report social drinking will be recruited from the community to participate in a 3-week EMA study. Using a social network interview, index participants will identify and recruit 3 of their friends to carry a Bluetooth beacon. Participants will complete a series of EMA reports on their own personal Android devices including random reports; morning reports; first-drink reports; and signal-contingent reports, which are triggered following the detection of a beacon carried by a peer participant. EMA will assess alcohol use and characteristics of the social environment, including who is nearby and who is drinking. For items about peer proximity and peer drinking, a customized peer list will be presented to participants. Feedback about the study protocol will be ascertained through weekly contact with both index and peer participants, followed by a qualitative interview at the end of the study. We will examine the feasibility and acceptability of recruitment, enrollment of participants and peers, and retention. Feasibility will be determined using indexes of eligibility, enrollment, and recruitment. Acceptability will be determined through participant enrollment and retention, protocol compliance, and participant-reported measures of acceptability. Feasibility and acceptability for peer participants will be informed by enrollment rates, latency to enrollment, compliance with carrying the beacon, and self-reported reasons for compliance or noncompliance with beacon procedures. Finally, EMA data about peer proximity and peer drinking will support the validity of the peer selection process. Results: Participant recruitment began in February 2023, and enrollment was completed in December 2023. Results will be reported in 2025. Conclusions: The protocol allows us to examine the feasibility and acceptability of a Bluetooth-based sensor for the detection of social contact between index participants and their friends, including social interactions during real-world drinking situations. Data from this study will inform just-in-time adaptive interventions seeking to address drinking in the natural environment by providing personalized feedback about a high-risk social context and alerting an individual that they are in a potentially unsafe situation. International Registered Report Identifier (IRRID): DERR1-10.2196/50650 %M 38842927 %R 10.2196/50650 %U https://www.researchprotocols.org/2024/1/e50650 %U https://doi.org/10.2196/50650 %U http://www.ncbi.nlm.nih.gov/pubmed/38842927 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e49450 %T Investigating Health and Well-Being Challenges Faced by an Aging Workforce in the Construction and Nursing Industries: Computational Linguistic Analysis of Twitter Data %A Li,Weicong %A Tang,Liyaning Maggie %A Montayre,Jed %A Harris,Celia B %A West,Sancia %A Antoniou,Mark %+ The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Locked Bag 1797, Penrith, 2751, Australia, 61 61 2 97726673, m.antoniou@westernsydney.edu.au %K social media %K construction %K nursing %K aging %K health and well-being %K Twitter %D 2024 %7 5.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Construction and nursing are critical industries. Although both careers involve physically and mentally demanding work, the risks to workers during the COVID-19 pandemic are not well understood. Nurses (both younger and older) are more likely to experience the ill effects of burnout and stress than construction workers, likely due to accelerated work demands and increased pressure on nurses during the COVID-19 pandemic. In this study, we analyzed a large social media data set using advanced natural language processing techniques to explore indicators of the mental status of workers across both industries before and during the COVID-19 pandemic. Objective: This social media analysis aims to fill a knowledge gap by comparing the tweets of younger and older construction workers and nurses to obtain insights into any potential risks to their mental health due to work health and safety issues. Methods: We analyzed 1,505,638 tweets published on Twitter (subsequently rebranded as X) by younger and older (aged <45 vs >45 years) construction workers and nurses. The study period spanned 54 months, from January 2018 to June 2022, which equates to approximately 27 months before and 27 months after the World Health Organization declared COVID-19 a global pandemic on March 11, 2020. The tweets were analyzed using big data analytics and computational linguistic analyses. Results: Text analyses revealed that nurses made greater use of hashtags and keywords (both monograms and bigrams) associated with burnout, health issues, and mental health compared to construction workers. The COVID-19 pandemic had a pronounced effect on nurses’ tweets, and this was especially noticeable in younger nurses. Tweets about health and well-being contained more first-person singular pronouns and affect words, and health-related tweets contained more affect words. Sentiment analyses revealed that, overall, nurses had a higher proportion of positive sentiment in their tweets than construction workers. However, this changed markedly during the COVID-19 pandemic. Since early 2020, sentiment switched, and negative sentiment dominated the tweets of nurses. No such crossover was observed in the tweets of construction workers. Conclusions: The social media analysis revealed that younger nurses had language use patterns consistent with someone experiencing the ill effects of burnout and stress. Older construction workers had more negative sentiments than younger workers, who were more focused on communicating about social and recreational activities rather than work matters. More broadly, these findings demonstrate the utility of large data sets enabled by social media to understand the well-being of target populations, especially during times of rapid societal change. %M 38838308 %R 10.2196/49450 %U https://www.jmir.org/2024/1/e49450 %U https://doi.org/10.2196/49450 %U http://www.ncbi.nlm.nih.gov/pubmed/38838308 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50149 %T Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study %A Chuang,Hai-Hua %A Lin,Chen %A Lee,Li-Ang %A Chang,Hsiang-Chih %A She,Guan-Jie %A Lin,Yu-Hsuan %+ Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 35053, Taiwan, 886 37 206 166 ext 36383, yuhsuanlin@nhri.edu.tw %K actigraphy %K body composition %K circadian rhythm %K human-smartphone interaction %K interdaily stability %K obesity %D 2024 %7 5.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. Objective: We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. Methods: We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. Results: Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. Conclusions: Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted. %M 38838328 %R 10.2196/50149 %U https://www.jmir.org/2024/1/e50149 %U https://doi.org/10.2196/50149 %U http://www.ncbi.nlm.nih.gov/pubmed/38838328 %0 Journal Article %@ 2561-7605 %I %V 7 %N %P e53020 %T Characterizing Walking Behaviors in Aged Residential Care Using Accelerometry, With Comparison Across Care Levels, Cognitive Status, and Physical Function: Cross-Sectional Study %A Mc Ardle,Ríona %A Taylor,Lynne %A Cavadino,Alana %A Rochester,Lynn %A Del Din,Silvia %A Kerse,Ngaire %K residential aged care facility %K cognitive dysfunction %K mobility limitation %K accelerometry %K physical activity %K aged residential care %D 2024 %7 4.6.2024 %9 %J JMIR Aging %G English %X Background: Walking is important for maintaining physical and mental well-being in aged residential care (ARC). Walking behaviors are not well characterized in ARC due to inconsistencies in assessment methods and metrics as well as limited research regarding the impact of care environment, cognition, or physical function on these behaviors. It is recommended that walking behaviors in ARC are assessed using validated digital methods that can capture low volumes of walking activity. Objective: This study aims to characterize and compare accelerometry-derived walking behaviors in ARC residents across different care levels, cognitive abilities, and physical capacities. Methods: A total of 306 ARC residents were recruited from the Staying UpRight randomized controlled trial from 3 care levels: rest home (n=164), hospital (n=117), and dementia care (n=25). Participants’ cognitive status was classified as mild (n=87), moderate (n=128), or severe impairment (n=61); physical function was classified as high-moderate (n=74) and low-very low (n=222) using the Montreal Cognitive Assessment and the Short Physical Performance Battery cutoff scores, respectively. To assess walking, participants wore an accelerometer (Axivity AX3; dimensions: 23×32.5×7.6 mm; weight: 11 g; sampling rate: 100 Hz; range: ±8 g; and memory: 512 MB) on their lower back for 7 days. Outcomes included volume (ie, daily time spent walking, steps, and bouts), pattern (ie, mean walking bout duration and alpha), and variability (of bout length) of walking. Analysis of covariance was used to assess differences in walking behaviors between groups categorized by level of care, cognition, or physical function while controlling for age and sex. Tukey honest significant difference tests for multiple comparisons were used to determine where significant differences occurred. The effect sizes of group differences were calculated using Hedges g (0.2-0.4: small, 0.5-0.7: medium, and 0.8: large). Results: Dementia care residents showed greater volumes of walking (P<.001; Hedges g=1.0-2.0), with longer (P<.001; Hedges g=0.7-0.8), more variable (P=.008 vs hospital; P<.001 vs rest home; Hedges g=0.6-0.9) bouts compared to other care levels with a lower alpha score (vs hospital: P<.001; Hedges g=0.9, vs rest home: P=.004; Hedges g=0.8). Residents with severe cognitive impairment took longer (P<.001; Hedges g=0.5-0.6), more variable (P<.001; Hedges g=0.4-0.6) bouts, compared to those with mild and moderate cognitive impairment. Residents with low-very low physical function had lower walking volumes (total walk time and bouts per day: P<.001; steps per day: P=.005; Hedges g=0.4-0.5) and higher variability (P=.04; Hedges g=0.2) compared to those with high-moderate capacity. Conclusions: ARC residents across different levels of care, cognition, and physical function demonstrate different walking behaviors. However, ARC residents often present with varying levels of both cognitive and physical abilities, reflecting their complex multimorbid nature, which should be considered in further work. This work has demonstrated the importance of considering a nuanced framework of digital outcomes relating to volume, pattern, and variability of walking behaviors among ARC residents. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618001827224; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=376298&isReview=true %R 10.2196/53020 %U https://aging.jmir.org/2024/1/e53020 %U https://doi.org/10.2196/53020 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55798 %T Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis %A Ohno,Yukiko %A Kato,Riri %A Ishikawa,Haruki %A Nishiyama,Tomohiro %A Isawa,Minae %A Mochizuki,Mayumi %A Aramaki,Eiji %A Aomori,Tohru %+ Faculty of Pharmacy, Takasaki University of Health and Welfare, 37-1 Nakaorui-machi, Takasaki-shi, Gunma, 370-0033, Japan, 81 273521290, aomori-t@takasaki-u.ac.jp %K natural language processing %K NLP %K named entity recognition %K pharmaceutical care records %K machine learning %K cefazolin sodium %K electronic medical record %K EMR %K extraction %K Japanese %D 2024 %7 4.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians’ records, it has yet to be widely applied to pharmaceutical care records. Objective: In this study, we aimed to investigate the feasibility of automatic extraction of the information regarding patients’ diseases and symptoms from pharmaceutical care records. The verification was performed using Medical Named Entity Recognition-Japanese (MedNER-J), a Japanese disease-extraction system designed for physicians’ records. Methods: MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F1-score. Results: The F1-scores of NER for subjective, objective, assessment, and plan data were 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive-negative classification, the F1-scores were 0.28, 0.39, 0.64, and 0.077, respectively. The F1-scores of NER for objective (0.70) and assessment data (0.76) were higher than those for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F1-score of NER and positive-negative classification was high for assessment data alone (F1-score=0.64), which was attributed to the similarity of its description format and contents to those of the training data. Conclusions: MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records. %M 38833694 %R 10.2196/55798 %U https://formative.jmir.org/2024/1/e55798 %U https://doi.org/10.2196/55798 %U http://www.ncbi.nlm.nih.gov/pubmed/38833694 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e47070 %T COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application–Based Prospective Observational Cohort Study %A Lang,Anna-Lena %A Hohmuth,Nils %A Višković,Vukašin %A Konigorski,Stefan %A Scholz,Stefan %A Balzer,Felix %A Remschmidt,Cornelius %A Leistner,Rasmus %+ d4l Data4Life gGmbH, c/o Digital Health Cluster (DHC) im Hasso-Plattner-Institut, Rudolf-Breitscheid-Straße 187, Potsdam, 14482, Germany, 49 015756025551, annalena.lang.26@gmail.com %K COVID-19 %K SARS-CoV-2 %K COVID-19 vaccines %K BNT162b2 %K vaccine effectiveness %K participatory disease surveillance %K web application %K digital public health %K vaccination %K Germany %K effectiveness %K data collection %K disease surveillance %K tool %D 2024 %7 4.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. Objective: This study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. Methods: Using a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. Results: We enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. Conclusions: Our data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools. %M 38833299 %R 10.2196/47070 %U https://www.jmir.org/2024/1/e47070 %U https://doi.org/10.2196/47070 %U http://www.ncbi.nlm.nih.gov/pubmed/38833299 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e49562 %T Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach %A Azizi,Mehrnoosh %A Jamali,Ali Akbar %A Spiteri,Raymond J %+ Department of Computer Science, University of Saskatchewan, S425 Thorvaldson Building, 110 Science Place, Saskatoon, SK, S7N5C9, Canada, 1 306 966 2909, spiteri@cs.usask.ca %K machine learning %K dementia %K Alzheimer disease %K COVID-19 %K X (Twitter) %K natural language processing %D 2024 %7 4.6.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: During the pandemic, patients with dementia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, therefore, identifying posts (formerly tweets) relevant to dementia can be an important support for patients with dementia and their caregivers. However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. Objective: The objective of this study was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. Methods: We used a combination of natural language processing and ML algorithms with manually annotated posts to identify posts relevant to dementia and COVID-19. We used 3 data sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. Results: Our results showed that (pretrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms tested, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an area under the curve of 83.53%. ALBERT substantially outperformed the other algorithms tested, further emphasizing the superior performance of transfer learning algorithms in the classification of posts. Conclusions: Transfer learning algorithms such as ALBERT are highly effective in identifying topic-specific posts, even when trained with limited or adjacent data, highlighting their superiority over other ML algorithms and applicability to other studies involving analysis of social media posts. Such an automated approach reduces the workload of manual coding of posts and facilitates their analysis for researchers and policy makers to support patients with dementia and their caregivers and other vulnerable populations. %M 38833288 %R 10.2196/49562 %U https://formative.jmir.org/2024/1/e49562 %U https://doi.org/10.2196/49562 %U http://www.ncbi.nlm.nih.gov/pubmed/38833288 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e47682 %T A Taxonomy for Health Information Systems %A Janssen,Anna %A Donnelly,Candice %A Shaw,Tim %+ Faculty of Medicine and Health, The University of Sydney, Level 2, Charles Perkins Centre D17, Sydney, 2066, Australia, 61 02 9036 9406, anna.janssen@sydney.edu.au %K eHealth %K digital health %K electronic health data %K data revolution %K actionable data %K mobile phone %D 2024 %7 31.5.2024 %9 Viewpoint %J J Med Internet Res %G English %X The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems. %M 38820575 %R 10.2196/47682 %U https://www.jmir.org/2024/1/e47682 %U https://doi.org/10.2196/47682 %U http://www.ncbi.nlm.nih.gov/pubmed/38820575 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e49320 %T Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review %A Guardado,Sharon %A Karampela,Maria %A Isomursu,Minna %A Grundstrom,Casandra %+ Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, Oulu, 90570, Finland, 358 504388396, sharon.guardadomedina@oulu.fi %K patient-generated health data %K mHealth %K health care professionals %K mobile technologies %K self-management %D 2024 %7 31.5.2024 %9 Review %J J Med Internet Res %G English %X Background: Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients’ behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context. Objective: This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them. Methods: A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses. Results: The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients’ devices. PGHD about patients’ behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies. Conclusions: PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce. International Registered Report Identifier (IRRID): RR2-10.2196/39389 %M 38820580 %R 10.2196/49320 %U https://www.jmir.org/2024/1/e49320 %U https://doi.org/10.2196/49320 %U http://www.ncbi.nlm.nih.gov/pubmed/38820580 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e49907 %T Controlling Inputter Variability in Vignette Studies Assessing Web-Based Symptom Checkers: Evaluation of Current Practice and Recommendations for Isolated Accuracy Metrics %A Meczner,András %A Cohen,Nathan %A Qureshi,Aleem %A Reza,Maria %A Sutaria,Shailen %A Blount,Emily %A Bagyura,Zsolt %A Malak,Tamer %+ Healthily, 167-169 Great Portland Street, London, W1W 5PF, United Kingdom, meczner@gmail.com %K symptom checker %K accuracy %K vignette studies %K variability %K methods %K triage %K evaluation %K vignette %K performance %K metrics %K mobile phone %D 2024 %7 31.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The rapid growth of web-based symptom checkers (SCs) is not matched by advances in quality assurance. Currently, there are no widely accepted criteria assessing SCs’ performance. Vignette studies are widely used to evaluate SCs, measuring the accuracy of outcome. Accuracy behaves as a composite metric as it is affected by a number of individual SC- and tester-dependent factors. In contrast to clinical studies, vignette studies have a small number of testers. Hence, measuring accuracy alone in vignette studies may not provide a reliable assessment of performance due to tester variability. Objective: This study aims to investigate the impact of tester variability on the accuracy of outcome of SCs, using clinical vignettes. It further aims to investigate the feasibility of measuring isolated aspects of performance. Methods: Healthily’s SC was assessed using 114 vignettes by 3 groups of 3 testers who processed vignettes with different instructions: free interpretation of vignettes (free testers), specified chief complaints (partially free testers), and specified chief complaints with strict instruction for answering additional symptoms (restricted testers). κ statistics were calculated to assess agreement of top outcome condition and recommended triage. Crude and adjusted accuracy was measured against a gold standard. Adjusted accuracy was calculated using only results of consultations identical to the vignette, following a review and selection process. A feasibility study for assessing symptom comprehension of SCs was performed using different variations of 51 chief complaints across 3 SCs. Results: Intertester agreement of most likely condition and triage was, respectively, 0.49 and 0.51 for the free tester group, 0.66 and 0.66 for the partially free group, and 0.72 and 0.71 for the restricted group. For the restricted group, accuracy ranged from 43.9% to 57% for individual testers, averaging 50.6% (SD 5.35%). Adjusted accuracy was 56.1%. Assessing symptom comprehension was feasible for all 3 SCs. Comprehension scores ranged from 52.9% and 68%. Conclusions: We demonstrated that by improving standardization of the vignette testing process, there is a significant improvement in the agreement of outcome between testers. However, significant variability remained due to uncontrollable tester-dependent factors, reflected by varying outcome accuracy. Tester-dependent factors, combined with a small number of testers, limit the reliability and generalizability of outcome accuracy when used as a composite measure in vignette studies. Measuring and reporting different aspects of SC performance in isolation provides a more reliable assessment of SC performance. We developed an adjusted accuracy measure using a review and selection process to assess data algorithm quality. In addition, we demonstrated that symptom comprehension with different input methods can be feasibly compared. Future studies reporting accuracy need to apply vignette testing standardization and isolated metrics. %M 38820578 %R 10.2196/49907 %U https://formative.jmir.org/2024/1/e49907 %U https://doi.org/10.2196/49907 %U http://www.ncbi.nlm.nih.gov/pubmed/38820578 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e56333 %T Finite Element Analysis of Pelvic Floor Biomechanical Models to Elucidate the Mechanism for Improving Urination and Defecation Dysfunction in Older Adults: Protocol for a Model Development and Validation Study %A Wang,Rui %A Liu,Guangtian %A Jing,Liwei %A Zhang,Jing %A Li,Chenyang %A Gong,Lichao %+ School of Nursing, Capital Medical University, 10 Xitoutiao, You'anmenwai, Fengtai District, Beijing, 100069, China, 86 13021000866, lwjing2004@ccmu.edu.cn %K elderly %K older adults %K pelvic cavity %K finite element analysis %K biomechanical model %K protocol %K urination %K incontinence %K aging %K bowel dysfunction %D 2024 %7 31.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The population is constantly aging, and most older adults will experience many potential physiological changes as they age, leading to functional decline. Urinary and bowel dysfunction is the most common obstacle in older people. At present, the analysis of pelvic floor histological changes related to aging has not been fully elucidated, and the mechanism of improving intestinal control ability in older people is still unclear. Objective: The purpose of this study is to describe how the finite element method will be used to understand the mechanical characteristics of and physiological changes in the pelvic cavity during the rehabilitation process, providing theoretical support for the mechanism for improving urination and defecation dysfunction in older individuals. Methods: We will collect magnetic resonance imaging (MRI) and computed tomography (CT) data of the pelvic cavity of one male and one female volunteer older than 60 years and use the finite element method to construct a 3D computer simulation model of the pelvic cavity. By simulating different physiological states, such as the Valsalva maneuver and bowel movement, we will verify the accuracy of the constructed model, investigate the effects of different neuromuscular functional changes, and quantify the impact proportions of the pelvic floor muscle group, core muscle group, and sacral nerve. Results: At present, we have registered the study in the Chinese Clinical Trial Registry and collected MRI and CT data for an older male and an older female patient. Next, the construction and analysis of the finite element model will be accomplished according to the study plan. We expect to complete the construction and analysis of the finite element model by July 2024 and publish the research results by October 2025. Conclusions: Our study will build finite element models of the pelvic floor of older men and older women, and we shall elucidate the relationship between the muscles of the pelvic floor, back, abdomen, and hips and the ability of older adults to control bowel movements. The results of this study will provide theoretical support for elucidating the mechanism for improving urination and defecation dysfunction through rehabilitation. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400080749; https://www.chictr.org.cn/showproj.html?proj=193428 International Registered Report Identifier (IRRID): DERR1-10.2196/56333 %M 38820582 %R 10.2196/56333 %U https://www.researchprotocols.org/2024/1/e56333 %U https://doi.org/10.2196/56333 %U http://www.ncbi.nlm.nih.gov/pubmed/38820582 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54974 %T Harnessing ChatGPT for Thematic Analysis: Are We Ready? %A Lee,V Vien %A van der Lubbe,Stephanie C C %A Goh,Lay Hoon %A Valderas,Jose Maria %+ Division of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, NUHS Tower Block, Level 9, 1E Kent Ridge Road, Singapore, 119228, Singapore, 65 67723874, jmvalderas@nus.edu.sg %K ChatGPT %K thematic analysis %K natural language processing %K NLP %K medical research %K qualitative research %K qualitative data %K technology %K viewpoint %K efficiency %D 2024 %7 31.5.2024 %9 Viewpoint %J J Med Internet Res %G English %X ChatGPT (OpenAI) is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the use of ChatGPT in three core phases of thematic analysis within a medical context: (1) direct coding of transcripts, (2) generating themes from a predefined list of codes, and (3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data. While ChatGPT may not adequately capture the full context of each participant, it can serve as an additional member of the analysis team, contributing to researcher triangulation through knowledge building and sensemaking. %M 38819896 %R 10.2196/54974 %U https://www.jmir.org/2024/1/e54974 %U https://doi.org/10.2196/54974 %U http://www.ncbi.nlm.nih.gov/pubmed/38819896 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e46895 %T Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study %A Cormack,Francesca %A McCue,Maggie %A Skirrow,Caroline %A Cashdollar,Nathan %A Taptiklis,Nick %A van Schaik,Tempest %A Fehnert,Ben %A King,James %A Chrones,Lambros %A Sarkey,Sara %A Kroll,Jasmin %A Barnett,Jennifer H %+ Cambridge Cognition, Tunbridge Court, Bottisham, Cambridge, CB25 9TU, United Kingdom, 44 7961910560, jasmin.kroll@camcog.com %K cognition %K depression %K digital biomarkers %K ecological momentary assessment %K mobile health %K remote testing %D 2024 %7 31.5.2024 %9 Original Paper %J JMIR Ment Health %G English %X Background: Cognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques. Objective: The objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity. Methods: A total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models. Results: Participants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01). Conclusions: The current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood. %M 38819909 %R 10.2196/46895 %U https://mental.jmir.org/2024/1/e46895 %U https://doi.org/10.2196/46895 %U http://www.ncbi.nlm.nih.gov/pubmed/38819909 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e56668 %T Tablet-Based Cognitive and Eye Movement Measures as Accessible Tools for Schizophrenia Assessment: Multisite Usability Study %A Morita,Kentaro %A Miura,Kenichiro %A Toyomaki,Atsuhito %A Makinodan,Manabu %A Ohi,Kazutaka %A Hashimoto,Naoki %A Yasuda,Yuka %A Mitsudo,Takako %A Higuchi,Fumihiro %A Numata,Shusuke %A Yamada,Akiko %A Aoki,Yohei %A Honda,Hiromitsu %A Mizui,Ryo %A Honda,Masato %A Fujikane,Daisuke %A Matsumoto,Junya %A Hasegawa,Naomi %A Ito,Satsuki %A Akiyama,Hisashi %A Onitsuka,Toshiaki %A Satomura,Yoshihiro %A Kasai,Kiyoto %A Hashimoto,Ryota %+ Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, 187 8553, Japan, 81 42 346 2046, ryotahashimoto55@ncnp.go.jp %K schizophrenia %K cognitive function %K eye movement %K diagnostic biomarkers %K digital health tools %D 2024 %7 30.5.2024 %9 Original Paper %J JMIR Ment Health %G English %X Background: Schizophrenia is a complex mental disorder characterized by significant cognitive and neurobiological alterations. Impairments in cognitive function and eye movement have been known to be promising biomarkers for schizophrenia. However, cognitive assessment methods require specialized expertise. To date, data on simplified measurement tools for assessing both cognitive function and eye movement in patients with schizophrenia are lacking. Objective: This study aims to assess the efficacy of a novel tablet-based platform combining cognitive and eye movement measures for classifying schizophrenia. Methods: Forty-four patients with schizophrenia, 67 healthy controls, and 41 patients with other psychiatric diagnoses participated in this study from 10 sites across Japan. A free-viewing eye movement task and 2 cognitive assessment tools (Codebreaker task from the THINC-integrated tool and the CognitiveFunctionTest app) were used for conducting assessments in a 12.9-inch iPad Pro. We performed comparative group and logistic regression analyses for evaluating the diagnostic efficacy of the 3 measures of interest. Results: Cognitive and eye movement measures differed significantly between patients with schizophrenia and healthy controls (all 3 measures; P<.001). The Codebreaker task showed the highest classification effectiveness in distinguishing schizophrenia with an area under the receiver operating characteristic curve of 0.90. Combining cognitive and eye movement measures further improved accuracy with a maximum area under the receiver operating characteristic curve of 0.94. Cognitive measures were more effective in differentiating patients with schizophrenia from healthy controls, whereas eye movement measures better differentiated schizophrenia from other psychiatric conditions. Conclusions: This multisite study demonstrates the feasibility and effectiveness of a tablet-based app for assessing cognitive functioning and eye movements in patients with schizophrenia. Our results suggest the potential of tablet-based assessments of cognitive function and eye movement as simple and accessible evaluation tools, which may be useful for future clinical implementation. %M 38815257 %R 10.2196/56668 %U https://mental.jmir.org/2024/1/e56668 %U https://doi.org/10.2196/56668 %U http://www.ncbi.nlm.nih.gov/pubmed/38815257 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e51977 %T Web Application to Enable Online Social Interactions in a Parkinson Disease Risk Cohort: Feasibility Study and Social Network Analysis %A Li,Xiancheng %A Gill,Aneet %A Panzarasa,Pietro %A Bestwick,Jonathan %A Schrag,Anette %A Noyce,Alastair %A De Simoni,Anna %+ Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, United Kingdom, 44 2078822520, a.desimoni@qmul.ac.uk %K pilot studies %K network analysis %K Parkinson disease %K risk factors %K risk %K risk cohort %K social interaction %K development %K neurodegenerative disease %K neurodegenerative %K United Kingdom %K feasibility %K design %K pilot %K engagement %K users %K online forum %K online network %K online %K regression analysis %D 2024 %7 24.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: There is evidence that social interaction has an inverse association with the development of neurodegenerative diseases. PREDICT-Parkinson Disease (PREDICT-PD) is an online UK cohort study that stratifies participants for risk of future Parkinson disease (PD). Objective: This study aims to explore the methodological approach and feasibility of assessing the digital social characteristics of people at risk of developing PD and their social capital within the PREDICT-PD platform, making hypotheses about the relationship between web-based social engagement and potential predictive risk indicators of PD. Methods: A web-based application was built to enable social interaction through the PREDICT-PD portal. Feedback from existing members of the cohort was sought and informed the design of the pilot. Dedicated staff used weekly engagement activities, consisting of PD-related research, facts, and queries, to stimulate discussion. Data were collected by the hosting platform. We examined the pattern of connections generated over time through the cumulative number of posts and replies and ego networks using social network analysis. We used network metrics to describe the bonding, bridging, and linking of social capital among participants on the platform. Relevant demographic data and Parkinson risk scores (expressed as an odd 1:x) were analyzed using descriptive statistics. Regression analysis was conducted to estimate the relationship between risk scores (after log transformation) and network measures. Results: Overall, 219 participants took part in a 4-month pilot forum embedded in the study website. In it, 200 people (n=80, 40% male and n=113, 57% female) connected in a large group, where most pairs of users could reach one another either directly or indirectly through other users. A total of 59% (20/34) of discussions were spontaneously started by participants. Participation was asynchronous, with some individuals acting as “brokers” between groups of discussions. As more participants joined the forum and connected to one another through online posts, distinct groups of connected users started to emerge. This pilot showed that a forum application within the cohort web platform was feasible and acceptable and fostered digital social interaction. Matching participants’ web-based social engagement with previously collected data at individual level in the PREDICT-PD study was feasible, showing potential for future analyses correlating online network characteristics with the risk of PD over time, as well as testing digital social engagement as an intervention to modify the risk of developing neurodegenerative diseases. Conclusions: The results from the pilot suggest that an online forum can serve as an intervention to enhance social connectedness and investigate whether patterns of online engagement can impact the risk of developing PD through long-term follow-up. This highlights the potential of leveraging online platforms to study the role of social capital in moderating PD risk and underscores the feasibility of such approaches in future research or interventions. %M 38788211 %R 10.2196/51977 %U https://formative.jmir.org/2024/1/e51977 %U https://doi.org/10.2196/51977 %U http://www.ncbi.nlm.nih.gov/pubmed/38788211 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e53067 %T Visual “Scrollytelling”: Mapping Aquatic Selfie-Related Incidents in Australia %A Cornell,Samuel %A Peden,Amy E %+ School of Population Health, University of New South Wales, High St, Kensington, Sydney, 2052, Australia, 61 (2) 9065 38, s.cornell@unsw.edu.au %K selfie %K map %K social media %K selfies %K scrollama %K JavaScript %K scrollytelling %K Mapbox %K incidence %K incidents %K incident %K fatality %K fatalities %K injury %K injuries %K retrieval %K prevalence %K image %K images %K photo %K photos %K photograph %K photographs %K prevalence %K Australia %K emergency %K visualization %K visualizations %K interactive %K location %K geography %K geographic %K geographical %K spatial %K artificial intelligence %K longitude %K latitude %K visual representation %K visual representations %D 2024 %7 23.5.2024 %9 Research Letter %J Interact J Med Res %G English %X %M 38781002 %R 10.2196/53067 %U https://www.i-jmr.org/2024/1/e53067 %U https://doi.org/10.2196/53067 %U http://www.ncbi.nlm.nih.gov/pubmed/38781002 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e40689 %T Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review %A Choi,Adrien %A Ooi,Aysel %A Lottridge,Danielle %+ School of Computer Science, Faculty of Science, University of Auckland, 38 Princes Street, Auckland, 1010, New Zealand, 64 9 373 7599 ext 82930, d.lottridge@auckland.ac.nz %K digital phenotyping %K passive sensing %K stress %K anxiety %K depression %K PRISMA %K Preferred Reporting Items for Systematic Reviews and Meta-Analyses %K mobile phone %D 2024 %7 23.5.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping involves capturing continuous behavioral data via digital smartphone devices to monitor human behavior and can potentially identify milder symptoms before they become serious. Objective: This systematic literature review aimed to answer the following questions: (1) what is the evidence of the effectiveness of digital phenotyping using smartphones in identifying behavioral patterns related to stress, anxiety, and mild depression? and (2) in particular, which smartphone sensors are found to be effective, and what are the associated challenges? Methods: We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process to identify 36 papers (reporting on 40 studies) to assess the key smartphone sensors related to stress, anxiety, and mild depression. We excluded studies conducted with nonadult participants (eg, teenagers and children) and clinical populations, as well as personality measurement and phobia studies. As we focused on the effectiveness of digital phenotyping using smartphones, results related to wearable devices were excluded. Results: We categorized the studies into 3 major groups based on the recruited participants: studies with students enrolled in universities, studies with adults who were unaffiliated to any particular organization, and studies with employees employed in an organization. The study length varied from 10 days to 3 years. A range of passive sensors were used in the studies, including GPS, Bluetooth, accelerometer, microphone, illuminance, gyroscope, and Wi-Fi. These were used to assess locations visited; mobility; speech patterns; phone use, such as screen checking; time spent in bed; physical activity; sleep; and aspects of social interactions, such as the number of interactions and response time. Of the 40 included studies, 31 (78%) used machine learning models for prediction; most others (n=8, 20%) used descriptive statistics. Students and adults who experienced stress, anxiety, or depression visited fewer locations, were more sedentary, had irregular sleep, and accrued increased phone use. In contrast to students and adults, less mobility was seen as positive for employees because less mobility in workplaces was associated with higher performance. Overall, travel, physical activity, sleep, social interaction, and phone use were related to stress, anxiety, and mild depression. Conclusions: This study focused on understanding whether smartphone sensors can be effectively used to detect behavioral patterns associated with stress, anxiety, and mild depression in nonclinical participants. The reviewed studies provided evidence that smartphone sensors are effective in identifying behavioral patterns associated with stress, anxiety, and mild depression. %M 38780995 %R 10.2196/40689 %U https://mhealth.jmir.org/2024/1/e40689 %U https://doi.org/10.2196/40689 %U http://www.ncbi.nlm.nih.gov/pubmed/38780995 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e51013 %T Using a Semiautomated Procedure (CleanADHdata.R Script) to Clean Electronic Adherence Monitoring Data: Tutorial %A Bandiera,Carole %A Pasquier,Jérôme %A Locatelli,Isabella %A Schneider,Marie P %+ School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, Geneva, 1205, Switzerland, 41 223795316, marie.schneider@unige.ch %K medication adherence %K digital technology %K digital pharmacy %K electronic adherence monitoring %K data management %K data cleaning %K research methodology %K algorithms %K R %K semiautomated %K code %K coding %K computer science %K computer programming %K medications %K computer script %D 2024 %7 22.5.2024 %9 Tutorial %J JMIR Form Res %G English %X Background: Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence. Objective: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users. Methods: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients’ demographic data. The script formats the data longitudinally and calculates each day’s medication implementation. Results: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Δ+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence. Conclusions: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies. %M 38776539 %R 10.2196/51013 %U https://formative.jmir.org/2024/1/e51013 %U https://doi.org/10.2196/51013 %U http://www.ncbi.nlm.nih.gov/pubmed/38776539 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e54933 %T Patients and Stakeholders’ Perspectives Regarding the Privacy, Security, and Confidentiality of Data Collected via Mobile Health Apps in Saudi Arabia: Protocol for a Mixed Method Study %A Alhammad,Nasser %A Alajlani,Mohannad %A Abd-alrazaq,Alaa %A Arvanitis,Theodoros %A Epiphaniou,Gregory %+ Institute of Digital Healthcare, WMG, University of Warwick, Millburn House, Coventry, CV4 7AL, United Kingdom, 44 558885007, N.alhammad@seu.edu.sa %K awareness %K data privacy %K confidentiality %K security %K health care %K patients %K Saudi Arabia %K mHealth %K mobile apps %D 2024 %7 22.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: There is data paucity regarding users’ awareness of privacy concerns and the resulting impact on the acceptance of mobile health (mHealth) apps, especially in the Saudi context. Such information is pertinent in addressing users’ needs in the Kingdom of Saudi Arabia (KSA). Objective: This article presents a study protocol for a mixed method study to assess the perspectives of patients and stakeholders regarding the privacy, security, and confidentiality of data collected via mHealth apps in the KSA and the factors affecting the adoption of mHealth apps. Methods: A mixed method study design will be used. In the quantitative phase, patients and end users of mHealth apps will be randomly recruited from various provinces in Saudi Arabia with a high population of mHealth users. The research instrument will be developed based on the emerging themes and findings from the interview conducted among stakeholders, app developers, health care professionals, and users of mHealth apps (n=25). The survey will focus on (1) how to improve patients’ awareness of data security, privacy, and confidentiality; (2) feedback on the current mHealth apps in terms of data security, privacy, and confidentiality; and (3) the features that might improve data security, privacy, and confidentiality of mHealth apps. Meanwhile, specific sections of the questionnaire will focus on patients’ awareness, privacy concerns, confidentiality concerns, security concerns, perceived usefulness, perceived ease of use, and behavioral intention. Qualitative data will be analyzed thematically using NVivo version 12. Descriptive statistics, regression analysis, and structural equation modeling will be performed using SPSS and partial least squares structural equation modeling. Results: The ethical approval for this research has been obtained from the Biomedical and Scientific Research Ethics Committee, University of Warwick, and the Medical Research and Ethics Committee Ministry of Health in the KSA. The qualitative phase is ongoing and 15 participants have been interviewed. The interviews for the remaining 10 participants will be completed by November 25, 2023. Preliminary thematic analysis is still ongoing. Meanwhile, the quantitative phase will commence by December 10, 2023, with 150 participants providing signed and informed consent to participate in the study. Conclusions: The mixed methods study will elucidate the antecedents of patients’ awareness and concerns regarding the privacy, security, and confidentiality of data collected via mHealth apps in the KSA. Furthermore, pertinent findings on the perspectives of stakeholders and health care professionals toward the aforementioned issues will be gleaned. The results will assist policy makers in developing strategies to improve Saudi users’/patients’ adoption of mHealth apps and addressing the concerns raised to benefit significantly from these advanced health care modalities. International Registered Report Identifier (IRRID): DERR1-10.2196/54933 %M 38776540 %R 10.2196/54933 %U https://www.researchprotocols.org/2024/1/e54933 %U https://doi.org/10.2196/54933 %U http://www.ncbi.nlm.nih.gov/pubmed/38776540 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e50812 %T A Web-Based, Respondent-Driven Sampling Survey Among Men Who Have Sex With Men (Kai Noi): Description of Methods and Characteristics %A Karuchit,Samart %A Thiengtham,Panupit %A Tanpradech,Suvimon %A Srinor,Watcharapol %A Yingyong,Thitipong %A Naiwatanakul,Thananda %A Northbrook,Sanny %A Hladik,Wolfgang %+ Informatics Section, Business Services Office, US Centers for Disease Control and Prevention, DDC7 Bldg, 3rd Fl. Ministry of Public Health, Tivanon Road, Nonthaburi, 11000, Thailand, 66 2 580 0669 ext 364, hqd5@cdc.gov %K online respondent-driven sampling %K web-based respondent-driven sampling %K virtual architecture %K men who have sex with men %K Thailand %K MSM %K Asia %K Asian %K gay %K homosexual %K homosexuality %K sexual minority %K sexual minorities %K biobehavioral %K surveillance %K respondent driven sampling %K survey %K surveys %K web app %K web application %K coding %K PHP %K web based %K automation %K automated %K design %K architecture %K information system %K information systems %K online sampling %K HIV %K sexually transmitted infection %K STI %K sexually transmitted disease %K STD %K sexual transmission %K sexually transmitted %K RDS %K webRDS %D 2024 %7 20.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Thailand’s HIV epidemic is heavily concentrated among men who have sex with men (MSM), and surveillance efforts are mostly based on case surveillance and local biobehavioral surveys. Objective: We piloted Kai Noi, a web-based respondent-driven sampling (RDS) survey among MSM. Methods: We developed an application coded in PHP that facilitated all procedures and events typically used in an RDS office for use on the web, including e-coupon validation, eligibility screening, consent, interview, peer recruitment, e-coupon issuance, and compensation. All procedures were automated and e-coupon ID numbers were randomly generated. Participants’ phone numbers were the principal means to detect and prevent duplicate enrollment. Sampling took place across Thailand; residents of Bangkok were also invited to attend 1 of 10 clinics for an HIV-related blood draw with additional compensation. Results: Sampling took place from February to June 2022; seeds (21 at the start, 14 added later) were identified through banner ads, micromessaging, and in online chat rooms. Sampling reached all 6 regions and almost all provinces. Fraudulent (duplicate) enrollment using “borrowed” phone numbers was identified and led to the detection and invalidation of 318 survey records. A further 106 participants did not pass an attention filter question (asking recruits to select a specific categorical response) and were excluded from data analysis, leading to a final data set of 1643 valid participants. Only one record showed signs of straightlining (identical adjacent responses). None of the Bangkok respondents presented for a blood draw. Conclusions: We successfully developed an application to implement web-based RDS among MSM across Thailand. Measures to minimize, detect, and eliminate fraudulent survey enrollment are imperative in web-based surveys offering compensation. Efforts to improve biomarker uptake are needed to fully tap the potential of web-based sampling and data collection. %M 38767946 %R 10.2196/50812 %U https://formative.jmir.org/2024/1/e50812 %U https://doi.org/10.2196/50812 %U http://www.ncbi.nlm.nih.gov/pubmed/38767946 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e52577 %T “Notification! You May Have Cancer.” Could Smartphones and Wearables Help Detect Cancer Early? %A Scott,Suzanne E %A Thompson,Matthew J %+ Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom, 44 2078823550, suzanne.scott@qmul.ac.uk %K wearables %K early diagnosis %K cancer %K challenges %K diagnosis %K wearable %K detect %K detection %K smartphone %K cancer diagnosis %K symptoms %K monitoring %K monitor %K implementation %K anxiety %K health care service %K mobile phone %D 2024 %7 20.5.2024 %9 Viewpoint %J JMIR Cancer %G English %X This viewpoint paper considers the authors’ perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources. %M 38767941 %R 10.2196/52577 %U https://cancer.jmir.org/2024/1/e52577 %U https://doi.org/10.2196/52577 %U http://www.ncbi.nlm.nih.gov/pubmed/38767941 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e51059 %T Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial %A Manz,Christopher R %A Schriver,Emily %A Ferrell,William J %A Williamson,Joelle %A Wakim,Jonathan %A Khan,Neda %A Kopinsky,Michael %A Balachandran,Mohan %A Chen,Jinbo %A Patel,Mitesh S %A Takvorian,Samuel U %A Shulman,Lawrence N %A Bekelman,Justin E %A Barnett,Ian J %A Parikh,Ravi B %+ Division of Health Policy, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Room 1102, Philadelphia, PA, 19104, United States, 1 3524224285, Ravi.Parikh@pennmedicine.upenn.edu %K wearables %K accelerometers %K patient-reported outcomes %K step counts %K oncology %K accelerometer %K patient-generated health data %K cancer %K death %K chemotherapy %K symptoms %K gastrointestinal cancer %K lung cancer %K monitoring %K symptom burden %K risk %K hospitalization %K mobile phone %D 2024 %7 17.5.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. Objective: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. Methods: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. Results: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI –277 to –213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change –892), nausea score (–677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. Conclusions: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. Trial Registration: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-054675 %M 38758583 %R 10.2196/51059 %U https://www.jmir.org/2024/1/e51059 %U https://doi.org/10.2196/51059 %U http://www.ncbi.nlm.nih.gov/pubmed/38758583 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e51496 %T Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review %A Zhang,Yujie %A Fu,Jiaqi %A Lai,Jie %A Deng,Shisi %A Guo,Zihan %A Zhong,Chuhan %A Tang,Jianyao %A Cao,Wenqiong %A Wu,Yanni %+ Nanfang Hospital, Southern Medical University, No 1838 Guangzhou Avenue North, Baiyun District, Guangdong Province, Guangzhou, 510515, China, 86 02061641192, yanniwuSMU@126.com %K qualitative research %K informed consent %K ethics approval %K privacy %K internet community %D 2024 %7 17.5.2024 %9 Review %J J Med Internet Res %G English %X Background: The internet community has become a significant source for researchers to conduct qualitative studies analyzing users’ views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data. Objective: This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care. Methods: We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Results: After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users’ written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users’ privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users’ written content (both direct and paraphrased quotes) and papers that did not contain users’ written content (P<.001). Conclusions: Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities. %M 38758590 %R 10.2196/51496 %U https://www.jmir.org/2024/1/e51496 %U https://doi.org/10.2196/51496 %U http://www.ncbi.nlm.nih.gov/pubmed/38758590 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e53327 %T Person-Generated Health Data in Women’s Health: Scoping Review %A Karim,Jalisa Lynn %A Wan,Rachel %A Tabet,Rhea S %A Chiu,Derek S %A Talhouk,Aline %+ Department of Obstetrics and Gynaecology, University of British Columbia, 593 - 828 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada, 1 604 875 3111, a.talhouk@ubc.ca %K digital health %K women’s health %K mobile health %K health app %K wearables %K femtech %K self-tracking %K personalized health %K person-generated health data %K patient-generated health data %K scoping review %K mobile phone %D 2024 %7 16.5.2024 %9 Review %J J Med Internet Res %G English %X Background: The increased pervasiveness of digital health technology is producing large amounts of person-generated health data (PGHD). These data can empower people to monitor their health to promote prevention and management of disease. Women make up one of the largest groups of consumers of digital self-tracking technology. Objective: In this scoping review, we aimed to (1) identify the different areas of women’s health monitored using PGHD from connected health devices, (2) explore personal metrics collected through these technologies, and (3) synthesize facilitators of and barriers to women’s adoption and use of connected health devices. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we searched 5 databases for articles published between January 1, 2015, and February 29, 2020. Papers were included if they targeted women or female individuals and incorporated digital health tools that collected PGHD outside a clinical setting. Results: We included a total of 406 papers in this review. Articles on the use of PGHD for women steadily increased from 2015 to 2020. The health areas that the articles focused on spanned several topics, with pregnancy and the postpartum period being the most prevalent followed by cancer. Types of digital health used to collect PGHD included mobile apps, wearables, websites, the Internet of Things or smart devices, 2-way messaging, interactive voice response, and implantable devices. A thematic analysis of 41.4% (168/406) of the papers revealed 6 themes regarding facilitators of and barriers to women’s use of digital health technology for collecting PGHD: (1) accessibility and connectivity, (2) design and functionality, (3) accuracy and credibility, (4) audience and adoption, (5) impact on community and health service, and (6) impact on health and behavior. Conclusions: Leading up to the COVID-19 pandemic, the adoption of digital health tools to address women’s health concerns was on a steady rise. The prominence of tools related to pregnancy and the postpartum period reflects the strong focus on reproductive health in women’s health research and highlights opportunities for digital technology development in other women’s health topics. Digital health technology was most acceptable when it was relevant to the target audience, was seen as user-friendly, and considered women’s personalization preferences while also ensuring accuracy of measurements and credibility of information. The integration of digital technologies into clinical care will continue to evolve, and factors such as liability and health care provider workload need to be considered. While acknowledging the diversity of individual needs, the use of PGHD can positively impact the self-care management of numerous women’s health journeys. The COVID-19 pandemic has ushered in increased adoption and acceptance of digital health technology. This study could serve as a baseline comparison for how this field has evolved as a result. International Registered Report Identifier (IRRID): RR2-10.2196/26110 %M 38754098 %R 10.2196/53327 %U https://www.jmir.org/2024/1/e53327 %U https://doi.org/10.2196/53327 %U http://www.ncbi.nlm.nih.gov/pubmed/38754098 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e52843 %T National Public Health Dashboards: Protocol for a Scoping Review %A Yanovitzky,Itzhak %A Stahlman,Gretchen %A Quow,Justine %A Ackerman,Matthew %A Perry,Yehuda %A Kim,Miriam %+ School of Communication & Information, Rutgers University, 4 Huntington St, New Brunswick, NJ, 08901, United States, 1 848 932 8852, itzhak@rutgers.edu %K dashboard %K scoping review %K public health %K design %K development %K implementation %K evaluation %K user need %K protocol %K data dashboards %K audiences %K audience %K systematic treatment %K public health data dashboards %K PRISMA-ScR %K snowballing techniques %K gray literature sources %K evidence-informed framework %K framework %K COVID-19 %K pandemic %D 2024 %7 16.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The COVID-19 pandemic highlighted the importance of robust public health data systems and the potential utility of data dashboards for ensuring access to critical public health data for diverse groups of stakeholders and decision makers. As dashboards are becoming ubiquitous, it is imperative to consider how they may be best integrated with public health data systems and the decision-making routines of diverse audiences. However, additional progress on the continued development, improvement, and sustainability of these tools requires the integration and synthesis of a largely fragmented scholarship regarding the purpose, design principles and features, successful implementation, and decision-making supports provided by effective public health data dashboards across diverse users and applications. Objective: This scoping review aims to provide a descriptive and thematic overview of national public health data dashboards including their purpose, intended audiences, health topics, design elements, impact, and underlying mechanisms of use and usefulness of these tools in decision-making processes. It seeks to identify gaps in the current literature on the topic and provide the first-of-its-kind systematic treatment of actionability as a critical design element of public health data dashboards. Methods: The scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The review considers English-language, peer-reviewed journal papers, conference proceedings, book chapters, and reports that describe the design, implementation, and evaluation of a public health dashboard published between 2000 and 2023. The search strategy covers scholarly databases (CINAHL, PubMed, Medline, and Web of Science) and gray literature sources and uses snowballing techniques. An iterative process of testing for and improving intercoder reliability was implemented to ensure that coders are properly trained to screen documents according to the inclusion criteria prior to beginning the full review of relevant papers. Results: The search process initially identified 2544 documents, including papers located via databases, gray literature searching, and snowballing. Following the removal of duplicate documents (n=1416), nonrelevant items (n=839), and items classified as literature reviews and background information (n=73), 216 documents met the inclusion criteria: US case studies (n=90) and non-US case studies (n=126). Data extraction will focus on key variables, including public health data characteristics; dashboard design elements and functionalities; intended users, usability, logistics, and operation; and indicators of usefulness and impact reported. Conclusions: The scoping review will analyze the goals, design, use, usefulness, and impact of public health data dashboards. The review will also inform the continued development and improvement of these tools by analyzing and synthesizing current practices and lessons emerging from the literature on the topic and proposing a theory-grounded and evidence-informed framework for designing, implementing, and evaluating public health data dashboards. International Registered Report Identifier (IRRID): DERR1-10.2196/52843 %M 38753428 %R 10.2196/52843 %U https://www.researchprotocols.org/2024/1/e52843 %U https://doi.org/10.2196/52843 %U http://www.ncbi.nlm.nih.gov/pubmed/38753428 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e49189 %T The Google Health Digital Well-Being Study: Protocol for a Digital Device Use and Well-Being Study %A McDuff,Daniel %A Barakat,Andrew %A Winbush,Ari %A Jiang,Allen %A Cordeiro,Felicia %A Crowley,Ryann %A Kahn,Lauren E %A Hernandez,John %A Allen,Nicholas B %+ Google, 1600 Amphitheatre Parkway, Unit D, Mountain View, CA, 94043, United States, 1 6176060531, dmcduff@google.com %K digital %K health %K well-being %K mobile %K google health %K digital health %K well-being %K mhealth %K digital device %K smartphone %D 2024 %7 14.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The impact of digital device use on health and well-being is a pressing question. However, the scientific literature on this topic, to date, is marred by small and unrepresentative samples, poor measurement of core constructs, and a limited ability to address the psychological and behavioral mechanisms that may underlie the relationships between device use and well-being. Recent authoritative reviews have made urgent calls for future research projects to address these limitations. The critical role of research is to identify which patterns of use are associated with benefits versus risks and who is more vulnerable to harmful versus beneficial outcomes, so that we can pursue evidence-based product design, education, and regulation aimed at maximizing benefits and minimizing the risks of smartphones and other digital devices. Objective: The objective of this study is to provide normative data on objective patterns of smartphone use. We aim to (1) identify how patterns of smartphone use impact well-being and identify groups of individuals who show similar patterns of covariation between smartphone use and well-being measures across time; (2) examine sociodemographic and personality or mental health predictors and which patterns of smartphone use and well-being are associated with pre-post changes in mental health and functioning; (3) discover which nondevice behavior patterns mediate the association between device use and well-being; (4) identify and explore recruitment strategies to increase and improve the representation of traditionally underrepresented populations; and (5) provide a real-world baseline of observed stress, mood, insomnia, physical activity, and sleep across a representative population. Methods: This is a prospective, nonrandomized study to investigate the patterns and relationships among digital device use, sensor-based measures (including both behavioral and physiological signals), and self-reported measures of mental health and well-being. The study duration is 4 weeks per participant and includes passive sensing based on smartphone sensors, and optionally a wearable (Fitbit), for the complete study period. The smartphone device will provide activity, location, phone unlocks and app usage, and battery status information. Results: At the time of submission, the study infrastructure and app have been designed and built, the institutional review board of the University of Oregon has approved the study protocol, and data collection is underway. Data from 4182 enrolled and consented participants have been collected as of March 27, 2023. We have made many efforts to sample a study population that matches the general population, and the demographic breakdown we have been able to achieve, to date, is not a perfect match. Conclusions: The impact of digital devices on mental health and well-being raises important questions. The Digital Well-Being Study is designed to help answer questions about the association between patterns of smartphone use and well-being. International Registered Report Identifier (IRRID): DERR1-10.2196/49189 %M 38743938 %R 10.2196/49189 %U https://www.researchprotocols.org/2024/1/e49189 %U https://doi.org/10.2196/49189 %U http://www.ncbi.nlm.nih.gov/pubmed/38743938 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54343 %T Impact of Incentives on Physician Participation in Research Surveys: Randomized Experiment %A Hawa,Saadiya %A Bane,Shalmali %A Kinsler,Kayla %A Rector,Amadeia %A Chaichian,Yashaar %A Falasinnu,Titilola %A Simard,Julia F %+ Department of Epidemiology and Population Health, Stanford School of Medicine, Alway Building, 300 Pasteur Drive, M121L, MC: 5405, Stanford, CA, 94305, United States, 1 650 7239680, jsimard@stanford.edu %K internet survey %K incentive %K physician recruitment %K internet surveys %K online survey %K online surveys %K web-based survey %K web-based surveys %K survey %K surveys %K incentives %K monetary incentive %K monetary incentives %K physician participation %K physician participant %K physician participants %K physician %K physicians %K doctor participation %K doctor participant %K doctor participants %K doctor %K doctors %K neurologist %K neurologists %D 2024 %7 14.5.2024 %9 Short Paper %J JMIR Form Res %G English %X Background: Web-based surveys can be effective data collection instruments; however, participation is notoriously low, particularly among professionals such as physicians. Few studies have explored the impact of varying amounts of monetary incentives on survey completion. Objective: This study aims to conduct a randomized study to assess how different incentive amounts influenced survey participation among neurologists in the United States. Methods: We distributed a web-based survey using standardized email text to 21,753 individuals randomly divided into 5 equal groups (≈4351 per group). In phase 1, each group was assigned to receive either nothing or a gift card for US $10, $20, $50, or $75, which was noted in the email subject and text. After 4 reminders, phase 2 began and each remaining individual was offered a US $75 gift card to complete the survey. We calculated and compared the proportions who completed the survey by phase 1 arm, both before and after the incentive change, using a chi-square test. As a secondary outcome, we also looked at survey participation as opposed to completion. Results: For the 20,820 emails delivered, 879 (4.2%) recipients completed the survey; of the 879 recipients, 622 (70.8%) were neurologists. Among the neurologists, most were male (412/622, 66.2%), White (430/622, 69.1%), non-Hispanic (592/622, 95.2%), graduates of American medical schools (465/622, 74.8%), and board certified (598/622, 96.1%). A total of 39.7% (247/622) completed their neurology residency more than 20 years ago, and 62.4% (388/622) practiced in an urban setting. For phase 1, the proportions of respondents completing the survey increased as the incentive amount increased (46/4185, 1.1%; 76/4165, 1.8%; 86/4160, 2.1%; 104/4162, 2.5%; and 119/4148, 2.9%, for US $0, $10, $20, $50, and $75, respectively; P<.001). In phase 2, the survey completion rate for the former US $0 arm increased to 3% (116/3928). Those originally offered US $10, $20, $50, and $75 who had not yet participated were less likely to participate compared with the former US $0 arm (116/3928, 3%; 90/3936, 2.3%; 80/3902, 2.1%; 88/3845, 2.3%; and 74/3878, 1.9%, for US $0, $10, $20, $50, and $75, respectively; P=.03). For our secondary outcome of survey participation, a trend similar to that of survey completion was observed in phase 1 (55/4185, 1.3%; 85/4165, 2%; 96/4160, 2.3%; 118/4162, 2.8%; and 135/4148, 3.3%, for US $0, $10, $20, $50, and $75, respectively; P<.001) and phase 2 (116/3928, 3%; 90/3936, 2.3%; 80/3902, 2.1%; 88/3845, 2.3%; and 86/3845, 2.2%, for US $0, $10, $20, $50, and $75, respectively; P=.10). Conclusions: As expected, monetary incentives can boost physician survey participation and completion, with a positive correlation between the amount offered and participation. %M 38743466 %R 10.2196/54343 %U https://formative.jmir.org/2024/1/e54343 %U https://doi.org/10.2196/54343 %U http://www.ncbi.nlm.nih.gov/pubmed/38743466 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e50679 %T Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches %A Gandrup,Julie %A Selby,David A %A Dixon,William G %+ Centre for Epidemiology Versus Arthritis, University of Manchester, Oxford Rd, Stopford Building, Manchester, M13 9PT, United Kingdom, 44 1613066000, will.dixon@manchester.ac.uk %K rheumatoid arthritis %K flare %K patient-generated health data %K smartphone %K mobile health %K machine learning %K arthritis %K rheumatic %K rheumatism %K joint %K joints %K arthritic %K musculoskeletal %K flares %K classify %K classification %K symptom %K symptoms %K mobile phone %D 2024 %7 14.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening. Objective: This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app. Methods: Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used. Predictors were several summary features of the daily symptom scores (eg, pain and fatigue) collected in the week leading up to the flare question. We fitted 3 binary classifiers: logistic regression with and without elastic net regularization, a random forest, and naive Bayes. Performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. For the best-performing model, we considered sensitivity and specificity for different thresholds in order to illustrate different ways in which the predictive model could behave in a clinical setting. Results: The data comprised an average of 60.6 daily reports and 10.5 weekly reports per participant. Participants reported a median of 2 (IQR 0.75-4.25) flares each over a median follow-up time of 81 (IQR 79-82) days. AUCs were broadly similar between models, but logistic regression with elastic net regularization had the highest AUC of 0.82. At a cutoff requiring specificity to be 0.80, the corresponding sensitivity to detect flares was 0.60 for this model. The positive predictive value (PPV) in this population was 53%, and the negative predictive value (NPV) was 85%. Given the prevalence of flares, the best PPV achieved meant only around 2 of every 3 positive predictions were correct (PPV 0.65). By prioritizing a higher NPV, the model correctly predicted over 9 in every 10 non-flare weeks, but the accuracy of predicted flares fell to only 1 in 2 being correct (NPV and PPV of 0.92 and 0.51, respectively). Conclusions: Predicting self-reported flares based on daily symptom scorings in the preceding week using machine learning methods was feasible. The observed predictive accuracy might improve as we obtain more data, and these exploratory results need to be validated in an external cohort. In the future, analysis of frequently collected patient-generated data may allow us to predict flares before they unfold, opening opportunities for just-in-time adaptative interventions. Depending on the nature and implication of an intervention, different cutoff values for an intervention decision need to be considered, as well as the level of predictive certainty required. %M 38743480 %R 10.2196/50679 %U https://formative.jmir.org/2024/1/e50679 %U https://doi.org/10.2196/50679 %U http://www.ncbi.nlm.nih.gov/pubmed/38743480 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e53790 %T Best Practices and Recommendations for Research Using Virtual Real-Time Data Collection: Protocol for Virtual Data Collection Studies %A Sanchez,Jasmin %A Trofholz,Amanda %A Berge,Jerica M %+ Department of Family Medicine and Community Health, University of Minnesota, 717 Delaware St SE Suite 454, Minneapolis, MN, 55414, United States, 1 2245879545, sanch559@umn.edu %K real-time data collection %K remote research %K virtual data collection %K virtual research protocol %K virtual research visits %D 2024 %7 14.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The COVID-19 pandemic and the subsequent need for social distancing required the immediate pivoting of research modalities. Research that had previously been conducted in person had to pivot to remote data collection. Researchers had to develop data collection protocols that could be conducted remotely with limited or no evidence to guide the process. Therefore, the use of web-based platforms to conduct real-time research visits surged despite the lack of evidence backing these novel approaches. Objective: This paper aims to review the remote or virtual research protocols that have been used in the past 10 years, gather existing best practices, and propose recommendations for continuing to use virtual real-time methods when appropriate. Methods: Articles (n=22) published from 2013 to June 2023 were reviewed and analyzed to understand how researchers conducted virtual research that implemented real-time protocols. “Real-time” was defined as data collection with a participant through a live medium where a participant and research staff could talk to each other back and forth in the moment. We excluded studies for the following reasons: (1) studies that collected participant or patient measures for the sole purpose of engaging in a clinical encounter; (2) studies that solely conducted qualitative interview data collection; (3) studies that conducted virtual data collection such as surveys or self-report measures that had no interaction with research staff; (4) studies that described research interventions but did not involve the collection of data through a web-based platform; (5) studies that were reviews or not original research; (6) studies that described research protocols and did not include actual data collection; and (7) studies that did not collect data in real time, focused on telehealth or telemedicine, and were exclusively intended for medical and not research purposes. Results: Findings from studies conducted both before and during the COVID-19 pandemic suggest that many types of data can be collected virtually in real time. Results and best practice recommendations from the current protocol review will be used in the design and implementation of a substudy to provide more evidence for virtual real-time data collection over the next year. Conclusions: Our findings suggest that virtual real-time visits are doable across a range of participant populations and can answer a range of research questions. Recommended best practices for virtual real-time data collection include (1) providing adequate equipment for real-time data collection, (2) creating protocols and materials for research staff to facilitate or guide participants through data collection, (3) piloting data collection, (4) iteratively accepting feedback, and (5) providing instructions in multiple forms. The implementation of these best practices and recommendations for future research are further discussed in the paper. International Registered Report Identifier (IRRID): DERR1-10.2196/53790 %M 38743477 %R 10.2196/53790 %U https://www.researchprotocols.org/2024/1/e53790 %U https://doi.org/10.2196/53790 %U http://www.ncbi.nlm.nih.gov/pubmed/38743477 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e53623 %T The Real-World Usability, Feasibility, and Performance Distributions of Deploying a Digital Toolbox of Computerized Assessments to Remotely Evaluate Brain Health: Development and Usability Study %A Attarha,Mouna %A Mahncke,Henry %A Merzenich,Michael %+ Posit Science, 160 Pine St Suite 200, San Francisco, CA, 94111, United States, 1 415 394 3100, Mouna.attarha@positscience.com %K web-based cognitive assessment %K remote data collection %K neurocognition %K cognitive profiles %K normative assessment data %K brain health %K cognitive status %K assessment accessibility %D 2024 %7 13.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: An ongoing global challenge is managing brain health and understanding how performance changes across the lifespan. Objective: We developed and deployed a set of self-administrable, computerized assessments designed to measure key indexes of brain health across the visual and auditory sensory modalities. In this pilot study, we evaluated the usability, feasibility, and performance distributions of the assessments in a home-based, real-world setting without supervision. Methods: Potential participants were untrained users who self-registered on an existing brain training app called BrainHQ. Participants were contacted via a recruitment email and registered remotely to complete a demographics questionnaire and 29 unique assessments on their personal devices. We examined participant engagement, descriptive and psychometric properties of the assessments, associations between performance and self-reported demographic variables, cognitive profiles, and factor loadings. Results: Of the 365,782 potential participants contacted via a recruitment email, 414 (0.11%) registered, of whom 367 (88.6%) completed at least one assessment and 104 (25.1%) completed all 29 assessments. Registered participants were, on average, aged 63.6 (SD 14.8; range 13-107) years, mostly female (265/414, 64%), educated (329/414, 79.5% with a degree), and White (349/414, 84.3% White and 48/414, 11.6% people of color). A total of 72% (21/29) of the assessments showed no ceiling or floor effects or had easily modifiable score bounds to eliminate these effects. When correlating performance with self-reported demographic variables, 72% (21/29) of the assessments were sensitive to age, 72% (21/29) of the assessments were insensitive to gender, 93% (27/29) of the assessments were insensitive to race and ethnicity, and 93% (27/29) of the assessments were insensitive to education-based differences. Assessments were brief, with a mean duration of 3 (SD 1.0) minutes per task. The pattern of performance across the assessments revealed distinctive cognitive profiles and loaded onto 4 independent factors. Conclusions: The assessments were both usable and feasible and warrant a full normative study. A digital toolbox of scalable and self-administrable assessments that can evaluate brain health at a glance (and longitudinally) may lead to novel future applications across clinical trials, diagnostics, and performance optimization. %M 38739916 %R 10.2196/53623 %U https://formative.jmir.org/2024/1/e53623 %U https://doi.org/10.2196/53623 %U http://www.ncbi.nlm.nih.gov/pubmed/38739916 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e44948 %T Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study %A Wolff,Christian %A Steinheimer,Patrick %A Warmerdam,Elke %A Dahmen,Tim %A Slusallek,Philipp %A Schlinkmann,Christian %A Chen,Fei %A Orth,Marcel %A Pohlemann,Tim %A Ganse,Bergita %+ Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Kirrberger Straße 1, Building 57, Homburg/Saar, 66421, Germany, 49 684116 ext 31570, bergita.ganse@uks.eu %K podiatry %K podiatric medicine %K movement analysis %K ground reaction forces %K wearables %K slope %K gait analysis %K monitoring %K gait %K rehabilitation %K treatment %K sensor %K injury %K postoperative treatment %K sensors %K personalized medicine %K movement %K digital health %K pedography %K baropedography %D 2024 %7 8.5.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. Objective: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. Methods: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: −20%, −15%, −10%, −5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. Results: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. Conclusions: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory. %M 38718385 %R 10.2196/44948 %U https://www.jmir.org/2024/1/e44948 %U https://doi.org/10.2196/44948 %U http://www.ncbi.nlm.nih.gov/pubmed/38718385 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e55211 %T Association of Rest-Activity Rhythm and Risk of Developing Dementia or Mild Cognitive Impairment in the Middle-Aged and Older Population: Prospective Cohort Study %A Haghayegh,Shahab %A Gao,Chenlu %A Sugg,Elizabeth %A Zheng,Xi %A Yang,Hui-Wen %A Saxena,Richa %A Rutter,Martin K %A Weedon,Michael %A Ibanez,Agustin %A Bennett,David A %A Li,Peng %A Gao,Lei %A Hu,Kun %+ Massachusetts General Hospital, 149 13th Street, 4th Floor, Boston, MA, 02129, United States, 1 5129543436, shaghayegh@mgh.harvard.edu %K circadian rhythm %K dementia %K actigraphy %K cognitive decline %K RAR %K rest-activity rhythms %K cognitive impairment %D 2024 %7 7.5.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults. Objective: We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults. Methods: We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease. Results: During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non–shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase. Conclusions: Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults. %M 38713911 %R 10.2196/55211 %U https://publichealth.jmir.org/2024/1/e55211 %U https://doi.org/10.2196/55211 %U http://www.ncbi.nlm.nih.gov/pubmed/38713911 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 7 %N %P e53461 %T Accuracy of a Web-Based Time-Use Diary (MEDAL) in Assessing Children’s Meal Intakes With Food Photography by Parents as Reference: Instrument Validation Study %A Chong,Kar Mun %A Chia,Airu %A Shah Budin,Nur Syahirah %A Poh,Bee Koon %A Jamil,Nor Aini %A Koh,Denise %A Chong,Mary Foong-Fong %A Wong,Jyh Eiin %+ Center for Community Health Studies (ReaCH), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz,, Kuala Lumpur, 50300, Malaysia, 60 3 9289 7683, wjeiin@ukm.edu.my %K children %K dietary intake %K time-use diary %K food photography %K accuracy %K mobile phone %D 2024 %7 7.5.2024 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: My E-Diary for Activities and Lifestyle (MEDAL) is a web-based time-use diary developed to assess the diet and movement behaviors of Asian school children. Objective: This study aims to determine the accuracy of MEDAL in assessing the dietary intake of Malaysian school children, using photographs of the children’s meals taken by their parents as an objective reference. Methods: A convenience sample of 46 children aged 10 to 11 years recorded their daily meals in MEDAL for 4 days (2 weekdays and 2 weekend days). Their parents took photographs of the meals and snacks of their children before and after consumption during the 4-day period and sent them along with a brief description of food and drinks consumed via an instant SMS text messaging app. The accuracy of the children’s reports of the food they had consumed was determined by comparing their MEDAL reports to the photographs of the food sent by their parents. Results: Overall, the match, omission, and intrusion rates were 62% (IQR 46%-86%), 39% (IQR 16%-55%), and 20% (IQR 6%-44%), respectively. Carbohydrate-based items from the food categories “rice and porridge”; “breads, spreads, and cereals”; and “noodles, pasta, and potatoes” were reported most accurately (total match rates: 68%-76%). “Snack and dessert” items were omitted most often (omission rate: 54%). Furthermore, side dishes from “vegetables and mushrooms,” “eggs and tofu,” “meat and fish,” and “curry” food groups were often omitted (omission rates: 42%-46%). Items from “milk, cheese, and yogurt”; “snacks and desserts”; and “drinks” food groups intruded most often (intrusion rates: 37%-46%). Compared to the items reported by the boys, those reported by the girls had higher match rates (69% vs 53%) and lesser omission rates (31% vs 49%; P=.03, respectively). Conclusions: In conclusion, children aged 10 to 11 years can self-report all their meals in MEDAL, although some items are omitted or intruded. Therefore, MEDAL is a tool that can be used to assess the dietary intake of Malaysian school children. %M 38713499 %R 10.2196/53461 %U https://pediatrics.jmir.org/2024/1/e53461 %U https://doi.org/10.2196/53461 %U http://www.ncbi.nlm.nih.gov/pubmed/38713499 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55452 %T Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study %A de Graaf,Debbie %A de Vries,Nienke M %A van de Zande,Tessa %A Schimmel,Janneke J P %A Shin,Sooyoon %A Kowahl,Nathan %A Barman,Poulami %A Kapur,Ritu %A Marks Jr,William J %A van 't Hul,Alex %A Bloem,Bastiaan R %+ Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Reinier Postlaan 4, Nijmegen, 6525 GC, Netherlands, 31 243613450, debbie.degraaf@radboudumc.nl %K Parkinson disease %K COPD %K chronic obstructive pulmonary disease %K physical activity %K physical capacity %K wearable devices %K walking %K exercise %K locomotion %K home-based %K wearable %K wearables %K wearable sensor %K dementia %K smartwatch %K StepWatch %K treatment %D 2024 %7 7.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life. Objective: We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test. Methods: This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT. Results: In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024. Conclusions: The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person’s physical capacity in real life, which makes it possible to personalize treatment options. Trial Registration: ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075 International Registered Report Identifier (IRRID): DERR1-10.2196/55452 %M 38713508 %R 10.2196/55452 %U https://www.researchprotocols.org/2024/1/e55452 %U https://doi.org/10.2196/55452 %U http://www.ncbi.nlm.nih.gov/pubmed/38713508 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55917 %T Impact of Electronic Patient-Reported Outcomes on Unplanned Consultations and Hospitalizations in Patients With Cancer Undergoing Systemic Therapy: Results of a Patient-Reported Outcome Study Compared With Matched Retrospective Data %A Trojan,Andreas %A Kühne,Christian %A Kiessling,Michael %A Schumacher,Johannes %A Dröse,Stefan %A Singer,Christian %A Jackisch,Christian %A Thomssen,Christoph %A Kullak-Ublick,Gerd A %+ Oncology, Breast Center Zürichsee, Seestrasse 88, Horgen, 8810, Switzerland, 41 76 34 30 200, trojan@1st.ch %K systemic cancer therapy %K electronic patient-reported outcome %K ePRO %K ePROs %K Consilium Care %K medidux %K unplanned consultation %K hospitalization %K hospitalizations %K hospitalized %K cancer %K oncology %K side effect %K side effects %K adverse %K chemotherapy %K patient reported outcome %K PRO %K PROs %K mobile health %K mHealth %K app %K apps %K application %K applications %K mobile phone %D 2024 %7 6.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The evaluation of electronic patient-reported outcomes (ePROs) is increasingly being used in clinical studies of patients with cancer and enables structured and standardized data collection in patients’ everyday lives. So far, few studies or analyses have focused on the medical benefit of ePROs for patients. Objective: The current exploratory analysis aimed to obtain an initial indication of whether the use of the Consilium Care app (recently renamed medidux; mobile Health AG) for structured and regular self-assessment of side effects by ePROs had a recognizable effect on incidences of unplanned consultations and hospitalizations of patients with cancer compared to a control group in a real-world care setting without app use. To analyze this, the incidences of unplanned consultations and hospitalizations of patients with cancer using the Consilium Care app that were recorded by the treating physicians as part of the patient reported outcome (PRO) study were compared retrospectively to corresponding data from a comparable population of patients with cancer collected at 2 Swiss oncology centers during standard-of-care treatment. Methods: Patients with cancer in the PRO study (178 included in this analysis) receiving systemic therapy in a neoadjuvant or noncurative setting performed a self-assessment of side effects via the Consilium Care app over an observational period of 90 days. In this period, unplanned (emergency) consultations and hospitalizations were documented by the participating physicians. The incidence of these events was compared with retrospective data obtained from 2 Swiss tumor centers for a matched cohort of patients with cancer. Results: Both patient groups were comparable in terms of age and gender ratio, as well as the distribution of cancer entities and Joint Committee on Cancer stages. In total, 139 patients from each group were treated with chemotherapy and 39 with other therapies. Looking at all patients, no significant difference in events per patient was found between the Consilium group and the control group (odds ratio 0.742, 90% CI 0.455-1.206). However, a multivariate regression model revealed that the interaction term between the Consilium group and the factor “chemotherapy” was significant at the 5% level (P=.048). This motivated a corresponding subgroup analysis that indicated a relevant reduction of the risk for the intervention group in the subgroup of patients who underwent chemotherapy. The corresponding odds ratio of 0.53, 90% CI 0.288-0.957 is equivalent to a halving of the risk for patients in the Consilium group and suggests a clinically relevant effect that is significant at a 2-sided 10% level (P=.08, Fisher exact test). Conclusions: A comparison of unplanned consultations and hospitalizations from the PRO study with retrospective data from a comparable cohort of patients with cancer suggests a positive effect of regular app-based ePROs for patients receiving chemotherapy. These data are to be verified in the ongoing randomized PRO2 study (registered on ClinicalTrials.gov; NCT05425550). Trial Registration: ClinicalTrials.gov NCT03578731; https://www.clinicaltrials.gov/ct2/show/NCT03578731 International Registered Report Identifier (IRRID): RR2-10.2196/29271 %M 38710048 %R 10.2196/55917 %U https://formative.jmir.org/2024/1/e55917 %U https://doi.org/10.2196/55917 %U http://www.ncbi.nlm.nih.gov/pubmed/38710048 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e57978 %T The Evaluation of Generative AI Should Include Repetition to Assess Stability %A Zhu,Lingxuan %A Mou,Weiming %A Hong,Chenglin %A Yang,Tao %A Lai,Yancheng %A Qi,Chang %A Lin,Anqi %A Zhang,Jian %A Luo,Peng %+ Department of Oncology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, Guangzhou, China, 86 020 61643888, luopeng@smu.edu.cn %K large language model %K generative AI %K ChatGPT %K artificial intelligence %K health care %D 2024 %7 6.5.2024 %9 Commentary %J JMIR Mhealth Uhealth %G English %X The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field. %M 38688841 %R 10.2196/57978 %U https://mhealth.jmir.org/2024/1/e57978 %U https://doi.org/10.2196/57978 %U http://www.ncbi.nlm.nih.gov/pubmed/38688841 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e51526 %T Assessing the Efficacy of ChatGPT Versus Human Researchers in Identifying Relevant Studies on mHealth Interventions for Improving Medication Adherence in Patients With Ischemic Stroke When Conducting Systematic Reviews: Comparative Analysis %A Ruksakulpiwat,Suebsarn %A Phianhasin,Lalipat %A Benjasirisan,Chitchanok %A Ding,Kedong %A Ajibade,Anuoluwapo %A Kumar,Ayanesh %A Stewart,Cassie %+ Department of Medical Nursing, Faculty of Nursing, Mahidol University, 2 Wang Lang Road, Siriraj, Bangkok Noi, Bangkok, 10700, Thailand, 66 984782692, suebsarn25@gmail.com %K ChatGPT %K systematic reviews %K medication adherence %K mobile health %K mHealth %K ischemic stroke %K mobile phone %D 2024 %7 6.5.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted. Objective: This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews. Methods: This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method. Results: A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies. Conclusions: Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field. %M 38710069 %R 10.2196/51526 %U https://mhealth.jmir.org/2024/1/e51526 %U https://doi.org/10.2196/51526 %U http://www.ncbi.nlm.nih.gov/pubmed/38710069 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e46282 %T Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective %A Wei,Lai %A Wang,Stephen Jia %+ School of Design, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, China (Hong Kong), 852 2766 5478, stephen.j.wang@polyu.edu.hk %K motion tracking %K daily living %K physical activity %K health care application %K design %K public health %K systematic review %K mobile phone %D 2024 %7 6.5.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Motion tracking technologies serve as crucial links between physical activities and health care insights, facilitating data acquisition essential for analyzing and intervening in physical activity. Yet, systematic methodologies for evaluating motion tracking data, especially concerning user activity recognition in health care applications, remain underreported. Objective: This study aims to systematically review motion tracking in daily living and physical activities, emphasizing the critical interaction among devices, users, and environments from a design perspective, and to analyze the process involved in health care application research. It intends to delineate the design and application intricacies in health care contexts, focusing on enhancing motion tracking data’s accuracy and applicability for health monitoring and intervention strategies. Methods: Using a systematic review, this research scrutinized motion tracking data and their application in health care and wellness, examining studies from Scopus, Web of Science, EBSCO, and PubMed databases. The review used actor network theory and data-enabled design to understand the complex interplay between humans, devices, and environments within these applications. Results: Out of 1501 initially identified studies, 54 (3.66%) were included for in-depth analysis. These articles predominantly used accelerometer and gyroscope sensors (n=43, 80%) to monitor and analyze motion, demonstrating a strong preference for these technologies in capturing both dynamic and static activities. While incorporating portable devices (n=11, 20%) and multisensor configurations (n=16, 30%), the application of sensors across the body (n=15, 28%) and within physical spaces (n=17, 31%) highlights the diverse applications of motion tracking technologies in health care research. This diversity reflects the application’s alignment with activity types ranging from daily movements to specialized scenarios. The results also reveal a diverse participant pool, including the general public, athletes, and specialized groups, with a focus on healthy individuals (n=31, 57%) and athletes (n=14, 26%). Despite this extensive application range, the focus primarily on laboratory-based studies (n=39, 72%) aimed at professional uses, such as precise activity identification and joint functionality assessment, emphasizes a significant challenge in translating findings from controlled environments to the dynamic conditions of everyday physical activities. Conclusions: This study’s comprehensive investigation of motion tracking technology in health care research reveals a significant gap between the methods used for data collection and their practical application in real-world scenarios. It proposes an innovative approach that includes designers in the research process, emphasizing the importance of incorporating data-enabled design framework. This ensures that motion data collection is aligned with the dynamic and varied nature of daily living and physical activities. Such integration is crucial for developing health applications that are accessible, intuitive, and tailored to meet diverse user needs. By leveraging a multidisciplinary approach that combines design, engineering, and health sciences, the research opens new pathways for enhancing the usability and effectiveness of health technologies. %M 38709547 %R 10.2196/46282 %U https://mhealth.jmir.org/2024/1/e46282 %U https://doi.org/10.2196/46282 %U http://www.ncbi.nlm.nih.gov/pubmed/38709547 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e53550 %T Integrating Virtual Mindfulness-Based Stress Reduction Into Inflammatory Bowel Disease Care: Mixed Methods Feasibility Trial %A Chappell,Kaitlyn Delaney %A Meakins,Diana %A Marsh-Joyal,Melanie %A Bihari,Allison %A Goodman,Karen J %A Le Melledo,Jean-Michel %A Lim,Allen %A Peerani,Farhad %A Kroeker,Karen Ivy %+ Division of Gastroenterology, Department of Medicine, University of Alberta, 130 University Campus NW, Edmonton, AB, T6G 2X8, Canada, 1 780 492 4873, karen.kroeker@ualberta.ca %K inflammatory bowel disease %K psychosocial care %K multidisciplinary care %K quality of care %K quality of life %K mental health %K adult %K adults %K anxiety %K depression %K IBD %K virtual mindfulness %K feasibility trial %K clinic %K health facility %K Canada %K semistructured interview %K psychiatrist %K psychiatrists %K videoconferencing %K effectiveness %K v-MBSR %K coping %K coping strategy %D 2024 %7 6.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Individuals with inflammatory bowel disease (IBD) experience cycles of aggressive physical symptoms including abdominal pain, diarrhea, and fatigue. These acute symptoms regress and return, and chronic symptoms and complications often linger. The nature of the disease can also cause individuals to experience psychological distress including symptoms of anxiety and depression; however, unlike the physical symptoms of IBD, these psychological symptoms often remain untreated. Objective: This study aims to evaluate the feasibility, acceptability, and effectiveness of virtual mindfulness-based stress reduction (v-MBSR) for adults with IBD. Methods: IBD patients with self-reported anxiety or depression were recruited from clinics in Alberta, Canada to participate in an 8-week v-MSBR intervention. Eligible patients participated in v-MBSR delivered by psychiatrists using a videoconferencing platform. Primary feasibility outcomes included trial uptake, adherence, attendance, and attrition rates. Secondary effectiveness outcomes included measures of anxiety, depression, quality of life (QoL), and mindfulness. Effectiveness data were collected at 3 time points: baseline, at intervention completion, and 6 months after completion. To further assess feasibility and acceptability, participants were invited to participate in a semistructured interview after completing v-MBSR. Results: A total of 16 of the 64 (25%) referred patients agreed to participate in v-MBSR with the most common reason for decline being a lack of time while 7 of the 16 (43.8%) participants completed the program and experienced encouraging effects including decreased anxiety and depression symptoms and increased health-related QoL with both improvements persisting at 6-month follow-up. Participants described improved coping strategies and disease management techniques as benefits of v-MBSR. Conclusions: Patients with IBD were interested in a psychiatrist-led virtual anxiety management intervention, but results demonstrate v-MBSR may be too time intensive for some patients with IBD patients. v-MBSR was acceptable to those who completed the intervention, and improvements to anxiety, depression, and QoL were promising and sustainable. Future studies should attempt to characterize the patients with IBD who may benefit most from interventions like v-MBSR. %M 38709548 %R 10.2196/53550 %U https://formative.jmir.org/2024/1/e53550 %U https://doi.org/10.2196/53550 %U http://www.ncbi.nlm.nih.gov/pubmed/38709548 %0 Journal Article %@ 2291-5222 %I %V 12 %N %P e53596 %T User Experience of Persons Using Ingestible Sensor–Enabled Pre-Exposure Prophylaxis to Prevent HIV Infection: Cross-Sectional Survey Study %A Browne,Sara %A Umlauf,Anya %A Moore,David J %A Benson,Constance A %A Vaida,Florin %K ingestible sensor %K sensor %K sensors %K oral %K UX %K user experience %K HIV prevention %K medication adherence %K HIV %K prevention %K prophylaxis %K STI %K STD %K sexually transmitted %K sexual transmission %K drug %K drugs %K pharmacy %K pharmacies %K pharmacology %K pharmacotherapy %K pharmaceutic %K pharmaceutics %K pharmaceuticals %K pharmaceutical %K medication %K medications %K adherence %K compliance %K sexually transmitted infection %K sexually transmitted disease %D 2024 %7 3.5.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: A digital health technology’s success or failure depends on how it is received by users. Objectives: We conducted a user experience (UX) evaluation among persons who used the Food and Drug Administration–approved Digital Health Feedback System incorporating ingestible sensors (ISs) to capture medication adherence, after they were prescribed oral pre-exposure prophylaxis (PrEP) to prevent HIV infection. We performed an association analysis with baseline participant characteristics, to see if “personas” associated with positive or negative UX emerged. Methods: UX data were collected upon exit from a prospective intervention study of adults who were HIV negative, prescribed oral PrEP, and used the Digital Health Feedback System with IS-enabled tenofovir disoproxil fumarate plus emtricitabine (IS-Truvada). Baseline demographics; urine toxicology; and self-report questionnaires evaluating sleep (Pittsburgh Sleep Quality Index), self-efficacy, habitual self-control, HIV risk perception (Perceived Risk of HIV Scale 8-item), and depressive symptoms (Patient Health Questionnaire–8) were collected. Participants with ≥28 days in the study completed a Likert-scale UX questionnaire of 27 questions grouped into 4 domain categories: overall experience, ease of use, intention of future use, and perceived utility. Means and IQRs were computed for participant total and domain subscores, and linear regressions modeled baseline participant characteristics associated with UX responses. Demographic characteristics of responders versus nonresponders were compared using the Fisher exact and Wilcoxon rank-sum tests. Results: Overall, 71 participants were enrolled (age: mean 37.6, range 18-69 years; n=64, 90% male; n=55, 77% White; n=24, 34% Hispanic; n=68, 96% housed; and n=53, 75% employed). No demographic differences were observed in the 63 participants who used the intervention for ≥28 days. Participants who completed the questionnaire were more likely to be housed (52/53, 98% vs 8/10, 80%; P=.06) and less likely to have a positive urine toxicology (18/51, 35% vs 7/10, 70%; P=.08), particularly methamphetamine (4/51, 8% vs 4/10, 40%; P=.02), than noncompleters. Based on IQR values, ≥75% of participants had a favorable UX based on the total score (median 3.78, IQR 3.17-4.20), overall experience (median 4.00, IQR 3.50-4.50), ease of use (median 3.72, IQR 3.33-4.22), and perceived utility (median 3.72, IQR 3.22-4.25), and ≥50% had favorable intention of future use (median 3.80, IQR 2.80-4.40). Following multipredictor modeling, self-efficacy was significantly associated with the total score (0.822, 95% CI 0.405-1.240; P<.001) and all subscores (all P<.05). Persons with more depressive symptoms reported better perceived utility (P=.01). Poor sleep was associated with a worse overall experience (−0.07, 95% CI −0.133 to −0.006; P=.03). Conclusions: The UX among persons using IS-enabled PrEP (IS-Truvada) to prevent HIV infection was positive. Association analysis of baseline participant characteristics linked higher self-efficacy with positive UX, more depressive symptoms with higher perceived utility, and poor sleep with negative UX. Trial Registration: ClinicalTrials.gov NCT03693040; https://clinicaltrials.gov/study/NCT03693040 %R 10.2196/53596 %U https://mhealth.jmir.org/2024/1/e53596 %U https://doi.org/10.2196/53596 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e49396 %T Assessment of Stress and Well-Being of Japanese Employees Using Wearable Devices for Sleep Monitoring Combined With Ecological Momentary Assessment: Pilot Observational Study %A Kinoshita,Shotaro %A Hanashiro,Sayaka %A Tsutsumi,Shiori %A Shiga,Kiko %A Kitazawa,Momoko %A Wada,Yasuyo %A Inaishi,Jun %A Kashiwagi,Kazuhiro %A Fukami,Toshikazu %A Mashimo,Yasumasa %A Minato,Kazumichi %A Kishimoto,Taishiro %+ Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, #7F Azabudai Hills Mori JP Tower, 1-3-1 Azabudai, Minato-Ku, Tokyo, 106-0041, Japan, 81 3 5363 3829, tkishimoto@keio.jp %K wearable device %K sleep feedback %K well-being %K stress %K ecological momentary assessment %K feasibility study %D 2024 %7 2.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Poor sleep quality can elevate stress levels and diminish overall well-being. Japanese individuals often experience sleep deprivation, and workers have high levels of stress. Nevertheless, research examining the connection between objective sleep assessments and stress levels, as well as overall well-being, among Japanese workers is lacking. Objective: This study aims to investigate the correlation between physiological data, including sleep duration and heart rate variability (HRV), objectively measured through wearable devices, and 3 states (sleepiness, mood, and energy) assessed through ecological momentary assessment (EMA) and use of rating scales for stress and well-being. Methods: A total of 40 office workers (female, 20/40, 50%; mean age 40.4 years, SD 11.8 years) participated in the study. Participants were asked to wear a wearable wristband device for 8 consecutive weeks. EMA regarding sleepiness, mood, and energy levels was conducted via email messages sent by participants 4 times daily, with each session spaced 3 hours apart. This assessment occurred on 8 designated days within the 8-week timeframe. Participants’ stress levels and perception of well-being were assessed using respective self-rating questionnaires. Subsequently, participants were categorized into quartiles based on their stress and well-being scores, and the sleep patterns and HRV indices recorded by the Fitbit Inspire 2 were compared among these groups. The Mann-Whitney U test was used to assess differences between the quartiles, with adjustments made for multiple comparisons using the Bonferroni correction. Furthermore, EMA results and the sleep and HRV indices were subjected to multilevel analysis for a comprehensive evaluation. Results: The EMA achieved a total response rate of 87.3%, while the Fitbit Inspire 2 wear rate reached 88.0%. When participants were grouped based on quartiles of well-being and stress-related scores, significant differences emerged. Specifically, individuals in the lowest stress quartile or highest subjective satisfaction quartile retired to bed earlier (P<.001 and P=.01, respectively), whereas those in the highest stress quartile exhibited greater variation in the midpoint of sleep (P<.001). A multilevel analysis unveiled notable relationships: intraindividual variability analysis indicated that higher energy levels were associated with lower deviation of heart rate during sleep on the preceding day (β=–.12, P<.001), and decreased sleepiness was observed on days following longer sleep durations (β=–.10, P<.001). Furthermore, interindividual variability analysis revealed that individuals with earlier midpoints of sleep tended to exhibit higher energy levels (β=–.26, P=.04). Conclusions: Increased sleep variabilities, characterized by unstable bedtime or midpoint of sleep, were correlated with elevated stress levels and diminished well-being. Conversely, improved sleep indices (eg, lower heart rate during sleep and earlier average bedtime) were associated with heightened daytime energy levels. Further research with a larger sample size using these methodologies, particularly focusing on specific phenomena such as social jet lag, has the potential to yield valuable insights. Trial Registration: UMIN-CTR UMIN000046858; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053392 %M 38696237 %R 10.2196/49396 %U https://formative.jmir.org/2024/1/e49396 %U https://doi.org/10.2196/49396 %U http://www.ncbi.nlm.nih.gov/pubmed/38696237 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e54622 %T Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study %A Hurwitz,Eric %A Butzin-Dozier,Zachary %A Master,Hiral %A O'Neil,Shawn T %A Walden,Anita %A Holko,Michelle %A Patel,Rena C %A Haendel,Melissa A %+ Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, United States, 1 9198436475, eric_hurwitz@med.unc.edu %K wearable device %K All of Us %K postpartum depression %K machine learning %K Fitbit %K mobile phone %D 2024 %7 2.5.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. Objective: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. Methods: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. Results: Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method’s specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. Conclusions: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies. %M 38696234 %R 10.2196/54622 %U https://mhealth.jmir.org/2024/1/e54622 %U https://doi.org/10.2196/54622 %U http://www.ncbi.nlm.nih.gov/pubmed/38696234 %0 Journal Article %@ 2291-5222 %I %V 12 %N %P e50620 %T Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study) %A Steen-Olsen,Emma Balch %A Pappot,Helle %A Hjerming,Maiken %A Hanghoej,Signe %A Holländer-Mieritz,Cecilie %K smart T-shirt %K AYA %K oncology %K home monitoring %K patients' perspective %K perspective %K perspectives %K experiences %K experience %K youth %K adolescent %K adolescents %K smart %K monitoring %K biometric %K sensor %K sensors %K young adult %K young adults %K feasibility %K cancer %K cancers %K electrode %K electrodes %K adherence %K mobile phone %D 2024 %7 1.5.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking. Objectives: The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period. Methods: This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire. Results: A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device. Conclusions: Although the primary end point was not reached, the patients’ experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use. Trial Registration: ClinicalTrials.gov NCT05235594; https://clinicaltrials.gov/study/NCT05235594 International Registered Report Identifier (IRRID): RR2-10.2196/37626 %R 10.2196/50620 %U https://mhealth.jmir.org/2024/1/e50620 %U https://doi.org/10.2196/50620 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e53311 %T Using Routine Data to Improve Lesbian, Gay, Bisexual, and Transgender Health %A Saunders,Catherine L %+ Department of Psychiatry, University of Cambridge, Herchel Smith Building, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SZ, United Kingdom, 44 1223337106, cs834@medschl.cam.ac.uk %K lesbian %K gay %K bisexual %K trans %K LGBTQ+ %K routine data %K England %K United Kingdom %K health %K viewpoint %K sexual orientation %K health services %K infrastructure data %K policy %K gender %K health outcome %K epidemiology %K risk prediction %K risk %D 2024 %7 1.5.2024 %9 Viewpoint %J Interact J Med Res %G English %X The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced—descriptive epidemiology, risk prediction, intervention development, and impact evaluation—and are discussed as frameworks for translating data into research with the potential to improve health. %M 38691398 %R 10.2196/53311 %U https://www.i-jmr.org/2024/1/e53311 %U https://doi.org/10.2196/53311 %U http://www.ncbi.nlm.nih.gov/pubmed/38691398 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e50035 %T Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study %A Kluge,Felix %A Brand,Yonatan E %A Micó-Amigo,M Encarna %A Bertuletti,Stefano %A D'Ascanio,Ilaria %A Gazit,Eran %A Bonci,Tecla %A Kirk,Cameron %A Küderle,Arne %A Palmerini,Luca %A Paraschiv-Ionescu,Anisoara %A Salis,Francesca %A Soltani,Abolfazl %A Ullrich,Martin %A Alcock,Lisa %A Aminian,Kamiar %A Becker,Clemens %A Brown,Philip %A Buekers,Joren %A Carsin,Anne-Elie %A Caruso,Marco %A Caulfield,Brian %A Cereatti,Andrea %A Chiari,Lorenzo %A Echevarria,Carlos %A Eskofier,Bjoern %A Evers,Jordi %A Garcia-Aymerich,Judith %A Hache,Tilo %A Hansen,Clint %A Hausdorff,Jeffrey M %A Hiden,Hugo %A Hume,Emily %A Keogh,Alison %A Koch,Sarah %A Maetzler,Walter %A Megaritis,Dimitrios %A Niessen,Martijn %A Perlman,Or %A Schwickert,Lars %A Scott,Kirsty %A Sharrack,Basil %A Singleton,David %A Vereijken,Beatrix %A Vogiatzis,Ioannis %A Yarnall,Alison %A Rochester,Lynn %A Mazzà,Claudia %A Del Din,Silvia %A Mueller,Arne %+ Novartis Biomedical Research, Novartis Pharma AG, Fabrikstrasse 2, Basel, 4056, Switzerland, 41 795544701, felix.kluge@novartis.com %K digital mobility outcomes %K validation %K wearable sensor %K walking %K digital health %K inertial measurement unit %K accelerometer %K Mobilise-D %D 2024 %7 1.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. Objective: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. Methods: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. Results: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. Conclusions: Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial Registration: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-050785 %M 38691395 %R 10.2196/50035 %U https://formative.jmir.org/2024/1/e50035 %U https://doi.org/10.2196/50035 %U http://www.ncbi.nlm.nih.gov/pubmed/38691395 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e53442 %T Digital Food Frequency Questionnaire Assessing Adherence to the Norwegian Food–Based Dietary Guidelines and Other National Lifestyle Recommendations: Instrument Validation Study %A Henriksen,Hege Berg %A Knudsen,Markus Dines %A Hjartåker,Anette %A Blomhoff,Rune %A Carlsen,Monica Hauger %+ Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Sognsvannsveien 9, PO Box 1046, Blindern, Oslo, 0372, Norway, 47 99459673, h.b.henriksen@medisin.uio.no %K digital food frequency questionnaire %K lifestyle assessment tool %K relative validity %K physical activity %K Norwegian food–based dietary guidelines %K Norway %K Norwegian %K food %K foods %K diet %K dietary %K lifestyle %K assessment %K digital questionnaire %K investigation %K food intake %K dietary intake %K dietary intakes %K observation %K monitoring %K youths %K adolescent %K adolescents %K teen %K teens %K teenager %K teenagers %K cross-sectional study %D 2024 %7 30.4.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Valid assessment tools are needed when investigating adherence to national dietary and lifestyle guidelines. Objective: The relative validity of the new digital food frequency questionnaire, the DIGIKOST-FFQ, against 7-day weighed food records and activity sensors was investigated. Methods: In total, 77 participants were included in the validation study and completed the DIGIKOST-FFQ and the weighed food record, and of these, 56 (73%) also used the activity sensors. The DIGIKOST-FFQ estimates the intake of foods according to the Norwegian food–based dietary guidelines (FBDGs) in addition to lifestyle factors. Results: At the group level, the DIGIKOST-FFQ showed good validity in estimating intakes according to the Norwegian FBDG. The median differences were small and well below portion sizes for all foods except “water” (median difference 230 g/day). The DIGIKOST-FFQ was able to rank individual intakes for all foods (r=0.2-0.7). However, ranking estimates of vegetable intakes should be interpreted with caution. Between 69% and 88% of the participants were classified into the same or adjacent quartile for foods and between 71% and 82% for different activity intensities. The Bland-Altman plots showed acceptable agreements between DIGIKOST-FFQ and the reference methods. The absolute amount of time in “moderate to vigorous intensity” was underestimated with the DIGIKOST-FFQ. However, estimated time in “moderate to vigorous intensity,” “vigorous intensity,” and “sedentary time” showed acceptable correlations and good agreement between the methods. The DIGIKOST-FFQ was able to identify adherence to the Norwegian FBDG and physical activity recommendations. Conclusions: The DIGIKOST-FFQ gave valid estimates of dietary intakes and was able to identify individuals with different degrees of adherence to the Norwegian FBDG and physical activity recommendations. Moderate physical activity was underreported, water was overreported, and vegetables showed poor correlation, which are important to consider when interpreting the data. Good agreement was observed between the methods in estimating dietary intakes and time in “moderate to vigorous physical activity,” “sedentary time,” and “sleep.” %M 38687986 %R 10.2196/53442 %U https://www.jmir.org/2024/1/e53442 %U https://doi.org/10.2196/53442 %U http://www.ncbi.nlm.nih.gov/pubmed/38687986 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e53441 %T Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study %A Vidal Bustamante,Constanza M %A Coombs III,Garth %A Rahimi-Eichi,Habiballah %A Mair,Patrick %A Onnela,Jukka-Pekka %A Baker,Justin T %A Buckner,Randy L %+ Department of Psychology, Harvard University, 52 Oxford Street, Northwest Building, East Wing, Room 295.06, Cambridge, MA, 02138, United States, 1 617 384 8230, constanzavidalbustamante@gmail.com %K deep phenotyping %K individualized models %K intensive longitudinal data %K sleep %K stress %K actigraphy %K accelerometer %K wearable %K mobile phone %K digital health %D 2024 %7 30.4.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Heightened stress and insufficient sleep are common in the transition to college, often co-occur, and have both been linked to negative health outcomes. A challenge concerns disentangling whether perceived stress precedes or succeeds changes in sleep. These day-to-day associations may vary across individuals, but short study periods and group-level analyses in prior research may have obscured person-specific phenotypes. Objective: This study aims to obtain stable estimates of lead-lag associations between perceived stress and objective sleep duration in the individual, unbiased by the group, by developing an individual-level linear model that can leverage intensive longitudinal data while remaining parsimonious. Methods: In total, 55 college students (n=6, 11% second-year students and n=49, 89% first-year students) volunteered to provide daily self-reports of perceived stress via a smartphone app and wore an actigraphy wristband for the estimation of daily sleep duration continuously throughout the academic year (median usable daily observations per participant: 178, IQR 65.5). The individual-level linear model, developed in a Bayesian framework, included the predictor and outcome of interest and a covariate for the day of the week to account for weekly patterns. We validated the model on the cohort of second-year students (n=6, used as a pilot sample) by applying it to variables expected to correlate positively within individuals: objective sleep duration and self-reported sleep quality. The model was then applied to the fully independent target sample of first-year students (n=49) for the examination of bidirectional associations between daily stress levels and sleep duration. Results: Proof-of-concept analyses captured expected associations between objective sleep duration and subjective sleep quality in every pilot participant. Target analyses revealed negative associations between sleep duration and perceived stress in most of the participants (45/49, 92%), but their temporal association varied. Of the 49 participants, 19 (39%) showed a significant association (probability of direction>0.975): 8 (16%) showed elevated stress in the day associated with shorter sleep later that night, 5 (10%) showed shorter sleep associated with elevated stress the next day, and 6 (12%) showed both directions of association. Of note, when analyzed using a group-based multilevel model, individual estimates were systematically attenuated, and some even reversed sign. Conclusions: The dynamic interplay of stress and sleep in daily life is likely person specific. Paired with intensive longitudinal data, our individual-level linear model provides a precision framework for the estimation of stable real-world behavioral and psychological dynamics and may support the personalized prioritization of intervention targets for health and well-being. %M 38687600 %R 10.2196/53441 %U https://formative.jmir.org/2024/1/e53441 %U https://doi.org/10.2196/53441 %U http://www.ncbi.nlm.nih.gov/pubmed/38687600 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e52075 %T Effectiveness of a Smartphone App–Based Intervention With Bluetooth-Connected Monitoring Devices and a Feedback System in Heart Failure (SMART-HF Trial): Randomized Controlled Trial %A Yoon,Minjae %A Lee,Seonhwa %A Choi,Jah Yeon %A Jung,Mi-Hyang %A Youn,Jong-Chan %A Shim,Chi Young %A Choi,Jin-Oh %A Kim,Eung Ju %A Kim,Hyungseop %A Yoo,Byung-Su %A Son,Yeon Joo %A Choi,Dong-Ju %+ Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seognam, 13620, Republic of Korea, 82 317877007, djchoi@snubh.org %K heart failure %K mobile applications %K mobile health %K self-care %K vital sign monitoring %K mobile phone %D 2024 %7 29.4.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Current heart failure (HF) guidelines recommend a multidisciplinary approach, discharge education, and self-management for HF. However, the recommendations are challenging to implement in real-world clinical settings. Objective: We developed a mobile health (mHealth) platform for HF self-care to evaluate whether a smartphone app–based intervention with Bluetooth-connected monitoring devices and a feedback system can help improve HF symptoms. Methods: In this prospective, randomized, multicenter study, we enrolled patients 20 years of age and older, hospitalized for acute HF, and who could use a smartphone from 7 tertiary hospitals in South Korea. In the intervention group (n=39), the apps were automatically paired with Bluetooth-connected monitoring devices. The patients could enter information on vital signs, HF symptoms, diet, medications, and exercise regimen into the app daily and receive feedback or alerts on their input. In the control group (n=38), patients could only enter their blood pressure, heart rate, and weight using conventional, non-Bluetooth devices and could not receive any feedback or alerts from the app. The primary end point was the change in dyspnea symptom scores from baseline to 4 weeks, assessed using a questionnaire. Results: At 4 weeks, the change in dyspnea symptom score from baseline was significantly greater in the intervention group than in the control group (mean –1.3, SD 2.1 vs mean –0.3, SD 2.3; P=.048). A significant reduction was found in body water composition from baseline to the final measurement in the intervention group (baseline level mean 7.4, SD 2.5 vs final level mean 6.6, SD 2.5; P=.003). App adherence, which was assessed based on log-in or the percentage of days when symptoms were first observed, was higher in the intervention group than in the control group. Composite end points, including death, rehospitalization, and urgent HF visits, were not significantly different between the 2 groups. Conclusions: The mobile-based health platform with Bluetooth-connected monitoring devices and a feedback system demonstrated improvement in dyspnea symptoms in patients with HF. This study provides evidence and rationale for implementing mobile app–based self-care strategies and feedback for patients with HF. Trial Registration: ClinicalTrials.gov NCT05668000; https://clinicaltrials.gov/study/NCT05668000 %M 38683665 %R 10.2196/52075 %U https://www.jmir.org/2024/1/e52075 %U https://doi.org/10.2196/52075 %U http://www.ncbi.nlm.nih.gov/pubmed/38683665 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e51874 %T Objective Assessment of Physical Activity at Home Using a Novel Floor-Vibration Monitoring System: Validation and Comparison With Wearable Activity Trackers and Indirect Calorimetry Measurements %A Nakajima,Yuki %A Kitayama,Asami %A Ohta,Yuji %A Motooka,Nobuhisa %A Kuno-Mizumura,Mayumi %A Miyachi,Motohiko %A Tanaka,Shigeho %A Ishikawa-Takata,Kazuko %A Tripette,Julien %+ Center for Interdisciplinary AI and Data Science, Ochanomizu University, 2-1-1 Otsuka, Bunkyo, 112-8610, Japan, 81 03 5978 2032 ext 2032, tripette.julien@ocha.ac.jp %K smart home system %K physical behavior %K physical activity %K activity tracker %K floor vibration %K housework-related activity %K home-based activity %K mobile phone %D 2024 %7 25.4.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The self-monitoring of physical activity is an effective strategy for promoting active lifestyles. However, accurately assessing physical activity remains challenging in certain situations. This study evaluates a novel floor-vibration monitoring system to quantify housework-related physical activity. Objective: This study aims to assess the validity of step-count and physical behavior intensity predictions of a novel floor-vibration monitoring system in comparison with the actual number of steps and indirect calorimetry measurements. The accuracy of the predictions is also compared with that of research-grade devices (ActiGraph GT9X). Methods: The Ocha-House, located in Tokyo, serves as an independent experimental facility equipped with high-sensitivity accelerometers installed on the floor to monitor vibrations. Dedicated data processing software was developed to analyze floor-vibration signals and calculate 3 quantitative indices: floor-vibration quantity, step count, and moving distance. In total, 10 participants performed 4 different housework-related activities, wearing ActiGraph GT9X monitors on both the waist and wrist for 6 minutes each. Concurrently, floor-vibration data were collected, and the energy expenditure was measured using the Douglas bag method to determine the actual intensity of activities. Results: Significant correlations (P<.001) were found between the quantity of floor vibrations, the estimated step count, the estimated moving distance, and the actual activity intensities. The step-count parameter extracted from the floor-vibration signal emerged as the most robust predictor (r2=0.82; P<.001). Multiple regression models incorporating several floor-vibration–extracted parameters showed a strong association with actual activity intensities (r2=0.88; P<.001). Both the step-count and intensity predictions made by the floor-vibration monitoring system exhibited greater accuracy than those of the ActiGraph monitor. Conclusions: Floor-vibration monitoring systems seem able to produce valid quantitative assessments of physical activity for selected housework-related activities. In the future, connected smart home systems that integrate this type of technology could be used to perform continuous and accurate evaluations of physical behaviors throughout the day. %M 38662415 %R 10.2196/51874 %U https://formative.jmir.org/2024/1/e51874 %U https://doi.org/10.2196/51874 %U http://www.ncbi.nlm.nih.gov/pubmed/38662415 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e51540 %T Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study %A Ahmed,Md Sabbir %A Hasan,Tanvir %A Islam,Salekul %A Ahmed,Nova %+ Design Inclusion and Access Lab, North South University, Plot # 15, Block B, Bashundhara R/A, Dhaka, 1229, Bangladesh, 880 1781920068, msg2sabbir@gmail.com %K depressive symptoms %K app usage rhythm %K behavioral markers %K personalization %K multitask learning framework %D 2024 %7 24.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Understanding a student’s depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. Objective: The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. Methods: Through a countrywide study, we collected 2952 students’ raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. Results: After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days’ app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. Conclusions: Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage–based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms’ capability to find subtle differences. International Registered Report Identifier (IRRID): DERR1-10.2196/51540 %M 38657238 %R 10.2196/51540 %U https://www.researchprotocols.org/2024/1/e51540 %U https://doi.org/10.2196/51540 %U http://www.ncbi.nlm.nih.gov/pubmed/38657238 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54645 %T Factor Analysis of Patients Who Find Tablets or Capsules Difficult to Swallow Due to Their Large Size: Using the Personal Health Record Infrastructure of Electronic Medication Notebooks %A Asano,Masaki %A Imai,Shungo %A Shimizu,Yuri %A Kizaki,Hayato %A Ito,Yukiko %A Tsuchiya,Makoto %A Kuriyama,Ryoko %A Yoshida,Nao %A Shimada,Masanori %A Sando,Takanori %A Ishijima,Tomo %A Hori,Satoko %+ Division of Drug Informatics, Faculty of Pharmacy and Graduate School of Pharmaceutical Sciences, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan, 81 354002650, satokoh@keio.jp %K tablet %K tablets %K capsules %K capsule %K size %K personal health record %K electronic medication notebook %K patient preference %K drug %K drugs %K pharmacy %K pharmacies %K pharmacology %K pharmacotherapy %K pharmaceutic %K pharmaceutics %K pharmaceuticals %K pharmaceutical %K medication %K medications %K preference %K preferences %K pill %K pills %K machine learning %K decision tree %K swallow %K swallowing %K throat %K pharynx %K risk %K risks %K dysphagia %K speech %K mobile phone %D 2024 %7 24.4.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Understanding patient preference regarding taking tablet or capsule formulations plays a pivotal role in treatment efficacy and adherence. Therefore, these preferences should be taken into account when designing formulations and prescriptions. Objective: This study investigates the factors affecting patient preference in patients who have difficulties swallowing large tablets or capsules and aims to identify appropriate sizes for tablets and capsules. Methods: A robust data set was developed based on a questionnaire survey conducted from December 1, 2022, to December 7, 2022, using the harmo smartphone app operated by harmo Co, Ltd. The data set included patient input regarding their tablet and capsule preferences, personal health records (including dispensing history), and drug formulation information (available from package inserts). Based on the medication formulation information, 6 indices were set for each of the tablets or capsules that were considered difficult to swallow owing to their large size and concomitant tablets or capsules (used as controls). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of each index. The index demonstrating the highest area under the curve of the ROC was selected as the best index to determine the tablet or capsule size that leads to swallowing difficulties. From the generated ROCs, the point with the highest discriminative performance that maximized the Youden index was identified, and the optimal threshold for each index was calculated. Multivariate logistic regression analysis was performed to identify the risk factors contributing to difficulty in swallowing oversized tablets or capsules. Additionally, decision tree analysis was performed to estimate the combined risk from several factors, using risk factors that were significant in the multivariate logistic regression analysis. Results: This study analyzed 147 large tablets or capsules and 624 control tablets or capsules. The “long diameter + short diameter + thickness” index (with a 21.5 mm threshold) was identified as the best indicator for causing swallowing difficulties in patients. The multivariate logistic regression analysis (including 132 patients with swallowing difficulties and 1283 patients without) results identified the following contributory risk factors: aged <50 years (odds ratio [OR] 1.59, 95% CI 1.03-2.44), female (OR 2.54, 95% CI 1.70-3.78), dysphagia (OR 3.54, 95% CI 2.22-5.65), and taking large tablets or capsules (OR 9.74, 95% CI 5.19-18.29). The decision tree analysis results suggested an elevated risk of swallowing difficulties for patients with taking large tablets or capsules. Conclusions: This study identified the most appropriate index and threshold for indicating that a given tablet or capsule size will cause swallowing difficulties, as well as the contributory risk factors. Although some sampling biases (eg, only including smartphone users) may exist, our results can guide the design of patient-friendly formulations and prescriptions, promoting better medication adherence. %M 38657229 %R 10.2196/54645 %U https://www.jmir.org/2024/1/e54645 %U https://doi.org/10.2196/54645 %U http://www.ncbi.nlm.nih.gov/pubmed/38657229 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e49512 %T Momentary Factors and Study Characteristics Associated With Participant Burden and Protocol Adherence: Ecological Momentary Assessment %A Tate,Allan D %A Fertig,Angela R %A de Brito,Junia N %A Ellis,Émilie M %A Carr,Christopher Patrick %A Trofholz,Amanda %A Berge,Jerica M %+ Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, 202 Miller Hall, 101 Buck Rd, Health Sciences Campus, Athens, GA, 30602, United States, 1 706 542 6317, allan.tate@uga.edu %K adherence %K burden %K data quality %K ecological momentary assessment %K mental health %K mHealth %K mobile health %K participant adherence %K public health %K stress %K study design %K survey burden %K survey %D 2024 %7 24.4.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Ecological momentary assessment (EMA) has become a popular mobile health study design to understand the lived experiences of dynamic environments. The numerous study design choices available to EMA researchers, however, may quickly increase participant burden and could affect overall adherence, which could limit the usability of the collected data. Objective: This study quantifies what study design, participant attributes, and momentary factors may affect self-reported burden and adherence. Methods: The EMA from the Phase 1 Family Matters Study (n=150 adult Black, Hmong, Latino or Latina, Native American, Somali, and White caregivers; n=1392 observation days) was examined to understand how participant self-reported survey burden was related to both design and momentary antecedents of adherence. The daily burden was measured by the question “Overall, how difficult was it for you to fill out the surveys today?” on a 5-item Likert scale (0=not at all and 4=extremely). Daily protocol adherence was defined as completing at least 2 signal-contingent surveys, 1 event-contingent survey, and 1 end-of-day survey each. Stress and mood were measured earlier in the day, sociodemographic and psychosocial characteristics were reported using a comprehensive cross-sectional survey, and EMA timestamps for weekends and weekdays were used to parameterize time-series models to evaluate prospective correlates of end-of-day study burden. Results: The burden was low at 1.2 (SD 1.14) indicating “a little” burden on average. Participants with elevated previous 30-day chronic stress levels (mean burden difference: 0.8; P=.04), 1 in 5 more immigrant households (P=.02), and the language primarily spoken in the home (P=.04; 3 in 20 more non-English–speaking households) were found to be population attributes of elevated moderate-high burden. Current and 1-day lagged nonadherence were correlated with elevated 0.39 and 0.36 burdens, respectively (P=.001), and the association decayed by the second day (β=0.08; P=.47). Unit increases in momentary antecedents, including daily depressed mood (P=.002) and across-day change in stress (P=.008), were positively associated with 0.15 and 0.07 higher end-of-day burdens after controlling for current-day adherence. Conclusions: The 8-day EMA implementation appeared to capture momentary sources of stress and depressed mood without substantial burden to a racially or ethnically diverse and immigrant or refugee sample of parents. Attention to sociodemographic attributes (eg, EMA in the primary language of the caregiver) was important for minimizing participant burden and improving data quality. Momentary stress and depressed mood were strong determinants of participant-experienced EMA burden and may affect adherence to mobile health study protocols. There were no strong indicators of EMA design attributes that created a persistent burden for caregivers. EMA stands to be an important observational design to address dynamic public health challenges related to human-environment interactions when the design is carefully tailored to the study population and to study research objectives. %M 38656787 %R 10.2196/49512 %U https://formative.jmir.org/2024/1/e49512 %U https://doi.org/10.2196/49512 %U http://www.ncbi.nlm.nih.gov/pubmed/38656787 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e50368 %T Preferences on Governance Models for Mental Health Data: Qualitative Study With Young People %A Carey,Emma Grace %A Adeyemi,Faith Oluwasemilore %A Neelakantan,Lakshmi %A Fernandes,Blossom %A Fazel,Mina %A Ford,Tamsin %A , %A Burn,Anne-Marie %+ Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain and Mind Sciences, Forvie Site, Robinson Way, Cambridge, CB2 0SZ, United Kingdom, 44 01223 336961, amb278@cam.ac.uk %K young people %K mental health %K data %K governance %K deliberative democracy %K mobile phone %D 2024 %7 23.4.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Improving access to mental health data to accelerate research and improve mental health outcomes is a potentially achievable goal given the substantial data that can now be collected from mobile devices. Smartphones can provide a useful mechanism for collecting mental health data from young people, especially as their use is relatively ubiquitous in high-resource settings such as the United Kingdom and they have a high capacity to collect active and passive data. This raises the interesting opportunity to establish a large bank of mental health data from young people that could be accessed by researchers worldwide, but it is important to clarify how to ensure that this is done in an appropriate manner aligned with the values of young people. Objective: In this study, we discussed the preferences of young people in the United Kingdom regarding the governance, sharing, and use of their mental health data with the establishment of a global data bank in mind. We aimed to determine whether young people want and feel safe to share their mental health data; if so, with whom; and their preferences in doing so. Methods: Young people (N=46) were provided with 2 modules of educational material about data governance models and background in scientific research. We then conducted 2-hour web-based group sessions using a deliberative democracy methodology to reach a consensus where possible. Findings were analyzed using the framework method. Results: Young people were generally enthusiastic about contributing data to mental health research. They believed that broader availability of mental health data could be used to discover what improves or worsens mental health and develop new services to support young people. However, this enthusiasm came with many concerns and caveats, including distributed control of access to ensure appropriate use, distributed power, and data management that included diverse representation and sufficient ethical training for applicants and data managers. Conclusions: Although it is feasible to use smartphones to collect mental health data from young people in the United Kingdom, it is essential to carefully consider the parameters of such a data bank. Addressing and embedding young people’s preferences, including the need for robust procedures regarding how their data are managed, stored, and accessed, will set a solid foundation for establishing any global data bank. %M 38652525 %R 10.2196/50368 %U https://formative.jmir.org/2024/1/e50368 %U https://doi.org/10.2196/50368 %U http://www.ncbi.nlm.nih.gov/pubmed/38652525 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e47428 %T Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation %A Zhu,Yue %A Zhang,Ran %A Yin,Shuluo %A Sun,Yihui %A Womer,Fay %A Liu,Rongxun %A Zeng,Sheng %A Zhang,Xizhe %A Wang,Fei %+ Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Gulou District, Nanjing City, China, Nanjing, 210000, China, 1 86 02583295953, zhangxizhe@njmu.edu.cn %K dietary behaviors %K digital marker %K depression %K mental health %K appetite disturbance %K behavioral monitoring %K eating pattern %K electronic record %K digital health %K behavioral %K surveillance %D 2024 %7 22.4.2024 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device–based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings. Objective: The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression. Methods: A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression. Results: The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64). Conclusions: This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression. %M 38648087 %R 10.2196/47428 %U https://publichealth.jmir.org/2024/1/e47428 %U https://doi.org/10.2196/47428 %U http://www.ncbi.nlm.nih.gov/pubmed/38648087 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55388 %T Evaluation of Prompts to Simplify Cardiovascular Disease Information Generated Using a Large Language Model: Cross-Sectional Study %A Mishra,Vishala %A Sarraju,Ashish %A Kalwani,Neil M %A Dexter,Joseph P %+ Data Science Initiative, Harvard University, Science and Engineering Complex 1.312-10, 150 Western Avenue, Allston, MA, 02134, United States, 1 8023381330, jdexter@fas.harvard.edu %K artificial intelligence %K ChatGPT %K GPT %K digital health %K large language model %K NLP %K language model %K language models %K prompt engineering %K health communication %K generative %K health literacy %K natural language processing %K patient-physician communication %K health communication %K prevention %K cardiology %K cardiovascular %K heart %K education %K educational %K human-in-the-loop %K machine learning %D 2024 %7 22.4.2024 %9 Research Letter %J J Med Internet Res %G English %X In this cross-sectional study, we evaluated the completeness, readability, and syntactic complexity of cardiovascular disease prevention information produced by GPT-4 in response to 4 kinds of prompts. %M 38648104 %R 10.2196/55388 %U https://www.jmir.org/2024/1/e55388 %U https://doi.org/10.2196/55388 %U http://www.ncbi.nlm.nih.gov/pubmed/38648104 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e56883 %T Psychometric Evaluation of a Tablet-Based Tool to Detect Mild Cognitive Impairment in Older Adults: Mixed Methods Study %A McMurray,Josephine %A Levy,AnneMarie %A Pang,Wei %A Holyoke,Paul %+ Lazaridis School of Business & Economics, Wilfrid Laurier University, 73 George St, Brantford, ON, N3T3Y3, Canada, 1 548 889 4492, jmcmurray@wlu.ca %K cognitive dysfunction %K dementia neuropsychological tests %K evaluation study %K technology %K aged %K mobile phone %D 2024 %7 19.4.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: With the rapid aging of the global population, the prevalence of mild cognitive impairment (MCI) and dementia is anticipated to surge worldwide. MCI serves as an intermediary stage between normal aging and dementia, necessitating more sensitive and effective screening tools for early identification and intervention. The BrainFx SCREEN is a novel digital tool designed to assess cognitive impairment. This study evaluated its efficacy as a screening tool for MCI in primary care settings, particularly in the context of an aging population and the growing integration of digital health solutions. Objective: The primary objective was to assess the validity, reliability, and applicability of the BrainFx SCREEN (hereafter, the SCREEN) for MCI screening in a primary care context. We conducted an exploratory study comparing the SCREEN with an established screening tool, the Quick Mild Cognitive Impairment (Qmci) screen. Methods: A concurrent mixed methods, prospective study using a quasi-experimental design was conducted with 147 participants from 5 primary care Family Health Teams (FHTs; characterized by multidisciplinary practice and capitated funding) across southwestern Ontario, Canada. Participants included health care practitioners, patients, and FHT administrative executives. Individuals aged ≥55 years with no history of MCI or diagnosis of dementia rostered in a participating FHT were eligible to participate. Participants were screened using both the SCREEN and Qmci. The study also incorporated the Geriatric Anxiety Scale–10 to assess general anxiety levels at each cognitive screening. The SCREEN’s scoring was compared against that of the Qmci and the clinical judgment of health care professionals. Statistical analyses included sensitivity, specificity, internal consistency, and test-retest reliability assessments. Results: The study found that the SCREEN’s longer administration time and complex scoring algorithm, which is proprietary and unavailable for independent analysis, presented challenges. Its internal consistency, indicated by a Cronbach α of 0.63, was below the acceptable threshold. The test-retest reliability also showed limitations, with moderate intraclass correlation coefficient (0.54) and inadequate κ (0.15) values. Sensitivity and specificity were consistent (63.25% and 74.07%, respectively) between cross-tabulation and discrepant analysis. In addition, the study faced limitations due to its demographic skew (96/147, 65.3% female, well-educated participants), the absence of a comprehensive gold standard for MCI diagnosis, and financial constraints limiting the inclusion of confirmatory neuropsychological testing. Conclusions: The SCREEN, in its current form, does not meet the necessary criteria for an optimal MCI screening tool in primary care settings, primarily due to its longer administration time and lower reliability. As the number of digital health technologies increases and evolves, further testing and refinement of tools such as the SCREEN are essential to ensure their efficacy and reliability in real-world clinical settings. This study advocates for continued research in this rapidly advancing field to better serve the aging population. International Registered Report Identifier (IRRID): RR2-10.2196/25520 %M 38640480 %R 10.2196/56883 %U https://www.jmir.org/2024/1/e56883 %U https://doi.org/10.2196/56883 %U http://www.ncbi.nlm.nih.gov/pubmed/38640480 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e50136 %T Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data %A Siepe,Björn Sebastian %A Sander,Christian %A Schultze,Martin %A Kliem,Andreas %A Ludwig,Sascha %A Hegerl,Ulrich %A Reich,Hanna %+ Psychological Methods Lab, Department of Psychology, University of Marburg, Gutenbergstraße 18, Marburg, 35032, Germany, 49 6421 28 23616, bjoern.siepe@uni-marburg.de %K depression %K time series analysis %K network analysis %K experience sampling %K idiography %K time varying %K mobile phone %D 2024 %7 18.4.2024 %9 Original Paper %J JMIR Ment Health %G English %X Background: As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed. Objective: To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time. Methods: Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models. Results: The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants. Conclusions: Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes. %M 38635978 %R 10.2196/50136 %U https://mental.jmir.org/2024/1/e50136 %U https://doi.org/10.2196/50136 %U http://www.ncbi.nlm.nih.gov/pubmed/38635978 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e38170 %T Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach %A Qian,Weicheng %A Cooke,Aranock %A Stanley,Kevin Gordon %A Osgood,Nathaniel David %+ Department of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, SK, S7N 5C9, Canada, 1 3069661947, weicheng.qian@usask.ca %K smartphone-based sensing %K proximity contact data %K transmission models %K agent-based simulation %K health informatics %K mobile phone %D 2024 %7 17.4.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. Objective: We aim to compare GPS colocated versus Bluetooth beacon–derived proximity contact data for their impacts on transmission models’ results under community and types of diseases. Methods: We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. Results: Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). Conclusions: We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future. %M 38422493 %R 10.2196/38170 %U https://www.jmir.org/2024/1/e38170 %U https://doi.org/10.2196/38170 %U http://www.ncbi.nlm.nih.gov/pubmed/38422493 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e57226 %T Smartphone App–Delivered Mindfulness-Based Intervention for Mild Traumatic Brain Injury in Adolescents: Protocol for a Feasibility Randomized Controlled Trial %A Ledoux,Andrée-Anne %A Zemek,Roger %A Cairncross,Molly %A Silverberg,Noah %A Sicard,Veronik %A Barrowman,Nicholas %A Goldfield,Gary %A Gray,Clare %A Harris,Ashley D %A Jaworska,Natalia %A Reed,Nick %A Saab,Bechara J %A Smith,Andra %A Walker,Lisa %+ Children's Hospital of Eastern Ontario Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada, 1 6137377600 ext 4104, aledoux@cheo.on.ca %K pediatric %K concussion %K persisting symptoms after concussion %K mindfulness %K randomized controlled trial %K feasibility RCT %K psychological intervention %K youth %K digital therapeutics %K eHealth %K mobile health %K mHealth %K mobile phone %D 2024 %7 11.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Concussion in children and adolescents is a significant public health concern, with 30% to 35% of patients at risk for prolonged emotional, cognitive, sleep, or physical symptoms. These symptoms negatively impact a child’s quality of life while interfering with their participation in important neurodevelopmental activities such as schoolwork, socializing, and sports. Early psychological intervention following a concussion may improve the ability to regulate emotions and adapt to postinjury symptoms, resulting in the greater acceptance of change; reduced stress; and recovery of somatic, emotional, and cognitive symptoms. Objective: The primary objective of this study is to assess the feasibility of conducting a parallel-group (1:1) randomized controlled trial (RCT) to evaluate a digital therapeutics (DTx) mindfulness-based intervention (MBI) in adolescents aged 12 to <18 years. The attention-matched comparator intervention (a math game also used in previous RCTs) will be delivered on the same DTx platform. Both groups will be provided with the standard of care guidelines. The secondary objective is to examine intervention trends for quality of life; resilience; self-efficacy; cognition such as attention, working memory, and executive functioning; symptom burden; and anxiety and depression scores at 4 weeks after concussion, which will inform a more definitive RCT. A subsample will be used to examine whether those randomized to the experimental intervention group have different brain-based imaging patterns compared with those randomized to the control group. Methods: This study is a double-blind Health Canada–regulated trial. A total of 70 participants will be enrolled within 7 days of concussion and randomly assigned to receive the 4-week DTx MBI (experimental group) or comparator intervention. Feasibility will be assessed based on the recruitment rate, treatment adherence to both interventions, and retention. All outcome measures will be evaluated before the intervention (within 7 days after injury) and at 1, 2, and 4 weeks after the injury. A subset of 60 participants will undergo magnetic resonance imaging within 72 hours and at 4 weeks after recruitment to identify the neurophysiological mechanisms underlying the potential benefits from MBI training in adolescents following a concussion. Results: The recruitment began in October 2022, and the data collection is expected to be completed by September 2024. Data collection and management is still in progress; therefore, data analysis is yet to be conducted. Conclusions: This trial will confirm the feasibility and resolve uncertainties to inform a future definitive multicenter efficacy RCT. If proven effective, a smartphone-based MBI has the potential to be an accessible and low-risk preventive treatment for youth at risk of experiencing prolonged postconcussion symptoms and complications. Trial Registration: ClinicalTrials.gov NCT05105802; https://classic.clinicaltrials.gov/ct2/show/NCT05105802 International Registered Report Identifier (IRRID): DERR1-10.2196/57226 %M 38602770 %R 10.2196/57226 %U https://www.researchprotocols.org/2024/1/e57226 %U https://doi.org/10.2196/57226 %U http://www.ncbi.nlm.nih.gov/pubmed/38602770 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e56143 %T Time Efficiency, Reliability, and User Satisfaction of the Tooth Memo App for Recording Oral Health Information: Cross-Sectional Questionnaire Study %A Detsomboonrat,Palinee %A Pisarnturakit,Pagaporn Pantuwadee %+ Department of Community Dentistry, Faculty of Dentistry, Chulalongkorn University, 34 Henry Dunant Road, Patumwan, Bangkok, 10330, Thailand, 66 22188545, pagaporn.p@chula.ac.th %K capability %K health survey %K oral health %K mobile apps %K personal health information %K PHI %K satisfaction %K tooth %K teeth %K oral %K dental %K dentist %K dentistry %K data entry %K data collection %K mHealth %K mobile health %K app %K apps %K applications %K periodontal %K survey %K questionnaire %K questionnaires %D 2024 %7 10.4.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Digitalizing oral health data through an app can help manage the extensive data obtained through oral health surveys. The Tooth Memo app collects data from oral health surveys and personal health information. Objective: This study aims to evaluate the evaluate the time efficiency, reliability, and user satisfaction of the Tooth Memo app. Methods: There are 2 sections in the Tooth Memo app: oral health survey and personal oral health record. For the oral health survey section of the Tooth Memo app, different data entry methods were compared and user satisfaction was evaluated. Fifth-year dental students had access to the oral health survey section in the Tooth Memo app during their clinical work. The time required for data entry, analysis, and summary of oral health survey data by 3 methods, that is, pen-and-paper (manual), Tooth Memo app on iOS device, and Tooth Memo app on Android device were compared among 3 data recorders who entered patients’ information on decayed, missing, and filled permanent teeth (DMFT) index and community periodontal index (CPI), which were read aloud from the database of 103 patients by another dental personnel. The interobserver reliability of the 3 different data-entering procedures was evaluated by percent disagreement and kappa statistic values. Laypeople had access to the personal oral health record section of this app, and their satisfaction was evaluated through a Likert scale questionnaire. The satisfaction assessments for both sections of the Tooth Memo app involved the same set of questions on the app design, usage, and overall satisfaction. Results: Of the 103 dental records on DMFT and CPI, 5.2% (177/3399) data points were missing in the manual data entries, but no data on tooth status were missing in the Android and iOS methods. Complete CPI information was provided by all 3 methods. Transferring data from paper to computer took an average of 55 seconds per case. The manual method required 182 minutes more than the iOS or Android methods to clean the missing data and transfer and analyze the tooth status data of 103 patients. The users, that is, 109 fifth-year dental students and 134 laypeople, expressed high satisfaction with using the Tooth Memo app. The overall satisfaction with the oral health survey ranged between 3 and 10, with an average (SD) of 7.86 (1.46). The overall satisfaction with the personal oral health record ranged between 4 and 10, with an average (SD) of 8.09 (1.28). Conclusions: The Tooth Memo app was more efficacious than manual data entry for collecting data of oral health surveys. Dental personnel as well as general users reported high satisfaction when using this app. %M 38598287 %R 10.2196/56143 %U https://formative.jmir.org/2024/1/e56143 %U https://doi.org/10.2196/56143 %U http://www.ncbi.nlm.nih.gov/pubmed/38598287 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e54801 %T Association of Arterial Stiffness With Mid- to Long-Term Home Blood Pressure Variability in the Electronic Framingham Heart Study: Cohort Study %A Wang,Xuzhi %A Zhang,Yuankai %A Pathiravasan,Chathurangi H %A Ukonu,Nene C %A Rong,Jian %A Benjamin,Emelia J %A McManus,David D %A Larson,Martin G %A Vasan,Ramachandran S %A Hamburg,Naomi M %A Murabito,Joanne M %A Liu,Chunyu %A Mitchell,Gary F %+ Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA, 02118, United States, 1 6176385104, liuc@bu.edu %K arterial stiffness %K mobile health %K mHealth %K blood pressure %K blood pressure variability %K risk factors %D 2024 %7 8.4.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Short-term blood pressure variability (BPV) is associated with arterial stiffness in patients with hypertension. Few studies have examined associations between arterial stiffness and digital home BPV over a mid- to long-term time span, irrespective of underlying hypertension. Objective: This study aims to investigate if arterial stiffness traits were associated with subsequent mid- to long-term home BPV in the electronic Framingham Heart Study (eFHS). We hypothesized that higher arterial stiffness was associated with higher home BPV over up to 1-year follow-up. Methods: At a Framingham Heart Study research examination (2016-2019), participants underwent arterial tonometry to acquire measures of arterial stiffness (carotid-femoral pulse wave velocity [CFPWV]; forward pressure wave amplitude [FWA]) and wave reflection (reflection coefficient [RC]). Participants who agreed to enroll in eFHS were provided with a digital blood pressure (BP) cuff to measure home BP weekly over up to 1-year follow-up. Participants with less than 3 weeks of BP readings were excluded. Linear regression models were used to examine associations of arterial measures with average real variability (ARV) of week-to-week home systolic (SBP) and diastolic (DBP) BP adjusting for important covariates. We obtained ARV as an average of the absolute differences of consecutive home BP measurements. ARV considers not only the dispersion of the BP readings around the mean but also the order of BP readings. In addition, ARV is more sensitive to measurement-to-measurement BPV compared with traditional BPV measures. Results: Among 857 eFHS participants (mean age 54, SD 9 years; 508/857, 59% women; mean SBP/DBP 119/76 mm Hg; 405/857, 47% hypertension), 1 SD increment in FWA was associated with 0.16 (95% CI 0.09-0.23) SD increments in ARV of home SBP and 0.08 (95% CI 0.01-0.15) SD increments in ARV of home DBP; 1 SD increment in RC was associated with 0.14 (95% CI 0.07-0.22) SD increments in ARV of home SBP and 0.11 (95% CI 0.04-0.19) SD increments in ARV of home DBP. After adjusting for important covariates, there was no significant association between CFPWV and ARV of home SBP, and similarly, no significant association existed between CFPWV and ARV of home DBP (P>.05). Conclusions: In eFHS, higher FWA and RC were associated with higher mid- to long-term ARV of week-to-week home SBP and DBP over 1-year follow-up in individuals across the BP spectrum. Our findings suggest that higher aortic stiffness and wave reflection are associated with higher week-to-week variation of BP in a home-based setting over a mid- to long-term time span. %M 38587880 %R 10.2196/54801 %U https://cardio.jmir.org/2024/1/e54801 %U https://doi.org/10.2196/54801 %U http://www.ncbi.nlm.nih.gov/pubmed/38587880 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e51171 %T Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study %A Yoo,Richard M %A Viggiano,Ben T %A Pundi,Krishna N %A Fries,Jason A %A Zahedivash,Aydin %A Podchiyska,Tanya %A Din,Natasha %A Shah,Nigam H %K consumer wearable devices %K atrial fibrillation %K postmarket surveillance %K surveillance %K monitoring %K artificial intelligence %K machine learning %K natural language processing %K NLP %K wearable %K wearables %K labeler %K heart %K cardiology %K arrhythmia %K diagnose %K diagnosis %K labeling %K classifier %K EHR %K electronic health record %K electronic health records %K consumer %K consumers %K device %K devices %K evaluation %D 2024 %7 4.4.2024 %9 %J JMIR Med Inform %G English %X Background: With the capability to render prediagnoses, consumer wearables have the potential to affect subsequent diagnoses and the level of care in the health care delivery setting. Despite this, postmarket surveillance of consumer wearables has been hindered by the lack of codified terms in electronic health records (EHRs) to capture wearable use. Objective: We sought to develop a weak supervision–based approach to demonstrate the feasibility and efficacy of EHR-based postmarket surveillance on consumer wearables that render atrial fibrillation (AF) prediagnoses. Methods: We applied data programming, where labeling heuristics are expressed as code-based labeling functions, to detect incidents of AF prediagnoses. A labeler model was then derived from the predictions of the labeling functions using the Snorkel framework. The labeler model was applied to clinical notes to probabilistically label them, and the labeled notes were then used as a training set to fine-tune a classifier called Clinical-Longformer. The resulting classifier identified patients with an AF prediagnosis. A retrospective cohort study was conducted, where the baseline characteristics and subsequent care patterns of patients identified by the classifier were compared against those who did not receive a prediagnosis. Results: The labeler model derived from the labeling functions showed high accuracy (0.92; F1-score=0.77) on the training set. The classifier trained on the probabilistically labeled notes accurately identified patients with an AF prediagnosis (0.95; F1-score=0.83). The cohort study conducted using the constructed system carried enough statistical power to verify the key findings of the Apple Heart Study, which enrolled a much larger number of participants, where patients who received a prediagnosis tended to be older, male, and White with higher CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 years, diabetes, stroke, vascular disease, age 65-74 years, sex category) scores (P<.001). We also made a novel discovery that patients with a prediagnosis were more likely to use anticoagulants (525/1037, 50.63% vs 5936/16,560, 35.85%) and have an eventual AF diagnosis (305/1037, 29.41% vs 262/16,560, 1.58%). At the index diagnosis, the existence of a prediagnosis did not distinguish patients based on clinical characteristics, but did correlate with anticoagulant prescription (P=.004 for apixaban and P=.01 for rivaroxaban). Conclusions: Our work establishes the feasibility and efficacy of an EHR-based surveillance system for consumer wearables that render AF prediagnoses. Further work is necessary to generalize these findings for patient populations at other sites. %R 10.2196/51171 %U https://medinform.jmir.org/2024/1/e51171 %U https://doi.org/10.2196/51171 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e53684 %T Experimentally Induced Reductions in Alcohol Consumption and Brain, Cognitive, and Clinical Outcomes in Older Persons With and Those Without HIV Infection (30-Day Challenge Study): Protocol for a Nonrandomized Clinical Trial %A Cook,Robert L %A Richards,Veronica L %A Gullett,Joseph M %A Lerner,Brenda D G %A Zhou,Zhi %A Porges,Eric C %A Wang,Yan %A Kahler,Christopher W %A Barnett,Nancy P %A Li,Zhigang %A Pallikkuth,Suresh %A Thomas,Emmanuel %A Rodriguez,Allan %A Bryant,Kendall J %A Ghare,Smita %A Barve,Shirish %A Govind,Varan %A Dévieux,Jessy G %A Cohen,Ronald A %A , %+ Southern HIV and Alcohol Research Consortium, University of Florida, 2004 Mowry Road, Department of Epidemiology, Gainesville, FL, 32610, United States, 1 3522735869, cookrl@ufl.edu %K alcohol %K contingency management %K biosensor %K HIV infection %K cognitive function %D 2024 %7 2.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Both alcohol consumption and HIV infection are associated with worse brain, cognitive, and clinical outcomes in older adults. However, the extent to which brain and cognitive dysfunction is reversible with reduction or cessation of drinking is unknown. Objective: The 30-Day Challenge study was designed to determine whether reduction or cessation of drinking would be associated with improvements in cognition, reduction of systemic and brain inflammation, and improvement in HIV-related outcomes in adults with heavy drinking. Methods: The study design was a mechanistic experimental trial, in which all participants received an alcohol reduction intervention followed by repeated assessments of behavioral and clinical outcomes. Persons were eligible if they were 45 years of age or older, had weekly alcohol consumption of 21 or more drinks (men) or 14 or more drinks (women), and were not at high risk of alcohol withdrawal. After a baseline assessment, participants received an intervention consisting of contingency management (money for nondrinking days) for at least 30 days followed by a brief motivational interview. After this, participants could either resume drinking or not. Study questionnaires, neurocognitive assessments, neuroimaging, and blood, urine, and stool samples were collected at baseline, 30 days, 90 days, and 1 year after enrollment. Results: We enrolled 57 persons with heavy drinking who initiated the contingency management protocol (mean age 56 years, SD 4.6 years; 63%, n=36 male, 77%, n=44 Black, and 58%, n=33 people with HIV) of whom 50 completed 30-day follow-up and 43 the 90-day follow-up. The planned study procedures were interrupted and modified due to the COVID-19 pandemic of 2020-2021. Conclusions: This was the first study seeking to assess changes in brain (neuroimaging) and cognition after alcohol intervention in nontreatment-seeking people with HIV together with people without HIV as controls. Study design strengths, limitations, and lessons for future study design considerations are discussed. Planned analyses are in progress, after which deidentified study data will be available for sharing. Trial Registration: ClinicalTrials.gov NCT03353701; https://clinicaltrials.gov/study/NCT03353701 International Registered Report Identifier (IRRID): DERR1-10.2196/53684 %M 38564243 %R 10.2196/53684 %U https://www.researchprotocols.org/2024/1/e53684 %U https://doi.org/10.2196/53684 %U http://www.ncbi.nlm.nih.gov/pubmed/38564243 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e56083 %T The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study %A Yeshurun,Shlomo %A Cramer,Tomer %A Souroujon,Daniel %A Mor,Merav %+ Metaflow Ltd, HaArba’a St 30, Tel-Aviv, 6473926, Israel, 972 37684062, shlomoyesh@gmail.com %K app %K applications %K association %K BMI %K body mass index %K carbohydrate %K carbon dioxide %K consumption %K correlate %K correlation %K diet %K dietary %K exhalation %K exhale %K food %K Lumen %K macronutrient %K meal %K metabolic flexibility %K metabolic %K metabolism %K mHealth %K mobile health %K nutrient %K nutrition %K nutritional %K obese %K obesity %K postprandial %K prandial %K retrospective %K weight %D 2024 %7 1.4.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Metabolic flexibility is the ability of the body to rapidly switch between fuel sources based on their accessibility and metabolic requirements. High metabolic flexibility is associated with improved health outcomes and a reduced risk of several metabolic disorders. Metabolic flexibility can be improved through lifestyle changes, such as increasing physical activity and eating a balanced macronutrient diet. Lumen is a small handheld device that measures metabolic fuel usage through exhaled carbon dioxide (CO2), which allows individuals to monitor their metabolic flexibility and make lifestyle changes to enhance it. Objective: This retrospective study aims to examine the postprandial CO2 response to meals logged by Lumen users and its relationship with macronutrient intake and BMI. Methods: We analyzed deidentified data from 2607 Lumen users who logged their meals and measured their exhaled CO2 before and after those meals between May 1, 2023, and October 18, 2023. A linear mixed model was fitted to test the association between macronutrient consumption, BMI, age, and gender to the postprandial CO2 response, followed by a 2-way ANOVA. Results: The model demonstrated significant associations (P<.001) between CO2 response after meals and both BMI and carbohydrate intake (BMI: β=–0.112, 95% CI –0.156 to –0.069; carbohydrates: β=0.046, 95% CI 0.034-0.058). In addition, a 2-way ANOVA revealed that higher carbohydrate intake resulted in a higher CO2 response compared to low carbohydrate intake (F2,2569=24.23; P<.001), and users with high BMI showed modest responses to meals compared with low BMI (F2,2569=5.88; P=.003). Conclusions: In this study, we show that Lumen’s CO2 response is influenced both by macronutrient consumption and BMI. The results of this study highlight a distinct pattern of reduced metabolic flexibility in users with obesity, indicating the value of Lumen for assessing postprandial metabolic flexibility. %M 38439744 %R 10.2196/56083 %U https://mhealth.jmir.org/2024/1/e56083 %U https://doi.org/10.2196/56083 %U http://www.ncbi.nlm.nih.gov/pubmed/38439744 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e51298 %T Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study %A Castro Ribeiro,Thais %A García Pagès,Esther %A Ballester,Laura %A Vilagut,Gemma %A García Mieres,Helena %A Suárez Aragonès,Víctor %A Amigo,Franco %A Bailón,Raquel %A Mortier,Philippe %A Pérez Sola,Víctor %A Serrano-Blanco,Antoni %A Alonso,Jordi %A Aguiló,Jordi %+ CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Calle Monforte de Lemos, 3-5, Pabellón 11, Madrid, 28029, Spain, 34 935868430, thais.castro@uab.cat %K mental health %K mental well-being %K mobile health %K mHealth %K remote monitoring %K physiological variables %K experimental protocol %K depression %K anxiety %D 2024 %7 29.3.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual’s well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. Objective: This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. Methods: This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. Results: Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. Conclusions: This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. Trial Registration: OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH International Registered Report Identifier (IRRID): DERR1-10.2196/51298 %M 38551647 %R 10.2196/51298 %U https://www.researchprotocols.org/2024/1/e51298 %U https://doi.org/10.2196/51298 %U http://www.ncbi.nlm.nih.gov/pubmed/38551647 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e53857 %T Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study %A Kilshaw,Robyn E %A Boggins,Abigail %A Everett,Olivia %A Butner,Emma %A Leifker,Feea R %A Baucom,Brian R W %+ Department of Psychology, University of Utah, 380 S 1530 E BEH S 502, Salt Lake City, UT, 84112, United States, 1 (801) 581 6124, robyn.kilshaw@psych.utah.edu %K audio data %K computational psychiatry %K data repository %K digital phenotyping %K machine learning %K passive sensor data %D 2024 %7 27.3.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. Objective: Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. Methods: We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). Results: Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. Conclusions: This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field’s move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. International Registered Report Identifier (IRRID): DERR1-10.2196/53857 %M 38536220 %R 10.2196/53857 %U https://www.researchprotocols.org/2024/1/e53857 %U https://doi.org/10.2196/53857 %U http://www.ncbi.nlm.nih.gov/pubmed/38536220 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e52652 %T Remote Evaluation of Sleep and Circadian Rhythms in Older Adults With Mild Cognitive Impairment and Dementia: Protocol for a Feasibility and Acceptability Mixed Methods Study %A Gabb,Victoria Grace %A Blackman,Jonathan %A Morrison,Hamish Duncan %A Biswas,Bijetri %A Li,Haoxuan %A Turner,Nicholas %A Russell,Georgina M %A Greenwood,Rosemary %A Jolly,Amy %A Trender,William %A Hampshire,Adam %A Whone,Alan %A Coulthard,Elizabeth %+ Bristol Medical School, University of Bristol, Bristol Brain Centre, Elgar House, Southmead Road, Bristol, BS10 5NB, United Kingdom, 44 117 456 0700, victoria.gabb@bristol.ac.uk %K feasibility %K sleep %K mild cognitive impairment %K dementia %K Lewy body disease %K Alzheimer disease %K Parkinson %K wearable devices %K research %K mobile phone %K electroencephalography %K accelerometery %K mobile applications %K application %K app %K cognitive %K cognitive impairment %K sleeping %K sleep disturbance %K risk factor %K Alzheimer %K wearable %K wearables %K acceptability %K smart device %D 2024 %7 22.3.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Sleep disturbances are a potentially modifiable risk factor for neurodegenerative dementia secondary to Alzheimer disease (AD) and Lewy body disease (LBD). Therefore, we need to identify the best methods to study sleep in this population. Objective: This study will assess the feasibility and acceptability of various wearable devices, smart devices, and remote study tasks in sleep and cognition research for people with AD and LBD. Methods: We will deliver a feasibility and acceptability study alongside a prospective observational cohort study assessing sleep and cognition longitudinally in the home environment. Adults aged older than 50 years who were diagnosed with mild to moderate dementia or mild cognitive impairment (MCI) due to probable AD or LBD and age-matched controls will be eligible. Exclusion criteria include lack of capacity to consent to research, other causes of MCI or dementia, and clinically significant sleep disorders. Participants will complete a cognitive assessment and questionnaires with a researcher and receive training and instructions for at-home study tasks across 8 weeks. At-home study tasks include remote sleep assessments using wearable devices (electroencephalography headband and actigraphy watch), app-based sleep diaries, online cognitive assessments, and saliva samples for melatonin- and cortisol-derived circadian markers. Feasibility outcomes will be assessed relating to recruitment and retention, data completeness, data quality, and support required. Feedback on acceptability and usability will be collected throughout the study period and end-of-study interviews will be analyzed using thematic analysis. Results: Recruitment started in February 2022. Data collection is ongoing, with final data expected in February 2024 and data analysis and publication of findings scheduled for the summer of 2024. Conclusions: This study will allow us to assess if remote testing using smart devices and wearable technology is a viable alternative to traditional sleep measurements, such as polysomnography and questionnaires, in older adults with and without MCI or dementia due to AD or LBD. Understanding participant experience and the barriers and facilitators to technology use for research purposes and remote research in this population will assist with the development of, recruitment to, and retention within future research projects studying sleep and cognition outside of the clinic or laboratory. International Registered Report Identifier (IRRID): DERR1-10.2196/52652 %M 38517469 %R 10.2196/52652 %U https://www.researchprotocols.org/2024/1/e52652 %U https://doi.org/10.2196/52652 %U http://www.ncbi.nlm.nih.gov/pubmed/38517469 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e48265 %T Performance Differences of a Touch-Based Serial Reaction Time Task in Healthy Older Participants and Older Participants With Cognitive Impairment on a Tablet: Experimental Study %A Mychajliw,Christian %A Holz,Heiko %A Minuth,Nathalie %A Dawidowsky,Kristina %A Eschweiler,Gerhard Wilhelm %A Metzger,Florian Gerhard %A Wortha,Franz %+ Geriatric Center, University Hospital for Psychiatry and Psychotherapy, University of Tübingen, Calwerstraße 14, Tübingen, 72076, Germany, 49 07071 ext 2985358, christian.mychajliw@med.uni-tuebingen.de %K serial reaction time task %K SRTT %K implicit learning %K mobile digital assessments %K cognitive impairment %K neurodegeneration %K tablet-based testing %K mild cognitive impairment %K MCI %K dementia %K Alzheimer %K neuropsychology %K aging %K older individuals %D 2024 %7 21.3.2024 %9 Original Paper %J JMIR Aging %G English %X Background: Digital neuropsychological tools for diagnosing neurodegenerative diseases in the older population are becoming more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined. The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the serial reaction time task (SRTT). Objective: This study aims to develop and empirically test a digital tablet–based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups. Methods: A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI—diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning. Results: Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=−3.64, SE 0.86; z=−4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (β=−0.34; SE 0.12; t=−2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%). Conclusions: Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice. %M 38512340 %R 10.2196/48265 %U https://aging.jmir.org/2024/1/e48265 %U https://doi.org/10.2196/48265 %U http://www.ncbi.nlm.nih.gov/pubmed/38512340 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e55178 %T Using In-Shoe Inertial Measurement Unit Sensors to Understand Daily-Life Gait Characteristics in Patients With Distal Radius Fractures During 6 Months of Recovery: Cross-Sectional Study %A Yamamoto,Akiko %A Yamada,Eriku %A Ibara,Takuya %A Nihey,Fumiyuki %A Inai,Takuma %A Tsukamoto,Kazuya %A Waki,Tomohiko %A Yoshii,Toshitaka %A Kobayashi,Yoshiyuki %A Nakahara,Kentaro %A Fujita,Koji %+ Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan, 81 3 5803 5279, fujiorth@tmd.ac.jp %K distal radius fracture %K gait analysis %K daily life %K long-term results %K gait %K sensor %K sensors %K walk %K walking %K fracture %K fractures %K wearable %K wearables %K recover %K rehabilitation %K spatiotemporal %K inertial measurement %K fragility %K postmenopausal %K menopause %K grip %K surgery %K surgical %K orthopedic %K postoperative %K orthopedics %K fall %K falls %K bone %K bones %K wrist %K radius %K radial %D 2024 %7 20.3.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A distal radius fracture (DRF) is a common initial fragility fracture among women in their early postmenopausal period, which is associated with an increased risk of subsequent fractures. Gait assessments are valuable for evaluating fracture risk; inertial measurement units (IMUs) have been widely used to assess gait under free-living conditions. However, little is known about long-term changes in patients with DRF, especially concerning daily-life gait. We hypothesized that, in the long term, the daily-life gait parameters in patients with DRF could enable us to reveal future risk factors for falls and fractures. Objective: This study assessed the spatiotemporal characteristics of patients with DRF at 4 weeks and 6 months of recovery. Methods: We recruited 16 women in their postmenopausal period with DRF as their first fragility fracture (mean age 62.3, SD 7.0 years) and 28 matched healthy controls (mean age 65.6, SD 8.0 years). Daily-life gait assessments and physical assessments, such as hand grip strength (HGS), were performed using an in-shoe IMU sensor. Participants’ results were compared with those of the control group, and their recovery was assessed for 6 months after the fracture. Results: In the fracture group, at 4 weeks after DRF, lower foot height in the swing phase (P=.049) and higher variability of stride length (P=.03) were observed, which improved gradually. However, the dorsiflexion angle in the fracture group tended to be lower consistently during 6 months (at 4 weeks: P=.06; during 6 months: P=.07). As for the physical assessments, the fracture group showed lower HGS at all time points (at 4 weeks: P<.001; during 6 months: P=.04), despite significant improvement at 6 months (P<.001). Conclusions: With an in-shoe IMU sensor, we discovered the recovery of spatiotemporal gait characteristics 6 months after DRF surgery without the participants’ awareness. The consistently unchanged dorsiflexion angle in the swing phase and lower HGS could be associated with fracture risk, implying the high clinical importance of appropriate interventions for patients with DRF to prevent future fractures. These results could be applied to a screening tool for evaluating the risk of falls and fractures, which may contribute to constructing a new health care system using wearable devices in the near future. %M 38506913 %R 10.2196/55178 %U https://mhealth.jmir.org/2024/1/e55178 %U https://doi.org/10.2196/55178 %U http://www.ncbi.nlm.nih.gov/pubmed/38506913 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55999 %T Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis %A Jacobucci,Ross %A Ammerman,Brooke %A Ram,Nilam %+ Department of Psychology, University of Notre Dame, 390 Corbett Family Hall, Notre Dame, IN, 46556, United States, 1 574 631 6650, rjacobuc@nd.edu %K screenomics %K digital phenotyping %K passive assessment %K intensive time sampling %K suicide risk %K suicidal behaviors %K risk detection %K Comparative Analysis %K suicide %K suicidal %K risk %K risks %K behavior %K behaviors %K detection %K prediction %K Smartphone-Based %K screenomic %K case review %K participant %K participants %K smartphone %K smartphones %K suicidal ideation %D 2024 %7 20.3.2024 %9 Case Report %J JMIR Form Res %G English %X Background: Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots. Objective: This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research. Methods: To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots—text—captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one’s phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots). Results: Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness. Conclusions: Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual’s psychological processes in the time leading up to a suicidal crisis. %M 38506916 %R 10.2196/55999 %U https://formative.jmir.org/2024/1/e55999 %U https://doi.org/10.2196/55999 %U http://www.ncbi.nlm.nih.gov/pubmed/38506916 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e51249 %T Smartwatch Versus Routine Tremor Documentation: Descriptive Comparison %A van Alen,Catharina Marie %A Brenner,Alexander %A Warnecke,Tobias %A Varghese,Julian %+ Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Bldg A11, Münster, 48149, Germany, 49 0251 8354 714, julian.varghese@uni-muenster.de %K Parkinson disease %K tremor %K smart wearables %K smartwatch %K mobile apps %K movement disorders %K tremor documentation %K tremor occurrence %K tremor score %D 2024 %7 20.3.2024 %9 Research Letter %J JMIR Form Res %G English %X We addressed the limitations of subjective clinical tremor assessment by comparing routine neurological evaluation with a Tremor Occurrence Score derived from smartwatch sensor data, among 142 participants with Parkinson disease and 77 healthy controls. Our findings highlight the potential of smartwatches for automated tremor detection as a valuable addition to conventional assessments, applicable in both clinical and home settings. %M 38506919 %R 10.2196/51249 %U https://formative.jmir.org/2024/1/e51249 %U https://doi.org/10.2196/51249 %U http://www.ncbi.nlm.nih.gov/pubmed/38506919 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e50135 %T Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study %A Lang,Anna-Lena %A Bruhn,Rosa-Lotta %A Fehling,Maya %A Heidenreich,Anouk %A Reisdorf,Jonathan %A Khanyaree,Ifrah %A Henningsen,Maike %A Remschmidt,Cornelius %+ Data4Life gGmbH, c/o Digital Health Cluster im Hasso-Plattner-Institut, Potsdam, 14482, Germany, 49 15756025551, annalena.lang.26@gmail.com %K women’s health %K menstrual cycle %K premenstrual syndrome %K PMS %K mobile app %K wearable device %K sensor data %K digital health %D 2024 %7 12.3.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Despite its importance to women’s reproductive health and its impact on women’s daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures. Objective: The study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle. Methods: This prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle. Results: The average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458). Conclusions: We demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices. %M 38470472 %R 10.2196/50135 %U https://mhealth.jmir.org/2024/1/e50135 %U https://doi.org/10.2196/50135 %U http://www.ncbi.nlm.nih.gov/pubmed/38470472 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 11 %N %P e51116 %T Evaluating the Experiences of Occupational Therapists and Children Using the SensoGrip Pressure-Sensitive Pen in a Handwriting Intervention: Multimethods Study %A Rettinger,Lena %A Schönthaler,Erna %A Kerschbaumer,Andrea %A Hauser,Carina %A Klupper,Carissa %A Aichinger,Lea %A Werner,Franz %+ Health Assisting Engineering, FH Campus Wien, University of Applied Sciences, Favoritenstrasse 226, Vienna, 1100, Austria, 43 606 68 77 ext 4382, lena.rettinger@fh-campuswien.ac.at %K handwriting %K handwriting pressure %K pen %K children %K occupational therapy %K assistive technology %K tablet %K app %D 2024 %7 7.3.2024 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: The acquisition of handwriting skills is essential for a child’s academic success, self-confidence, and general school performance. Nevertheless, an estimated 5% to 27% of children face handwriting challenges, where the ability to modulate pressure on the pencil and lead on the paper is a key motor component. Objective: We aimed to investigate the experience with and usability of the SensoGrip system, a pressure-measuring pen system with personalized real-time feedback about pressure modulation, in a clinical setting with children and occupational therapists (OTs). Methods: A multimethods study was conducted, incorporating qualitative interviews and questionnaires with children, user diaries, focus group discussions, and a usability questionnaire with OTs, along with a questionnaire for parents. Results: The study involved OTs (n=8), children with handwriting difficulties (n=16), and their parents (n=16), each of whom used the SensoGrip system in up to 5 therapy sessions. OTs reported that the SensoGrip system helped to focus the child’s awareness on handwriting pressure and to measure it objectively. The system received high acceptance and usability ratings from the OTs—usefulness: median score of 4 out of 7; ease of use and ease of learning: median score of 6 out of 7; and satisfaction: median score of 6 out of 7. Participants appreciated that it fosters pressure awareness and motivation to draw and write. Conclusions: The SensoGrip pressure-sensing system with real-time feedback is a promising tool for pediatric occupational therapy. It supports children with handwriting difficulties to adjust their pressure application during the task. In the future, controlled quantitative trials are warranted to further examine the system’s impact. %M 38451584 %R 10.2196/51116 %U https://rehab.jmir.org/2024/1/e51116 %U https://doi.org/10.2196/51116 %U http://www.ncbi.nlm.nih.gov/pubmed/38451584 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e52442 %T Six-Month Pilot Testing of a Digital Health Tool to Support Effective Self-Care in People With Heart Failure: Mixed Methods Study %A Keogh,Alison %A Brennan,Carol %A Johnston,William %A Dickson,Jane %A Leslie,Stephen J %A Burke,David %A Megyesi,Peter %A Caulfield,Brian %+ Insight Centre Data Analytics, University College Dublin, Belfield, Dublin, D04V1W8, Ireland, 353 17167777, Alison.keogh@ucd.ie %K digital health %K heart failure %K cardiology %K self-care %K behavior change %K eHealth %K mHealth %K mobile health %K mobile app %K mobile phone %K elderly %K self-care %K self-management %K digital tools %K digital tool %K human-centered design %K app %K apps %K applications %K wearables %K wearable %K Fitbit %K usability %K adherence %K feasibility %K congestive heart failure %K cardiac failure %K myocardial failure %K heart decompensation %D 2024 %7 1.3.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Digital tools may support people to self-manage their heart failure (HF). Having previously outlined the human-centered design development of a digital tool to support self-care of HF, the next step was to pilot the tool over a period of time to establish people’s acceptance of it in practice. Objective: This study aims to conduct an observational pilot study to examine the usability, adherence, and feasibility of a digital health tool for HF within the Irish health care system. Methods: A total of 19 participants with HF were provided with a digital tool comprising a mobile app and the Fitbit Charge 4 and Aria Air smart scales for a period of 6 months. Changes to their self-care were assessed before and after the study with the 9-item European HF Self-care Behavior Scale (EHFScBS) and the Minnesota Living with HF Questionnaire (MLwHFQ) using a Wilcoxon signed rank test. After the study, 3 usability questionnaires were implemented and descriptively analyzed: the System Usability Scale (SUS), Wearable Technology Motivation Scale (WTMS), and Comfort Rating Scale (CRS). Participants also undertook a semistructured interview regarding their experiences with the digital tool. Interviews were analyzed deductively using the Theoretical Domains Framework. Results: Participants wore their devices for an average of 86.2% of the days in the 6-month testing period ranging from 40.6% to 98%. Although improvements in the EHFScBS and MLwHFQ were seen, these changes were not significant (P=.10 and P=.70, respectively, where P>.03, after a Bonferroni correction). SUS results suggest that the usability of this system was not acceptable with a median score of 58.8 (IQR 55.0-60.0; range 45.0-67.5). Participants demonstrated a strong motivation to use the system according to the WTMS (median 6.0, IQR 5.0-7.0; range 1.0-7.0), whereas the Fitbit was considered very comfortable as demonstrated by the low CRS results (median 0.0, IQR 0.0-0.0; range 0.0-2.0). According to participant interviews, the digital tool supported self-management through increased knowledge, improved awareness, decision-making, and confidence in their own data, and improving their social support through a feeling of comfort in being watched. Conclusions: The digital health tool demonstrated high levels of adherence and acceptance among participants. Although the SUS results suggest low usability, this may be explained by participants uncertainty that they were using it fully, rather than it being unusable, especially given the experiences documented in their interviews. The digital tool targeted key self-management behaviors and feelings of social support. However, a number of changes to the tool, and the health service, are required before it can be implemented at scale. A full-scale feasibility trial conducted at a wider level is required to fully determine its potential effectiveness and wider implementation needs. %M 38427410 %R 10.2196/52442 %U https://formative.jmir.org/2024/1/e52442 %U https://doi.org/10.2196/52442 %U http://www.ncbi.nlm.nih.gov/pubmed/38427410 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e47359 %T Iterative Patient Testing of a Stimuli-Responsive Swallowing Activity Sensor to Promote Extended User Engagement During the First Year After Radiation: Multiphase Remote and In-Person Observational Cohort Study %A Shinn,Eileen H %A Garden,Adam S %A Peterson,Susan K %A Leupi,Dylan J %A Chen,Minxing %A Blau,Rachel %A Becerra,Laura %A Rafeedi,Tarek %A Ramirez,Julian %A Rodriquez,Daniel %A VanFossen,Finley %A Zehner,Sydney %A Mercier,Patrick P %A Wang,Joseph %A Hutcheson,Kate %A Hanna,Ehab %A Lipomi,Darren J %+ Department of Behavioral Science, University of Texas, MD Anderson Cancer Center, 1155 Herman Pressler, Unit 1330, PO Box 301439, Houston, TX, 77230-1330, United States, 1 713 745 0870, eshinn@mdanderson.org %K user-centered design %K patients with head and neck cancer %K dysphagia throat sensor %D 2024 %7 28.2.2024 %9 Original Paper %J JMIR Cancer %G English %X Background: Frequent sensor-assisted monitoring of changes in swallowing function may help improve detection of radiation-associated dysphagia before it becomes permanent. While our group has prototyped an epidermal strain/surface electromyography sensor that can detect minute changes in swallowing muscle movement, it is unknown whether patients with head and neck cancer would be willing to wear such a device at home after radiation for several months. Objective: We iteratively assessed patients’ design preferences and perceived barriers to long-term use of the prototype sensor. Methods: In study 1 (questionnaire only), survivors of pharyngeal cancer who were 3-5 years post treatment and part of a larger prospective study were asked their design preferences for a hypothetical throat sensor and rated their willingness to use the sensor at home during the first year after radiation. In studies 2 and 3 (iterative user testing), patients with and survivors of head and neck cancer attending visits at MD Anderson’s Head and Neck Cancer Center were recruited for two rounds of on-throat testing with prototype sensors while completing a series of swallowing tasks. Afterward, participants were asked about their willingness to use the sensor during the first year post radiation. In study 2, patients also rated the sensor’s ease of use and comfort, whereas in study 3, preferences were elicited regarding haptic feedback. Results: The majority of respondents in study 1 (116/138, 84%) were willing to wear the sensor 9 months after radiation, and participant willingness rates were similar in studies 2 (10/14, 71.4%) and 3 (12/14, 85.7%). The most prevalent reasons for participants’ unwillingness to wear the sensor were 9 months being excessive, unwanted increase in responsibility, and feeling self-conscious. Across all three studies, the sensor’s ability to detect developing dysphagia increased willingness the most compared to its appearance and ability to increase adherence to preventive speech pathology exercises. Direct haptic signaling was also rated highly, especially to indicate correct sensor placement and swallowing exercise performance. Conclusions: Patients and survivors were receptive to the idea of wearing a personalized risk sensor for an extended period during the first year after radiation, although this may have been limited to well-educated non-Hispanic participants. A significant minority of patients expressed concern with various aspects of the sensor’s burden and its appearance. Trial Registration: ClinicalTrials.gov NCT03010150; https://clinicaltrials.gov/study/NCT03010150 %M 38416544 %R 10.2196/47359 %U https://cancer.jmir.org/2024/1/e47359 %U https://doi.org/10.2196/47359 %U http://www.ncbi.nlm.nih.gov/pubmed/38416544 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e48954 %T Designing and Validating a Novel Method for Assessing Delay Discounting Associated With Health Behaviors: Ecological Momentary Assessment Study %A Luken,Amanda %A Rabinowitz,Jill A %A Wells,Jonathan L %A Sosnowski,David W %A Strickland,Justin C %A Thrul,Johannes %A Kirk,Gregory D %A Maher,Brion S %+ Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, 624 N Broadway, Baltimore, MD, 212055, United States, 1 4432878287, brion@jhu.edu %K delay discounting %K measurement %K Monetary Choice Questionnaire %K ecological momentary assessment %K substance use %K substance abuse %K questionnaire %K validity %K validation %K measurement %K monetary %K reward %K rewards %K survey %K mobile phone %D 2024 %7 27.2.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Delay discounting quantifies an individual’s preference for smaller, short-term rewards over larger, long-term rewards and represents a transdiagnostic factor associated with numerous adverse health outcomes. Rather than a fixed trait, delay discounting may vary over time and place, influenced by individual and contextual factors. Continuous, real-time measurement could inform adaptive interventions for various health conditions. Objective: The goals of this paper are 2-fold. First, we present and validate a novel, short, ecological momentary assessment (EMA)–based delay discounting scale we developed. Second, we assess this tool’s ability to reproduce known associations between delay discounting and health behaviors (ie, substance use and craving) using a convenience-based sample. Methods: Participants (N=97) were adults (age range 18-71 years), recruited on social media. In phase 1, data were collected on participant sociodemographic characteristics, and delay discounting was evaluated via the traditional Monetary Choice Questionnaire (MCQ) and our novel method (ie, 7-item time-selection and 7-item monetary-selection scales). During phase 2 (approximately 6 months later), participants completed the MCQ, our novel delay discounting measures, and health outcomes questions. The correlations between our method and the traditional MCQ within and across phases were examined. For scale reduction, a random number of items were iteratively selected, and the correlation between the full and random scales was assessed. We then examined the association between our time- and monetary-selection scales assessed during phase 2 and the percentage of assessments that participants endorsed using or craving alcohol, tobacco, or cannabis. Results: In total, 6 of the 7 individual time-selection items were highly correlated with the full scale (r>0.89). Both time-selection (r=0.71; P<.001) and monetary-selection (r=0.66; P<.001) delay discounting rates had high test-retest reliability across phases 1 and 2. Phase 1 MCQ delay discounting function highly correlated with phase 1 (r=0.76; P<.001) and phase 2 (r=0.45; P<.001) time-selection delay discounting scales. One or more randomly chosen time-selection items were highly correlated with the full scale (r>0.94). Greater delay discounting measured via the time-selection measure (adjusted mean difference=5.89, 95% CI 1.99-9.79), but not the monetary-selection scale (adjusted mean difference=–0.62, 95% CI –3.57 to 2.32), was associated with more past-hour tobacco use endorsement in follow-up surveys. Conclusions: This study evaluated a novel EMA-based scale’s ability to validly and reliably assess delay discounting. By measuring delay discounting with fewer items and in situ via EMA in natural environments, researchers may be better able to identify individuals at risk for poor health outcomes. %M 38412027 %R 10.2196/48954 %U https://formative.jmir.org/2024/1/e48954 %U https://doi.org/10.2196/48954 %U http://www.ncbi.nlm.nih.gov/pubmed/38412027 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e44406 %T Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review %A Gheisari,Mehdi %A Ghaderzadeh,Mustafa %A Li,Huxiong %A Taami,Tania %A Fernández-Campusano,Christian %A Sadeghsalehi,Hamidreza %A Afzaal Abbasi,Aaqif %+ School of Nursing and Health Sciences of Boukan, Urmia University of Medical Sciences, Kurdistan Blv Boukan, Urmia, 5951715161, Iran, 98 9129378390, Mustaf.ghaderzadeh@sbmu.ac.ir %K COVID-19 %K detection %K diagnosis %K internet of things %K cloud computing %K mobile applications %K mobile app %K mobile apps %K artificial intelligence: AI %K mobile phone %K smartphone %D 2024 %7 22.2.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. Objective: With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. Methods: In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. Results: Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)–based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. Conclusions: Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients. %M 38231538 %R 10.2196/44406 %U https://mhealth.jmir.org/2024/1/e44406 %U https://doi.org/10.2196/44406 %U http://www.ncbi.nlm.nih.gov/pubmed/38231538 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54194 %T Measurement of Head Circumference Using a Smartphone: Feasibility Cohort Study %A Yordanov,Stefan %A Akhter,Kalsoom %A Quan Teh,Jye %A Naushahi,Jawad %A Jalloh,Ibrahim %+ Academic Division of Neurosurgery, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, University of Cambridge, Hills Rd, Cambridge, CB2 0QQ, United Kingdom, 44 01223 805000 ext 348134, yordanov.stefan@yahoo.com %K head circumference %K HC %K hydrocephalus %K neurosurgery %K pediatric neurosurgery %K paediatric neurosurgery %K neurology %K neuro %K neurosurgeon %K neurologist %K mobile health %K mHealth %K app %K apps %K application %K applications %K digital health %K smartphone %K smartphones %K pediatric %K pediatrics %K paediatric %K paediatrics %K infant %K infants %K infancy %K baby %K babies %K neonate %K neonates %K neonatal %K toddler %K toddlers %K child %K children %D 2024 %7 14.2.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Accurate head circumference (HC) measurement is essential when assessing neonates and infants. Tape measure HC measurements are prone to errors, particularly when performed by parents/guardians, due to individual differences in head shape, hair style and texture, subject cooperation, and examiner techniques, including tape measure placement and tautness. There is, therefore, the need for a more reliable method. Objective: The primary objective of this study was to evaluate the validity, reliability, and consistency of HC app measurement compared to the current standard of practice, serving as a proof-of-concept for use by health care professionals. Methods: We recruited infants attending the neurosurgery clinic, and parents/guardians were approached and consented to participate in the study. Along with the standard head circumference measurement, measurements were taken with the head circumference app (HC app) developed in-house, and we also collected baseline medical history and characteristics. For the statistical analysis, we used RStudio (version 4.1.1). In summary, we analyzed covariance and intraclass correlation coefficient (ICC) to compare the measurement's within-rater and interrater reliability. The F test was used to analyze the variance between measurements and the Bland-Altman agreement, t test, and correlation coefficients were used to compare the tape measurement to the measures taken by the HC app. We also used nonvalidated questionnaires to explore parental or guardians’ experiences, assess their views on app utility, and collect feedback. Results: The total number of recruited patients was 37. Comparison between the app measurements and the measurements with a tape measure showed poor reliability (ICC=0.177) and wide within-app variations (ICC=0.341). The agreement between the measurements done by parents/guardians and the tape measurements done by the researcher was good (ICC=0.901). Parental/guardian feedback was overall very positive, with most of the parents/guardians reporting that the app was easy to use (n=31, 84%) and that they are happy to use the app in an unsupervised setting, provided that they are assured of the measurement quality. Conclusions: We developed this project as a proof-of-concept study, and as such, the app has shown great potential to be used both in a clinical setting and by parents/guardians in their own homes. %M 38354022 %R 10.2196/54194 %U https://formative.jmir.org/2024/1/e54194 %U https://doi.org/10.2196/54194 %U http://www.ncbi.nlm.nih.gov/pubmed/38354022 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e51057 %T Reflective Engagement With a Digital Physical Activity Intervention Among People Living With and Beyond Breast Cancer: Mixed Methods Study %A Robertson,Michael C %A Cox-Martin,Emily %A Basen-Engquist,Karen %A Lyons,Elizabeth J %+ Department of Family and Preventive Medicine, TSET Health Promotion Research Center, University of Oklahoma Health Sciences Center, 655 Research Pkwy #400, Oklahoma City, OK, 73104, United States, 1 405 271 6872, michael-robertson@ouhsc.edu %K survivors of cancer %K exercise %K acceptance and commitment therapy %K fatigue %K mindfulness %K motivation %K behavioral sciences %D 2024 %7 9.2.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: People living with and beyond breast cancer can face internal barriers to physical activity (eg, fatigue and pain). Digital interventions that promote psychological acceptance and motivation may help this population navigate these barriers. The degree to which individuals (1) adhere to intervention protocols and (2) reflect on and internalize intervention content may predict intervention efficacy. Objective: The objective of this study was to characterize the nature of reflective processes brought about by an 8-week acceptance- and mindfulness-based physical activity intervention for insufficiently active survivors of breast cancer (n=75). Furthermore, we explored the potential utility of a metric of reflective processes for predicting study outcomes. Methods: Of the intervention’s 8 weekly modules, 7 (88%) included an item that asked participants to reflect on what they found to be most useful. Two coders conducted directed content analysis on participants’ written responses. They assessed each comment’s depth of reflection using an existing framework (ranging from 0 to 4, with 0=simple description and 4=fundamental change with consideration of social and ethical issues). The coders identified themes within the various levels of reflection. We fit multiple linear regression models to evaluate whether participants’ (1) intervention adherence (ie, number of modules completed) and (2) the mean level of the depth of reflection predicted study outcomes. Results: Participants were aged on average 57.2 (SD 11.2) years, mostly non-Hispanic White (58/75, 77%), and mostly overweight or obese (54/75, 72%). Of the 407 responses to the item prompting personal reflection, 70 (17.2%) were rated as reflection level 0 (ie, description), 247 (60.7%) were level 1 (ie, reflective description), 74 (18.2%) were level 2 (ie, dialogic reflection), 14 (3.4%) were level 3 (ie, transformative reflection), and 2 (0.5%) were level 4 (ie, critical reflection). Lower levels of reflection were characterized by the acquisition of knowledge or expressing intentions. Higher levels were characterized by personal insight, commentary on behavior change processes, and a change of perspective. Intervention adherence was associated with increases in self-reported weekly bouts of muscle-strengthening exercise (B=0.26, SE 0.12, 95% CI 0.02-0.50) and decreases in sleep disturbance (B=−1.04, SE 0.50, 95% CI −0.06 to −2.02). The mean level of reflection was associated with increases in psychological acceptance (B=3.42, SE 1.70, 95% CI 0.09-6.75) and motivation for physical activity (ie, integrated regulation: B=0.55, SE 0.25, 95% CI 0.06-1.04). Conclusions: We identified a useful method for understanding the reflective processes that can occur during digital behavior change interventions serving people living with and beyond breast cancer. Intervention adherence and the depth of reflection each predicted changes in study outcomes. Deeper reflection on intervention content was associated with beneficial changes in the determinants of sustained behavior change. More research is needed to investigate the relations among digital behavior change intervention use, psychological processes, and intervention efficacy. %M 38335025 %R 10.2196/51057 %U https://mhealth.jmir.org/2024/1/e51057 %U https://doi.org/10.2196/51057 %U http://www.ncbi.nlm.nih.gov/pubmed/38335025 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e50196 %T A Smartphone Food Record App Developed for the Dutch National Food Consumption Survey: Relative Validity Study %A Ocké,Marga %A Dinnissen,Ceciel Simone %A van den Bogaard,Coline %A Beukers,Marja %A Drijvers,José %A Sanderman-Nawijn,Eline %A van Rossum,Caroline %A Toxopeus,Ido %+ National Institute for Public Health and the Environment, Antonie van Leeuwenhoeklaan 9, Bilthoven, 3721 MA, Netherlands, 31 088 689 8989, ceciel.dinnissen@rivm.nl %K relative validity %K smartphone food record %K 24-hour dietary recall %K mobile app %K national food consumption surveys %K smartphone %K food %K food consumption %K app %K diet %K dietary intake %K nutrients %K survey %K mobile phone %D 2024 %7 9.2.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In the Dutch National Food Consumption Survey, dietary intake has been assessed since 2003 through 24-hour dietary recalls using the GloboDiet software. A new self-administered smartphone food record app called DitEetIk! was developed for potential use in future surveys. Objective: This study aims to evaluate the data collected using the DitEetIk! app and its relative validity for food group, energy, and nutrient intake compared with the previous dietary assessment method (GloboDiet 24-hour dietary recalls). Methods: A total of 300 participants aged 18 to 79 years were recruited from a consumer panel. Participants were asked to keep a record of their consumption using the DitEetIk! app on 3 nonconsecutive days. Trained dietitians conducted a 24-hour dietary recall interview by telephone using the GloboDiet software (International Agency for Research on Cancer) regarding 1 of 3 DitEetIk! recording days. Nutrient intake was calculated using the NEVO database (version 2021/7.0). Relative validity was studied by comparing data from GloboDiet 24-hour dietary recalls and the DitEetIk app for the same day. Participants with implausible records, defined as days with energy intake of <0.6 or >3.0 basal metabolic rate, were excluded from the analyses. For 19 food groups and 29 nutrients, differences in median intake were assessed using the Wilcoxon signed rank test, and Spearman correlation coefficients were calculated. Bland-Altman plots with mean differences and 95% limits of agreement were created for energy intake and the contribution to energy intake from fat, carbohydrates, and protein. Results: A total of 227 participants completed a combination of a DitEetIk! app recording day and a 24-hour dietary recall interview for the same day. Of this group, 211 participants (n=104, 49.3% men and n=107, 50.7% women) had plausible recording days. Of all recorded food items, 12.8% (114/894) were entered via food barcode scanning, and 18.9% (169/894) were searched at the brand level. For 31% (5/16) of the food groups, the median intake assessed using the DitEetIk! app was >10% lower than that assessed using 24-hour dietary recalls; this was the case for fruit (P=.005), added fats (P=.001), milk and milk products (P=.02), cereal products (P=.01), and sauces (P<.001). This was also the case for 14% (4/29) of the nutrients (all P<.001). Regarding mean intake, differences were generally smaller. Regarding energy intake, the mean difference and 95% limits of agreement were 14 kcal (−1096 to 1124). Spearman correlation coefficients between intake assessed using the DitEetIk! app and 24-hour dietary recalls ranged from 0.48 to 0.88 (median 0.78) for food groups and from 0.58 to 0.90 (median 0.72) for nutrients. Conclusions: Compared with GloboDiet 24-hour dietary recalls, the DitEetIk! app assessed similar mean energy intake levels but somewhat lower median intake levels for several food groups and nutrients. %M 38335009 %R 10.2196/50196 %U https://mhealth.jmir.org/2024/1/e50196 %U https://doi.org/10.2196/50196 %U http://www.ncbi.nlm.nih.gov/pubmed/38335009 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e53597 %T Digital Phenotyping for Real-Time Monitoring of Nonsuicidal Self-Injury: Protocol for a Prospective Observational Study %A Ahn,Chan-Young %A Lee,Jong-Sun %+ Department of Psychology, Kangwon National University, Kangwondaehak-gil, Chuncheon-si, 24341, Republic of Korea, 82 0332506853, sunny597@gmail.com %K nonsuicidal self-injury %K NSSI %K digital phenotyping %K digital phenotype %K wearable device %K wearable %K wearables %K wrist worn %K mood %K emotion %K emotions %K heart rate %K step %K sleep %K machine learning %K multilevel modeling %K ecological momentary assessment %K EMA %K self-injury %K self-harm %K psychiatry %K psychiatric %K mental health %K predict %K prediction %K predictions %K predictor %K predictors %K predictive %D 2024 %7 8.2.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Nonsuicidal self-injury (NSSI) is a major global health concern. The limitations of traditional clinical and laboratory-based methodologies are recognized, and there is a pressing need to use novel approaches for the early detection and prevention of NSSI. Unfortunately, there is still a lack of basic knowledge of a descriptive nature on NSSI, including when, how, and why self-injury occurs in everyday life. Digital phenotyping offers the potential to predict and prevent NSSI by assessing objective and ecological measurements at multiple points in time. Objective: This study aims to identify real-time predictors and explain an individual’s dynamic course of NSSI. Methods: This study will use a hybrid approach, combining elements of prospective observational research with non–face-to-face study methods. This study aims to recruit a cohort of 150 adults aged 20 to 29 years who have self-reported engaging in NSSI on 5 or more days within the past year. Participants will be enrolled in a longitudinal study conducted at 3-month intervals, spanning 3 long-term follow-up phases. The ecological momentary assessment (EMA) technique will be used via a smartphone app. Participants will be prompted to complete a self-injury and suicidality questionnaire and a mood appraisal questionnaire 3 times a day for a duration of 14 days. A wrist-worn wearable device will be used to collect heart rate, step count, and sleep patterns from participants. Dynamic structural equation modeling and machine learning approaches will be used. Results: Participant recruitment and data collection started in October 2023. Data collection and analysis are expected to be completed by December 2024. The results will be published in a peer-reviewed journal and presented at scientific conferences. Conclusions: The insights gained from this study will not only shed light on the underlying mechanisms of NSSI but also pave the way for the development of tailored and culturally sensitive treatment options that can effectively address this major mental health concern. International Registered Report Identifier (IRRID): DERR1-10.2196/53597 %M 38329791 %R 10.2196/53597 %U https://www.researchprotocols.org/2024/1/e53597 %U https://doi.org/10.2196/53597 %U http://www.ncbi.nlm.nih.gov/pubmed/38329791 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e46347 %T Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study %A King,Zachary D %A Yu,Han %A Vaessen,Thomas %A Myin-Germeys,Inez %A Sano,Akane %+ Department of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, TX, 77005, United States, 1 713 348 0000, zdk2@rice.edu %K mobile health %K mHealth %K affect inference %K study design %K ecological momentary assessment %K EMA %K just-in-time adaptive interventions %K JITAIs %K receptivity %K mobile phone %D 2024 %7 7.2.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants’ responses when implementing new survey delivery methods. Objective: This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants’ reported emotional state. We examine the factors that affect participants’ receptivity to EMAs in a 10-day wearable and EMA–based emotional state–sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs. Methods: We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity. Results: Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions. Conclusions: Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points. %M 38324358 %R 10.2196/46347 %U https://mhealth.jmir.org/2024/1/e46347 %U https://doi.org/10.2196/46347 %U http://www.ncbi.nlm.nih.gov/pubmed/38324358 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54299 %T Remote Self-Administration of Cognitive Screeners for Older Adults Prior to a Primary Care Visit: Pilot Cross-Sectional Study of the Reliability and Usability of the MyCog Mobile Screening App %A Young,Stephanie Ruth %A Dworak,Elizabeth McManus %A Byrne,Greg Joseph %A Jones,Callie Madison %A Yao,Lihua %A Yoshino Benavente,Julia Noelani %A Diaz,Maria Varela %A Curtis,Laura %A Gershon,Richard %A Wolf,Michael %A Nowinski,Cindy J %+ Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, 625 North Michigan Avenue 21st Floor, Chicago, IL, 60611-3008, United States, 1 3125033303, stephanieruth.young@northwestern.edu %K cognitive screening %K cognitive %K cognition %K psychometric %K usability %K feasibility %K early detection %K dementia %K Alzheimer’s disease, Alzheimer's %K Alzheimer’s disease and age-related dementia %K mHealth, mobile health apps %K detection %K screening %K mobile health %K mobile phone %K app %K apps %K applications %K applications %K user experience %K smartphone %K smartphones %K gerontology %K geriatric %K geriatrics %K older adult %K older adults %K elder %K elderly %K older person %K older people %K ageing %K aging %K aged %D 2024 %7 7.2.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Routine cognitive screening is essential in the early detection of dementia, but time constraints in primary care settings often limit clinicians’ ability to conduct screenings. MyCog Mobile is a newly developed cognitive screening system that patients can self-administer on their smartphones before a primary care visit, which can help save clinics’ time, encourage broader screening practices, and increase early detection of cognitive decline. Objective: The goal of this pilot study was to examine the feasibility, acceptability, and initial psychometric properties of MyCog Mobile. Research questions included (1) Can older adults complete MyCog Mobile remotely without staff support? (2) Are the internal consistency and test-retest reliability of the measures acceptable? and (3) How do participants rate the user experience of MyCog Mobile? Methods: A sample of adults aged 65 years and older (N=51) self-administered the MyCog Mobile measures remotely on their smartphones twice within a 2- to 3-week interval. The pilot version of MyCog Mobile includes 4 activities: MyFaces measures facial memory, MySorting measures executive functioning, MySequences measures working memory, and MyPictures measures episodic memory. After their first administration, participants also completed a modified version of the Simplified System Usability Scale (S-SUS) and 2 custom survey items. Results: All participants in the sample passed the practice items and completed each measure. Findings indicate that the Mobile Toolbox assessments measure the constructs well (internal consistency 0.73 to 0.91) and are stable over an approximately 2-week delay (test-retest reliability 0.61 to 0.71). Participants’ rating of the user experience (mean S-SUS score 73.17, SD 19.27) indicated that older adults found the usability of MyCog Mobile to be above average. On free-response feedback items, most participants provided positive feedback or no feedback at all, but some indicated a need for clarity in certain task instructions, concerns about participants’ abilities, desire to be able to contact a support person or use in-app technical support, and desire for additional practice items. Conclusions: Pilot evidence suggests that the MyCog Mobile cognitive screener can be reliably self-administered by older adults on their smartphones. Participants in our study generally provided positive feedback about the MyCog Mobile experience and rated the usability of the app highly. Based on participant feedback, we will conduct further usability research to improve support functionality, optimize task instructions and practice opportunities, and ensure that patients feel comfortable using MyCog Mobile. The next steps include a clinical validation study that compares MyCog Mobile to gold-standard assessments and tests the sensitivity and specificity of the measures for identifying dementia. %M 38324368 %R 10.2196/54299 %U https://formative.jmir.org/2024/1/e54299 %U https://doi.org/10.2196/54299 %U http://www.ncbi.nlm.nih.gov/pubmed/38324368 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e45910 %T Sleep Patterns of Premedical Undergraduate Students: Pilot Study and Protocol Evaluation %A Rajput,Gargi %A Gao,Andy %A Wu,Tzu-Chun %A Tsai,Ching-Tzu %A Molano,Jennifer %A Wu,Danny T Y %+ Department of Biomedical Informatics, College of Medicine, University of Cincinnati, 231 Albert Sabin Way, ML0840, Cincinnati, OH, 45267, United States, 1 5135586464, wutz@ucmail.uc.edu %K patient-generated health data %K Fitbit wearables %K sleep quality %K premedical college students %K sleep %K sleep hygiene %K student %K colleges %K university %K postsecondary %K higher education %K survey %K sleep pattern %K medical student %K adolescence %K behavior change %D 2024 %7 2.2.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Poor sleep hygiene persists in college students today, despite its heavy implications on adolescent development and academic performance. Although sleep patterns in undergraduates have been broadly investigated, no study has exclusively assessed the sleep patterns of premedical undergraduate students. A gap also exists in the knowledge of how students perceive their sleep patterns compared to their actual sleep patterns. Objective: This study aims to address 2 research questions: What are the sleep patterns of premedical undergraduate students? Would the proposed study protocol be feasible to examine the perception of sleep quality and promote sleep behavioral changes in premedical undergraduate students? Methods: An anonymous survey was conducted with premedical students in the Medical Science Baccalaureate program at an R1: doctoral university in the Midwest United States to investigate their sleep habits and understand their demographics. The survey consisted of both Pittsburg Sleep Quality Index (PSQI) questionnaire items (1-9) and participant demographic questions. To examine the proposed protocol feasibility, we recruited 5 students from the survey pool for addressing the perception of sleep quality and changes. These participants followed a 2-week protocol wearing Fitbit Inspire 2 watches and underwent preassessments, midassessments, and postassessments. Participants completed daily reflections and semistructured interviews along with PSQI questionnaires during assessments. Results: According to 103 survey responses, premedical students slept an average of 7.1 hours per night. Only a quarter (26/103) of the participants experienced good sleep quality (PSQI<5), although there was no significant difference (P=.11) in the proportions of good (PSQI<5) versus poor sleepers (PSQI≥5) across cohorts. When students perceived no problem at all in their sleep quality, 50% (14/28) of them actually had poor sleep quality. Among the larger proportion of students who perceived sleep quality as only a slight problem, 26% (11/43) of them presented poor sleep quality. High stress levels were associated with poor sleep quality. This study reveals Fitbit as a beneficial tool in raising sleep awareness. Participants highlighted Fitbit elements that aid in comprehension such as being able to visualize their sleep stage breakdown and receive an overview of their sleep pattern by simply looking at their Fitbit sleep scores. In terms of protocol evaluation, participants believed that assessments were conducted within the expected duration, and they did not have a strong opinion about the frequency of survey administration. However, Fitbit was found to provide notable variation daily, leading to missing data. Moreover, the Fitbit app’s feature description was vague and could lead to confusion. Conclusions: Poor sleep quality experienced by unaware premedical students points to a need for raising sleep awareness and developing effective interventions. Future work should refine our study protocol based on lessons learned and health behavior theories and use Fitbit as an informatics solution to promote healthy sleep behaviors. %M 38306175 %R 10.2196/45910 %U https://formative.jmir.org/2024/1/e45910 %U https://doi.org/10.2196/45910 %U http://www.ncbi.nlm.nih.gov/pubmed/38306175 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e47850 %T User Requirements in Developing a Novel Dietary Assessment Tool for Children: Mixed Methods Study %A van der Heijden,Zoë %A de Gooijer,Femke %A Camps,Guido %A Lucassen,Desiree %A Feskens,Edith %A Lasschuijt,Marlou %A Brouwer-Brolsma,Elske %+ Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, Wageningen, 6700 AA, Netherlands, 31 7480 100, zoe.vanderheijden@wur.nl %K diet %K children %K dietary assessment %K recall %K technological innovation %K mobile health %K mHealth %K mobile phone %D 2024 %7 1.2.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The prevalence of childhood obesity and comorbidities is rising alarmingly, and diet is an important modifiable determinant. Numerous dietary interventions in children have been developed to reduce childhood obesity and overweight rates, but their long-term effects are unsatisfactory. Stakeholders call for more personalized approaches, which require detailed dietary intake data. In the case of primary school children, caregivers are key to providing such dietary information. However, as school-aged children are not under the full supervision of one specific caregiver anymore, data are likely to be biased. Recent technological advancements provide opportunities for the role of children themselves, which would serve the overall quality of the obtained dietary data. Objective: This study aims to conduct a child-centered exploratory sequential mixed methods study to identify user requirements for a dietary assessment tool for children aged 5 to 6 years. Methods: Formative, nonsystematic narrative literature research was undertaken to delineate initial user requirements and inform prototype ideation in an expert panel workshop (n=11). This yielded 3 prototype dietary assessment tools: FoodBear (tangible piggy bank), myBear (smartphone or tablet app), and FoodCam (physical camera). All 3 prototypes were tested for usability by means of a usability task (video analyses) and user experience (This or That method) among 14 Dutch children aged 5 to 6 years (n=8, 57% boys and n=6, 43% girls). Results: Most children were able to complete FoodBear’s (11/14, 79%), myBear’s (10/14, 71%), and FoodCam’s (9/14, 64%) usability tasks, but all children required assistance (14/14, 100%) and most of the children encountered usability problems (13/14, 93%). Usability issues were related to food group categorization and recognition, frustrations owing to unsatisfactory functioning of (parts) of the prototypes, recall of food products, and the distinction between eating moments. No short-term differences in product preference between the 3 prototypes were observed, but autonomy, challenge, gaming elements, being tablet based, appearance, social elements, and time frame were identified as determinants of liking the product. Conclusions: Our results suggest that children can play a complementary role in dietary data collection to enhance the data collected by their parents. Incorporation of a training program, auditory or visual prompts, reminders and feedback, a user-friendly and intuitive interaction design, child-friendly food groups or icons, and room for children’s autonomy were identified as requirements for the future development of a novel and usable dietary assessment tool for children aged 5 to 6 years. Our findings can serve as valuable guidance for ongoing innovations in the field of children’s dietary assessment and the provision of personalized dietary support. %M 38300689 %R 10.2196/47850 %U https://formative.jmir.org/2024/1/e47850 %U https://doi.org/10.2196/47850 %U http://www.ncbi.nlm.nih.gov/pubmed/38300689 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54274 %T Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study %A Gannon,Hannah %A Larsson,Leyla %A Chimhuya,Simbarashe %A Mangiza,Marcia %A Wilson,Emma %A Kesler,Erin %A Chimhini,Gwendoline %A Fitzgerald,Felicity %A Zailani,Gloria %A Crehan,Caroline %A Khan,Nushrat %A Hull-Bailey,Tim %A Sassoon,Yali %A Baradza,Morris %A Heys,Michelle %A Chiume,Msandeni %+ Population, Policy and Practice, Institute of Child Health, University College London, 30 Guildford Street, London, WC1N 1EH, United Kingdom, 44 (0) 20 7905 ext 2600, h.gannon@ucl.ac.uk %K mobile health %K mHealth %K neonatology %K digital health %K mobile apps %K newborn %K Malawi, Zimbabwe %K usability %K clinical decision support %D 2024 %7 26.1.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Despite an increase in hospital-based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics, there is significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardizing care. This could be achieved through digital clinical decision support (CDS) tools. Neotree is a digital, quality improvement platform that incorporates CDS, aiming to improve neonatal care in low-resource health care facilities. Before this study, first-phase CDS development included developing and implementing neonatal resuscitation algorithms, creating initial versions of CDS to address a range of neonatal conditions, and a Delphi study to review key algorithms. Objective: This second-phase study aims to codevelop and implement neonatal digital CDS algorithms in Malawi and Zimbabwe. Methods: Overall, 11 diagnosis-specific web-based workshops with Zimbabwean, Malawian, and UK neonatal experts were conducted (August 2021 to April 2022) encompassing the following: (1) review of available evidence, (2) review of country-specific guidelines (Essential Medicines List and Standard Treatment Guidelinesfor Zimbabwe and Care of the Infant and Newborn, Malawi), and (3) identification of uncertainties within the literature for future studies. After agreement of clinical content, the algorithms were programmed into a test script, tested with the respective hospital’s health care professionals (HCPs), and refined according to their feedback. Once finalized, the algorithms were programmed into the Neotree software and implemented at the tertiary-level implementation sites: Sally Mugabe Central Hospital in Zimbabwe and Kamuzu Central Hospital in Malawi, in December 2021 and May 2022, respectively. In Zimbabwe, usability was evaluated through 2 usability workshops and usability questionnaires: Post-Study System Usability Questionnaire (PSSUQ) and System Usability Scale (SUS). Results: Overall, 11 evidence-based diagnostic and management algorithms were tailored to local resource availability. These refined algorithms were then integrated into Neotree. Where national management guidelines differed, country-specific guidelines were created. In total, 9 HCPs attended the usability workshops and completed the SUS, among whom 8 (89%) completed the PSSUQ. Both usability scores (SUS mean score 75.8 out of 100 [higher score is better]; PSSUQ overall score 2.28 out of 7 [lower score is better]) demonstrated high usability of the CDS function but highlighted issues around technical complexity, which continue to be addressed iteratively. Conclusions: This study describes the successful development and implementation of the only known neonatal CDS system, incorporated within a bedside data capture system with the ability to deliver up-to-date management guidelines, tailored to local resource availability. This study highlighted the importance of collaborative participatory design. Further implementation evaluation is planned to guide and inform the development of health system and program strategies to support newborn HCPs, with the ultimate goal of reducing preventable neonatal morbidity and mortality in low-resource settings. %M 38277198 %R 10.2196/54274 %U https://formative.jmir.org/2024/1/e54274 %U https://doi.org/10.2196/54274 %U http://www.ncbi.nlm.nih.gov/pubmed/38277198 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50132 %T Development and Evaluation of a Smartphone-Based Chatbot Coach to Facilitate a Balanced Lifestyle in Individuals With Headaches (BalanceUP App): Randomized Controlled Trial %A Ulrich,Sandra %A Gantenbein,Andreas R %A Zuber,Viktor %A Von Wyl,Agnes %A Kowatsch,Tobias %A Künzli,Hansjörg %+ School of Applied Psychology, Zurich University of Applied Sciences, Pfingstweidstrasse 96, 2, Zurich, 8005, Switzerland, 41 58 934 ext 8451, sandra.ulrich@zhaw.ch %K chatbot %K mobile health %K mHealth %K smartphone %K headache management %K psychoeducation %K behavior change %K stress management %K mental well-being %K lifestyle %K mindfulness %K relaxation %K mobile phone %D 2024 %7 24.1.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Primary headaches, including migraine and tension-type headaches, are widespread and have a social, physical, mental, and economic impact. Among the key components of treatment are behavior interventions such as lifestyle modification. Scalable conversational agents (CAs) have the potential to deliver behavior interventions at a low threshold. To our knowledge, there is no evidence of behavioral interventions delivered by CAs for the treatment of headaches. Objective: This study has 2 aims. The first aim was to develop and test a smartphone-based coaching intervention (BalanceUP) for people experiencing frequent headaches, delivered by a CA and designed to improve mental well-being using various behavior change techniques. The second aim was to evaluate the effectiveness of BalanceUP by comparing the intervention and waitlist control groups and assess the engagement and acceptance of participants using BalanceUP. Methods: In an unblinded randomized controlled trial, adults with frequent headaches were recruited on the web and in collaboration with experts and allocated to either a CA intervention (BalanceUP) or a control condition. The effects of the treatment on changes in the primary outcome of the study, that is, mental well-being (as measured by the Patient Health Questionnaire Anxiety and Depression Scale), and secondary outcomes (eg, psychosomatic symptoms, stress, headache-related self-efficacy, intention to change behavior, presenteeism and absenteeism, and pain coping) were analyzed using linear mixed models and Cohen d. Primary and secondary outcomes were self-assessed before and after the intervention, and acceptance was assessed after the intervention. Engagement was measured during the intervention using self-reports and usage data. Results: A total of 198 participants (mean age 38.7, SD 12.14 y; n=172, 86.9% women) participated in the study (intervention group: n=110; waitlist control group: n=88). After the intervention, the intention-to-treat analysis revealed evidence for improved well-being (treatment: β estimate=–3.28, 95% CI –5.07 to –1.48) with moderate between-group effects (Cohen d=–0.66, 95% CI –0.99 to –0.33) in favor of the intervention group. We also found evidence of reduced somatic symptoms, perceived stress, and absenteeism and presenteeism, as well as improved headache management self-efficacy, application of behavior change techniques, and pain coping skills, with effects ranging from medium to large (Cohen d=0.43-1.05). Overall, 64.8% (118/182) of the participants used coaching as intended by engaging throughout the coaching and completing the outro. Conclusions: BalanceUP was well accepted, and the results suggest that coaching delivered by a CA can be effective in reducing the burden of people who experience headaches by improving their well-being. Trial Registration: German Clinical Trials Register DRKS00017422; https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS00017422 %M 38265863 %R 10.2196/50132 %U https://www.jmir.org/2024/1/e50132 %U https://doi.org/10.2196/50132 %U http://www.ncbi.nlm.nih.gov/pubmed/38265863 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e47157 %T A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study %A Daryabeygi-Khotbehsara,Reza %A Rawstorn,Jonathan C %A Dunstan,David W %A Shariful Islam,Sheikh Mohammed %A Abdelrazek,Mohamed %A Kouzani,Abbas Z %A Thummala,Poojith %A McVicar,Jenna %A Maddison,Ralph %+ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Hw, Melbourne Burwood, 3125, Australia, 61 3 924 45936, reza.d@deakin.edu.au %K activity tracker %K algorithms %K deep neural network %K machine learning %K real-time data %K Sedentary behaviOR Detector %K sedentary behavior %K SORD %K standing %K validation %K walking %K wearables %D 2024 %7 24.1.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. Objective: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. Methods: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. Results: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=–0.3% to 0.9%), 1.19% for standing (LoA=–1.5% to 3.42%), and –4.71% for walking (LoA=–9.26% to –0.16%). The mean biases between SORD and activPAL were –3.45% for sitting and reclining (LoA=–11.59% to 4.68%), 7.45% for standing (LoA=–5.04% to 19.95%), and –5.40% for walking (LoA=–11.44% to 0.64%). Conclusions: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior. %M 38265864 %R 10.2196/47157 %U https://formative.jmir.org/2024/1/e47157 %U https://doi.org/10.2196/47157 %U http://www.ncbi.nlm.nih.gov/pubmed/38265864 %0 Journal Article %@ 2291-5222 %I %V 12 %N %P e47632 %T The Goldilocks Dilemma on Balancing User Response and Reflection in mHealth Interventions: Observational Study %A Nelson,Lyndsay A %A Spieker,Andrew J %A LeStourgeon,Lauren M %A Greevy Jr,Robert A %A Molli,Samuel %A Roddy,McKenzie K %A Mayberry,Lindsay S %K engagement %K mobile phone %K text messaging %K messaging %K SMS %K diabetes %K diabetic %K mobile health %K mHealth %K technology %K user response %K users %K quality of life %K engagement %K mHealth management %K management %K socioeconomic %K effectiveness %K support person %K support worker %K support persons %K text message %K text messages %K reflection %K behavior change %D 2024 %7 19.1.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) has the potential to radically improve health behaviors and quality of life; however, there are still key gaps in understanding how to optimize mHealth engagement. Most engagement research reports only on system use without consideration of whether the user is reflecting on the content cognitively. Although interactions with mHealth are critical, cognitive investment may also be important for meaningful behavior change. Notably, content that is designed to request too much reflection could result in users’ disengagement. Understanding how to strike the balance between response burden and reflection burden has critical implications for achieving effective engagement to impact intended outcomes. Objective: In this observational study, we sought to understand the interplay between response burden and reflection burden and how they impact mHealth engagement. Specifically, we explored how varying the response and reflection burdens of mHealth content would impact users’ text message response rates in an mHealth intervention. Methods: We recruited support persons of people with diabetes for a randomized controlled trial that evaluated an mHealth intervention for diabetes management. Support person participants assigned to the intervention (n=148) completed a survey and received text messages for 9 months. During the 2-year randomized controlled trial, we sent 4 versions of a weekly, two-way text message that varied in both reflection burden (level of cognitive reflection requested relative to that of other messages) and response burden (level of information requested for the response relative to that of other messages). We quantified engagement by using participant-level response rates. We compared the odds of responding to each text and used Poisson regression to estimate associations between participant characteristics and response rates. Results: The texts requesting the most reflection had the lowest response rates regardless of response burden (high reflection and low response burdens: median 10%, IQR 0%-40%; high reflection and high response burdens: median 23%, IQR 0%-51%). The response rate was highest for the text requesting the least reflection (low reflection and low response burdens: median 90%, IQR 61%-100%) yet still relatively high for the text requesting medium reflection (medium reflection and low response burdens: median 75%, IQR 38%-96%). Lower odds of responding were associated with higher reflection burden (P<.001). Younger participants and participants who had a lower socioeconomic status had lower response rates to texts with more reflection burden, relative to those of their counterparts (all P values were <.05). Conclusions: As reflection burden increased, engagement decreased, and we found more disparities in engagement across participants’ characteristics. Content encouraging moderate levels of reflection may be ideal for achieving both cognitive investment and system use. Our findings provide insights into mHealth design and the optimization of both engagement and effectiveness. %R 10.2196/47632 %U https://mhealth.jmir.org/2024/1/e47632 %U https://doi.org/10.2196/47632 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e44214 %T Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial %A White,Katie M %A Carr,Ewan %A Leightley,Daniel %A Matcham,Faith %A Conde,Pauline %A Ranjan,Yatharth %A Simblett,Sara %A Dawe-Lane,Erin %A Williams,Laura %A Henderson,Claire %A Hotopf,Matthew %+ Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom, 44 020 7848 0002, katie.white@kcl.ac.uk %K remote measurement %K technology %K engagement %K app %K depression %K smartphones %K wearable devices %K engagement %K symptom tracking %K self-awareness %K community %K mobile phone %D 2024 %7 19.1.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Multiparametric remote measurement technologies (RMTs), which comprise smartphones and wearable devices, have the potential to revolutionize understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the utmost importance for the validity of predictive analytical methods and long-term use and can be conceptualized as both objective engagement (data availability) and subjective engagement (system usability and experiential factors). Positioning the design of user interfaces within the theoretical framework of the Behavior Change Wheel can help maximize effectiveness. In-app components containing information from credible sources, visual feedback, and access to support provide an opportunity to promote engagement with RMTs while minimizing team resources. Randomized controlled trials are the gold standard in quantifying the effects of in-app components on engagement with RMTs in patients with MDD. Objective: This study aims to evaluate whether a multiparametric RMT system with theoretically informed notifications, visual progress tracking, and access to research team contact details could promote engagement with remote symptom tracking over and above the system as usual. We hypothesized that participants using the adapted app (intervention group) would have higher engagement in symptom monitoring, as measured by objective and subjective engagement. Methods: A 2-arm, parallel-group randomized controlled trial (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms used the RADAR-base system, comprising a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50, 50%) also had access to additional in-app components. The primary outcome was objective engagement, measured as the percentage of weekly questionnaires completed during follow-up. The secondary outcomes measured subjective engagement (system engagement, system usability, and emotional self-awareness). Results: The levels of completion of the Patient Health Questionnaire-8 (PHQ-8) were similar between the control (67/97, 69%) and intervention (66/97, 68%) arms (P value for the difference between the arms=.83, 95% CI −9.32 to 11.65). The intervention group participants reported slightly higher user engagement (1.93, 95% CI −1.91 to 5.78), emotional self-awareness (1.13, 95% CI −2.93 to 5.19), and system usability (2.29, 95% CI −5.93 to 10.52) scores than the control group participants at follow-up; however, all CIs were wide and included 0. Process evaluation suggested that participants saw the in-app components as helpful in increasing task completion. Conclusions: The adapted system did not increase objective or subjective engagement in remote symptom tracking in our research cohort. This study provides an important foundation for understanding engagement with RMTs for research and the methodologies by which this work can be replicated in both community and clinical settings. Trial Registration: ClinicalTrials.gov NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474 International Registered Report Identifier (IRRID): RR2-10.2196/32653 %M 38241070 %R 10.2196/44214 %U https://mhealth.jmir.org/2024/1/e44214 %U https://doi.org/10.2196/44214 %U http://www.ncbi.nlm.nih.gov/pubmed/38241070 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 11 %N %P e49222 %T Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study %A Wadle,Lisa-Marie %A Ebner-Priemer,Ulrich W %A Foo,Jerome C %A Yamamoto,Yoshiharu %A Streit,Fabian %A Witt,Stephanie H %A Frank,Josef %A Zillich,Lea %A Limberger,Matthias F %A Ablimit,Ayimnisagul %A Schultz,Tanja %A Gilles,Maria %A Rietschel,Marcella %A Sirignano,Lea %+ Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Hertzstrasse 16, Bldg 06.31, Karlsruhe, 76187, Germany, 49 72160847543, lisa.wadle@kit.edu %K ambulatory assessment %K experience sampling %K ecological momentary assessment %K speech features %K speech pattern %K depression %K sleep deprivation therapy %K mobile phone %D 2024 %7 18.1.2024 %9 Original Paper %J JMIR Ment Health %G English %X Background: The use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs. Objective: Previous research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessments to compare depressive and nondepressive episodes within the same patient. To our knowledge, no study has used a series of measurements within patients with depression (eg, intensive longitudinal data) to model the dynamic ebb and flow of subjectively reported depression and concomitant speech samples. However, such data are indispensable for detecting and ultimately preventing upcoming episodes. Methods: In this study, we captured voice samples and momentary affect ratings over the course of 3 weeks in a sample of patients (N=30) with an acute depressive episode receiving stationary care. Patients underwent sleep deprivation therapy, a chronotherapeutic intervention that can rapidly improve depression symptomatology. We hypothesized that within-person variability in depressive and affective momentary states would be reflected in the following 3 speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) from open-source Speech and Music Interpretation by Large-Space Extraction (openSMILE; audEERING GmbH) and extracted them from a transcript. We analyzed the speech features along with self-reported momentary affect ratings, using multilevel linear regression analysis. We analyzed an average of 32 (SD 19.83) assessments per patient. Results: Analyses revealed that pitch variability, speech pauses, and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; furthermore, speech pauses and speech rate were associated with negative affect, and speech pauses were additionally associated with calmness. Specifically, pitch variability was negatively associated with improved momentary states (ie, lower pitch variability was linked to lower depression severity as well as higher positive affect, valence, and energetic arousal). Speech pauses were negatively associated with improved momentary states, whereas speech rate was positively associated with improved momentary states. Conclusions: Pitch variability, speech pauses, and speech rate are promising features for the development of clinical prediction technologies to improve patient care as well as timely diagnosis and monitoring of treatment response. Our research is a step forward on the path to developing an automated depression monitoring system, facilitating individually tailored treatments and increased patient empowerment. %M 38236637 %R 10.2196/49222 %U https://mental.jmir.org/2024/1/e49222 %U https://doi.org/10.2196/49222 %U http://www.ncbi.nlm.nih.gov/pubmed/38236637 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e47177 %T SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment %A Gunsilius,Chloe Zimmerman %A Heffner,Joseph %A Bruinsma,Sienna %A Corinha,Madison %A Cortinez,Maria %A Dalton,Hadley %A Duong,Ellen %A Lu,Joshua %A Omar,Aisulu %A Owen,Lucy Long Whittington %A Roarr,Bradford Nazario %A Tang,Kevin %A Petzschner,Frederike H %+ Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Carney Institute for Brain Science, 4th floor, 164 Angell Street, Providence, RI, 02912, United States, 1 401 863 6272, chloe_zimmerman@brown.edu %K acute pain %K acute-chronic pain transition %K chronic pain %K clinical outcome measurement %K digital health %K ecological momentary assessment %K EMA %K ESM %K experience sampling methodology %K mHealth %K mobile health %K pain management %K pain self-management %K patient reported outcomes %K smartphone app %D 2024 %7 12.1.2024 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users’ self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain. %M 38214952 %R 10.2196/47177 %U https://mhealth.jmir.org/2024/1/e47177 %U https://doi.org/10.2196/47177 %U http://www.ncbi.nlm.nih.gov/pubmed/38214952 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e47091 %T Assessing and Improving Data Integrity in Web-Based Surveys: Comparison of Fraud Detection Systems in a COVID-19 Study %A Bonett,Stephen %A Lin,Willey %A Sexton Topper,Patrina %A Wolfe,James %A Golinkoff,Jesse %A Deshpande,Aayushi %A Villarruel,Antonia %A Bauermeister,José %+ School of Nursing, University of Pennsylvania, 418 Curie Boulevard, Philadelphia, PA, 19104, United States, 1 2155734299, stepdo@nursing.upenn.edu %K web-based survey %K data quality %K fraud %K survey methodology %K COVID-19 %K survey %K fraud detection %K Philadelphia %K data privacy %K data protection %K privacy %K security %K data %K information security %K data validation %K cross-sectional %K web-based %D 2024 %7 12.1.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Web-based surveys increase access to study participation and improve opportunities to reach diverse populations. However, web-based surveys are vulnerable to data quality threats, including fraudulent entries from automated bots and duplicative submissions. Widely used proprietary tools to identify fraud offer little transparency about the methods used, effectiveness, or representativeness of resulting data sets. Robust, reproducible, and context-specific methods of accurately detecting fraudulent responses are needed to ensure integrity and maximize the value of web-based survey research. Objective: This study aims to describe a multilayered fraud detection system implemented in a large web-based survey about COVID-19 attitudes, beliefs, and behaviors; examine the agreement between this fraud detection system and a proprietary fraud detection system; and compare the resulting study samples from each of the 2 fraud detection methods. Methods: The PhillyCEAL Common Survey is a cross-sectional web-based survey that remotely enrolled residents ages 13 years and older to assess how the COVID-19 pandemic impacted individuals, neighborhoods, and communities in Philadelphia, Pennsylvania. Two fraud detection methods are described and compared: (1) a multilayer fraud detection strategy developed by the research team that combined automated validation of response data and real-time verification of study entries by study personnel and (2) the proprietary fraud detection system used by the Qualtrics (Qualtrics) survey platform. Descriptive statistics were computed for the full sample and for responses classified as valid by 2 different fraud detection methods, and classification tables were created to assess agreement between the methods. The impact of fraud detection methods on the distribution of vaccine confidence by racial or ethnic group was assessed. Results: Of 7950 completed surveys, our multilayer fraud detection system identified 3228 (40.60%) cases as valid, while the Qualtrics fraud detection system identified 4389 (55.21%) cases as valid. The 2 methods showed only “fair” or “minimal” agreement in their classifications (κ=0.25; 95% CI 0.23-0.27). The choice of fraud detection method impacted the distribution of vaccine confidence by racial or ethnic group. Conclusions: The selection of a fraud detection method can affect the study’s sample composition. The findings of this study, while not conclusive, suggest that a multilayered approach to fraud detection that includes conservative use of automated fraud detection and integration of human review of entries tailored to the study’s specific context and its participants may be warranted for future survey research. %M 38214962 %R 10.2196/47091 %U https://formative.jmir.org/2024/1/e47091 %U https://doi.org/10.2196/47091 %U http://www.ncbi.nlm.nih.gov/pubmed/38214962 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e47781 %T Adolescent and Parent Perspectives on Digital Phenotyping in Youths With Chronic Pain: Cross-Sectional Mixed Methods Survey Study %A Nestor,Bridget A %A Chimoff,Justin %A Koike,Camila %A Weitzman,Elissa R %A Riley,Bobbie L %A Uhl,Kristen %A Kossowsky,Joe %+ Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, 1 Autumn St, Boston, MA, 02215, United States, 1 617 355 0965, bridget.nestor@childrens.harvard.edu %K acceptability %K adolescent %K chronic pain %K digital phenotyping %K mobile health %K pediatric %D 2024 %7 11.1.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Digital phenotyping is a promising methodology for capturing moment-to-moment data that can inform individually adapted and timely interventions for youths with chronic pain. Objective: This study aimed to investigate adolescent and parent endorsement, perceived utility, and concerns related to passive data stream collection through smartphones for digital phenotyping for clinical and research purposes in youths with chronic pain. Methods: Through multiple-choice and open-response survey questions, we assessed the perspectives of patient-parent dyads (103 adolescents receiving treatment for chronic pain at a pediatric hospital with an average age of 15.6, SD 1.6 years, and 99 parents with an average age of 47.8, SD 6.3 years) on passive data collection from the following 9 smartphone-embedded passive data streams: accelerometer, apps, Bluetooth, SMS text message and call logs, keyboard, microphone, light, screen, and GPS. Results: Quantitative and qualitative analyses indicated that adolescents and parent endorsement and perceived utility of digital phenotyping varied by stream, though participants generally endorsed the use of data collected by passive stream (35%-75.7% adolescent endorsement for clinical use and 37.9%-74.8% for research purposes; 53.5%-81.8% parent endorsement for clinical and 52.5%-82.8% for research purposes) if a certain level of utility could be provided. For adolescents and parents, adjusted logistic regression results indicated that the perceived utility of each stream significantly predicted the likelihood of endorsement of its use in both clinical practice and research (Ps<.05). Adolescents and parents alike identified accelerometer, light, screen, and GPS as the passive data streams with the highest utility (36.9%-47.5% identifying streams as useful). Similarly, adolescents and parents alike identified apps, Bluetooth, SMS text message and call logs, keyboard, and microphone as the passive data streams with the least utility (18.5%-34.3% identifying streams as useful). All participants reported primary concerns related to privacy, accuracy, and validity of the collected data. Passive data streams with the greatest number of total concerns were apps, Bluetooth, call and SMS text message logs, keyboard, and microphone. Conclusions: Findings support the tailored use of digital phenotyping for this population and can help refine this methodology toward an acceptable, feasible, and ethical implementation of real-time symptom monitoring for assessment and intervention in youths with chronic pain. %M 38206665 %R 10.2196/47781 %U https://www.jmir.org/2024/1/e47781 %U https://doi.org/10.2196/47781 %U http://www.ncbi.nlm.nih.gov/pubmed/38206665 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e50293 %T Dissemination Strategies for mHealth Apps: Systematic Review %A Moungui,Henri Claude %A Nana-Djeunga,Hugues Clotaire %A Anyiang,Che Frankline %A Cano,Mireia %A Ruiz Postigo,Jose Antonio %A Carrion,Carme %+ Universitat Oberta de Catalunya, Rambla del Poblenou, 156, Barcelona, 08018, Spain, 34 672192283, henrimoungui@yahoo.fr %K mobile health %K mHealth %K mobile health apps %K mHealth apps %K dissemination %K marketing strategies %K digital marketing %K engagement %K onboarding %K systematic review %K systematic %K market %K marketing %K app %K apps %K adoption %K consumer %K mobile phone %D 2024 %7 5.1.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Among the millions of mobile apps in existence, thousands fall under the category of mobile health (mHealth). Although the utility of mHealth apps has been demonstrated for disease diagnosis, treatment data management, and health promotion strategies, to be effective they must reach and be used by their target audience. An appropriate marketing strategy can ensure that apps reach potential users and potentially convert them to actual users. Such a strategy requires definitions of target end users, communication channels, and advertising content, as well as a timeline for effectively reaching and motivating end users to adopt and maintain engagement with the mHealth app. Objective: The aim of this study was to identify strategies and elements that ensure that end users adopt and remain engaged with mHealth apps. Methods: A systematic search of the PubMed, PsycINFO, Scopus, and CINAHL databases was conducted for suitable studies published between January 1, 2018, and September 30, 2022. Two researchers independently screened studies for inclusion, extracted data, and assessed the risk of bias. The main outcome was dissemination strategies for mHealth apps. Results: Of the 648 papers retrieved from the selected databases, only 10 (1.5%) met the inclusion criteria. The marketing strategies used in these studies to inform potential users of the existence of mHealth apps and motivate download included both paid and unpaid strategies and used various channels, including social media, emails, printed posters, and face-to-face communication. Most of the studies reported a combination of marketing concepts used to advertise their mHealth apps. Advertising messages included instructions on where and how to download and install the apps. In most of the studies (6/10, 60%), instructions were oriented toward how to use the apps and maintain engagement with a health intervention. The most frequently used paid marketing platform was Facebook Ads Manager (2/10, 20%). Advertising performance was influenced by many factors, including but not limited to advertising content. In 1 (10%) of the 10 studies, animated graphics generated the greatest number of clicks compared with other image types. The metrics used to assess marketing strategy effectiveness were number of downloads; nonuse rate; dropout rate; adherence rate; duration of app use; and app usability over days, weeks, or months. Additional indicators such as cost per click, cost per install, and clickthrough rate were mainly used to assess the cost-effectiveness of paid marketing campaigns. Conclusions: mHealth apps can be disseminated via paid and unpaid marketing strategies using various communication channels. The effects of these strategies are reflected in download numbers and user engagement with mHealth apps. Further research could provide guidance on a framework for disseminating mHealth apps and encouraging their routine use. %M 38180796 %R 10.2196/50293 %U https://mhealth.jmir.org/2024/1/e50293 %U https://doi.org/10.2196/50293 %U http://www.ncbi.nlm.nih.gov/pubmed/38180796 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e46744 %T Documentation Completeness and Nurses’ Perceptions of a Novel Electronic App for Medical Resuscitation in the Emergency Room: Mixed Methods Approach %A Cheung,Kin %A Yip,Chak Sum %+ School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Hong Kong, China (Hong Kong), 852 2766 6773, kin.cheung@polyu.edu.hk %K tablet computer %K nursing documentation %K paper resuscitation record %K electronic resuscitation record %K medical resuscitation %K electronic medical record %K documentation %K resuscitation %K electronic health record %K nurses’ perception %K traditional paper record %K nurse %D 2024 %7 5.1.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Complete documentation of critical care events in the accident and emergency department (AED) is essential. Due to the fast-paced and complex nature of resuscitation cases, missing data is a common issue during emergency situations. Objective: This study aimed to evaluate the impact of a tablet-based resuscitation record on documentation completeness during medical resuscitations and nurses’ perceptions of the use of the tablet app. Methods: A mixed methods approach was adopted. To collect quantitative data, randomized retrospective reviews of paper-based resuscitation records before implementation of the tablet (Pre-App Paper; n=176), paper-based resuscitation records after implementation of the tablet (Post-App Paper; n=176), and electronic tablet-based resuscitation records (Post-App Electronic; n=176) using a documentation completeness checklist were conducted. The checklist was validated by 4 experts in the emergency medicine field. The content validity index (CVI) was calculated using the scale CVI (S-CVI). The universal agreement S-CVI was 0.822, and the average S-CVI was 0.939. The checklist consisted of the following 5 domains: basic information, vital signs, procedures, investigations, and medications. To collect qualitative data, nurses’ perceptions of the app for electronic resuscitation documentation were obtained using individual interviews. Reporting of the qualitative data was guided by Consolidated Criteria for Reporting Qualitative Studies (COREQ) to enhance rigor. Results: A significantly higher documentation rate in all 5 domains (ie, basic information, vital signs, procedures, investigations, and medications) was present with Post-App Electronic than with Post-App Paper, but there were no significant differences in the 5 domains between Pre-App Paper and Post-App Paper. The qualitative analysis resulted in main categories of “advantages of tablet-based documentation of resuscitation records,” “challenges with tablet-based documentation of resuscitation records,” and “areas for improvement of tablet-based resuscitation records.” Conclusions: This study demonstrated that higher documentation completion rates are achieved with electronic tablet-based resuscitation records than with traditional paper records. During the transition period, the nurse documenters faced general problems with resuscitation documentation such as multitasking and unique challenges such as software updates and a need to familiarize themselves with the app’s layout. Automation should be considered during future app development to improve documentation and redistribute more time for patient care. Nurses should continue to provide feedback on the app’s usability and functionality during app refinement to ensure a successful transition and future development of electronic documentation records. %M 38180801 %R 10.2196/46744 %U https://mhealth.jmir.org/2024/1/e46744 %U https://doi.org/10.2196/46744 %U http://www.ncbi.nlm.nih.gov/pubmed/38180801 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e51125 %T Selecting an Ecological Momentary Assessment Platform: Tutorial for Researchers %A Henry,Lauren M %A Hansen,Eleanor %A Chimoff,Justin %A Pokstis,Kimberly %A Kiderman,Miryam %A Naim,Reut %A Kossowsky,Joe %A Byrne,Meghan E %A Lopez-Guzman,Silvia %A Kircanski,Katharina %A Pine,Daniel S %A Brotman,Melissa A %+ Emotion and Development Branch, National Institute of Mental Health, 9000 Rockville Pike, Building 15K, Bethesda, MD, 20892, United States, 1 301 480 3895, lauren.henry@nih.gov %K ecological momentary assessment %K methodology %K psychology and psychiatry %K child and adolescent %K in vivo and real time %D 2024 %7 4.1.2024 %9 Tutorial %J J Med Internet Res %G English %X Background: Although ecological momentary assessment (EMA) has been applied in psychological research for decades, delivery methods have evolved with the proliferation of digital technology. Technological advances have engendered opportunities for enhanced accessibility, convenience, measurement precision, and integration with wearable sensors. Notwithstanding, researchers must navigate novel complexities in EMA research design and implementation. Objective: In this paper, we aimed to provide guidance on platform selection for clinical scientists launching EMA studies. Methods: Our team includes diverse specialties in child and adolescent behavioral and mental health with varying expertise on EMA platforms (eg, users and developers). We (2 research sites) evaluated EMA platforms with the goal of identifying the platform or platforms with the best fit for our research. We created a list of extant EMA platforms; conducted a web-based review; considered institutional security, privacy, and data management requirements; met with developers; and evaluated each of the candidate EMA platforms for 1 week. Results: We selected 2 different EMA platforms, rather than a single platform, for use at our 2 research sites. Our results underscore the importance of platform selection driven by individualized and prioritized laboratory needs; there is no single, ideal platform for EMA researchers. In addition, our project generated 11 considerations for researchers in selecting an EMA platform: (1) location; (2) developer involvement; (3) sample characteristics; (4) onboarding; (5) survey design features; (6) sampling scheme and scheduling; (7) viewing results; (8) dashboards; (9) security, privacy, and data management; (10) pricing and cost structure; and (11) future directions. Furthermore, our project yielded a suggested timeline for the EMA platform selection process. Conclusions: This study will guide scientists initiating studies using EMA, an in vivo, real-time research tool with tremendous promise for facilitating advances in psychological assessment and intervention. %M 38175682 %R 10.2196/51125 %U https://www.jmir.org/2024/1/e51125 %U https://doi.org/10.2196/51125 %U http://www.ncbi.nlm.nih.gov/pubmed/38175682 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e39211 %T Comparing Self-Reported Running Distance and Pace With a Commercial Fitness Watch Data: Reliability Study %A Bullock,Garrett %A Stocks,Joanne %A Feakins,Benjamin %A Alizadeh,Zahra %A Arundale,Amelia %A Kluzek,Stefan %+ Wake Forest School of Medicine, 475 Vine St, Winston-Salem, NC, 27411, United States, 1 3367144264, garrettbullock@gmail.com %K GPS %K Garmin %K training load %K running %K exercise %K fitness %K wearables %K running %K running distance %K pace %K pace distance %D 2024 %7 4.1.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: There is substantial evidence exploring the reliability of running distance self-reporting and GPS wearable technology, but there are currently no studies investigating the reliability of participant self-reporting in comparison to GPS wearable technology. There is also a critical sports science and medical research gap due to a paucity of reliability studies assessing self-reported running pace. Objective: The purpose of this study was to assess the reliability of weekly self-reported running distance and pace compared to a commercial GPS fitness watch, stratified by sex and age. These data will give clinicians and sports researchers insights into the reliability of runners’ self-reported pace, which may improve training designs and rehabilitation prescriptions. Methods: A prospective study of recreational runners was performed. Weekly running distance and average running pace were captured through self-report and a fitness watch. Baseline characteristics collected included age and sex. Intraclass correlational coefficients were calculated for weekly running distance and running pace for self-report and watch data. Bland-Altman plots assessed any systemic measurement error. Analyses were then stratified by sex and age. Results: Younger runners reported improved weekly distance reliability (median 0.93, IQR 0.92-0.94). All ages demonstrated similar running pace reliability. Results exhibited no discernable systematic bias. Conclusions: Weekly self-report demonstrated good reliability for running distance and moderate reliability for running pace in comparison to the watch data. Similar reliability was observed for male and female participants. Younger runners demonstrated improved running distance reliability, but all age groups exhibited similar pace reliability. Running pace potentially should be monitored through technological means to increase precision. %M 38175696 %R 10.2196/39211 %U https://formative.jmir.org/2024/1/e39211 %U https://doi.org/10.2196/39211 %U http://www.ncbi.nlm.nih.gov/pubmed/38175696 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e48834 %T Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study %A Chen,Hung-Hsun %A Lu,Henry Horng-Shing %A Weng,Wei-Hung %A Lin,Yu-Hsuan %+ Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road Zhunan, Miaoli County, 35053, Taiwan, 886 37 206 166 ext 36383, yuhsuanlin@nhri.edu.tw %K human-smartphone interaction %K digital phenotyping %K work hours %K machine learning %K deep learning %K probability in work mode %K one-dimensional convolutional neural network %K extreme gradient-boosted trees %D 2023 %7 29.12.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Traditional methods for investigating work hours rely on an employee’s physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. Objective: In this study, we aimed to develop a novel approach called “probability in work mode,” which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. Methods: To capture human-smartphone interactions and GPS locations, we used the “Staff Hours” app, developed by our team, to passively and continuously record participants’ screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. Results: Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes. Conclusions: Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being. %M 38157232 %R 10.2196/48834 %U https://www.jmir.org/2023/1/e48834 %U https://doi.org/10.2196/48834 %U http://www.ncbi.nlm.nih.gov/pubmed/38157232 %0 Journal Article %@ 2817-092X %I JMIR Publications %V 2 %N %P e50660 %T Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation %A Sriraam,Natarajan %A Raghu,S %A Gommer,Erik D %A Hilkman,Danny M W %A Temel,Yasin %A Vasudeva Rao,Shyam %A Hegde,Alangar Satyaranjandas %A L Kubben,Pieter %+ Center for Medical Electronics and Computing, Ramaiah Institute of Technology, MSRIT Post, M S Ramaiah Nagar, Bengaluru, 560054, India, 91 9632294999, sriraam@msrit.edu %K Android %K epileptic seizures %K mobile health %K mHealth %K mobile phone–based epilepsy monitoring %K support vector machine %K seizure %K epileptic %K epilepsy %K monitoring %K smartphone %K smartphones %K mobile phone %K neurology %K neuroscience %K electroencephalography %K EEG %K brain %K classification %K detect %K detection %K neurological %K electroencephalogram %K diagnose %K diagnosis %K diagnostic %K imaging %D 2023 %7 19.12.2023 %9 Original Paper %J JMIR Neurotech %G English %X Background: Implementing automated seizure detection in long-term electroencephalography (EEG) analysis enables the remote monitoring of patients with epilepsy, thereby improving their quality of life. Objective: The objective of this study was to explore an mHealth (mobile health) solution by investigating the feasibility of smartphones for processing large EEG recordings for the remote monitoring of patients with epilepsy. Methods: We developed a mobile app to automatically analyze and classify epileptic seizures using EEG. We used the cross-database model developed in our previous study, incorporating successive decomposition index and matrix determinant as features, adaptive median feature baseline correction for overcoming interdatabase feature variation, and postprocessing-based support vector machine for classification using 5 different EEG databases. The Sezect (Seizure Detect) Android app was built using the Chaquopy software development kit, which uses the Python language in Android Studio. Various durations of EEG signals were tested on different smartphones to check the feasibility of the Sezect app. Results: We observed a sensitivity of 93.5%, a specificity of 97.5%, and a false detection rate of 1.5 per hour for EEG recordings using the Sezect app. The various mobile phones did not differ substantially in processing time, which indicates a range of phone models can be used for implementation. The computational time required to process real-time EEG data via smartphones and the classification results suggests that our mHealth app could be a valuable asset for monitoring patients with epilepsy. Conclusions: Smartphones have multipurpose use in health care, offering tools that can improve the quality of patients’ lives. %R 10.2196/50660 %U https://neuro.jmir.org/2023/1/e50660 %U https://doi.org/10.2196/50660 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e44762 %T Narrowing the Patient–Physician Gap Based on Self-Reporting and Monthly Hepatologist Feedback for Patients With Alcohol-Related Liver Disease: Interventional Pilot Study Using a Journaling Smartphone App %A Yamashiki,Noriyo %A Kawabata,Kyoko %A Murata,Miki %A Ikeda,Shunichiro %A Fujimaki,Takako %A Suwa,Kanehiko %A Seki,Toshihito %A Aramaki,Eiji %A Naganuma,Makoto %+ Department of Gastroenterology and Hepatology, Kansai Medical University Medical Center, Fumizono 10-15, Moriguchi, Osaka, 570-8507, Japan, 81 6 6992 1001, yamashno@takii.kmu.ac.jp %K alcohol-related liver disease %K alcohol use disorder %K alcoholism %K smartphone %K mobile health %D 2023 %7 19.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Screening and intervention for alcohol use disorders (AUDs) are recommended to improve the prognosis of patients with alcohol-related liver disease (ALD). Most patients’ smartphone app diaries record drinking behavior for self-monitoring. A smartphone app can be expected to also be helpful for physicians because it can provide rich patient information to hepatologists, leading to suitable feedback. We conducted this prospective pilot study to assess the use of a smartphone app as a journaling tool and as a self-report–based feedback source for patients with ALD. Objective: The aims of this study were assessment of whether journaling (self-report) and self-report–based feedback can help patients maintain abstinence and improve liver function data. Methods: This pilot study used a newly developed smartphone journaling app for patients, with input data that physicians can review. After patients with ALD were screened for harmful alcohol use, some were invited to use the smartphone journaling app for 8 weeks. Their self-reported alcohol intake, symptoms, and laboratory data were recorded at entry, week 4, and week 8. Biomarkers for alcohol use included gamma glutamyl transferase (GGT), percentage of carbohydrate-deficient transferrin to transferrin (%CDT), and GGT-CDT (GGT-CDT= 0.8 × ln[GGT] + 1.3 × ln[%CDT]). At each visit, their recorded data were reviewed by a hepatologist to evaluate changes in alcohol consumption and laboratory data. The relation between those outcomes and app usage was also investigated. Results: Of 14 patients agreeing to participate, 10 completed an 8-week follow-up, with diary input rates between 44% and 100% of the expected days. Of the 14 patients, 2 withdrew from clinical follow-up, and 2 additional patients never used the smartphone journaling app. Using the physician’s view, a treating hepatologist gave feedback via comments to patients at each visit. Mean self-reported alcohol consumption dropped from baseline (100, SD 70 g) to week 4 (13, SD 25 g; P=.002) and remained lower at week 8 (13, SD 23 g; P=.007). During the study, 5 patients reported complete abstinence. No significant changes were found in mean GGT and mean %CDT alone, but the mean GGT-CDT combination dropped significantly from entry (5.2, SD 1.2) to the week 4 visit (4.8, SD 1.1; P=.02) and at week 8 (4.8, SD 1.0; P=.01). During the study period, decreases in mean total bilirubin (3.0, SD 2.4 mg/dL to 2.4, SD 1.9 mg/dL; P=.01) and increases in mean serum albumin (3.0, SD 0.9 g/dL to 3.3, SD 0.8 g/dL; P=.009) were recorded. Conclusions: These pilot study findings revealed that a short-term intervention with a smartphone journaling app used by both patients and treatment-administering hepatologists was associated with reduced drinking and improved liver function. Trial Registration: UMIN CTR UMIN000045285; http://tinyurl.com/yvvk38tj %R 10.2196/44762 %U https://formative.jmir.org/2023/1/e44762/ %U https://doi.org/10.2196/44762 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e51336 %T Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study %A Kawai,Keita %A Iwamoto,Kunihiro %A Miyata,Seiko %A Okada,Ippei %A Fujishiro,Hiroshige %A Noda,Akiko %A Nakagome,Kazuyuki %A Ozaki,Norio %A Ikeda,Masashi %+ Department of Psychiatry, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa, Nagoya, 466-8550, Japan, 81 52 744 2282, iwamoto@med.nagoya-u.ac.jp %K consumer sleep-tracking device %K polysomnography %K portable sleep EEG monitor %K electroencephalography %K EEG %K psychiatric disorders %K sleep logs %K sleep state misperception %K polysomnography %K sleep study %K wearable %K psychiatric disorder %K sleep %K disturbances %K quality of sleep %K Fitbit %K mHealth %K wearables %K psychiatry %K electroencephalogram %D 2023 %7 13.12.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive. Objective: This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders. Methods: We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs. Results: The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=–0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=–0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments. Conclusions: Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders. %M 38090797 %R 10.2196/51336 %U https://www.jmir.org/2023/1/e51336 %U https://doi.org/10.2196/51336 %U http://www.ncbi.nlm.nih.gov/pubmed/38090797 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46778 %T Digital Phenotyping for Monitoring Mental Disorders: Systematic Review %A Bufano,Pasquale %A Laurino,Marco %A Said,Sara %A Tognetti,Alessandro %A Menicucci,Danilo %+ Institute of Clinical Physiology, National Research Council, via Giuseppe Moruzzi,1, Pisa, 56124, Italy, 39 0503152181, marco.laurino@cnr.it %K digital phenotyping %K mobile %K mental health %K smartphone %K mobile sensing %K passive sensing %K active sensing %K digital phenotype %K digital biomarker %K mobile phone %D 2023 %7 13.12.2023 %9 Review %J J Med Internet Res %G English %X Background: The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. Objective: This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. Methods: Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. Results: We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. Conclusions: Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation). %M 38090800 %R 10.2196/46778 %U https://www.jmir.org/2023/1/e46778 %U https://doi.org/10.2196/46778 %U http://www.ncbi.nlm.nih.gov/pubmed/38090800 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47873 %T Sit-to-Stand Video Analysis–Based App for Diagnosing Sarcopenia and Its Relationship With Health-Related Risk Factors and Frailty in Community-Dwelling Older Adults: Diagnostic Accuracy Study %A Ruiz-Cárdenas,Juan D %A Montemurro,Alessio %A Martínez-García,María del Mar %A Rodríguez-Juan,Juan J %+ Physiotherapy Department, Faculty of Physiotherapy, Podiatry and Occupational Therapy, Universidad Católica de Murcia, Avenida Los Jerónimos s/n 135, Murcia, 30107, Spain, 34 968278800, jdruiz@ucam.edu %K sarcopenia %K power %K calf circumference %K diagnosis %K screening %K affordable %K community dwelling %K older adults %K smartphone %D 2023 %7 8.12.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Probable sarcopenia is determined by a reduction in muscle strength assessed with the handgrip strength test or 5 times sit-to-stand test, and it is confirmed with a reduction in muscle quantity determined by dual-energy X-ray absorptiometry or bioelectrical impedance analysis. However, these parameters are not implemented in clinical practice mainly due to a lack of equipment and time constraints. Nowadays, the technical innovations incorporated in most smartphone devices, such as high-speed video cameras, provide the opportunity to develop specific smartphone apps for measuring kinematic parameters related with sarcopenia during a simple sit-to-stand transition. Objective: We aimed to create and validate a sit-to-stand video analysis–based app for diagnosing sarcopenia in community-dwelling older adults and to analyze its construct validity with health-related risk factors and frailty. Methods: A total of 686 community-dwelling older adults (median age: 72 years; 59.2% [406/686] female) were recruited from elderly social centers. The index test was a sit-to-stand video analysis–based app using muscle power and calf circumference as proxies of muscle strength and muscle quantity, respectively. The reference standard was obtained by different combinations of muscle strength (handgrip strength or 5 times sit-to-stand test result) and muscle quantity (appendicular skeletal mass or skeletal muscle index) as recommended by the European Working Group on Sarcopenia in Older People-2 (EWGSOP2). Sensitivity, specificity, positive and negative predictive values, and area under the curve (AUC) of the receiver operating characteristic curve were calculated to determine the diagnostic accuracy of the app. Construct validity was evaluated using logistic regression to identify the risks associated with health-related outcomes and frailty (Fried phenotype) among those individuals who were classified as having sarcopenia by the index test. Results: Sarcopenia prevalence varied from 2% to 11% according to the different combinations proposed by the EWGSOP2 guideline. Sensitivity, specificity, and AUC were 70%-83.3%, 77%-94.9%, and 80.5%-87.1%, respectively, depending on the diagnostic criteria used. Likewise, positive and negative predictive values were 10.6%-43.6% and 92.2%-99.4%, respectively. These results proved that the app was reliable to rule out the disease. Moreover, those individuals who were diagnosed with sarcopenia according to the index test showed more odds of having health-related adverse outcomes and frailty compared to their respective counterparts, regardless of the definition proposed by the EWGSOP2. Conclusions: The app showed good diagnostic performance for detecting sarcopenia in well-functioning Spanish community-dwelling older adults. Individuals with sarcopenia diagnosed by the app showed more odds of having health-related risk factors and frailty compared to their respective counterparts. These results highlight the potential use of this app in clinical settings. Trial Registration: ClinicalTrials.gov NCT05148351; https://clinicaltrials.gov/study/NCT05148351 International Registered Report Identifier (IRRID): RR2-10.3390/s22166010 %M 38064268 %R 10.2196/47873 %U https://www.jmir.org/2023/1/e47873 %U https://doi.org/10.2196/47873 %U http://www.ncbi.nlm.nih.gov/pubmed/38064268 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e39791 %T Internet Tool to Support Self-Assessment and Self-Swabbing of Sore Throat: Development and Feasibility Study %A Lown,Mark %A Smith,Kirsten A %A Muller,Ingrid %A Woods,Catherine %A Maund,Emma %A Rogers,Kirsty %A Becque,Taeko %A Hayward,Gail %A Moore,Michael %A Little,Paul %A Glogowska,Margaret %A Hay,Alastair %A Stuart,Beth %A Mantzourani,Efi %A Wilcox,Christopher R %A Thompson,Natalie %A Francis,Nick A %+ Nuffield Department of Primary Health Care Sciences, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 01865617872, margaret.glogowska@phc.ox.ac.uk %K sore throat %K ear, neck, throat %K pharyngitis %K self-assessment %K self-swabbing %K primary care %K throat %K development %K feasibility %K web-based tool %K tool %K antibiotics %K develop %K self-assess %K symptoms %K diagnostic testing %K acceptability %K adult %K children %K social media %K saliva %K swab %K inflammation %K samples %K support %K clinical %K antibiotic %K web-based support tool %K think-aloud %K neck %K tonsil %K tongue %K teeth %K dental %K dentist %K tooth %K laboratory %K lab %K oral %K oral health %K mouth %K mobile phone %D 2023 %7 8.12.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Sore throat is a common problem and a common reason for the overuse of antibiotics. A web-based tool that helps people assess their sore throat, through the use of clinical prediction rules, taking throat swabs or saliva samples, and taking throat photographs, has the potential to improve self-management and help identify those who are the most and least likely to benefit from antibiotics. Objective: We aimed to develop a web-based tool to help patients and parents or carers self-assess sore throat symptoms and take throat photographs, swabs, and saliva samples for diagnostic testing. We then explored the acceptability and feasibility of using the tool in adults and children with sore throats. Methods: We used the Person-Based Approach to develop a web-based tool and then recruited adults and children with sore throats who participated in this study by attending general practices or through social media advertising. Participants self-assessed the presence of FeverPAIN and Centor score criteria and attempted to photograph their throat and take throat swabs and saliva tests. Study processes were observed via video call, and participants were interviewed about their views on using the web-based tool. Self-assessed throat inflammation and pus were compared to clinician evaluation of patients’ throat photographs. Results: A total of 45 participants (33 adults and 12 children) were recruited. Of these, 35 (78%) and 32 (71%) participants completed all scoring elements for FeverPAIN and Centor scores, respectively, and most (30/45, 67%) of them reported finding self-assessment relatively easy. No valid response was provided for swollen lymph nodes, throat inflammation, and pus on the throat by 11 (24%), 9 (20%), and 13 (29%) participants respectively. A total of 18 (40%) participants provided a throat photograph of adequate quality for clinical assessment. Patient assessment of inflammation had a sensitivity of 100% (3/3) and specificity of 47% (7/15) compared with the clinician-assessed photographs. For pus on the throat, the sensitivity was 100% (3/3) and the specificity was 71% (10/14). A total of 89% (40/45), 93% (42/45), 89% (40/45), and 80% (30/45) of participants provided analyzable bacterial swabs, viral swabs, saliva sponges, and saliva drool samples, respectively. Participants were generally happy and confident in providing samples, with saliva samples rated as slightly more acceptable than swab samples. Conclusions: Most adult and parent participants were able to use a web-based intervention to assess the clinical features of throat infections and generate scores using clinical prediction rules. However, some had difficulties assessing clinical signs, such as lymph nodes, throat pus, and inflammation, and scores were assessed as sensitive but not specific. Many participants had problems taking photographs of adequate quality, but most were able to take throat swabs and saliva samples. %M 38064265 %R 10.2196/39791 %U https://www.jmir.org/2023/1/e39791 %U https://doi.org/10.2196/39791 %U http://www.ncbi.nlm.nih.gov/pubmed/38064265 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 8 %N %P e51515 %T Measuring Heart Rate Accurately in Patients With Parkinson Disease During Intense Exercise: Usability Study of Fitbit Charge 4 %A Colonna,Giulia %A Hoye,Jocelyn %A de Laat,Bart %A Stanley,Gelsina %A Ibrahimy,Alaaddin %A Tinaz,Sule %A Morris,Evan D %+ Department of Radiology and Biomedical Imaging, Yale University, 40 Temple St, New Haven, CT, 06520, United States, 1 (203) 737, colonna.1844724@studenti.uniroma1.it %K Fitbit %K heart rate measurements %K Parkinson disease %K exercise %K accuracy %K intensity %K heart rate %K wearable %K neurodegenerative disease %K aerobic exercise %K physical exercise %K program %K device %D 2023 %7 8.12.2023 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Parkinson disease (PD) is the second most common neurodegenerative disease, affecting approximately 1% of the world’s population. Increasing evidence suggests that aerobic physical exercise can be beneficial in mitigating both motor and nonmotor symptoms of the disease. In a recent pilot study of the role of exercise on PD, we sought to confirm exercise intensity by monitoring heart rate (HR). For this purpose, we asked participants to wear a chest strap HR monitor (Polar Electro Oy) and the Fitbit Charge 4 (Fitbit Inc) wrist-worn HR monitor as a potential proxy due to its convenience. Polar H10 has been shown to provide highly accurate R-R interval measurements. Therefore, we treated it as the gold standard in this study. It has been shown that Fitbit Charge 4 has comparable accuracy to Polar H10 in healthy participants. It has yet to be determined if the Fitbit is as accurate as Polar H10 in patients with PD during rest and exercise. Objective: This study aimed to compare Fitbit Charge 4 to Polar H10 for monitoring HR in patients with PD at rest and during an intensive exercise program. Methods: A total of 596 exercise sessions from 11 (6 male and 5 female) participants were collected simultaneously with both devices. Patients with early-stage PD (Hoehn and Yahr ≤2) were enrolled in a 6-month exercise program designed for patients with PD. They participated in 3 one-hour exercise sessions per week. They wore both Fitbit and Polar H10 during each session. Sessions included rest, warm-up, intense exercise, and cool-down periods. We calculated the bias in the HR of the Fitbit Charge 4 at rest (5 min) and during intense exercise (20 min) by comparing the mean HR during each of the periods to the respective means measured by Polar H10 (HRFitbit – HRPolar). We also measured the sensitivity and specificity of Fitbit Charge 4 to detect average HRs that exceed the threshold for intensive exercise, defined as 70% of an individual’s theoretical maximum HR. Different types of correlations between the 2 devices were investigated. Results: The mean bias was 1.68 beats per minute (bpm) at rest and 6.29 bpm during high-intensity exercise, with an overestimation by Fitbit Charge 4 in both conditions. The mean bias of the Fitbit across both rest and intensive exercise periods was 3.98 bpm. The device’s sensitivity in identifying high-intensity exercise sessions was 97.14%. The correlation between the 2 devices was nonlinear, suggesting Fitbit’s tendency to saturate at high values of HR. Conclusions: The performance of Fitbit Charge 4 is comparable to Polar H10 for assessing exercise intensity in a cohort of patients with PD (mean bias 3.98 bpm). The device could be considered a reasonable surrogate for more cumbersome chest-worn devices in future studies of clinical cohorts. %M 38875680 %R 10.2196/51515 %U https://biomedeng.jmir.org/2023/1/e51515 %U https://doi.org/10.2196/51515 %U http://www.ncbi.nlm.nih.gov/pubmed/38875680 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e46866 %T Designing a Clinician-Centered Wearable Data Dashboard (CarePortal): Participatory Design Study %A Sadhu,Shehjar %A Solanki,Dhaval %A Brick,Leslie A %A Nugent,Nicole R %A Mankodiya,Kunal %+ University of Rhode Island, 45 Upper College Rd, Kingston, RI, 02881, United States, 1 7746412663, shehjar_sadhu@uri.edu %K digital health %K wearables %K smart watch %K smartwatch %K symptom monitoring %K mobile health %K mHealth %K participatory design %K stress management %K monitoring %K eHealth %K wearable technology %K remote monitoring %K physical stress %K psychological stress %K stress %K data interpretation %K visualization %K questionnaire %K decision-making %K mobile phone %D 2023 %7 5.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The recent growth of eHealth is unprecedented, especially after the COVID-19 pandemic. Within eHealth, wearable technology is increasingly being adopted because it can offer the remote monitoring of chronic and acute conditions in daily life environments. Wearable technology may be used to monitor and track key indicators of physical and psychological stress in daily life settings, providing helpful information for clinicians. One of the key challenges is to present extensive wearable data to clinicians in an easily interpretable manner to make informed decisions. Objective: The purpose of this research was to design a wearable data dashboard, named CarePortal, to present analytic visualizations of wearable data that are meaningful to clinicians. The study was divided into 2 main research objectives: to understand the needs of clinicians regarding wearable data interpretation and visualization and to develop a system architecture for a web application to visualize wearable data and related analytics. Methods: We used a wearable data set collected from 116 adolescent participants who experienced trauma. For 2 weeks, participants wore a Microsoft Band that logged physiological sensor data such as heart rate (HR). A total of 834 days of HR data were collected. To design the CarePortal dashboard, we used a participatory design approach that interacted directly with clinicians (stakeholders) with backgrounds in clinical psychology and neuropsychology. A total of 8 clinicians were recruited from the Rhode Island Hospital and the University of Massachusetts Memorial Health. The study involved 5 stages of participatory workshops and began with an understanding of the needs of clinicians. A User Experience Questionnaire was used at the end of the study to quantitatively evaluate user experience. Physiological metrics such as daily and hourly maximum, minimum, average, and SD of HR and HR variability, along with HR-based activity levels, were identified. This study investigated various data visualization graphing methods for wearable data, including radar charts, stacked bar plots, scatter plots combined with line plots, simple bar plots, and box plots. Results: We created a CarePortal dashboard after understanding the clinicians’ needs. Results from our workshops indicate that overall clinicians preferred aggregate information such as daily HR instead of continuous HR and want to see trends in wearable sensor data over a period (eg, days). In the User Experience Questionnaire, a score of 1.4 was received, which indicated that CarePortal was exciting to use (question 5), and a similar score was received, indicating that CarePortal was the leading edge (question 8). On average, clinicians reported that CarePortal was supportive and can be useful in making informed decisions. Conclusions: We concluded that the CarePortal dashboard integrated with wearable sensor data visualization techniques would be an acceptable tool for clinicians to use in the future. %M 38051573 %R 10.2196/46866 %U https://formative.jmir.org/2023/1/e46866 %U https://doi.org/10.2196/46866 %U http://www.ncbi.nlm.nih.gov/pubmed/38051573 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e49135 %T Using Mobile Ecological Momentary Assessment to Understand Consumption and Context Around Online Food Delivery Use: Pilot Feasibility and Acceptability Study %A Jia,Si Si %A Allman-Farinelli,Margaret %A Roy,Rajshri %A Phongsavan,Philayrath %A Hyun,Karice %A Gibson,Alice Anne %A Partridge,Stephanie Ruth %+ School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown NSW 2006, Sydney, 2006, Australia, 61 2 8627 1697, sisi.jia@sydney.edu.au %K ecological momentary assessment %K mobile applications %K mobile apps %K feasibility studies %K online food delivery %K smartphone %K young adult %K adolescent %K food environment %K consumer behavior %K mobile phone %D 2023 %7 29.11.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile ecological momentary assessment (EMA) is a powerful tool for collecting real-time and contextual data from individuals. As our reliance on online technologies to increase convenience accelerates, the way we access food is changing. Online food delivery (OFD) services may further encourage unhealthy food consumption habits, given the high availability of energy-dense, nutrient-poor foods. We used EMA to understand the real-time effects of OFD on individuals’ food choices and consumption behaviors. Objective: The primary aims of this pilot study were to assess the feasibility and acceptability of using EMA in young users of OFD and compare 2 different EMA sampling methods. The secondary aims were to gather data on OFD events and their context and examine any correlations between demographics, lifestyle chronic disease risk factors, and OFD use. Methods: This study used EMA methods via a mobile app (mEMASense, ilumivu Inc). Existing users of OFD services aged 16 to 35 years in Australia who had access to a smartphone were recruited. Participants were randomly assigned to 1 of 2 groups: signal-contingent or event-contingent. The signal-contingent group was monitored over 3 days between 7 AM and 10 PM. They received 5 prompts each day to complete EMA surveys via the smartphone app. In contrast, the event-contingent group was monitored over 7 days and was asked to self-report any instance of OFD. Results: A total of 102 participants were analyzed, with 53 participants in the signal-contingent group and 49 participants in the event-contingent group. Compliance rates, indicating the feasibility of signal-contingent and event-contingent protocols, were similar at 72.5% (574/792) and 73.2% (251/343), respectively. Feedback from the participants suggested that the EMA app was not easy to use, which affected their acceptability of the study. Participants in the event-contingent group were 3.53 (95% CI 1.52-8.17) times more likely to have had an OFD event captured during the study. Pizza (23/124, 18.5%) and fried chicken (18/124, 14.5%) comprised a bulk of the 124 OFD orders captured. Most orders were placed at home (98/124, 79%) for 1 person (68/124, 54.8%). Age (incidence rate ratio 0.95, 95% CI 0.91-0.99; P=.03) and dependents (incidence rate ratio 2.01, 95% CI 1.16-3.49; P=.01) were significantly associated with the number of OFD events in a week after adjusting for gender, socioeconomic status, diet quality score, and perceived stress levels. Conclusions: This pilot study showed that EMA using an event-contingent sampling approach may be a better method to capture OFD events and context than signal-contingent sampling. The compliance rates showed that both sampling methods were feasible and acceptable. Although the findings from this study have gathered some insight on the consumption and context of OFD in young people, further studies are required to develop targeted interventions. %M 38019563 %R 10.2196/49135 %U https://mhealth.jmir.org/2023/1/e49135 %U https://doi.org/10.2196/49135 %U http://www.ncbi.nlm.nih.gov/pubmed/38019563 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e50701 %T Diagnostic Accuracy of Single-Lead Electrocardiograms Using the Kardia Mobile App and the Apple Watch 4: Validation Study %A Klier,Kristina %A Koch,Lucas %A Graf,Lisa %A Schinköthe,Timo %A Schmidt,Annette %+ Institute of Sport Science, University of the Bundeswehr Munich, Werner-Heisenberg-Weg 39, Neubiberg, 85577, Germany, 49 8960042382, kristina.klier@unibw.de %K accuracy %K electrocardiography %K eHealth %K mHealth %K mobile health %K app %K applications %K mobile monitoring %K electrocardiogram %K ECG %K telemedicine %K diagnostic %K diagnosis %K monitoring %K heart %K cardiology %K mobile phone %D 2023 %7 23.11.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: To date, the 12-lead electrocardiogram (ECG) is the gold standard for cardiological diagnosis in clinical settings. With the advancements in technology, a growing number of smartphone apps and gadgets for recording, visualizing, and evaluating physical performance as well as health data is available. Although this new smart technology is innovative and time- and cost-efficient, less is known about its diagnostic accuracy and reliability. Objective: This study aimed to examine the agreement between the mobile single-lead ECG measurements of the Kardia Mobile App and the Apple Watch 4 compared to the 12-lead gold standard ECG in healthy adults under laboratory conditions. Furthermore, it assessed whether the measurement error of the devices increases with an increasing heart rate. Methods: This study was designed as a prospective quasi-experimental 1-sample measurement, in which no randomization of the sampling was carried out. In total, ECGs at rest from 81 participants (average age 24.89, SD 8.58 years; n=58, 72% male) were recorded and statistically analyzed. Bland-Altman plots were created to graphically illustrate measurement differences. To analyze the agreement between the single-lead ECGs and the 12-lead ECG, Pearson correlation coefficient (r) and Lin concordance correlation coefficient (CCCLin) were calculated. Results: The results showed a higher agreement for the Apple Watch (mean deviation QT: 6.85%; QT interval corrected for heart rate using Fridericia formula [QTcF]: 7.43%) than Kardia Mobile (mean deviation QT: 9.53%; QTcF: 9.78%) even if both tend to underestimate QT and QTcF intervals. For Kardia Mobile, the QT and QTcF intervals correlated significantly with the gold standard (rQT=0.857 and rQTcF=0.727; P<.001). CCCLin corresponded to an almost complete heuristic agreement for the QT interval (0.835), whereas the QTcF interval was in the range of strong agreement (0.682). Further, for the Apple Watch, Pearson correlations were highly significant and in the range of a large effect (rQT=0.793 and rQTcF=0.649; P<.001). CCCLin corresponded to a strong heuristic agreement for both the QT (0.779) and QTcF (0.615) intervals. A small negative correlation between the measurement error and increasing heart rate could be found of each the devices and the reference. Conclusions: Smart technology seems to be a promising and reliable approach for nonclinical health monitoring. Further research is needed to broaden the evidence regarding its validity and usability in different target groups. %M 37995111 %R 10.2196/50701 %U https://cardio.jmir.org/2023/1/e50701 %U https://doi.org/10.2196/50701 %U http://www.ncbi.nlm.nih.gov/pubmed/37995111 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e46937 %T Association Between the Characteristics of mHealth Apps and User Input During Development and Testing: Secondary Analysis of App Assessment Data %A Frey,Anna-Lena %A Baines,Rebecca %A Hunt,Sophie %A Kent,Rachael %A Andrews,Tim %A Leigh,Simon %+ Organisation for the Review of Care and Health Apps, V2, Sci-Tech Daresbury, Keckwick Lane, Daresbury, WA4 4FS, United Kingdom, 44 01925 606542, anna.frey@orchahealth.com %K patient and public involvement %K user involvement %K mobile apps %K digital health %K mobile health %K quality assessment %D 2023 %7 22.11.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: User involvement is increasingly acknowledged as a central part of health care innovation. However, meaningful user involvement during the development and testing of mobile health apps is often not fully realized. Objective: This study aims to examine in which areas user input is most prevalent and whether there is an association between user inclusion and compliance with best practices for mobile health apps. Methods: A secondary analysis was conducted on an assessment data set of 1595 health apps. The data set contained information on whether the apps had been developed or tested with user input and whether they followed best practices across several domains. Background information was also available regarding the apps’ country of origin, targeted condition areas, subjective user ratings, download numbers, and risk (as per the National Institute for Health and Care Excellence Evidence Standards Framework [ESF]). Descriptive statistics, Mann-Whitney U tests, and Pearson chi-square analyses were applied to the data. Results: User involvement was reported by 8.71% (139/1595) of apps for only the development phase, by 33.67% (537/1595) of apps for only the testing phase, by 21.88% (349/1595) of apps for both phases, and by 35.74% (570/1595) of apps for neither phase. The highest percentage of health apps with reported user input during development was observed in Denmark (19/24, 79%); in the condition areas of diabetes (38/79, 48%), cardiology (15/32, 47%), pain management (20/43, 47%), and oncology (25/54, 46%); and for high app risk (ESF tier 3a; 105/263, 39.9%). The highest percentage of health apps with reported user input during testing was observed in Belgium (10/11, 91%), Sweden (29/34, 85%), and France (13/16, 81%); in the condition areas of neurodiversity (42/52, 81%), respiratory health (58/76, 76%), cardiology (23/32, 72%), and diabetes (56/79, 71%); and for high app risk (ESF tier 3a; 176/263, 66.9%). Notably, apps that reported seeking user input during testing demonstrated significantly more downloads than those that did not (P=.008), and user inclusion was associated with better compliance with best practices in clinical assurance, data privacy, risk management, and user experience. Conclusions: The countries and condition areas in which the highest percentage of health apps with user involvement were observed tended to be those with higher digital maturity in health care and more funding availability, respectively. This suggests that there may be a trade-off between developers’ willingness or ability to involve users and the need to meet challenges arising from infrastructure limitations and financial constraints. Moreover, the finding of a positive association between user inclusion and compliance with best practices indicates that, where no other guidance is available, users may benefit from prioritizing health apps developed with user input as the latter may be a proxy for broader app quality. %M 37991822 %R 10.2196/46937 %U https://mhealth.jmir.org/2023/1/e46937 %U https://doi.org/10.2196/46937 %U http://www.ncbi.nlm.nih.gov/pubmed/37991822 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43368 %T German Version of the Mobile Agnew Relationship Measure: Translation and Validation Study %A von Wulffen,Clemens %A Marciniak,Marta Anna %A Rohde,Judith %A Kalisch,Raffael %A Binder,Harald %A Tuescher,Oliver %A Kleim,Birgit %+ Department of Psychology, University of Zurich, Lenggstrasse 31, Zürich, 8032, Switzerland, 41 583842436, marta.marciniak@uzh.ch %K therapeutic alliance %K digital therapeutic alliance %K mental health apps %K mHealth %K mobile health %K translation %K validation %K mobile phone %D 2023 %7 13.11.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The mobile Agnew Relationship Measure (mARM) is a self-report questionnaire for the evaluation of digital mental health interventions and their interactions with users. With the global increase in digital mental health intervention research, translated measures are required to conduct research with local populations. Objective: The aim of this study was to translate and validate the original English version of the mARM into a German version (mARM-G). Methods: A total of 2 native German speakers who spoke English as their second language conducted forward translation of the original items. This version was then back translated by 2 native German speakers with a fluent knowledge of English. An independent bilingual reviewer then compared these drafts and created a final German version. The mARM-G was validated by 15 experts in the field of mobile app development and 15 nonexperts for content validity and face validity; 144 participants were recruited to conduct reliability testing as well as confirmatory factor analysis. Results: The content validity index of the mARM-G was 0.90 (expert ratings) and 0.79 (nonexperts). The face validity index was 0.89 (experts) and 0.86 (nonexperts). Internal consistency for the entire scale was Cronbach α=.91. Confirmatory factor analysis results were as follows: the chi-square statistic to df ratio was 1.66. Comparative Fit Index was 0.87 and the Tucker-Lewis Index was 0.86. The root mean square error of approximation was 0.07. Conclusions: The mARM-G is a valid and reliable tool that can be used for future studies in German-speaking countries. %M 37955952 %R 10.2196/43368 %U https://www.jmir.org/2023/1/e43368 %U https://doi.org/10.2196/43368 %U http://www.ncbi.nlm.nih.gov/pubmed/37955952 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e42916 %T Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study %A El Dahr,Yola %A Perquier,Florence %A Moloney,Madison %A Woo,Guyyunge %A Dobrin-De Grace,Roksana %A Carvalho,Daniela %A Addario,Nicole %A Cameron,Emily E %A Roos,Leslie E %A Szatmari,Peter %A Aitken,Madison %+ Cundill Centre for Child and Youth Depression, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada, 1 416 535 8501 ext 34091, madison.aitken@camh.ca %K ambulatory assessment %K children %K ecological momentary assessment %K longitudinal %K parents %K survey %D 2023 %7 9.11.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Intensive longitudinal data collection, including ecological momentary assessment (EMA), has the potential to reduce recall biases, collect more ecologically valid data, and increase our understanding of dynamic associations between variables. EMA is typically administered using an application that is downloaded on participants’ devices, which presents cost and privacy concerns that may limit its use. Research Electronic Data Capture (REDCap), a web-based survey application freely available to nonprofit organizations, may allow researchers to overcome these barriers; however, at present, little guidance is available to researchers regarding the setup of EMA in REDCap, especially for those who are new to using REDCap or lack advanced programming expertise. Objective: We provide an example of a simplified EMA setup in REDCap. This study aims to demonstrate the feasibility of this approach. We provide information on survey completion and user behavior in a sample of parents and children recruited across Canada. Methods: We recruited 66 parents and their children (aged 9-13 years old) from an existing longitudinal cohort study to participate in a study on risk and protective factors for children’s mental health. Parents received survey prompts (morning and evening) by email or SMS text message for 14 days, twice daily. Each survey prompt contained 2 sections, one for parents and one for children to complete. Results: The completion rates were good (mean 82%, SD 8%) and significantly higher on weekdays than weekends and in dyads with girls than dyads with boys. Children were available to respond to their own survey questions most of the time (in 1134/1498, 75.7% of surveys submitted). The number of assessments submitted was significantly higher, and response times were significantly faster among participants who selected SMS text message survey notifications compared to email survey notifications. The average response time was 47.0 minutes after the initial survey notification, and the use of reminder messages increased survey completion. Conclusions: Our results support the feasibility of using REDCap for EMA studies with parents and children. REDCap also has features that can accommodate EMA studies by recruiting participants across multiple time zones and providing different survey delivery methods. Offering the option of SMS text message survey notifications and reminders may be an important way to increase completion rates and the timeliness of responses. REDCap is a potentially useful tool for researchers wishing to implement EMA in settings in which cost or privacy are current barriers. Researchers should weigh these benefits with the potential limitations of REDCap and this design, including staff time to set up, monitor, and clean the data outputs of the project. %M 37943593 %R 10.2196/42916 %U https://formative.jmir.org/2023/1/e42916 %U https://doi.org/10.2196/42916 %U http://www.ncbi.nlm.nih.gov/pubmed/37943593 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e48754 %T Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis %A Abd-alrazaq,Alaa %A AlSaad,Rawan %A Harfouche,Manale %A Aziz,Sarah %A Ahmed,Arfan %A Damseh,Rafat %A Sheikh,Javaid %+ AI Center for Precision Health, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Ezdan Street, Doha, M343A8, Qatar, 974 44928812, aaa4027@qatar-med.cornell.edu %K anxiety %K artificial intelligence %K wearable devices %K machine learning %K systematic review %K mobile phone %D 2023 %7 8.11.2023 %9 Review %J J Med Internet Res %G English %X Background: Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently. Objective: This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety. Methods: Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies–Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate. Results: Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods. Conclusions: Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI. Trial Registration: PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560 %M 37938883 %R 10.2196/48754 %U https://www.jmir.org/2023/1/e48754 %U https://doi.org/10.2196/48754 %U http://www.ncbi.nlm.nih.gov/pubmed/37938883 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47219 %T Community-Based Digital Contact Tracing of Emerging Infectious Diseases: Design and Implementation Study With Empirical COVID-19 Cases %A Wang,Hsiao-Chi %A Lin,Ting-Yu %A Yao,Yu-Chin %A Hsu,Chen-Yang %A Yang,Chang-Jung %A Chen,Tony Hsiu-Hsi %A Yeh,Yen-Po %+ Changhua County Public Health Bureau, No.162, Sec. 2, Jhongshan Rd., Changhua County, 500, Taiwan, 886 4 7115141, yeh.leego@gmail.com %K COVID-19 %K digital contact tracing %K public health %K surveillance %D 2023 %7 8.11.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Contact tracing for containing emerging infectious diseases such as COVID-19 is resource intensive and requires digital transformation to enable timely decision-making. Objective: This study demonstrates the design and implementation of digital contact tracing using multimodal health informatics to efficiently collect personal information and contain community outbreaks. The implementation of digital contact tracing was further illustrated by 3 empirical SARS-CoV-2 infection clusters. Methods: The implementation in Changhua, Taiwan, served as a demonstration of the multisectoral informatics and connectivity between electronic health systems needed for digital contact tracing. The framework incorporates traditional travel, occupation, contact, and cluster approaches and a dynamic contact process enabled by digital technology. A centralized registry system, accessible only to authorized health personnel, ensures privacy and data security. The efficiency of the digital contact tracing system was evaluated through a field study in Changhua. Results: The digital contact tracing system integrates the immigration registry, communicable disease report system, and national health records to provide real-time information about travel, occupation, contact, and clusters for potential contacts and to facilitate a timely assessment of the risk of COVID-19 transmission. The digitalized system allows for informed decision-making regarding quarantine, isolation, and treatment, with a focus on personal privacy. In the first cluster infection, the system monitored 665 contacts and isolated 4 (0.6%) cases; none of the contacts (0/665, 0%) were infected during quarantine. The estimated reproduction number of 0.92 suggests an effective containment strategy for preventing community-acquired outbreak. The system was also used in a cluster investigation involving foreign workers, where none of the 462 contacts (0/462, 0%) tested positive for SARS-CoV-2. Conclusions: By integrating the multisectoral database, the contact tracing process can be digitalized to provide the information required for risk assessment and decision-making in a timely manner to contain a community-acquired outbreak when facing the outbreak of emerging infectious disease. %M 37938887 %R 10.2196/47219 %U https://www.jmir.org/2023/1/e47219 %U https://doi.org/10.2196/47219 %U http://www.ncbi.nlm.nih.gov/pubmed/37938887 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e46980 %T Assessing the Effect of Extreme Weather on Population Health Using Consumer-Grade Wearables in Rural Burkina Faso: Observational Panel Study %A Koch,Mara %A Matzke,Ina %A Huhn,Sophie %A Sié,Ali %A Boudo,Valentin %A Compaoré,Guillaume %A Maggioni,Martina Anna %A Bunker,Aditi %A Bärnighausen,Till %A Dambach,Peter %A Barteit,Sandra %+ Heidelberg Institute of Global Health, Faculty of Medicine, University Hospital, Heidelberg University, Im Neuenheimer Feld 130.3, Heidelberg, 69120, Germany, 49 6221 5634030, barteit@uni-heidelberg.de %K wearable %K consumer-grade wearable %K sleep %K activity %K heart rate %K climate change %K heat %K rain %K weather %K sub-Saharan Africa %K global health %K public health %K mobile phone %D 2023 %7 8.11.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Extreme weather, including heat and extreme rainfall, is projected to increase owing to climate change, which can have adverse impacts on human health. In particular, rural populations in sub-Saharan Africa are at risk because of a high burden of climate-sensitive diseases and low adaptive capacities. However, there is a lack of data on the regions that are anticipated to be most exposed to climate change. Improved public health surveillance is essential for better decision-making and health prioritization and to identify risk groups and suitable adaptation measures. Digital technologies such as consumer-grade wearable devices (wearables) may generate objective measurements to guide data-driven decision-making. Objective: The main objective of this observational study was to examine the impact of weather exposure on population health in rural Burkina Faso using wearables. Specifically, this study aimed to assess the relationship between individual daily activity (steps), sleep duration, and heart rate (HR), as estimated by wearables, and exposure to heat and heavy rainfall. Methods: Overall, 143 participants from the Nouna health and demographic surveillance system in Burkina Faso wore the Withings Pulse HR wearable 24/7 for 11 months. We collected continuous weather data using 5 weather stations throughout the study region. The heat index and wet-bulb globe temperature (WBGT) were calculated as measures of heat. We used linear mixed-effects models to quantify the relationship between exposure to heat and rainfall and the wearable parameters. Participants kept activity journals and completed a questionnaire on their perception of and adaptation to heat and other weather exposure. Results: Sleep duration decreased significantly (P<.001) with higher heat exposure, with approximately 15 minutes shorter sleep duration during heat stress nights with a heat index value of ≥25 °C. Many participants (55/137, 40.1%) reported that heat affected them the most at night. During the day, most participants (133/137, 97.1%) engaged in outdoor physical work such as farming, housework, or fetching water. During the rainy season, when WBGT was highest, daily activity was highest and increased when the daily maximum WBGT surpassed 30 °C during the rainiest month. In the hottest month, daily activity decreased per degree increase in WBGT for values >30 °C. Nighttime HR showed no significant correlation with heat exposure. Daytime HR data were insufficient for analysis. We found no negative health impact associated with heavy rainfall. With increasing rainfall, sleep duration increased, average nightly HR decreased, and activity decreased. Conclusions: During the study period, participants were frequently exposed to heat and heavy rainfall. Heat was particularly associated with impaired sleep and daily activity. Essential tasks such as harvesting, fetching water, and caring for livestock expose this population to weather that likely has an adverse impact on their health. Further research is essential to guide interventions safeguarding vulnerable communities. %M 37938879 %R 10.2196/46980 %U https://mhealth.jmir.org/2023/1/e46980 %U https://doi.org/10.2196/46980 %U http://www.ncbi.nlm.nih.gov/pubmed/37938879 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e44034 %T Participant Engagement and Adherence to Providing Smartwatch and Patient-Reported Outcome Data: Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Real-World Study %A Nowell,William B %A Curtis,Jeffrey R %A Zhao,Hong %A Xie,Fenglong %A Stradford,Laura %A Curtis,David %A Gavigan,Kelly %A Boles,Jessica %A Clinton,Cassie %A Lipkovich,Ilya %A Venkatachalam,Shilpa %A Calvin,Amy %A Hayes,Virginia S %+ Global Healthy Living Foundation, 515 N Midland Ave, Upper Nyack, NY, 10960, United States, 1 9163963097, lstradford@ghlf.org %K real-world evidence %K real-world data %K patients %K rheumatoid arthritis %K patient-reported outcomes %K patient-generated health data %K mobile technology %K wearable digital technology %K mobile phone %D 2023 %7 7.11.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Digital health studies using electronic patient-reported outcomes (ePROs) and wearables bring new challenges, including the need for participants to consistently provide trial data. Objective: This study aims to characterize the engagement, protocol adherence, and data completeness among participants with rheumatoid arthritis enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study. Methods: Participants were invited to participate in this app-based study, which included a 14-day run-in and an 84-day main study. In the run-in period, data were collected via the ArthritisPower mobile app to increase app familiarity and identify the individuals who were motivated to participate. Successful completers of the run-in period were mailed a wearable smartwatch, and automated and manual prompts were sent to participants, reminding them to complete app input or regularly wear and synchronize devices, respectively, during the main study. Study coordinators monitored participant data and contacted participants via email, SMS text messaging, and phone to resolve adherence issues per a priori rules, in which consecutive spans of missing data triggered participant contact. Adherence to data collection during the main study period was defined as providing requested data for >70% of 84 days (daily ePRO, ≥80% daily smartwatch data) or at least 9 of 12 weeks (weekly ePRO). Results: Of the 470 participants expressing initial interest, 278 (59.1%) completed the run-in period and qualified for the main study. Over the 12-week main study period, 87.4% (243/278) of participants met the definition of adherence to protocol-specified data collection for weekly ePRO, and 57.2% (159/278) did so for daily ePRO. For smartwatch data, 81.7% (227/278) of the participants adhered to the protocol-specified data collection. In total, 52.9% (147/278) of the participants met composite adherence. Conclusions: Compared with other digital health rheumatoid arthritis studies, a short run-in period appears useful for identifying participants likely to engage in a study that collects data via a mobile app and wearables and gives participants time to acclimate to study requirements. Automated or manual prompts (ie, “It’s time to sync your smartwatch”) may be necessary to optimize adherence. Adherence varies by data collection type (eg, ePRO vs smartwatch data). International Registered Report Identifier (IRRID): RR2-10.2196/14665 %M 37934559 %R 10.2196/44034 %U https://humanfactors.jmir.org/2023/1/e44034 %U https://doi.org/10.2196/44034 %U http://www.ncbi.nlm.nih.gov/pubmed/37934559 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e50983 %T Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study %A Lee,Taeyoung %A Cho,Younghoon %A Cha,Kwang Su %A Jung,Jinhwan %A Cho,Jungim %A Kim,Hyunggug %A Kim,Daewoo %A Hong,Joonki %A Lee,Dongheon %A Keum,Moonsik %A Kushida,Clete A %A Yoon,In-Young %A Kim,Jeong-Whun %+ Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Gyeonggi-do, Seongnam-si, 13620, Republic of Korea, 82 10 3079 7405, kimemails7@gmail.com %K consumer sleep trackers %K wearables %K nearables %K airables %K sleep monitoring %K sleep stage %K comparative study %K polysomnography %K multicenter study %K deep learning %K artificial intelligence %K Fitbit Sense 2, Amazon Halo Rise, SleepRoutine %D 2023 %7 2.11.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. Objective: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. Methods: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. Results: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. Conclusions: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep. %M 37917155 %R 10.2196/50983 %U https://mhealth.jmir.org/2023/1/e50983 %U https://doi.org/10.2196/50983 %U http://www.ncbi.nlm.nih.gov/pubmed/37917155 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e49876 %T Using a Novel Connected Device for the Collection of Puffing Topography Data for the Vuse Solo Electronic Nicotine Delivery System in a Real-World Setting: Prospective Ambulatory Clinical Study %A Underly,Robert %A Dull,Gary M %A Nudi,Evan %A Pionk,Timothy %A Prevette,Kristen %A Smith,Jeffrey %+ Reynolds American Incorporated Services Company, 1100 Reynolds Blvd, Winston-Salem, NC, 27105, United States, 1 3367410909, underlr1@rjrt.com %K topography %K electronic cigarette %K e-cigarette %K electronic nicotine delivery system %K ENDS %K ambulatory puffing %K use behavior %K sessions %K mobile phone %D 2023 %7 30.10.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Over the last decade, the use of electronic nicotine delivery systems (ENDSs) has risen, whereas studies that describe how consumers use these products have been limited. Most studies related to ENDS use have involved study designs focused on use in a central location environment or attempted to measure use outcomes through subjective self-reported end points. The development of accurate and reliable tools to collect data in a naturalistic real-world environment is necessary to capture the complexities of ENDS use. Using connected devices in a real-world setting provides a convenient and objective approach to collecting behavioral outcomes with ENDS. Objective: The Product Use and Behavior instrument was developed and used to capture the use of the Vuse Solo ENDS in an ambulatory setting to best replicate real-world use behavior. This study aims to determine overall mean values for topography outcomes while also providing a definition for an ENDS use session. Methods: A prospective ambulatory clinical study was performed with the Product Use and Behavior instrument. Participants (n=75) were aged between 21 and 60 years, considered in good health, and were required to be established regular users of ENDSs. To better understand use behavior within the population, the sample was sorted into percentiles with bins based on daily puff counts. To frame these data in the relevant context, they were binned into low-, moderate-, and high-use categories (10th to 40th, 40th to 70th, and 70th to 100th percentiles, respectively), with the low-use group representing the nonintense category, the high-use group representing the intense category, and the moderate-use group being reflective of the average consumer. Results: Participants with higher daily use took substantially more puffs per use session (6.71 vs 4.40) and puffed more frequently (interpuff interval: 32.78 s vs 61.66 s) than participants in the low-use group. Puff duration remained consistent across the low-, moderate‑, and high-use groups (2.10 s, 2.18 s, and 2.19 s, respectively). The moderate-use group had significantly shorter session lengths (P<.001) than the high- and low-use groups, which did not differ significantly from each other (P=.16). Conclusions: Using connected devices allows for a convenient and robust approach to the collection of behavioral outcomes related to product use in an ambulatory setting. By using the variables captured with these tools, it becomes possible to move away from predefined periods of use to better understand topography outcomes and define use sessions. The data presented here offer a possible method to define these sessions. These data also begin to frame international standards used for the analytical assessments of ENDSs in the correct context and begin to shed light on the differences between standardized testing regimens and actual use behavior. Trial Registration: Clinicaltrials.gov NCT04226404; https://clinicaltrials.gov/study/NCT04226404 %M 37902830 %R 10.2196/49876 %U https://formative.jmir.org/2023/1/e49876 %U https://doi.org/10.2196/49876 %U http://www.ncbi.nlm.nih.gov/pubmed/37902830 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e47167 %T Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study %A Sükei,Emese %A Romero-Medrano,Lorena %A de Leon-Martinez,Santiago %A Herrera López,Jesús %A Campaña-Montes,Juan José %A Olmos,Pablo M %A Baca-Garcia,Enrique %A Artés,Antonio %+ Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Ed. Torres Quevedo, 3rd Fl., Av. de la Universidad, 30, Leganés, 28911, Spain, 34 916 248 741, esukei@tsc.uc3m.es %K WHODAS %K functional limitations %K mobile sensing %K passive ecological momentary assessment %K predictive modeling %K interpretable machine learning %K machine learning %K disability %K clinical outcome %D 2023 %7 30.10.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients’ functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. Objective: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. Methods: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Results: Our machine learning–based models for predicting patients’ WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Conclusions: Our findings show the feasibility of using machine learning–based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models’ decisions—an important aspect in clinical practice. %M 37902823 %R 10.2196/47167 %U https://formative.jmir.org/2023/1/e47167 %U https://doi.org/10.2196/47167 %U http://www.ncbi.nlm.nih.gov/pubmed/37902823 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e46338 %T Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study %A G Ravindran,Kiran K %A della Monica,Ciro %A Atzori,Giuseppe %A Lambert,Damion %A Hassanin,Hana %A Revell,Victoria %A Dijk,Derk-Jan %+ Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Surrey Clinical Research Building, Egerton Road, Guildford, GU27XP, United Kingdom, 44 01483683709, k.guruswamyravindran@surrey.ac.uk %K contactless sleep technologies %K evaluation %K nearables %K polysomnography %K older adults %K sleep %K Withings sleep analyzer %K Emfit %K Somnofy %D 2023 %7 25.10.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people. Objective: This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population. Methods: Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard. Results: The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21). Conclusions: These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals. %M 37878360 %R 10.2196/46338 %U https://mhealth.jmir.org/2023/1/e46338 %U https://doi.org/10.2196/46338 %U http://www.ncbi.nlm.nih.gov/pubmed/37878360 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45549 %T Smartphone Apps for Containing the COVID-19 Pandemic in Germany: Qualitative Interview Study With Experts Based on Grounded Theory %A Krämer,Dennis %A Brachem,Elisabeth %A Schneider-Reuter,Lydia %A D'Angelo,Isabella %A Vollmann,Jochen %A Haltaufderheide,Joschka %+ Faculty of Social Sciences, Georg-August-University Göttingen, Sprangerweg 2, Göttingen, 37075, Germany, 49 5513925656, dennis.kraemer@uni-goettingen.de %K Corona-Warn-App %K COVID-19 pandemic %K eHealth %K Germany %K health technology %K mobile phone %K qualitative research %K sovereignty %K transparency %D 2023 %7 20.10.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Smartphone apps, including those for digital contact tracing (DCT), played a crucial role in containing infections during the COVID-19 pandemic. Their primary function is to generate and disseminate information to disrupt transmissions based on various events, such as encounters, vaccinations, locations, or infections. Although the functionality of these apps has been extensively studied, there is still a lack of qualitative research addressing critical issues. Objective: We will demonstrate that the use of DCT presents a challenge due to the tension between continuous health monitoring and uncertainties related to transparency and user sovereignty. On one hand, DCT enables the monitoring of various risk factors, including data-based calculations of infection probabilities. On the other hand, continuous risk management is intertwined with several uncertainties, including the unclear storage of personal data, who has access to it, and how it will be used in the future. Methods: We focus on the German “Corona-Warn-App” and support our argument with empirical data from 19 expert interviews conducted between 2020 and 2021. The interviews were conducted using a semistructured questionnaire and analyzed according to the principles of grounded theory. Results: Our data underscores 3 dimensions: transparency, data sovereignty, and the east-west divide. While transparency is considered an essential foundation for establishing trust in the use of DCT by providing a sense of security, data sovereignty is seen as a high value during the pandemic, protecting users from an undesired loss of control. The aspect of the east-west divide highlights the idea of incorporating sociocultural values and standards into technology, emphasizing that algorithms and data-driven elements, such as distance indicators, encounters, and isolations, are also influenced by sociocultural factors. Conclusions: The effective use of DCT for pandemic containment relies on achieving a balance between individual control and technological prevention. Maximizing the technological benefits of these tools is crucial. However, users must also be mindful of the information they share and maintain control over their shared data. %M 37862068 %R 10.2196/45549 %U https://www.jmir.org/2023/1/e45549 %U https://doi.org/10.2196/45549 %U http://www.ncbi.nlm.nih.gov/pubmed/37862068 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45764 %T Evaluating the Effects of Rewards and Schedule Length on Response Rates to Ecological Momentary Assessment Surveys: Randomized Controlled Trials %A Edney,Sarah %A Goh,Claire Marie %A Chua,Xin Hui %A Low,Alicia %A Chia,Janelle %A S Koek,Daphne %A Cheong,Karen %A van Dam,Rob %A Tan,Chuen Seng %A Müller-Riemenschneider,Falk %+ Physical Activity and Nutrition Determinants in Asia Programme, Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, Singapore, 117549, Singapore, 65 6516 4988, sarah.edney@nus.edu.sg %K experience sampling %K ambulatory assessment %K compliance %K mobile phone %D 2023 %7 19.10.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Ecological momentary assessments (EMAs) are short, repeated surveys designed to collect information on experiences in real-time, real-life contexts. Embedding periodic bursts of EMAs within cohort studies enables the study of experiences on multiple timescales and could greatly enhance the accuracy of self-reported information. However, the burden on participants may be high and should be minimized to optimize EMA response rates. Objective: We aimed to evaluate the effects of study design features on EMA response rates. Methods: Embedded within an ongoing cohort study (Health@NUS), 3 bursts of EMAs were implemented over a 7-month period (April to October 2021). The response rate (percentage of completed EMA surveys from all sent EMA surveys; 30-42 individual EMA surveys sent/burst) for each burst was examined. Following a low response rate in burst 1, changes were made to the subsequent implementation strategy (SMS text message announcements instead of emails). In addition, 2 consecutive randomized controlled trials were conducted to evaluate the efficacy of 4 different reward structures (with fixed and bonus components) and 2 different schedule lengths (7 or 14 d) on changes to the EMA response rate. Analyses were conducted from 2021 to 2022 using ANOVA and analysis of covariance to examine group differences and mixed models to assess changes across all 3 bursts. Results: Participants (N=384) were university students (n=232, 60.4% female; mean age 23, SD 1.3 y) in Singapore. Changing the reward structure did not significantly change the response rate (F3,380=1.75; P=.16). Changing the schedule length did significantly change the response rate (F1,382=6.23; P=.01); the response rate was higher for the longer schedule (14 d; mean 48.34%, SD 33.17%) than the shorter schedule (7 d; mean 38.52%, SD 33.44%). The average response rate was higher in burst 2 and burst 3 (mean 50.56, SD 33.61 and mean 48.34, SD 33.17, respectively) than in burst 1 (mean 25.78, SD 30.12), and the difference was statistically significant (F2,766=93.83; P<.001). Conclusions: Small changes to the implementation strategy (SMS text messages instead of emails) may have contributed to increasing the response rate over time. Changing the available rewards did not lead to a significant difference in the response rate, whereas changing the schedule length did lead to a significant difference in the response rate. Our study provides novel insights on how to implement EMA surveys in ongoing cohort studies. This knowledge is essential for conducting high-quality studies using EMA surveys. Trial Registration: ClinicalTrials.gov NCT05154227; https://clinicaltrials.gov/ct2/show/NCT05154227 %M 37856188 %R 10.2196/45764 %U https://www.jmir.org/2023/1/e45764 %U https://doi.org/10.2196/45764 %U http://www.ncbi.nlm.nih.gov/pubmed/37856188 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e39995 %T Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study %A Tutunji,Rayyan %A Kogias,Nikos %A Kapteijns,Bob %A Krentz,Martin %A Krause,Florian %A Vassena,Eliana %A Hermans,Erno J %+ Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Kapittelweg, Nijmegen, 6525EN, Netherlands, 31 36168494, rayyan.tutunji@donders.ru.nl %K biosensor %K devices %K ecological momentary assessments %K experience sampling %K machine learning %K mental disorder %K mental health %K monitoring %K physiological %K prevention %K psychological %K smartwatches %K stress %K wearables %D 2023 %7 19.10.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Increasing efforts toward the prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. Wearable devices have emerged as a possible solution to aid in this process, but their use in real-life stress detection has not been systematically investigated. Objective: We aimed to determine the utility of ecological momentary assessments (EMA) and physiological arousal measured through wearable devices in detecting ecologically relevant stress states. Methods: Using EMA combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (ie, a high-stakes examination week) on physiological arousal and affect compared to a control week without examinations in first-year medical and biomedical science students (51/83, 61.4% female). We first used generalized linear mixed-effects models with maximal fitting approaches to investigate the impact of examination periods on subjective stress exposure, mood, and physiological arousal. We then used machine learning models to investigate whether we could use EMA, wearable biosensors, or the combination of both to classify momentary data (ie, beeps) as belonging to examination or control weeks. We tested both individualized models using a leave-one-beep-out approach and group-based models using a leave-one-subject-out approach. Results: During stressful high-stakes examination (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal decreased on average during the examination week. Time-resolved analyses revealed peaks in physiological arousal associated with both momentary self-reported stress exposure and self-reported positive affect. Mediation models revealed that the decreased physiological arousal in the examination week was mediated by lower positive affect during the same period. We then used machine learning to show that while individualized EMA outperformed EPA in its ability to classify beeps as originating from examinations or from control weeks (1603/4793, 33.45% and 1648/4565, 36.11% error rates, respectively), a combination of EMA and EPA yields optimal classification (1363/4565, 29.87% error rate). Finally, when comparing individualized models to group-based models, we found that the individualized models significantly outperformed the group-based models across all 3 inputs (EMA, EPA, and the combination). Conclusions: This study underscores the potential of wearable biosensors for stress-related mental health monitoring. However, it emphasizes the necessity of psychological context in interpreting physiological arousal captured by these devices, as arousal can be related to both positive and negative contexts. Moreover, our findings support a personalized approach in which momentary stress is optimally detected when referenced against an individual’s own data. %M 37856180 %R 10.2196/39995 %U https://www.jmir.org/2023/1/e39995 %U https://doi.org/10.2196/39995 %U http://www.ncbi.nlm.nih.gov/pubmed/37856180 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e51019 %T First-Year Experience of Managing Urology Patients With Home Uroflowmetry: Descriptive Retrospective Analysis %A Bladt,Lola %A Kashtiara,Ardavan %A Platteau,Wouter %A De Wachter,Stefan %A De Win,Gunter %+ Product Development, Faculty of Design Sciences, University of Antwerp, Paardenmarkt 90/94, Antwerp, 2000, Belgium, 32 497848014, lola.bladt@uantwerpen.be %K lower urinary tract symptoms %K home uroflowmetry %K automated bladder diary %K homeflow %K hospiflow %K mobile phone %D 2023 %7 17.10.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Lower urinary tract symptoms affect a large number of people of all ages and sexes. The clinical assessment typically involves a bladder diary and uroflowmetry test. Conventional paper-based diaries are affected by low patient compliance, whereas in-clinic uroflowmetry measurement face challenges such as patient stress and inconvenience factors. Home uroflowmetry and automated bladder diaries are believed to overcome these limitations. Objective: In this study, we present our first-year experience of managing urological patients using Minze homeflow, which combines home uroflowmetry and automated bladder diaries. Our objective was 2-fold: first, to provide a description of the reasons for using homeflow and second, to compare the data obtained from homeflow with the data obtained from in-clinic uroflowmetry (hospiflow). Methods: A descriptive retrospective analysis was conducted using Minze homeflow between July 2019 and July 2020 at a tertiary university hospital. The device comprises a Bluetooth-connected gravimetric uroflowmeter, a patient smartphone app, and a cloud-based clinician portal. Descriptive statistics, Bland-Altman plots, the McNemar test, and the Wilcoxon signed rank test were used for data analysis. Results: The device was offered to 166 patients, including 91 pediatric and 75 adult patients. In total, 3214 homeflows and 129 hospiflows were recorded. Homeflow proved valuable for diagnosis, particularly in cases where hospiflow was unreliable or unsuccessful, especially in young children. It confirmed or excluded abnormal hospiflow results and provided comprehensive data with multiple measurements taken at various bladder volumes, urge levels, and times of the day. As a result, we found that approximately one-fourth of the patients with abnormal flow curves in the clinic had normal bell-shaped flow curves at home. Furthermore, homeflow offers the advantage of providing an individual’s plot of maximum flow rate (Q-max) versus voided volume as well as an average or median result. Our findings revealed that a considerable percentage of patients (22/76, 29% for pediatric patients and 24/50, 48% for adult patients) had a Q-max measurement from hospiflow falling outside the range of homeflow measurements. This discrepancy may be attributed to the unnatural nature of the hospiflow test, resulting in nonrepresentative uroflow curves and an underestimation of Q-max, as confirmed by the Bland-Altman plot analysis. The mean difference for Q-max was −3.1 mL/s (with an upper limit of agreement of 13 mL/s and a lower limit of agreement of −19.2 mL/s), which was statistically significant (Wilcoxon signed rank test: V=2019.5; P<.001). Given its enhanced reliability, homeflow serves as a valuable tool not only for diagnosis but also for follow-up, allowing for the evaluation of treatment effectiveness and home monitoring of postoperative and recurrent interventions. Conclusions: Our first-year experience with Minze homeflow demonstrated its feasibility and usefulness in the diagnosis and follow-up of various patient categories. Homeflow provided more reliable and comprehensive voiding data compared with hospiflow. %M 37847531 %R 10.2196/51019 %U https://formative.jmir.org/2023/1/e51019 %U https://doi.org/10.2196/51019 %U http://www.ncbi.nlm.nih.gov/pubmed/37847531 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e49096 %T Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study %A Jenciūtė,Gabrielė %A Kasputytė,Gabrielė %A Bunevičienė,Inesa %A Korobeinikova,Erika %A Vaitiekus,Domas %A Inčiūra,Arturas %A Jaruševičius,Laimonas %A Bunevičius,Romas %A Krikštolaitis,Ričardas %A Krilavičius,Tomas %A Juozaitytė,Elona %A Bunevičius,Adomas %+ Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, 710 W 168th St, New York, NY, 10032, United States, 1 617 417 7174, a.bunevicius@yahoo.com %K cancer %K digital phenotyping %K biomarkers %K oncology %K digital phenotype %K biomarker %K data collection %K data generation %K monitor %K monitoring %K data collection %K predict %K prediction %K model %K models %K mobile phone %D 2023 %7 10.10.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. Objective: We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. Methods: We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. Results: As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. Conclusions: This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. International Registered Report Identifier (IRRID): PRR1-10.2196/49096 %M 37815850 %R 10.2196/49096 %U https://www.researchprotocols.org/2023/1/e49096 %U https://doi.org/10.2196/49096 %U http://www.ncbi.nlm.nih.gov/pubmed/37815850 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e47267 %T Acceptability of a Pain History Assessment and Education Chatbot (Dolores) Across Age Groups in Populations With Chronic Pain: Development and Pilot Testing %A Andrews,Nicole Emma %A Ireland,David %A Vijayakumar,Pranavie %A Burvill,Lyza %A Hay,Elizabeth %A Westerman,Daria %A Rose,Tanya %A Schlumpf,Mikaela %A Strong,Jenny %A Claus,Andrew %+ RECOVER Injury Research Centre, The University of Queensland, Level 7, Surgical Treatment and Rehabilitation Service (STARS), 296 Herston Rd, Herston, 4029, Australia, 61 418762617, n.andrews@uq.edu.au %K chronic pain %K education %K neurophysiology %K neuroscience %K conversation agent %K chatbot %K age %K young adult %K adolescence %K adolescent %K pain %K patient education %K usability %K acceptability %K mobile health %K mHealth %K mobile app %K health app %K youth %K mobile phone %D 2023 %7 6.10.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The delivery of education on pain neuroscience and the evidence for different treatment approaches has become a key component of contemporary persistent pain management. Chatbots, or more formally conversation agents, are increasingly being used in health care settings due to their versatility in providing interactive and individualized approaches to both capture and deliver information. Research focused on the acceptability of diverse chatbot formats can assist in developing a better understanding of the educational needs of target populations. Objective: This study aims to detail the development and initial pilot testing of a multimodality pain education chatbot (Dolores) that can be used across different age groups and investigate whether acceptability and feedback were comparable across age groups following pilot testing. Methods: Following an initial design phase involving software engineers (n=2) and expert clinicians (n=6), a total of 60 individuals with chronic pain who attended an outpatient clinic at 1 of 2 pain centers in Australia were recruited for pilot testing. The 60 individuals consisted of 20 (33%) adolescents (aged 10-18 years), 20 (33%) young adults (aged 19-35 years), and 20 (33%) adults (aged >35 years) with persistent pain. Participants spent 20 to 30 minutes completing interactive chatbot activities that enabled the Dolores app to gather a pain history and provide education about pain and pain treatments. After the chatbot activities, participants completed a custom-made feedback questionnaire measuring the acceptability constructs pertaining to health education chatbots. To determine the effect of age group on the acceptability ratings and feedback provided, a series of binomial logistic regression models and cumulative odds ordinal logistic regression models with proportional odds were generated. Results: Overall, acceptability was high for the following constructs: engagement, perceived value, usability, accuracy, responsiveness, adoption intention, esthetics, and overall quality. The effect of age group on all acceptability ratings was small and not statistically significant. An analysis of open-ended question responses revealed that major frustrations with the app were related to Dolores’ speech, which was explored further through a comparative analysis. With respect to providing negative feedback about Dolores’ speech, a logistic regression model showed that the effect of age group was statistically significant (χ22=11.7; P=.003) and explained 27.1% of the variance (Nagelkerke R2). Adults and young adults were less likely to comment on Dolores’ speech compared with adolescent participants (odds ratio 0.20, 95% CI 0.05-0.84 and odds ratio 0.05, 95% CI 0.01-0.43, respectively). Comments were related to both speech rate (too slow) and quality (unpleasant and robotic). Conclusions: This study provides support for the acceptability of pain history and education chatbots across different age groups. Chatbot acceptability for adolescent cohorts may be improved by enabling the self-selection of speech characteristics such as rate and personable tone. %M 37801342 %R 10.2196/47267 %U https://formative.jmir.org/2023/1/e47267 %U https://doi.org/10.2196/47267 %U http://www.ncbi.nlm.nih.gov/pubmed/37801342 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e51845 %T Ecological Momentary Assessment of Midlife Adults’ Daily Stress: Protocol for the Stress Reports in Variable Environments (STRIVE) App Study %A Jordan,Evan J %A Shih,Patrick C %A Nelson,Erik J %A Carter,Stephen J %A Schootman,Mario %A Prather,Aric A %A Yao,Xing %A Peters,Chasie D %A Perry,Canaan S E %+ Department of Health and Wellness Design, School of Public Health - Bloomington, Indiana University, 1025 E Seventh St, Bloomington, IN, 47405, United States, 1 8128553528, ejjordan@iu.edu %K activity trackers %K built environment %K ecological momentary assessment %K heart rate monitoring %K life stress %K physical activity %K spatial analysis %K wearable technology %D 2023 %7 5.10.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Daily stressors are associated with cognitive decline and increased risk of heart disease, depression, and other debilitating chronic illnesses in midlife adults. Daily stressors tend to occur at home or at work and are more frequent in urban versus rural settings. Conversely, spending time in natural environments such as parks or forests, or even viewing nature-themed images in a lab setting, is associated with lower levels of perceived stress and is hypothesized to be a strong stress “buffer,” reducing perceived stress even after leaving the natural setting. However, many studies of daily stress have not captured environmental contexts and relied on end-of-day recall instead of in-the-moment data capture. With new technology, these limitations can be addressed to enhance knowledge of the daily stress experience. Objective: We propose to use our novel custom-built Stress Reports in Variable Environments (STRIVE) ecological momentary assessment mobile phone app to measure the experience of daily stress of midlife adults in free-living conditions. Using our app to capture data in real time will allow us to determine (1) where and when daily stress occurs for midlife adults, (2) whether midlife adults’ daily stressors are linked to certain elements of the built and natural environment, and (3) how ecological momentary assessment measurement of daily stress is similar to and different from a modified version of the popular Daily Inventory of Stressful Events measurement tool that captures end-of-day stress reports (used in the Midlife in the United States [MIDUS] survey). Methods: We will enroll a total of 150 midlife adults living in greater Indianapolis, Indiana, in this study on a rolling basis for 3-week periods. As those in underrepresented minority groups and low-income areas have previously been found to experience greater levels of stress, we will use stratified sampling to ensure that half of our study sample is composed of underrepresented minorities (eg, Black, American Indian, Hispanic, or Native Pacific Islanders) and approximately one-third of our sample falls within low-, middle-, and high-income brackets. Results: This project is funded by the National Institute on Aging from December 2022 to November 2024. Participant enrollment began in August 2023 and is expected to finish in July 2024. Data will be spatiotemporally analyzed to determine where and when stress occurs for midlife adults. Pictures of stressful environments will be qualitatively analyzed to determine the common elements of stressful environments. Data collected by the STRIVE app will be compared with retrospective Daily Inventory of Stressful Events data. Conclusions: Completing this study will expand our understanding of midlife adults’ experience of stress in free-living conditions and pave the way for data-driven individual and community-based intervention designs to promote health and well-being in midlife adults. International Registered Report Identifier (IRRID): DERR1-10.2196/51845 %M 37796561 %R 10.2196/51845 %U https://www.researchprotocols.org/2023/1/e51845 %U https://doi.org/10.2196/51845 %U http://www.ncbi.nlm.nih.gov/pubmed/37796561 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e46521 %T Assessment of Upper Extremity Function in Multiple Sclerosis: Feasibility of a Digital Pinching Test %A Graves,Jennifer S %A Elantkowski,Marcin %A Zhang,Yan-Ping %A Dondelinger,Frank %A Lipsmeier,Florian %A Bernasconi,Corrado %A Montalban,Xavier %A Midaglia,Luciana %A Lindemann,Michael %+ F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, 4070, Switzerland, 41 61 687 79 09, florian.lipsmeier@roche.com %K multiple sclerosis %K smartphone sensor %K digital health technology tools %K upper extremity function %K hand-motor dexterity %D 2023 %7 2.10.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The development of touchscreen-based assessments of upper extremity function could benefit people with multiple sclerosis (MS) by allowing convenient, quantitative assessment of their condition. The Pinching Test forms a part of the Floodlight smartphone app (F. Hoffmann-La Roche Ltd, Basel, Switzerland) for people with MS and was designed to capture upper extremity function. Objective: This study aimed to evaluate the Pinching Test as a tool for remotely assessing upper extremity function in people with MS. Methods: Using data from the 24-week, prospective feasibility study investigating the Floodlight Proof-of-Concept app for remotely assessing MS, we examined 13 pinching, 11 inertial measurement unit (IMU)–based, and 13 fatigability features of the Pinching Test. We assessed the test-retest reliability using intraclass correlation coefficients [second model, first type; ICC(2,1)], age- and sex-adjusted cross-sectional Spearman rank correlation, and known-groups validity (data aggregation: median [all features], SD [fatigability features]). Results: We evaluated data from 67 people with MS (mean Expanded Disability Status Scale [EDSS]: 2.4 [SD 1.4]) and 18 healthy controls. In this cohort of early MS, pinching features were reliable [ICC(2,1)=0.54-0.81]; correlated with standard clinical assessments, including the Nine-Hole Peg Test (9HPT) (|r|=0.26-0.54; 10/13 features), EDSS (|r|=0.25-0.36; 7/13 features), and the arm items of the 29-item Multiple Sclerosis Impact Scale (MSIS-29) (|r|=0.31-0.52; 7/13 features); and differentiated people with MS-Normal from people with MS-Abnormal (area under the curve: 0.68-0.78; 8/13 features). IMU-based features showed similar test-retest reliability [ICC(2,1)=0.47-0.84] but showed little correlations with standard clinical assessments. In contrast, fatigability features (SD aggregation) correlated with 9HPT time (|r|=0.26-0.61; 10/13 features), EDSS (|r|=0.26-0.41; 8/13 features), and MSIS-29 arm items (|r|=0.32-0.46; 7/13 features). Conclusions: The Pinching Test provides a remote, objective, and granular assessment of upper extremity function in people with MS that can potentially complement standard clinical evaluation. Future studies will validate it in more advanced MS. Trial Registration: ClinicalTrials.gov NCT02952911; https://clinicaltrials.gov/study/NCT02952911 %M 37782540 %R 10.2196/46521 %U https://formative.jmir.org/2023/1/e46521 %U https://doi.org/10.2196/46521 %U http://www.ncbi.nlm.nih.gov/pubmed/37782540 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e47567 %T Decision Trade-Offs in Ecological Momentary Assessments and Digital Wearables Uptake: Protocol for a Discrete Choice Experiment %A El-Toukhy,Sherine %A Pike,James Russell %A Zuckerman,Gabrielle %A Hegeman,Phillip %+ Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, 11545 Rockville Pike, Rockville, MD, United States, 1 3015944743, sherine.el-toukhy@nih.gov %K digital wearables %K discrete choice experiment %K ecological momentary assessment %K mHealth %K mobile health %K remote monitoring technology %D 2023 %7 25.9.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Ecological momentary assessments (EMAs) and digital wearables (DW) are commonly used remote monitoring technologies that capture real-time data in people’s natural environments. Real-time data are core to personalized medical care and intensively adaptive health interventions. The utility of such personalized care is contingent on user uptake and continued use of EMA and DW. Consequently, it is critical to understand user preferences that may increase the uptake of EMA and DW. Objective: The study aims to quantify users’ preferences of EMA and DW, examine variations in users’ preferences across demographic and behavioral subgroups, and assess the association between users’ preferences and intentions to use EMA and DW. Methods: We will administer 2 discrete choice experiments (DCEs) paired with self-report surveys on the internet to a total of 3260 US adults through Qualtrics. The first DCE will assess participants’ EMA preferences using a choice-based conjoint design that will ask participants to compare the relative importance of prompt frequency, number of questions per prompt, prompt type, health topic, and assessment duration. The second DCE will measure participants’ DW preferences using a maximum difference scaling design that will quantify the relative importance of device characteristics, effort expectancy, social influence, and facilitating technical, health care, and market factors. Hierarchical Bayesian multinomial logistic regression models will be used to generate subject-specific preference utilities. Preference utilities will be compared across demographic (ie, sex, age, race, and ethnicity) and behavioral (ie, substance use, physical activity, dietary behavior, and sleep duration) subgroups. Regression models will determine whether specific utilities are associated with attitudes toward or intentions to use EMA and DW. Mixture models will determine the associations of attitudes toward and intentions to use EMA and DW with latent profiles of user preferences. Results: The institutional review board approved the study on December 19, 2022. Data collection started on January 20, 2023, and concluded on May 4, 2023. Data analysis is currently underway. Conclusions: The study will provide evidence on users’ preferences of EMA and DW features that can improve initial uptake and potentially continued use of these remote monitoring tools. The sample size and composition allow for subgroup analysis by demographics and health behaviors and will provide evidence on associations between users’ preferences and intentions to uptake EMA and DW. Limitations include the cross-sectional nature of the study, which limits our ability to measure direct behavior. Rather, we capture behavioral intentions for EMA and DW uptake. The nonprobability sample limits the generalizability of the results and introduces self-selection bias related to the demographic and behavioral characteristics of participants who belong to web-based survey panels. International Registered Report Identifier (IRRID): DERR1-10.2196/47567 %M 37747771 %R 10.2196/47567 %U https://www.researchprotocols.org/2023/1/e47567 %U https://doi.org/10.2196/47567 %U http://www.ncbi.nlm.nih.gov/pubmed/37747771 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e49476 %T Usability of Electronic Patient-Reported Outcome Measures for Older Patients With Cancer: Secondary Analysis of Data from an Observational Single Center Study %A Riedl,David %A Lehmann,Jens %A Rothmund,Maria %A Dejaco,Daniel %A Grote,Vincent %A Fischer,Michael J %A Rumpold,Gerhard %A Holzner,Bernhard %A Licht,Thomas %+ Ludwig Boltzmann Institute for Rehabilitation Research, Kurbadstrasse 14, Vienna, 1100, Austria, 43 13615220, david.riedl@rehabilitation.lbg.ac.at %K patient-reported outcomes %K completion rate %K geriatric %K age %K patient reported %K elderly %K older adults %K older adult %K cancer %K oncology %K survivor %K survivors %K questionnaire %K questionnaires %K self-reported %K geriatrics %K gerontology %K survey %K surveys %K mobile phone %D 2023 %7 21.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient-reported outcomes are considered the gold standard for assessing subjective health status in oncology patients. Electronic assessment of patient-reported outcomes (ePRO) has become increasingly popular in recent years in both clinical trials and practice. However, there is limited evidence on how well older patients with cancer can complete ePRO assessments. Objective: We aimed to investigate how well adult patients with cancer of different age ranges could complete ePRO assessments at home and in a treatment facility and to identify factors associated with the ability to complete questionnaires electronically. Methods: This retrospective longitudinal single-center study involved survivors of cancer who participated in inpatient rehabilitation. Patients completed ePRO assessments before rehabilitation at home (T1) and after rehabilitation at the facility (T2). We analyzed the rate of patients who could complete the ePRO assessment at T1 and T2, the proportion of patients who required assistance, and the time it took patients to complete standardized questionnaires. Multivariate logistic regression analyses were conducted to identify predictors of ePRO completion rate and the need for assistance. Results: Between 2017 and 2022, a total of 5571 patients were included in this study. Patients had a mean age of 60.3 (SD 12.2) years (range 18 to 93 years), and 1135 (20.3%) of them were classified as geriatric patients (>70 years). While more than 90% (5060/5571) of all patients completed the ePRO assessment, fewer patients in the age group of >70 years (924/1135, 81.4% at T1 vs 963/1135, 84.8% at T2) completed the assessment. Approximately 19% (1056/5571) of patients reported a need for assistance with the ePRO assessment at home, compared to 6.8% (304/4483) at the institution. Patients older than 70 years had a significantly higher need for assistance than those in younger age groups. Moreover, a gender difference was observed, with older women reporting a higher need for assistance than men (71-80 years: women requiring assistance 215/482, 44.6% vs men 96/350, 27.4%; P<.001 and >80 years: women 102/141, 72.3% vs men 57/112, 50.9%; P<.001). On average, patients needed 4.9 (SD 3.20) minutes to remotely complete a 30-item questionnaire (European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire) and patients in the older age groups took significantly longer compared to younger age groups. Lower age and higher physical functioning were the clearest predictors for both the ePRO completion rate and the need for assistance in the multivariate regression analysis. Conclusions: This study’s results indicate that ePRO assessment is feasible in older individuals with cancer, but older patients may require assistance (eg, from relatives) to complete home-based assessments. It may be more feasible to conduct assessments in-house in this population. Additionally, it is crucial to carefully consider which resources are necessary and available to support patients in using ePRO devices. %M 37733409 %R 10.2196/49476 %U https://www.jmir.org/2023/1/e49476 %U https://doi.org/10.2196/49476 %U http://www.ncbi.nlm.nih.gov/pubmed/37733409 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46396 %T Digital Health Implementation Strategies Coproduced With Adults With Acquired Brain Injury, Their Close Others, and Clinicians: Mixed Methods Study With Collaborative Autoethnography and Network Analysis %A Miao,Melissa %A Morrow,Rosemary %A Salomon,Alexander %A Mcculloch,Ben %A Evain,Jean-Christophe %A Wright,Meg Rebecca %A Murphy,Marie Therese %A Welsh,Monica %A Williams,Liz %A Power,Emma %A Rietdijk,Rachael %A Debono,Deborah %A Brunner,Melissa %A Togher,Leanne %+ Graduate School of Health, Faculty of Health, University of Technology Sydney, 100 Broadway, Sydney, Australia, 61 2 9514 1448, melissa.miao@uts.edu.au %K complexity %K implementation science %K internet interventions %K brain injury %K stroke %K traumatic brain injury %K delivery of health care %K caregivers %K digital health %K psychosocial interventions %K psychosocial %K mobile phone %D 2023 %7 19.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Acquired brain injuries (ABIs), such as stroke and traumatic brain injury, commonly cause cognitive-communication disorders, in which underlying cognitive difficulties also impair communication. As communication is an exchange with others, close others such as family and friends also experience the impact of cognitive-communication impairment. It is therefore an internationally recommended best practice for speech-language pathologists to provide communication support to both people with ABI and the people who communicate with them. Current research also identifies a need for neurorehabilitation professionals to support digital communication, such as social media use, after ABI. However, with >135 million people worldwide affected by ABI, alternate and supplementary service delivery models are needed to meet these communication needs. The “Social Brain Toolkit” is a novel suite of 3 interventions to deliver communication rehabilitation via the internet. However, digital health implementation is complex, and minimal guidance exists for ABI. Objective: This study aimed to support the implementation of the Social Brain Toolkit by coproducing implementation knowledge with people with ABI, people who communicate with people with ABI, clinicians, and leaders in digital health implementation. Methods: A maximum variation sample (N=35) of individuals with living experience of ABI, close others, clinicians, and digital health implementation leaders participated in an explanatory sequential mixed methods design. Stakeholders quantitatively prioritized 4 of the 7 theoretical domains of the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework as being the most important for Social Brain Toolkit implementation. Qualitative interview and focus group data collection focused on these 4 domains. Data were deductively analyzed against the NASSS framework with stakeholder coauthors to determine implementation considerations and strategies. A collaborative autoethnography of the research was conducted. Interrelationships between considerations and strategies were identified through a post hoc network analysis. Results: Across the 4 prioritized domains of “condition,” “technology,” “value proposition,” and “adopters,” 48 digital health implementation considerations and 52 tailored developer and clinician implementation strategies were generated. Benefits and challenges of coproduction were identified. The post hoc network analysis revealed 172 unique relationships between the identified implementation considerations and strategies, with user and persona testing and responsive design identified as the potentially most impactful strategies. Conclusions: People with ABI, close others, clinicians, and digital health leaders coproduced new knowledge of digital health implementation considerations for adults with ABI and the people who communicate with them, as well as tailored implementation strategies. Complexity-informed network analyses offered a data-driven method to identify the 2 most potentially impactful strategies. Although the study was limited by a focus on 4 NASSS domains and the underrepresentation of certain demographics, the wealth of actionable implementation knowledge produced supports future coproduction of implementation research with mutually beneficial outcomes for stakeholders and researchers. International Registered Report Identifier (IRRID): RR2-10.2196/35080 %M 37725413 %R 10.2196/46396 %U https://www.jmir.org/2023/1/e46396 %U https://doi.org/10.2196/46396 %U http://www.ncbi.nlm.nih.gov/pubmed/37725413 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45760 %T Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study %A Kwun,Ju-Seung %A Lee,Jang Hoon %A Park,Bo Eun %A Park,Jong Sung %A Kim,Hyeon Jeong %A Kim,Sun-Hwa %A Jeon,Ki-Hyun %A Cho,Hyoung-won %A Kang,Si-Hyuck %A Lee,Wonjae %A Youn,Tae-Jin %A Chae,In-Ho %A Yoon,Chang-Hwan %+ Cardiovascular Center, Seoul National University Bundang Hospital, 82, Gumi-Ro 173, Bundang-Gu, Seongnam-si, 13620, Republic of Korea, 82 317877052, kunson2@snu.ac.kr %K arrhythmias %K atrial fibrillation %K wearable electronic device %K patch electrocardiogram monitor %K electrocardiogram %K adult %K AT-Patch %K heart failure %K mobile phone %D 2023 %7 18.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation. Objective: We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates. Methods: In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ≥19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ≥2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists. Results: Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ≥4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively. Conclusions: This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation. Trial Registration: ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268 %M 37721791 %R 10.2196/45760 %U https://www.jmir.org/2023/1/e45760 %U https://doi.org/10.2196/45760 %U http://www.ncbi.nlm.nih.gov/pubmed/37721791 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47179 %T Remote Symptom Monitoring With Ecological Momentary Computerized Adaptive Testing: Pilot Cohort Study of a Platform for Frequent, Low-Burden, and Personalized Patient-Reported Outcome Measures %A Harrison,Conrad %A Trickett,Ryan %A Wormald,Justin %A Dobbs,Thomas %A Lis,Przemysław %A Popov,Vesselin %A Beard,David J %A Rodrigues,Jeremy %+ Surgical Intervention Trials Unit, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, The Botnar Research Centre, Old Road, Oxford, OX3 7LD, United Kingdom, 44 1865 227374, conrad.harrison@ndorms.ox.ac.uk %K patient-reported outcome measures %K ecological momentary assessment %K computerized adaptive testing %K EMCAT %K symptom monitoring %K monitoring %K assessment %K smartphone app %K trauma %K arthritis %K usability %K mobile phone %D 2023 %7 14.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Remote patient-reported outcome measure (PROM) data capture can provide useful insights into research and clinical practice and deeper insights can be gained by administering assessments more frequently, for example, in ecological momentary assessment. However, frequent data collection can be limited by the burden of multiple, lengthy questionnaires. This burden can be reduced with computerized adaptive testing (CAT) algorithms that select only the most relevant items from a PROM for an individual respondent. In this paper, we propose “ecological momentary computerized adaptive testing” (EMCAT): the use of CAT algorithms to reduce PROM response burden and facilitate high-frequency data capture via a smartphone app. We develop and pilot a smartphone app for performing EMCAT using a popular hand surgery PROM. Objective: The aim of this study is to determine the feasibility of EMCAT as a system for remote PROM administration. Methods: We built the EMCAT web app using Concerto, an open-source CAT platform maintained by the Psychometrics Centre, University of Cambridge, and hosted it on an Amazon Web Service cloud server. The platform is compatible with any questionnaire that has been parameterized with item response theory or Rasch measurement theory. For this study, the PROM we chose was the patient evaluation measure, which is commonly used in hand surgery. CAT algorithms were built using item response theory models derived from UK Hand Registry data. In the pilot study, we enrolled 40 patients with hand trauma or thumb-base arthritis, across 2 sites, between July 13, 2022, and September 14, 2022. We monitored their symptoms with the patient evaluation measure, via EMCAT, over a 12-week period. Patients were assessed thrice weekly, once daily, or thrice daily. We additionally administered full-length PROM assessments at 0, 6, and 12 weeks, and the User Engagement Scale at 12 weeks. Results: The use of EMCAT significantly reduced the length of the PROM (median 2 vs 11 items) and the time taken to complete it (median 8.8 seconds vs 1 minute 14 seconds). Very similar scores were obtained when EMCAT was administered concurrently with the full-length PROM, with a mean error of <0.01 on a logit (z score) scale. The median response rate in the daily assessment group was 93%. The median perceived usability score of the User Engagement Scale was 4.0 (maximum possible score 5.0). Conclusions: EMCAT reduces the burden of PROM assessments, enabling acceptable high-frequency, remote PROM data capture. This has potential applications in both research and clinical practice. In research, EMCAT could be used to study temporal variations in symptom severity, for example, recovery trajectories after surgery. In clinical practice, EMCAT could be used to monitor patients remotely, prompting early intervention if a patient’s symptom trajectory causes clinical concern. Trial Registration: ISRCTN 19841416; https://www.isrctn.com/ISRCTN19841416 %M 37707947 %R 10.2196/47179 %U https://www.jmir.org/2023/1/e47179 %U https://doi.org/10.2196/47179 %U http://www.ncbi.nlm.nih.gov/pubmed/37707947 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46523 %T Bot or Not? Detecting and Managing Participant Deception When Conducting Digital Research Remotely: Case Study of a Randomized Controlled Trial %A Loebenberg,Gemma %A Oldham,Melissa %A Brown,Jamie %A Dinu,Larisa %A Michie,Susan %A Field,Matt %A Greaves,Felix %A Garnett,Claire %+ UCL Tobacco and Alcohol Research Group, University College London, 1-19 Torrington Place, London, WC1E 7HB, United Kingdom, 44 20 7679 8781, gemma.loebenberg@ucl.ac.uk %K artificial intelligence %K false information %K mHealth applications %K participant deception %K participant %K recruit %K research subject %K web-based studies %D 2023 %7 14.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Evaluating digital interventions using remote methods enables the recruitment of large numbers of participants relatively conveniently and cheaply compared with in-person methods. However, conducting research remotely based on participant self-report with little verification is open to automated “bots” and participant deception. Objective: This paper uses a case study of a remotely conducted trial of an alcohol reduction app to highlight and discuss (1) the issues with participant deception affecting remote research trials with financial compensation; and (2) the importance of rigorous data management to detect and address these issues. Methods: We recruited participants on the internet from July 2020 to March 2022 for a randomized controlled trial (n=5602) evaluating the effectiveness of an alcohol reduction app, Drink Less. Follow-up occurred at 3 time points, with financial compensation offered (up to £36 [US $39.23]). Address authentication and telephone verification were used to detect 2 kinds of deception: “bots,” that is, automated responses generated in clusters; and manual participant deception, that is, participants providing false information. Results: Of the 1142 participants who enrolled in the first 2 months of recruitment, 75.6% (n=863) of them were identified as bots during data screening. As a result, a CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) was added, and after this, no more bots were identified. Manual participant deception occurred throughout the study. Of the 5956 participants (excluding bots) who enrolled in the study, 298 (5%) were identified as false participants. The extent of this decreased from 110 in November 2020, to a negligible level by February 2022 including a number of months with 0. The decline occurred after we added further screening questions such as attention checks, removed the prominence of financial compensation from social media advertising, and added an additional requirement to provide a mobile phone number for identity verification. Conclusions: Data management protocols are necessary to detect automated bots and manual participant deception in remotely conducted trials. Bots and manual deception can be minimized by adding a CAPTCHA, attention checks, a requirement to provide a phone number for identity verification, and not prominently advertising financial compensation on social media. Trial Registration: ISRCTN Number ISRCTN64052601; https://doi.org/10.1186/ISRCTN64052601 %M 37707943 %R 10.2196/46523 %U https://www.jmir.org/2023/1/e46523 %U https://doi.org/10.2196/46523 %U http://www.ncbi.nlm.nih.gov/pubmed/37707943 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46675 %T Cardiff Online Cognitive Assessment in a National Sample: Cross-Sectional Web-Based Study %A Lynham,Amy Joanne %A Jones,Ian R %A Walters,James T R %+ Division of Psychological Medicine, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ, United Kingdom, 44 2920688434, waltersjt@cardiff.ac.uk %K cognition %K digital assessment %K mental health %K mobile phone %K normative data %K web-based %K cognitive assessment %K CONCA %D 2023 %7 13.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Psychiatric disorders are associated with cognitive impairment. We have developed a web-based, 9-task cognitive battery to measure the core domains affected in people with psychiatric disorders. To date, this assessment has been used to collect data on a clinical sample of participants with psychiatric disorders. Objective: The aims of this study were (1) to establish a briefer version of the battery (called the Cardiff Online Cognitive Assessment [CONCA]) that can give a valid measure of cognitive ability (“g”) and (2) to collect normative data and demonstrate CONCA’s application in a health population sample. Methods: Based on 6 criteria and data from our previous study, we selected 5 out of the original 9 tasks to include in CONCA. These included 3 core tasks that were sufficient to derive a measure of “g” and 2 optional tasks. Participants from a web-based national cohort study (HealthWise Wales) were invited to complete CONCA. Completion rates, sample characteristics, performance distributions, and associations between cognitive performance and demographic characteristics and mental health measures were examined. Results: A total of 3679 participants completed at least one CONCA task, of which 3135 completed all 3 core CONCA tasks. Performance on CONCA was associated with age (B=–0.05, SE 0.002; P<.001), device (tablet computer: B=–0.26, SE 0.05; P<.001; smartphone: B=–0.46, SE 0.05; P<.001), education (degree: B=1.68, SE 0.14; P<.001), depression symptoms (B=–0.04, SE 0.01; P<.001), and anxiety symptoms (B=–0.04, SE 0.01; P<.001). Conclusions: CONCA provides a valid measure of “g,” which can be derived using as few as 3 tasks that take no more than 15 minutes. Performance on CONCA showed associations with demographic characteristics in the expected direction and was associated with current depression and anxiety symptoms. The effect of device on cognitive performance is an important consideration for research using web-based assessments. %M 37703073 %R 10.2196/46675 %U https://www.jmir.org/2023/1/e46675 %U https://doi.org/10.2196/46675 %U http://www.ncbi.nlm.nih.gov/pubmed/37703073 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e38481 %T Using the AllerSearch Smartphone App to Assess the Association Between Dry Eye and Hay Fever: mHealth-Based Cross-Sectional Study %A Inomata,Takenori %A Sung,Jaemyoung %A Nakamura,Masahiro %A Iwagami,Masao %A Akasaki,Yasutsugu %A Fujio,Kenta %A Nakamura,Masahiro %A Ebihara,Nobuyuki %A Ide,Takuma %A Nagao,Masashi %A Okumura,Yuichi %A Nagino,Ken %A Fujimoto,Keiichi %A Eguchi,Atsuko %A Hirosawa,Kunihiko %A Midorikawa-Inomata,Akie %A Muto,Kaori %A Fujisawa,Kumiko %A Kikuchi,Yota %A Nojiri,Shuko %A Murakami,Akira %+ Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan, 81 338133111, tinoma@juntendo.ac.jp %K dry eye %K hay fever %K mobile health %K personalized medicine %K smartphone %K pollinosis %K rhinitis %K allergic conjunctivitis %K nasal symptom score %K nonnasal symptom score %K Ocular Surface Disease Index %K Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire %K mobile phone %D 2023 %7 12.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Dry eye (DE) and hay fever (HF) show synergistic exacerbation of each other’s pathology through inflammatory pathways. Objective: This study aimed to investigate the association between DE and HF comorbidity and the related risk factors. Methods: A cross-sectional observational study was conducted using crowdsourced multidimensional data from individuals who downloaded the AllerSearch smartphone app in Japan between February 2018 and May 2020. AllerSearch collected the demographics, medical history, lifestyle and residential information, HF status, DE symptoms, and HF-related quality of life. HF symptoms were evaluated using the nasal symptom score (0-15 points) and nonnasal symptom score (0-12 points). HF was defined by the participants’ responses to the questionnaire as HF, non-HF, or unknown. Symptomatic DE was defined as an Ocular Surface Disease Index total score (0-100 points), with a threshold score of 13 points. HF-related quality of life was assessed using the Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire (0-68 points). We conducted a multivariable linear regression analysis to examine the association between the severity of DE and HF symptoms. We subsequently conducted a multivariable logistic regression analysis to identify the factors associated with symptomatic DE (vs nonsymptomatic DE) among individuals with HF. Dimension reduction via Uniform Manifold Approximation and Projection stratified the comorbid DE and HF symptoms. The symptom profiles in each cluster were identified using hierarchical heat maps. Results: This study included 11,284 participants, classified into experiencing HF (9041 participants), non-HF (720 participants), and unknown (1523 participants) groups. The prevalence of symptomatic DE among individuals with HF was 49.99% (4429/9041). Severe DE symptoms were significantly associated with severe HF symptoms: coefficient 1.33 (95% CI 1.10-1.57; P<.001) for mild DE, coefficient 2.16 (95% CI 1.84-2.48; P<.001) for moderate DE, and coefficient 3.80 (95% CI 3.50-4.11; P<.001) for severe DE. The risk factors for comorbid symptomatic DE among individuals with HF were identified as female sex; lower BMI; medicated hypertension; history of hematologic, collagen, heart, liver, respiratory, or atopic disease; tomato allergy; current and previous mental illness; pet ownership; living room and bedrooms furnished with materials other than hardwood, carpet, tatami, and vinyl; discontinuation of contact lens use during the HF season; current contact lens use; smoking habits; and sleep duration of <6 hours per day. Uniform Manifold Approximation and Projection stratified the heterogeneous comorbid DE and HF symptoms into 14 clusters. In the hierarchical heat map, cluster 9 was comorbid with the most severe HF and DE symptoms, and cluster 1 showed severe HF symptoms with minimal DE-related symptoms. Conclusions: This crowdsourced study suggested a significant association between severe DE and HF symptoms. Detecting DE among individuals with HF could allow effective prevention and interventions through concurrent treatment for ocular surface management along with HF treatment. %M 37698897 %R 10.2196/38481 %U https://www.jmir.org/2023/1/e38481 %U https://doi.org/10.2196/38481 %U http://www.ncbi.nlm.nih.gov/pubmed/37698897 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e47388 %T Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study %A Bin,Kaio Jia %A De Pretto,Lucas Ramos %A Sanchez,Fábio Beltrame %A De Souza e Castro,Fabio Pacheco Muniz %A Ramos,Vinicius Delgado %A Battistella,Linamara Rizzo %+ Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, Rua Domingo de Soto, 100, Jardim Vila Mariana, São Paulo, 04116-040, Brazil, 55 1151807987, kaiobin@gmail.com %K smartwatch %K digital health %K telemedicine %K wearable %K telemonitoring %K mobile health %K General Data Protection Regulation %K GDPR %K Lei Geral de Proteção de Dados %K LGPD %K digital platform %K clinical intervention %K sensitive data %K clinical trial %K mobile phone %D 2023 %7 12.9.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome. Objective: This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws. Methods: A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home. Results: In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were −1.280% (SD 5.679%), −1.399 (SD 19.112) mm Hg, −1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life. Conclusions: On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring. %M 37698916 %R 10.2196/47388 %U https://formative.jmir.org/2023/1/e47388 %U https://doi.org/10.2196/47388 %U http://www.ncbi.nlm.nih.gov/pubmed/37698916 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44772 %T Psychometric Evaluation of the Modes of Health Information Acquisition, Sharing, and Use Questionnaire: Prospective Cross-Sectional Observational Study %A Jones,Lenette M %A Piscotty Jr,Ronald J %A Sullivan,Stephen %A Manzor Mitrzyk,Beatriz %A Ploutz-Snyder,Robert J %A Ghosh,Bidisha %A Veinot,Tiffany %+ School of Nursing, University of Michigan, Room 2180, 400 N. Ingalls, Ann Arbor, MI, 48109, United States, 1 17347631371, lenettew@umich.edu %K psychometric evaluation %K health information behavior %K construct validity %K reliability %K chronic illness %K MHIASU %K hypertension %D 2023 %7 11.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Health information is a critical resource for individuals with health concerns and conditions, such as hypertension. Enhancing health information behaviors may help individuals to better manage chronic illness. The Modes of Health Information Acquisition, Sharing, and Use (MHIASU) is a 23-item questionnaire that measures how individuals with health risks or chronic illness acquire, share, and use health information. Yet this measure has not been psychometrically evaluated in a large national sample. Objective: The objective of this study was to evaluate the psychometric properties of the self-administered MHIASU in a large, diverse cohort of individuals living with a chronic illness. Methods: Sharing Information, a prospective, observational study, was launched in August 2018 and used social media campaigns to advertise to Black women. Individuals who were interested in participating clicked on the advertisements and were redirected to a Qualtrics eligibility screener. To meet eligibility criteria individuals had to self-identify as a Black woman, be diagnosed with hypertension by a health care provider, and live in the United States. A total of 320 Black women with hypertension successfully completed the eligibility screener and then completed a web-based version of the MHIASU questionnaire. We conducted a psychometric evaluation of the MHIASU using exploratory factor analysis. The evaluation included item review, construct validity, and reliability. Results: Construct validity was established using exploratory factor analysis with principal axis factoring. The analysis was constricted to the expected domains. Interitem correlations were examined for possible item extraction. There were no improvements in factor structure with the removal of items with high interitem correlation (n=3), so all items of the MHIASU were retained. As anticipated, the instrument was found to have 3 subscales: acquisition, sharing, and use. Reliability was high for all 3 subscales, as evidenced by Cronbach α scores of .81 (acquisition), .81 (sharing), and .93 (use). Factor 3 (use of health information) explained the maximum variance (74%). Conclusions: Construct validity and reliability of the web-based, self-administered MHIASU was demonstrated in a large national cohort of Black women with hypertension. Although this sample was highly educated and may have had higher digital literacy compared to other samples not recruited via social media, the population captured (Black women living with hypertension) are often underrepresented in research and are particularly vulnerable to this chronic condition. Future studies can use the MHIASU to examine health information behavior in other diverse populations managing health concerns and conditions. %M 37695669 %R 10.2196/44772 %U https://www.jmir.org/2023/1/e44772 %U https://doi.org/10.2196/44772 %U http://www.ncbi.nlm.nih.gov/pubmed/37695669 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e49020 %T Wellness in Nursing Education to Promote Resilience and Reduce Burnout: Protocol for a Holistic Multidimensional Wellness Intervention and Longitudinal Research Study Design in Nursing Education %A Strout,Kelley %A Schwartz-Mette,Rebecca %A McNamara,Jade %A Parsons,Kayla %A Walsh,Dyan %A Bonnet,Jen %A O'Brien,Liam M %A Robinson,Kathryn %A Sibley,Sean %A Smith,Annie %A Sapp,Maile %A Sprague,Lydia %A Sabegh,Nima Sajedi %A Robinson,Kaitlin %A Henderson,Amanda %+ School of Nursing, University of Maine, Dunn Hall, Orono, ME, 04469, United States, 1 2075812601, kelley.strout@maine.edu %K nursing workforce %K academic performance %K burnout %K resilience %K wellness %K nursing %K education %K nursing education %K protocol %K nursing students %K students %K holistic %K implementation %K workforce %D 2023 %7 8.9.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: The United States faces a nursing shortage driven by a burnout epidemic among nurses and nursing students. Nursing students are an integral population to fuel the nursing workforce at high risk of burnout and increased rates of perceived stress. Objective: The aim of this paper is to describe WellNurse, a holistic, interdisciplinary, multidimensional longitudinal research study that examines evidence-based interventions intended to reduce burnout and increase resilience among graduate and undergraduate nursing students. Methods: Graduate and undergraduate nursing students matriculated at a large public university in the northeastern United States are eligible to enroll in this ongoing, longitudinal cohort study beginning in March 2021. Participants complete a battery of health measurements twice each semester during the fourth week and the week before final examinations. The measures include the Perceived Stress Scale, the Satisfaction with Life Scale, the Oldenburg Burnout Inventory, the Brief Resilience Scale, and the Pittsburgh Sleep Quality Index. Participants are eligible to enroll in a variety of interventions, including mindfulness-based stress reduction, mindful eating, fitness training, and massage therapy. Those who enroll in specific, targeted interventions complete additional measures designed to target the aim of the intervention. All participants receive a free Fitbit device. Additional environmental changes are being implemented to further promote a culture that supports academic well-being, including recruiting a diverse student population through evidence-based holistic admissions, inclusive teaching design, targeted resilience and stress reduction workshops, and cultural shifts within classrooms and curricula. The study design protocol is registered at Open Science Framework DOI 10.17605/OSF.IO/NCBPE. Results: The project was funded on January 1, 2022. Data collection started in March 2022. A total of 267 participants have been recruited. Results will be published after each semester starting in December 2023. WellNurse evaluation follows the Rapid Cycle Quality Improvement framework to continuously monitor ongoing project processes, activity outcomes, and progress toward reducing burnout and increasing resilience. Rapid Cycle Quality Improvement promotes the ability to alter WellNurse interventions, examine multiple interventions, and test their effectiveness among the nursing education population to identify the most effective interventions. Conclusions: Academic nursing organizations must address student burnout risk and increase resilience to produce a future workforce that provides high-quality patient care to a diverse population. Findings from WellNurse will support evidence-based implementations for public baccalaureate and master’s nursing programs in the United States. International Registered Report Identifier (IRRID): DERR1-10.2196/49020 %R 10.2196/49020 %U https://www.researchprotocols.org/2023/1/e49020/ %U https://doi.org/10.2196/49020 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 12 %N %P e45258 %T Mobile Apps Aimed at Preventing and Handling Unintentional Injuries in Children Aged <7 Years: Systematic Review %A Schulze,Annett %A Lindemann,Ann-Kathrin %A Brand,Fabian %A Geppert,Johanna %A Menning,Axel %A Stehr,Paula %A Reifegerste,Doreen %A Rossmann,Constanze %+ Department of Risk Communication, German Federal Institute for Risk Assessment, Max-Dohrn-Str 8-10, Berlin, 10589, Germany, 49 18412 52002, annett.schulze@bfr.bund.de %K mobile health %K mHealth %K caregiver %K parental %K prevention %K first aid %K pediatric %K review method %K injuries %K health app %K needs %K mobile phone %D 2023 %7 6.9.2023 %9 Review %J Interact J Med Res %G English %X Background: Despite various global health crises, the prevention and handling of unintentional childhood injuries remains an important public health objective. Although several systematic reviews have examined the effectiveness of different child injury prevention measures, these reviews did not address the evaluation of mobile communication intervention tools. Whether and how mobile apps were evaluated provides information on the extent to which communication theories, models, and evidence-based knowledge were considered. Previous studies have shown that the effectiveness of mobile apps increases when theories and evidence are considered during their development. Objective: This systematic review aimed to identify research on mobile apps dealing with the prevention and handling of unintentional injuries in children and examine the theoretical and methodological approaches thereof. In addition, this review analyzed the different needs of various target groups of the mobile apps described in the articles. Methods: In total, 8 electronic databases, ranging from interdisciplinary to medical and technical as well as social sciences databases, were searched for original research articles or brief reports in peer-reviewed journals or conference proceedings. Moreover, this review encompassed a systematic scan of articles published in the BMJ journal Injury Prevention. These steps were followed by a snowball search based on the literature references in the articles identified through the initial screening. The articles had to be written in English or German, published between 2008 and 2021, and evaluate mobile apps dealing with the prevention and handling of unintentional child injuries. The identified 5 studies were analyzed by 5 independent researchers using an inductive approach. Furthermore, the quality of the studies was assessed using the Mixed Methods Appraisal Tool. Results: A total of 5 articles were included and assessed with regard to overall quality of theoretical and methodological foundations, assessed variables, the focal app’s architecture, and the needs of the study participants. The overall study quality was moderate, although part of this classification was due to a lack of details reported in the studies. Each study examined 1 mobile app aimed at parents and other caregivers. Each study assessed at least 1 usability- or user experience-related variable, whereas the needs of the included study participants were detailed in only 20% (1/5) of the cases. However, none of the studies referred to theories such as the Technology Acceptance Model during the development of the apps. Conclusions: The future development and evaluation of apps dealing with the prevention and handling of child injuries should combine insights into existing models on user experience and usability with established theories on mobile information behavior. This theory-based approach will increase the validity of such evaluation studies. %M 37672312 %R 10.2196/45258 %U https://www.i-jmr.org/2023/1/e45258 %U https://doi.org/10.2196/45258 %U http://www.ncbi.nlm.nih.gov/pubmed/37672312 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 4 %N %P e45370 %T User and Usability Testing of a Web-Based Genetics Education Tool for Parkinson Disease: Mixed Methods Study %A Han,Noah %A Paul,Rachel A %A Bardakjian,Tanya %A Kargilis,Daniel %A Bradbury,Angela R %A Chen-Plotkin,Alice %A Tropea,Thomas F %+ Department of Neurology, Perelman School of Medicine, University of Pennsylvania, 330 South 9th Street, Philadelphia, PA, 19107, United States, 1 215 829 7731, Thomas.Tropea@pennmedicine.upenn.edu %K Parkinson disease %K genetic testing %K teleneurology %K patient education %K neurology %K genetic %K usability %K user testing %K web-based %K internet-based %K web-based resource %K mobile phone %D 2023 %7 30.8.2023 %9 Original Paper %J JMIR Bioinform Biotech %G English %X Background: Genetic testing is essential to identify research participants for clinical trials enrolling people with Parkinson disease (PD) carrying a variant in the glucocerebrosidase (GBA) or leucine-rich repeat kinase 2 (LRRK2) genes. The limited availability of professionals trained in neurogenetics or genetic counseling is a major barrier to increased testing. Telehealth solutions to increase access to genetics education can help address issues around counselor availability and offer options to patients and family members. Objective: As an alternative to pretest genetic counseling, we developed a web-based genetics education tool focused on GBA and LRRK2 testing for PD called the Interactive Multimedia Approach to Genetic Counseling to Inform and Educate in Parkinson’s Disease (IMAGINE-PD) and conducted user testing and usability testing. The objective was to conduct user and usability testing to obtain stakeholder feedback to improve IMAGINE-PD. Methods: Genetic counselors and PD and neurogenetics subject matter experts developed content for IMAGINE-PD specifically focused on GBA and LRRK2 genetic testing. Structured interviews were conducted with 11 movement disorder specialists and 13 patients with PD to evaluate the content of IMAGINE-PD in user testing and with 12 patients with PD to evaluate the usability of a high-fidelity prototype according to the US Department of Health and Human Services Research-Based Web Design & Usability Guidelines. Qualitative data analysis informed changes to create a final version of IMAGINE-PD. Results: Qualitative data were reviewed by 3 evaluators. Themes were identified from feedback data of movement disorder specialists and patients with PD in user testing in 3 areas: content such as the topics covered, function such as website navigation, and appearance such as pictures and colors. Similarly, qualitative analysis of usability testing feedback identified additional themes in these 3 areas. Key points of feedback were determined by consensus among reviewers considering the importance of the comment and the frequency of similar comments. Refinements were made to IMAGINE-PD based on consensus recommendations by evaluators within each theme at both user testing and usability testing phases to create a final version of IMAGINE-PD. Conclusions: User testing for content review and usability testing have informed refinements to IMAGINE-PD to develop this focused, genetics education tool for GBA and LRRK2 testing. Comparison of this stakeholder-informed intervention to standard telegenetic counseling approaches is ongoing. %R 10.2196/45370 %U https://bioinform.jmir.org/2023/1/e45370 %U https://doi.org/10.2196/45370 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e41833 %T Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study %A Wyant,Kendra %A Moshontz,Hannah %A Ward,Stephanie B %A Fronk,Gaylen E %A Curtin,John J %+ Department of Psychology, University of Wisconsin-Madison, 1202 W Johnson St, Madison, WI, 53706, United States, 1 (608) 262 1040, jjcurtin@wisc.edu %K personal sensing %K digital therapeutics %K mobile health %K smartphone %K alcohol use disorder %K self-report %K alcohol use %K symptom monitoring %K mental health %K acceptability %K alcohol intake %K mobile phone %D 2023 %7 28.8.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications. Objective: We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses. Methods: Participants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants’ choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method. Results: Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04). Conclusions: These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement. %M 37639300 %R 10.2196/41833 %U https://mhealth.jmir.org/2023/1/e41833 %U https://doi.org/10.2196/41833 %U http://www.ncbi.nlm.nih.gov/pubmed/37639300 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e45986 %T A Bespoke Electronic Health Journal for Monitoring Response to Botulinum Toxin in Treatment of Cervical Dystonia: Open-Label Observational Study of User Experience %A Edwards,Colin %A Borton,Rebecca %A Ross,Anita %A Molloy,Fiona %+ patientMpower Ltd, 10-13 Thomas St, The Digital Hub, Dublin, D08PX8H, Ireland, 353 872599131, colin.edwards@merlinconsulting.ie %K cervical dystonia %K electronic health journal %K user experience %K user acceptance testing %K botulinum toxin %K diary %K acceptability %K user testing %K symptom control %K spasm %K muscle pain %K spasmodic torticollis %D 2023 %7 23.8.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The mainstay of treatment for cervical dystonia (CD) is regular botulinum toxin injections every 3-4 months. Clinical evaluation of response is dependent on the patient’s recall of how well symptoms responded to the previous injection. A mobile health app could assist both patients and health care professionals to monitor treatment benefits and side effects to assist with the selection of muscle and toxin dose to be injected at the next visit. The DystoniaDiary is a bespoke electronic health journal for monitoring symptoms of CD and response to treatment. Objective: The objective of this study was to assess the acceptability and utility of the DystoniaDiary in patients with CD treated with botulinum toxins as part of their usual care. Methods: In this open-label, single-center, single-arm observational study, patients attending a botulinum toxin injection clinic were invited to download the DystoniaDiary app. Patients selected up to 3 of their most troublesome CD symptoms (from a predefined list) and were prompted every 3 days to rate the control of these symptoms on a scale from 0 (very badly) to 100 (very well). Dates of onset and wearing off of response to injected botulinum toxin and responses to the Cervical Dystonia Impact Profile (CDIP-58) questionnaire at baseline and week 6 were also recorded in the app. Results: A total of 34 patients installed DystoniaDiary. Twenty-five patients (25/34, 74%) recorded data for ≥12 weeks and 21 patients (21/34, 62%) for ≥16 weeks. Median time between the first and last data input was 140 days with a median of 13 recordings per patient. User experience questionnaires at weeks 4 and 12 (20 respondents) indicated that the majority of respondents found the DystoniaDiary app easy to install and use, liked using it, would recommend it to others (19/20), and wished to continue using it (16/20). A smaller proportion indicated that the DystoniaDiary gave a greater sense of control in managing their CD (13/20). There was interindividual variation in patients’ perceptions of control of their symptoms after botulinum toxin injection. Response to treatment was apparent in the symptom control scores for some patients, whereas the severity of other patients’ symptoms did not appear to change after treatment. Conclusions: This observational study demonstrated that the DystoniaDiary app was perceived as useful and acceptable for a large proportion of this sample of patients with CD attending a botulinum toxin clinic. Patients with CD appear to be willing to regularly record symptom severity for at least the duration of a botulinum injection treatment cycle (12-16 weeks). This app may be useful in monitoring and optimizing individual patient responses to botulinum toxin injection. %M 37610807 %R 10.2196/45986 %U https://formative.jmir.org/2023/1/e45986 %U https://doi.org/10.2196/45986 %U http://www.ncbi.nlm.nih.gov/pubmed/37610807 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47366 %T Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study %A Liu,Jen-Hsuan %A Shih,Chih-Yuan %A Huang,Hsien-Liang %A Peng,Jen-Kuei %A Cheng,Shao-Yi %A Tsai,Jaw-Shiun %A Lai,Feipei %+ Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, 7 Chung-Shan South Road, Taipei, 100225, Taiwan, 886 2 2312 3456 ext 62147, jawshiun@ntu.edu.tw %K artificial intelligence %K end-of-life care %K machine learning %K palliative care %K survival prediction %K terminal cancer %K wearable device %D 2023 %7 18.8.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. Objective: This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. Methods: This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning–based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. Results: From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. Conclusions: We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. Trial Registration: ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907 %M 37594793 %R 10.2196/47366 %U https://www.jmir.org/2023/1/e47366 %U https://doi.org/10.2196/47366 %U http://www.ncbi.nlm.nih.gov/pubmed/37594793 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42950 %T An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review %A Tam,William %A Alajlani,Mohannad %A Abd-alrazaq,Alaa %+ Weill Cornell Medicine, 8C9R+735, Education City, Al Luqta St, Ar-Rayyan, Doha, 8C9R+735, Qatar, 974 4492 8000, aaa4027@qatar-med.cornell.edu %K Parkinson disease %K wearable devices %K machine learning %K hospital %K secondary care %K United Kingdom %K scoping review %D 2023 %7 18.8.2023 %9 Review %J J Med Internet Res %G English %X Background: The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. Objective: In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. Methods: A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. Results: Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. Conclusions: This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape. %M 37594791 %R 10.2196/42950 %U https://www.jmir.org/2023/1/e42950 %U https://doi.org/10.2196/42950 %U http://www.ncbi.nlm.nih.gov/pubmed/37594791 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 12 %N %P e45504 %T Use of Wearable Devices for Peak Oxygen Consumption Measurement in Clinical Cardiology: Case Report and Literature Review %A Bayshtok,Gabriella %A Tiosano,Shmuel %A Furer,Ariel %+ Leviev Heart Center, Sheba Medical Center, Derech Sheba 2, Ramat Gan, Israel, 972 52 927 7372, furera@gmail.com %K cardiac fitness %K cardiac patient %K cardiorespiratory fitness %K CRF %K clinical cardiology %K oxygen consumption %K peak VO2 %K smartwatch %K wearable device %D 2023 %7 15.8.2023 %9 Case Report %J Interact J Med Res %G English %X Background: Oxygen consumption is an important index to evaluate in cardiac patients, particularly those with heart failure, and is measured in the setting of advanced cardiopulmonary exercise testing. However, technological advances now allow for the estimation of this parameter in many consumer and medical-grade wearable devices, making it available for the medical provider at the initial evaluation of patients. We report a case of an apparently healthy male aged 40 years who presented for evaluation due to an Apple Watch (Apple Inc) notification of low cardiac fitness. This alert triggered a thorough workup, revealing a diagnosis of familial nonischemic cardiomyopathy with severely reduced left ventricular systolic function. While the use of wearable devices for the measurement of oxygen consumption and related parameters is promising, further studies are needed for validation. Objective: The aim of this report is to investigate the potential utility of wearable devices as a screening and risk stratification tool for cardiac fitness for the general population and those with increased cardiovascular risk, particularly through the measurement of peak oxygen consumption (VO2). We discuss the possible advantages of measuring oxygen consumption using wearables and propose its integration into routine patient evaluation and follow-up processes. With the current evidence and limitations, we encourage researchers and clinicians to explore bringing wearable devices into clinical practice. Methods: The case was identified at Sheba Medical Center, and the patient’s cardiac fitness was monitored through an Apple Watch Series 6. The patient underwent a comprehensive cardiac workup following his presentation. Subsequently, we searched the literature for articles relating to the clinical utility of peak VO2 monitoring and available wearable devices. Results: The Apple Watch data provided by the patient demonstrated reduced peak VO2, a surrogate index for cardiac fitness, which improved after treatment initiation. A cardiological workup confirmed familial nonischemic cardiomyopathy with severely reduced left ventricular systolic function. A review of the literature revealed the potential clinical benefit of peak VO2 monitoring in both cardiac and noncardiac scenarios. Additionally, several devices on the market were identified that could allow for accurate oxygen consumption measurement; however, future studies and approval by the Food and Drug Administration (FDA) are still necessary. Conclusions: This case report highlights the potential utility of peak VO2 measurements by wearable devices for early identification and screening of cardiac fitness for the general population and those at increased risk of cardiovascular disease. The integration of wearable devices into routine patient evaluation may allow for earlier presentation in the diagnostic workflow. Cardiac fitness can be serially measured using the wearable device, allowing for close monitoring of functional capacity parameters. Devices need to be used with caution, and further studies are warranted. %M 37581915 %R 10.2196/45504 %U https://www.i-jmr.org/2023/1/e45504 %U https://doi.org/10.2196/45504 %U http://www.ncbi.nlm.nih.gov/pubmed/37581915 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e47380 %T Understanding Mental Health Clinicians’ Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study %A Nghiem,Jodie %A Adler,Daniel A %A Estrin,Deborah %A Livesey,Cecilia %A Choudhury,Tanzeem %+ College of Computing and Information Science, Cornell Tech, 2 W Loop Rd, New York, NY, 10044, United States, 1 2155953769, daa243@cornell.edu %K digital technology %K clinical decision support %K mobile health %K mHealth %K qualitative research %K mental health %K clinician %K perception %K patient-generated health data %K mobile app %K digital app %K wearables %K mobile phone %D 2023 %7 10.8.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. Objective: We conducted a qualitative study to understand mental health clinicians’ perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants’ current experiences with and visions for using passive PGHD. Methods: Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. Results: Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven—we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data—participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients’ mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action—participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy—participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. Conclusions: Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data–sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians. %M 37561561 %R 10.2196/47380 %U https://formative.jmir.org/2023/1/e47380 %U https://doi.org/10.2196/47380 %U http://www.ncbi.nlm.nih.gov/pubmed/37561561 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e45693 %T Using Continuous Passive Assessment Technology to Describe Health and Behavior Patterns Preceding and Following a Cancer Diagnosis in Older Adults: Proof-of-Concept Case Series Study %A Wu,Chao-Yi %A Tibbitts,Deanne %A Beattie,Zachary %A Dodge,Hiroko %A Shannon,Jackilen %A Kaye,Jeffrey %A Winters-Stone,Kerri %+ Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, 10-003C, Charlestown, MA, 02129, United States, 1 617 724 2428, chwu3@mgh.harvard.edu %K sensor %K quality of life %K physical activity %K medication %K monitoring %K function %K mobile phone %D 2023 %7 10.8.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Describing changes in health and behavior that precede and follow a sentinel health event, such as a cancer diagnosis, is challenging because of the lack of longitudinal, objective measurements that are collected frequently enough to capture varying trajectories of change leading up to and following the event. A continuous passive assessment system that continuously monitors older adults’ physical activity, weight, medication-taking behavior, pain, health events, and mood could enable the identification of more specific health and behavior patterns leading up to a cancer diagnosis and whether and how patterns change thereafter. Objective: In this study, we conducted a proof-of-concept retrospective analysis, in which we identified new cancer diagnoses in older adults and compared trajectories of change in health and behaviors before and after cancer diagnosis. Methods: Participants were 10 older adults (mean age 71.8, SD 4.9 years; 3/10, 30% female) with various self-reported cancer types from a larger prospective cohort study of older adults. A technology-agnostic assessment platform using multiple devices provided continuous data on daily physical activity via wearable sensors (actigraphy); weight via a Wi-Fi–enabled digital scale; daily medication-taking behavior using electronic Bluetooth-enabled pillboxes; and weekly pain, health events, and mood with online, self-report surveys. Results: Longitudinal linear mixed-effects models revealed significant differences in the pre- and postcancer trajectories of step counts (P<.001), step count variability (P=.004), weight (P<.001), pain severity (P<.001), hospitalization or emergency room visits (P=.03), days away from home overnight (P=.01), and the number of pillbox door openings (P<.001). Over the year preceding a cancer diagnosis, there were gradual reductions in step counts and weight and gradual increases in pain severity, step count variability, hospitalization or emergency room visits, and days away from home overnight compared with 1 year after the cancer diagnosis. Across the year after the cancer diagnosis, there was a gradual increase in the number of pillbox door openings compared with 1 year before the cancer diagnosis. There was no significant trajectory change from the pre– to post–cancer diagnosis period in terms of low mood (P=.60) and loneliness (P=.22). Conclusions: A home-based, technology-agnostic, and multidomain assessment platform could provide a unique approach to monitoring different types of behavior and health markers in parallel before and after a life-changing health event. Continuous passive monitoring that is ecologically valid, less prone to bias, and limits participant burden could greatly enhance research that aims to improve early detection efforts, clinical care, and outcomes for people with cancer. %M 37561574 %R 10.2196/45693 %U https://formative.jmir.org/2023/1/e45693 %U https://doi.org/10.2196/45693 %U http://www.ncbi.nlm.nih.gov/pubmed/37561574 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e28848 %T A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach %A Ahmed,Md Sabbir %A Ahmed,Nova %+ Design Inclusion and Access Lab, North South University, Plot #15, Block #B, Bashundhara, Dhaka, 1229, Bangladesh, 880 1781920068, msg2sabbir@gmail.com %K smartphone %K depression %K explainable machine learning %K low-resource settings %K real-time system %K students %D 2023 %7 10.8.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial. Additionally, due to the requirement of running systems in the background for prolonged periods, existing systems can be resource inefficient. As a result, these systems can be infeasible in low-resource settings. Objective: Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time. Another objective was to explain the machine learning (ML) models that were best for identifying depression. Methods: We developed a fast tool that retrieves the past 7 days’ app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data and responses to the Patient Health Questionnaire-9. To identify depressed and nondepressed students, we developed a diverse set of ML models: linear, tree-based, and neural network–based models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches: filter, wrapper, and embedded methods. We developed and validated the models using the nested cross-validation method. Additionally, we explained the best ML models through the Shapley additive explanations (SHAP) method. Results: Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). Feature importance analysis suggested app usage behavioral markers containing diurnal usage patterns as being more important than aggregated data-based markers. In addition, a SHAP analysis of our best models presented behavioral markers that were related to depression. For instance, students who were not depressed spent more time on education apps on weekdays, whereas those who were depressed used a higher number of photo and video apps and also had a higher deviation in using photo and video apps over the morning, afternoon, evening, and night time periods of the weekend. Conclusions: Due to our system’s fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed and take steps for intervention. %M 37561568 %R 10.2196/28848 %U https://formative.jmir.org/2023/1/e28848 %U https://doi.org/10.2196/28848 %U http://www.ncbi.nlm.nih.gov/pubmed/37561568 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e44388 %T Assessing Mood With the Identifying Depression Early in Adolescence Chatbot (IDEABot): Development and Implementation Study %A Viduani,Anna %A Cosenza,Victor %A Fisher,Helen L %A Buchweitz,Claudia %A Piccin,Jader %A Pereira,Rivka %A Kohrt,Brandon A %A Mondelli,Valeria %A van Heerden,Alastair %A Araújo,Ricardo Matsumura %A Kieling,Christian %+ Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Rua Ramiro Barcelos, 2400, Porto Alegre, 90035003, Brazil, 55 5133085624, ckieling@ufrgs.com %K depression %K adolescent %K ambulatory assessment %K chatbot %K smartphone %K digital mental health %K mobile phone %D 2023 %7 7.8.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Mental health status assessment is mostly limited to clinical or research settings, but recent technological advances provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on their smartphones, such as chatbots, could be a useful approach to capture subjective reports of mood in the moment. Objective: This study aimed to describe the development and implementation of the Identifying Depression Early in Adolescence Chatbot (IDEABot), a WhatsApp-based tool designed for collecting intensive longitudinal data on adolescents’ mood. Methods: The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It supports the administration and collection of self-reported structured items or questionnaires and audio responses. The development explored WhatsApp’s default features, such as emojis and recorded audio messages, and focused on scripting relevant and acceptable conversations. The IDEABot supports 5 types of interactions: textual and audio questions, administration of a version of the Short Mood and Feelings Questionnaire, unprompted interactions, and a snooze function. Six adolescents (n=4, 67% male participants and n=2, 33% female participants) aged 16 to 18 years tested the initial version of the IDEABot and were engaged to codevelop the final version of the app. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo study. Results: The adolescents assessed the initial version of the IDEABot as enjoyable and made suggestions for improvements that were subsequently implemented. The IDEABot’s final version follows a structured script with the choice of answer based on exact text matches throughout 15 days. The implementation of the IDEABot in 2 waves of the IDEA-RiSCo sample (140 and 132 eligible adolescents in the second- and third-year follow-ups, respectively) evidenced adequate engagement indicators, with good acceptance for using the tool (113/140, 80.7% and 122/132, 92.4% for second- and third-year follow-up use, respectively), low attrition (only 1/113, 0.9% and 1/122, 0.8%, respectively, failed to engage in the protocol after initial interaction), and high compliance in terms of the proportion of responses in relation to the total number of elicited prompts (12.8, SD 3.5; 91% out of 14 possible interactions and 10.57, SD 3.4; 76% out of 14 possible interactions, respectively). Conclusions: The IDEABot is a frugal app that leverages an existing app already in daily use by our target population. It follows a simple rule-based approach that can be easily tested and implemented in diverse settings and possibly diminishes the burden of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears as an acceptable and potentially scalable tool for gathering momentary information that can enhance our understanding of mood fluctuations and development. %M 37548996 %R 10.2196/44388 %U https://humanfactors.jmir.org/2023/1/e44388 %U https://doi.org/10.2196/44388 %U http://www.ncbi.nlm.nih.gov/pubmed/37548996 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e45079 %T The Use of a Decision Support System (MyFood) to Assess Dietary Intake Among Free-Living Older Adults in Norway: Evaluation Study %A Severinsen,Frida %A Andersen,Lene Frost %A Paulsen,Mari Mohn %+ Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Postboks 1046, Blindern, Oslo, 0317, Norway, 47 95772048, m.m.paulsen@medisin.uio.no %K dietary assessment %K malnutrition %K eHealth %K validation study %K older adults %K mobile phone %D 2023 %7 3.8.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The proportion of older adults in the world is constantly increasing, and malnutrition is a common challenge among the older adults aged ≥65 years. This poses a need for better tools to prevent, assess, and treat malnutrition among older adults. MyFood is a decision support system developed with the intention to prevent and treat malnutrition. Objective: This study aimed to evaluate the ability of the MyFood app to estimate the intake of energy, protein, fluids, and food and beverage items among free-living older adults aged ≥65 years, primarily at an individual level and secondarily at a group level. In addition, the aim was to measure the experiences of free-living older adults using the app. Methods: Participants were instructed to record their dietary intake in the MyFood app for 4 consecutive days. In addition, each participant completed two 24-hour recalls, which were used as a reference method to evaluate the dietary assessment function in the MyFood app. Differences in the estimations of energy, protein, fluid, and food groups were analyzed at both the individual and group levels, by comparing the recorded intake in MyFood with the 2 corresponding recalls and by comparing the mean of all 4 recording days with the mean of the 2 recalls, respectively. A short, study-specific questionnaire was used to measure the participants’ experiences with the app. Results: This study included 35 free-living older adults residing in Norway. Approximately half of the participants had ≥80% agreement between MyFood and the 24-hour recalls for energy intake on both days. For protein and fluids, approximately 60% of the participants had ≥80% agreement on the first day of comparison. Dinner was the meal with the lowest agreement between the methods, at both the individual and group levels. MyFood tended to underestimate the intake of energy, protein, fluid, and food items at both the individual and group levels. The food groups that achieved the greatest agreement between the 2 methods were eggs, yogurt, self-composed dinner, and hot beverages. All participants found the app easy to use, and 74% (26/35) of the participants reported that the app was easy to navigate. Conclusions: The results showed that the MyFood app tended to underestimate the participants’ dietary intake compared with the 24-hour recalls at both the individual and group levels. The app’s ability to estimate intake within food groups was greater for eggs, yogurt, and self-composed dinner than for spreads, mixed meals, vegetables, and snacks. The app was well accepted among the study participants and may be a useful tool among free-living older adults, given that the users are provided follow-up and support in how to record their dietary intake. %M 37535420 %R 10.2196/45079 %U https://mhealth.jmir.org/2023/1/e45079 %U https://doi.org/10.2196/45079 %U http://www.ncbi.nlm.nih.gov/pubmed/37535420 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e44323 %T The Influence of Greenspace Exposure on Affect in People With and Those Without Schizophrenia: Exploratory Study %A Kangarloo,Tairmae %A Mote,Jasmine %A Abplanalp,Samuel %A Gold,Alisa %A James,Peter %A Gard,David %A Fulford,Daniel %+ Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Ave, Boston, MA, 02215, United States, 1 6035681431, tairmaek@bu.edu %K greenspace %K mental health %K mobile technology %K affect %K smartphone %K sensing %K schizophrenia %K natural vegetation %K mental health %K exposure %K assessment %K mechanism %D 2023 %7 3.8.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Exposure to natural vegetation (ie, “greenspace”) is related to beneficial outcomes, including higher positive and lower negative affect, in individuals with and those without mental health concerns. Researchers have yet to examine dynamic associations between greenspace exposure and affect within individuals over time. Smartphone-based ecological momentary assessment (EMA) and passive sensors (eg, GPS, microphone) allow for frequent sampling of data that may reveal potential moment-to-moment mechanisms through which greenspace exposure impacts mental health. Objective: In this study, we examined associations between greenspace exposure and affect (both self-reported and inferred through speech) in people with and those without schizophrenia spectrum disorder (SSD) at the daily level using smartphones. Methods: Twenty people with SSD and 14 healthy controls reported on their current affect 3 times per day over 7 days using smartphone-based EMA. Affect expressed through speech was labeled from ambient audio data collected via the phone’s microphone using Linguistic Inquiry and Word Count (LIWC). Greenspace exposure, defined as the normalized difference vegetation index (NDVI), was quantified based on continuous geo-location data collected from the phone’s GPS. Results: Overall, people with SSD used significantly more positive affect words (P=.04) and fewer anger words (P=.04) than controls. Groups did not significantly differ in mean EMA-reported positive or negative affect, LIWC total word count, or NDVI exposure. Greater greenspace exposure showed small to moderate associations with lower EMA-reported negative affect across groups. In controls, greenspace exposure on a given day was associated with significantly lower EMA-reported anxiety on that day (b=–0.40, P=.03, 95% CI –0.76 to –0.04) but significantly higher use of negative affect words (b=0.66, P<.001, 95% CI 0.29-1.04). There were no significant associations between greenspace exposure and affect at the daily level among participants with SSD. Conclusions: Our findings speak to the utility of passive and active smartphone assessments for identifying potential mechanisms through which greenspace exposure influences mental health. We identified preliminary evidence that greenspace exposure could be associated with improved mental health by reducing experiences of negative affect. Future directions will focus on furthering our understanding of the relationship between greenspace exposure and affect on individuals with and those without SSD. %M 37535418 %R 10.2196/44323 %U https://formative.jmir.org/2023/1/e44323 %U https://doi.org/10.2196/44323 %U http://www.ncbi.nlm.nih.gov/pubmed/37535418 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e41412 %T Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study %A Murray,Aja %A Ushakova,Anastasia %A Zhu,Xinxin %A Yang,Yi %A Xiao,Zhuoni %A Brown,Ruth %A Speyer,Lydia %A Ribeaud,Denis %A Eisner,Manuel %+ Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, United Kingdom, 44 0131 650 3455, aja.murray@ed.ac.uk %K ecological momentary assessment %K experience sampling %K machine learning %K recruitment %K sampling %D 2023 %7 2.8.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Ecological momentary assessment (EMA) is widely used in health research to capture individuals’ experiences in the flow of daily life. The majority of EMA studies, however, rely on nonprobability sampling approaches, leaving open the possibility of nonrandom participation concerning the individual characteristics of interest in EMA research. Knowledge of the factors that predict participation in EMA research is required to evaluate this possibility and can also inform optimal recruitment strategies. Objective: This study aimed to examine the extent to which being willing to participate in EMA research is related to respondent characteristics and to identify the most critical predictors of participation. Methods: We leveraged the availability of comprehensive data on a general young adult population pool of potential EMA participants and used and compared logistic regression, classification and regression trees, and random forest approaches to evaluate respondents’ characteristic predictors of willingness to participate in the Decades-to-Minutes EMA study. Results: In unadjusted logistic regression models, gender, migration background, anxiety, attention deficit hyperactivity disorder symptoms, stress, and prosociality were significant predictors of participation willingness; in logistic regression models, mutually adjusting for all predictors, migration background, tobacco use, and social exclusion were significant predictors. Tree-based approaches also identified migration status, tobacco use, and prosociality as prominent predictors. However, overall, willingness to participate in the Decades-to-Minutes EMA study was only weakly predictable from respondent characteristics. Cross-validation areas under the curve for the best models were only in the range of 0.56 to 0.57. Conclusions: Results suggest that migration background is the single most promising target for improving EMA participation and sample representativeness; however, more research is needed to improve prediction of participation in EMA studies in health. %M 37531181 %R 10.2196/41412 %U https://www.jmir.org/2023/1/e41412 %U https://doi.org/10.2196/41412 %U http://www.ncbi.nlm.nih.gov/pubmed/37531181 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44428 %T Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective %A Woelfle,Tim %A Bourguignon,Lucie %A Lorscheider,Johannes %A Kappos,Ludwig %A Naegelin,Yvonne %A Jutzeler,Catherine Ruth %+ Department of Health Sciences and Technology, ETH Zurich, Lengghalde 2, Zürich, 8008, Switzerland, 41 044 344 99 50, catherine.jutzeler@hest.ethz.ch %K multiple sclerosis %K digital biomarkers %K digital health technologies %K digital mobility outcomes %K wearables %K sensors %K inertial motion unit %K accelerometry %K actigraphy %K review %D 2023 %7 27.7.2023 %9 Review %J J Med Internet Res %G English %X Background: Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains. Objective: This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades. Methods: We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms “multiple sclerosis” and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Results: Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good. Conclusions: Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS. Trial Registration: PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249 %M 37498655 %R 10.2196/44428 %U https://www.jmir.org/2023/1/e44428 %U https://doi.org/10.2196/44428 %U http://www.ncbi.nlm.nih.gov/pubmed/37498655 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47735 %T The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review %A Duarte,Miguel %A Pereira-Rodrigues,Pedro %A Ferreira-Santos,Daniela %+ Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, Porto, 4200-319, Portugal, 351 225 513 600, m.duarte722@gmail.com %K obstructive sleep apnea %K diagnosis %K digital tools %K smartphone %K wearables %K sensor %K polysomnography %K systematic review %K mobile phone %D 2023 %7 26.7.2023 %9 Review %J J Med Internet Res %G English %X Background: Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard. Objective: This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population. Methods: We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures. Results: We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat—a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events. Conclusions: These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings. Trial Registration: PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748 %M 37494079 %R 10.2196/47735 %U https://www.jmir.org/2023/1/e47735 %U https://doi.org/10.2196/47735 %U http://www.ncbi.nlm.nih.gov/pubmed/37494079 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e48812 %T G Tolerance Prediction Model Using Mobile Device–Measured Cardiac Force Index for Military Aircrew: Observational Study %A Kuo,Ming-Hao %A Lin,You-Jin %A Huang,Wun-Wei %A Chiang,Kwo-Tsao %A Tu,Min-Yu %A Chu,Chi-Ming %A Lai,Chung-Yu %+ Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Rm 8118, No 161, Sec 6, Minquan E Rd, Neihu Dist, Taipei City, 11490, Taiwan, 886 287923100 ext 19066, multi0912@gmail.com %K G force %K baroreflex %K anti-G straining maneuver %K G tolerance %K cardiac force index %K anti-G suit %K relaxed G tolerance %K straining G tolerance %K cardiac force ratio %D 2023 %7 26.7.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: During flight, G force compels blood to stay in leg muscles and reduces blood flow to the heart. Cardiovascular responses activated by the autonomic nerve system and strengthened by anti-G straining maneuvers can alleviate the challenges faced during G loading. To our knowledge, no definite cardiac information measured using a mobile health device exists for analyzing G tolerance. However, our previous study developed the cardiac force index (CFI) for analyzing the G tolerance of military aircrew. Objective: This study used the CFI to verify participants’ cardiac performance when walking and obtained a formula for predicting an individual’s G tolerance during centrifuge training. Methods: Participants from an air force aircrew undertook high-G training from January 2020 to December 2022. Their heart rate (HR) in beats per minute and activity level per second were recorded using the wearable BioHarness 3.0 device. The CFI was computed using the following formula: weight × activity / HR during resting or walking. Relaxed G tolerance (RGT) and straining G tolerance (SGT) were assessed at a slowly increasing rate of G loading (0.1 G/s) during training. Other demographic factors were included in the multivariate regression to generate a model for predicting G tolerance from the CFI. Results: A total of 213 eligible trainees from a military aircrew were recruited. The average age was 25.61 (SD 3.66) years, and 13.1% (28/213) of the participants were women. The mean resting CFI and walking CFI (WCFI) were 0.016 (SD 0.001) and 0.141 (SD 0.037) kg × G/beats per minute, respectively. The models for predicting RGT and SGT were as follows: RGT = 0.066 × age + 0.043 × (WCFI × 100) – 0.037 × height + 0.015 × systolic blood pressure – 0.010 × HR + 7.724 and SGT = 0.103 × (WCFI × 100) − 0.069 × height + 0.018 × systolic blood pressure + 15.899. Thus, the WCFI is a positive factor for predicting the RGT and SGT before centrifuge training. Conclusions: The WCFI is a vital component of the formula for estimating G tolerance prior to training. The WCFI can be used to monitor physiological conditions against G stress. %M 37494088 %R 10.2196/48812 %U https://mhealth.jmir.org/2023/1/e48812 %U https://doi.org/10.2196/48812 %U http://www.ncbi.nlm.nih.gov/pubmed/37494088 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46026 %T Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study %A Schooley,Benjamin L %A Ahmed,Abdulaziz %A Maxwell,Justine %A Feldman,Sue S %+ University of Alabama at Birmingham, 1716 9th Street South, SHP 590K, Birmingham, AL, 35294, United States, 1 205 975 0809, sfeldman@uab.edu %K COVID-19 %K risk assessment %K symptom tracker %K passport application %K surveillance %K mobile app %K multiple linear regression %K healthcheck %K public health informatics %K decision support system %K health information technology %D 2023 %7 25.7.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Some of the most vexing issues with the COVID-19 pandemic were the inability of facilities and events, such as schools and work areas, to track symptoms to mitigate the spread of the disease. To combat these challenges, many turned to the implementation of technology. Technology solutions to mitigate repercussions of the COVID-19 pandemic include tools that provide guidelines and interfaces to influence behavior, reduce exposure to the disease, and enable policy-driven avenues to return to a sense of normalcy. This paper presents the implementation and early evaluation of a return-to-work COVID-19 symptom and risk assessment tool. The system was implemented across 34 institutions of health and education in Alabama, including more than 174,000 users with over 4 million total uses and more than 86,000 reports of exposure risk between July 2020 and April 2021. Objective: This study aimed to explore the usage of technology, specifically a COVID-19 symptom and risk assessment tool, to mitigate exposure to COVID-19 within public spaces. More specifically, the objective was to assess the relationship between user-reported symptoms and exposure via a mobile health app, with confirmed COVID-19 cases reported by the Alabama Department of Public Health (ADPH). Methods: This cross-sectional study evaluated the relationship between confirmed COVID-19 cases and user-reported COVID-19 symptoms and exposure reported through the Healthcheck web-based mobile application. A dependent variable for confirmed COVID-19 cases in Alabama was obtained from ADPH. Independent variables (ie, health symptoms and exposure) were collected through Healthcheck survey data and included measures assessing COVID-19–related risk levels and symptoms. Multiple linear regression was used to examine the relationship between ADPH-confirmed diagnosis of COVID-19 and self-reported health symptoms and exposure via Healthcheck that were analyzed across the state population but not connected at the individual patient level. Results: Regression analysis showed that the self-reported information collected by Healthcheck significantly affects the number of COVID-19–confirmed cases. The results demonstrate that the average number of confirmed COVID-19 cases increased by 5 (high risk: β=5.10; P=.001), decreased by 24 (sore throat: β=−24.03; P=.001), and increased by 21 (nausea or vomiting: β=21.67; P=.02) per day for every additional self-report of symptoms by Healthcheck survey respondents. Congestion or runny nose was the most frequently reported symptom. Sore throat, low risk, high risk, nausea, or vomiting were all statistically significant factors. Conclusions: The use of technology allowed organizations to remotely track a population as it is related to COVID-19. Healthcheck was a platform that aided in symptom tracking, risk assessment, and evaluation of status for admitting individuals into public spaces for people in the Alabama area. The confirmed relationship between symptom and exposure self-reporting using an app and population-wide confirmed cases suggests that further investigation is needed to determine the opportunity for such apps to mitigate disease spread at a community and individual level. %M 37490320 %R 10.2196/46026 %U https://www.jmir.org/2023/1/e46026 %U https://doi.org/10.2196/46026 %U http://www.ncbi.nlm.nih.gov/pubmed/37490320 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45658 %T Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review %A Lawson,Lauren %A Mc Ardle,Ríona %A Wilson,Sarah %A Beswick,Emily %A Karimi,Radin %A Slight,Sarah P %+ School of Pharmacy, Population Health Sciences Institute, Newcastle University, King George VI Building, Newcastle Upon Tyne, NE1 7RU, United Kingdom, 44 7739174547, sarah.slight@newcastle.ac.uk %K mild cognitive impairment %K MCI %K functional status %K activities of daily living %K instrumental activities of daily living %K IADLs %K digital technology %K mobile phone %D 2023 %7 25.7.2023 %9 Review %J J Med Internet Res %G English %X Background: Subtle impairments in instrumental activities of daily living (IADLs) can be a key predictor of disease progression and are considered central to functional independence. Mild cognitive impairment (MCI) is a syndrome associated with significant changes in cognitive function and mild impairment in complex functional abilities. The early detection of functional decline through the identification of IADL impairments can aid early intervention strategies. Digital health technology is an objective method of capturing IADL-related behaviors. However, it is unclear how these IADL-related behaviors have been digitally assessed in the literature and what differences can be observed between MCI and normal aging. Objective: This review aimed to identify the digital methods and metrics used to assess IADL-related behaviors in people with MCI and report any statistically significant differences in digital endpoints between MCI and normal aging and how these digital endpoints change over time. Methods: A total of 16,099 articles were identified from 8 databases (CINAHL, Embase, MEDLINE, ProQuest, PsycINFO, PubMed, Web of Science, and Scopus), out of which 15 were included in this review. The included studies must have used continuous remote digital measures to assess IADL-related behaviors in adults characterized as having MCI by clinical diagnosis or assessment. This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: Ambient technology was the most commonly used digital method to assess IADL-related behaviors in the included studies (14/15, 93%), with passive infrared motion sensors (5/15, 33%) and contact sensors (5/15, 33%) being the most prevalent types of methods. Digital technologies were used to assess IADL-related behaviors across 5 domains: activities outside of the home, everyday technology use, household and personal management, medication management, and orientation. Other recognized domains—culturally specific tasks and socialization and communication—were not assessed. Of the 79 metrics recorded among 11 types of technologies, 65 (82%) were used only once. There were inconsistent findings around differences in digital IADL endpoints across the cognitive spectrum, with limited longitudinal assessment of how they changed over time. Conclusions: Despite the broad range of metrics and methods used to digitally assess IADL-related behaviors in people with MCI, several IADLs relevant to functional decline were not studied. Measuring multiple IADL-related digital endpoints could offer more value than the measurement of discrete IADL outcomes alone to observe functional decline. Key recommendations include the development of suitable core metrics relevant to IADL-related behaviors that are based on clinically meaningful outcomes to aid the standardization and further validation of digital technologies against existing IADL measures. Increased longitudinal monitoring is necessary to capture changes in digital IADL endpoints over time in people with MCI. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022326861; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=326861 %M 37490331 %R 10.2196/45658 %U https://www.jmir.org/2023/1/e45658 %U https://doi.org/10.2196/45658 %U http://www.ncbi.nlm.nih.gov/pubmed/37490331 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45599 %T Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review %A Thomas,Jonah J C %A Daley,Amanda J %A Esliger,Dale W %A Kettle,Victoria E %A Coombe,April %A Stamatakis,Emmanuel %A Sanders,James P %+ School of Sport, Exercise and Health Science, Loughborough University, Epinal Way, Loughborough, LE113TU, United Kingdom, 44 01509222222, j.j.c.thomas@lboro.ac.uk %K sedentary behavior %K device measured %K data harmonization %K open science %K big data %D 2023 %7 19.7.2023 %9 Review %J J Med Internet Res %G English %X Background: Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue. Objective: This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design. Methods: Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources. Results: A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets. Conclusions: This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity. %M 37467026 %R 10.2196/45599 %U https://www.jmir.org/2023/1/e45599 %U https://doi.org/10.2196/45599 %U http://www.ncbi.nlm.nih.gov/pubmed/37467026 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42093 %T Comparing Adherence to the Experience Sampling Method Among Patients With Schizophrenia Spectrum Disorder and Unaffected Individuals: Observational Study From the Multicentric DiAPAson Project %A Zarbo,Cristina %A Zamparini,Manuel %A Nielssen,Olav %A Casiraghi,Letizia %A Rocchetti,Matteo %A Starace,Fabrizio %A de Girolamo,Giovanni %A , %+ Unit of Epidemiological and Evaluation Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, Brescia, 25125, Italy, 39 3896875449, cristinazarbo@gmail.com %K ecological momentary assessment %K multicenter study %K mobile application %K mobile app %K compliance %K psychosis %D 2023 %7 18.7.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The Experience Sampling Method (ESM) is a valid method of remotely recording activities and mood, but the predictors of adherence to ESM in patients with Schizophrenia Spectrum Disorder (SSD) are not known. Studies on adherence are significant as they highlight the strengths and weaknesses of ESM-based study designs and allow the development of recommendations and practical guidelines for implementing future studies or treatment plans. Objective: The aim of this study was to compare the adherence to ESM in patients with SSD and unaffected control individuals, investigate their patterns, and report the predictors of adherence. Methods: In total, 131 patients with SSD (74 in residential facilities and 57 outpatients) and 115 unaffected control individuals were recruited at 10 different centers in Italy as part of the DiAPAson project. Demographic information, symptom severity, disability level, and level of function were recorded for the clinical sample. Participants were evaluated for daily time use and mood through a smartphone-based ESM 8 times a day for 7 consecutive days. Adherence was measured by the response rate to ESM notifications. Results were analyzed using the chi-square test, ANOVA, Kruskal-Wallis test, and Friedman test, and a logistic regression model. Results: The overall adherence rate in this study was 50% for residents, 59% for outpatients, and 78% for unaffected control individuals. Indeed, patients with SSD had a lower rate of adherence to ESM than the unaffected control group (P≤.001), independent of time slot, day of monitoring, or day of the week. No differences in adherence rates between weekdays and weekends were found among the 3 groups. The adherence rate was the lowest in the late evening time slot (8 PM to 12 AM) and days 6-7 of the study for both patients with SSD and unaffected control individuals. The adherence rate among patients with SSD was not predicted by sociodemographic characteristics, cognitive function, or other clinical features. A higher adherence rate (ie, ≥70%) among patients with SSD was predicted by higher collaboration skills (odds ratio [OR] 2.952; P=.046) and self-esteem (OR 3.394; P=.03), and lower positive symptom severity (OR 0.835; P=.04). Conclusions: Adherence to ESM prompts for both patients with SSD and unaffected control individuals decreased during late evening and after 6 days of monitoring. Higher self-esteem and collaboration skills predicted higher adherence to ESM among patients with SSD, while higher positive symptom scores predicted lower adherence rates. This study provides important information to guide protocols for future studies using ESM. Future clinical or research studies should set ESM monitoring to waking hours, limit the number of days of monitoring, select patients with more collaborative skills and avoid those with marked positive symptoms, provide intensive training sessions, and improve participants’ self-confidence with technologies. International Registered Report Identifier (IRRID): RR2-10.1186/s12888-020-02588-y %M 37463030 %R 10.2196/42093 %U https://www.jmir.org/2023/1/e42093 %U https://doi.org/10.2196/42093 %U http://www.ncbi.nlm.nih.gov/pubmed/37463030 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e44529 %T Association of Depressive Symptom Trajectory With Physical Activity Collected by mHealth Devices in the Electronic Framingham Heart Study: Cohort Study %A Wang,Xuzhi %A Pathiravasan,Chathurangi H %A Zhang,Yuankai %A Trinquart,Ludovic %A Borrelli,Belinda %A Spartano,Nicole L %A Lin,Honghuang %A Nowak,Christopher %A Kheterpal,Vik %A Benjamin,Emelia J %A McManus,David D %A Murabito,Joanne M %A Liu,Chunyu %+ Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA, 02118, United States, 1 6176385104, liuc@bu.edu %K depression %K mobile health %K risk factors %K physical activity %K eCohort %K Framingham Heart Study %D 2023 %7 14.7.2023 %9 Original Paper %J JMIR Ment Health %G English %X Background: Few studies have examined the association between depressive symptom trajectories and physical activity collected by mobile health (mHealth) devices. Objective: We aimed to investigate if antecedent depressive symptom trajectories predict subsequent physical activity among participants in the electronic Framingham Heart Study (eFHS). Methods: We performed group-based multi-trajectory modeling to construct depressive symptom trajectory groups using both depressive symptoms (Center for Epidemiological Studies-Depression [CES-D] scores) and antidepressant medication use in eFHS participants who attended 3 Framingham Heart Study research exams over 14 years. At the third exam, eFHS participants were instructed to use a smartphone app for submitting physical activity index (PAI) surveys. In addition, they were provided with a study smartwatch to track their daily step counts. We performed linear mixed models to examine the association between depressive symptom trajectories and physical activity including app-based PAI and smartwatch-collected step counts over a 1-year follow-up adjusting for age, sex, wear hour, BMI, smoking status, and other health variables. Results: We identified 3 depressive symptom trajectory groups from 722 eFHS participants (mean age 53, SD 8.5 years; n=432, 60% women). The low symptom group (n=570; mean follow-up 287, SD 109 days) consisted of participants with consistently low CES-D scores, and a small proportion reported antidepressant use. The moderate symptom group (n=71; mean follow-up 280, SD 118 days) included participants with intermediate CES-D scores, who showed the highest and increasing likelihood of reporting antidepressant use across 3 exams. The high symptom group (n=81; mean follow-up 252, SD 116 days) comprised participants with the highest CES-D scores, and the proportion of antidepressant use fell between the other 2 groups. Compared to the low symptom group, the high symptom group had decreased PAI (mean difference –1.09, 95% CI –2.16 to –0.01) and the moderate symptom group walked fewer daily steps (823 fewer, 95% CI –1421 to –226) during the 1-year follow-up. Conclusions: Antecedent depressive symptoms or antidepressant medication use was associated with lower subsequent physical activity collected by mHealth devices in eFHS. Future investigation of interventions to improve mood including via mHealth technologies to help promote people’s daily physical activity is needed. %M 37450333 %R 10.2196/44529 %U https://mental.jmir.org/2023/1/e44529 %U https://doi.org/10.2196/44529 %U http://www.ncbi.nlm.nih.gov/pubmed/37450333 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e44003 %T Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review %A Zepeda-Echavarria,Alejandra %A van de Leur,Rutger R %A van Sleuwen,Meike %A Hassink,Rutger J %A Wildbergh,Thierry X %A Doevendans,Pieter A %A Jaspers,Joris %A van Es,René %+ Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, Netherlands, 31 88 757 3453, R.vanEs@umcutrecht.nl %K electrocardiogram %K mobile ECG %K home use ECG %K wearables %K medical devices %K ECG clinical validation, ECG technical characteristics %D 2023 %7 7.7.2023 %9 Review %J JMIR Cardio %G English %X Background: Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments. Objective: This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence. Methods: We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices’ technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases. Results: From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation. Conclusions: ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities. %M 37418308 %R 10.2196/44003 %U https://cardio.jmir.org/2023/1/e44003 %U https://doi.org/10.2196/44003 %U http://www.ncbi.nlm.nih.gov/pubmed/37418308 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e43340 %T Smartwatch-Based Maximum Oxygen Consumption Measurement for Predicting Acute Mountain Sickness: Diagnostic Accuracy Evaluation Study %A Ye,Xiaowei %A Sun,Mengjia %A Yu,Shiyong %A Yang,Jie %A Liu,Zhen %A Lv,Hailin %A Wu,Boji %A He,Jingyu %A Wang,Xuhong %A Huang,Lan %+ Institute of Cardiovascular Diseases of People's Liberation Army, The Second Affiliated Hospital, Army Medical University (Third Military Medical University), No 183, Xinqiao Street, Shapingba District, Chongqing, 400037, China, 86 23 68755601, huanglan260@126.com %K VO2max %K maximum oxygen consumption %K smartwatch %K cardiopulmonary exercise test %K acute mountain sickness %D 2023 %7 6.7.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cardiorespiratory fitness plays an important role in coping with hypoxic stress at high altitudes. However, the association of cardiorespiratory fitness with the development of acute mountain sickness (AMS) has not yet been evaluated. Wearable technology devices provide a feasible assessment of cardiorespiratory fitness, which is quantifiable as maximum oxygen consumption (VO2max) and may contribute to AMS prediction. Objective: We aimed to determine the validity of VO2max estimated by the smartwatch test (SWT), which can be self-administered, in order to overcome the limitations of clinical VO2max measurements. We also aimed to evaluate the performance of a VO2max-SWT–based model in predicting susceptibility to AMS. Methods: Both SWT and cardiopulmonary exercise test (CPET) were performed for VO2max measurements in 46 healthy participants at low altitude (300 m) and in 41 of them at high altitude (3900 m). The characteristics of the red blood cells and hemoglobin levels in all the participants were analyzed by routine blood examination before the exercise tests. The Bland-Altman method was used for bias and precision assessment. Multivariate logistic regression was performed to analyze the correlation between AMS and the candidate variables. A receiver operating characteristic curve was used to evaluate the efficacy of VO2max in predicting AMS. Results: VO2max decreased after acute high altitude exposure, as measured by CPET (25.20 [SD 6.46] vs 30.17 [SD 5.01] at low altitude; P<.001) and SWT (26.17 [SD 6.71] vs 31.28 [SD 5.17] at low altitude; P<.001). Both at low and high altitudes, VO2max was slightly overestimated by SWT but had considerable accuracy as the mean absolute percentage error (<7%) and mean absolute error (<2 mL·kg–1·min–1), with a relatively small bias compared with VO2max-CPET. Twenty of the 46 participants developed AMS at 3900 m, and their VO2max was significantly lower than that of those without AMS (CPET: 27.80 [SD 4.55] vs 32.00 [SD 4.64], respectively; P=.004; SWT: 28.00 [IQR 25.25-32.00] vs 32.00 [IQR 30.00-37.00], respectively; P=.001). VO2max-CPET, VO2max-SWT, and red blood cell distribution width-coefficient of variation (RDW-CV) were found to be independent predictors of AMS. To increase the prediction accuracy, we used combination models. The combination of VO2max-SWT and RDW-CV showed the largest area under the curve for all parameters and models, which increased the area under the curve from 0.785 for VO2max-SWT alone to 0.839. Conclusions: Our study demonstrates that the smartwatch device can be a feasible approach for estimating VO2max. In both low and high altitudes, VO2max-SWT showed a systematic bias toward a calibration point, slightly overestimating the proper VO2max when investigated in healthy participants. The SWT-based VO2max at low altitude is an effective indicator of AMS and helps to better identify susceptible individuals following acute high-altitude exposure, particularly by combining the RDW-CV at low altitude. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200059900; https://www.chictr.org.cn/showproj.html?proj=170253 %M 37410528 %R 10.2196/43340 %U https://mhealth.jmir.org/2023/1/e43340 %U https://doi.org/10.2196/43340 %U http://www.ncbi.nlm.nih.gov/pubmed/37410528 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e47098 %T Using Wearable Passive Sensing to Predict Binge Eating in Response to Negative Affect Among Individuals With Transdiagnostic Binge Eating: Protocol for an Observational Study %A Presseller,Emily K %A Lampe,Elizabeth W %A Zhang,Fengqing %A Gable,Philip A %A Guetterman,Timothy C %A Forman,Evan M %A Juarascio,Adrienne S %+ Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut St., Stratton Hall 2nd. Floor, Philadelphia, PA, 19104, United States, 1 2155537100, emily.k.presseller@drexel.edu %K affect %K binge eating %K heart rate %K heart rate variability %K electrodermal activity %K ecological momentary assessment %K wearable sensors %K ecological momentary intervention %D 2023 %7 6.7.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Binge eating (BE), characterized by eating a large amount of food accompanied by a sense of loss of control over eating, is a public health crisis. Negative affect is a well-established antecedent for BE. The affect regulation model of BE posits that elevated negative affect increases momentary risk for BE, as engaging in BE alleviates negative affect and reinforces the behavior. The eating disorder field’s capacity to identify moments of elevated negative affect, and thus BE risk, has exclusively relied on ecological momentary assessment (EMA). EMA involves the completion of surveys in real time on one’s smartphone to report behavioral, cognitive, and emotional symptoms throughout the day. Although EMA provides ecologically valid information, EMA surveys are often delivered only 5-6 times per day, involve self-report of affect intensity only, and are unable to assess affect-related physiological arousal. Wearable, psychophysiological sensors that measure markers of affect arousal including heart rate, heart rate variability, and electrodermal activity may augment EMA surveys to improve accurate real-time prediction of BE. These sensors can objectively and continuously measure biomarkers of nervous system arousal that coincide with affect, thus allowing them to measure affective trajectories on a continuous timescale, detect changes in negative affect before the individual is consciously aware of them, and reduce user burden to improve data completeness. However, it is unknown whether sensor features can distinguish between positive and negative affect states, given that physiological arousal may occur during both negative and positive affect states. Objective: The aims of this study are (1) to test the hypothesis that sensor features will distinguish positive and negative affect states in individuals with BE with >60% accuracy and (2) test the hypothesis that a machine learning algorithm using sensor data and EMA-reported negative affect to predict the occurrence of BE will predict BE with greater accuracy than an algorithm using EMA-reported negative affect alone. Methods: This study will recruit 30 individuals with BE who will wear Fitbit Sense 2 wristbands to passively measure heart rate and electrodermal activity and report affect and BE on EMA surveys for 4 weeks. Machine learning algorithms will be developed using sensor data to distinguish instances of high positive and high negative affect (aim 1) and to predict engagement in BE (aim 2). Results: This project will be funded from November 2022 to October 2024. Recruitment efforts will be conducted from January 2023 through March 2024. Data collection is anticipated to be completed in May 2024. Conclusions: This study is anticipated to provide new insight into the relationship between negative affect and BE by integrating wearable sensor data to measure affective arousal. The findings from this study may set the stage for future development of more effective digital ecological momentary interventions for BE. International Registered Report Identifier (IRRID): DERR1-10.2196/47098 %M 37410522 %R 10.2196/47098 %U https://www.researchprotocols.org/2023/1/e47098 %U https://doi.org/10.2196/47098 %U http://www.ncbi.nlm.nih.gov/pubmed/37410522 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e47053 %T A Technology-Enriched Approach to Studying Microlongitudinal Aging Among Adults Aged 18 to 85 Years: Protocol for the Labs Without Walls Study %A Brady,Brooke %A Zhou,Shally %A Ashworth,Daniel %A Zheng,Lidan %A Eramudugolla,Ranmalee %A Huque,Md Hamidul %A Anstey,Kaarin Jane %+ School of Psychology, University of New South Wales, 139 Barker Street, Randwick, 2031, Australia, 61 0410128221, b.brady@unsw.edu.au %K life-course aging %K longitudinal research %K subjective age %K gender %K cognition %K sensory function %K app %K mobile app %K eHealth %K mobile health %K mHealth %K measurement burst design %K ecological momentary assessment %K health information technology %K personalized health %K mobile phone %D 2023 %7 6.7.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Traditional longitudinal aging research involves studying the same individuals over a long period, with measurement intervals typically several years apart. App-based studies have the potential to provide new insights into life-course aging by improving the accessibility, temporal specificity, and real-world integration of data collection. We developed a new research app for iOS named Labs Without Walls to facilitate the study of life-course aging. Combined with data collected using paired smartwatches, the app collects complex data including data from one-time surveys, daily diary surveys, repeated game-like cognitive and sensory tasks, and passive health and environmental data. Objective: The aim of this protocol is to describe the research design and methods of the Labs Without Walls study conducted between 2021 and 2023 in Australia. Methods: Overall, 240 Australian adults will be recruited, stratified by age group (18-25, 26-35, 36-45, 46-55, 56-65, 66-75, and 76-85 years) and sex at birth (male and female). Recruitment procedures include emails to university and community networks, as well as paid and unpaid social media advertisements. Participants will be invited to complete the study onboarding either in person or remotely. Participants who select face-to-face onboarding (n=approximately 40) will be invited to complete traditional in-person cognitive and sensory assessments to be cross-validated against their app-based counterparts. Participants will be sent an Apple Watch and headphones for use during the study period. Participants will provide informed consent within the app and then begin an 8-week study protocol, which includes scheduled surveys, cognitive and sensory tasks, and passive data collection using the app and a paired watch. At the conclusion of the study period, participants will be invited to rate the acceptability and usability of the study app and watch. We hypothesize that participants will be able to successfully provide e-consent, input survey data through the Labs Without Walls app, and have passive data collected over 8 weeks; participants will rate the app and watch as user-friendly and acceptable; the app will allow for the study of daily variability in self-perceptions of age and gender; and data will allow for the cross-validation of app- and laboratory-based cognitive and sensory tasks. Results: Recruitment began in May 2021, and data collection was completed in February 2023. The publication of preliminary results is anticipated in 2023. Conclusions: This study will provide evidence regarding the acceptability and usability of the research app and paired watch for studying life-course aging processes on multiple timescales. The feedback obtained will be used to improve future iterations of the app, explore preliminary evidence for intraindividual variability in self-perceptions of aging and gender expression across the life span, and explore the associations between performance on app-based cognitive and sensory tests and that on similar traditional cognitive and sensory tests. International Registered Report Identifier (IRRID): DERR1-10.2196/47053 %M 37410527 %R 10.2196/47053 %U https://www.researchprotocols.org/2023/1/e47053 %U https://doi.org/10.2196/47053 %U http://www.ncbi.nlm.nih.gov/pubmed/37410527 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46783 %T Use of Electronic Ecological Momentary Assessment Methodologies in Physical Activity, Sedentary Behavior, and Sleep Research in Young Adults: Systematic Review %A Hartson,Kimberly R %A Huntington-Moskos,Luz %A Sears,Clara G %A Genova,Gina %A Mathis,Cara %A Ford,Wessly %A Rhodes,Ryan E %+ School of Nursing, University of Louisville, 555 South Floyd Street, Louisville, KY, 40202, United States, 1 502 852 8388, kimberly.rapp@louisville.edu %K ecological momentary assessment %K young adults %K 24-hour movement behaviors %K physical activity %K sedentary behavior %K sleep %K mobile phone %D 2023 %7 29.6.2023 %9 Review %J J Med Internet Res %G English %X Background: Recent technological advances allow for the repeated sampling of real-time data in natural settings using electronic ecological momentary assessment (eEMA). These advances are particularly meaningful for investigating physical activity, sedentary behavior, and sleep in young adults who are in a critical life stage for the development of healthy lifestyle behaviors. Objective: This study aims to describe the use of eEMA methodologies in physical activity, sedentary behavior, and sleep research in young adults. Methods: The PubMed, CINAHL, PsycINFO, Embase, and Web of Science electronic databases were searched through August 2022. Inclusion criteria were use of eEMA; sample of young adults aged 18 to 25 years; at least 1 measurement of physical activity, sedentary behavior, or sleep; English language; and a peer-reviewed report of original research. Study reports were excluded if they were abstracts, protocols, or reviews. The risk of bias assessment was conducted using the National Heart, Lung, and Blood Institute’s Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Screening, data extraction, and risk of bias assessments were conducted by independent authors, with discrepancies resolved by consensus. Descriptive statistics and narrative synthesis were used to identify overarching patterns within the following categories guided by the Checklist for Reporting Ecological Momentary Assessments Studies: study characteristics, outcomes and measures, eEMA procedures, and compliance. Results: The search resulted in 1221 citations with a final sample of 37 reports describing 35 unique studies. Most reports (28/37, 76%) were published in the last 5 years (2017-2022), used observational designs (35/37, 95%), consisted of samples of college students or apprentices (28/35, 80%), and were conducted in the United States (22/37, 60%). The sample sizes ranged from 14 to 1584 young adults. Physical activity was measured more frequently (28/37, 76%) than sleep (16/37, 43%) or sedentary behavior (4/37, 11%). Of the 37 studies, 11 (30%) reports included 2 movement behaviors and no reports included 3 movement behaviors. eEMA was frequently used to measure potential correlates of movement behaviors, such as emotional states or feelings (25/37, 68%), cognitive processes (7/37, 19%), and contextual factors (9/37, 24%). There was wide variability in the implementation and reporting of eEMA procedures, measures, missing data, analysis, and compliance. Conclusions: The use of eEMA methodologies in physical activity, sedentary behavior, and sleep research in young adults has greatly increased in recent years; however, reports continue to lack standardized reporting of features unique to the eEMA methodology. Additional areas in need of future research include the use of eEMA with more diverse populations and the incorporation of all 3 movement behaviors within a 24-hour period. The findings are intended to assist investigators in the design, implementation, and reporting of physical activity, sedentary behavior, and sleep research using eEMA in young adults. Trial Registration: PROSPERO CRD42021279156; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021279156 %M 37384367 %R 10.2196/46783 %U https://www.jmir.org/2023/1/e46783 %U https://doi.org/10.2196/46783 %U http://www.ncbi.nlm.nih.gov/pubmed/37384367 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e43107 %T Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study %A Rao,Kaushal %A Speier,William %A Meng,Yiwen %A Wang,Jinhan %A Ramesh,Nidhi %A Xie,Fenglong %A Su,Yujie %A Nowell,W Benjamin %A Curtis,Jeffrey R %A Arnold,Corey %+ Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, 1825 University Blvd, Shelby 121H, Birmingham, AL, 35233, United States, 1 205 937 0585, jcurtis@uab.edu %K rheumatoid arthritis %K rheumatic %K rheumatism %K Fitbit %K classification %K physical data %K digital health %K activity tracker %K mobile health %K machine learning %K model %K patient reported %K outcome measure %K PROMIS %K nonclinical monitoring %K mHealth %K tracker %K wearable %K arthritis %K mobile phone %D 2023 %7 26.6.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The increasing use of activity trackers in mobile health studies to passively collect physical data has shown promise in lessening participation burden to provide actively contributed patient-reported outcome (PRO) information. Objective: The aim of this study was to develop machine learning models to classify and predict PRO scores using Fitbit data from a cohort of patients with rheumatoid arthritis. Methods: Two different models were built to classify PRO scores: a random forest classifier model that treated each week of observations independently when making weekly predictions of PRO scores, and a hidden Markov model that additionally took correlations between successive weeks into account. Analyses compared model evaluation metrics for (1) a binary task of distinguishing a normal PRO score from a severe PRO score and (2) a multiclass task of classifying a PRO score state for a given week. Results: For both the binary and multiclass tasks, the hidden Markov model significantly (P<.05) outperformed the random forest model for all PRO scores, and the highest area under the curve, Pearson correlation coefficient, and Cohen κ coefficient were 0.750, 0.479, and 0.471, respectively. Conclusions: While further validation of our results and evaluation in a real-world setting remains, this study demonstrates the ability of physical activity tracker data to classify health status over time in patients with rheumatoid arthritis and enables the possibility of scheduling preventive clinical interventions as needed. If patient outcomes can be monitored in real time, there is potential to improve clinical care for patients with other chronic conditions. %M 37017471 %R 10.2196/43107 %U https://formative.jmir.org/2023/1/e43107 %U https://doi.org/10.2196/43107 %U http://www.ncbi.nlm.nih.gov/pubmed/37017471 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43826 %T Evaluating Declines in Compliance With Ecological Momentary Assessment in Longitudinal Health Behavior Research: Analyses From a Clinical Trial %A Tonkin,Sarah %A Gass,Julie %A Wray,Jennifer %A Maguin,Eugene %A Mahoney,Martin %A Colder,Craig %A Tiffany,Stephen %A Hawk Jr,Larry W %+ Stephenson Cancer Center, Tobacco Settlement Endowment Trust Health Promotion Research Center, University of Oklahoma Health Sciences Center, 655 Research Park Way, Suite 400, Oklahoma City, OK, 73104, United States, 1 3013670453, sarah-tonkin@ouhsc.edu %K ecological momentary assessment %K compliance %K health behavior %K methodology %K longitudinal %K health behavior %K smoking %K smoker %K cessation %K quit %K adherence %K dropout %K RCT %K cigar %K retention %D 2023 %7 22.6.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Ecological momentary assessment (EMA) is increasingly used to evaluate behavioral health processes over extended time periods. The validity of EMA for providing representative, real-world data with high temporal precision is threatened to the extent that EMA compliance drops over time. Objective: This research builds on prior short-term studies by evaluating the time course of EMA compliance over 9 weeks and examines predictors of weekly compliance rates among cigarette-using adults. Methods: A total of 257 daily cigarette-using adults participating in a randomized controlled trial for smoking cessation completed daily smartphone EMA assessments, including 1 scheduled morning assessment and 4 random assessments per day. Weekly EMA compliance was calculated and multilevel modeling assessed the rate of change in compliance over the 9-week assessment period. Participant and study characteristics were examined as predictors of overall compliance and changes in compliance rates over time. Results: Compliance was higher for scheduled morning assessments (86%) than for random assessments (58%) at the beginning of the EMA period (P<.001). EMA compliance declined linearly across weeks, and the rate of decline was greater for morning assessments (2% per week) than for random assessments (1% per week; P<.001). Declines in compliance were stronger for younger participants (P<.001), participants who were employed full-time (P=.03), and participants who subsequently dropped out of the study (P<.001). Overall compliance was higher among White participants compared to Black or African American participants (P=.001). Conclusions: This study suggests that EMA compliance declines linearly but modestly across lengthy EMA protocols. In general, these data support the validity of EMA for tracking health behavior and hypothesized treatment mechanisms over the course of several months. Future work should target improving compliance among subgroups of participants and investigate the extent to which rapid declines in EMA compliance might prove useful for triggering interventions to prevent study dropout. Trial Registration: ClinicalTrials.gov NCT03262662; https://clinicaltrials.gov/ct2/show/NCT03262662 %M 37347538 %R 10.2196/43826 %U https://www.jmir.org/2023/1/e43826 %U https://doi.org/10.2196/43826 %U http://www.ncbi.nlm.nih.gov/pubmed/37347538 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e43990 %T Using Smartphone Survey and GPS Data to Inform Smoking Cessation Intervention Delivery: Case Study %A Luken,Amanda %A Desjardins,Michael R %A Moran,Meghan B %A Mendelson,Tamar %A Zipunnikov,Vadim %A Kirchner,Thomas R %A Naughton,Felix %A Latkin,Carl %A Thrul,Johannes %+ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, Baltimore, MD, 21205, United States, 1 732 690 2886, aluken95@gmail.com %K adult %K application %K case study %K cessation %K delivery %K GIS %K GPS %K health interventions %K mHealth %K mobile phone %K smartphone application %K smartphone %K smoker %K smoking cessation %K smoking %D 2023 %7 16.6.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Interest in quitting smoking is common among young adults who smoke, but it can prove challenging. Although evidence-based smoking cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences—spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual’s phone enters the perimeter. Despite growth in personalized and ubiquitous smoking cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information. Objective: This study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting 4 case studies using a combination of self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process of deploying coping messages when young adults enter geofence boundaries. Methods: Data came from an ecological momentary assessment study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay area. Participants reported smoking and nonsmoking events through a smartphone app for 30 days, and GPS data was recorded by the app. We sampled 4 cases along ecological momentary assessment compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each 3-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for 3 types of zones (census blocks, 500 ft2 fishnet grids, and 1000 ft2 fishnet grids). Descriptive comparisons were made across the 4 cases to better understand the strengths and limitations of each geofence construction method. Results: The number of reported past 30-day smoking events ranged from 12 to 177 for the 4 cases. Each 3-hour geofence for 3 of the 4 cases captured over 50% of smoking events. The 1000 ft2 fishnet grid captured the highest percentage of smoking events compared to census blocks across the 4 cases. Across 3-hour periods except for 3:00 AM-5:59 AM for 1 case, geofences contained an average of 36.4%-100% of smoking events. Findings showed that fishnet grid geofences may capture more smoking events compared to census blocks. Conclusions: Our findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform the delivery of intervention messages. %M 37327031 %R 10.2196/43990 %U https://mhealth.jmir.org/2023/1/e43990 %U https://doi.org/10.2196/43990 %U http://www.ncbi.nlm.nih.gov/pubmed/37327031 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45028 %T Ecological Momentary Assessment of Cognition in Clinical and Community Samples: Reliability and Validity Study %A Singh,Shifali %A Strong,Roger %A Xu,Irene %A Fonseca,Luciana M %A Hawks,Zoe %A Grinspoon,Elizabeth %A Jung,Lanee %A Li,Frances %A Weinstock,Ruth S %A Sliwinski,Martin J %A Chaytor,Naomi S %A Germine,Laura T %+ McLean Hospital, 1010 Pleasant Street, Belmont, MA, 02478, United States, 1 617 855 2675, ssingh@mclean.harvard.edu %K ecological momentary assessment %K cognition %K digital neuropsychology %K remote assessment %K digital technology %K type 1 diabetes, teleneuropsychology %K reliability %K validity %K cognitive functioning %K psychological %K physiological %K glucose %K community %D 2023 %7 2.6.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The current methods of evaluating cognitive functioning typically rely on a single time point to assess and characterize an individual’s performance. However, cognitive functioning fluctuates within individuals over time in relation to environmental, psychological, and physiological contexts. This limits the generalizability and diagnostic utility of single time point assessments, particularly among individuals who may exhibit large variations in cognition depending on physiological or psychological context (eg, those with type 1 diabetes [T1D], who may have fluctuating glucose concentrations throughout the day). Objective: We aimed to report the reliability and validity of cognitive ecological momentary assessment (EMA) as a method for understanding between-person differences and capturing within-person variation in cognition over time in a community sample and sample of adults with T1D. Methods: Cognitive performance was measured 3 times a day for 15 days in the sample of adults with T1D (n=198, recruited through endocrinology clinics) and for 10 days in the community sample (n=128, recruited from TestMyBrain, a web-based citizen science platform) using ultrabrief cognitive tests developed for cognitive EMA. Our cognitive EMA platform allowed for remote, automated assessment in participants’ natural environments, enabling the measurement of within-person cognitive variation without the burden of repeated laboratory or clinic visits. This allowed us to evaluate reliability and validity in samples that differed in their expected degree of cognitive variability as well as the method of recruitment. Results: The results demonstrate excellent between-person reliability (ranging from 0.95 to 0.99) and construct validity of cognitive EMA in both the sample of adults with T1D and community sample. Within-person reliability in both samples (ranging from 0.20 to 0.80) was comparable with that observed in previous studies in healthy older adults. As expected, the full-length baseline and EMA versions of TestMyBrain tests correlated highly with one another and loaded together on the expected cognitive domains when using exploratory factor analysis. Interruptions had higher negative impacts on accuracy-based outcomes (β=−.34 to −.26; all P values <.001) than on reaction time–based outcomes (β=−.07 to −.02; P<.001 to P=.40). Conclusions: We demonstrated that ultrabrief mobile assessments are both reliable and valid across 2 very different clinic versus community samples, despite the conditions in which cognitive EMAs are administered, which are often associated with more noise and variability. The psychometric characteristics described here should be leveraged appropriately depending on the goals of the cognitive assessment (eg, diagnostic vs everyday functioning) and the population being studied. %M 37266996 %R 10.2196/45028 %U https://www.jmir.org/2023/1/e45028 %U https://doi.org/10.2196/45028 %U http://www.ncbi.nlm.nih.gov/pubmed/37266996 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e46216 %T Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation %A Tran,Hai Hong %A Hong,Jung Kyung %A Jang,Hyeryung %A Jung,Jinhwan %A Kim,Jongmok %A Hong,Joonki %A Lee,Minji %A Kim,Jeong-Whun %A Kushida,Clete A %A Lee,Dongheon %A Kim,Daewoo %A Yoon,In-Young %+ Department of Psychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea, 82 31 787 7433, iyoon@snu.ac.kr %K respiratory sounds %K sleep stages %K deep learning %K smartphone %K home environment %D 2023 %7 1.6.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. Objective: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. Methods: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. Results: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). Conclusions: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only. %M 37261889 %R 10.2196/46216 %U https://www.jmir.org/2023/1/e46216 %U https://doi.org/10.2196/46216 %U http://www.ncbi.nlm.nih.gov/pubmed/37261889 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e45825 %T The Impact of Individuals’ Social Environments on Contact Tracing App Use: Survey Study %A Sadeghi,Atiyeh %A Pape,Sebastian %A Harborth,David %+ Chair of Mobile Business & Multilateral Security, Faculty of Economics and Business, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, Frankfurt, 60323, Germany, 49 69798 ext 34701, sebastian.pape@m-chair.de %K contact tracing app %K corona warning app %K Corona-Warn-App %K social influence %K usage %K COVID-19 %D 2023 %7 31.5.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of today, it has been downloaded approximately 45 million times. Objective: This study aims to investigate the influence of (non)users’ social environments on the usage of the CWA during 2 periods with relatively lower death rates and higher death rates caused by SARS-CoV-2. Methods: We conducted a longitudinal survey study in Germany with 833 participants in 2 waves to investigate how participants perceive their peer groups’ opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We analyzed these questions with generalized estimating equations. Further, 2 related sample tests were performed to test for differences between users of the CWA and nonusers and between the 2 points in time (wave 1 with the highest death rates observable during the pandemic in Germany versus wave 2 with significantly lower death rates). Results: Participants perceived that peer groups have a positive opinion toward using the CWA, with more positive opinions by the media, family doctors, politicians, and virologists/Robert Koch Institute and a lower, only slightly negative opinion originating from social media. Users of the CWA perceived their peer groups’ opinions about using the app as more positive than nonusers do. Furthermore, the perceived positive opinion of the media (P=.001) and politicians (P<.001) was significantly lower in wave 2 compared with that in wave 1. The perceived opinion of friends and family (P<.001) as well as their perceived influence (P=.02) among nonusers toward using the CWA was significantly higher in the latter period compared with that in wave 1. The influence of virologists (in Germany primarily communicated via the Robert Koch Institute) had the highest positive effect on using the CWA (B=0.363, P<.001). We only found 1 decreasing effect of the influence of politicians (B=–0.098, P=.04). Conclusions: Opinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that the influence of virologists/Robert Koch Institute and family/friends exerts the strongest effect on participants’ decisions to use the CWA while politicians had a slightly negative influence. Our results also indicate that it is crucial to accompany the introduction of such a contact tracing app with explanations and a media campaign to support its adoption that is backed up by political decision makers and subject matter experts. %M 37256683 %R 10.2196/45825 %U https://humanfactors.jmir.org/2023/1/e45825 %U https://doi.org/10.2196/45825 %U http://www.ncbi.nlm.nih.gov/pubmed/37256683 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e45203 %T Reliability and Validity of Noncognitive Ecological Momentary Assessment Survey Response Times as an Indicator of Cognitive Processing Speed in People’s Natural Environment: Intensive Longitudinal Study %A Hernandez,Raymond %A Hoogendoorn,Claire %A Gonzalez,Jeffrey S %A Jin,Haomiao %A Pyatak,Elizabeth A %A Spruijt-Metz,Donna %A Junghaenel,Doerte U %A Lee,Pey-Jiuan %A Schneider,Stefan %+ Center of Economic and Social Research, University of Southern California, 635 Downey Way, VPD, Los Angeles, CA, 90089, United States, 1 213 821 1899, hern939@usc.edu %K cognitive performance %K processing speed %K ecological momentary assessment %K ambulatory assessment %K type 1 diabetes %K survey response times %K paradata %K chronic illness %K smartphone %K mobile health %K mHealth %K mobile phone %D 2023 %7 30.5.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Various populations with chronic conditions are at risk for decreased cognitive performance, making assessment of their cognition important. Formal mobile cognitive assessments measure cognitive performance with greater ecological validity than traditional laboratory-based testing but add to participant task demands. Given that responding to a survey is considered a cognitively demanding task itself, information that is passively collected as a by-product of ecological momentary assessment (EMA) may be a means through which people’s cognitive performance in their natural environment can be estimated when formal ambulatory cognitive assessment is not feasible. We specifically examined whether the item response times (RTs) to EMA questions (eg, mood) can serve as approximations of cognitive processing speed. Objective: This study aims to investigate whether the RTs from noncognitive EMA surveys can serve as approximate indicators of between-person (BP) differences and momentary within-person (WP) variability in cognitive processing speed. Methods: Data from a 2-week EMA study investigating the relationships among glucose, emotion, and functioning in adults with type 1 diabetes were analyzed. Validated mobile cognitive tests assessing processing speed (Symbol Search task) and sustained attention (Go-No Go task) were administered together with noncognitive EMA surveys 5 to 6 times per day via smartphones. Multilevel modeling was used to examine the reliability of EMA RTs, their convergent validity with the Symbol Search task, and their divergent validity with the Go-No Go task. Other tests of the validity of EMA RTs included the examination of their associations with age, depression, fatigue, and the time of day. Results: Overall, in BP analyses, evidence was found supporting the reliability and convergent validity of EMA question RTs from even a single repeatedly administered EMA item as a measure of average processing speed. BP correlations between the Symbol Search task and EMA RTs ranged from 0.43 to 0.58 (P<.001). EMA RTs had significant BP associations with age (P<.001), as expected, but not with depression (P=.20) or average fatigue (P=.18). In WP analyses, the RTs to 16 slider items and all 22 EMA items (including the 16 slider items) had acceptable (>0.70) WP reliability. After correcting for unreliability in multilevel models, EMA RTs from most combinations of items showed moderate WP correlations with the Symbol Search task (ranged from 0.29 to 0.58; P<.001) and demonstrated theoretically expected relationships with momentary fatigue and the time of day. The associations between EMA RTs and the Symbol Search task were greater than those between EMA RTs and the Go-No Go task at both the BP and WP levels, providing evidence of divergent validity. Conclusions: Assessing the RTs to EMA items (eg, mood) may be a method of approximating people’s average levels of and momentary fluctuations in processing speed without adding tasks beyond the survey questions. %M 37252787 %R 10.2196/45203 %U https://mhealth.jmir.org/2023/1/e45203 %U https://doi.org/10.2196/45203 %U http://www.ncbi.nlm.nih.gov/pubmed/37252787 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42073 %T Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study %A Concheiro-Moscoso,Patricia %A Groba,Betania %A Alvarez-Estevez,Diego %A Miranda-Duro,María del Carmen %A Pousada,Thais %A Nieto-Riveiro,Laura %A Mejuto-Muiño,Francisco Javier %A Pereira,Javier %+ Faculty of Health Sciences, Oza Campus, Universidade da Coruña (University of A Coruña), A Coruña, 15006, Spain, 34 881015870, b.groba@udc.es %K sleep %K health promotion %K occupation %K polysomnography %K Xiaomi Mi Band 5 %K Internet of Things %D 2023 %7 19.5.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Polysomnography is the gold standard for measuring and detecting sleep patterns. In recent years, activity wristbands have become popular because they record continuous data in real time. Hence, comprehensive validation studies are needed to analyze the performance and reliability of these devices in the recording of sleep parameters. Objective: This study compared the performance of one of the best-selling activity wristbands, the Xiaomi Mi Band 5, against polysomnography in measuring sleep stages. Methods: This study was carried out at a hospital in A Coruña, Spain. People who were participating in a polysomnography study at a sleep unit were recruited to wear a Xiaomi Mi Band 5 simultaneously for 1 night. The total sample consisted of 45 adults, 25 (56%) with sleep disorders (SDis) and 20 (44%) without SDis. Results: Overall, the Xiaomi Mi Band 5 displayed 78% accuracy, 89% sensitivity, 35% specificity, and a Cohen κ value of 0.22. It significantly overestimated polysomnography total sleep time (P=.09), light sleep (N1+N2 stages of non–rapid eye movement [REM] sleep; P=.005), and deep sleep (N3 stage of non-REM sleep; P=.01). In addition, it underestimated polysomnography wake after sleep onset and REM sleep. Moreover, the Xiaomi Mi Band 5 performed better in people without sleep problems than in those with sleep problems, specifically in detecting total sleep time and deep sleep. Conclusions: The Xiaomi Mi Band 5 can be potentially used to monitor sleep and to detect changes in sleep patterns, especially for people without sleep problems. However, additional studies are necessary with this activity wristband in people with different types of SDis. Trial Registration: ClinicalTrials.gov NCT04568408; https://clinicaltrials.gov/ct2/show/NCT04568408 International Registered Report Identifier (IRRID): RR2-10.3390/ijerph18031106 %M 37204853 %R 10.2196/42073 %U https://www.jmir.org/2023/1/e42073 %U https://doi.org/10.2196/42073 %U http://www.ncbi.nlm.nih.gov/pubmed/37204853 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44352 %T Using Digital Technology to Quantify Habitual Physical Activity in Community Dwellers With Cognitive Impairment: Systematic Review %A Mc Ardle,Ríona %A Jabbar,Khalid Abdul %A Del Din,Silvia %A Thomas,Alan J %A Robinson,Louise %A Kerse,Ngaire %A Rochester,Lynn %A Callisaya,Michele %+ Newcastle University, Room 3.27, The Catalyst, 3 Science Square, Newcastle Helix, Newcastle, NE4 5TG, United Kingdom, 44 7476700757, riona.mcardle@ncl.ac.uk %K dementia %K cognitive dysfunction %K physical activity %K digital technology %K wearable electronic devices %K remote sensing technology %K systematic review %K community %K wearables %K cognitive impairment %K support %K clinicians %K sensing %D 2023 %7 18.5.2023 %9 Review %J J Med Internet Res %G English %X Background: Participating in habitual physical activity (HPA) can support people with dementia and mild cognitive impairment (MCI) to maintain functional independence. Digital technology can continuously measure HPA objectively, capturing nuanced measures relating to its volume, intensity, pattern, and variability. Objective: To understand HPA participation in people with cognitive impairment, this systematic review aims to (1) identify digital methods and protocols; (2) identify metrics used to assess HPA; (3) describe differences in HPA between people with dementia, MCI, and controls; and (4) make recommendations for measuring and reporting HPA in people with cognitive impairment. Methods: Key search terms were input into 6 databases: Scopus, Web of Science, Psych Articles, PsychInfo, MEDLINE, and Embase. Articles were included if they included community dwellers with dementia or MCI, reported HPA metrics derived from digital technology, were published in English, and were peer reviewed. Articles were excluded if they considered populations without dementia or MCI diagnoses, were based in aged care settings, did not concern digitally derived HPA metrics, or were only concerned with physical activity interventions. Key outcomes extracted included the methods and metrics used to assess HPA and differences in HPA outcomes across the cognitive spectrum. Data were synthesized narratively. An adapted version of the National Institute of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was used to assess the quality of articles. Due to significant heterogeneity, a meta-analysis was not feasible. Results: A total of 3394 titles were identified, with 33 articles included following the systematic review. The quality assessment suggested that studies were moderate-to-good quality. Accelerometers worn on the wrist or lower back were the most prevalent methods, while metrics relating to volume (eg, daily steps) were most common for measuring HPA. People with dementia had lower volumes, intensities, and variability with different daytime patterns of HPA than controls. Findings in people with MCI varied, but they demonstrated different patterns of HPA compared to controls. Conclusions: This review highlights limitations in the current literature, including lack of standardization in methods, protocols, and metrics; limited information on validity and acceptability of methods; lack of longitudinal research; and limited associations between HPA metrics and clinically meaningful outcomes. Limitations of this review include the exclusion of functional physical activity metrics (eg, sitting/standing) and non-English articles. Recommendations from this review include suggestions for measuring and reporting HPA in people with cognitive impairment and for future research including validation of methods, development of a core set of clinically meaningful HPA outcomes, and further investigation of socioecological factors that may influence HPA participation. Trial Registration: PROSPERO CRD42020216744; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216744   %M 37200065 %R 10.2196/44352 %U https://www.jmir.org/2023/1/e44352 %U https://doi.org/10.2196/44352 %U http://www.ncbi.nlm.nih.gov/pubmed/37200065 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e44986 %T Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study %A Braund,Taylor A %A O’Dea,Bridianne %A Bal,Debopriyo %A Maston,Kate %A Larsen,Mark %A Werner-Seidler,Aliza %A Tillman,Gabriel %A Christensen,Helen %+ Faculty of Medicine and Health, University of New South Wales, High St, Kensington, 2052, Australia, 61 290659255, t.braund@blackdog.org.au %K adolescents %K anxiety %K depression %K digital phenotype %K keystroke dynamics %K keystroke metadata %K smartphone %K students %D 2023 %7 15.5.2023 %9 Original Paper %J JMIR Ment Health %G English %X Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children’s Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. Trial Registration: Australian and New Zealand Clinical Trial Registry, ACTRN12619000855123; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664&isReview=true %M 37184904 %R 10.2196/44986 %U https://mental.jmir.org/2023/1/e44986 %U https://doi.org/10.2196/44986 %U http://www.ncbi.nlm.nih.gov/pubmed/37184904 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e41220 %T Applying the UTAUT2 Model to Smart Eyeglasses to Detect and Prevent Falls Among Older Adults and Examination of Associations With Fall-Related Functional Physical Capacities: Survey Study %A Hellec,Justine %A Hayotte,Meggy %A Chorin,Frédéric %A Colson,Serge S %A d'Arripe-Longueville,Fabienne %+ Université Côte d'Azur, LAMHESS, Campus STAPS, Sciences du Sport, 261, Boulevard du Mercantour, Nice, 06205, France, 33 489153905, justine.hellec@univ-cotedazur.fr %K Unified Theory of Acceptance and Use of Technology 2 %K fall prevention %K fall detection %K older people %K older adults %K facilitating conditions %K effort expectancy %K smart eyeglasses %D 2023 %7 12.5.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: As people age, their physical capacities (eg, walking and balance) decline and the risk of falling rises. Yet, classic fall detection devices are poorly accepted by older adults. Because they often wear eyeglasses as they go about their daily activities, daily monitoring to detect and prevent falls with smart eyeglasses might be more easily accepted. Objective: On the basis of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study evaluated (1) the acceptability of smart eyeglasses for the detection and prevention of falls by older adults and (2) the associations with selected fall-related functional physical capacities. Methods: A total of 142 volunteer older adults (mean age 74.9 years, SD 6.5 years) completed the UTAUT2 questionnaire adapted for smart eyeglasses and then performed several physical tests: a unipodal balance test with eyes open and closed, a 10-m walk test, and a 6-minute walk test. An unsupervised analysis classified the participants into physical performance groups. Multivariate ANOVAs were performed to identify differences in acceptability constructs according to the performance group. Results: The UTAUT2 questionnaire adapted for eyeglasses presented good psychometric properties. Performance expectancy (β=.21, P=.005), social influence (β=.18, P=.007), facilitating conditions (β=.17, P=.04), and habit (β=.40, P<.001) were significant contributors to the behavioral intention to use smart eyeglasses (R²=0.73). The unsupervised analysis based on fall-related functional physical capacities created 3 groups of physical performance: low, intermediate, and high. Effort expectancy in the low performance group (mean 3.99, SD 1.46) was lower than that in the other 2 groups (ie, intermediate: mean 4.68, SD 1.23; high: mean 5.09, SD 1.41). Facilitating conditions in the high performance group (mean 5.39, SD 1.39) were higher than those in the other 2 groups (ie, low: mean 4.31, SD 1.68; intermediate: mean 4.66, SD 1.51). Conclusions: To our knowledge, this study is the first to examine the acceptability of smart eyeglasses in the context of fall detection and prevention in older adults and to associate acceptability with fall-related functional physical capacities. The older adults with higher physical performances, and possibly lower risks of falling, reported greater acceptability of smart eyeglasses for fall prevention and detection than their counterparts exhibiting low physical performances. %M 37171835 %R 10.2196/41220 %U https://www.jmir.org/2023/1/e41220 %U https://doi.org/10.2196/41220 %U http://www.ncbi.nlm.nih.gov/pubmed/37171835 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e43033 %T The Effect of Periodic Email Prompts on Participant Engagement With a Behavior Change mHealth App: Longitudinal Study %A Agachi,Elena %A Bijmolt,Tammo H A %A van Ittersum,Koert %A Mierau,Jochen O %+ Department of Marketing, Faculty of Economics and Business, University of Groningen, Nettelbosje 2, Groningen, 9747 AE, Netherlands, 31 50 363 3686, e.agachi@rug.nl %K mobile health %K behavior change %K mobile app %K digital health %K engagement %K retention %K email %K hidden Markov model %D 2023 %7 11.5.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Following the need for the prevention of noncommunicable diseases, mobile health (mHealth) apps are increasingly used for promoting lifestyle behavior changes. Although mHealth apps have the potential to reach all population segments, providing accessible and personalized services, their effectiveness is often limited by low participant engagement and high attrition rates. Objective: This study concerns a large-scale, open-access mHealth app, based in the Netherlands, focused on improving the lifestyle behaviors of its participants. The study examines whether periodic email prompts increased participant engagement with the mHealth app and how this effect evolved over time. Points gained from the activities in the app were used as an objective measure of participant engagement with the program. The activities considered were physical workouts tracked through the mHealth app and interactions with the web-based coach. Methods: The data analyzed covered 22,797 unique participants over a period of 78 weeks. A hidden Markov model (HMM) was used for disentangling the overtime effects of periodic email prompts on participant engagement with the mHealth app. The HMM accounted for transitions between latent activity states, which generated the observed measure of points received in a week. Results: The HMM indicated that, on average, 70% (15,958/22,797) of the participants were in the inactivity state, gaining 0 points in total per week; 18% (4103/22,797) of the participants were in the average activity state, gaining 27 points per week; and 12% (2736/22,797) of the participants were in the high activity state, gaining 182 points per week. Receiving and opening a generic email was associated with a 3 percentage point increase in the likelihood of becoming active in that week, compared with the weeks when no email was received. Examining detailed email categories revealed that the participants were more likely to increase their activity level following emails that were in line with the program’s goal, such as emails regarding health campaigns, while being resistant to emails that deviated from the program’s goal, such as emails regarding special deals. Conclusions: Participant engagement with a behavior change mHealth app can be positively influenced by email prompts, albeit to a limited extent. Given the relatively low costs associated with emails and the high population reach that mHealth apps can achieve, such instruments can be a cost-effective means of increasing participant engagement in the stride toward improving program effectiveness. %M 37166974 %R 10.2196/43033 %U https://mhealth.jmir.org/2023/1/e43033 %U https://doi.org/10.2196/43033 %U http://www.ncbi.nlm.nih.gov/pubmed/37166974 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e41345 %T Mental Health in Urban Environments: Uncovering the Black Box of Person-Place Interactions Requires Interdisciplinary Approaches %A Kanning,Martina %A Yi,Li %A Yang,Chih-Hsiang %A Niermann,Christina %A Fina,Stefan %+ Department of Sport Science, University of Konstanz, Univeristätsstraße 10, Konstanz, 78464, Germany, 49 7531 88 3154, martina.kanning@uni-konstanz.de %K physical activity %K urban health %K ambulatory assessment %K environment %K mental health %K real-time data %K within-subject association %D 2023 %7 11.5.2023 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Living in urban environments affects individuals’ mental health through different pathways. For instance, physical activity and social participation are seen as mediators. However, aiming to understand underlying mechanisms, it is necessary to consider that the individual is interacting with its environment. In this regard, this viewpoint discusses how urban health research benefits from integration of socioecological and interdisciplinary perspectives, combined with innovative ambulatory data assessments that enable researchers to integrate different data sources. It is stated that neither focusing on the objective and accurate assessment of the environment (from the perspective of spatial sciences) nor focusing on subjectively measured individual variables (from the public health as well as a psychosocial perspective) alone is suitable to further develop the field. Addressing person-place interactions requires an interdisciplinary view on the level of theory (eg, which variables should be focused on?), assessment methods (eg, combination of time-varying objective and subjective measures), as well as data analysis and interpretation. Firstly, this viewpoint gives an overview on previous findings addressing the relationship of environmental characteristics to physical activity and mental health outcomes. We emphasize the need for approaches that allow us to appropriately assess the real-time interaction between a person and a specific environment and examine within-subject associations. This requires the assessment of environmental features, the spatial-temporal behavior of the individual, and the subjective experiences of the situation together with other individual factors, such as momentary affective states. Therefore, we finally focused on triggered study designs as an innovative ambulatory data assessment approach that allows us to capture real-time data in predefined situations (eg, while walking through a specific urban area). %M 37166963 %R 10.2196/41345 %U https://mhealth.jmir.org/2023/1/e41345 %U https://doi.org/10.2196/41345 %U http://www.ncbi.nlm.nih.gov/pubmed/37166963 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e45405 %T Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study %A Anmella,Gerard %A Corponi,Filippo %A Li,Bryan M %A Mas,Ariadna %A Sanabra,Miriam %A Pacchiarotti,Isabella %A Valentí,Marc %A Grande,Iria %A Benabarre,Antoni %A Giménez-Palomo,Anna %A Garriga,Marina %A Agasi,Isabel %A Bastidas,Anna %A Cavero,Myriam %A Fernández-Plaza,Tabatha %A Arbelo,Néstor %A Bioque,Miquel %A García-Rizo,Clemente %A Verdolini,Norma %A Madero,Santiago %A Murru,Andrea %A Amoretti,Silvia %A Martínez-Aran,Anabel %A Ruiz,Victoria %A Fico,Giovanna %A De Prisco,Michele %A Oliva,Vincenzo %A Solanes,Aleix %A Radua,Joaquim %A Samalin,Ludovic %A Young,Allan H %A Vieta,Eduard %A Vergari,Antonio %A Hidalgo-Mazzei,Diego %+ Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, Villarroel St, 170, Barcelona, Catalonia, 08036, Spain, 34 932275400 ext 4189, dahidalg@clinic.cat %K depression %K mania %K bipolar disorder %K major depressive disorder %K machine learning %K deep learning %K physiological data %K digital biomarker %K wearable %K Empatica E4 %D 2023 %7 4.5.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. Objective: Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data. Methods: We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses. Results: Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI>0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52). Conclusions: Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes. %M 36939345 %R 10.2196/45405 %U https://mhealth.jmir.org/2023/1/e45405 %U https://doi.org/10.2196/45405 %U http://www.ncbi.nlm.nih.gov/pubmed/36939345 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e39862 %T Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study %A Bae,Sang Won %A Suffoletto,Brian %A Zhang,Tongze %A Chung,Tammy %A Ozolcer,Melik %A Islam,Mohammad Rahul %A Dey,Anind K %+ Human-Computer Interaction and Human-Centered AI Systems Lab, AI for Healthcare Lab, School of Systems and Enterprises, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, United States, 1 4122658616, sbae4@stevens.edu %K alcohol consumption %K binge-drinking event %K BDE %K behavioral prediction model %K machine learning %K smartphone sensors %K passive sensing %K explainable artificial intelligence %K XAI %K just-in-time adaptive interventions %K JITAIs %K mobile phone %D 2023 %7 4.5.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could improve intervention impact. Objective: We aimed to determine the feasibility of developing a machine learning (ML) model to accurately predict future, that is, same-day BDEs 1 to 6 hours prior BDEs, using smartphone sensor data and to identify the most informative phone sensor features associated with BDEs on weekends and weekdays to determine the key features that explain prediction model performance. Methods: We collected phone sensor data from 75 young adults (aged 21 to 25 years; mean 22.4, SD 1.9 years) with risky drinking behavior who reported their drinking behavior over 14 weeks. The participants in this secondary analysis were enrolled in a clinical trial. We developed ML models testing different algorithms (eg, extreme gradient boosting [XGBoost] and decision tree) to predict same-day BDEs (vs low-risk drinking events and non-drinking periods) using smartphone sensor data (eg, accelerometer and GPS). We tested various “prediction distance” time windows (more proximal: 1 hour; distant: 6 hours) from drinking onset. We also tested various analysis time windows (ie, the amount of data to be analyzed), ranging from 1 to 12 hours prior to drinking onset, because this determines the amount of data that needs to be stored on the phone to compute the model. Explainable artificial intelligence was used to explore interactions among the most informative phone sensor features contributing to the prediction of BDEs. Results: The XGBoost model performed the best in predicting imminent same-day BDEs, with 95% accuracy on weekends and 94.3% accuracy on weekdays (F1-score=0.95 and 0.94, respectively). This XGBoost model needed 12 and 9 hours of phone sensor data at 3- and 6-hour prediction distance from the onset of drinking on weekends and weekdays, respectively, prior to predicting same-day BDEs. The most informative phone sensor features for BDE prediction were time (eg, time of day) and GPS-derived features, such as the radius of gyration (an indicator of travel). Interactions among key features (eg, time of day and GPS-derived features) contributed to the prediction of same-day BDEs. Conclusions: We demonstrated the feasibility and potential use of smartphone sensor data and ML for accurately predicting imminent (same-day) BDEs in young adults. The prediction model provides “windows of opportunity,” and with the adoption of explainable artificial intelligence, we identified “key contributing features” to trigger just-in-time adaptive intervention prior to the onset of BDEs, which has the potential to reduce the likelihood of BDEs in young adults. Trial Registration: ClinicalTrials.gov NCT02918565; https://clinicaltrials.gov/ct2/show/NCT02918565 %M 36809294 %R 10.2196/39862 %U https://formative.jmir.org/2023/1/e39862 %U https://doi.org/10.2196/39862 %U http://www.ncbi.nlm.nih.gov/pubmed/36809294 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e40524 %T Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation %A Hearn,Jason %A Van den Eynde,Jef %A Chinni,Bhargava %A Cedars,Ari %A Gottlieb Sen,Danielle %A Kutty,Shelby %A Manlhiot,Cedric %+ Blalock-Taussig-Thomas Heart Center, Johns Hopkins University, 1800 Orleans Street, Baltimore, MD, 21287, United States, 1 410 614 8481, cmanlhi1@jhmi.edu %K wearables %K time series %K data reliability %K prediction models %K hear rate %K monitoring %K data %K reliability %K clinical %K sleep %K data set %K cardiac %K physiological %K accuracy %K consumer %K wearables %K device %D 2023 %7 3.5.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated. Objective: The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings. Methods: Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set. Results: The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE. Conclusions: In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models. %M 37133921 %R 10.2196/40524 %U https://cardio.jmir.org/2023/1/e40524 %U https://doi.org/10.2196/40524 %U http://www.ncbi.nlm.nih.gov/pubmed/37133921 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e41545 %T Delivering a Postpartum Weight Loss Intervention via Facebook or In-Person Groups: Results From a Randomized Pilot Feasibility Trial %A Waring,Molly E %A Pagoto,Sherry L %A Moore Simas,Tiffany A %A Blackman Carr,Loneke T %A Eamiello,Madison L %A Libby,Brooke A %A Rudin,Lauren R %A Heersping,Grace E %+ Department of Allied Health Sciences, University of Connecticut, 358 Mansfield Rd, Unit 1101, Storrs, CT, 06269, United States, 1 8604861446, molly.waring@uconn.edu %K postpartum weight loss %K Facebook %K social media %K pilot study %K feasibility %K mobile phone %D 2023 %7 27.4.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Postpartum weight retention contributes to weight gain and obesity. Remotely delivered lifestyle interventions may be able to overcome barriers to attending in-person programs during this life phase. Objective: This study aimed to conduct a randomized feasibility pilot trial of a 6-month postpartum weight loss intervention delivered via Facebook or in-person groups. Feasibility outcomes were recruitment, sustained participation, contamination, retention, and feasibility of study procedures. Percent weight loss at 6 and 12 months were exploratory outcomes. Methods: Women with overweight or obesity who were 8 weeks to 12 months post partum were randomized to receive a 6-month behavioral weight loss intervention based on the Diabetes Prevention Program lifestyle intervention via Facebook or in-person groups. Participants completed assessments at baseline, 6 months, and 12 months. Sustained participation was defined by intervention meeting attendance or visible engagement in the Facebook group. We calculated percent weight change for participants who provided weight at each follow-up. Results: Among individuals not interested in the study, 68.6% (72/105) were not interested in or could not attend in-person meetings and 2.9% (3/105) were not interested in the Facebook condition. Among individuals excluded at screening, 18.5% (36/195) were ineligible owing to reasons related to the in-person condition, 12.3% (24/195) related to the Facebook condition, and 2.6% (5/195) were unwilling to be randomized. Randomized participants (n=62) were a median of 6.1 (IQR 3.1-8.3) months post partum, with a median BMI of 31.7 (IQR 28.2-37.4) kg/m2. Retention was 92% (57/62) at 6 months and 94% (58/62) at 12 months. The majority (21/30, 70%) of Facebook and 31% (10/32) of in-person participants participated in the last intervention module. Half (13/26, 50%) of Facebook and 58% (15/26) of in-person participants would be likely or very likely to participate again if they had another baby, and 54% (14/26) and 70% (19/27), respectively, would be likely or very likely to recommend the program to a friend. In total, 96% (25/26) of Facebook participants reported that it was convenient or very convenient to log into the Facebook group daily compared with 7% (2/27) of in-person participants who said it was convenient or very convenient to attend group meetings each week. Average weight loss was 3.0% (SD 7.2%) in the Facebook condition and 5.4% (SD 6.8%) in the in-person condition at 6 months, and 2.8% (SD 7.4%) in the Facebook condition and 4.8% (SD 7.6%) in the in-person condition at 12 months. Conclusions: Barriers to attending in-person meetings hampered recruitment efforts and intervention participation. Although women found the Facebook group convenient and stayed engaged in the group, weight loss appeared lower. Research is needed to further develop care models for postpartum weight loss that balance accessibility with efficacy. Trial Registration: ClinicalTrials.gov, NCT03700736; https://clinicaltrials.gov/ct2/show/NCT03700736 %M 37103991 %R 10.2196/41545 %U https://mhealth.jmir.org/2023/1/e41545 %U https://doi.org/10.2196/41545 %U http://www.ncbi.nlm.nih.gov/pubmed/37103991 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e39588 %T Impact of Human Mobility on COVID-19 Transmission According to Mobility Distance, Location, and Demographic Factors in the Greater Bay Area of China: Population-Based Study %A Xia,Jizhe %A Yin,Kun %A Yue,Yang %A Li,Qingquan %A Wang,Xiling %A Hu,Dongsheng %A Wang,Xiong %A Du,Zhanwei %A Cowling,Ben J %A Chen,Erzhen %A Zhou,Ying %+ Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Ruijin Second Road 197, HuangPu District, Shanghai, 200000, China, 86 64370045 ext 600603, zy12941@rjh.com.cn %K COVID-19 %K mobility restriction %K mobility distance %K demographic factors %K locations %D 2023 %7 26.4.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Mobility restriction was one of the primary measures used to restrain the spread of COVID-19 globally. Governments implemented and relaxed various mobility restriction measures in the absence of evidence for almost 3 years, which caused severe adverse outcomes in terms of health, society, and economy. Objective: This study aimed to quantify the impact of mobility reduction on COVID-19 transmission according to mobility distance, location, and demographic factors in order to identify hotspots of transmission and guide public health policies. Methods: Large volumes of anonymized aggregated mobile phone position data between January 1 and February 24, 2020, were collected for 9 megacities in the Greater Bay Area, China. A generalized linear model (GLM) was established to test the association between mobility volume (number of trips) and COVID-19 transmission. Subgroup analysis was also performed for sex, age, travel location, and travel distance. Statistical interaction terms were included in a variety of models that express different relations between involved variables. Results: The GLM analysis demonstrated a significant association between the COVID-19 growth rate ratio (GR) and mobility volume. A stratification analysis revealed a higher effect of mobility volume on the COVID-19 GR among people aged 50-59 years (GR decrease of 13.17% per 10% reduction in mobility volume; P<.001) than among other age groups (GR decreases of 7.80%, 10.43%, 7.48%, 8.01%, and 10.43% for those aged ≤18, 19-29, 30-39, 40-49, and ≥60 years, respectively; P=.02 for the interaction). The impact of mobility reduction on COVID-19 transmission was higher for transit stations and shopping areas (instantaneous reproduction number [Rt] decreases of 0.67 and 0.53 per 10% reduction in mobility volume, respectively) than for workplaces, schools, recreation areas, and other locations (Rt decreases of 0.30, 0.37, 0.44, and 0.32, respectively; P=.02 for the interaction). The association between mobility volume reduction and COVID-19 transmission was lower with decreasing mobility distance as there was a significant interaction between mobility volume and mobility distance with regard to Rt (P<.001 for the interaction). Specifically, the percentage decreases in Rt per 10% reduction in mobility volume were 11.97% when mobility distance increased by 10% (Spring Festival), 6.74% when mobility distance remained unchanged, and 1.52% when mobility distance declined by 10%. Conclusions: The association between mobility reduction and COVID-19 transmission significantly varied according to mobility distance, location, and age. The substantially higher impact of mobility volume on COVID-19 transmission for longer travel distance, certain age groups, and specific travel locations highlights the potential to optimize the effectiveness of mobility restriction strategies. The results from our study demonstrate the power of having a mobility network using mobile phone data for surveillance that can monitor movement at a detailed level to measure the potential impacts of future pandemics. %M 36848228 %R 10.2196/39588 %U https://publichealth.jmir.org/2023/1/e39588 %U https://doi.org/10.2196/39588 %U http://www.ncbi.nlm.nih.gov/pubmed/36848228 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e38920 %T Reintroducing the Effortless Assessment Research System (EARS) %A Lind,Monika N %A Kahn,Lauren E %A Crowley,Ryann %A Reed,Wyatt %A Wicks,Geordie %A Allen,Nicholas B %+ Center for Digital Mental Health, University of Oregon, Straub Hall, Eugene, OR, 97403, United States, 1 541 346 4075, nallen3@uoregon.edu %K mobile sensing %K passive sensing %K personal sensing %K digital phenotyping %K ecological momentary assessment %K digital mental health %D 2023 %7 26.4.2023 %9 Viewpoint %J JMIR Ment Health %G English %X This paper reintroduces the Effortless Assessment Research System (EARS), 4 years and 10,000 participants after its initial launch. EARS is a mobile sensing tool that affords researchers the opportunity to collect naturalistic, behavioral data via participants’ naturalistic smartphone use. The first section of the paper highlights improvements made to EARS via a tour of EARS’s capabilities—the most important of which is the expansion of EARS to the iOS operating system. Other improvements include better keyboard integration for the collection of typed text; full control of survey design and administration for research teams; and the addition of a researcher-facing EARS dashboard, which facilitates survey design, the enrollment of participants, and the tracking of participants. The second section of the paper goes behind the scenes to describe 3 challenges faced by the EARS developers—remote participant enrollment and tracking, keeping EARS running in the background, and continuous attention and effort toward data protection—and how those challenges shaped the design of the app. %M 37099361 %R 10.2196/38920 %U https://mental.jmir.org/2023/1/e38920 %U https://doi.org/10.2196/38920 %U http://www.ncbi.nlm.nih.gov/pubmed/37099361 %0 Journal Article %@ 2817-092X %I JMIR Publications %V 2 %N %P e43351 %T A Novel System to Monitor Tic Attacks for Tourette Syndrome Using Machine Learning and Wearable Technology: Preliminary Survey Study and Proposal for a New Sensing Device %A Rajinikanth,Agni %A Clark,Davis Kevin %A Kapsetaki,Marianna Evangelia %+ Faculty of Life Sciences, Division of Biosciences, University College London, Gower Street, London, WC1E 6BT, United Kingdom, 44 07392970494, marianna.kapsetaki.15@ucl.ac.uk %K Tourette syndrome %K neurological diseases %K tic attacks %K wearable technology %K movement disorders %K tremor monitoring %K biosensing technology %K automatic tic detection %D 2023 %7 25.4.2023 %9 Original Paper %J JMIR Neurotech %G English %X Background: Tourette syndrome is a neurological disorder that is characterized by repeated unintentional physical movement and vocal sounds, better known as tics. Cases of mild Tourette can have tics numerous times throughout the day, while severe cases may have tics every 5 to 10 seconds. At certain times, typically during high levels of stress, tics become chained in an incessant, continuous fashion—this is known as a tic attack. Tic attacks incapacitate the patient, rendering it difficult for them to move, perform daily actions, and even communicate with others. Caretakers—usually guardians, family members, or nurses—can help reduce the time tic attacks last with their presence and by providing emotional support to the patient. Objective: We describe TSBand, a wearable wristband that uses machine learning algorithms and a variety of sensors to monitor for tic attacks and notify caretakers when an attack occurs. Methods: We conducted a research survey with 70 Tourette patients to determine the usability and functionality of TSBand; internal review board approval was not required. Results: This study has resulted in a smart wristband prototype that costs US $62.74; it uses movement, heart rate, sweat, and body temperature to detect tic attacks using a hybrid local outlier factoring and regression algorithm. An audio tic attack detection mechanism is also included, using recurrent neural networks, and a manually activated backup button and backup audio mechanism are fitted to alert caretakers on the personalized companion app. Conclusions: TSBand enables the caretaker to provide support faster and prevent excessive self-harm or injury during the attack. It is an affordable and effective solution, solving a problem that many Tourette patients, often children, face. This study has not had the opportunity to test TSBand with any Tourette patients, and we aim to perform rigorous testing and analysis after grant funding is secured. %R 10.2196/43351 %U https://neuro.jmir.org/2023/1/e43351 %U https://doi.org/10.2196/43351 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e40722 %T Direct Support Professionals’ Perspectives on Using Technology to Help Support Adults With Autism Spectrum Disorder: Mixed Methods Study %A Simmons,Christina A %A Moretti,Abigail E %A Lobo,Andrea F %A Tremoulet,Patrice D %+ Department of Psychology, Rowan University, 201 Mullica Hill Rd, Robinson Hall, Glassboro, NJ, 08028, United States, 1 8562564500 ext 53777, tremoulet@rowan.edu %K technology %K data collection %K documentation %K direct support professionals %K autism %K mobile phone %D 2023 %7 25.4.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Documentation is a critical responsibility for direct support professionals (DSPs) who work with adults with autism spectrum disorder (ASD); however, it contributes significantly to their workload. Targeted efforts must be made to mitigate the burden of necessary data collection and documentation, which contributes to high DSP turnover rates and poor job satisfaction. Objective: This mixed methods study aimed to explore how technology could assist DSPs who work with adults with ASD and prioritize aspects of technology that would be most useful for future development efforts. Methods: In the first study, 15 DSPs who worked with adults with ASD participated in 1 of the 3 online focus groups. The topics included daily tasks, factors that would influence the adoption of technology, and how DSPs would like to interact with technologies to provide information about their clients. Responses were thematically analyzed across focus groups and ranked by salience. In the second study, 153 DSPs across the United States rated the usefulness of technology features and data entry methods and provided qualitative responses on their concerns regarding the use of technology for data collection and documentation. Quantitative responses were ranked based on their usefulness across participants, and rank-order correlations were calculated between different work settings and age groups. The qualitative responses were thematically analyzed. Results: In study 1, participants described difficulties with paper-and-pencil data collection, noted benefits and concerns about using technology instead, identified benefits and concerns about particular technology features, and specified work-environment factors that impact data collection. In study 2, participants rated multiple features of technology as useful, with the highest usefulness percentages endorsed for task views (ie, by shift, client, and DSP), logging completed tasks, and setting reminders for specific tasks. Participants also rated most data entry methods (eg, typing on a phone or tablet, typing on a keyboard, and choosing from options on a touch screen) as useful. Rank-order correlations indicated that the usefulness of technology features and data entry methods differed across work settings and age groups. Across both studies, DSPs cited some concerns with technology, such as confidentiality, reliability and accuracy, complexity and efficiency, and data loss from technology failure. Conclusions: Understanding the challenges faced by DSPs who work with adults with ASD, and their thoughts about using technology to meet those challenges, represents an essential first step toward developing technology solutions that can increase DSPs’ effectiveness and job satisfaction. The survey results indicate that technology innovations should incorporate multiple features to account for different needs across DSPs, settings, and age groups. Future research should explore barriers to adopting data collection and documentation tools and elicit input from agency directors, families, and others interested in reviewing data about adults with ASD. %M 37097738 %R 10.2196/40722 %U https://formative.jmir.org/2023/1/e40722 %U https://doi.org/10.2196/40722 %U http://www.ncbi.nlm.nih.gov/pubmed/37097738 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e38774 %T Effect of the Data Collection Method on Mobile Phone Survey Participation in Bangladesh and Tanzania: Secondary Analyses of a Randomized Crossover Trial %A Pariyo,George %A Meghani,Ankita %A Gibson,Dustin %A Ali,Joseph %A Labrique,Alain %A Khan,Iqbal Ansary %A Kibria,Gulam Muhammed Al %A Masanja,Honorati %A Hyder,Adnan Ali %A Ahmed,Saifuddin %+ Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, E8141, Baltimore, MD, 21205, United States, 1 4434779403, gkibria1@jhu.edu %K mobile phone survey %K interactive voice response survey %K non-communicable disease surveillance %K response rate %K cooperation rate %K phone %K risk %K survey %K public health %K interview %K voice %K response %K cooperation %K female %K women %K rural %K school %K countries %K non-communicable disease %K surveillance %K interactive survey %D 2023 %7 20.4.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Mobile phone surveys provide a novel opportunity to collect population-based estimates of public health risk factors; however, nonresponse and low participation challenge the goal of collecting unbiased survey estimates. Objective: This study compares the performance of computer-assisted telephone interview (CATI) and interactive voice response (IVR) survey modalities for noncommunicable disease risk factors in Bangladesh and Tanzania. Methods: This study used secondary data from a randomized crossover trial. Between June 2017 and August 2017, study participants were identified using the random digit dialing method. Mobile phone numbers were randomly allocated to either a CATI or IVR survey. The analysis examined survey completion, contact, response, refusal, and cooperation rates of those who received the CATI and IVR surveys. Differences in survey outcomes between modes were assessed using multilevel, multivariable logistic regression models to adjust for confounding covariates. These analyses were adjusted for clustering effects by mobile network providers. Results: For the CATI surveys, 7044 and 4399 phone numbers were contacted in Bangladesh and Tanzania, respectively, and 60,863 and 51,685 phone numbers, respectively, were contacted for the IVR survey. The total numbers of completed interviews in Bangladesh were 949 for CATI and 1026 for IVR and in Tanzania were 447 for CATI and 801 for IVR. Response rates for CATI were 5.4% (377/7044) in Bangladesh and 8.6% (376/4391) in Tanzania; response rates for IVR were 0.8% (498/60,377) in Bangladesh and 1.1% (586/51,483) in Tanzania. The distribution of the survey population was significantly different from the census distribution. In both countries, IVR respondents were younger, were predominantly male, and had higher education levels than CATI respondents. IVR respondents had a lower response rate than CATI respondents in Bangladesh (adjusted odds ratio [AOR]=0.73, 95% CI 0.54-0.99) and Tanzania (AOR=0.32, 95% CI 0.16-0.60). The cooperation rate was also lower with IVR than with CATI in Bangladesh (AOR=0.12, 95% CI 0.07-0.20) and Tanzania (AOR=0.28, 95% CI 0.14-0.56). Both in Bangladesh (AOR=0.33, 95% CI 0.25-0.43) and Tanzania (AOR=0.09, 95% CI 0.06-0.14), there were fewer completed interviews with IVR than with CATI; however, there were more partial interviews with IVR than with CATI in both countries. Conclusions: There were lower completion, response, and cooperation rates with IVR than with CATI in both countries. This finding suggests that, to increase representativeness in certain settings, a selective approach may be needed to design and deploy mobile phone surveys to increase population representativeness. Overall, CATI surveys may offer a promising approach for surveying potentially under-represented groups like women, rural residents, and participants with lower levels of education in some countries. %M 37079373 %R 10.2196/38774 %U https://formative.jmir.org/2023/1/e38774 %U https://doi.org/10.2196/38774 %U http://www.ncbi.nlm.nih.gov/pubmed/37079373 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e45309 %T Geolocation Patterns, Wi-Fi Connectivity Rates, and Psychiatric Symptoms Among Urban Homeless Youth: Mixed Methods Study Using Self-report and Smartphone Data %A Ilyas,Yousaf %A Hassanbeigi Daryani,Shahrzad %A Kiriella,Dona %A Pachwicewicz,Paul %A Boley,Randy A %A Reyes,Karen M %A Smith,Dale L %A Zalta,Alyson K %A Schueller,Stephen M %A Karnik,Niranjan S %A Stiles-Shields,Colleen %+ Institute for Juvenile Research, Department of Psychiatry, College of Medicine, University of Illinois Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, United States, 1 3122730185, ecss@uic.edu %K mHealth %K mobile health %K smartphones %K geolocation %K Wi-Fi %K youth experiencing homelessness %K mobile phone %K homelessness %K youth %D 2023 %7 18.4.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Despite significant research done on youth experiencing homelessness, few studies have examined movement patterns and digital habits in this population. Examining these digital behaviors may provide useful data to design new digital health intervention models for youth experiencing homelessness. Specifically, passive data collection (data collected without extra steps for a user) may provide insights into lived experience and user needs without putting an additional burden on youth experiencing homelessness to inform digital health intervention design. Objective: The objective of this study was to explore patterns of mobile phone Wi-Fi usage and GPS location movement among youth experiencing homelessness. Additionally, we further examined the relationship between usage and location as correlated with depression and posttraumatic stress disorder (PTSD) symptoms. Methods: A total of 35 adolescent and young adult participants were recruited from the general community of youth experiencing homelessness for a mobile intervention study that included installing a sensor data acquisition app (Purple Robot) for up to 6 months. Of these participants, 19 had sufficient passive data to conduct analyses. At baseline, participants completed self-reported measures for depression (Patient Health Questionnaire-9 [PHQ-9]) and PTSD (PTSD Checklist for DSM-5 [PCL-5]). Behavioral features were developed and extracted from phone location and usage data. Results: Almost all participants (18/19, 95%) used private networks for most of their noncellular connectivity. Greater Wi-Fi usage was associated with a higher PCL-5 score (P=.006). Greater location entropy, representing the amount of variability in time spent across identified clusters, was also associated with higher severity in both PCL-5 (P=.007) and PHQ-9 (P=.045) scores. Conclusions: Location and Wi-Fi usage both demonstrated associations with PTSD symptoms, while only location was associated with depression symptom severity. While further research needs to be conducted to establish the consistency of these findings, they suggest that the digital patterns of youth experiencing homelessness offer insights that could be used to tailor digital interventions. %M 37071457 %R 10.2196/45309 %U https://formative.jmir.org/2023/1/e45309 %U https://doi.org/10.2196/45309 %U http://www.ncbi.nlm.nih.gov/pubmed/37071457 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e37347 %T Health Monitoring Using Smart Home Technologies: Scoping Review %A Morita,Plinio P %A Sahu,Kirti Sundar %A Oetomo,Arlene %+ School of Public Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 31372, plinio.morita@uwaterloo.ca %K monitor %K smart home %K ambient assisted living %K active assisted living %K AAL %K assisted living %K review %K internet of things %K aging %K gerontology %K elder %K older adult %K older people %K geriatric %K digital health %K eHealth %K smart technology %K older population %K independent living %K big data %K machine learning %K algorithm %K deep learning %D 2023 %7 13.4.2023 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring. Objective: This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. Methods: Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed. Results: Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy. Conclusions: This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps—in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector. %M 37052984 %R 10.2196/37347 %U https://mhealth.jmir.org/2023/1/e37347 %U https://doi.org/10.2196/37347 %U http://www.ncbi.nlm.nih.gov/pubmed/37052984 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45332 %T Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach %A Razavi,Rouzbeh %A Xue,Guisen %+ Department of Management and Information Systems, Kent State University, 800 E Summit St, Kent, OH, 44240, United States, 1 2163198890, rrazavi@kent.edu %K micronutrient deficiencies %K micronutrient %K food label %K food %K nutrition %K nutrient %K diet %K machine learning %K algorithm %K predict %K predictive model %K nutrition mobile applications %K mobile app %K health app %K mHealth %K mobile health %D 2023 %7 12.4.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Micronutrient deficiencies represent a major global health issue, with over 2 billion individuals experiencing deficiencies in essential vitamins and minerals. Food labels provide consumers with information regarding the nutritional content of food items and have been identified as a potential tool for improving diets. However, due to governmental regulations and the physical limitations of the labels, food labels often lack comprehensive information about the vitamins and minerals present in foods. As a result, information about most of the micronutrients is absent from existing food labels. Objective: This paper aims to examine the possibility of using machine learning algorithms to predict unreported micronutrients such as vitamin A (retinol), vitamin C, vitamin B1 (thiamin), vitamin B2 (riboflavin), vitamin B3 (niacin), vitamin B6, vitamin B12, vitamin E (alpha-tocopherol), vitamin K, and minerals such as magnesium, zinc, phosphorus, selenium, manganese, and copper from nutrition information provided on existing food labels. If unreported micronutrients can be predicted with acceptable accuracies from existing food labels using machine learning predictive models, such models can be integrated into mobile apps to provide consumers with additional micronutrient information about foods and help them make more informed diet decisions. Methods: Data from the Food and Nutrient Database for Dietary Studies (FNDDS) data set, representing a total of 5624 foods, were used to train a diverse set of machine learning classification and regression algorithms to predict unreported vitamins and minerals from existing food label data. For each model, hyperparameters were adjusted, and the models were evaluated using repeated cross-validation to ensure that the reported results were not subject to overfitting. Results: According to the results, while predicting the exact quantity of vitamins and minerals is shown to be challenging, with regression R2 varying in a wide range from 0.28 (for magnesium) to 0.92 (for manganese), the classification models can accurately predict the category (“low,” “medium,” or “high”) level of all minerals and vitamins with accuracies exceeding 0.80. The highest classification accuracies for specific micronutrients are achieved for vitamin B12 (0.94) and phosphorus (0.94), while the lowest are for vitamin E (0.81) and selenium (0.83). Conclusions: This study demonstrates the feasibility of predicting unreported micronutrients from existing food labels using machine learning algorithms. The results show that the approach has the potential to significantly improve consumer knowledge about the micronutrient content of the foods they consume. Integrating these predictive models into mobile apps can enhance their accessibility and engagement with consumers. The implications of this research for public health are noteworthy, underscoring the potential of technology to augment consumers’ understanding of the micronutrient content of their diets while also facilitating the tracking of food intake and providing personalized recommendations based on the micronutrient content and individual preferences. %M 37043261 %R 10.2196/45332 %U https://www.jmir.org/2023/1/e45332 %U https://doi.org/10.2196/45332 %U http://www.ncbi.nlm.nih.gov/pubmed/37043261 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e44275 %T Wearable Sensor and Mobile App–Based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study %A Takano,Ayumi %A Ono,Koki %A Nozawa,Kyosuke %A Sato,Makito %A Onuki,Masaki %A Sese,Jun %A Yumoto,Yosuke %A Matsushita,Sachio %A Matsumoto,Toshihiko %+ Department of Mental Health and Psychiatric Nursing, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan, 81 3 5803 5348, ayumi-takano@umin.ac.jp %K alcohol and drug use %K alcoholism %K digital health %K drug use %K ecological momentary assessment %K ecological momentary intervention %K electronic health record %K Fitbit %K machine learning %K mHealth %K mobile app %K self-monitoring %K wearables devices %D 2023 %7 11.4.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Digital health technologies using mobile apps and wearable devices are a promising approach to the investigation of substance use in the real world and for the analysis of predictive factors or harms from substance use. Moreover, consecutive repeated data collection enables the development of predictive algorithms for substance use by machine learning methods. Objective: We developed a new self-monitoring mobile app to record daily substance use, triggers, and cravings. Additionally, a wearable activity tracker (Fitbit) was used to collect objective biological and behavioral data before, during, and after substance use. This study aims to describe a model using machine learning methods to determine substance use. Methods: This study is an ongoing observational study using a Fitbit and a self-monitoring app. Participants of this study were people with health risks due to alcohol or methamphetamine use. They were required to record their daily substance use and related factors on the self-monitoring app and to always wear a Fitbit for 8 weeks, which collected the following data: (1) heart rate per minute, (2) sleep duration per day, (3) sleep stages per day, (4) the number of steps per day, and (5) the amount of physical activity per day. Fitbit data will first be visualized for data analysis to confirm typical Fitbit data patterns for individual users. Next, machine learning and statistical analysis methods will be performed to create a detection model for substance use based on the combined Fitbit and self-monitoring data. The model will be tested based on 5-fold cross-validation, and further preprocessing and machine learning methods will be conducted based on the preliminary results. The usability and feasibility of this approach will also be evaluated. Results: Enrollment for the trial began in September 2020, and the data collection finished in April 2021. In total, 13 people with methamphetamine use disorder and 36 with alcohol problems participated in this study. The severity of methamphetamine or alcohol use disorder assessed by the Drug Abuse Screening Test-10 or the Alcohol Use Disorders Identification Test-10 was moderate to severe. The anticipated results of this study include understanding the physiological and behavioral data before, during, and after alcohol or methamphetamine use and identifying individual patterns of behavior. Conclusions: Real-time data on daily life among people with substance use problems were collected in this study. This new approach to data collection might be helpful because of its high confidentiality and convenience. The findings of this study will provide data to support the development of interventions to reduce alcohol and methamphetamine use and associated negative consequences. International Registered Report Identifier (IRRID): DERR1-10.2196/44275 %M 37040162 %R 10.2196/44275 %U https://www.researchprotocols.org/2023/1/e44275 %U https://doi.org/10.2196/44275 %U http://www.ncbi.nlm.nih.gov/pubmed/37040162 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42482 %T Time of Day Preferences and Daily Temporal Consistency for Predicting the Sustained Use of a Commercial Meditation App: Longitudinal Observational Study %A Berardi,Vincent %A Fowers,Rylan %A Rubin,Gavriella %A Stecher,Chad %+ Department of Psychology, Chapman University, 1 University Drive, Orange, CA, 92866, United States, 1 7145165883, berardi@chapman.edu %K behavioral habits %K habit formation %K mindfulness meditation %K mobile health %K health app %K app usage %K meditation app %K temporal analysis %K circadian rhythm %K healthy life style %K physical activity %K mental well being %K habit %K mindfulness %K meditation %K wellbeing %K mental health %K longitudinal %K observational %K advice %K morning %D 2023 %7 10.4.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The intensive data typically collected by mobile health (mHealth) apps allows factors associated with persistent use to be investigated, which is an important objective given users’ well-known struggles with sustaining healthy behavior. Objective: Data from a commercial meditation app (n=14,879; 899,071 total app uses) were analyzed to assess the validity of commonly given habit formation advice to meditate at the same time every day, preferably in the morning. Methods: First, the change in probability of meditating in 4 nonoverlapping time windows (morning, midday, evening, and late night) on a given day over the first 180 days after creating a meditation app account was calculated via generalized additive mixed models. Second, users’ time of day preferences were calculated as the percentage of all meditation sessions that occurred within each of the 4 time windows. Additionally, the temporal consistency of daily meditation behavior was calculated as the entropy of the timing of app usage sessions. Linear regression was used to examine the effect of time of day preference and temporal consistency on two outcomes: (1) short-term engagement, defined as the number of meditation sessions completed within the sixth and seventh month of a user’s account, and (2) long-term use, defined as the days until a user’s last observed meditation session. Results: Large reductions in the probability of meditation at any time of day were seen over the first 180 days after creating an account, but this effect was smallest for morning meditation sessions (63.4% reduction vs reductions ranging from 67.8% to 74.5% for other times). A greater proportion of meditation in the morning was also significantly associated with better short-term engagement (regression coefficient B=2.76, P<.001) and long-term use (B=50.6, P<.001). The opposite was true for late-night meditation sessions (short-term: B=–2.06, P<.001; long-term: B=–51.7, P=.001). Significant relationships were not found for midday sessions (any outcome) or for evening sessions when examining long-term use. Additionally, temporal consistency in the performance of morning meditation sessions was associated with better short-term engagement (B=–1.64, P<.001) but worse long-term use (B=55.8, P<.001). Similar-sized temporal consistency effects were found for all other time windows. Conclusions: Meditating in the morning was associated with higher rates of maintaining a meditation practice with the app. This is consistent with findings from other studies that have hypothesized that the strength of existing morning routines and circadian rhythms may make the morning an ideal time to build new habits. In the long term, less temporal consistency in meditation sessions was associated with more persistent app use, suggesting there are benefits from maintaining flexibility in behavior performance. These findings improve our understanding of how to promote enduring healthy lifestyles and can inform the design of mHealth strategies for maintaining behavior changes. %M 37036755 %R 10.2196/42482 %U https://www.jmir.org/2023/1/e42482 %U https://doi.org/10.2196/42482 %U http://www.ncbi.nlm.nih.gov/pubmed/37036755 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e38066 %T Smartphone Keyboard Interaction Monitoring as an Unobtrusive Method to Approximate Rest-Activity Patterns: Experience Sampling Study Investigating Interindividual and Metric-Specific Variations %A Smolders,Karin %A Druijff-van de Woestijne,Gerrieke %A Meijer,Kim %A Mcconchie,Hannah %A de Kort,Yvonne %+ Eindhoven University of Technology, Human-Technology Interaction group, Groene Loper, Eindhoven, 5600MB, Netherlands, 31 402474491, k.c.h.j.smolders@tue.nl %K smartphone keyboard interactions monitoring %K rest-activity patters %K sleep quality %K chronotype %K trait self-control %K mobile phone %D 2023 %7 7.4.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Sleep is an important determinant of individuals’ health and behavior during the wake phase. Novel research methods for field assessments are required to enable the monitoring of sleep over a prolonged period and across a large number of people. The ubiquity of smartphones offers new avenues for detecting rest-activity patterns in everyday life in a noninvasive an inexpensive manner and on a large scale. Recent studies provided evidence for the potential of smartphone interaction monitoring as a novel tracking method to approximate rest-activity patterns based on the timing of smartphone activity and inactivity throughout the 24-hour day. These findings require further replication and more detailed insights into interindividual variations in the associations and deviations with commonly used metrics for monitoring rest-activity patterns in everyday life. Objective: This study aimed to replicate and expand on earlier findings regarding the associations and deviations between smartphone keyboard–derived and self-reported estimates of the timing of the onset of the rest and active periods and the duration of the rest period. Moreover, we aimed to quantify interindividual variations in the associations and time differences between the 2 assessment modalities and to investigate to what extent general sleep quality, chronotype, and trait self-control moderate these associations and deviations. Methods: Students were recruited to participate in a 7-day experience sampling study with parallel smartphone keyboard interaction monitoring. Multilevel modeling was used to analyze the data. Results: In total, 157 students participated in the study, with an overall response rate of 88.9% for the diaries. The results revealed moderate to strong relationships between the keyboard-derived and self-reported estimates, with stronger associations for the timing-related estimates (β ranging from .61 to .78) than for the duration-related estimates (β=.51 and β=.52). The relational strength between the time-related estimates was lower, but did not substantially differ for the duration-related estimates, among students experiencing more disturbances in their general sleep quality. Time differences between the keyboard-derived and self-reported estimates were, on average, small (<0.5 hours); however, large discrepancies were also registered for quite some nights. The time differences between the 2 assessment modalities were larger for both timing-related and rest duration–related estimates among students who reported more disturbances in their general sleep quality. Chronotype and trait self-control did not significantly moderate the associations and deviations between the 2 assessment modalities. Conclusions: We replicated the positive potential of smartphone keyboard interaction monitoring for estimating rest-activity patterns among populations of regular smartphone users. Chronotype and trait self-control did not significantly influence the metrics’ accuracy, whereas general sleep quality did: the behavioral proxies obtained from smartphone interactions appeared to be less powerful among students who experienced lower general sleep quality. The generalization and underlying process of these findings require further investigation. %M 37027202 %R 10.2196/38066 %U https://www.jmir.org/2023/1/e38066 %U https://doi.org/10.2196/38066 %U http://www.ncbi.nlm.nih.gov/pubmed/37027202 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e44393 %T Evaluation of the Effectiveness of Therapy for Anxiety in Williams Beuren Syndrome Using a Smartphone App: Protocol for a Single-Case Experiment %A Lehman,Natacha %A Trouillet,Raphaël %A Genevieve,David %+ EA4556 Laboratoire Epsylon, Université Paul Valery Montpellier 3, Rte de Mende, Montpellier, 34090, France, 33 617437801, natacha.lehman@gmail.com %K single case experimental design %K cognitive behavioral therapy %K intellectual disability %K rare disease %K Williams syndrome %K smartphone application %K ecological momentary assessment %D 2023 %7 3.4.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Williams syndrome (WS-OMIM 194050, orphaned number: Orpha 904) is a rare condition mostly associated with intellectual disability. People with Williams syndrome are 8 times more likely to have anxiety disorders than the general population. Therapeutic solutions to treat the anxiety remain limited, particularly nonpharmacological therapy. However, cognitive behavioral therapy (CBT) has been found efficacious in managing anxiety disorders and can be used for people with intellectual disability. Objective: This paper describes a protocol to assess the efficiency of a CBT program based on digital support for people with Williams syndrome and anxiety based on a research methodology designed for rare diseases. Methods: We will recruit 5 individuals with Williams syndrome and anxiety. They will participate in 9 CBT sessions. Participants will perform daily self-assessments of anxiety using a digital app, which will allow for ecological and repeated evaluation of their anxiety. This digital app will provide support for each therapy session. Anxiety and quality of life will be externally assessed before and after the program and at a 3-month follow-up. This is a single-case intervention research design with multiple baselines implying repeated measures of judgment criteria. The present protocol ensures high internal validity and will help identify encouraging contributions for later clinical trials. Results: Participant recruitment and data collection began in September 2019, and we project that the study findings will be available for dissemination by spring 2023. Conclusions: This study will allow the assessment of the efficiency of a CBT program based on digital support to treat anxiety in people with Williams syndrome. Finally, the program could be used as an example of nonpharmacological therapy for rare diseases. Trial Registration: ClinicalTrials.gov ID: NCT03827525; https://clinicaltrials.gov/ct2/show/NCT03827525 International Registered Report Identifier (IRRID): DERR1-10.2196/44393 %M 37010888 %R 10.2196/44393 %U https://www.researchprotocols.org/2023/1/e44393 %U https://doi.org/10.2196/44393 %U http://www.ncbi.nlm.nih.gov/pubmed/37010888 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e41117 %T Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study %A Millard,Louise A C %A Johnson,Laura %A Neaves,Samuel R %A Flach,Peter A %A Tilling,Kate %A Lawlor,Deborah A %+ Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom, 44 0117 455 7676, louise.millard@bristol.ac.uk %K digital health %K data collection %K voice-based approaches %K Amazon Alexa %K self-reported data %K food and drink %D 2023 %7 31.3.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Voice-based systems such as Amazon Alexa may be useful for collecting self-reported information in real time from participants of epidemiology studies using verbal input. In epidemiological research studies, self-reported data tend to be collected using short, infrequent questionnaires, in which the items require participants to select from predefined options, which may lead to errors in the information collected and lack of coverage. Voice-based systems give the potential to collect self-reported information “continuously” over several days or weeks. At present, to the best of our knowledge, voice-based systems have not been used or evaluated for collecting epidemiological data. Objective: We aimed to demonstrate the technical feasibility of using Alexa to collect information from participants, investigate participant acceptability, and provide an initial evaluation of the validity of the collected data. We used food and drink information as an exemplar. Methods: We recruited 45 staff members and students at the University of Bristol (United Kingdom). Participants were asked to tell Alexa what they ate or drank for 7 days and to also submit this information using a web-based form. Questionnaires asked for basic demographic information, about their experience during the study, and the acceptability of using Alexa. Results: Of the 37 participants with valid data, most (n=30, 81%) were aged 20 to 39 years and 23 (62%) were female. Across 29 participants with Alexa and web entries corresponding to the same intake event, 60.1% (357/588) of Alexa entries contained the same food and drink information as the corresponding web entry. Most participants reported that Alexa interjected, and this was worse when entering the food and drink information (17/35, 49% of participants said this happened often; 1/35, 3% said this happened always) than when entering the event date and time (6/35, 17% of participants said this happened often; 1/35, 3% said this happened always). Most (28/35, 80%) said they would be happy to use a voice-controlled system for future research. Conclusions: Although there were some issues interacting with the Alexa skill, largely because of its conversational nature and because Alexa interjected if there was a pause in speech, participants were mostly willing to participate in future research studies using Alexa. More studies are needed, especially to trial less conversational interfaces. %M 37000476 %R 10.2196/41117 %U https://mhealth.jmir.org/2023/1/e41117 %U https://doi.org/10.2196/41117 %U http://www.ncbi.nlm.nih.gov/pubmed/37000476 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 6 %N %P e42461 %T Physical Activity Surveillance in Children and Adolescents Using Smartphone Technology: Systematic Review %A Nasruddin,Nur Izzatun Nasriah %A Murphy,Joey %A Armstrong,Miranda Elaine Glynis %+ Centre for Exercise, Nutrition and Health Sciences, School for Policy Studies, University of Bristol, 8 Priory Road, Bristol, BS8 1TZ, United Kingdom, 44 117 455 2103, izzah.nasruddin@bristol.ac.uk %K physical activity %K surveillance %K children %K adolescents %K smartphone technology %K smartphone apps %K smartphone %K technology %K application %K database %K mobile phone %D 2023 %7 29.3.2023 %9 Review %J JMIR Pediatr Parent %G English %X Background: Self-reported physical activity (PA) questionnaires have traditionally been used for PA surveillance in children and adolescents, especially in free-living conditions. Objective measures are more accurate at measuring PA, but high cost often creates a barrier for their use in low- and middle-income settings. The advent of smartphone technology has greatly influenced mobile health and has offered new opportunities in health research, including PA surveillance. Objective: This review aimed to systematically explore the use of smartphone technology for PA surveillance in children and adolescents, specifically focusing on the use of smartphone apps. Methods: A literature search was conducted using 5 databases (PubMed, Scopus, CINAHL, MEDLINE, and Web of Science) and Google Scholar to identify articles relevant to the topic that were published from 2008 to 2023. Articles were included if they included children and adolescents within the age range of 5 to 18 years; used smartphone technology as PA surveillance; had PA behavioral outcomes such as energy expenditure, step count, and PA levels; were written in English; and were published between 2008 and 2023. Results: We identified and analyzed 8 studies (5 cross-sectional studies and 3 cohort studies). All participants were aged 12-18 years, and all studies were conducted in high-income countries only. Participants were recruited from schools, primary care facilities, and voluntarily. Five studies used mobile apps specifically and purposely developed for the study, whereas 3 studies used mobile apps downloadable from the Apple App Store and Android Play Store. PA surveillance using these apps was conducted from 24 hours to 4 weeks. Conclusions: Evidence of PA surveillance using smartphone technology in children and adolescents was insufficient, which demonstrated the knowledge gap. Additional research is needed to further study the feasibility and validity of smartphone apps for PA surveillance among children and adolescents, especially in low- and middle-income countries. %M 36989033 %R 10.2196/42461 %U https://pediatrics.jmir.org/2023/1/e42461 %U https://doi.org/10.2196/42461 %U http://www.ncbi.nlm.nih.gov/pubmed/36989033 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e41050 %T Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study %A Hunter,Victoria %A Shapiro,Allison %A Chawla,Devika %A Drawnel,Faye %A Ramirez,Ernesto %A Phillips,Elizabeth %A Tadesse-Bell,Sara %A Foschini,Luca %A Ukachukwu,Vincent %+ Roche Products Limited, 6 Falcon Way, Shire Park, Welwyn Garden City, AL7 1TW, United Kingdom, 44 7785642250, vincent.ukachukwu@roche.com %K influenza %K influenza-like illness %K wearable sensor %K person-generated health care data %D 2023 %7 23.3.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. Objective: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non–care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. Methods: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care–seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. Results: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. Conclusions: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non–care-seeking populations, which may have future application in health care resource planning. Trial Registration: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800 %M 36951890 %R 10.2196/41050 %U https://www.jmir.org/2023/1/e41050 %U https://doi.org/10.2196/41050 %U http://www.ncbi.nlm.nih.gov/pubmed/36951890 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e37469 %T Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis %A Aalbers,George %A Hendrickson,Andrew T %A Vanden Abeele,Mariek MP %A Keijsers,Loes %+ Department of Cognitive Science & Artificial Intelligence, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, Netherlands, 31 13 466 9111, h.j.g.aalbers@tilburguniversity.edu %K mobile health %K mobile phone %K digital phenotype %K digital biomarker %K machine learning %K personalized models %D 2023 %7 23.3.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Stress is an important predictor of mental health problems such as burnout and depression. Acute stress is considered adaptive, whereas chronic stress is viewed as detrimental to well-being. To aid in the early detection of chronic stress, machine learning models are increasingly trained to learn the quantitative relation from digital footprints to self-reported stress. Prior studies have investigated general principles in population-wide studies, but the extent to which the findings apply to individuals is understudied. Objective: We aimed to explore to what extent machine learning models can leverage features of smartphone app use log data to recognize momentary subjective stress in individuals, which of these features are most important for predicting stress and represent potential digital markers of stress, the nature of the relations between these digital markers and stress, and the degree to which these relations differ across people. Methods: Student participants (N=224) self-reported momentary subjective stress 5 times per day up to 60 days in total (44,381 observations); in parallel, dedicated smartphone software continuously logged their smartphone app use. We extracted features from the log data (eg, time spent on app categories such as messenger apps and proxies for sleep duration and onset) and trained machine learning models to predict momentary subjective stress from these features using 2 approaches: modeling general relations at the group level (nomothetic approach) and modeling relations for each person separately (idiographic approach). To identify potential digital markers of momentary subjective stress, we applied explainable artificial intelligence methodology (ie, Shapley additive explanations). We evaluated model accuracy on a person-to-person basis in out-of-sample observations. Results: We identified prolonged use of messenger and social network site apps and proxies for sleep duration and onset as the most important features across modeling approaches (nomothetic vs idiographic). The relations of these digital markers with momentary subjective stress differed from person to person, as did model accuracy. Sleep proxies, messenger, and social network use were heterogeneously related to stress (ie, negative in some and positive or zero in others). Model predictions correlated positively and statistically significantly with self-reported stress in most individuals (median person-specific correlation=0.15-0.19 for nomothetic models and median person-specific correlation=0.00-0.09 for idiographic models). Conclusions: Our findings indicate that smartphone log data can be used for identifying digital markers of stress and also show that the relation between specific digital markers and stress differs from person to person. These findings warrant follow-up studies in other populations (eg, professionals and clinical populations) and pave the way for similar research using physiological measures of stress. %M 36951924 %R 10.2196/37469 %U https://mhealth.jmir.org/2023/1/e37469 %U https://doi.org/10.2196/37469 %U http://www.ncbi.nlm.nih.gov/pubmed/36951924 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e42646 %T Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review %A Langener,Anna M %A Stulp,Gert %A Kas,Martien J %A Bringmann,Laura F %+ Groningen Institute for Evolutionary Life Sciences, Nijenborgh 7, Groningen, 9747 AG, Netherlands, 31 050 363 8, langener95@gmail.com %K social context %K experience sampling method %K egocentric network %K digital phenotyping %K passive measures %K ambulatory assessment %K mobile phone %D 2023 %7 17.3.2023 %9 Review %J JMIR Ment Health %G English %X Background: Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people’s social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people’s social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. Objective: Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. Methods: We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. Conclusions: We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants’ burden and form a holistic perspective on the social environment. %M 36930210 %R 10.2196/42646 %U https://mental.jmir.org/2023/1/e42646 %U https://doi.org/10.2196/42646 %U http://www.ncbi.nlm.nih.gov/pubmed/36930210 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e41685 %T Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study %A Weber,Kyle S %A Godkin,F Elizabeth %A Cornish,Benjamin F %A McIlroy,William E %A Van Ooteghem,Karen %+ Department of Kinesiology and Health Sciences, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada, 1 (519) 888 4567, kvanooteghem@uwaterloo.ca %K neurodegenerative disease %K aging %K older adults %K wearable sensors %K physical activity %K activity intensity %K activity monitoring %K exercise prescription %K accelerometry %K health technology %D 2023 %7 15.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Accurate measurement of daily physical activity (PA) is important as PA is linked to health outcomes in older adults and people living with complex health conditions. Wrist-worn accelerometers are widely used to estimate PA intensity, including walking, which composes much of daily PA. However, there is concern that wrist-derived PA data in these cohorts is unreliable due to slow gait speed, mobility aid use, disease-related symptoms that impact arm movement, and transient activities of daily living. Despite the potential for error in wrist-derived PA intensity estimates, their use has become ubiquitous in research and clinical application. Objective: The goals of this work were to (1) determine the accuracy of wrist-based estimates of PA intensity during known walking periods in older adults and people living with cerebrovascular disease (CVD) or neurodegenerative disease (NDD) and (2) explore factors that influence wrist-derived intensity estimates. Methods: A total of 35 older adults (n=23 with CVD or NDD) wore an accelerometer on the dominant wrist and ankle for 7 to 10 days of continuous monitoring. Stepping was detected using the ankle accelerometer. Analyses were restricted to gait bouts ≥60 seconds long with a cadence ≥80 steps per minute (LONG walks) to identify periods of purposeful, continuous walking likely to reflect moderate-intensity activity. Wrist accelerometer data were analyzed within LONG walks using 15-second epochs, and published intensity thresholds were applied to classify epochs as sedentary, light, or moderate-to-vigorous physical activity (MVPA). Participants were stratified into quartiles based on the percent of walking epochs classified as sedentary, and the data were examined for differences in behavioral or demographic traits between the top and bottom quartiles. A case series was performed to illustrate factors and behaviors that can affect wrist-derived intensity estimates during walking. Results: Participants averaged 107.7 (SD 55.8) LONG walks with a median cadence of 107.3 (SD 10.8) steps per minute. Across participants, wrist-derived intensity classification was 22.9% (SD 15.8) sedentary, 27.7% (SD 14.6) light, and 49.3% (SD 25.5) MVPA during LONG walks. All participants measured a statistically lower proportion of wrist-derived activity during LONG walks than expected (all P<.001), and 80% (n=28) of participants had at least 20 minutes of LONG walking time misclassified as sedentary based on wrist-derived intensity estimates. Participants in the highest quartile of wrist-derived sedentary classification during LONG walks were significantly older (t16=4.24, P<.001) and had more variable wrist movement (t16=2.13, P=.049) compared to those in the lowest quartile. Conclusions: The current best practice wrist accelerometer method is prone to misclassifying activity intensity during walking in older adults and people living with complex health conditions. A multidevice approach may be warranted to advance methods for accurately assessing PA in these groups. %M 36920452 %R 10.2196/41685 %U https://formative.jmir.org/2023/1/e41685 %U https://doi.org/10.2196/41685 %U http://www.ncbi.nlm.nih.gov/pubmed/36920452 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e41178 %T Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study %A Zhuparris,Ahnjili %A Maleki,Ghobad %A Koopmans,Ingrid %A Doll,Robert J %A Voet,Nicoline %A Kraaij,Wessel %A Cohen,Adam %A van Brummelen,Emilie %A De Maeyer,Joris H %A Groeneveld,Geert Jan %+ Centre for Human Drug Research (CHDR), Zernikedreef 8, Leiden, 2333 CL, Netherlands, 31 0715246400, ggroeneveld@chdr.nl %K facioscapulohumeral muscular dystrophy %K FSHD %K smartphone %K wearables %K machine learning %K Time Up and Go %K regression %K mobile phone %K neuromuscular disease %K mHealth %K mobile health %D 2023 %7 15.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions. Objective: We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic. Methods: In total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data. Results: The single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively. Conclusions: We demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period. Trial Registration: ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735 %M 36920465 %R 10.2196/41178 %U https://formative.jmir.org/2023/1/e41178 %U https://doi.org/10.2196/41178 %U http://www.ncbi.nlm.nih.gov/pubmed/36920465 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e39425 %T Objective Prediction of Next-Day’s Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study %A Jafarlou,Salar %A Lai,Jocelyn %A Azimi,Iman %A Mousavi,Zahra %A Labbaf,Sina %A Jain,Ramesh C %A Dutt,Nikil %A Borelli,Jessica L %A Rahmani,Amir %+ Donald Bren School of Information and Computer Sciences, University of California, Irvine, Donald Bren Hall, 6210, Irvine, CA, 92697, United States, 1 (949) 824 7427, jafarlos@uci.edu %K wearable devices %K mental health %K affective computing %D 2023 %7 15.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Affective states are important aspects of healthy functioning; as such, monitoring and understanding affect is necessary for the assessment and treatment of mood-based disorders. Recent advancements in wearable technologies have increased the use of such tools in detecting and accurately estimating mental states (eg, affect, mood, and stress), offering comprehensive and continuous monitoring of individuals over time. Objective: Previous attempts to model an individual’s mental state relied on subjective measurements or the inclusion of only a few objective monitoring modalities (eg, smartphones). This study aims to investigate the capacity of monitoring affect using fully objective measurements. We conducted a comparatively long-term (12-month) study with a holistic sampling of participants’ moods, including 20 affective states. Methods: Longitudinal physiological data (eg, sleep and heart rate), as well as daily assessments of affect, were collected using 3 modalities (ie, smartphone, watch, and ring) from 20 college students over a year. We examined the difference between the distributions of data collected from each modality along with the differences between their rates of missingness. Out of the 20 participants, 7 provided us with 200 or more days’ worth of data, and we used this for our predictive modeling setup. Distributions of positive affect (PA) and negative affect (NA) among the 7 selected participants were observed. For predictive modeling, we assessed the performance of different machine learning models, including random forests (RFs), support vector machines (SVMs), multilayer perceptron (MLP), and K-nearest neighbor (KNN). We also investigated the capability of each modality in predicting mood and the most important features of PA and NA RF models. Results: RF was the best-performing model in our analysis and performed mood and stress (nervousness) prediction with ~81% and ~72% accuracy, respectively. PA models resulted in better performance compared to NA. The order of the most important modalities in predicting PA and NA was the smart ring, phone, and watch, respectively. SHAP (Shapley Additive Explanations) analysis showed that sleep and activity-related features were the most impactful in predicting PA and NA. Conclusions: Generic machine learning–based affect prediction models, trained with population data, outperform existing methods, which use the individual’s historical information. Our findings indicated that our mood prediction method outperformed the existing methods. Additionally, we found that sleep and activity level were the most important features for predicting next-day PA and NA, respectively. %M 36920456 %R 10.2196/39425 %U https://formative.jmir.org/2023/1/e39425 %U https://doi.org/10.2196/39425 %U http://www.ncbi.nlm.nih.gov/pubmed/36920456 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e39546 %T A Call to Expand the Scope of Digital Phenotyping %A De Boer,Christopher %A Ghomrawi,Hassan %A Zeineddin,Suhail %A Linton,Samuel %A Kwon,Soyang %A Abdullah,Fizan %+ Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, 225 E Chicago Ave, Chicago, IL, 60611, United States, 1 312 227 4210, fabdullah@luriechildrens.org %K digital phenotyping %K wearables %K digital health %K data collection %K real-time %K data %K digital devices %K smartphones %K phenotype %K quantification %K phenotyping %K wearable devices %K tracking %K monitoring %K clinical data %K applcaition %K implementation %D 2023 %7 14.3.2023 %9 Viewpoint %J J Med Internet Res %G English %X Digital phenotyping refers to near–real-time data collection from personal digital devices, particularly smartphones, to better quantify the human phenotype. Methodology using smartphones is often considered the gold standard by many for passive data collection within the field of digital phenotyping, which limits its applications mainly to adults or adolescents who use smartphones. However, other technologies, such as wearable devices, have evolved considerably in recent years to provide similar or better quality passive physiologic data of clinical relevance, thus expanding the potential of digital phenotyping applications to other patient populations. In this perspective, we argue for the continued expansion of digital phenotyping to include other potential gold standards in addition to smartphones and provide examples of currently excluded technologies and populations who may uniquely benefit from this technology. %M 36917148 %R 10.2196/39546 %U https://www.jmir.org/2023/1/e39546 %U https://doi.org/10.2196/39546 %U http://www.ncbi.nlm.nih.gov/pubmed/36917148 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e45355 %T Predicting Pain in People With Sickle Cell Disease in the Day Hospital Using the Commercial Wearable Apple Watch: Feasibility Study %A Stojancic,Rebecca Sofia %A Subramaniam,Arvind %A Vuong,Caroline %A Utkarsh,Kumar %A Golbasi,Nuran %A Fernandez,Olivia %A Shah,Nirmish %+ Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Division of Hematology, Duke University Hospital, 40 Duke Medicine Cir, Clinic 2N, Durham, NC, 27710, United States, 1 919 684 0628, rsstojan@ncsu.edu %K sickle cell disease %K vaso-occlusive crises %K mobile health %K consumer wearable %K Apple Watch %K machine learning %K pain %K prediction %K smartwatch %K wearable %K predict %D 2023 %7 14.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Sickle cell disease (SCD) is a genetic red blood cell disorder associated with severe complications including chronic anemia, stroke, and vaso-occlusive crises (VOCs). VOCs are unpredictable, difficult to treat, and the leading cause of hospitalization. Recent efforts have focused on the use of mobile health technology to develop algorithms to predict pain in people with sickle cell disease. Combining the data collection abilities of a consumer wearable, such as the Apple Watch, and machine learning techniques may help us better understand the pain experience and find trends to predict pain from VOCs. Objective: The aim of this study is to (1) determine the feasibility of using the Apple Watch to predict the pain scores in people with sickle cell disease admitted to the Duke University SCD Day Hospital, referred to as the Day Hospital, and (2) build and evaluate machine learning algorithms to predict the pain scores of VOCs with the Apple Watch. Methods: Following approval of the institutional review board, patients with sickle cell disease, older than 18 years, and admitted to Day Hospital for a VOC between July 2021 and September 2021 were approached to participate in the study. Participants were provided with an Apple Watch Series 3, which is to be worn for the duration of their visit. Data collected from the Apple Watch included heart rate, heart rate variability (calculated), and calories. Pain scores and vital signs were collected from the electronic medical record. Data were analyzed using 3 different machine learning models: multinomial logistic regression, gradient boosting, and random forest, and 2 null models, to assess the accuracy of pain scores. The evaluation metrics considered were accuracy (F1-score), area under the receiving operating characteristic curve, and root-mean-square error (RMSE). Results: We enrolled 20 patients with sickle cell disease, all of whom identified as Black or African American and consisted of 12 (60%) females and 8 (40%) males. There were 14 individuals diagnosed with hemoglobin type SS (70%). The median age of the population was 35.5 (IQR 30-41) years. The median time each individual spent wearing the Apple Watch was 2 hours and 17 minutes and a total of 15,683 data points were collected across the population. All models outperformed the null models, and the best-performing model was the random forest model, which was able to predict the pain scores with an accuracy of 84.5%, and a RMSE of 0.84. Conclusions: The strong performance of the model in all metrics validates feasibility and the ability to use data collected from a noninvasive device, the Apple Watch, to predict the pain scores during VOCs. It is a novel and feasible approach and presents a low-cost method that could benefit clinicians and individuals with sickle cell disease in the treatment of VOCs. %M 36917171 %R 10.2196/45355 %U https://formative.jmir.org/2023/1/e45355 %U https://doi.org/10.2196/45355 %U http://www.ncbi.nlm.nih.gov/pubmed/36917171 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e38072 %T Tracking Changes in Mobility Before and After the First SARS-CoV-2 Vaccination Using Global Positioning System Data in England and Wales (Virus Watch): Prospective Observational Community Cohort Study %A Nguyen,Vincent %A Liu,Yunzhe %A Mumford,Richard %A Flanagan,Benjamin %A Patel,Parth %A Braithwaite,Isobel %A Shrotri,Madhumita %A Byrne,Thomas %A Beale,Sarah %A Aryee,Anna %A Fong,Wing Lam Erica %A Fragaszy,Ellen %A Geismar,Cyril %A Navaratnam,Annalan M D %A Hardelid,Pia %A Kovar,Jana %A Pope,Addy %A Cheng,Tao %A Hayward,Andrew %A Aldridge,Robert %A , %+ Centre for Public Health Data Science, Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 20 3549 5541, r.aldridge@ucl.ac.uk %K COVID-19 %K SARS-CoV-2 %K vaccination %K global positioning system %K GPS %K movement tracking %K geographical tracking %K mobile app %K health application %K surveillance %K public health %K mHealth %K mobile surveillance %K tracking device %K geolocation %D 2023 %7 8.3.2023 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Evidence suggests that individuals may change adherence to public health policies aimed at reducing the contact, transmission, and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination when they are not fully vaccinated. Objective: We aimed to estimate changes in median daily travel distance of our cohort from their registered addresses before and after receiving a SARS-CoV-2 vaccine. Methods: Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants, and vaccination status was collected from January 2021 onward. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute toward our tracker subcohort, which uses the GPS via a smartphone app to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. Results: We analyzed the daily travel distance of 249 vaccinated adults. From 157 days prior to vaccination until the day before vaccination, the median daily travel distance was 9.05 (IQR 8.06-10.09) km. From the day of vaccination to 105 days after vaccination, the median daily travel distance was 10.08 (IQR 8.60-12.42) km. From 157 days prior to vaccination until the vaccination date, there was a daily median decrease in mobility of 40.09 m (95% CI –50.08 to –31.10; P<.001). After vaccination, there was a median daily increase in movement of 60.60 m (95% CI 20.90-100; P<.001). Restricting the analysis to the third national lockdown (January 4, 2021, to April 5, 2021), we found a median daily movement increase of 18.30 m (95% CI –19.20 to 55.80; P=.57) in the 30 days prior to vaccination and a median daily movement increase of 9.36 m (95% CI 38.6-149.00; P=.69) in the 30 days after vaccination. Conclusions: Our study demonstrates the feasibility of collecting high-volume geolocation data as part of research projects and the utility of these data for understanding public health issues. Our various analyses produced results that ranged from no change in movement after vaccination (during the third national lock down) to an increase in movement after vaccination (considering all periods, up to 105 days after vaccination), suggesting that, among Virus Watch participants, any changes in movement distances after vaccination are small. Our findings may be attributable to public health measures in place at the time such as movement restrictions and home working that applied to the Virus Watch cohort participants during the study period. %M 36884272 %R 10.2196/38072 %U https://publichealth.jmir.org/2023/1/e38072 %U https://doi.org/10.2196/38072 %U http://www.ncbi.nlm.nih.gov/pubmed/36884272 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e43296 %T Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study %A Niemeijer,Koen %A Mestdagh,Merijn %A Verdonck,Stijn %A Meers,Kristof %A Kuppens,Peter %+ Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Tiensestraat 102, Post box 3717, Leuven, 3000, Belgium, 32 16 37 2580, koen.niemeijer@kuleuven.be %K digital phenotyping %K mobile health %K mHealth %K mobile sensing %K passive sensing %K ambulatory assessment %K experience sampling %K ecological momentary assessment %K smartphones %K mobile phone %D 2023 %7 7.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection. Objective: In this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities. Methods: To create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-week pilot study in which we delivered ESM questionnaires while collecting mobile sensing data to evaluate the app’s sampling reliability and perceived user experience. As m-Path is already widely used, the ease of use of the ESM system was not investigated. Results: Data from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 value per second using summary statistics, the entire SQLite database contained 84,299,462 observations and was 18.30 GB in size. The reliability of sampling frequency in the pilot study was satisfactory for most sensors, based on the absolute number of collected observations. However, the relative coverage rate—the ratio between the actual and expected number of measurements—was below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants’ perceived user experience. Conclusions: To better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM. %M 36881444 %R 10.2196/43296 %U https://formative.jmir.org/2023/1/e43296 %U https://doi.org/10.2196/43296 %U http://www.ncbi.nlm.nih.gov/pubmed/36881444 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e35045 %T Comparison of a Daily Smartphone App and Retrospective Questionnaire Measures of Adherence to Nicotine Replacement Therapy Among Pregnant Women: Observational Study %A Emery,Joanne %A Huang,Yue %A Naughton,Felix %A Cooper,Sue %A McDaid,Lisa %A Dickinson,Anne %A Clark,Miranda %A Kinahan-Goodwin,Darren %A Thomson,Ross %A Phillips,Lucy %A Lewis,Sarah %A Coleman,Tim %+ School of Health Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, United Kingdom, 44 1603 456161, joanne.emery@uea.ac.uk %K smoking cessation %K pregnancy %K nicotine replacement therapy %K treatment adherence measurement %K smartphone app %K questionnaires %K ecological momentary assessment %K mHealth %K mobile health %K smoking %K nicotine %D 2023 %7 7.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Few studies have investigated how to best measure adherence to smoking cessation medications, but continuous usage measures are recommended. Objective: In this first study of its kind, we compared methods for measuring adherence to nicotine replacement therapy (NRT) among pregnant women, investigating the completeness and validity of data collected from daily assessments using a smartphone app versus data collected from retrospective questionnaires. Methods: Women aged ≥16 years who were daily smokers and <25 weeks pregnant were offered smoking-cessation counseling and encouraged to use NRT. For 28 days after setting a quit date (QD), women were asked to report NRT use daily to a smartphone app and to questionnaires administered in person or remotely at 7 and 28 days. For both data collection methods, we provided up to £25 (~US $30) as compensation for the time taken providing research data. Data completeness and NRT use reported to the app and in questionnaires were compared. For each method, we also correlated mean daily nicotine doses reported within 7 days of the QD with Day 7 saliva cotinine concentrations. Results: Of the 438 women assessed for eligibility, 40 participated and 35 accepted NRT. More participants (31/35) submitted NRT usage data to the app by Day 28 (median 25, IQR 11 days) than completed the Day 28 questionnaire (24/35) or either of the two questionnaires (27/35). Data submitted to the app showed a lower reported duration of NRT use compared to that indicated in the questionnaire (median for app 24 days, IQR 10.25; median for questionnaire 28 days, IQR 4.75; P=.007), and there appeared to be specific cases of overreporting to the questionnaire. Mean daily nicotine doses between the QD and Day 7 were lower when calculated using app data (median for app 40 mg, IQR 52.1; median for questionnaire 40 mg, IQR 63.1; P=.001), and some large outliers were evident for the questionnaire. Mean daily nicotine doses, adjusted for cigarettes smoked, were not associated with cotinine concentrations for either method (app rs=0.184, P=.55; questionnaire rs=0.031, P=.92), but the small sample size meant that the analysis was likely underpowered. Conclusions: Daily assessment of NRT use via a smartphone app facilitated more complete data (a higher response rate) than questionnaires, and reporting rates over 28 days were encouraging among pregnant women. App data had better face validity; retrospective questionnaires appeared to overestimate NRT use for some participants. %M 36881452 %R 10.2196/35045 %U https://formative.jmir.org/2023/1/e35045 %U https://doi.org/10.2196/35045 %U http://www.ncbi.nlm.nih.gov/pubmed/36881452 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e41115 %T A Digital Infrastructure for Cardiovascular Patient Care Based on Mobile Health Data and Patient-Reported Outcomes: Concept Details of the Helios TeleWear Project Including Preliminary Experiences %A Leiner,Johannes %A König,Sebastian %A Mouratis,Konstantinos %A Kim,Igor %A Schmitz,Pia %A Joshi,Tanvi %A Schanner,Carolin %A Wohlrab,Lisa %A Hohenstein,Sven %A Pellissier,Vincent %A Nitsche,Anne %A Kuhlen,Ralf %A Hindricks,Gerhard %A Bollmann,Andreas %+ Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, Struempellstrasse 39, Leipzig, 04289, Germany, 49 341865251573, johannes.leiner@helios-gesundheit.de %K mHealth %K wearable %K patient-reported outcomes %K electrocardiogram %K cardiovascular disease %K atrial fibrillation %K telemedicine %K mobile health %K telehealth %D 2023 %7 3.3.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Mobile health (mHealth) approaches are already having a fundamental impact on clinical practice in cardiovascular medicine. A variety of different health apps and wearable devices for capturing health data such as electrocardiograms (ECGs) exist. However, most mHealth technologies focus on distinct variables without integrating patients’ quality of life, and the impact on clinical outcome measures of implementing those digital solutions into cardiovascular health care is still to be determined. Objective: Within this document, we describe the TeleWear project, which was recently initiated as an approach for contemporary patient management integrating mobile-collected health data and the standardized mHealth-guided measurement of patient-reported outcomes (PROs) in patients with cardiovascular disease. Methods: The specifically designed mobile app and clinical frontend form the central elements of our TeleWear infrastructure. Because of its flexible framework, the platform allows far-reaching customization with the possibility to add different mHealth data sources and respective questionnaires (patient-reported outcome measures). Results: With initial focus on patients with cardiac arrhythmias, a feasibility study is currently carried out to assess wearable-recorded ECG and PRO transmission and its evaluation by physicians using the TeleWear app and clinical frontend. First experiences made during the feasibility study yielded positive results and confirmed the platform’s functionality and usability. Conclusions: TeleWear represents a unique mHealth approach comprising PRO and mHealth data capturing. With the currently running TeleWear feasibility study, we aim to test and further develop the platform in a real-world setting. A randomized controlled trial including patients with atrial fibrillation that investigates PRO- and ECG-based clinical management based on the established TeleWear infrastructure will evaluate its clinical benefits. Widening the spectrum of health data collection and interpretation beyond the ECG and use of the TeleWear infrastructure in different patient subcohorts with focus on cardiovascular diseases are further milestones of the project with the ultimate goal to establish a comprehensive telemedical center entrenched by mHealth. %M 36867450 %R 10.2196/41115 %U https://formative.jmir.org/2023/1/e41115 %U https://doi.org/10.2196/41115 %U http://www.ncbi.nlm.nih.gov/pubmed/36867450 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e38732 %T The Vaping and Patterns of e-Cigarette Use Research Study: Protocol for a Web-Based Cohort Study %A Hardesty,Jeffrey J %A Crespi,Elizabeth %A Nian,Qinghua %A Sinamo,Joshua K %A Breland,Alison B %A Eissenberg,Thomas %A Welding,Kevin %A Kennedy,Ryan David %A Cohen,Joanna E %+ Institute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, 2213 McElddery St, Fourth Floor, Baltimore, MD, 21205, United States, 1 410 502 8835, jhardesty@jhu.edu %K internet %K web-based %K cohort %K survey %K e-cigarettes %K electronic nicotine delivery systems %K ENDS %K tobacco %K recruitment %K data collection %K strategies %K lessons learned %K mobile phone %D 2023 %7 2.3.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: In total, 3.2% of American adults report using e-cigarettes every day or some days. The Vaping and Patterns of E-cigarette Use Research (VAPER) Study is a web-based longitudinal survey designed to observe patterns in device and liquid use that suggest the benefits and unintended consequences of potential e-cigarette regulations. The heterogeneity of the e-cigarette devices and liquids on the market, the customizability of the devices and liquids, and the lack of standardized reporting requirements result in unique measurement challenges. Furthermore, bots and survey takers who submit falsified responses are threats to data integrity that require mitigation strategies. Objective: This paper aims to describe the protocols for 3 waves of the VAPER Study and discuss recruitment and data processing experiences and lessons learned, including the benefits and limitations of bot- and fraudulent survey taker–related strategies. Methods: American adults (aged ≥21 years) who use e-cigarettes ≥5 days per week are recruited from up to 404 Craigslist catchment areas covering all 50 states. The questionnaire measures and skip logic are designed to accommodate marketplace heterogeneity and user customization (eg, different skip logic pathways for different device types and customizations). To reduce reliance on self-report data, we also require participants to submit a photo of their device. All data are collected using REDCap (Research Electronic Data Capture; Vanderbilt University). Incentives are US $10 Amazon gift codes delivered by mail to new participants and electronically to returning participants. Those lost to follow-up are replaced. Several strategies are applied to maximize the odds that participants who receive incentives are not bots and are likely to possess an e-cigarette (eg, required identity check and photo of a device). Results: In total, 3 waves of data were collected between 2020 and 2021 (wave 1: n=1209; wave 2: n=1218; wave 3: n=1254). Retention from waves 1 to 2 was 51.94% (628/1209), and 37.55% (454/1209) of the wave 1 sample completed all 3 waves. These data were mostly generalizable to daily e-cigarette users in the United States, and poststratification weights were generated for future analyses. Our data offer a detailed examination of users’ device features and specifications, liquid characteristics, and key behaviors, which can provide more insights into the benefits and unintended consequences of potential regulations. Conclusions: Relative to existing e-cigarette cohort studies, this study methodology has some advantages, including efficient recruitment of a lower-prevalence population and collection of detailed data relevant to tobacco regulatory science (eg, device wattage). The web-based nature of the study requires several bot- and fraudulent survey taker–related risk-mitigation strategies, which can be time-intensive. When these risks are addressed, web-based cohort studies can be successful. We will continue to explore methods for maximizing recruitment efficiency, data quality, and participant retention in subsequent waves. International Registered Report Identifier (IRRID): DERR1-10.2196/38732 %M 36862467 %R 10.2196/38732 %U https://www.researchprotocols.org/2023/1/e38732 %U https://doi.org/10.2196/38732 %U http://www.ncbi.nlm.nih.gov/pubmed/36862467 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e40921 %T Menstrual Tracking Mobile App Review by Consumers and Health Care Providers: Quality Evaluations Study %A Ko,Siyeon %A Lee,Jisan %A An,Doyeon %A Woo,Hyekyung %+ Department of Health Administration, College of Nursing & Health, Kongju National University, 56 Gongjudaehak-ro, Gongju-Si, Chungcheongnam-do, Republic of Korea, 82 10 3350 3486, hkwoo@kongju.ac.kr %K mobile app %K period %K menstrual cycle %K mHealth %K mobile health %K evaluation %K women’s health %K health care provider %K consumer %K menstrual app %K digital health app %K health screening %K consumer satisfaction %D 2023 %7 1.3.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Women’s menstrual cycle is an important component of their overall health. Physiological cycles and associated symptoms can be monitored continuously and used as indicators in various fields. Menstrual apps are accessible and can be used to promote overall female health. However, no study has evaluated these apps’ functionality from both consumers’ and health care providers’ perspectives. As such, the evidence indicating whether the menstrual apps available on the market provide user satisfaction is insufficient. Objective: This study was performed to investigate the key content and quality of menstrual apps from the perspectives of health care providers and consumers. We also analyzed the correlations between health care provider and consumer evaluation scores. On the basis of this analysis, we offer technical and policy recommendations that could increase the usability and convenience of future app. Methods: We searched the Google Play Store and iOS App Store using the keywords “period” and “menstrual cycle” in English and Korean and identified relevant apps. An app that met the following inclusion criteria was selected as a research app: nonduplicate; with >10,000 reviews; last updated ≤180 days ago; relevant to this topic; written in Korean or English; available free of charge; and currently operational. App quality was evaluated by 6 consumers and 4 health care providers using Mobile Application Rating Scale (MARS) and user version of the Mobile Application Rating Scale (uMARS). We then analyzed the correlations among MARS scores, uMARS scores, star ratings, and the number of reviews. Results: Of the 34 apps, 31 (91%) apps could be used to predict the menstrual cycle, and 2 (6%) apps provided information pertinent to health screening. All apps that scored highly in the MARS evaluation offer a symptom logging function and provide the user with personalized notifications. The “Bom Calendar” app had the highest MARS (4.51) and uMARS (4.23) scores. The MARS (2.22) and uMARS (4.15) scores for the “Menstrual calendar—ovulation & pregnancy calendar” app were different. In addition, there was no relationship between MARS and uMARS scores (r=0.32; P=.06). Conclusions: We compared consumer and health care provider ratings for menstrual apps. Continuous monitoring of app quality from consumer and health care provider perspectives is necessary to guide their development and update content. %M 36857125 %R 10.2196/40921 %U https://mhealth.jmir.org/2023/1/e40921 %U https://doi.org/10.2196/40921 %U http://www.ncbi.nlm.nih.gov/pubmed/36857125 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e40736 %T Associations Between Product Type and Intensity of Tobacco and Cannabis Co-use on the Same Day Among Young Adult Smokers: Smartphone-Based Daily-Diary Study %A Nguyen,Nhung %A Thrul,Johannes %A Neilands,Torsten B %A Ling,Pamela M %+ Center for Tobacco Control Research and Education, University of California San Francisco, 530 Parnassus Ave, San Francisco, CA, 94143, United States, 1 415 476 2265, Nhung.Nguyen@ucsf.edu %K tobacco %K cannabis %K substance co-use %K young adults %K intensive longitudinal data %K EMA %K mHealth %K smartphone-based data collection %K data collection %K smartphone data %K substance use %D 2023 %7 20.2.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Co-use of tobacco and cannabis is highly prevalent among young US adults. Same-day co-use of tobacco and cannabis (ie, use of both substances on the same day) may increase the extent of use and negative health consequences among young adults. However, much remains unknown about same-day co-use of tobacco and cannabis, in part due to challenges in measuring this complex behavior. Nuanced understanding of tobacco and cannabis co-use in terms of specific products and intensity (ie, quantity of tobacco and cannabis use within a day) is critical to inform prevention and intervention efforts. Objective: We used a daily-diary data collection method via smartphone to capture occurrence of tobacco and cannabis co-use within a day. We examined (1) whether the same route of administration would facilitate co-use of 2 substances on the same day and (2) whether participants would use more tobacco on a day when they use more cannabis. Methods: This smartphone-based study collected 2891 daily assessments from 147 cigarette smokers (aged 18-26 years, n=76, 51.7% female) during 30 consecutive days. Daily assessments measured type (ie, cigarette, cigarillo, or e-cigarette) and intensity (ie, number of cigarettes or cigarillos smoked or number of times vaping e-cigarettes per day) of tobacco use and type (ie, combustible, vaporized, or edible) and intensity (ie, number of times used per day) of cannabis use. We estimated multilevel models to examine day-level associations between types of cannabis use and each type of tobacco use, as well as day-level associations between intensities of using cannabis and tobacco. All models controlled for demographic covariates, day-level alcohol use, and time effects (ie, study day and weekend vs weekday). Results: Same-day co-use was reported in 989 of the total 2891 daily assessments (34.2%). Co-use of cigarettes and combustible cannabis (885 of the 2891 daily assessments; 30.6%) was most commonly reported. Participants had higher odds of using cigarettes (adjusted odds ratio [AOR] 1.92, 95% CI 1.31-2.81) and cigarillos (AOR 244.29, 95% CI 35.51-1680.62) on days when they used combustible cannabis. Notably, participants had higher odds of using e-cigarettes on days when they used vaporized cannabis (AOR 23.21, 95% CI 8.66-62.24). Participants reported a greater intensity of using cigarettes (AOR 1.35, 95% CI 1.23-1.48), cigarillos (AOR 2.04, 95% CI 1.70-2.46), and e-cigarettes (AOR 1.48, 95% CI 1.16-1.88) on days when they used more cannabis. Conclusions: Types and intensities of tobacco and cannabis use within a day among young adult smokers were positively correlated, including co-use of vaporized products. Prevention and intervention efforts should address co-use and pay attention to all forms of use and timeframes of co-use (eg, within a day or at the same time), including co-use of e-cigarettes and vaporized cannabis, to reduce negative health outcomes. %M 36806440 %R 10.2196/40736 %U https://mhealth.jmir.org/2023/1/e40736 %U https://doi.org/10.2196/40736 %U http://www.ncbi.nlm.nih.gov/pubmed/36806440 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e38439 %T Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study %A Barata,Filipe %A Cleres,David %A Tinschert,Peter %A Iris Shih,Chen-Hsuan %A Rassouli,Frank %A Boesch,Maximilian %A Brutsche,Martin %A Fleisch,Elgar %+ Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8092, Switzerland, 41 44 632 35 0, fbarata@ethz.ch %K cough monitoring %K ward monitoring %K mobile sensing %K machine learning %K convolutional neural network %K COVID-19 %K mobile phone %D 2023 %7 20.2.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. Objective: This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. Methods: Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. Results: In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were −1.0 (95% CI −12.3 to 10.2) and −0.9 (95% CI −6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. Conclusions: The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward. %M 36655551 %R 10.2196/38439 %U https://formative.jmir.org/2023/1/e38439 %U https://doi.org/10.2196/38439 %U http://www.ncbi.nlm.nih.gov/pubmed/36655551 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e44123 %T The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review %A Alamoudi,Doaa %A Breeze,Emma %A Crawley,Esther %A Nabney,Ian %+ Department of Computer Science, University of Bristol, Merchant Venturers’ Building, Woodland Road, Bristol, BS8 1UB, United Kingdom, 44 117 928 3000, d.alamoudi@bristol.ac.uk %K mHealth %K digital %K health %K mental health %K insomnia %K technology %K sleep %K risk %K cardiovascular disease %K diabetes %K men %K mortality %K sleep disorder %K anxiety %K depression %K heart disease %K obesity %K dementia %K sensor %K intervention %K young adult %D 2023 %7 17.2.2023 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Since the era of smartphones started in early 2007, they have steadily turned into an accepted part of our lives. Poor sleep is a health problem that needs to be closely monitored before it causes severe mental health problems, such as anxiety or depression. Sleep disorders (eg, acute insomnia) can also develop to chronic insomnia if not treated early. More specifically, mental health problems have been recognized to have casual links to anxiety, depression, heart disease, obesity, dementia, diabetes, and cancer. Several researchers have used mobile sensors to monitor sleep and to study changes in individual mood that may cause depression and anxiety. Objective: Extreme sleepiness and insomnia not only influence physical health, they also have a significant impact on mental health, such as by causing depression, which has a prevalence of 18% to 21% among young adults aged 16 to 24 in the United Kingdom. The main body of this narrative review explores how passive data collection through smartphone sensors can be used in predicting anxiety and depression. Methods: A narrative review of the English language literature was performed. We investigated the use of smartphone sensors as a method of collecting data from individuals, regardless of whether the data source was active or passive. Articles were found from a search of Google Scholar records (from 2013 to 2020) with keywords including “mobile phone,” “mobile applications,” “health apps,” “insomnia,” “mental health,” “sleep monitoring,” “depression,” “anxiety,” “sleep disorder,” “lack of sleep,” “digital phenotyping,” “mobile sensing,” “smartphone sensors,” and “sleep detector.” Results: The 12 articles presented in this paper explain the current practices of using smartphone sensors for tracking sleep patterns and detecting changes in mental health, especially depression and anxiety over a period of time. Several researchers have been exploring technological methods to detect sleep using smartphone sensors. Researchers have also investigated changes in smartphone sensors and linked them with mental health and well-being. Conclusions: The conducted review provides an overview of the possibilities of using smartphone sensors unobtrusively to collect data related to sleeping pattern, depression, and anxiety. This provides a unique research opportunity to use smartphone sensors to detect insomnia and provide early detection or intervention for mental health problems such as depression and anxiety if insomnia is detected. %M 36800211 %R 10.2196/44123 %U https://mhealth.jmir.org/2023/1/e44123 %U https://doi.org/10.2196/44123 %U http://www.ncbi.nlm.nih.gov/pubmed/36800211 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 7 %N %P e41691 %T A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study %A Han,Dong %A Ding,Eric Y %A Cho,Chaeho %A Jung,Haewook %A Dickson,Emily L %A Mohagheghian,Fahimeh %A Peitzsch,Andrew G %A DiMezza,Danielle %A Tran,Khanh-Van %A McManus,David D %A Chon,Ki H %+ Department of Biomedical Engineering, University of Connecticut, A B Brownwell Building, Room 217, 260 Glenbrook Road, Unit 3247, Storrs, CT, 06269, United States, 1 860 486 5838, dong.han@uconn.edu %K atrial fibrillation %K stroke %K smartwatch app %K smartphone apps %K wearable devices %K user experience %K older adults %K mobile phone %D 2023 %7 13.2.2023 %9 Original Paper %J JMIR Cardio %G English %X Background: The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group. Objective: This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians. Methods: The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server. Results: We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is “easy to use.” For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection. Conclusions: This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system’s usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment. Trial Registration: ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394 International Registered Report Identifier (IRRID): RR2-10.1016/j.cvdhj.2021.07.002 %M 36780211 %R 10.2196/41691 %U https://cardio.jmir.org/2023/1/e41691 %U https://doi.org/10.2196/41691 %U http://www.ncbi.nlm.nih.gov/pubmed/36780211 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e42177 %T Exploring the Cross-cultural Acceptability of Digital Tools for Pain Self-reporting: Qualitative Study %A Ali,Syed Mustafa %A Lee,Rebecca R %A McBeth,John %A James,Ben %A McAlister,Sean %A Chiarotto,Alessandro %A Dixon,William G %A van der Veer,Sabine N %+ Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, United Kingdom, 44 161 275 1644, syedmustafa.ali@manchester.ac.uk %K chronic pain %K pain perception %K cross-cultural comparison %K pain measurement %K mobile app %K mobile phone %D 2023 %7 8.2.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Culture and ethnicity influence how people communicate about their pain. This makes it challenging to develop pain self-report tools that are acceptable across ethnic groups. Objective: We aimed to inform the development of cross-culturally acceptable digital pain self-report tools by better understanding the similarities and differences between ethnic groups in pain experiences and self-reporting needs. Methods: Three web-based workshops consisting of a focus group and a user requirement exercise with people who self-identified as being of Black African (n=6), South Asian (n=10), or White British (n=7) ethnicity were conducted. Results: Across ethnic groups, participants shared similar lived experiences and challenges in communicating their pain to health care professionals. However, there were differences in beliefs about the causes of pain, attitudes toward pain medication, and experiences of how stigma and gender norms influenced pain-reporting behavior. Despite these differences, they agreed on important aspects for pain self-report, but participants from non-White backgrounds had additional language requirements such as culturally appropriate pain terminologies to reduce self-reporting barriers. Conclusions: To improve the cross-cultural acceptability and equity of digital pain self-report tools, future developments should address the differences among ethnic groups on pain perceptions and beliefs, factors influencing pain reporting behavior, and language requirements. %M 36753324 %R 10.2196/42177 %U https://humanfactors.jmir.org/2023/1/e42177 %U https://doi.org/10.2196/42177 %U http://www.ncbi.nlm.nih.gov/pubmed/36753324 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e36764 %T Nominal Versus Realized Costs of Recruiting and Retaining a National Sample of Sexual Minority Adolescents in the United States: Longitudinal Study %A Mamey,Mary Rose %A Schrager,Sheree M %A Rhoades,Harmony %A Goldbach,Jeremy T %+ University of Southern California, 3620 S Vermont Ave, Los Angeles, CA, 90089, United States, 1 949 933 4700, maryrosemamey@gmail.com %K cost analysis %K study recruitment %K longitudinal retention %K sexual minority adolescents %K mobile phone %D 2023 %7 2.2.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Web-based recruitment for research studies is becoming increasingly popular and necessary. When compared with the traditional methods of recruitment, these methods may enable researchers to reach more diverse participants in less time. Social media use is highly prevalent among adolescents, and the unique context of social media may be particularly important for the recruitment of sexual minority young people who would not be captured by traditional methods. Objective: This paper described the details of a national web-based study recruitment approach aimed at sexual minority adolescents across the United States, focusing on important details of this relatively novel approach, including cost, time efficiency, and retention outcomes. Methods: This study recruited sexual minority adolescents aged 14-17 years living in the United States through targeted advertisements on Facebook, Instagram, and YouTube and through respondent-driven sampling (RDS). Potential participants completed eligibility screening surveys and were automatically directed to a baseline survey if they were eligible. After baseline survey completion, additional data checks were implemented, and the remaining participants were contacted for recruitment into a longitudinal study (surveys every 6 months for 3 years). Results: Recruitment lasted 44 weeks, and 9843 participants accessed the initial screening survey, with 2732 (27.76%) meeting the eligibility criteria and completing the baseline survey. Of those, 2558 (93.63%) were determined to have provided nonfraudulent, usable study data and 1076 (39.39%) subsequently enrolled in the longitudinal study. Of the baseline sample, 79.05% (2022/2558) was recruited through Facebook and Instagram, 3.05% (78/2558) through YouTube, and 17.9% (458/2558) through RDS. The average cost of recruiting a participant into the study was US $12.98, but the recruitment cost varied by method or platform, with a realized cost of US $13 per participant on Facebook and Instagram, US $24 on YouTube, and US $10 through RDS. Participant differences (sex assigned at birth, race and ethnicity, sexual orientation, region, and urbanicity) were identified between platforms and methods both in terms of overall number of participants and cost per participant. Facebook and Instagram were the most time efficient (approximately 15 days to recruit 100 participants), whereas RDS was the least time efficient (approximately 70 days to recruit 100 participants). Participants recruited through YouTube were the most likely to be longitudinally retained, followed by Facebook and Instagram, and then RDS. Conclusions: Large differences exist in study recruitment cost and efficiency when using social media and RDS. Demographic, region, and urbanicity differences in recruitment methods highlight the need for attention to demographic diversity when planning and implementing recruitment across platforms. Finally, it is more cost-effective to retain than recruit samples, and this study provided evidence that with thorough screening and data quality practices, social media recruitment can result in diverse, highly involved study populations. %M 36729597 %R 10.2196/36764 %U https://www.jmir.org/2023/1/e36764 %U https://doi.org/10.2196/36764 %U http://www.ncbi.nlm.nih.gov/pubmed/36729597 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e44296 %T Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study %A Park,Junghwan %A Norman,Gregory J %A Klasnja,Predrag %A Rivera,Daniel E %A Hekler,Eric %+ Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, United States, 1 858 429 9370, jup014@ucsd.edu %K mobile health %K mHealth %K physical activity %K walk %K prediction %K classification %K multilayered perceptron %K microrandomized trial %K MRT %K just-in-time adaptive intervention %K JITAI %K prevention %K female %K development %K validation %K application %D 2023 %7 27.1.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical inactivity is associated with numerous health risks, including cancer, cardiovascular disease, type 2 diabetes, increased health care expenditure, and preventable, premature deaths. The majority of Americans fall short of clinical guideline goals (ie, 8000-10,000 steps per day). Behavior prediction algorithms could enable efficacious interventions to promote physical activity by facilitating delivery of nudges at appropriate times. Objective: The aim of this paper is to develop and validate algorithms that predict walking (ie, >5 min) within the next 3 hours, predicted from the participants’ previous 5 weeks’ steps-per-minute data. Methods: We conducted a retrospective, closed cohort, secondary analysis of a 6-week microrandomized trial of the HeartSteps mobile health physical-activity intervention conducted in 2015. The prediction performance of 6 algorithms was evaluated, as follows: logistic regression, radial-basis function support vector machine, eXtreme Gradient Boosting (XGBoost), multilayered perceptron (MLP), decision tree, and random forest. For the MLP, 90 random layer architectures were tested for optimization. Prior 5-week hourly walking data, including missingness, were used for predictors. Whether the participant walked during the next 3 hours was used as the outcome. K-fold cross-validation (K=10) was used for the internal validation. The primary outcome measures are classification accuracy, the Mathew correlation coefficient, sensitivity, and specificity. Results: The total sample size included 6 weeks of data among 44 participants. Of the 44 participants, 31 (71%) were female, 26 (59%) were White, 36 (82%) had a college degree or more, and 15 (34%) were married. The mean age was 35.9 (SD 14.7) years. Participants (n=3, 7%) who did not have enough data (number of days <10) were excluded, resulting in 41 (93%) participants. MLP with optimized layer architecture showed the best performance in accuracy (82.0%, SD 1.1), whereas XGBoost (76.3%, SD 1.5), random forest (69.5%, SD 1.0), support vector machine (69.3%, SD 1.0), and decision tree (63.6%, SD 1.5) algorithms showed lower performance than logistic regression (77.2%, SD 1.2). MLP also showed superior overall performance to all other tried algorithms in Mathew correlation coefficient (0.643, SD 0.021), sensitivity (86.1%, SD 3.0), and specificity (77.8%, SD 3.3). Conclusions: Walking behavior prediction models were developed and validated. MLP showed the highest overall performance of all attempted algorithms. A random search for optimal layer structure is a promising approach for prediction engine development. Future studies can test the real-world application of this algorithm in a “smart” intervention for promoting physical activity. %M 36705954 %R 10.2196/44296 %U https://mhealth.jmir.org/2023/1/e44296 %U https://doi.org/10.2196/44296 %U http://www.ncbi.nlm.nih.gov/pubmed/36705954 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e42866 %T The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement %A de Angel,Valeria %A Adeleye,Fadekemi %A Zhang,Yuezhou %A Cummins,Nicholas %A Munir,Sara %A Lewis,Serena %A Laporta Puyal,Estela %A Matcham,Faith %A Sun,Shaoxiong %A Folarin,Amos A %A Ranjan,Yatharth %A Conde,Pauline %A Rashid,Zulqarnain %A Dobson,Richard %A Hotopf,Matthew %+ Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, E3.08, 3rd Floor East Wing, de Crespigny park, London, SE5 8AF, United Kingdom, 44 20 7848 0002, valeria.de_angel@kcl.ac.uk %K depression %K anxiety %K digital health %K wearable devices %K smartphone %K passive sensing %K mobile health %K mHealth %K digital phenotyping %K mobile phone %D 2023 %7 24.1.2023 %9 Original Paper %J JMIR Ment Health %G English %X Background: Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. Objective: A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. Methods: A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. Results: The overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=−2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=−0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. Conclusions: Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term. %M 36692937 %R 10.2196/42866 %U https://mental.jmir.org/2023/1/e42866 %U https://doi.org/10.2196/42866 %U http://www.ncbi.nlm.nih.gov/pubmed/36692937 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e37716 %T mHealth Apps Targeting Obesity and Overweight in Young People: App Review and Analysis %A Vlahu-Gjorgievska,Elena %A Burazor,Andrea %A Win,Khin Than %A Trajkovik,Vladimir %+ School of Computing and Information Technology, University of Wollongong, Northfields Ave, Wollongong, 2522, Australia, 61 42214606, elenavg@uow.edu.au %K behavior change techniques %K user interface design patterns %K mHealth apps %K obesity %K lifestyle %K mobile app %K mobile health %K mobile phone %D 2023 %7 19.1.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Overweight and obesity have been linked to several serious health problems and medical conditions. With more than a quarter of the young population having weight problems, the impacts of overweight and obesity on this age group are particularly critical. Mobile health (mHealth) apps that support and encourage positive health behaviors have the potential to achieve better health outcomes. These apps represent a unique opportunity for young people (age range 10-24 years), for whom mobile phones are an indispensable part of their everyday living. However, despite the potential of mHealth apps for improved engagement in health interventions, user adherence to these health interventions in the long term is low. Objective: The aims of this research were to (1) review and analyze mHealth apps targeting obesity and overweight and (2) propose guidelines for the inclusion of user interface design patterns (UIDPs) in the development of mHealth apps for obese young people that maximizes the impact and retention of behavior change techniques (BCTs). Methods: A search for apps was conducted in Google Play Store using the following search string: [“best weight loss app for obese teens 2020”] OR [“obesity applications for teens”] OR [“popular weight loss applications”]. The most popular apps available in both Google Play and Apple App Store that fulfilled the requirements within the inclusion criteria were selected for further analysis. The designs of 17 mHealth apps were analyzed for the inclusion of BCTs supported by various UIDPs. Based on the results of the analysis, BCT-UI design guidelines were developed. The usability of the guidelines was presented using a prototype app. Results: The results of our analysis showed that only half of the BCTs are implemented in the reviewed apps, with a subset of those BCTs being supported by UIDPs. Based on these findings, we propose design guidelines that associate the BCTs with UIDPs. The focus of our guidelines is the implementation of BCTs using design patterns that are impactful for the young people demographics. The UIDPs are classified into 6 categories, with each BCT having one or more design patterns appropriate for its implementation. The applicability of the proposed guidelines is presented by mock-ups of the mHealth app “Morphe,” intended for young people (age range 10-24 years). The presented use cases showcase the 5 main functionalities of Morphe: learn, challenge, statistics, social interaction, and settings. Conclusions: The app analysis results showed that the implementation of BCTs using UIDPs is underutilized. The purposed guidelines will help developers in designing mHealth apps for young people that are easy to use and support behavior change. Future steps involve the development and deployment of the Morphe app and the validation of its usability and effectiveness. %M 36656624 %R 10.2196/37716 %U https://mhealth.jmir.org/2023/1/e37716 %U https://doi.org/10.2196/37716 %U http://www.ncbi.nlm.nih.gov/pubmed/36656624 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e40602 %T Engagement With mHealth COVID-19 Digital Biomarker Measurements in a Longitudinal Cohort Study: Mixed Methods Evaluation %A Rennie,Kirsten L %A Lawlor,Emma R %A Yassaee,Arrash %A Booth,Adam %A Westgate,Kate %A Sharp,Stephen J %A Tyrrell,Carina S B %A Aral,Mert %A Wareham,Nicholas J %+ Medical Research Council Epidemiology Unit, University of Cambridge, Box 285, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, United Kingdom, 44 1223 330315, kirsten.rennie@mrc-epid.cam.ac.uk %K smartphone %K apps %K engagement %K COVID-19 %K pandemic %K cohort studies %K epidemiology %K mobile health %K digital health %K biomarker %K mobile phone %D 2023 %7 13.1.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic accelerated the interest in implementing mobile health (mHealth) in population-based health studies, but evidence is lacking on engagement and adherence in studies. We conducted a fully remote study for ≥6 months tracking COVID-19 digital biomarkers and symptoms using a smartphone app nested within an existing cohort of adults. Objective: We aimed to investigate participant characteristics associated with initial and sustained engagement in digital biomarker collection from a bespoke smartphone app and if engagement changed over time or because of COVID-19 factors and explore participants’ reasons for consenting to the smartphone substudy and experiences related to initial and continued engagement. Methods: Participants in the Fenland COVID-19 study were invited to the app substudy from August 2020 to October 2020 until study closure (April 30, 2021). Participants were asked to complete digital biomarker modules (oxygen saturation, body temperature, and resting heart rate [RHR]) and possible COVID-19 symptoms in the app 3 times per week. Participants manually entered the measurements, except RHR that was measured using the smartphone camera. Engagement was categorized by median weekly frequency of completing the 3 digital biomarker modules (categories: 0, 1-2, and ≥3 times per week). Sociodemographic and health characteristics of those who did or did not consent to the substudy and by engagement category were explored. Semistructured interviews were conducted with 35 participants who were purposively sampled by sex, age, educational attainment, and engagement category, and data were analyzed thematically; 63% (22/35) of the participants consented to the app substudy, and 37% (13/35) of the participants did not consent. Results: A total of 62.61% (2524/4031) of Fenland COVID-19 study participants consented to the app substudy. Of those, 90.21% (2277/2524) completed the app onboarding process. Median time in the app substudy was 34.5 weeks (IQR 34-37) with no change in engagement from 0 to 3 months or 3 to 6 months. Completion rates (≥1 per week) across the study between digital biomarkers were similar (RHR: 56,517/77,664, 72.77%; temperature: 56,742/77,664, 73.06%; oxygen saturation: 57,088/77,664, 73.51%). Older age groups and lower managerial and intermediate occupations were associated with higher engagement, whereas working, being a current smoker, being overweight or obese, and high perceived stress were associated with lower engagement. Continued engagement was facilitated through routine and personal motivation, and poor engagement was caused by user error and app or equipment malfunctions preventing data input. From these results, we developed key recommendations to improve engagement in population-based mHealth studies. Conclusions: This mixed methods study demonstrated both high initial and sustained engagement in a large mHealth COVID-19 study over a ≥6-month period. Being nested in a known cohort study enabled the identification of participant characteristics and factors associated with engagement to inform future applications in population-based health research. %M 36194866 %R 10.2196/40602 %U https://www.jmir.org/2023/1/e40602 %U https://doi.org/10.2196/40602 %U http://www.ncbi.nlm.nih.gov/pubmed/36194866 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e42053 %T The PTSD Family Coach App in Veteran Family Members: Pilot Randomized Controlled Trial %A van Stolk-Cooke,Katherine %A Wielgosz,Joseph %A Hallenbeck,Haijing Wu %A Chang,Andrew %A Rosen,Craig %A Owen,Jason %A Kuhn,Eric %+ Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Palo Alto, CA, 94304, United States, 1 860 335 2021, cvscooke@stanford.edu %K posttraumatic stress disorder %K PTSD %K veterans %K family %K mobile apps %D 2023 %7 5.1.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Posttraumatic stress disorder (PTSD) among US military veterans can adversely impact their concerned significant others (CSOs; eg, family members and romantic partners). Mobile apps can be tailored to support CSO mental health through psychoeducation, coping skills, and stress monitoring. Objective: This study assessed the feasibility, acceptability, and potential efficacy of PTSD Family Coach 1.0, a free, publicly available app that includes psychoeducation, stress management tools, self-assessments, and features for connecting to alternative supports, compared with a psychoeducation-only version of the app for cohabitating CSOs of veterans with PTSD. Methods: A total of 200 participants with an average age of 39 (SD 8.44) years, primarily female (193/200, 97%), and White (160/200, 80%) were randomized to self-guided use of either PTSD Family Coach 1.0 (n=104) or a psychoeducation-only app (n=96) for 4 weeks. Caregiver burden, stress, depression, anxiety, beliefs about treatment, CSO self-efficacy, and relationship functioning assessed using measures of dyadic adjustment, social constraints, and communication danger signs were administered via a web survey at baseline and after treatment. User satisfaction and app helpfulness were assessed after treatment. Data were analyzed using linear mixed methods. Results: Overall, 50.5% (101/200) of randomized participants used their allocated app. Participants found PTSD Family Coach 1.0 somewhat satisfying (mean 4.88, SD 1.11) and moderately helpful (mean 2.99, SD 0.97) to use. Linear mixed effects models revealed no significant differences in outcomes by condition for caregiver burden (P=.45; Cohen d=0.1, 95% CI −0.2 to 0.4), stress (P=.64; Cohen d=0.1, 95% CI −0.4 to 0.6), depression (P=.93; Cohen d= 0.0, 95% CI −0.3 to 0.3), anxiety (P=.55; Cohen d=−0.1, 95% CI −0.4 to 0.2), beliefs about treatment (P=.71; Cohen d=0.1, 95% CI −0.2 to 0.3), partner self-efficacy (P=.59; Cohen d=−0.1, 95% CI −0.4 to 0.2), dyadic adjustment (P=.08; Cohen d=−0.2, 95% CI −0.5 to 0.0), social constraints (P=.05; Cohen d=0.3, 95% CI 0.0-0.6), or communication danger signs (P=.90; Cohen d=−0.0, 95% CI −0.3 to 0.3). Post hoc analyses collapsing across conditions revealed a significant between-group effect on stress for app users versus nonusers (β=−3.62; t281=−2.27; P=.02). Conclusions: Approximately half of the randomized participants never used their allocated app, and participants in the PTSD Family Coach 1.0 condition only opened the app approximately 4 times over 4 weeks, suggesting limitations to this app version’s feasibility. PTSD Family Coach 1.0 users reported moderately favorable impressions of the app, suggesting preliminary acceptability. Regarding efficacy, no significant difference was found between PTSD Family Coach 1.0 users and psychoeducation app users across any outcome of interest. Post hoc analyses suggested that app use regardless of treatment condition was associated with reduced stress. Further research that improves app feasibility and establishes efficacy in targeting the domains most relevant to CSOs is warranted. Trial Registration: ClinicalTrials.gov NCT02486705; https://clinicaltrials.gov/ct2/show/NCT02486705 %M 36602852 %R 10.2196/42053 %U https://formative.jmir.org/2023/1/e42053 %U https://doi.org/10.2196/42053 %U http://www.ncbi.nlm.nih.gov/pubmed/36602852 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 4 %P e40210 %T Acceptability and Feasibility of Wearable Transdermal Alcohol Sensors: Systematic Review %A Brobbin,Eileen %A Deluca,Paolo %A Hemrage,Sofia %A Drummond,Colin %+ Department of Addictions, Institute of Psychiatry, Psychology & Neuroscience, King's College London, Addiction Science Building, 4 Windsor Walk, London, SE5 8BB, United Kingdom, 44 0207 836 545, eileen.brobbin@kcl.ac.uk %K alcohol consumption %K alcohol monitoring %K digital technology %K transdermal alcohol sensors %K wearables %K acceptability %K feasibility %K monitoring %K sensors %K real-time feedback %K health promotion %K alcohol intake %D 2022 %7 23.12.2022 %9 Review %J JMIR Hum Factors %G English %X Background: Transdermal alcohol sensors (TASs) have the potential to be used to monitor alcohol consumption objectively and continuously. These devices can provide real-time feedback to the user, researcher, or health professional and measure alcohol consumption and peaks of use, thereby addressing some of the limitations of the current methods, including breathalyzers and self-reports. Objective: This systematic review aims to evaluate the acceptability and feasibility of the currently available TAS devices. Methods: A systematic search was conducted in CINAHL, EMBASE, Google Scholar, MEDLINE, PsycINFO, PubMed, and Scopus bibliographic databases in February 2021. Two members of our study team independently screened studies for inclusion, extracted data, and assessed the risk of bias. The study’s methodological quality was appraised using the Mixed Methods Appraisal Tool. The primary outcome was TAS acceptability. The secondary outcome was feasibility. The data are presented as a narrative synthesis. Results: We identified and analyzed 22 studies. Study designs included laboratory- and ambulatory-based studies, mixed designs, randomized controlled trials, and focus groups, and the length the device was worn ranged from days to weeks. Although views on TASs were generally positive with high compliance, some factors were indicated as potential barriers and there are suggestions to overcome these. Conclusions: There is a lack of research investigating the acceptability and feasibility of TAS devices as a tool to monitor alcohol consumption in clinical and nonclinical populations. Although preliminary evidence suggests their potential in short-term laboratory-based studies with volunteers, more research is needed to establish long-term daily use with other populations, specifically, in the clinical and the criminal justice system. Trial Registration: PROSPERO CRD42021231027; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=231027 %M 36563030 %R 10.2196/40210 %U https://humanfactors.jmir.org/2022/4/e40210 %U https://doi.org/10.2196/40210 %U http://www.ncbi.nlm.nih.gov/pubmed/36563030 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 12 %P e38479 %T Using Continuous Glucose Monitoring to Detect and Intervene on Dietary Restriction in Individuals With Binge Eating: The SenseSupport Withdrawal Design Study %A Juarascio,Adrienne S %A Srivastava,Paakhi %A Presseller,Emily K %A Lin,Mandy %A Patarinski,Anna G G %A Manasse,Stephanie M %A Forman,Evan M %+ Center for Weight, Eating, and Lifestyle Science, Drexel University, 3201 Chestnut St., Philadelphia, PA, 19104, United States, 1 215 553 7154, asj32@drexel.edu %K binge eating %K loss-of-control eating %K continuous glucose monitoring %K mobile phone %D 2022 %7 14.12.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Dietary restraint is a key factor for maintaining engagement in binge eating among individuals with binge eating disorder (BED) and bulimia nervosa (BN). Reducing dietary restraint is a mechanism of change in cognitive behavioral therapy (CBT) for individuals with BN and BED. However, many individuals who undergo CBT fail to adequately reduce dietary restraint during treatment, perhaps owing to difficulty in using treatment skills (eg, regular eating) to reduce dietary restraint during their daily lives. The SenseSupport system, a novel just-in-time, adaptive intervention (JITAI) system that uses continuous glucose monitoring to detect periods of dietary restraint, may improve CBT to reduce dietary restraint during treatment by providing real-time interventions. Objective: This study aimed to describe the feasibility, acceptability, and initial evaluation of SenseSupport. We presented feasibility, acceptability, target engagement, and initial treatment outcome data from a small trial using an ABAB (A=continuous glucose monitoring data sharing and JITAIs-Off, B=continuous glucose monitoring data sharing and JITAIs-On) design (in which JITAIs were turned on for 2 weeks and then turned off for 2 weeks throughout the treatment). Methods: Participants (N=30) were individuals with BED or BN engaging in ≥3 episodes of ≥5 hours without eating per week at baseline. Participants received 12 sessions of CBT and wore continuous glucose monitors to detect eating behaviors and inform the delivery of JITAIs. Participants completed 4 assessments and reported eating disorder behaviors, dietary restraint, and barriers to app use weekly throughout treatment. Results: Retention was high (25/30, 83% after treatment). However, the rates of continuous glucose monitoring data collection were low (67.4% of expected glucose data were collected), and therapists and participants reported frequent app-related issues. Participants reported that the SenseSupport system was comfortable, minimally disruptive, and easy to use. The only form of dietary restraint that decreased significantly more rapidly during JITAIs-On periods relative to JITAIs-Off periods was the desire for an empty stomach (t43=1.69; P=.049; Cohen d=0.25). There was also a trend toward greater decrease in overall restraint during JITAs-On periods compared with JITAIs-Off periods, but these results were not statistically significant (t43=1.60; P=.06; Cohen d=0.24). There was no significant difference in change in the frequency of binge eating during JITAIs-On periods compared with JITAIs-Off periods (P=.23). Participants demonstrated clinically significant, large decreases in binge eating (t24=10.36; P<.001; Cohen d=2.07), compensatory behaviors (t24=3.40; P=.001; Cohen d=0.68), and global eating pathology (t24=6.25; P<.001; Cohen d=1.25) from pre- to posttreatment. Conclusions: This study describes the successful development and implementation of the first intervention system combining passive continuous glucose monitors and JITAIs to augment CBT for binge-spectrum eating disorders. Despite the lower-than-anticipated collection of glucose data, the high acceptability and promising treatment outcomes suggest that the SenseSupport system warrants additional investigation via future, fully powered clinical trials. Trial Registration: ClinicalTrials.gov NCT04126694; https://clinicaltrials.gov/ct2/show/NCT04126694 %M 36515992 %R 10.2196/38479 %U https://formative.jmir.org/2022/12/e38479 %U https://doi.org/10.2196/38479 %U http://www.ncbi.nlm.nih.gov/pubmed/36515992 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 12 %P e38785 %T The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review %A Girousse,Eva %A Vuillerme,Nicolas %+ AGEIS, Université Grenoble Alpes, La Tronche, Grenoble, 38706, France, 33 4 7663 7104, nicolas.vuillerme@univ-grenoble-alpes.fr %K smartphones %K anxiety %K depression %K college students %K smartphone %K data %K monitor %K students %K systematic review %K public health %K mental conditions %K disorder %K strength %K limitation %D 2022 %7 14.12.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: College students are particularly at risk of depression and anxiety. These disorders have a serious impact on public health and affect patients’ daily lives. The potential for using smartphones to monitor these mental conditions, providing passively collected physiological and behavioral data, has been reported among the general population. However, research on the use of passive smartphone data to monitor anxiety and depression among specific populations of college students has never been reviewed. Objective: This review’s objectives are (1) to provide an overview of the use of passive smartphone data to monitor depression and anxiety among college students, given their specific type of smartphone use and living setting, and (2) to evaluate the different methods used to assess those smartphone data, including their strengths and limitations. Methods: This review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent investigators will review English-language, full-text, peer-reviewed papers extracted from PubMed and Web of Science that measure passive smartphone data and levels of depression or anxiety among college students. A preliminary search was conducted in February 2022 as a proof of concept. Results: Our preliminary search identified 115 original articles, 8 of which met our eligibility criteria. Our planned full study will include an article selection flowchart, tables, and figures representing the main information extracted on the use of passive smartphone data to monitor anxiety and depression among college students. Conclusions: The planned review will summarize the published research on using passive smartphone data to monitor anxiety and depression among college students. The review aims to better understand whether and how passive smartphone data are associated with indicators of depression and anxiety among college students. This could be valuable in order to provide a digital solution for monitoring mental health issues in this specific population by enabling easier identification and follow-up of the patients. Trial Registration: PROSPERO CRD42022316263; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=316263 International Registered Report Identifier (IRRID): DERR1-10.2196/38785 %M 36515983 %R 10.2196/38785 %U https://www.researchprotocols.org/2022/12/e38785 %U https://doi.org/10.2196/38785 %U http://www.ncbi.nlm.nih.gov/pubmed/36515983 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 12 %P e42225 %T The Persian Version of the Mobile Application Rating Scale (MARS-Fa): Translation and Validation Study %A Barzegari,Saeed %A Sharifi Kia,Ali %A Bardus,Marco %A Stoyanov,Stoyan R %A GhaziSaeedi,Marjan %A Rafizadeh,Mouna %+ Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston campus, Edgbaston, B15 2TT, United Kingdom, 44 0121 414 3344, m.bardus@bham.ac.uk %K mobile application rating scale %K Farsi %K mobile apps %K validation %K smartphone addiction %K Persian %K Iran %K development %K mobile health %K mHealth %K scale %K validate %K reliability %K measurement tool %K assessment tool %D 2022 %7 5.12.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Approximately 110 million Farsi speakers worldwide have access to a growing mobile app market. Despite restrictions and international sanctions, Iran’s internal mobile health app market is growing, especially for Android-based apps. However, there is a need for guidelines for developing health apps that meet international quality standards. There are also no tools in Farsi that assess health app quality. Developers and researchers who operate in Farsi could benefit from such quality assessment tools to improve their outputs. Objective: This study aims to translate and culturally adapt the Mobile Application Rating Scale in Farsi (MARS-Fa). This study also evaluates the validity and reliability of the newly developed MARS-Fa tool. Methods: We used a well-established method to translate and back translate the MARS-Fa tool with a group of Iranian and international experts in Health Information Technology and Psychology. The final translated version of the tool was tested on a sample of 92 apps addressing smartphone addiction. Two trained reviewers completed an independent assessment of each app in Farsi and English. We reported reliability and construct validity estimates for the objective scales (engagement, functionality, aesthetics, and information quality). Reliability was based on the evaluation of intraclass correlation coefficients, Cronbach α and Spearman-Brown split-half reliability indicators (for internal consistency), as well as Pearson correlations for test-retest reliability. Construct validity included convergent and discriminant validity (through item-total correlations within the objective scales) and concurrent validity using Pearson correlations between the objective and subjective scores. Results: After completing the translation and cultural adaptation, the MARS-Fa tool was used to assess the selected apps for smartphone addiction. The MARS-Fa total scale showed good interrater reliability (intraclass correlation coefficient=0.83, 95% CI 0.74-0.89) and good internal consistency (Cronbach α=.84); Spearman-Brown split-half reliability for both raters was 0.79 to 0.93. The instrument showed excellent test-retest reliability (r=0.94). The correlations among the MARS-Fa subdomains and the total score were all significant and above r=0.40, suggesting good convergent and discriminant validity. The MARS-Fa was positively and significantly correlated with subjective quality (r=0.90, P<.001), and so were the objective subdomains of engagement (r=0.85, P<.001), information quality (r=0.80, P<.001), aesthetics (r=0.79, P<.001), and functionality (r=0.57, P<.001), indicating concurrent validity. Conclusions: The MARS-Fa is a reliable and valid instrument to assess mobile health apps. This instrument could be adopted by Farsi-speaking researchers and developers who want to evaluate the quality of mobile apps. While we tested the tool with a sample of apps addressing smartphone addiction, the MARS-Fa could assess other domains or issues since the Mobile App Rating Scale has been used to rate apps in different contexts and languages. %M 36469402 %R 10.2196/42225 %U https://formative.jmir.org/2022/12/e42225 %U https://doi.org/10.2196/42225 %U http://www.ncbi.nlm.nih.gov/pubmed/36469402 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 11 %P e40339 %T The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study %A Watanabe,Kazuhiro %A Tsutsumi,Akizumi %+ Department of Public Health, Kitasato University School of Medicine, 1-15-1 Kitazato, Minami-ku, Sagamihara, 252-0374, Japan, 81 42 778 9352, kzwatanabe-tky@umin.ac.jp %K digital biomarkers %K mobile health %K mental health %K psychological distress %K depression %K anxiety %K physical activity %D 2022 %7 30.11.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Digital data on physical activity are useful for self-monitoring and preventing depression and anxiety. Although previous studies have reported machine or deep learning models that use physical activity for passive monitoring of depression and anxiety, there are no models for workers. The working population has different physical activity patterns from other populations, which is based on commuting, holiday patterns, physical demands, occupations, and industries. These working conditions are useful in optimizing the model used in predicting depression and anxiety. Further, recurrent neural networks increase predictive accuracy by using previous inputs on physical activity, depression, and anxiety. Objective: This study evaluated the performance of a deep learning model optimized for predicting depression and anxiety in workers. Psychological distress was considered a depression and anxiety indicator. Methods: A 2-week longitudinal study was conducted with workers in urban areas in Japan. Absent workers were excluded. In a daily survey, psychological distress was measured using a self-reported questionnaire. As features, activity time by intensity was determined using the Google Fit application. Additionally, we measured age, gender, occupations, employment status, work shift types, working hours, and whether the response date was a working or nonworking day. A deep learning model, using long short-term memory, was developed and validated to predict psychological distress the next day, using features of the previous day. Further, a 5-fold cross-validation method was used to evaluate the performance of the aforementioned model. As the primary indicator of performance, classification accuracy for the severity of the psychological distress (light, subthreshold, and severe) was considered. Results: A total of 1661 days of supervised data were obtained from 236 workers, who were aged between 20 and 69 years. The overall classification accuracy for psychological distress was 76.3% (SD 0.04%). The classification accuracy for severe-, subthreshold-, and light-level psychological distress was 51.1% (SD 0.05%), 60.6% (SD 0.05%), and 81.6% (SD 0.04%), respectively. The model predicted a light-level psychological distress the next day after the participants had been involved in 3 peaks of activity (in the morning, noon, and evening) on the previous day. Lower activity levels were predicted as subthreshold- and severe-level psychological distress. Different predictive results were observed on the basis of occupations and whether the previous day was a working or nonworking day. Conclusions: The developed deep learning model showed a similar performance as in previous studies and, in particular, high accuracy for light-level psychological distress. Working conditions and long short-term memory were useful in maintaining the model performance for monitoring depression and anxiety, using digitally recorded physical activity in workers. The developed model can be implemented in mobile apps and may further be practically used by workers to self-monitor and maintain their mental health state. %M 36449342 %R 10.2196/40339 %U https://formative.jmir.org/2022/11/e40339 %U https://doi.org/10.2196/40339 %U http://www.ncbi.nlm.nih.gov/pubmed/36449342 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 11 %P e42146 %T Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review %A Winkler,Tanita %A Büscher,Rebekka %A Larsen,Mark Erik %A Kwon,Sam %A Torous,John %A Firth,Joseph %A Sander,Lasse B %+ Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Hebelstraße 29, Freiburg, 79104, Germany, 49 7612035519, Lasse.Sander@mps.uni-freiburg.de %K suicide prediction %K passive sensing %K review %K systematic review %K sensors %K suicidal thoughts and behaviors %K digital markers %K behavioral markers %D 2022 %7 29.11.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Suicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STBs) is key to preventing attempts. We discuss passive sensing of digital and behavioral markers to enhance the detection and prediction of STBs. Objective: The paper presents the protocol for a systematic review that aims to summarize existing research on passive sensing of STBs and evaluate whether the STB prediction can be improved using passive sensing compared to prior prediction models. Methods: A systematic search will be conducted in the scientific databases MEDLINE, PubMed, Embase, PsycINFO, and Web of Science. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STBs. The predictive value of passive sensing will be the primary outcome. The practical implications and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STBs. Results: The review process started in July 2022 with data extraction in September 2022. Results are expected in December 2022. Conclusions: Despite intensive research efforts, the ability to predict STBs is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve STB prediction. Future research will be stimulated since gaps in the current literature will be identified and promising next steps toward clinical implementation will be outlined. Trial Registration: OSF Registries osf-registrations-hzxua-v1; https://osf.io/hzxua International Registered Report Identifier (IRRID): DERR1-10.2196/42146 %M 36445737 %R 10.2196/42146 %U https://www.researchprotocols.org/2022/11/e42146 %U https://doi.org/10.2196/42146 %U http://www.ncbi.nlm.nih.gov/pubmed/36445737 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 11 %P e39322 %T Analyzing Person-Place Interactions During Walking Episodes: Innovative Ambulatory Assessment Approach of Walking-Triggered e-Diaries %A Kanning,Martina %A Bollenbach,Lukas %A Schmitz,Julian %A Niermann,Christina %A Fina,Stefan %+ Department of Sport Science, University of Konstanz, Universitätsstraße 10, Konstanz, 78464, Germany, 49 7531 883651, martina.kanning@uni-konstanz.de %K ecological momentary assessment %K active transport %K socio-ecological model %K subjective well-being %K mental health %K urban health %K GEMA %K geographically explicit ecological momentary assessment %K behaviour change %K walking %K experience %K environment %K monitoring %K activity %K tracking %K e-diary %K assessment %D 2022 %7 25.11.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Walking behavior is positively associated with physiological and mental health as much evidence has already shown. Walking is also becoming a critical issue for health promotion in urban environments as it is the most often used form of active mobility and helps to replace carbon dioxide emissions from motorized forms of transport. It therefore contributes to mitigate the negative effects of climate change and heat islands within cities. However, to promote walking among urban dwellers and to utilize its health-enhancing potential, we need to know more about the way in which physical and social environments shape individual experiences during walking episodes. Such person-place interactions could not adequately be analyzed in former studies owing to methodological constraints. Objective: This study introduces walking-triggered e-diaries as an innovative ambulatory assessment approach for time-varying associations, and investigates its accuracy with 2 different validation strategies. Methods: The walking trigger consists of a combination of movement acceleration via an accelerometer and mobile positioning of the cellphone via GPS and transmission towers to track walking activities. The trigger starts an e-diary whenever a movement acceleration exceeds a predetermined threshold and participants' locations are identified as nonstationary outside a predefined place of residence. Every 420 (±300) seconds, repeated e-diaries were prompted as long as the trigger conditions were met. Data were assessed on 10 consecutive days. First, to investigate accuracy, we reconstructed walking routes and calculated a percentage score for all triggered prompts in relation to all walking routes where a prompt could have been triggered. Then, to provide data about its specificity, we used momentary self-reports and objectively assessed movement behavior to describe activity levels before the trigger prompted an e-diary. Results: Data of 67 participants could be analyzed and the walking trigger led to 3283 e-diary prompts, from which 2258 (68.8%) were answered. Regarding accuracy, the walking trigger prompted an e-diary on 732 of 842 (86.9%) reconstructed walking routes. Further, in 838 of 1206 (69.5%) triggered e-diaries, participants self-reported that they were currently walking outdoors. Steps and acceleration movement was higher during these self-reported walking episodes than when participants denied walking outdoors (steps: 106 vs 32; acceleration>0.2 g in 58.4% vs 19% of these situations). Conclusions: Accuracy analysis revealed that walking-triggered e-diaries are suitable to collect different data of individuals' current experiences in situations in which a person walks outdoors. Combined with environmental data, such an approach increases knowledge about person-place interactions and provides the possibility to gain knowledge about user preferences for health-enhancing urban environments. From a methodological viewpoint, however, specificity analysis showed how changes in trigger conditions (eg, increasing the threshold for movement acceleration) lead to changes in accuracy. %M 36427231 %R 10.2196/39322 %U https://formative.jmir.org/2022/11/e39322 %U https://doi.org/10.2196/39322 %U http://www.ncbi.nlm.nih.gov/pubmed/36427231 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 11 %P e42853 %T Digital Pattern Recognition for the Identification of Various Hypospadias Parameters via an Artificial Neural Network: Protocol for the Development and Validation of a System and Mobile App %A Wahyudi,Irfan %A Utomo,Chandra Prasetyo %A Djauzi,Samsuridjal %A Fathurahman,Muhamad %A Situmorang,Gerhard Reinaldi %A Rodjani,Arry %A Yonathan,Kevin %A Santoso,Budi %+ Department of Urology, Faculty of Medicine, Universitas Indonesia, Jl Dipenogoro No 71, Jakarta, 10430, Indonesia, 62 217867222, irf.wahyudi2011@gmail.com %K artificial intelligence %K digital recognition %K hypospadias %K machine learning %D 2022 %7 25.11.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Hypospadias remains the most prevalent congenital abnormality in boys worldwide. However, the limited infrastructure and number of pediatric urologists capable of diagnosing and managing the condition hinder the management of hypospadias in Indonesia. The use of artificial intelligence and image recognition is thought to be beneficial in improving the management of hypospadias cases in Indonesia. Objective: We aim to develop and validate a digital pattern recognition system and a mobile app based on an artificial neural network to determine various parameters of hypospadias. Methods: Hypospadias and normal penis images from an age-matched database will be used to train the artificial neural network. Images of 3 aspects of the penis (ventral, dorsal, and lateral aspects, which include the glans, shaft, and scrotum) will be taken from each participant. The images will be labeled with the following hypospadias parameters: hypospadias status, meatal location, meatal shape, the quality of the urethral plate, glans diameter, and glans shape. The data will be uploaded to train the image recognition model. Intrarater and interrater analyses will be performed, using the test images provided to the algorithm. Results: Our study is at the protocol development stage. A preliminary study regarding the system’s development and feasibility will start in December 2022. The results of our study are expected to be available by the end of 2023. Conclusions: A digital pattern recognition system using an artificial neural network will be developed and designed to improve the diagnosis and management of patients with hypospadias, especially those residing in regions with limited infrastructure and health personnel. International Registered Report Identifier (IRRID): PRR1-10.2196/42853 %M 36427238 %R 10.2196/42853 %U https://www.researchprotocols.org/2022/11/e42853 %U https://doi.org/10.2196/42853 %U http://www.ncbi.nlm.nih.gov/pubmed/36427238 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 11 %P e37683 %T Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review %A Guo,Christine C %A Chiesa,Patrizia Andrea %A de Moor,Carl %A Fazeli,Mir Sohail %A Schofield,Thomas %A Hofer,Kimberly %A Belachew,Shibeshih %A Scotland,Alf %+ Biogen Digital Health International GmbH, Biogen Inc, Neuhofstrasse 30, Baar, 6340, Switzerland, 41 79 414 69 23, alf.scotland@biogen.com %K motor function %K medical devices %K computers %K handheld %K smartwatch %K smartphone %K mobility %K wearable electronic devices %K Parkinson disease %K Parkinsonian disorders %K gait %K mobile phone %D 2022 %7 21.11.2022 %9 Review %J J Med Internet Res %G English %X Background: With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes. Objective: We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations. Methods: A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers. Results: A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care–related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care–related device, personal electronic devices, and entertainment consoles). Conclusions: Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change. %M 36409538 %R 10.2196/37683 %U https://www.jmir.org/2022/11/e37683 %U https://doi.org/10.2196/37683 %U http://www.ncbi.nlm.nih.gov/pubmed/36409538 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 11 %P e37501 %T Digital Connectedness in the Jackson Heart Study: Cross-sectional Study %A Anugu,Pramod %A Ansari,Md Abu Yusuf %A Min,Yuan-I %A Benjamin,Emelia J %A Murabito,Joanne %A Winters,Karen %A Turner,Erica %A Correa,Adolfo %+ University of Mississippi Medical Center, 2500 N State St, Jackson, MS, 39216, United States, 1 6018155771, panugu@umc.edu %K teleresearch %K mobile technology %K cardiovascular disease %K Jackson Heart Study %K mobile phone %D 2022 %7 21.11.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Although new approaches for data collection, such as mobile technology and teleresearch, have demonstrated new opportunities for the conduct of more timely and less costly surveys in community-based studies, literature on the feasibility of conducing cardiovascular disease research using mobile health (mHealth) platforms among middle-aged and older African Americans has been limited. Objective: The purpose of this study was to contribute to the knowledge regarding the penetrance of internet and mobile technologies, such as cellphones or smartphones in existing large cohort studies of cardiovascular disease. Methods: A digital connectedness survey was conducted in the Jackson Heart Study (JHS), a Mississippi-based African American cohort study, as part of the annual follow-up calls with participants from July 2017 to February 2019. Results: Of the 4024 participants contacted, 2564 (63.7%) completed the survey. Among survey respondents, 2262 (88.2%) reported use of internet or cellphone, and 1593 (62.1%) had a smartphone. Compared to nonusers (n=302), internet or cellphone users (n=2262) were younger (mean age 80.1, SD 8.0 vs 68.2, SD 11.3 years), more likely to be affluent (n=778, 40.1% vs n=39, 15.4%), and had greater than high school education (n=1636, 72.5% vs n=85, 28.1%). Internet or cellphone users were less likely to have cardiovascular disease history compared to nonusers (136/2262, 6.6% vs 41/302, 15.8%). The prevalence of current smoking and average BMI were similar between internet or cellphone users and nonusers. Among internet or cellphone users, 1316 (58.3%) reported use of email, 504 (22.3%) reported use of apps to track or manage health, and 1269 (56.1%) expressed interest in using JHS-developed apps. Conclusions: Our findings suggest that it is feasible to use mHealth technologies to collect survey data among African Americans already enrolled in a longitudinal study. Our findings also highlight the need for more efforts to reduce the age and education divide in access and use of internet and smartphones for tracking health and research in African American communities. %M 36409531 %R 10.2196/37501 %U https://www.jmir.org/2022/11/e37501 %U https://doi.org/10.2196/37501 %U http://www.ncbi.nlm.nih.gov/pubmed/36409531 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 11 %P e40797 %T Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review %A Lee,Peter %A Kim,Heepyung %A Zitouni,M Sami %A Khandoker,Ahsan %A Jelinek,Herbert F %A Hadjileontiadis,Leontios %A Lee,Uichin %A Jeong,Yong %+ Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro Yuseong gu, Daejeon, 34141, Republic of Korea, 82 423504324, yong@kaist.ac.kr %K Internet of Things %K IoT %K sensor technology %K smart helmet %K smart sensor %K wearable device %K mobile phone %D 2022 %7 15.11.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: As a form of the Internet of Things (IoT)–gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. Objective: This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? Methods: We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. Results: Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. Conclusions: A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality. %M 36378505 %R 10.2196/40797 %U https://mhealth.jmir.org/2022/11/e40797 %U https://doi.org/10.2196/40797 %U http://www.ncbi.nlm.nih.gov/pubmed/36378505 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 11 %P e36696 %T Health-Related Indicators Measured Using Earable Devices: Systematic Review %A Choi,Jin-Young %A Jeon,Seonghee %A Kim,Hana %A Ha,Jaeyoung %A Jeon,Gyeong-suk %A Lee,Jeong %A Cho,Sung-il %+ Department of Public Health Science, Graduate School of Public Health, Seoul National University, Bldg 220, Rm 703., 1 Gwanak-ro, Gwanak-gu., Seoul, 08826, Republic of Korea, 82 2 880 2717, persontime@hotmail.com %K digital public health %K earable %K wearable %K biomarker %K health status %K disease monitoring %K prevention strategy %K Internet of Things %K systematic review %K mobile phone %D 2022 %7 15.11.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Earable devices are novel, wearable Internet of Things devices that are user-friendly and have potential applications in mobile health care. The position of the ear is advantageous for assessing vital status and detecting diseases through reliable and comfortable sensing devices. Objective: Our study aimed to review the utility of health-related indicators derived from earable devices and propose an improved definition of disease prevention. We also proposed future directions for research on the health care applications of earable devices. Methods: A systematic review was conducted of the PubMed, Embase, and Web of Science databases. Keywords were used to identify studies on earable devices published between 2015 and 2020. The earable devices were described in terms of target health outcomes, biomarkers, sensor types and positions, and their utility for disease prevention. Results: A total of 51 articles met the inclusion criteria and were reviewed, and the frequency of 5 health-related characteristics of earable devices was described. The most frequent target health outcomes were diet-related outcomes (9/51, 18%), brain status (7/51, 14%), and cardiovascular disease (CVD) and central nervous system disease (5/51, 10% each). The most frequent biomarkers were electroencephalography (11/51, 22%), body movements (6/51, 12%), and body temperature (5/51, 10%). As for sensor types and sensor positions, electrical sensors (19/51, 37%) and the ear canal (26/51, 51%) were the most common, respectively. Moreover, the most frequent prevention stages were secondary prevention (35/51, 69%), primary prevention (12/51, 24%), and tertiary prevention (4/51, 8%). Combinations of ≥2 target health outcomes were the most frequent in secondary prevention (8/35, 23%) followed by brain status and CVD (5/35, 14% each) and by central nervous system disease and head injury (4/35, 11% each). Conclusions: Earable devices can provide biomarkers for various health outcomes. Brain status, healthy diet status, and CVDs were the most frequently targeted outcomes among the studies. Earable devices were mostly used for secondary prevention via monitoring of health or disease status. The potential utility of earable devices for primary and tertiary prevention needs to be investigated further. Earable devices connected to smartphones or tablets through cloud servers will guarantee user access to personal health information and facilitate comfortable wearing. %M 36239201 %R 10.2196/36696 %U https://mhealth.jmir.org/2022/11/e36696 %U https://doi.org/10.2196/36696 %U http://www.ncbi.nlm.nih.gov/pubmed/36239201 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 11 %P e40765 %T Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study %A Li,Sophia Xueying %A Halabi,Ramzi %A Selvarajan,Rahavi %A Woerner,Molly %A Fillipo,Isabell Griffith %A Banerjee,Sreya %A Mosser,Brittany %A Jain,Felipe %A Areán,Patricia %A Pratap,Abhishek %+ Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, 12th floor, Toronto, ON, M5T 1R8, Canada, 1 416 535 8501, Abhishek.Pratap@camh.ca %K participant recruitment %K participant retention %K decentralized studies %K active and passive data collection %K retention %K adherence %K compliance %K engagement %K smartphone %K mobile health %K mHealth %K sensor data %K clinical research %K data sharing %K recruitment %K mobile phone %D 2022 %7 14.11.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner. Objective: We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated. Methods: We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression. Results: We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants’ sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence. Conclusions: Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner. %M 36374539 %R 10.2196/40765 %U https://formative.jmir.org/2022/11/e40765 %U https://doi.org/10.2196/40765 %U http://www.ncbi.nlm.nih.gov/pubmed/36374539 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 11 %P e36583 %T The Effects of Tinnitus in Probabilistic Learning Tasks: Protocol for an Ecological Momentary Assessment Study %A Zhang,Lili %A Monacelli,Greta %A Vashisht,Himanshu %A Schlee,Winfried %A Langguth,Berthold %A Ward,Tomas %+ Insight Science Foundation Ireland Research Centre for Data Analytics, Dublin City University, Glasnevin Campus, Dublin, 9, Ireland, 353 0873747816, lili.zhang27@mail.dcu.ie %K chronic tinnitus %K computational modeling %K decision-making %K ecological momentary assessment %K mobile phone %D 2022 %7 11.11.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Chronic tinnitus is an increasing worldwide health concern, causing a significant burden to the health care system each year. The COVID-19 pandemic has seen a further increase in reported cases. For people with tinnitus, symptoms are exacerbated because of social isolation and the elevated levels of anxiety and depression caused by quarantines and lockdowns. Although it has been reported that patients with tinnitus can experience changes in cognitive capabilities, changes in adaptive learning via decision-making tasks for people with tinnitus have not yet been investigated. Objective: In this study, we aim to assess state- and trait-related impairments in adaptive learning ability on probabilistic learning tasks among people with tinnitus. Given that performance in such tasks can be quantified through computational modeling methods using a small set of neural-informed model parameters, such approaches are promising in terms of the assessment of tinnitus severity. We will first examine baseline differences in the characterization of decision-making under uncertainty between healthy individuals and people with tinnitus in terms of differences in the parameters of computational models in a cross-sectional experiment. We will also investigate whether these computational markers, which capture characteristics of decision-making, can be used to understand the cognitive impact of tinnitus symptom fluctuations through a longitudinal experimental design. Methods: We have developed a mobile app, AthenaCX, to deliver e-consent and baseline tinnitus and psychological assessments as well as regular ecological momentary assessments (EMAs) of perceived tinnitus loudness and a web-based aversive version of a probabilistic decision-making task, which can be triggered based on the participants’ responses to the EMA surveys. Computational models will be developed to fit participants’ choice data in the task, and cognitive parameters will be estimated to characterize participants’ current ability to adapt learning to the change of the simulated environment at each session when the task is triggered. Linear regression analysis will be conducted to evaluate the impacts of baseline tinnitus severity on adapting decision-making performance. Repeated measures linear regression analysis will be used to examine model-derived parameters of decision-making in measuring real-time perceived tinnitus loudness fluctuations. Results: Ethics approval was received in December 2020 from Dublin City University (DCUREC/2021/070). The implementation of the experiments, including both the surveys and the web-based decision-making task, has been prepared. Recruitment flyers have been shared with audiologists, and a video instruction has been created to illustrate to the participants how to participate in the experiment. We expect to finish data collection over 12 months and complete data analysis 6 months after this. The results are expected to be published in December 2023. Conclusions: We believe that EMA with context-aware triggering can facilitate a deeper understanding of the effects of tinnitus symptom severity upon decision-making processes as measured outside of the laboratory. International Registered Report Identifier (IRRID): PRR1-10.2196/36583 %M 36367761 %R 10.2196/36583 %U https://www.researchprotocols.org/2022/11/e36583 %U https://doi.org/10.2196/36583 %U http://www.ncbi.nlm.nih.gov/pubmed/36367761 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 11 %P e37624 %T The Management Perspective in Digital Health Literature: Systematic Review %A Angerer,Alfred %A Stahl,Johanna %A Krasniqi,Egzona %A Banning,Stefan %+ Healthcare Management, Winterthur Institute of Health Economics, Zurich University of Applied Sciences, Gertrudstrasse 15, Winterthur, 8401, Switzerland, 41 589346672, alfred.angerer@zhaw.ch %K digital health %K management %K health care management %K literature review %K health technology %K eHealth %K data health %K trend health %K tech health %D 2022 %7 10.11.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: New digital health technologies are considered one solution to challenges in the health sector, which include rising numbers of chronic diseases and increased health spending. As digitalization in health care is still in its infancy, there are many unanswered questions about the impact of digital health on management. Objective: This paper assesses the current state of knowledge in the field of digital health from a management perspective. It highlights research gaps within this field to determine future research opportunities. Methods: A systematic review of digital health literature was conducted using 3 databases. The chosen articles (N=38) were classified according to a taxonomy developed for the purpose, and research gaps were identified based on the topic areas discussed. Results: The literature review revealed a slight prevalence of practical (n=21, 55%) over theoretical (n=17, 45%) approaches. Most of the papers (n=23, 61%) deal with information technology (IT) and are, therefore, focused more on technology and less on management. The research question in most of the papers (n=31, 82%) deals with the creation of concepts, and very few (n=4, 11%) evaluate or even question existing solutions. Most consider the main reason for digitalization to be the optimization of operational processes (n=26, 68%), and 42% (n=16) deal with new business models. The topic area discussed most frequently was found to be eHealth (n=30, 79%). By contrast, the field of tech health with topics such as sensors receives the least attention (n=3, 8%), despite its significant potential for health care processes and strategy. Conclusions: Three main research propositions were identified. First, research into digital health innovation should not focus solely on the technology aspects but also on its implications for strategic and operational management. Second, the research community should target other domains besides eHealth. Third, we observed a lack of quantitative research on the real impact of digital health on organizations and their management. More quantitative evidence is required regarding the expected outcome and impact of the implementation of digital health solutions into our health care organizations. %M 36355426 %R 10.2196/37624 %U https://mhealth.jmir.org/2022/11/e37624 %U https://doi.org/10.2196/37624 %U http://www.ncbi.nlm.nih.gov/pubmed/36355426 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 11 %P e40347 %T Determination of Markers of Successful Implementation of Mental Health Apps for Young People: Systematic Review %A Bear,Holly Alice %A Ayala Nunes,Lara %A DeJesus,John %A Liverpool,Shaun %A Moltrecht,Bettina %A Neelakantan,Lakshmi %A Harriss,Elinor %A Watkins,Edward %A Fazel,Mina %+ Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, United Kingdom, 44 01865613127, holly.bear@psych.ox.ac.uk %K adolescent mental health %K smartphones %K mobile apps %K apps %K implementation science %K mobile phone %D 2022 %7 9.11.2022 %9 Review %J J Med Internet Res %G English %X Background: Smartphone apps have the potential to address some of the current issues facing service provision for young people’s mental health by improving the scalability of evidence-based mental health interventions. However, very few apps have been successfully implemented, and consensus on implementation measurement is lacking. Objective: This review aims to determine the proportion of evidence-based mental health and well-being apps that have been successfully adopted and sustained in real-world settings. A secondary aim is to establish if key implementation determinants such as coproduction, acceptability, feasibility, appropriateness, and engagement contribute toward successful implementation and longevity. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, an electronic search of 5 databases in 2021 yielded 18,660 results. After full-text screening, 34 articles met the full eligibility criteria, providing data on 29 smartphone apps studied with individuals aged 15 to 25 years. Results: Of 34 studies, only 10 (29%) studies were identified that were evaluating the effectiveness of 8 existing, commercially available mental health apps, and the remaining 24 (71%) studies reported the development and evaluation of 21 newly developed apps, of which 43% (9/21) were available, commercially or otherwise (eg, in mental health services), at the time of enquiry. Most studies addressed some implementation components including adoption, acceptability, appropriateness, feasibility, and engagement. Factors including high cost, funding constraints, and lengthy research processes impeded implementation. Conclusions: Without addressing common implementation drivers, there is considerable redundancy in the translation of mobile mental health research findings into practice. Studies should embed implementation strategies from the outset of the planned research, build collaborations with partners already working in the field (academic and commercial) to capitalize on existing interventions and platforms, and modify and evaluate them for local contexts or target problems and populations. Trial Registration: PROSPERO CRD42021224365; https://tinyurl.com/4umpn85f %M 36350704 %R 10.2196/40347 %U https://www.jmir.org/2022/11/e40347 %U https://doi.org/10.2196/40347 %U http://www.ncbi.nlm.nih.gov/pubmed/36350704 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 11 %P e41566 %T Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial %A Webb,Christian A %A Hirshberg,Matthew J %A Davidson,Richard J %A Goldberg,Simon B %+ Department of Counseling Psychology, University of Wisconsin – Madison, 315 Education Building, 1000 Bascom Mall, Madison, WI, 53706, United States, 1 608 265 8986, sbgoldberg@wisc.edu %K precision medicine %K prediction %K machine learning %K meditation %K mobile technology %K smartphone app %K mobile phone %D 2022 %7 8.11.2022 %9 Original Papetar %J J Med Internet Res %G English %X Background: Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps. Objective: This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training. Methods: Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a “Personalized Advantage Index” (PAI) reflecting an individual’s expected reduction in distress (primary outcome) from HMP versus control. Results: A significant group × PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit. Conclusions: Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them. Trial Registration: ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318 %M 36346668 %R 10.2196/41566 %U https://www.jmir.org/2022/11/e41566 %U https://doi.org/10.2196/41566 %U http://www.ncbi.nlm.nih.gov/pubmed/36346668 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 11 %P e37280 %T Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation %A Campo,David %A Elie,Valery %A de Gallard,Tristan %A Bartet,Pierre %A Morichau-Beauchant,Tristan %A Genain,Nicolas %A Fayol,Antoine %A Fouassier,David %A Pasteur-Rousseau,Adrien %A Puymirat,Etienne %A Nahum,Julien %+ Withings, 2 rue Maurice Hartmann, Issy Les Moulineaux, 92130, France, 33 637430705, valery.elie@withings.com %K atrial fibrillation %K mobile health %K mHealth %K diagnosis %K electrocardiogram %K ECG %K smartwatch %K smart technology %K wearable %K cardiology %K cardiac %K heart failure %K heart disease %K cardiovascular %K morbidity %K automatic detection %K algorithm %K physician %K sensor %K digital health %D 2022 %7 4.11.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Atrial fibrillation affects approximately 4% of the world’s population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation. Objective: We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. Methods: Eligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram). Results: A total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm. Conclusions: The algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch’s single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care. Trial Registration: ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386 %M 35481559 %R 10.2196/37280 %U https://formative.jmir.org/2022/11/e37280 %U https://doi.org/10.2196/37280 %U http://www.ncbi.nlm.nih.gov/pubmed/35481559 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 5 %N 4 %P e35510 %T Data Completeness and Concordance in the FeverApp Registry: Comparative Study %A Rathjens,Larisa %A Fingerhut,Ingo %A Martin,David %A Hamideh Kerdar,Sara %A Gwiasda,Moritz %A Schwarz,Silke %A Jenetzky,Ekkehart %+ Faculty of Health/School of Medicine, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, Witten, 58455, Germany, 49 2302 926 ext 7730, Ekkehart.Jenetzky@uni-wh.de %K registry %K data quality %K completeness %K concordance %K ecological momentary assessment %D 2022 %7 2.11.2022 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: The FeverApp registry uses ecological momentary assessment (EMA) to collect parental data on pediatric fever for scientific research. The mobile app FeverApp educates parents on safe fever management and serves as a fever diary. Objective: The focus of this study was to evaluate the completeness and concordance of the EMA-based FeverApp registry with regard to its data quality from a multilevel perspective. Methods: Structured descriptions of fever episodes by health care professionals from an office were used as reference. The number of children, their sociodemographic data, and agreement of fever episodes, with maximum temperature, intake of antipyretics and antibiotics, and physician visits, were compared with the entries in the corresponding physician’s reference records. The data quality indicators for completeness, meaning the extent to which the necessary data for the registry has actually been submitted, and concordance, which is the correspondence of the value of a data element with a reference source, were chosen to analyze whether EMA may be a suitable method for this kind of registry. Results: In both data sources, 1012 children were available for comparison over 16 months. The completeness of gender (1012/1012, 100%) and date of birth (1004/1012, 99.2%) information was high, and the mismatches were 0.69% (7/1012) and 1.19% (12/1012), respectively, between the sources. Of these 1012 children, 668 (66%) registered fever episodes in FeverApp. They relate to 534 families with 953 fever episodes in the reference records and 1452 episodes in the FeverApp registry. Of the 534 families, 183 (34.3%) refrained from visiting the office during fever episodes but nevertheless documented them in FeverApp. Largest part (766/1452, 52.75%) episodes were recorded exclusively in the FeverApp registry by 371 (371/534, 69.5%) families. The remaining 686 (47.2%) episodes of 391 (58.5%) children from 351 (65.7%) families were comparable with the reference data source in terms of physician visits, medication, and temperature. The completeness ranged, depending on the kind of variable, from 11.5% to 65% in the registry and from 7.6% to 42.6% in the office. The 953 fever episodes reported by the reference office consisted of 681 (71.5%) acute and 272 (28.5%) past episodes. In FeverApp, most past (262/272, 96.3%) but less acute (424/681, 62.3%) episodes have been entered. The concordance rates were varied: 90.2% for antibiotic use, 66.6% for antipyretic use, 61.7% for physician visits, and 16% for the highest temperature during the fever episode. Conclusions: Both sources delivered only partial data, and the rates of completeness and concordance depended on the kind of variable. However, the FeverApp registry showed higher documentation and precision rates than professional records for all considered variables. Therefore, EMA may play a unique supplement for research in ambulatory care. FeverApp could support pediatric offices, especially during the pandemic. %M 36322119 %R 10.2196/35510 %U https://pediatrics.jmir.org/2022/4/e35510 %U https://doi.org/10.2196/35510 %U http://www.ncbi.nlm.nih.gov/pubmed/36322119 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 10 %P e41306 %T Racial and Ethnic Differences in Outcomes of a 12-Week Digital Rehabilitation Program for Musculoskeletal Pain: Prospective Longitudinal Cohort Study %A Scheer,Justin %A Costa,Fabíola %A Molinos,Maria %A Areias,Anabela %A Janela,Dora %A Moulder,Robert G %A Lains,Jorge %A Bento,Virgílio %A Yanamadala,Vijay %A Cohen,Steven P %A Correia,Fernando Dias %+ Sword Health, Inc, 13937 Sprague Lane, Suite 100, Draper, UT, 84020, United States, 1 385 308 8034, fcorreia@swordhealth.com %K physical therapy %K telerehabilitation %K digital therapy %K eHealth %K telehealth %K musculoskeletal conditions %K race %K ethnicity %K pain %K diversity %K equity %K mobile phone %D 2022 %7 31.10.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Musculoskeletal (MSK) pain disproportionately affects people from different ethnic backgrounds through higher burden and less access to care. Digital care programs (DCPs) can improve access and help reduce inequities. However, the outcomes of such programs based on race and ethnicity have yet to be studied. Objective: We aimed to assess the impact of race and ethnicity on engagement and outcomes in a multimodal DCP for MSK pain. Methods: This was an ad hoc analysis of an ongoing decentralized single-arm investigation into engagement and clinical-related outcomes after a multimodal DCP in patients with MSK conditions. Patients were stratified by self-reported racial and ethnic group, and their engagement and outcome changes between baseline and 12 weeks were compared using latent growth curve analysis. Outcomes included program engagement (number of sessions), self-reported pain scores, likelihood of surgery, Generalized Anxiety Disorder 7-item scale, Patient Health Questionnaire 9-item, and Work Productivity and Activity Impairment. A minimum clinically important difference (MCID) of 30% was calculated for pain, and multivariable logistic regression was performed to evaluate race as an independent predictor of meeting the MCID. Results: A total of 6949 patients completed the program: 65.5% (4554/6949) of them were non-Hispanic White, 10.8% (749/6949) were Black, 9.7% (673/6949) were Asian, 9.2% (636/6949) were Hispanic, and 4.8% (337/6949) were of other racial or ethnic backgrounds. The population studied was diverse and followed the proportions of the US population. All groups reported high engagement and satisfaction, with Hispanic and Black patients ranking first among satisfaction despite lower engagement. Black patients had a higher likelihood to drop out (odds ratio [OR] 1.19, 95% CI 1.01-1.40, P=.04) than non-Hispanic White patients. Hispanic and Black patients reported the highest level of pain, surgical intent, work productivity, and impairment in activities of daily living at baseline. All race groups showed a significant improvement in all outcomes, with Black and Hispanic patients reporting the greatest improvements in clinical outcomes. Hispanic patients also had the highest response rate for pain (75.8%) and a higher OR of meeting the pain MCID (OR 1.74, 95% CI 1.24-2.45, P=.001), when compared with non-Hispanic White patients, independent of age, BMI, sex, therapy type, education level, and employment status. No differences in mental health outcomes were found between race and ethnic groups. Conclusions: This study advocates for the utility of a DCP in improving access to MSK care and promoting health equity. Engagement and satisfaction rates were high in all the groups. Black and Hispanic patients had higher MSK burden at baseline and lower engagement but also reported higher improvements, with Hispanic patients presenting a higher likelihood of pain improvement. %M 36189963 %R 10.2196/41306 %U https://www.jmir.org/2022/10/e41306 %U https://doi.org/10.2196/41306 %U http://www.ncbi.nlm.nih.gov/pubmed/36189963 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 10 %P e28082 %T Validity of Chatbot Use for Mental Health Assessment: Experimental Study %A Schick,Anita %A Feine,Jasper %A Morana,Stefan %A Maedche,Alexander %A Reininghaus,Ulrich %+ Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, 68219, Germany, 49 62117031941, anita.schick@zi-mannheim.de %K chatbot %K distress %K monitoring %K mobile health %K social desirability %K social presence %D 2022 %7 31.10.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mental disorders in adolescence and young adulthood are major public health concerns. Digital tools such as text-based conversational agents (ie, chatbots) are a promising technology for facilitating mental health assessment. However, the human-like interaction style of chatbots may induce potential biases, such as socially desirable responding (SDR), and may require further effort to complete assessments. Objective: This study aimed to investigate the convergent and discriminant validity of chatbots for mental health assessments, the effect of assessment mode on SDR, and the effort required by participants for assessments using chatbots compared with established modes. Methods: In a counterbalanced within-subject design, we assessed 2 different constructs—psychological distress (Kessler Psychological Distress Scale and Brief Symptom Inventory-18) and problematic alcohol use (Alcohol Use Disorders Identification Test-3)—in 3 modes (chatbot, paper-and-pencil, and web-based), and examined convergent and discriminant validity. In addition, we investigated the effect of mode on SDR, controlling for perceived sensitivity of items and individuals’ tendency to respond in a socially desirable way, and we also assessed the perceived social presence of modes. Including a between-subject condition, we further investigated whether SDR is increased in chatbot assessments when applied in a self-report setting versus when human interaction may be expected. Finally, the effort (ie, complexity, difficulty, burden, and time) required to complete the assessments was investigated. Results: A total of 146 young adults (mean age 24, SD 6.42 years; n=67, 45.9% female) were recruited from a research panel for laboratory experiments. The results revealed high positive correlations (all P<.001) of measures of the same construct across different modes, indicating the convergent validity of chatbot assessments. Furthermore, there were no correlations between the distinct constructs, indicating discriminant validity. Moreover, there were no differences in SDR between modes and whether human interaction was expected, although the perceived social presence of the chatbot mode was higher than that of the established modes (P<.001). Finally, greater effort (all P<.05) and more time were needed to complete chatbot assessments than for completing the established modes (P<.001). Conclusions: Our findings suggest that chatbots may yield valid results. Furthermore, an understanding of chatbot design trade-offs in terms of potential strengths (ie, increased social presence) and limitations (ie, increased effort) when assessing mental health were established. %M 36315228 %R 10.2196/28082 %U https://mhealth.jmir.org/2022/10/e28082 %U https://doi.org/10.2196/28082 %U http://www.ncbi.nlm.nih.gov/pubmed/36315228 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 10 %P e39085 %T Measurement Properties of Smartphone Approaches to Assess Physical Activity in Healthy Young People: Systematic Review %A Parmenter,Belinda %A Burley,Claire %A Stewart,Courtney %A Whife,Jesse %A Champion,Katrina %A Osman,Bridie %A Newton,Nicola %A Green,Olivia %A Wescott,Annie B %A Gardner,Lauren A %A Visontay,Rachel %A Birrell,Louise %A Bryant,Zachary %A Chapman,Cath %A Lubans,David R %A Sunderland,Matthew %A Slade,Tim %A Thornton,Louise %+ School of Health Sciences, University of New South Wales, Room 234, Level 2 Wallace Wurth Building, Corner of High Street and Botany Street, Kensington, NSW 2052, Australia, 61 0290653510, c.burley@unsw.edu.au %K smartphone %K mobile phone %K mHealth %K prevention %K risk %K physical activity %K sedentary behavior %K young people %D 2022 %7 21.10.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Physical inactivity is a preventable risk factor for several chronic diseases and one of the driving forces behind the growing global burden of disease. Recent evidence has shown that interventions using mobile smartphone apps can promote a significant increase in physical activity (PA) levels. However, the accuracy and reliability of using apps is unknown. Objective: The aim of our review was to determine the accuracy and reliability of using mobile apps to measure PA levels in young people. We conducted a systematic review guided by PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Methods: Studies published from 2007 to 2020 were sourced from 8 databases—Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsychINFO (EBSCOhost), CINAHL (EBSCOhost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library database. Studies were conducted in young people aged 10-24 years and without chronic illnesses, who evaluated a mobile app’s ability to measure PA. Primary outcomes included validity, reliability, and responsiveness of the measurement approach. Duplicate screening was conducted for eligibility, data extraction, and assessing the risk of bias. Results were reported as a systematic review. The main physical activity measures evaluated for each study were the following: total PA time (min/day or min/week), total moderate to vigorous PA per week, daily step count, intensity measure (heart rate), and frequency measure (days per week). Results: Of the 149 identified studies, 5 met the inclusion criteria (322 participants, 176 female; mean age 14, SD 3 years). A total of 3 studies measured criterion validity and compared PA measured via apps against PA measured via an Actigraph accelerometer. The 2 studies that reported on construct validity identified a significant difference between self-reported PA and the objective measure. Only 1 of the 5 apps examined was available to the public, and although this app was highly accepted by young people, the app recorded PA to be significantly different to participants’ self-reported PA. Conclusions: Overall, few studies assess the reliability, validity, and responsiveness of mobile apps to measure PA in healthy young people, with studies typically only reporting on one measurement property. Of the 3 studies that measured validity, all concluded that mobile phones were acceptable and valid tools. More research is needed into the validity and reliability of smartphone apps to measure PA levels in this population as well as in populations with other characteristics, including other age groups and those with chronic diseases. Trial Registration: PROSPERO CRD42019122242; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=122242 %M 36269659 %R 10.2196/39085 %U https://mhealth.jmir.org/2022/10/e39085 %U https://doi.org/10.2196/39085 %U http://www.ncbi.nlm.nih.gov/pubmed/36269659 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 7 %N 2 %P e40066 %T Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: Effectiveness Evaluation in Malakal, South Sudan %A Leidman,Eva %A Jatoi,Muhammad Ali %A Bollemeijer,Iris %A Majer,Jennifer %A Doocy,Shannon %+ Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, United States, 1 4049085125, eleidman@jhu.edu %K mobile health %K mHealth %K child nutrition %K anthropometry %K 3D imaging %K imaging %K accuracy %K measurement %K child stature %K software %K algorithm %K automated %K device %K child health %K pediatric health %K height %K length %K arm circumference %D 2022 %7 21.10.2022 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Adoption of 3D imaging systems in humanitarian settings requires accuracy comparable with manual measurement notwithstanding additional constraints associated with austere settings. Objective: This study aimed to evaluate the accuracy of child stature and mid–upper arm circumference (MUAC) measurements produced by the AutoAnthro 3D imaging system (third generation) developed by Body Surface Translations Inc. Methods: A study of device accuracy was embedded within a 2-stage cluster survey at the Malakal Protection of Civilians site in South Sudan conducted between September 2021 and October 2021. All children aged 6 to 59 months within selected households were eligible. For each child, manual measurements were obtained by 2 anthropometrists following the protocol used in the 2006 World Health Organization Child Growth Standards study. Scans were then captured by a different enumerator using a Samsung Galaxy 8 phone loaded with a custom software, AutoAnthro, and an Intel RealSense 3D scanner. The scans were processed using a fully automated algorithm. A multivariate logistic regression model was fit to evaluate the adjusted odds of achieving a successful scan. The accuracy of the measurements was visually assessed using Bland-Altman plots and quantified using average bias, limits of agreement (LoAs), and the 95% precision interval for individual differences. Key informant interviews were conducted remotely with survey enumerators and Body Surface Translations Inc developers to understand challenges in beta testing, training, data acquisition and transmission. Results: Manual measurements were obtained for 539 eligible children, and scan-derived measurements were successfully processed for 234 (43.4%) of them. Caregivers of at least 10.4% (56/539) of the children refused consent for scan capture; additional scans were unsuccessfully transmitted to the server. Neither the demographic characteristics of the children (age and sex), stature, nor MUAC were associated with availability of scan-derived measurements; team was significantly associated (P<.001). The average bias of scan-derived measurements in cm was −0.5 (95% CI −2.0 to 1.0) for stature and 0.7 (95% CI 0.4-1.0) for MUAC. For stature, the 95% LoA was −23.9 cm to 22.9 cm. For MUAC, the 95% LoA was −4.0 cm to 5.4 cm. All accuracy metrics varied considerably by team. The COVID-19 pandemic–related physical distancing and travel policies limited testing to validate the device algorithm and prevented developers from conducting in-person training and field oversight, negatively affecting the quality of scan capture, processing, and transmission. Conclusions: Scan-derived measurements were not sufficiently accurate for the widespread adoption of the current technology. Although the software shows promise, further investments in the software algorithms are needed to address issues with scan transmission and extreme field contexts as well as to enable improved field supervision. Differences in accuracy by team provide evidence that investment in training may also improve performance. %M 38875695 %R 10.2196/40066 %U https://biomedeng.jmir.org/2022/2/e40066 %U https://doi.org/10.2196/40066 %U http://www.ncbi.nlm.nih.gov/pubmed/38875695 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 10 %P e40452 %T The Family Level Assessment of Screen Use–Mobile Approach: Development of an Approach to Measure Children’s Mobile Device Use %A Perez,Oriana %A Kumar Vadathya,Anil %A Beltran,Alicia %A Barnett,R Matthew %A Hindera,Olivia %A Garza,Tatyana %A Musaad,Salma M %A Baranowski,Tom %A Hughes,Sheryl O %A Mendoza,Jason A %A Sabharwal,Ashutosh %A Veeraraghavan,Ashok %A O'Connor,Teresia M %+ United States Department of Agriculture/Agricultural Research Service Children's Nutrition Research Center, Baylor College of Medicine, 1100 Bates St, Houston, TX, 77030, United States, 1 713 798 6782, teresiao@bcm.edu %K screen time %K mobile media apps %K children %K mobile phone use %K tablet use %K mobile phone %D 2022 %7 21.10.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: There is a strong association between increased mobile device use and worse dietary habits, worse sleep outcomes, and poor academic performance in children. Self-report or parent-proxy report of children’s screen time has been the most common method of measuring screen time, which may be imprecise or biased. Objective: The objective of this study was to assess the feasibility of measuring the screen time of children on mobile devices using the Family Level Assessment of Screen Use (FLASH)–mobile approach, an innovative method that leverages the existing features of the Android platform. Methods: This pilot study consisted of 2 laboratory-based observational feasibility studies and 2 home-based feasibility studies in the United States. A total of 48 parent-child dyads consisting of a parent and child aged 6 to 11 years participated in the pilot study. The children had to have their own or shared Android device. The laboratory-based studies included a standardized series of tasks while using the mobile device or watching television, which were video recorded. Video recordings were coded by staff for a gold standard comparison. The home-based studies instructed the parent-child dyads to use their mobile device as they typically use it over 3 days. Parents received a copy of the use logs at the end of the study and completed an exit interview in which they were asked to review their logs and share their perceptions and suggestions for the improvement of the FLASH-mobile approach. Results: The final version of the FLASH-mobile approach resulted in user identification compliance rates of >90% for smartphones and >80% for tablets. For laboratory-based studies, a mean agreement of 73.6% (SD 16.15%) was achieved compared with the gold standard (human coding of video recordings) in capturing the target child’s mobile use. Qualitative feedback from parents and children revealed that parents found the FLASH-mobile approach useful for tracking how much time their child spends using the mobile device as well as tracking the apps they used. Some parents revealed concerns over privacy and provided suggestions for improving the FLASH-mobile approach. Conclusions: The FLASH-mobile approach offers an important new research approach to measure children’s use of mobile devices more accurately across several days, even when the child shares the device with other family members. With additional enhancement and validation studies, this approach can significantly advance the measurement of mobile device use among young children. %M 36269651 %R 10.2196/40452 %U https://formative.jmir.org/2022/10/e40452 %U https://doi.org/10.2196/40452 %U http://www.ncbi.nlm.nih.gov/pubmed/36269651 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 10 %P e41199 %T Rationale and Design of an Ecological Momentary Assessment Study Examining Predictors of Binge Eating Among Sexual Minority and Heterosexual Young Women: Protocol for the Health and Experiences in Real Life (HER Life) Study %A Heron,Kristin E %A Braitman,Abby L %A Dawson,Charlotte A %A Sandoval,Cassidy M %A Butler,Lauren V %A Moulder,Alicia %A Lewis,Robin J %+ Department of Psychology, Old Dominion University, 250 Mills Godwin Building, Norfolk, VA, 23529, United States, 1 757 683 5214, kheron@odu.edu %K sexual minority women %K ecological momentary assessment %K binge eating %K sexual minority stress %K negative affective states %K mobile phone %D 2022 %7 21.10.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Previous research has identified health disparities between sexual minority and heterosexual women, including increased rates of obesity and binge eating in sexual minority women. Established predictors of binge eating behavior include negative emotions and sociocultural processes; however, these studies are generally conducted in samples of young women where sexual identity is not known or reported. There is a dearth of research evaluating how sexual minority–specific factors (eg, minority stress and connectedness to the lesbian, gay, bisexual, transgender, and queer community) may affect binge eating in sexual minority women. In addition, no studies have examined these processes in racially diverse samples or considered how intersecting minority identities (eg, Black and sexual minority) may affect eating behaviors. Objective: The Health and Experiences in Real Life (HER Life) Project aims to clarify real-world predictors of binge eating in young heterosexual and sexual minority women using ecological momentary assessment. The role of affective, social, and health behavior factors in binge eating will be examined for all women (aim 1), and sexual minority–specific predictors will also be considered for sexual minority women participants (aim 2). Person-level moderators of race, body- and eating-related factors, and sexual minority–specific factors will also be examined to better understand how real-world binge eating predictors may differ for various demographic groups (aim 3). Methods: Researchers aim to recruit 150 sexual minority and 150 heterosexual women from across the United States, including at least 50 Black women for each group, using web-based recruitment methods. The eligibility criteria include identifying as a woman, being aged between 18 and 30 years, and having had at least two binge eating episodes in the last 2 weeks. Participants must endorse being only or mostly attracted to men (considered heterosexual) or only or mostly attracted to women or having a current or most recent female partner (considered sexual minority). Eligible participants complete an initial web-based baseline survey and then 14 days of ecological momentary assessment involving the completion of a morning and before-bed survey and 5 prompted surveys per day as well as a user-initiated survey after binge eating episodes. The data will be analyzed using a series of multilevel models. Results: Data collection started in February 2021. We have currently enrolled 129 sexual minority women and 146 heterosexual women. Data collection is expected to conclude in fall 2022. Conclusions: The Health and Experiences in Real Life Project aims to elucidate potential differences between sexual minority and heterosexual women in within-person factors predicting binge eating and inform eating disorder interventions for sexual minority women. The challenges in recruiting sexual minority women, including the determination of eligibility criteria and considerations for remote data collection, are discussed. International Registered Report Identifier (IRRID): DERR1-10.2196/41199 %M 36269642 %R 10.2196/41199 %U https://www.researchprotocols.org/2022/10/e41199 %U https://doi.org/10.2196/41199 %U http://www.ncbi.nlm.nih.gov/pubmed/36269642 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 10 %P e38963 %T Assessment of the Popularity and Perceived Effectiveness of Smartphone Tools That Track and Limit Smartphone Use: Survey Study and Machine Learning Analysis %A Aboujaoude,Elias %A Vera Cruz,Germano %A Rochat,Lucien %A Courtois,Robert %A Ben Brahim,Farah %A Khan,Riaz %A Khazaal,Yasser %+ Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, 401 Quarry Rd, Stanford, CA, 94305, United States, 1 650 498 9111, eaboujaoude@stanford.edu %K smartphone addiction %K internet addiction %K internet gaming disorder %K smartphone tools %K telepsychiatry %K machine learning %K telemedicine %K social media %K digital mental health interventions %K mobile phone %D 2022 %7 20.10.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established treatments, smartphone-provided tools that monitor or control smartphone use have become increasingly popular, and their dissemination has largely occurred without oversight from the mental health field. Objective: We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools. Methods: First, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions. Results: Smartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use. Conclusions: If proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non–smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential. %M 36264627 %R 10.2196/38963 %U https://www.jmir.org/2022/10/e38963 %U https://doi.org/10.2196/38963 %U http://www.ncbi.nlm.nih.gov/pubmed/36264627 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 10 %P e39759 %T Long-term Follow-up of Patients With Hernia Using the Hernia-Specific Quality-of-Life Mobile App: Feasibility Questionnaire Study %A Huang,Ching-Shui %A Tai,Feng-Chuan %A Lien,Heng-Hui %A Wong,Jia-Uei %A Huang,Chi-Cheng %+ Department of Surgery, Taipei Veterans General Hospital, No 201, Sec 2, Shipai Rd, Beitou District, Taipei, 106, Taiwan, 886 2 28757808, chishenh74@gmail.com %K hernia %K mobile app %K quality of life %K Hernia-Specific Quality-of-Life (HERQL) %K mobile health %K mHealth %K app %K self-management %D 2022 %7 19.10.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Hernia repair is one of the most common surgical procedures; however, the long-term outcomes are seldom reported due to incomplete follow-up. Objective: The aim of this study was to examine the use of a mobile app for the long-term follow-up of hernia recurrence, complication, and quality-of-life perception. Methods: A cloud-based corroborative system drove a mobile app with the HERQL (Hernia-Specific Quality-of-Life) questionnaire built in. Patients who underwent hernia repair were identified from medical records, and an invitation to participate in this study was sent through the post. Results: The response rate was 11.89% (311/2615) during the 1-year study period, whereas the recurrence rate was 1.0% (3/311). Causal relationships between symptomatic and functional domains of the HERQL questionnaire were indicated by satisfactory model fit indices and significant regression coefficients derived from structural equational modeling. Regarding patients’ last hernia surgeries, 88.7% (276/311) of the patients reported them to be satisfactory or very satisfactory, 68.5% (213/311) of patients reported no discomfort, and 61.1% (190/311) of patients never experienced mesh foreign body sensation. Subgroup analysis for the most commonly used mesh repairs found that mesh plug repair inevitably resulted in worse symptoms and quality-of-life perception from the group with groin hernias. Conclusions: The mobile app has the potential to enhance the quality of care for patients with hernia and facilitate outcomes research with more complete follow-up. %M 36260390 %R 10.2196/39759 %U https://formative.jmir.org/2022/10/e39759 %U https://doi.org/10.2196/39759 %U http://www.ncbi.nlm.nih.gov/pubmed/36260390 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 10 %P e38709 %T Electronic Health Diary Campaigns to Complement Longitudinal Assessments in Persons With Multiple Sclerosis: Nested Observational Study %A Sieber,Chloé %A Chiavi,Deborah %A Haag,Christina %A Kaufmann,Marco %A Horn,Andrea B %A Dressel,Holger %A Zecca,Chiara %A Calabrese,Pasquale %A Pot,Caroline %A Kamm,Christian Philipp %A von Wyl,Viktor %A , %+ Swiss Multiple Sclerosis Registry, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, Zurich, 8001, Switzerland, 41 44 634 63 80, viktor.vonwyl@uzh.ch %K registry %K multiple sclerosis %K digital health %K electronic health diary %K diary %K participation %K adherence %K patient-reported outcome %K natural language processing %K unstructured text %D 2022 %7 5.10.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Electronic health diaries hold promise in complementing standardized surveys in prospective health studies but are fraught with numerous methodological challenges. Objective: The study aimed to investigate participant characteristics and other factors associated with response to an electronic health diary campaign in persons with multiple sclerosis, identify recurrent topics in free-text diary entries, and assess the added value of structured diary entries with regard to current symptoms and medication intake when compared with survey-collected information. Methods: Data were collected by the Swiss Multiple Sclerosis Registry during a nested electronic health diary campaign and during a regular semiannual Swiss Multiple Sclerosis Registry follow-up survey serving as comparator. The characteristics of campaign participants were descriptively compared with those of nonparticipants. Diary content was analyzed using the Linguistic Inquiry and Word Count 2015 software (Pennebaker Conglomerates, Inc) and descriptive keyword analyses. The similarities between structured diary data and follow-up survey data on health-related quality of life, symptoms, and medication intake were examined using the Jaccard index. Results: Campaign participants (n=134; diary entries: n=815) were more often women, were not working full time, did not have a higher education degree, had a more advanced gait impairment, and were on average 5 years older (median age 52.5, IQR 43.25-59.75 years) than eligible nonparticipants (median age 47, IQR 38-55 years; n=524). Diary free-text entries (n=632; participants: n=100) most often contained references to the following standard Linguistic Inquiry and Word Count word categories: negative emotion (193/632, 30.5%), body parts or body functioning (191/632, 30.2%), health (94/632, 14.9%), or work (67/632, 10.6%). Analogously, the most frequently mentioned keywords (diary entries: n=526; participants: n=93) were “good,” “day,” and “work.” Similarities between diary data and follow-up survey data, collected 14 months apart (median), were high for health-related quality of life and stable for slow-changing symptoms such as fatigue or gait disorder. Similarities were also comparatively high for drugs requiring a regular application, including interferon beta-1a (Avonex) and glatiramer acetate (Copaxone), and for modern oral therapies such as fingolimod (Gilenya) and teriflunomide (Aubagio). Conclusions: Diary campaign participation seemed dependent on time availability and symptom burden and was enhanced by reminder emails. Electronic health diaries are a meaningful complement to regular structured surveys and can provide more detailed information regarding medication use and symptoms. However, they should ideally be embedded into promotional activities or tied to concrete research study tasks to enhance regular and long-term participation. %M 36197713 %R 10.2196/38709 %U https://mhealth.jmir.org/2022/10/e38709 %U https://doi.org/10.2196/38709 %U http://www.ncbi.nlm.nih.gov/pubmed/36197713 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 10 %P e40667 %T Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis %A Zhang,Yuezhou %A Folarin,Amos A %A Sun,Shaoxiong %A Cummins,Nicholas %A Vairavan,Srinivasan %A Qian,Linglong %A Ranjan,Yatharth %A Rashid,Zulqarnain %A Conde,Pauline %A Stewart,Callum %A Laiou,Petroula %A Sankesara,Heet %A Matcham,Faith %A White,Katie M %A Oetzmann,Carolin %A Ivan,Alina %A Lamers,Femke %A Siddi,Sara %A Simblett,Sara %A Rintala,Aki %A Mohr,David C %A Myin-Germeys,Inez %A Wykes,Til %A Haro,Josep Maria %A Penninx,Brenda W J H %A Narayan,Vaibhav A %A Annas,Peter %A Hotopf,Matthew %A Dobson,Richard J B %A , %+ Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SGDP Centre, IoPPN, PO Box 80, De Crespigny Park, Denmark Hill, London, SE5 8AF, United Kingdom, 44 75 7985 6617, yuezhou.zhang@kcl.ac.uk %K depression %K gait %K mobile health %K mHealth %K acceleration signals %K monitoring %K wearable devices %K mobile phones %K mental health %D 2022 %7 4.10.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. Objective: The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. Methods: We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. Results: Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=0.06). Conclusions: This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings. %M 36194451 %R 10.2196/40667 %U https://mhealth.jmir.org/2022/10/e40667 %U https://doi.org/10.2196/40667 %U http://www.ncbi.nlm.nih.gov/pubmed/36194451 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e40572 %T Feasibility of Measuring Screen Time, Activity, and Context Among Families With Preschoolers: Intensive Longitudinal Pilot Study %A Parker,Hannah %A Burkart,Sarah %A Reesor-Oyer,Layton %A Smith,Michal T %A Dugger,Roddrick %A von Klinggraeff,Lauren %A Weaver,R Glenn %A Beets,Michael W %A Armstrong,Bridget %+ Department of Exercise Science, Arnold School of Public Health, University of South Carolina, 921 Assembly St, Columbia, SC, 29208-3904, United States, 1 803 576 8418, ba12@mailbox.sc.edu %K ecological momentary assessment %K accelerometry %K objective digital media use %K screen time %K sleep %K activity %K preschool %K dyads %K mobile phone %D 2022 %7 29.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Digital media has made screen time more available across multiple contexts, but our understanding of the ways children and families use digital media has lagged behind the rapid adoption of this technology. Objective: This study evaluated the feasibility of an intensive longitudinal data collection protocol to objectively measure digital media use, physical activity, sleep, sedentary behavior, and socioemotional context among caregiver-child dyads. This paper also describes preliminary convergent validity of ecological momentary assessment (EMA) measures and preliminary agreement between caregiver self-reported phone use and phone use collected from passive mobile sensing. Methods: Caregivers and their preschool-aged child (3-5 years) were recruited to complete a 30-day assessment protocol. Within 30-days, caregivers completed 7 days of EMA to measure child behavior problems and caregiver stress. Caregivers and children wore an Axivity AX3 (Newcastle Upon Tyne) accelerometer to assess physical activity, sedentary behavior, and sleep. Phone use was assessed via passive mobile sensing; we used Chronicle for Android users and screenshots of iOS screen time metrics for iOS users. Participants were invited to complete a second 14-day protocol approximately 3-12 months after their first assessment. We used Pearson correlations to examine preliminary convergent validity between validated questionnaire measures of caregiver psychological functioning, child behavior, and EMA items. Root mean square errors were computed to examine the preliminary agreement between caregiver self-reported phone use and objective phone use. Results: Of 110 consenting participants, 105 completed all protocols (105/110, 95.5% retention rate). Compliance was defined a priori as completing ≥70%-75% of each protocol task. There were high compliance rates for passive mobile sensing for both Android (38/40, 95%) and iOS (64/65, 98%). EMA compliance was high (105/105, 100%), but fewer caregivers and children were compliant with accelerometry (62/99, 63% and 40/100, 40%, respectively). Average daily phone use was 383.4 (SD 157.0) minutes for Android users and 354.7 (SD 137.6) minutes for iOS users. There was poor agreement between objective and caregiver self-reported phone use; root mean square errors were 157.1 and 81.4 for Android and iOS users, respectively. Among families who completed the first assessment, 91 re-enrolled to complete the protocol a second time, approximately 7 months later (91/105, 86.7% retention rate). Conclusions: It is feasible to collect intensive longitudinal data on objective digital media use simultaneously with accelerometry and EMA from an economically and racially diverse sample of families with preschool-aged children. The high compliance and retention of the study sample are encouraging signs that these methods of intensive longitudinal data collection can be completed in a longitudinal cohort study. The lack of agreement between self-reported and objectively measured mobile phone use highlights the need for additional research using objective methods to measure digital media use. International Registered Report Identifier (IRRID): RR2-36240 %M 36173677 %R 10.2196/40572 %U https://formative.jmir.org/2022/9/e40572 %U https://doi.org/10.2196/40572 %U http://www.ncbi.nlm.nih.gov/pubmed/36173677 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e40468 %T Digital Platform to Continuously Monitor Patients Using a Smartwatch: Preliminary Report %A Bin,Kaio Jia %A De Pretto,Lucas Ramos %A Sanchez,Fabio Beltrame %A Battistella,Linamara Rizzo %+ Instituto de Medicina Física e Reabilitação do Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, Rua Domingo Soto 100, Jardim Vila Mariana, São Paulo, Brazil, 55 1126616208, kaiobin@gmail.com %K smartwatch %K digital health %K telemedicine %K wearable %K telemonitoring %K mobile health %K digital platform %K clinical intervention %K sensitive data %K clinical trial %D 2022 %7 15.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Monitoring vital signs such as oximetry, blood pressure, and heart rate is important to follow the evolution of patients. Smartwatches are a revolution in medicine allowing the collection of such data in a continuous and organic way. However, it is still a challenge to make this information available to health care professionals to make decisions during clinical follow-up. Objective: This study aims to build a digital solution that displays vital sign data from smartwatches, collected remotely, continuously, reliably, and from multiple users, with trigger warnings when abnormal results are identified. Methods: This is a single-center prospective study following the guidelines “Evaluating digital health products” from the UK Health Security Agency. A digital platform with 3 different applications was created to capture and display data from the mobile phones of volunteers with smartwatches. We selected 80 volunteers who were followed for 24 weeks each, and the synchronization interval between the smartwatch and digital solution was recorded for each vital sign collected. Results: In 14 weeks of project progress, we managed to recruit 80 volunteers, with 68 already registered in the digital solution. More than 2.8 million records have already been collected, without system downtime. Less than 5% of continuous heart rate measurements (bpm) were synchronized within 2 hours. However, approximately 70% were synchronized in less than 24 hours, and 90% were synchronized in less than 119 hours. Conclusions: The digital solution is working properly in its role of displaying data collected from smartwatches. Vital sign values are being monitored by the research team as part of the monitoring of volunteers. Although the digital solution proved unsuitable for monitoring urgent events, it is more than suitable for use in outpatient clinical use. This digital solution, which is based on cloud technology, can be applied in the future for telemonitoring in regions lacking health care professionals. Accuracy and reliability studies still need to be performed at the end of the 24-week follow-up. %M 36107471 %R 10.2196/40468 %U https://formative.jmir.org/2022/9/e40468 %U https://doi.org/10.2196/40468 %U http://www.ncbi.nlm.nih.gov/pubmed/36107471 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e38579 %T A Versatile and Scalable Platform That Streamlines Data Collection for Patient-Centered Studies: Usability and Feasibility Study %A Huang,Haley %A Aschettino,Sofia %A Lari,Nasim %A Lee,Ting-Hsuan %A Rosenberg,Sarah Stothers %A Ng,Xinyi %A Muthuri,Stella %A Bakshi,Anirudh %A Bishop,Korrin %A Ezzeldin,Hussein %+ Center for Biologics Evaluation and Research, United States Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, United States, 1 240 402 8629, hussein.ezzeldin@fda.hhs.gov %K mobile app %K patient experience data %K data-collection app %K mobile phone %K usability %K mHealth app %K feasibility %K user centered %K eHealth %K patient-generated data %D 2022 %7 14.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The Food and Drug Administration Center for Biologics Evaluation and Research (CBER) established the Biologics Effectiveness and Safety (BEST) Initiative with several objectives, including the expansion and enhancement of CBER’s access to fit-for-purpose data sources, analytics, tools, and infrastructures to improve the understanding of patient experiences with conditions related to CBER-regulated products. Owing to existing challenges in data collection, especially for rare disease research, CBER recognized the need for a comprehensive platform where study coordinators can engage with study participants and design and deploy studies while patients or caregivers could enroll, consent, and securely participate as well. Objective: This study aimed to increase awareness and describe the design, development, and novelty of the Survey of Health and Patient Experience (SHAPE) platform, its functionality and application, quality improvement efforts, open-source availability, and plans for enhancement. Methods: SHAPE is hosted in a Google Cloud environment and comprises 3 parts: the administrator application, participant app, and application programming interface. The administrator can build a study comprising a set of questionnaires and self-report entries through the app. Once the study is deployed, the participant can access the app, consent to the study, and complete its components. To build SHAPE to be scalable and flexible, we leveraged the open-source software development kit, Ionic Framework. This enabled the building and deploying of apps across platforms, including iOS, Android, and progressive web applications, from a single codebase by using standardized web technologies. SHAPE has been integrated with a leading Health Level 7 (HL7®) Fast Healthcare Interoperability Resources (FHIR®) application programming interface platform, 1upHealth, which allows participants to consent to 1-time data pull of their electronic health records. We used an agile-based process that engaged multiple stakeholders in SHAPE’s design and development. Results: SHAPE allows study coordinators to plan, develop, and deploy questionnaires to obtain important end points directly from patients or caregivers. Electronic health record integration enables access to patient health records, which can validate and enhance the accuracy of data-capture methods. The administrator can then download the study data into HL7® FHIR®–formatted JSON files. In this paper, we illustrate how study coordinators can use SHAPE to design patient-centered studies. We demonstrate its broad applicability through a hypothetical type 1 diabetes cohort study and an ongoing pilot study on metachromatic leukodystrophy to implement best practices for designing a regulatory-grade natural history study for rare diseases. Conclusions: SHAPE is an intuitive and comprehensive data-collection tool for a variety of clinical studies. Further customization of this versatile and scalable platform allows for multiple use cases. SHAPE can capture patient perspectives and clinical data, thereby providing regulators, clinicians, researchers, and patient advocacy organizations with data to inform drug development and improve patient outcomes. %M 36103218 %R 10.2196/38579 %U https://formative.jmir.org/2022/9/e38579 %U https://doi.org/10.2196/38579 %U http://www.ncbi.nlm.nih.gov/pubmed/36103218 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e33890 %T Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study %A Ren,Benny %A Xia,Cedric Huchuan %A Gehrman,Philip %A Barnett,Ian %A Satterthwaite,Theodore %+ Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, 1st floor Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, United States, 1 9177676698, bennyren@pennmedicine.upenn.edu %K mobile health %K mHealth %K hidden Markov model %K mental health %K circadian rhythm %K mobile phone %D 2022 %7 14.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Irregularities in circadian rhythms have been associated with adverse health outcomes. The regularity of rhythms can be quantified using passively collected smartphone data to provide clinically relevant biomarkers of routine. Objective: This study aims to develop a metric to quantify the regularity of activity rhythms and explore the relationship between routine and mood, as well as demographic covariates, in an outpatient psychiatric cohort. Methods: Passively sensed smartphone data from a cohort of 38 young adults from the Penn or Children’s Hospital of Philadelphia Lifespan Brain Institute and Outpatient Psychiatry Clinic at the University of Pennsylvania were fitted with 2-state continuous-time hidden Markov models representing active and resting states. The regularity of routine was modeled as the hour-of-the-day random effects on the probability of state transition (ie, the association between the hour-of-the-day and state membership). A regularity score, Activity Rhythm Metric, was calculated from the continuous-time hidden Markov models and regressed on clinical and demographic covariates. Results: Regular activity rhythms were associated with longer sleep durations (P=.009), older age (P=.001), and mood (P=.049). Conclusions: Passively sensed Activity Rhythm Metrics are an alternative to existing metrics but do not require burdensome survey-based assessments. Low-burden, passively sensed metrics based on smartphone data are promising and scalable alternatives to traditional measurements. %M 36103225 %R 10.2196/33890 %U https://formative.jmir.org/2022/9/e33890 %U https://doi.org/10.2196/33890 %U http://www.ncbi.nlm.nih.gov/pubmed/36103225 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e33606 %T Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning %A Perrett,Toby %A Masullo,Alessandro %A Damen,Dima %A Burghardt,Tilo %A Craddock,Ian %A Mirmehdi,Majid %+ University of Bristol, Digital Health, First Floor, 1 Cathedral Square, Bristol, BS1 5DD, United Kingdom, 44 117 45 50375, toby.perrett@bristol.ac.uk %K energy expenditure %K calories, calorimetry %K deep learning %K computer vision %D 2022 %7 14.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Calorimetry is both expensive and obtrusive but provides the only way to accurately measure energy expenditure in daily living activities of any specific person, as different people can use different amounts of energy despite performing the same actions in the same manner. Deep learning video analysis techniques have traditionally required a lot of data to train; however, recent advances in few-shot learning, where only a few training examples are necessary, have made developing personalized models without a calorimeter a possibility. Objective: The primary aim of this study is to determine which activities are most well suited to calibrate a vision-based personalized deep learning calorie estimation system for daily living activities. Methods: The SPHERE (Sensor Platform for Healthcare in a Residential Environment) Calorie data set is used, which features 10 participants performing 11 daily living activities totaling 4.5 hours of footage. Calorimeter and video data are available for all recordings. A deep learning method is used to regress calorie predictions from video. Results: Models are personalized with 32 seconds from all 11 actions in the data set, and mean square error (MSE) is taken against a calorimeter ground truth. The best single action for calibration is wipe (1.40 MSE). The best pair of actions are sweep and sit (1.09 MSE). This compares favorably to using a whole 30-minute sequence containing 11 actions to calibrate (1.06 MSE). Conclusions: A vision-based deep learning energy expenditure estimation system for a wide range of daily living activities can be calibrated to a specific person with footage and calorimeter data from 32 seconds of sweeping and 32 seconds of sitting. %M 36103223 %R 10.2196/33606 %U https://formative.jmir.org/2022/9/e33606 %U https://doi.org/10.2196/33606 %U http://www.ncbi.nlm.nih.gov/pubmed/36103223 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e31775 %T Objective Monitoring of Facioscapulohumeral Dystrophy During Clinical Trials Using a Smartphone App and Wearables: Observational Study %A Maleki,Ghobad %A Zhuparris,Ahnjili %A Koopmans,Ingrid %A Doll,Robert J %A Voet,Nicoline %A Cohen,Adam %A van Brummelen,Emilie %A Groeneveld,Geert Jan %A De Maeyer,Joris %+ Facio Therapies, Galileiweg 8, Leiden, 2333BD, Netherlands, 31 496109262, joris.demaeyer@facio-therapies.com %K facioscapulohumeral dystrophy %K FSHD %K smartphone %K wearables %K machine learning %K classification %K mobile phone %D 2022 %7 13.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients’ quality of life. Real-world data related to physical activity, sleep, and social behavior could potentially provide additional insight into the impact of the disease and might be useful in assessing treatment effects on aspects that are important contributors to the functioning and well-being of patients with FSHD. Objective: This study investigated the feasibility of using smartphones and wearables to capture symptoms related to FSHD based on a continuous collection of multiple features, such as the number of steps, sleep, and app use. We also identified features that can be used to differentiate between patients with FSHD and non-FSHD controls. Methods: In this exploratory noninterventional study, 58 participants (n=38, 66%, patients with FSHD and n=20, 34%, non-FSHD controls) were monitored using a smartphone monitoring app for 6 weeks. On the first and last day of the study period, clinicians assessed the participants’ FSHD clinical score and Timed Up-and-Go test time. Participants installed the app on their Android smartphones, were given a smartwatch, and were instructed to measure their weight and blood pressure on a weekly basis using a scale and blood pressure monitor. The user experience and perceived burden of the app on participants’ smartphones were assessed at 6 weeks using a questionnaire. With the data collected, we sought to identify the behavioral features that were most salient in distinguishing the 2 groups (patients with FSHD and non-FSHD controls) and the optimal time window to perform the classification. Results: Overall, the participants stated that the app was well tolerated, but 67% (39/58) noticed a difference in battery life using all 6 weeks of data, we classified patients with FSHD and non-FSHD controls with 93% accuracy, 100% sensitivity, and 80% specificity. We found that the optimal time window for the classification is the first day of data collection and the first week of data collection, which yielded an accuracy, sensitivity, and specificity of 95.8%, 100%, and 94.4%, respectively. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification. Conclusions: Remotely monitored data collection allowed for the collection of daily activity data in patients with FSHD and non-FSHD controls for 6 weeks. We demonstrated the initial ability to detect differences in features in patients with FSHD and non-FSHD controls using smartphones and wearables, mainly based on data related to physical and social activity. Trial Registration: ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735 %M 36098990 %R 10.2196/31775 %U https://formative.jmir.org/2022/9/e31775 %U https://doi.org/10.2196/31775 %U http://www.ncbi.nlm.nih.gov/pubmed/36098990 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 9 %P e35727 %T Wearable Devices in Diving: Scoping Review %A Bube,Benjamin %A Zanón,Bruno Baruque %A Lara Palma,Ana María %A Klocke,Heinrich %+ Faculty of Computer Science and Engineering Science, University of Applied Sciences Cologne, Steinmüllerallee 1, Gummersbach, 51643, Germany, 49 1601688162, benjaminbube@googlemail.com %K wearable device %K underwater communication %K head-up display %K safety device %K scuba diving %K free diving %D 2022 %7 6.9.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Wearables and their benefits for the safety and well-being of users have been widely studied and have had an enormous impact on the general development of these kinds of devices. Yet, the extent of research into the use and impact of wearable devices in the underwater environment is comparatively low. In the past 15 years, there has been an increased interest in research into wearables that are used underwater, as the use of such wearables has steadily grown over time. However, there has so far been no clear indication in the literature about the direction in which efforts for the design and construction of underwater wearable devices are developing. Therefore, the analysis presented in this scoping review establishes a good and powerful basis for the further development and orientation of current underwater wearables within the field. Objective: In this scoping review, we targeted wearable devices for underwater use to make a comprehensive map of their capabilities and features and discuss the general direction of the development of underwater wearables and the orientation of research into novel prototypes of these kinds of devices. Methods: In September 2021, we conducted an extensive search for existing literature on 4 databases and for grey literature to identify developed prototypes and early-stage products that were described and tested in water, could be worn and interacted with (eg, displays, buttons, etc), and were fully functional without external equipment. The studies were written in English, came from peer-reviewed academic sources, and were published between 2005 and 2021. We reviewed each title and abstract. The data extraction process was carried out by one author and verified by another author. Results: In total, 36 relevant studies were included. Among these, 4 different categories were identified; 18 studies dealt primarily with safety devices, 9 dealt with underwater communication devices, 7 dealt with head-up displays, and 2 dealt with underwater human-computer interaction approaches. Although the safety devices seemed to have gained the most interest at the time of this study, a clear trend toward underwater communication wearables was identified. Conclusions: This review sought to provide a first insight into the possibilities and challenges of the technologies that have been used in and for wearable devices that are meant for use in the underwater environment. Among these, underwater communication technologies have had the most significant influence on future developments. Moreover, a topic that has not received enough attention but should be further addressed is human-computer interaction. By developing underwater wearables that cover 2 or more of the technology categories that we identified, the extent of the benefits of such devices can be significantly increased in the future. %M 36066926 %R 10.2196/35727 %U https://mhealth.jmir.org/2022/9/e35727 %U https://doi.org/10.2196/35727 %U http://www.ncbi.nlm.nih.gov/pubmed/36066926 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e34280 %T Measurement of Heart Rate Using the Withings ScanWatch Device During Free-living Activities: Validation Study %A Giggins,Oonagh M %A Doyle,Julie %A Smith,Suzanne %A Crabtree,Daniel R %A Fraser,Matthew %+ NetwellCASALA, Dundalk Institute of Technology, Dublin Road, Dundalk, A91 K584, Ireland, 353 429370200 ext 2114, oonagh.giggins@dkit.ie %K heart rate %K photoplethysmography %K PPG %K wearable electronic device %K wrist-worn device %K validation study %K heart %K activity %K physical activity %K free-living activity %D 2022 %7 1.9.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Wrist-worn devices that incorporate photoplethysmography (PPG) sensing represent an exciting means of measuring heart rate (HR). A number of studies have evaluated the accuracy of HR measurements produced by these devices in controlled laboratory environments. However, it is also important to establish the accuracy of measurements produced by these devices outside the laboratory, in real-world, consumer use conditions. Objective: This study sought to examine the accuracy of HR measurements produced by the Withings ScanWatch during free-living activities. Methods: A sample of convenience of 7 participants volunteered (3 male and 4 female; mean age 64, SD 10 years; mean height 164, SD 4 cm; mean weight 77, SD 16 kg) to take part in this real-world validation study. Participants were instructed to wear the ScanWatch for a 12-hour period on their nondominant wrist as they went about their day-to-day activities. A Polar H10 heart rate sensor was used as the criterion measure of HR. Participants used a study diary to document activities undertaken during the 12-hour study period. These activities were classified according to the 11 following domains: desk work, eat or drink, exercise, gardening, household activities, self-care, shopping, sitting, sleep, travel, and walking. Validity was assessed using the Bland-Altman analysis, concordance correlation coefficient (CCC), and mean absolute percentage error (MAPE). Results: Across all activity domains, the ScanWatch measured HR with MAPE values <10%, except for the shopping activity domain (MAPE=10.8%). The activity domains that were more sedentary in nature (eg, desk work, eat or drink, and sitting) produced the most accurate HR measurements with a small mean bias and MAPE values <5%. Moderate to strong correlations (CCC=0.526-0.783) were observed between devices for all activity domains, except during the walking activity domain, which demonstrated a weak correlation (CCC=0.164) between devices. Conclusions: The results of this study show that the ScanWatch measures HR with a degree of accuracy that is acceptable for general consumer use; however, it would not be suitable in circumstances where more accurate measurements of HR are required, such as in health care or in clinical trials. Overall, the ScanWatch was less accurate at measuring HR during ambulatory activities (eg, walking, gardening, and household activities) compared to more sedentary activities (eg, desk work, eat or drink, and sitting). Further larger-scale studies examining this device in different populations and during different activities are required. %M 36048505 %R 10.2196/34280 %U https://formative.jmir.org/2022/9/e34280 %U https://doi.org/10.2196/34280 %U http://www.ncbi.nlm.nih.gov/pubmed/36048505 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e38716 %T Insights Into Needs and Preferences for Mental Health Support on Social Media and Through Mobile Apps Among Black Male University Students: Exploratory Qualitative Study %A Williams,Kofoworola D A %A Wijaya,Clarisa %A Stamatis,Caitlin A %A Abbott,Gabriel %A Lattie,Emily G %+ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, 10th floor, Chicago, IL, 60611, United States, 1 (312) 503 2922, kofoworola.williams@northwestern.edu %K Black or African American men %K college %K mental health %K social media %K mobile apps %K mobile phone %D 2022 %7 31.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Black college-aged men are less likely than their peers to use formal, therapeutic in-person services for mental health concerns. As the use of mobile technologies and social media platforms is steadily increasing, it is important to conduct work that examines the future utility of digital tools and technologies to improve access to and uptake of mental health services for Black men and Black men in college. Objective: The aim of this study was to identify and understand college-attending Black men’s needs and preferences for using digital health technologies and social media for stress and mental health symptom management. Methods: Interviews were conducted with Black male students (N=11) from 2 racially diverse universities in the Midwestern United States. Participants were asked questions related to their current mental health needs and interest in using social media platforms and mobile-based apps for their mental health concerns. A thematic analysis was conducted. Results: Four themes emerged from the data: current stress relief strategies, technology-based support needs and preferences (subthemes: mobile-based support and social media–based support), resource information dissemination considerations (subthemes: information-learning expectations and preferences and information-sharing preferences and behaviors), and technology-based mental health support design considerations (subtheme: relatability and representation). Participants were interested in using social media and digital technologies for their mental health concerns and needs, for example, phone notifications and visual-based mental health advertisements that promote awareness. Relatability in the context of representation was emphasized as a key factor for participants interested in using digital mental health tools. Examples of methods for increasing relatability included having tools disseminated by minority-serving organizations and including components explicitly portraying Black men engaging in mental health support strategies. The men also discussed wanting to receive recommendations for stress relief that have been proven successful, particularly for Black men. Conclusions: The findings from this study provide insights into design and dissemination considerations for future work geared toward developing mental health messaging and digital interventions for young Black men. %M 36044261 %R 10.2196/38716 %U https://formative.jmir.org/2022/8/e38716 %U https://doi.org/10.2196/38716 %U http://www.ncbi.nlm.nih.gov/pubmed/36044261 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 8 %P e25735 %T Birth and Death Notifications for Improving Civil Registration and Vital Statistics in Bangladesh: Pilot Exploratory Study %A Tahsina,Tazeen %A Iqbal,Afrin %A Rahman,Ahmed Ehsanur %A Chowdhury,Suman Kanti %A Chowdhury,Atique Iqbal %A Billah,Sk Masum %A Rahman,Ataur %A Parveen,Monira %A Ahmed,Lubana %A Rahman,Qazi Sadequr %A Ashrafi,Shah Ali Akbar %A Arifeen,Shams El %+ International Centre for Diarrhoeal Disease Research, Bangladesh, 68, Shahid Tajuddin Ahmed Sarani, Mohakhali, Dhaka, 1212, Bangladesh, 880 9827077, tahsina.tazeen@gmail.com %K notification %K registration %K birth %K death %K CRVS %K mobile phone %K mobile app %K mobile technology %K technology-based platform %K community health %K low- and middle-income countries %K mHealth %K Bangladesh %D 2022 %7 29.8.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Effective health policy formulation requires sound information of the numerical data and causes of deaths in a population. Currently, in Bangladesh, neither births nor deaths are fully and promptly registered. Birth registration in Bangladesh is around 54% nationally. Although the legal requirements are to register within 45 days of an event, only 4.5% of births and 35.9% of deaths were reported within the required time frame in 2020. This study adopted an innovative digital notification approach to improve the coverage of registration of these events at the community level. Objective: Our primary objective was to assess (1) the proportion of events identified by the new notification systems (success rate) and the contribution of the different notifiers individually and in combination (completeness) and (2) the proportion of events notified within specific time limits (timeliness of notifications) after introducing the innovative approach. Methods: We conducted a pilot study in 2016 in 2 subdistricts of Bangladesh to understand whether accurate, timely, and complete information on births and deaths can be collected and notified by facility-based service providers; community health workers, including those who routinely visit households; local government authorities; and key informants from the community. We designed a mobile technology–based platform, an app, and a call center through which the notifications were recorded. All notifications were verified through the confirmation of events by family members during visits to the concerned households. We undertook a household survey–based assessment at the end of the notification period. Results: Our innovative system gathered 13,377 notifications for births and deaths from all channels, including duplicate reports from multiple sources. Project workers were able to verify 92% of the births and 93% of the deaths through household visits. The household survey conducted among a subsample of the project population identified 1204 births and 341 deaths. After matching the notifications with the household survey, we found that the system was able to capture over 87% of the births in the survey areas. Health assistants and family welfare assistants were the primary sources of information. Notifications from facilities were very low for both events. Conclusions: The Global Civil Registration and Vital Statistics: Scaling Up Investment Plan 2015-2024 and the World Health Organization reiterated the importance of building an evidence base for improving civil registration and vital statistics. Our pilot innovation revealed that it is possible to coordinate with the routine health information system to note births and deaths as the first step to ensure registration. Health assistants could capture more than half of the notifications as a stand-alone source. %M 36036979 %R 10.2196/25735 %U https://publichealth.jmir.org/2022/8/e25735 %U https://doi.org/10.2196/25735 %U http://www.ncbi.nlm.nih.gov/pubmed/36036979 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e37994 %T Heart Rate Variability Biofeedback to Treat Anxiety in Young People With Autism Spectrum Disorder: Findings From a Home-Based Pilot Study %A Coulter,Helen %A Donnelly,Mark %A Mallett,John %A Kernohan,W George %+ Ulster University, Shore Road, Newtownabbey, BT370QB, United Kingdom, 44 2895365135, mp.donnelly@ulster.ac.uk %K autism %K anxiety %K biofeedback %K remote intervention %K mobile phone %D 2022 %7 26.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: People with autism spectrum disorder (ASD) frequently experience high levels of anxiety. Despite this, many clinical settings do not provide specialist ASD mental health services, and demand for professional support frequently outstrips supply. Across many sectors of health, investigators have explored digital health solutions to mitigate demand and extend the reach of professional practice beyond traditional clinical settings. Objective: This critical appraisal and pilot feasibility study examines heart rate variability (HRV) biofeedback as an approach to help young people with ASD to manage anxiety symptoms outside of formal settings. The aim is to explore the use of portable biofeedback devices to manage anxiety, while also highlighting the risks and benefits of this approach with this population. Methods: We assessed the feasibility of using home-based HRV biofeedback for self-management of anxiety in young people with ASD. We adopted coproduction, involving people with ASD, to facilitate development of the study design. Next, a separate pilot with 20 participants with ASD (n=16, 80% male participants and n=4, 20% female participants, aged 13-24 years; IQ>70) assessed adoption and acceptability of HRV biofeedback devices for home use over a 12-week period. Data were collected from both carers and participants through questionnaires and interviews; participants also provided single-lead electrocardiogram recordings as well as daily reports through smartphone on adoption and use of their device. Results: Pre-post participant questionnaires indicated a significant reduction in anxiety in children (t6=2.55; P=.04; Cohen d=0.99) as well as adults (t7=3.95; P=.006; Cohen d=0.54). Participant age was significantly negatively correlated with all HRV variables at baseline, namely high-frequency heart rate variability (HF-HRV: P=.02), the root mean square of successive differences in normal heartbeat contractions (RMSSD: P=.02) and the variability of normal-to-normal interbeat intervals (SDNN: P=.04). At follow-up, only SDNN was significantly negatively correlated with age (P=.05). Levels of ASD symptoms were positively correlated with heart rate both before (P=.04) and after the intervention (P=.01). The majority (311/474, 65.6%) of reports from participants indicated that the devices helped when used. Difficulties with the use of some devices and problems with home testing of HRV were noted. These initial findings are discussed within the context of the strengths and challenges of remotely delivering a biofeedback intervention for people with ASD. Conclusions: HRV biofeedback devices have shown promise in this pilot study. There is now a need for larger evaluation of biofeedback to determine which delivery methods achieve the greatest effect for people with ASD. Trial Registration: ClinicalTrials.gov NCT04955093; https://clinicaltrials.gov/ct2/show/NCT04955093 %M 36018712 %R 10.2196/37994 %U https://formative.jmir.org/2022/8/e37994 %U https://doi.org/10.2196/37994 %U http://www.ncbi.nlm.nih.gov/pubmed/36018712 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e36972 %T Passively Captured Interpersonal Social Interactions and Motion From Smartphones for Predicting Decompensation in Heart Failure: Observational Cohort Study %A Cakmak,Ayse S %A Perez Alday,Erick A %A Densen,Samuel %A Najarro,Gabriel %A Rout,Pratik %A Rozell,Christopher J %A Inan,Omer T %A Shah,Amit J %A Clifford,Gari D %+ Department of Biomedical Informatics, School of Medicine, Emory University, 100 Woodruff Circle, Atlanta, GA, 30322, United States, 1 404 727 6123, erick@dbmi.emory.edu %K heart failure %K mobile device %K social interaction %K heart disease %K mobile health %K hospitalization %D 2022 %7 24.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Heart failure (HF) is a major cause of frequent hospitalization and death. Early detection of HF symptoms using smartphone-based monitoring may reduce adverse events in a low-cost, scalable way. Objective: We examined the relationship of HF decompensation events with smartphone-based features derived from passively and actively acquired data. Methods: This was a prospective cohort study in which we monitored HF participants’ social and movement activities using a smartphone app and followed them for clinical events via phone and chart review and classified the encounters as compensated or decompensated by reviewing the provider notes in detail. We extracted motion, location, and social interaction passive features and self-reported quality of life weekly (active) with the short Kansas City Cardiomyopathy Questionnaire (KCCQ-12) survey. We developed and validated an algorithm for classifying decompensated versus compensated clinical encounters (hospitalizations or clinic visits). We evaluated models based on single modality as well as early and late fusion approaches combining patient-reported outcomes and passive smartphone data. We used Shapley additive explanation values to quantify the contribution and impact of each feature to the model. Results: We evaluated 28 participants with a mean age of 67 years (SD 8), among whom 11% (3/28) were female and 46% (13/28) were Black. We identified 62 compensated and 48 decompensated clinical events from 24 and 22 participants, respectively. The highest area under the precision-recall curve (AUCPr) for classifying decompensation was with a late fusion approach combining KCCQ-12, motion, and social contact features using leave-one-subject-out cross-validation for a 2-day prediction window. It had an AUCPr of 0.80, with an area under the receiver operator curve (AUC) of 0.83, a positive predictive value (PPV) of 0.73, a sensitivity of 0.77, and a specificity of 0.88 for a 2-day prediction window. Similarly, the 4-day window model had an AUC of 0.82, an AUCPr of 0.69, a PPV of 0.62, a sensitivity of 0.68, and a specificity of 0.87. Passive social data provided some of the most informative features, with fewer calls of longer duration associating with a higher probability of future HF decompensation. Conclusions: Smartphone-based data that includes both passive monitoring and actively collected surveys may provide important behavioral and functional health information on HF status in advance of clinical visits. This proof-of-concept study, although small, offers important insight into the social and behavioral determinants of health and the feasibility of using smartphone-based monitoring in this population. Our strong results are comparable to those of more active and expensive monitoring approaches, and underscore the need for larger studies to understand the clinical significance of this monitoring method. %M 36001367 %R 10.2196/36972 %U https://formative.jmir.org/2022/8/e36972 %U https://doi.org/10.2196/36972 %U http://www.ncbi.nlm.nih.gov/pubmed/36001367 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e38475 %T Reliability and Validity of Electronic Patient-Reported Outcomes Using the Smartphone App AllerSearch for Hay Fever: Prospective Observational Study %A Akasaki,Yasutsugu %A Inomata,Takenori %A Sung,Jaemyoung %A Okumura,Yuichi %A Fujio,Kenta %A Miura,Maria %A Hirosawa,Kunihiko %A Iwagami,Masao %A Nakamura,Masahiro %A Ebihara,Nobuyuki %A Nakamura,Masahiro %A Ide,Takuma %A Nagino,Ken %A Murakami,Akira %+ Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan, 81 338133111, tinoma@juntendo.ac.jp %K hay fever %K AllerSearch %K smartphone app %K mobile health %K mHealth %K patient-reported outcome %K reliability %K validity %K Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire %K JACQLQ %K questionnaire %K allergic conjunctivitis %D 2022 %7 23.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Hay fever is a highly prevalent, heterogenous, and multifactorial disease. Patients may benefit from longitudinal assessments using mobile health (mHealth) principles. We have previously attempted to establish an effective mHealth platform for patients with hay fever through AllerSearch, our in-house smartphone app that assesses electronic patient-reported outcomes through a questionnaire on hay fever and provides evidence-based advice. To be used by the public, an investigation on its reliability and validity is necessary. Objective: The aim of this paper is to assess the reliability and validity of subjective symptom data on hay fever collected through our app, AllerSearch. Methods: This study used a prospective observational design. The participants were patients aged ≥20 years recruited from a single university hospital between June 2, 2021, and January 26, 2022. We excluded patients who could not use smartphones as well as those with incomplete data records and outlier data. All participants answered the Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire (JACQLQ), first in the paper-and-pencil format and subsequently on AllerSearch on the same day. The JACQLQ comprises the following three domains: Domain I, with 9 items on ocular or nasal symptoms; Domain II, with 17 items on daily activity and psychological well-being; and Domain III, with 3 items on overall condition by face score. The concordance rate of each domain between the 2 platforms was calculated. The internal consistency of Domains I and II of the 2 platforms was assessed using Cronbach alpha coefficients, the concurrent validity of Domains I and II was assessed by calculating Pearson correlation coefficients, and the mean differences between the 2 platforms were assessed using Bland-Altman analysis. Results: In total, 22 participants were recruited; the data of 20 (91%) participants were analyzed. The average age was 65.4 (SD 12.8) years, and 80% (16/20) of the participants were women. The concordance rate of Domains I, II, and III between the paper-based and app-based JACQLQ was 0.78, 0.85, and 0.90, respectively. The internal consistency of Domains I and II between the 2 platforms was satisfactory (Cronbach alpha of .964 and .919, respectively). Pearson correlation analysis yielded a significant positive correlation between Domains I and II across the 2 platforms (r=0.920 and r=0.968, respectively). The mean difference in Domains I and II between the 2 platforms was 3.35 units (95% limits of agreement: –6.51 to 13.2). Conclusions: Our findings indicate that AllerSearch is a valid and reliable tool for the collection of electronic patient-reported outcomes to assess hay fever, contributing to the advantages of the mHealth platform. %M 35998022 %R 10.2196/38475 %U https://formative.jmir.org/2022/8/e38475 %U https://doi.org/10.2196/38475 %U http://www.ncbi.nlm.nih.gov/pubmed/35998022 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 8 %P e38958 %T A Gambling Just-In-Time Adaptive Intervention (GamblingLess: In-The-Moment): Protocol for a Microrandomized Trial %A Dowling,Nicki A %A Merkouris,Stephanie S %A Youssef,George J %A Lubman,Dan I %A Bagot,Kathleen L %A Hawker,Chloe O %A Portogallo,Hannah J %A Thomas,Anna C %A Rodda,Simone N %+ School of Psychology, Deakin University, 1 Gheringhap St, Geelong, 3220, Australia, 61 3 9244 5610, nicki.dowling@deakin.edu.au %K mobile health %K mHealth %K just-in-time adaptive intervention %K ecological momentary intervention %K microrandomized trial %K gambling %K addiction %K treatment %K intervention %K protocol %K relapse %K mobile phone %D 2022 %7 23.8.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: The presence of discrete but fluctuating precipitants, in combination with the dynamic nature of gambling episodes, calls for the development of tailored interventions delivered in real time, such as just-in-time adaptive interventions (JITAIs). JITAIs leverage mobile and wireless technologies to address dynamically changing individual needs by providing the type and amount of support required at the right time and only when needed. They have the added benefit of reaching underserved populations by providing accessible, convenient, and low-burden support. Despite these benefits, few JITAIs targeting gambling behavior are available. Objective: This study aims to redress this gap in service provision by developing and evaluating a theoretically informed and evidence-based JITAI for people who want to reduce their gambling. Delivered via a smartphone app, GamblingLess: In-The-Moment provides tailored cognitive-behavioral and third-wave interventions targeting cognitive processes explicated by the relapse prevention model (cravings, self-efficacy, and positive outcome expectancies). It aims to reduce gambling symptom severity (distal outcome) through short-term reductions in the likelihood of gambling episodes (primary proximal outcome) by improving craving intensity, self-efficacy, or expectancies (secondary proximal outcomes). The primary aim is to explore the degree to which the delivery of a tailored intervention at a time of cognitive vulnerability reduces the probability of a subsequent gambling episode. Methods: GamblingLess: In-The-Moment interventions are delivered to gamblers who are in a state of receptivity (available for treatment) and report a state of cognitive vulnerability via ecological momentary assessments 3 times a day. The JITAI will tailor the type, timing, and amount of support for individual needs. Using a microrandomized trial, a form of sequential factorial design, each eligible participant will be randomized to a tailored intervention condition or no intervention control condition at each ecological momentary assessment across a 28-day period. The microrandomized trial will be supplemented by a 6-month within-group follow-up evaluation to explore long-term effects on primary (gambling symptom severity) and secondary (gambling behavior, craving severity, self-efficacy, and expectancies) outcomes and an acceptability evaluation via postintervention surveys, app use and engagement indices, and semistructured interviews. In all, 200 participants will be recruited from Australia and New Zealand. Results: The project was funded in June 2019, with approval from the Deakin University Human Research Ethics Committee (2020-304). Stakeholder user testing revealed high acceptability scores. The trial began on March 29, 2022, and 84 participants have been recruited (as of June 24, 2022). Results are expected to be published mid-2024. Conclusions: GamblingLess: In-The-Moment forms part of a suite of theoretically informed and evidence-based web-based and mobile gambling interventions. This trial will provide important empirical data that can be used to facilitate the JITAI’s optimization to make it a more effective, efficient, and scalable tailored intervention. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12622000490774; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380757&isClinicalTrial=False International Registered Report Identifier (IRRID): PRR1-10.2196/38958 %M 35998018 %R 10.2196/38958 %U https://www.researchprotocols.org/2022/8/e38958 %U https://doi.org/10.2196/38958 %U http://www.ncbi.nlm.nih.gov/pubmed/35998018 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 8 %P e34734 %T Wearables for Measuring the Physical Activity and Sedentary Behavior of Patients With Axial Spondyloarthritis: Systematic Review %A Soulard,Julie %A Carlin,Thomas %A Knitza,Johannes %A Vuillerme,Nicolas %+ Université Grenoble Alpes, AGEIS, Batiment Jean Roget, Faculté de médecine, La Tronche, 38700, France, 33 476637104, juliesoulard.physio@gmail.com %K axial spondyloarthritis %K rheumatology %K physical activity %K sedentary behavior %K objective measures %K wearable %K mobile health %K mHealth %K eHealth %K systematic review %K mobile phone %D 2022 %7 22.8.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Axial spondyloarthritis (axSpA) is an inflammatory rheumatic disease associated with chronic back pain and restricted mobility and physical function. Increasing physical activity is a viable strategy for improving the health and quality of life of patients with axSpA. Thus, quantifying physical activity and sedentary behavior in this population is relevant to clinical outcomes and disease management. However, to the best of our knowledge, no systematic review to date has identified and synthesized the available evidence on the use of wearable devices to objectively measure the physical activity or sedentary behavior of patients with axSpA. Objective: This study aimed to review the literature on the use of wearable activity trackers as outcome measures for physical activity and sedentary behavior in patients with axSpA. Methods: PubMed, PEDro, and Cochrane electronic databases were searched in July 2021 for relevant original articles, with no limits on publication dates. Studies were included if they were original articles, targeted adults with a diagnosis of axSpA, and reported wearable device–measured physical activity or sedentary behavior among patients with axSpA. Data regarding the study’s characteristics, the sample description, the methods used for measuring physical activity and sedentary behavior (eg, wearable devices, assessment methods, and outcomes), and the main results of the physical activity and sedentary behavior assessments were extracted. Results: A total of 31 studies were initially identified; 13 (13/31, 42%) met the inclusion criteria, including 819 patients with axSpA. All the studies used accelerometer-based wearable devices to assess physical activity. Of the 13 studies, 4 (4/31, 31%) studies also reported outcomes related to sedentary behavior. Wearable devices were secured on the wrists (3/13 studies, 23%), lower back (3/13, 23%), right hip (3/13, 23%), waist (2/13, 15%), anterior thigh (1/13, 8%), or right arm (1/13, 8%). The methods for reporting physical activity and sedentary behavior were heterogeneous. Approximately 77% (10/13) of studies had a monitoring period of 1 week, including weekend days. Conclusions: To date, few studies have used wearable devices to quantify the physical activity and sedentary behavior of patients with axSpA. The methodologies and results were heterogeneous, and none of these studies assessed the psychometric properties of these wearables in this specific population. Further investigation in this direction is needed before using wearable device–measured physical activity and sedentary behavior as outcome measures in intervention studies in patients with axSpA. Trial Registration: PROSPERO CRD42020182398; https://tinyurl.com/ec22jzkt International Registered Report Identifier (IRRID): RR2-10.2196/23359 %M 35994315 %R 10.2196/34734 %U https://mhealth.jmir.org/2022/8/e34734 %U https://doi.org/10.2196/34734 %U http://www.ncbi.nlm.nih.gov/pubmed/35994315 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 3 %P e31991 %T Identifying Potential Gamification Elements for A New Chatbot for Families With Neurodevelopmental Disorders: User-Centered Design Approach %A Bui,Truong An %A Pohl,Megan %A Rosenfelt,Cory %A Ogourtsova,Tatiana %A Yousef,Mahdieh %A Whitlock,Kerri %A Majnemer,Annette %A Nicholas,David %A Demmans Epp,Carrie %A Zaiane,Osmar %A Bolduc,François V %+ Department of Pediatrics, University of Alberta, 4-588 Edmonton Clinic Health Academy, 11405-87 Ave, Edmonton, AB, T6G 1C9, Canada, 1 780 248 5569, fbolduc@ualberta.ca %K gamification %K chatbot %K neurodevelopmental disorders %K engagement %K mobile health %K mHealth %K eHealth %K focus group %K interview %K user-centered design %K health information technologies %D 2022 %7 19.8.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Chatbots have been increasingly considered for applications in the health care field. However, it remains unclear how a chatbot can assist users with complex health needs, such as parents of children with neurodevelopmental disorders (NDDs) who need ongoing support. Often, this population must deal with complex and overwhelming health information, which can make parents less likely to use a software that may be very helpful. An approach to enhance user engagement is incorporating game elements in nongame contexts, known as gamification. Gamification needs to be tailored to users; however, there has been no previous assessment of gamification use in chatbots for NDDs. Objective: We sought to examine how gamification elements are perceived and whether their implementation in chatbots will be well received among parents of children with NDDs. We have discussed some elements in detail as the initial step of the project. Methods: We performed a narrative literature review of gamification elements, specifically those used in health and education. Among the elements identified in the literature, our health and social science experts in NDDs prioritized five elements for in-depth discussion: goal setting, customization, rewards, social networking, and unlockable content. We used a qualitative approach, which included focus groups and interviews with parents of children with NDDs (N=21), to assess the acceptability of the potential implementation of these elements in an NDD-focused chatbot. Parents were asked about their opinions on the 5 elements and to rate them. Video and audio recordings were transcribed and summarized for emerging themes, using deductive and inductive thematic approaches. Results: From the responses obtained from 21 participants, we identified three main themes: parents of children with NDDs were familiar with and had positive experiences with gamification; a specific element (goal setting) was important to all parents, whereas others (customization, rewards, and unlockable content) received mixed opinions; and the social networking element received positive feedback, but concerns about information accuracy were raised. Conclusions: We showed for the first time that parents of children with NDDs support gamification use in a chatbot for NDDs. Our study illustrates the need for a user-centered design in the medical domain and provides a foundation for researchers interested in developing chatbots for populations that are medically vulnerable. Future studies exploring wide range of gamification elements with large number of potential users are needed to understand the impact of gamification elements in enhancing knowledge mobilization. %M 35984679 %R 10.2196/31991 %U https://humanfactors.jmir.org/2022/3/e31991 %U https://doi.org/10.2196/31991 %U http://www.ncbi.nlm.nih.gov/pubmed/35984679 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e38737 %T A “No-Code” App Design Platform for Mobile Health Research: Development and Usability Study %A Liu,Sam %A La,Henry %A Willms,Amanda %A Rhodes,Ryan E %+ School of Exercise Science, Physical and Health Education, University of Victoria, McKinnon Building 124, PO Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada, 1 250 721 8392, samliu@uvic.ca %K app development %K behavior change technique %K health promotion %K mobile health %K mobile application %K application development %K design platform %K platform development %K no-code mHealth app %K no-code app %K no-code %K end user %K participatory research %K Pathverse %K agile %K hybrid-agile %K software design %K software development %K software developer %K computer science %K BCT %K behavior change %K research tool %K research instrument %K digital platform %K mHealth %K mobile app %D 2022 %7 18.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: A challenge facing researchers conducting mobile health (mHealth) research is the amount of resources required to develop mobile apps. This can be a barrier to generating relevant knowledge in a timely manner. The recent rise of “no-code” software development platforms may overcome this challenge and enable researchers to decrease the cost and time required to develop mHealth research apps. Objective: We aimed to describe the development process and the lessons learned to build Pathverse, a no-code mHealth app design platform. Methods: The study took place between November 2019 and December 2021. We used a participatory research framework to develop the mHealth app design platform. In phase 1, we worked with researchers to gather key platform feature requirements and conducted an exploratory literature search to determine needs related to this platform. In phase 2, we used an agile software framework (Scrum) to develop the platform. Each development sprint cycle was 4 weeks in length. We created a minimum viable product at the end of 7 sprint cycles. In phase 3, we used a convenience sample of adults (n=5) to gather user feedback through usability and acceptability testing. In phase 4, we further developed the platform based on user feedback, following the V-model software development process. Results: Our team consulted end users (ie, researchers) and utilized behavior change technique taxonomy and behavior change models (ie, the multi-process action control framework) to guide the development of features. The first version of the Pathverse platform included features that allowed researchers to (1) design customized multimedia app content (eg, interactive lessons), (2) set content delivery logic (eg, only show new lessons when completing the previous lesson), (3) implement customized participant surveys, (4) provide self-monitoring tools, (5) set personalized goals, and (6) customize app notifications. Usability and acceptability testing revealed that researchers found the platform easy to navigate and that the features were intuitive to use. Potential improvements include the ability to deliver adaptive interventions and add features such as community group chat. Conclusions: To our knowledge, Pathverse is the first no-code mHealth app design platform for developing mHealth interventions for behavior. We successfully used behavior change models and the behavior change technique taxonomy to inform the feature requirements of Pathverse. Overall, the use of a participatory framework, combined with the agile and hybrid-agile software development process, enabled our team to successfully develop the Pathverse platform. %M 35980740 %R 10.2196/38737 %U https://formative.jmir.org/2022/8/e38737 %U https://doi.org/10.2196/38737 %U http://www.ncbi.nlm.nih.gov/pubmed/35980740 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 8 %P e37622 %T Digital Health Care Industry Ecosystem: Network Analysis %A Park,Yoonseo %A Park,Sewon %A Lee,Munjae %+ Department of Medical Humanities and Social Medicine, Ajou University School of Medicine, 206 World Cup-Ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Suwon, 16499, Republic of Korea, 82 31 219 5289, emunjae@ajou.ac.kr %K digital health care %K industrial ecosystem %K network analysis %K topic modeling %K South Korea %D 2022 %7 17.8.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: As the need for digital health care based on mobile devices is increasing, with the rapid development of digital technologies, especially in the face of the COVID-19 pandemic, gaining a better understanding of the industrial structure is needed to activate the use of digital health care. Objective: The aim of this study was to suggest measures to revitalize the digital health care industry by deriving the stakeholders and major issues with respect to the ecosystem of the industry. Methods: A total of 1822 newspaper articles were collected using Big Kings, a big data system for news, for a limited period from 2016 to August 2021, when the mobile health care project was promoted in Korea centered on public health centers. The R and NetMiner programs were used for network analysis. Results: The Korean government and the Ministry of Health and Welfare showed the highest centrality and appeared as major stakeholders, and their common major issues were “reviewing the introduction of telemedicine,” “concerns about bankruptcy of local clinics,” and “building an integrated platform for precision medicine.” In addition, the major stakeholders of medical institutions and companies were Seoul National University Hospital, Kangbuk Samsung Hospital, Ajou University Hospital, Samsung, and Vuno Inc. Conclusions: This analysis confirmed that the issues related to digital health care are largely composed of telemedicine, data, and health care business. For digital health care to develop as a national innovative growth engine and to be institutionalized, the development of a digital health care fee model that can improve the regulatory system and the cost-effectiveness of patient care, centering on the Ministry of Health and Welfare as a key stakeholder, is essential. %M 35976690 %R 10.2196/37622 %U https://www.jmir.org/2022/8/e37622 %U https://doi.org/10.2196/37622 %U http://www.ncbi.nlm.nih.gov/pubmed/35976690 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e37303 %T Validation of the Mobile App Version of the EQ-5D-5L Quality of Life Questionnaire Against the Gold Standard Paper-Based Version: Randomized Crossover Study %A Kamstra,Regina J M %A Boorsma,André %A Krone,Tanja %A van Stokkum,Robin M %A Eggink,Hannah M %A Peters,Ton %A Pasman,Wilrike J %+ Netherlands Organization for Applied Scientific Research (TNO), Utrechtseweg 48, Zeist, 3704 HE, Netherlands, 31 611873786, kristel.kamstra@tno.nl %K quality of life assessment %K EQ-5D-5L questionnaire %K mobile app %K test-retest reliability %K mobile phone %D 2022 %7 11.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Study participants and patients often perceive (long) questionnaires as burdensome. In addition, paper-based questionnaires are prone to errors such as (unintentionally) skipping questions or filling in a wrong type of answer. Such errors can be prevented with the emergence of mobile questionnaire apps. Objective: This study aimed to validate an innovative way to measure the quality of life using a mobile app based on the EQ-5D-5L questionnaire. This validation study compared the EQ-5D-5L questionnaire requested by a mobile app with the gold standard paper-based version of the EQ-5D-5L. Methods: This was a randomized, crossover, and open study. The main criteria for participation were participants should be aged ≥18 years, healthy at their own discretion, in possession of a smartphone with at least Android version 4.1 or higher or iOS version 9 or higher, digitally skilled in downloading the mobile app, and able to read and answer questionnaires in Dutch. Participants were recruited by a market research company that divided them into 2 groups balanced for age, gender, and education. Each participant received a digital version of the EQ-5D-5L questionnaire via a mobile app and the EQ-5D-5L paper-based questionnaire by postal mail. In the mobile app, participants received, for 5 consecutive days, 1 question in the morning and 1 question in the afternoon; as such, all questions were asked twice (at time point 1 [App T1] and time point 2 [App T2]). The primary outcomes were the correlations between the answers (scores) of each EQ-5D-5L question answered via the mobile app compared with the paper-based questionnaire to assess convergent validity. Results: A total of 255 participants (healthy at their own discretion), 117 (45.9%) men and 138 (54.1%) women in the age range of 18 to 64 years, completed the study. To ensure randomization, the measured demographics were checked and compared between groups. To compare the results of the electronic and paper-based questionnaires, polychoric correlation analysis was performed. All questions showed a high correlation (0.64-0.92; P<.001) between the paper-based and the mobile app–based questions at App T1 and App T2. The scores and their variance remained similar over the questionnaires, indicating no clear difference in the answer tendency. In addition, the correlation between the 2 app-based questionnaires was high (>0.73; P<.001), illustrating a high test-retest reliability, indicating it to be a reliable replacement for the paper-based questionnaire. Conclusions: This study indicates that the mobile app is a valid tool for measuring the quality of life and is as reliable as the paper-based version of the EQ-5D-5L, while reducing the response burden. %M 35969437 %R 10.2196/37303 %U https://formative.jmir.org/2022/8/e37303 %U https://doi.org/10.2196/37303 %U http://www.ncbi.nlm.nih.gov/pubmed/35969437 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 8 %P e38331 %T Quality of Life and Physical Activity in 629 Individuals With Sarcoidosis: Prospective, Cross-sectional Study Using Smartphones (Sarcoidosis App) %A Chu,Brian %A O'Connor,Daniel M %A Wan,Marilyn %A Barnett,Ian %A Shou,Haochang %A Judson,Marc %A Rosenbach,Misha %+ Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 2nd Floor, Maloney Building, Philadelphia, PA, 19104, United States, 1 2156627883, misha.rosenbach@uphs.upenn.edu %K sarcoidosis %K smartphone %K quality of life %K mobile app %K mobile health %K mHealth %K digital health %K rare disease %K physical activity %K exercise %K fitness %K development %K tracking %K recruit %K enroll %D 2022 %7 10.8.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Large gaps exist in understanding the symptomatic and functional impact of sarcoidosis, a rare multisystem granulomatous disease affecting fewer than 200,000 individuals in the United States. Smartphones could be used for prospective research, especially for rare diseases where organizing large cohorts can be challenging, given their near ubiquitous ownership and ability to track objective and subjective data with increasingly sophisticated technology. Objective: We aimed to investigate whether smartphones could assess the quality of life (QoL) and physical activity of a large cohort of individuals with sarcoidosis. Methods: We developed a mobile app (Sarcoidosis App) for a prospective, cross-sectional study on individuals with sarcoidosis. The Sarcoidosis App was made available on both Apple and Android smartphones. Individuals with sarcoidosis were recruited, consented, and enrolled entirely within the app. Surveys on sarcoidosis history, medical history, and medications were administered. Patients completed modules from the Sarcoidosis Assessment Tool, a validated patient-reported outcomes assessment of physical activity, fatigue, pain, skin symptoms, sleep, and lungs symptoms. Physical activity measured by smartphones was tracked as available. Results: From April 2018 to May 2020, the App was downloaded 2558 times, and 629 individuals enrolled (404, 64.2% female; mean age 51 years; 513, 81.6% White; 86, 13.7% Black). Two-thirds of participants had a college or graduate degree, and more than half of them reported an income greater than US $60,000. Both QoL related to physical activity (P<.001, ρ=0.250) and fatigue (P<.01, ρ=–0.203) correlated with actual smartphone-tracked physical activity. Overall, 19.0% (98/517) of participants missed at least 1 week of school or work in an observed month owing to sarcoidosis, and 44.4% (279/629) reported that finances “greatly” or “severely” affected by sarcoidosis. Furthermore, 71.2% (437/614) of participants reported taking medications for sarcoidosis, with the most common being prednisone, methotrexate, hydroxychloroquine, and infliximab. Moreover, 46.4% (244/526) reported medication side effects, most commonly due to prednisone. Conclusions: We demonstrate that smartphones can prospectively recruit, consent, and study physical activity, QoL, and medication usage in a large sarcoidosis cohort, using both passively collected objective data and qualitative surveys that did not require any in-person encounters. Our study’s limitations include the study population being weighted toward more educated and wealthier individuals, suggesting that recruitment was not representative of the full spectrum of patients with sarcoidosis in the United States. Our study provides a model for future smartphone-enabled clinical research for rare diseases and highlights key technical challenges that future research teams interested in smartphone-based research for rare diseases should anticipate. %M 35947439 %R 10.2196/38331 %U https://mhealth.jmir.org/2022/8/e38331 %U https://doi.org/10.2196/38331 %U http://www.ncbi.nlm.nih.gov/pubmed/35947439 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 8 %P e37547 %T Accuracy and Precision of Consumer-Grade Wearable Activity Monitors for Assessing Time Spent in Sedentary Behavior in Children and Adolescents: Systematic Review %A Martinko,Antonio %A Karuc,Josip %A Jurić,Petra %A Podnar,Hrvoje %A Sorić,Maroje %+ Faculty of Kinesiology, University of Zagreb, Horvaćanski zavoj 15, Zagreb, 10000, Croatia, 385 981302484, antonio.martinko@kif.hr %K accuracy %K precision %K sedentary behavior %K children %K adolescents %K wearable activity monitor %K eHealth %K digital health %K mobile health %K mHealth %K mobile phone %D 2022 %7 9.8.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: A large number of wearable activity monitor models are released and used each year by consumers and researchers. As more studies are being carried out on children and adolescents in terms of sedentary behavior (SB) assessment, knowledge about accurate and precise monitoring devices becomes increasingly important. Objective: The main aim of this systematic review was to investigate and communicate findings on the accuracy and precision of consumer-grade physical activity monitors in assessing the time spent in SB in children and adolescents. Methods: Searches of PubMed (MEDLINE), Scopus, SPORTDiscus (full text), ProQuest, Open Access Theses and Dissertations, DART Europe E-theses Portal, and Networked Digital Library of Theses and Dissertations electronic databases were performed. All relevant studies that compared different types of consumer-grade monitors using a comparison method in the assessment of SB, published in European languages from 2015 onward were considered for inclusion. The risk of bias was estimated using Consensus-Based Standards for the Selection of Health Status Measurement Instruments. For enabling comparisons of accuracy measures within the studied outcome domain, measurement accuracy interpretation was based on group mean or percentage error values and 90% CI. Acceptable limits were predefined as –10% to +10% error in controlled and free-living settings. For determining the number of studies with group error percentages that fall within or outside one of the sides from previously defined acceptable limits, two 1-sided tests of equivalence were carried out, and the direction of measurement error was examined. Results: A total of 8 studies complied with the predefined inclusion criteria, and 3 studies provided acceptable data for quantitative analyses. In terms of the presented accuracy comparisons, 14 were subsequently identified, with 6 of these comparisons being acceptable in terms of quantitative analysis. The results of the Cochran Q test indicated that the included studies did not share a common effect size (Q5=82.86; P<.001). I2, which represents the percentage of total variation across studies due to heterogeneity, amounted to 94%. The summary effect size based on the random effects model was not statistically significant (effect size=14.36, SE 12.04, 90% CI −5.45 to 34.17; P=.23). According to the equivalence test results, consumer-grade physical activity monitors did not generate equivalent estimates of SB in relation to the comparison methods. Majority of the studies (3/7, 43%) that reported the mean absolute percentage errors have reported values of <30%. Conclusions: This is the first study that has attempted to synthesize available evidence on the accuracy and precision of consumer-grade physical activity monitors in measuring SB in children and adolescents. We found very few studies on the accuracy and almost no evidence on the precision of wearable activity monitors. The presented results highlight the large heterogeneity in this area of research. Trial Registration: PROSPERO CRD42021251922; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=251922 %M 35943763 %R 10.2196/37547 %U https://mhealth.jmir.org/2022/8/e37547 %U https://doi.org/10.2196/37547 %U http://www.ncbi.nlm.nih.gov/pubmed/35943763 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 8 %P e39010 %T Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study %A Young,Alexander S %A Choi,Abigail %A Cannedy,Shay %A Hoffmann,Lauren %A Levine,Lionel %A Liang,Li-Jung %A Medich,Melissa %A Oberman,Rebecca %A Olmos-Ochoa,Tanya T %+ Semel Institute for Neuroscience & Human Behavior, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 300 UCLA Medical Plaza, Los Angeles, CA, 90095, United States, 1 310 794 7219, ayoung@ucla.edu %K serious mental illness %K mobile health %K mental health %K passive sensing %K health informatics %K behavior %K sensor %K self-tracking %K predict %K assessment %D 2022 %7 5.8.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Serious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms. Objective: The aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms. Methods: A mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms. Results: The study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021. Conclusions: Findings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes. Trial Registration: ClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252 International Registered Report Identifier (IRRID): DERR1-10.2196/39010 %M 35930336 %R 10.2196/39010 %U https://www.researchprotocols.org/2022/8/e39010 %U https://doi.org/10.2196/39010 %U http://www.ncbi.nlm.nih.gov/pubmed/35930336 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 3 %P e33754 %T Alignment Between Heart Rate Variability From Fitness Trackers and Perceived Stress: Perspectives From a Large-Scale In Situ Longitudinal Study of Information Workers %A Martinez,Gonzalo J %A Grover,Ted %A Mattingly,Stephen M %A Mark,Gloria %A D’Mello,Sidney %A Aledavood,Talayeh %A Akbar,Fatema %A Robles-Granda,Pablo %A Striegel,Aaron %+ Computer Science and Engineering, University of Notre Dame, 400 Main Building, Notre Dame, IN, 46556, United States, 1 (574) 631 8320, gonzalo.martinez@ieee.org %K stress measurement %K heart rate variability %K HRV %K perceived stress %K ecological momentary assessment %K EMA %K wearables %K fitness tracker %D 2022 %7 4.8.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Stress can have adverse effects on health and well-being. Informed by laboratory findings that heart rate variability (HRV) decreases in response to an induced stress response, recent efforts to monitor perceived stress in the wild have focused on HRV measured using wearable devices. However, it is not clear that the well-established association between perceived stress and HRV replicates in naturalistic settings without explicit stress inductions and research-grade sensors. Objective: This study aims to quantify the strength of the associations between HRV and perceived daily stress using wearable devices in real-world settings. Methods: In the main study, 657 participants wore a fitness tracker and completed 14,695 ecological momentary assessments (EMAs) assessing perceived stress, anxiety, positive affect, and negative affect across 8 weeks. In the follow-up study, approximately a year later, 49.8% (327/657) of the same participants wore the same fitness tracker and completed 1373 EMAs assessing perceived stress at the most stressful time of the day over a 1-week period. We used mixed-effects generalized linear models to predict EMA responses from HRV features calculated over varying time windows from 5 minutes to 24 hours. Results: Across all time windows, the models explained an average of 1% (SD 0.5%; marginal R2) of the variance. Models using HRV features computed from an 8 AM to 6 PM time window (namely work hours) outperformed other time windows using HRV features calculated closer to the survey response time but still explained a small amount (2.2%) of the variance. HRV features that were associated with perceived stress were the low frequency to high frequency ratio, very low frequency power, triangular index, and SD of the averages of normal-to-normal intervals. In addition, we found that although HRV was also predictive of other related measures, namely, anxiety, negative affect, and positive affect, it was a significant predictor of stress after controlling for these other constructs. In the follow-up study, calculating HRV when participants reported their most stressful time of the day was less predictive and provided a worse fit (R2=0.022) than the work hours time window (R2=0.032). Conclusions: A significant but small relationship between perceived stress and HRV was found. Thus, although HRV is associated with perceived stress in laboratory settings, the strength of that association diminishes in real-life settings. HRV might be more reflective of perceived stress in the presence of specific and isolated stressors and research-grade sensing. Relying on wearable-derived HRV alone might not be sufficient to detect stress in naturalistic settings and should not be considered a proxy for perceived stress but rather a component of a complex phenomenon. %M 35925662 %R 10.2196/33754 %U https://humanfactors.jmir.org/2022/3/e33754 %U https://doi.org/10.2196/33754 %U http://www.ncbi.nlm.nih.gov/pubmed/35925662 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 2 %P e38570 %T Prediction of VO2max From Submaximal Exercise Using the Smartphone Application Myworkout GO: Validation Study of a Digital Health Method %A Helgerud,Jan %A Haglo,Håvard %A Hoff,Jan %+ Medical Rehabilitation Clinic, Myworkout, Ingvald Ystgaards veg 23, Trondheim, 7047, Norway, 47 92621619, havard@treningsklinikken.no %K high-intensity interval training %K cardiovascular health %K physical inactivity %K endurance training %K measurement accuracy %D 2022 %7 4.8.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Physical inactivity remains the largest risk factor for the development of cardiovascular disease worldwide. Wearable devices have become a popular method of measuring activity-based outcomes and facilitating behavior change to increase cardiorespiratory fitness (CRF) or maximal oxygen consumption (VO2max) and reduce weight. However, it is critical to determine their accuracy in measuring these variables. Objective: This study aimed to determine the accuracy of using a smartphone and the application Myworkout GO for submaximal prediction of VO2max. Methods: Participants included 162 healthy volunteers: 58 women and 104 men (17-73 years old). The study consisted of 3 experimental tests randomized to 3 separate days. One-day VO2max was assessed with Metamax II, with the participant walking or running on the treadmill. On the 2 other days, the application Myworkout GO used standardized high aerobic intensity interval training (HIIT) on the treadmill to predict VO2max. Results: There were no significant differences between directly measured VO2max (mean 49, SD 14 mL/kg/min) compared with the VO2max predicted by Myworkout GO (mean 50, SD 14 mL/kg/min). The direct and predicted VO2max values were highly correlated, with an R2 of 0.97 (P<.001) and standard error of the estimate (SEE) of 2.2 mL/kg/min, with no sex differences. Conclusions: Myworkout GO accurately calculated VO2max, with an SEE of 4.5% in the total group. The submaximal HIIT session (4 x 4 minutes) incorporated in the application was tolerated well by the participants. We present health care providers and their patients with a more accurate and practical version of health risk estimation. This might increase physical activity and improve exercise habits in the general population. %M 35925653 %R 10.2196/38570 %U https://cardio.jmir.org/2022/2/e38570 %U https://doi.org/10.2196/38570 %U http://www.ncbi.nlm.nih.gov/pubmed/35925653 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 8 %P e33850 %T Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation %A Ng,Ada %A Wei,Boyang %A Jain,Jayalakshmi %A Ward,Erin A %A Tandon,S Darius %A Moskowitz,Judith T %A Krogh-Jespersen,Sheila %A Wakschlag,Lauren S %A Alshurafa,Nabil %+ McCormick School of Engineering, Northwestern University, 633 Clark St, Evanston, IL, 60208, United States, 1 8474913741, adang@u.northwestern.edu %K explainability %K just-in-time interventions %K machine learning %K prenatal stress %K stress prediction %K wearable %K mobile phone %D 2022 %7 2.8.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cognitive behavioral therapy–based interventions are effective in reducing prenatal stress, which can have severe adverse health effects on mothers and newborns if unaddressed. Predicting next-day physiological or perceived stress can help to inform and enable pre-emptive interventions for a likely physiologically and perceptibly stressful day. Machine learning models are useful tools that can be developed to predict next-day physiological and perceived stress by using data collected from the previous day. Such models can improve our understanding of the specific factors that predict physiological and perceived stress and allow researchers to develop systems that collect selected features for assessment in clinical trials to minimize the burden of data collection. Objective: The aim of this study was to build and evaluate a machine-learned model that predicts next-day physiological and perceived stress by using sensor-based, ecological momentary assessment (EMA)–based, and intervention-based features and to explain the prediction results. Methods: We enrolled pregnant women into a prospective proof-of-concept study and collected electrocardiography, EMA, and cognitive behavioral therapy intervention data over 12 weeks. We used the data to train and evaluate 6 machine learning models to predict next-day physiological and perceived stress. After selecting the best performing model, Shapley Additive Explanations were used to identify the feature importance and explainability of each feature. Results: A total of 16 pregnant women enrolled in the study. Overall, 4157.18 hours of data were collected, and participants answered 2838 EMAs. After applying feature selection, 8 and 10 features were found to positively predict next-day physiological and perceived stress, respectively. A random forest classifier performed the best in predicting next-day physiological stress (F1 score of 0.84) and next-day perceived stress (F1 score of 0.74) by using all features. Although any subset of sensor-based, EMA-based, or intervention-based features could reliably predict next-day physiological stress, EMA-based features were necessary to predict next-day perceived stress. The analysis of explainability metrics showed that the prolonged duration of physiological stress was highly predictive of next-day physiological stress and that physiological stress and perceived stress were temporally divergent. Conclusions: In this study, we were able to build interpretable machine learning models to predict next-day physiological and perceived stress, and we identified unique features that were highly predictive of next-day stress that can help to reduce the burden of data collection. %M 35917157 %R 10.2196/33850 %U https://mhealth.jmir.org/2022/8/e33850 %U https://doi.org/10.2196/33850 %U http://www.ncbi.nlm.nih.gov/pubmed/35917157 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e35396 %T Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study %A Hiraoka,Daisuke %A Inui,Tomohiko %A Kawakami,Eiryo %A Oya,Megumi %A Tsuji,Ayumu %A Honma,Koya %A Kawasaki,Yohei %A Ozawa,Yoshihito %A Shiko,Yuki %A Ueda,Hideki %A Kohno,Hiroki %A Matsuura,Kaoru %A Watanabe,Michiko %A Yakita,Yasunori %A Matsumiya,Goro %+ Department of Cardiovascular Surgery, University of Chiba, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8677, Japan, 81 08041941178, d.the.lion.hearted@gmail.com %K wearable device %K atrial fibrillation %K photoplethysmography %K cardiology %K heart %K mHealth %K mobile health %K pulse %K development %K pilot study %K Apple Watch %K sensor %K algorithm %K detection %K diagnose %K cardiac surgery %K machine learning %D 2022 %7 1.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Some attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions. Objective: This study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device. Methods: A total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch. Results: One of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve. Conclusions: We were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF. %M 35916709 %R 10.2196/35396 %U https://formative.jmir.org/2022/8/e35396 %U https://doi.org/10.2196/35396 %U http://www.ncbi.nlm.nih.gov/pubmed/35916709 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 8 %P e30149 %T Data-Driven User-Type Clustering of a Physical Activity Promotion App: Usage Data Analysis Study %A Kranzinger,Christina %A Venek,Verena %A Rieser,Harald %A Jungreitmayr,Sonja %A Ring-Dimitriou,Susanne %+ Salzburg Research Forschungsgesellschaft mbH, Jakob-⁠Haringer-⁠Straße 5/⁠3, Salzburg, 5020, Austria, 43 6622288 ext 252, christina.kranzinger@salzburgresearch.at %K active and assisted living %K app usage %K cluster analysis %K Jenks natural breaks algorithm %K Partitioning Around Medoids algorithm %K physical activity promotion %K usage groups %D 2022 %7 1.8.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical inactivity remains a leading risk factor for mortality worldwide. Owing to increasing sedentary behavior (activities in a reclining, seated, or lying position with low-energy expenditures), vehicle-based transport, and insufficient physical workload, the prevalence of physical activity decreases significantly with age. To promote sufficient levels of participation in physical activities, the research prototype Fit-mit-ILSE was developed with the goal of making adults aged ≥55 years physically fit and fit for the use of assistive technologies. The system combines active and assisted living technologies and smart services in the ILSE app. Objective: The clustering of health and fitness app user types, especially in the context of active and assisted living projects, has been mainly defined by experts through 1D cluster thresholds based on app usage frequency. We aimed to investigate and present data-driven methods for clustering app user types and to identify usage patterns based on the ILSE app function Fit at home. Methods: During the 2 phases of the field trials, ILSE app log data were collected from 165 participants. Using this data set, 2 data-driven approaches were applied for clustering to group app users who were similar to each other. First, the common approach of user-type clustering based on expert-defined thresholds was replaced by a data-driven derivation of the cluster thresholds using the Jenks natural breaks algorithm. Second, a multidimensional clustering approach using the Partitioning Around Medoids algorithm was explored to consider the detailed app usage pattern data. Results: Applying the Jenks clustering algorithm to the mean usage per day and clustering the users into 4 groups showed that most of the users (63/165, 38.2%) used the Fit at home function between once a week and every second day. More men were in the low usage group than women. In addition, the younger users were more often identified as moderate or high users than the older users, who were mainly classified as low users; moreover, the regional differences between Vienna and Salzburg were identified. In addition, the multidimensional approach identified 4 different user groups that differed mainly in terms of time of use, gender, and region. Overall, the younger women living in Salzburg were the users with highest average app usage. Conclusions: The application of different clustering approaches showed that data-driven calculations of user groups can complement expert-based definitions, provide objective thresholds for the analysis of app usage data, and identify groups that can be targeted individually based on their specific group characteristics. %M 35916687 %R 10.2196/30149 %U https://formative.jmir.org/2022/8/e30149 %U https://doi.org/10.2196/30149 %U http://www.ncbi.nlm.nih.gov/pubmed/35916687 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 8 %P e35206 %T Transdiagnostic Psychopathology in a Help-Seeking Population of an Early Recognition Center for Mental Disorders: Protocol for an Experience Sampling Study %A Rosen,Marlene %A Betz,Linda T %A Montag,Christian %A Kannen,Christopher %A Kambeitz,Joseph %+ Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Kerpener Straße 62, Cologne, 50937, Germany, 49 221 478 87166, marlene.rosen@uk-koeln.de %K help-seeking population %K phenotyping %K ecological momentary assessment %K symptom networks %K transdiagnositc psychiatry %K prevention %K early intervention %K psychiatry %K mental health %D 2022 %7 1.8.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Prevention in psychiatry provides a promising way to address the burden of mental illness. However, established approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of help-seeking populations that present at early recognition services. Conceptualizing the psychopathology manifested in help-seeking populations from a network perspective of interacting symptoms allows transdiagnostic investigations beyond binary disease categories. Furthermore, modern technologies such as smartphones facilitate the application of the Experience Sampling Method (ESM). Objective: This study is a combination of ESM with network analyses to provide valid insights beyond the established assessment instruments in a help-seeking population. Methods: We will examine 75 individuals (aged 18-40 years) of the help-seeking population of the Cologne early recognition center. For a maximally naturalistic sample, only minimal exclusion criteria will be applied. We will collect data for 14 days using a mobile app to assess 10 transdiagnostic symptoms (ie, depressive, anxious, and psychotic symptoms) as well as distress level 5 times a day. With these data, we will generate average group-level symptom networks and personalized symptom networks using a 2-step multilevel vector autoregressive model. Additionally, we will explore associations between symptom networks and sociodemographic, risk, and resilience factors, as well as psychosocial functioning. Results: The protocol was designed in February 2020 and approved by the Ethics Committee of the University Hospital Cologne in October 2020. The protocol was reviewed and funded by the Köln Fortune program in September 2020. Data collection began in November 2020 and was completed in November 2021. Of the 258 participants who were screened, 93 (36%) fulfilled the inclusion criteria and were willing to participate in the study. Of these 93 participants, 86 (92%) completed the study. The first results are expected to be published in 2022. Conclusions: This study will provide insights about the feasibility and utility of the ESM in a help-seeking population of an early recognition center. Providing the first explorative phenotyping of transdiagnostic psychopathology in this population, our study will contribute to the innovation of early recognition in psychiatry. The results will help pave the way for prevention and targeted early intervention in a broader patient group, and thus, enable greater intended effects in alleviating the burden of psychiatric disorders. International Registered Report Identifier (IRRID): DERR1-10.2196/35206 %M 35916702 %R 10.2196/35206 %U https://www.researchprotocols.org/2022/8/e35206 %U https://doi.org/10.2196/35206 %U http://www.ncbi.nlm.nih.gov/pubmed/35916702 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 7 %P e36665 %T Initial Psychometric Properties of 7 NeuroUX Remote Ecological Momentary Cognitive Tests Among People With Bipolar Disorder: Validation Study %A Moore,Raeanne C %A Parrish,Emma M %A Van Patten,Ryan %A Paolillo,Emily %A Filip,Tess F %A Bomyea,Jessica %A Lomas,Derek %A Twamley,Elizabeth W %A Eyler,Lisa T %A Depp,Colin A %+ Department of Psychiatry, University of California San Diego, 220 Dickinson St. Ste B (8231), San Diego, CA, 92103-8231, United States, 1 949 933 8063, r6moore@health.ucsd.edu %K neuropsychology %K mobile health %K ambulatory assessment %K ecological momentary assessment %K practice effects %K validity %K testing %K serious mental illness %K mobile phone %D 2022 %7 29.7.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: As smartphone technology has become nearly ubiquitous, there is a growing body of literature suggesting that ecological momentary cognitive testing (EMCT) offers advantages over traditional pen-and-paper psychological assessment. We introduce a newly developed platform for the self-administration of cognitive tests in ecologically valid ways. Objective: The aim of this study is to develop a Health Insurance Portability and Accountability Act–compliant EMCT smartphone-based platform for the frequent and repeated testing of cognitive abilities in everyday life. This study examines the psychometric properties of 7 mobile cognitive tests covering domains of processing speed, visual working memory, recognition memory, and response inhibition within our platform among persons with and without bipolar disorder (BD). Ultimately, if shown to have adequate psychometric properties, EMCTs may be useful in research on BD and other neurological and psychiatric illnesses. Methods: A total of 45 persons with BD and 21 demographically comparable healthy volunteer participants (aged 18-65 years) completed smartphone-based EMCTs 3 times daily for 14 days. Each EMCT session lasted approximately 1.5 minutes. Only 2 to 3 tests were administered in any given session, no test was administered more than once per day, and alternate test versions were administered in each session. Results: The mean adherence to the EMCT protocol was 69.7% (SD 20.5%), resulting in 3965 valid and complete tests across the full sample. Participants were significantly more likely to miss tests on later versus earlier study days. Adherence did not differ by diagnostic status, suggesting that BD does not interfere with EMCT participation. In most tests, age and education were related to EMCT performance in expected directions. The average performances on most EMCTs were moderately to strongly correlated with the National Institutes of Health Toolbox Cognition Battery. Practice effects were observed in 5 tests, with significant differences in practice effects by BD status in 3 tests. Conclusions: Although additional reliability and validity data are needed, this study provides initial psychometric support for EMCTs in the assessment of cognitive performance in real-world contexts in BD. %M 35904876 %R 10.2196/36665 %U https://www.jmir.org/2022/7/e36665 %U https://doi.org/10.2196/36665 %U http://www.ncbi.nlm.nih.gov/pubmed/35904876 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 7 %P e35701 %T Effects of a Gamified, Behavior Change Technique–Based Mobile App on Increasing Physical Activity and Reducing Anxiety in Adults With Autism Spectrum Disorder: Feasibility Randomized Controlled Trial %A Lee,Daehyoung %A Frey,Georgia C %A Cothran,Donetta J %A Harezlak,Jaroslaw %A Shih,Patrick C %+ Department of Applied Human Sciences, University of Minnesota Duluth, 123 Sports and Health Center, 1216 Ordean Court, Duluth, MN, 55812, United States, 1 2187267816, lee03284@d.umn.edu %K gamification %K behavior change techniques %K physical activity %K sedentary behavior %K anxiety %K autism %K mobile app %K mental health %K mHealth %K mobile phone %D 2022 %7 28.7.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical activity (PA) has an impact on physical and mental health in neurotypical populations, and addressing these variables may improve the prevalent burden of anxiety in adults with autism spectrum disorder (ASD). Gamified mobile apps using behavior change techniques present a promising way of increasing PA and reducing sedentary time, thus reducing anxiety in adults with ASD. Objective: This study aimed to compare the effectiveness of a gamified and behavior change technique–based mobile app, PuzzleWalk, versus a commercially available app, Google Fit, on increasing PA and reducing sedentary time as an adjunct anxiety treatment for this population. Methods: A total of 24 adults with ASD were assigned to either the PuzzleWalk or Google Fit group for 5 weeks using a covariate-adaptive randomization design. PA and anxiety were assessed over 7 days at 3 different data collection periods (ie, baseline, intervention start, and intervention end) using triaxial accelerometers and the Beck Anxiety Inventory. Group differences in outcome variables were assessed using repeated-measures analysis of covariance, adjusting for age, sex, and BMI. Results: The findings indicated that the PuzzleWalk group spent a significantly longer amount of time on app use compared with the Google Fit group (F2,38=5.07; P=.01; partial η2=0.21), whereas anxiety was unfavorably associated with increases in light PA and decreases in sedentary time after intervention (all P<.05). Conclusions: Further research is needed to clarify the determinants of physical and mental health and their interrelationship in adults with ASD to identify the factors that facilitate the use and adoption of mobile health technologies in these individuals. Despite these mixed results, the small changes in PA or anxiety may be clinically significant for adults with ASD. Trial Registration: ClinicalTrials.gov NCT05466617; https://clinicaltrials.gov/show/NCT05466617 %M 35900808 %R 10.2196/35701 %U https://formative.jmir.org/2022/7/e35701 %U https://doi.org/10.2196/35701 %U http://www.ncbi.nlm.nih.gov/pubmed/35900808 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 7 %P e38077 %T Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study %A Karas,Marta %A Muschelli,John %A Leroux,Andrew %A Urbanek,Jacek K %A Wanigatunga,Amal A %A Bai,Jiawei %A Crainiceanu,Ciprian M %A Schrack,Jennifer A %+ Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 410 303 1723, jschrac1@jhu.edu %K accelerometry %K actigraphy %K activity counts %K wearable computing %K monitor-independent movement summary %K MIMS %K physical activity %K aging %K older adult population %K wearable device %K health monitoring %K digital health %K wearable technology %K health technology %D 2022 %7 22.7.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures. Objective: We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data. Methods: High-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data. Results: Data were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published. Conclusions: The findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping. %M 35867392 %R 10.2196/38077 %U https://mhealth.jmir.org/2022/7/e38077 %U https://doi.org/10.2196/38077 %U http://www.ncbi.nlm.nih.gov/pubmed/35867392 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 7 %P e35197 %T Exploring an Artificial Intelligence–Based, Gamified Phone App Prototype to Track and Improve Food Choices of Adolescent Girls in Vietnam: Acceptability, Usability, and Likeability Study %A C Braga,Bianca %A Nguyen,Phuong H %A Aberman,Noora-Lisa %A Doyle,Frank %A Folson,Gloria %A Hoang,Nga %A Huynh,Phuong %A Koch,Bastien %A McCloskey,Peter %A Tran,Lan %A Hughes,David %A Gelli,Aulo %+ Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Ave, Boston, MA, 02111, United States, 1 6176363777, curi.bianca@tufts.edu %K adolescent %K dietary quality %K food choice %K gamification %K low- and middle-income country %K smartphone app %K mobile phone %D 2022 %7 21.7.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Adolescents’ consumption of healthy foods is suboptimal in low- and middle-income countries. Adolescents’ fondness for games and social media and the increasing access to smartphones make apps suitable for collecting dietary data and influencing their food choices. Little is known about how adolescents use phones to track and shape their food choices. Objective: This study aimed to examine the acceptability, usability, and likability of a mobile phone app prototype developed to collect dietary data using artificial intelligence–based image recognition of foods, provide feedback, and motivate users to make healthier food choices. The findings were used to improve the design of the app. Methods: A total of 4 focus group discussions (n=32 girls, aged 15-17 years) were conducted in Vietnam. Qualitative data were collected and analyzed by grouping ideas into common themes based on content analysis and ground theory. Results: Adolescents accepted most of the individual- and team-based dietary goals presented in the app prototype to help them make healthier food choices. They deemed the overall app wireframes, interface, and graphic design as acceptable, likable, and usable but suggested the following modifications: tailored feedback based on users’ medical history, anthropometric characteristics, and fitness goals; new language on dietary goals; provision of information about each of the food group dietary goals; wider camera frame to fit the whole family food tray, as meals are shared in Vietnam; possibility of digitally separating food consumption on shared meals; and more appealing graphic design, including unique badge designs for each food group. Participants also liked the app’s feedback on food choices in the form of badges, notifications, and statistics. A new version of the app was designed incorporating adolescent’s feedback to improve its acceptability, usability, and likability. Conclusions: A phone app prototype designed to track food choice and help adolescent girls from low- and middle-income countries make healthier food choices was found to be acceptable, likable, and usable. Further research is needed to examine the feasibility of using this technology at scale. %M 35862147 %R 10.2196/35197 %U https://formative.jmir.org/2022/7/e35197 %U https://doi.org/10.2196/35197 %U http://www.ncbi.nlm.nih.gov/pubmed/35862147 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 7 %P e32280 %T Inducing and Recording Acute Stress Responses on a Large Scale With the Digital Stress Test (DST): Development and Evaluation Study %A Norden,Matthias %A Hofmann,Amin Gerard %A Meier,Martin %A Balzer,Felix %A Wolf,Oliver T %A Böttinger,Erwin %A Drimalla,Hanna %+ Faculty of Technology, Bielefeld University, Postfach 10 01 31, Bielefeld, 33619, Germany, 49 521 106 12043, drimalla@techfak.uni-bielefeld.de %K stress induction %K smartphone %K stress reactivity %K Trier Social Stress Test %K TSST %K remote %K video recording %K acute stress %K digital health %K mobile health %K mHealth %K mobile phone %D 2022 %7 15.7.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Valuable insights into the pathophysiology and consequences of acute psychosocial stress have been gained using standardized stress induction experiments. However, most protocols are limited to laboratory settings, are labor-intensive, and cannot be scaled to larger cohorts or transferred to daily life scenarios. Objective: We aimed to provide a scalable digital tool that enables the standardized induction and recording of acute stress responses in outside-the-laboratory settings without any experimenter contact. Methods: On the basis of well-described stress protocols, we developed the Digital Stress Test (DST) and evaluated its feasibility and stress induction potential in a large web-based study. A total of 284 participants completed either the DST (n=103; 52/103, 50.5% women; mean age 31.34, SD 9.48 years) or an adapted control version (n=181; 96/181, 53% women; mean age 31.51, SD 11.18 years) with their smartphones via a web application. We compared their affective responses using the international Positive and Negative Affect Schedule Short Form before and after stress induction. In addition, we assessed the participants’ stress-related feelings indicated in visual analogue scales before, during, and after the procedure, and further analyzed the implemented stress-inducing elements. Finally, we compared the DST participants’ stress reactivity with the results obtained in a classic stress test paradigm using data previously collected in 4 independent Trier Social Stress Test studies including 122 participants overall. Results: Participants in the DST manifested significantly higher perceived stress indexes than the Control-DST participants at all measurements after the baseline (P<.001). Furthermore, the effect size of the increase in DST participants’ negative affect (d=0.427) lay within the range of effect sizes for the increase in negative affect in the previously conducted Trier Social Stress Test experiments (0.281-1.015). Conclusions: We present evidence that a digital stress paradigm administered by smartphone can be used for standardized stress induction and multimodal data collection on a large scale. Further development of the DST prototype and a subsequent validation study including physiological markers are outlined. %M 35838765 %R 10.2196/32280 %U https://www.jmir.org/2022/7/e32280 %U https://doi.org/10.2196/32280 %U http://www.ncbi.nlm.nih.gov/pubmed/35838765 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 7 %P e36666 %T Investigating Microtemporal Processes Underlying Health Behavior Adoption and Maintenance: Protocol for an Intensive Longitudinal Observational Study %A Wang,Shirlene %A Intille,Stephen %A Ponnada,Aditya %A Do,Bridgette %A Rothman,Alexander %A Dunton,Genevieve %+ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1875 N Soto St, 3rd Floor, Los Angeles, CA, 90032, United States, 1 3125327663, shirlenw@usc.edu %K emerging adulthood %K behavior change %K longitudinal data collection %K ecological momentary assessment %K sensing %K theory %K young adult %K weight gain %K EMA %K chronic disease %K physical activity %D 2022 %7 14.7.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Young adulthood (ages 18-29 years) is marked by substantial weight gain, leading to increased lifetime risks of chronic diseases. Engaging in sufficient levels of physical activity and sleep, and limiting sedentary time are important contributors to the prevention of weight gain. Dual-process models of decision-making and behavior that delineate reflective (ie, deliberative, slow) and reactive (ie, automatic, fast) processes shed light on different mechanisms underlying the adoption versus maintenance of these energy-balance behaviors. However, reflective and reactive processes may unfold at different time scales and vary across people. Objective: This paper describes the study design, recruitment, and data collection procedures for the Temporal Influences on Movement and Exercise (TIME) study, a 12-month intensive longitudinal data collection study to examine real-time microtemporal influences underlying the adoption and maintenance of physical activity, sedentary behavior, and sleep. Methods: Intermittent ecological momentary assessment (eg, intentions, self-control) and continuous, sensor-based passive monitoring (eg, location, phone/app use, activity levels) occur using smartwatches and smartphones. Data analyses will combine idiographic (person-specific, data-driven) and nomothetic (generalizable, theory-driven) approaches to build models that may predict within-subject variation in the likelihood of behavior “episodes” (eg, ≥10 minutes of physical activity, ≥120 minutes of sedentary time, ≥7 hours sleep) and “lapses” (ie, not attaining recommended levels for ≥7 days) as a function of reflective and reactive factors. Results: The study recruited young adults across the United States (N=246). Rolling recruitment began in March 2020 and ended August 2021. Data collection will continue until August 2022. Conclusions: Results from the TIME study will be used to build more predictive health behavior theories, and inform personalized behavior interventions to reduce obesity and improve public health. International Registered Report Identifier (IRRID): DERR1-10.2196/36666 %M 35834296 %R 10.2196/36666 %U https://www.researchprotocols.org/2022/7/e36666 %U https://doi.org/10.2196/36666 %U http://www.ncbi.nlm.nih.gov/pubmed/35834296 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 7 %P e35684 %T Wearing the Future—Wearables to Empower Users to Take Greater Responsibility for Their Health and Care: Scoping Review %A Kang,Harjeevan Singh %A Exworthy,Mark %+ College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom, 44 121 414 3344, harjeevankangmedicine@gmail.com %K wearable %K device %K tracker %K activity tracker %K fitness tracker %K technology %K MedTech %K HealthTech %K sensor %K monitor %K gadget %K smartwatch %K empowerment %K self-care %K management %K behavior %K responsibility %K attitude %K personalization %K mobile phone %K self-management %K smartphone %K wearable electronic devices %K health promotion %K health behavior %K mHealth %K digital health %K health care wearables %K scoping review %D 2022 %7 13.7.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Wearables refer to devices that are worn by individuals. In the health care field, wearables may assist with individual monitoring and diagnosis. In fact, the potential for wearable technology to assist with health care has received recognition from health systems around the world, including a place in the strategic Long Term Plan shared by the National Health Service in England. However, wearables are not limited to specialist medical devices used by patients. Leading technology companies, including Apple, have been exploring the capabilities of wearable health technology for health-conscious consumers. Despite advancements in wearable health technology, research is yet to be conducted on wearables and empowerment. Objective: This study aimed to identify, summarize, and synthesize knowledge on how wearable health technology can empower individuals to take greater responsibility for their health and care. Methods: This study was a scoping review with thematic analysis and narrative synthesis. Relevant guidance, such as the Arksey and O’Malley framework, was followed. In addition to searching gray literature, we searched MEDLINE, EMBASE, PsycINFO, HMIC, and Cochrane Library. Studies were included based on the following selection criteria: publication in English, publication in Europe or the United States, focus on wearables, relevance to the research, and the availability of the full text. Results: After identifying 1585 unique records and excluding papers based on the selection criteria, 20 studies were included in the review. On analysis of these 20 studies, 3 main themes emerged: the potential barriers to using wearables, the role of providers and the benefits to providers from promoting the use of wearables, and how wearables can drive behavior change. Conclusions: Considerable literature findings suggest that wearables can empower individuals by assisting with diagnosis, behavior change, and self-monitoring. However, greater adoption of wearables and engagement with wearable devices depend on various factors, including promotion and support from providers to encourage uptake; increased short-term investment to upskill staff, especially in the area of data analysis; and overcoming the barriers to use, particularly by improving device accuracy. Acting on these suggestions will require investment and constructive input from key stakeholders, namely users, health care professionals, and designers of the technology. As advancements in technology to make wearables viable health care devices have only come about recently, further studies will be important for measuring the effectiveness of wearables in empowering individuals. The investigation of user outcomes through large-scale studies would also be beneficial. Nevertheless, a significant challenge will be in the publication of research to keep pace with rapid developments related to wearable health technology. %M 35830222 %R 10.2196/35684 %U https://mhealth.jmir.org/2022/7/e35684 %U https://doi.org/10.2196/35684 %U http://www.ncbi.nlm.nih.gov/pubmed/35830222 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 7 %P e32593 %T A German Smartphone-Based Self-management Tool for Psoriasis: Community-Driven Development and Evaluation of Quality-of-Life Effects %A Brandl,Lea C %A Liebram,Claudia %A Schramm,Wendelin %A Pobiruchin,Monika %+ Institute of Telematics, University of Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany, 49 451 3101 6401, brandl@itm.uni-luebeck.de %K psoriasis %K self-management %K mobile apps %K quality of life %K mobile phones %K smartphones %D 2022 %7 7.7.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Psoriasis is a chronic disease characterized by inflammation, increased scaling, itching, and other symptoms. Psoriasis is not contagious, but patients have often felt shunned. Therefore, in addition to psoriasis symptoms, stress, anxiety, and depression can also affect quality of life (QoL). Surveys show that only a quarter of patients are satisfied with the success of their therapy. However, in addition to medical therapy, self-management can also make it easier to deal with chronic diseases like psoriasis. Objective: The aim of this project was to develop a smartphone-based self-management tool (SMT) specifically for patients with psoriasis using a community-driven process. The impact of the SMT on QoL as well as its acceptance and usability were evaluated. Methods: In collaboration with an internet-based self-help community, 2 user surveys were conducted to determine the requirements for a smartphone-based SMT. The surveys consisted of semistructured questionnaires asking for desired features in an SMT for psoriasis. A pilot study was conducted to evaluate QoL, acceptance, and usability. Community users were recruited to use the app for 21 days and complete the Dermatology Life Quality Index (DLQI) questionnaire at the beginning (T0) and end (T1). Afterward, participants were asked to complete another questionnaire on usability and ease of use. Results: SMT requirements were collected from 97 members of an internet-based community. The SMT was built as a progressive web app that communicates with a server back end and an Angular web app for content management. The app was used by 15 participants who also provided qualitative feedback, and 10 participants answered all questionnaires. The average DLQI score was 7.1 (SD 6.2) at T0 and 6.9 (SD 6.6) at T1. The minimal required sample size of 27 was not reached. Conclusions: The high degree of community participation in the development process and the responses during the requirement engineering process indicated that there is a general need for an independently developed SMT for patients with psoriasis. However, the feedback received after app use shows that the SMT does not meet the needs of the community. It can be concluded that a more customizable app is needed. The focus and needs of the users were very heterogeneous. Similar developments and research could benefit from the findings of this project. %M 35797109 %R 10.2196/32593 %U https://formative.jmir.org/2022/7/e32593 %U https://doi.org/10.2196/32593 %U http://www.ncbi.nlm.nih.gov/pubmed/35797109 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 3 %P e24376 %T Mobile Videoconferencing for Occupational Therapists’ Assessments of Patients’ Home Environments Prior to Hospital Discharge: Mixed Methods Feasibility and Comparative Study %A Latulippe,Karine %A Giroux,Dominique %A Guay,Manon %A Kairy,Dahlia %A Vincent,Claude %A Boivin,Katia %A Morales,Ernesto %A Obradovic,Natasa %A Provencher,Véronique %+ Center for Interdisciplinary Research in Rehabilitation of Metropolitan Montreal, 6363, chemin Hudson, bureau 061 Pavillon Lindsay de l’IURDPM, Montréal, QC, H3S 1M9, Canada, 1 418 435 8541, karine.latulippe@mcgill.ca %K caregivers %K feasibility %K mixed methods %K mobile videoconferencing %K mobile phone %K occupational therapy %K discharge planning %K home assessment %D 2022 %7 5.7.2022 %9 Original Paper %J JMIR Aging %G English %X Background: Occupational therapists who work in hospitals need to assess patients’ home environment in preparation for hospital discharge in order to provide recommendations (eg, technical aids) to support their independence and safety. Home visits increase performance in everyday activities and decrease the risk of falls; however, in some countries, home visits are rarely made prior to hospital discharge due to the cost and time involved. In most cases, occupational therapists rely on an interview with the patient or a caregiver to assess the home. The use of videoconferencing to assess patients’ home environments could be an innovative solution to allow better and more appropriate recommendations. Objective: The aim of this study was (1) to explore the added value of using mobile videoconferencing compared with standard procedure only and (2) to document the clinical feasibility of using mobile videoconferencing to assess patients’ home environments. Methods: Occupational therapists assessed home environments using, first, the standard procedure (interview), and then, videoconferencing (with the help of a family caregiver located in patients’ homes, using an electronic tablet). We used a concurrent mixed methods design. The occupational therapist's responsiveness to telehealth, time spent on assessment, patient’s occupational performance and satisfaction, and major events influencing the variables were collected as quantitative data. The perceptions of occupational therapists and family caregivers regarding the added value of using this method and the nature of changes made to recommendations as a result of the videoconference (if any) were collected as qualitative data, using questionnaires and semistructured interviews. Results: Eight triads (6 occupational therapists, 8 patients, and 8 caregivers) participated. The use of mobile videoconferencing generally led occupational therapists to modify the initial intervention plan (produced after the standard interview). Occupational therapists and caregivers perceived benefits in using mobile videoconferencing (eg, the ability to provide real-time comments or feedback), and they also perceived disadvantages (eg, videoconferencing requires additional time and greater availability of caregivers). Some occupational therapists believed that mobile videoconferencing added value to assessments, while others did not. Conclusions: The use of mobile videoconferencing in the context of hospital discharge planning has raised questions of clinical feasibility. Although mobile videoconferencing provides multiple benefits to hospital discharge, including more appropriate occupational therapist recommendations, time constraints made it more difficult to perceive the added value. However, with smartphone use, interdisciplinary team involvement, and patient participation in the videoconference visit, mobile videoconferencing can become an asset to hospital discharge planning. International Registered Report Identifier (IRRID): RR2-10.2196/11674 %M 35787486 %R 10.2196/24376 %U https://aging.jmir.org/2022/3/e24376 %U https://doi.org/10.2196/24376 %U http://www.ncbi.nlm.nih.gov/pubmed/35787486 %0 Journal Article %@ 2562-0959 %I JMIR Publications %V 5 %N 3 %P e35916 %T Experiences of Patient-Led Surveillance, Including Patient-Performed Teledermoscopy, in the MEL-SELF Pilot Randomized Controlled Trial: Qualitative Interview Study %A Drabarek,Dorothy %A Habgood,Emily %A Janda,Monika %A Hersch,Jolyn %A Ackermann,Deonna %A Low,Don %A Low,Cynthia %A Morton,Rachael L %A Dieng,Mbathio %A Cust,Anne E %A Morgan,Adelaide %A Smith,Elloise %A Bell,Katy L J %+ Sydney School of Public Health, University of Sydney, Rm 131 Edward Ford Building, Sydney, 2006, Australia, 61 2 9351 4823, katy.bell@sydney.edu.au %K melanoma %K self-surveillance %K digital technologies %K teledermoscopy %K teledermatology %K mHealth %K mobile phone %D 2022 %7 1.7.2022 %9 Original Paper %J JMIR Dermatol %G English %X Background: Current clinician-led melanoma surveillance models require frequent routinely scheduled clinic visits, with associated travel, cost, and time burden for patients. Patient-led surveillance is a new model of follow-up care that could reduce health care use such as clinic visits and medical procedures and their associated costs, increase access to care, and promote early diagnosis of a subsequent new melanoma after treatment of a primary melanoma. Understanding patient experiences may allow improvements in implementation. Objective: This study aims to explore patients’ experiences and perceptions of patient-led surveillance during the 6 months of participation in the MEL-SELF pilot randomized controlled trial. Patient-led surveillance comprised regular skin self-examination, use of a mobile dermatoscope to image lesions of concern, and a smartphone app to track and send images to a teledermatologist for review, in addition to usual care. Methods: Semistructured interviews were conducted with patients previously treated for melanoma localized to the skin in New South Wales, Australia, who were randomized to the patient-led surveillance (intervention group) in the trial. Thematic analysis was used to analyze the data with reference to the technology acceptance model. Results: We interviewed 20 patients (n=8, 40% women and n=12, 60% men; median age 62 years). Patients who were more adherent experienced benefits such as increased awareness of their skin and improved skin self-examination practice, early detection of melanomas, and opportunities to be proactive in managing their clinical follow-up. Most participants experienced difficulty in obtaining clear images and technical problems with the app. These barriers were overcome or persevered by participants with previous experience with digital technology and with effective help from a skin check partner (such as a spouse, sibling, or friend). Having too many or too few moles decreased perceived usefulness. Conclusions: Patients with melanoma are receptive to and experience benefits from patient-led surveillance using teledermoscopy. Increased provision of training and technical support to patients and their skin check partners may help to realize the full potential benefits of this new model of melanoma surveillance. %M 37632893 %R 10.2196/35916 %U https://derma.jmir.org/2022/3/e35916 %U https://doi.org/10.2196/35916 %U http://www.ncbi.nlm.nih.gov/pubmed/37632893 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 7 %P e36021 %T An mHealth Platform for Augmenting Behavioral Health in Primary Care: Longitudinal Feasibility Study %A Moon,Khatiya Chelidze %A Sobolev,Michael %A Grella,Megan %A Alvarado,George %A Sapra,Manish %A Ball,Trever %+ Zucker Hillside Hospital, Kaufmann Building, Suite k204, 75-59 263rd Street, Glen Oaks, NY, 11004, United States, 1 7184704367, kmoon2@northwell.edu %K collaborative care %K mobile health %K psychiatry %K depression %K virtual care %K psychoeducation %K mobile app %K mobile phone %D 2022 %7 1.7.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The collaborative care model is a well-established system of behavioral health care within primary care settings. There is potential for mobile health (mHealth) technology to augment collaborative behavioral health care in primary care settings, thereby improving scalability, efficiency, and clinical outcomes. Objective: We aimed to assess the feasibility of engaging with and the preliminary clinical outcomes of an mHealth platform that was used to augment an existing collaborative care program in primary care settings. Methods: We performed a longitudinal, single-arm feasibility study of an mHealth platform that was used to augment collaborative care. A total of 3 behavioral health care managers, who were responsible for coordinating disease management in 6 primary care practices, encouraged participants to use a mobile app to augment the collaborative model of behavioral health care. The mHealth platform’s functions included asynchronous chats with the behavioral health care managers, depression self-report assessments, and psychoeducational content. The primary outcome was the feasibility of engagement, which was based on the number and type of participant-generated actions that were completed in the app. The primary clinical end point was a comparison of the baseline and final assessments of the Patient Health Questionnaire-9. Results: Of the 245 individuals who were referred by their primary care provider for behavioral health services, 89 (36.3%) consented to app-augmented behavioral health care. Only 12% (11/89) never engaged with the app during the study period. Across all participants, we observed a median engagement of 7 (IQR 12; mean 10.4; range 0-130) actions in the app (participants: n=78). The chat function was the most popular, followed by psychoeducational content and assessments. The subgroup analysis revealed no significant differences in app usage by age (P=.42) or sex (P=.84). The clinical improvement rate in our sample was 73% (32/44), although follow-up assessments were only available for 49% (44/89) of participants. Conclusions: Our preliminary findings indicate the moderate feasibility of using mHealth technology to augment behavioral health care in primary care settings. The results of this study are applicable to improving the design and implementation of mobile apps in collaborative care. %M 35776491 %R 10.2196/36021 %U https://formative.jmir.org/2022/7/e36021 %U https://doi.org/10.2196/36021 %U http://www.ncbi.nlm.nih.gov/pubmed/35776491 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 6 %P e38582 %T Dynamic Regulatory Processes in the Transition From Suicidal Ideation to Action in Adults Leaving Inpatient Psychiatric Care: Protocol for an Intensive Longitudinal Study %A Victor,Sarah E %A Christensen,Kirsten %A Johnson,Sheri L %A Van Allen,Jason %A Brick,Leslie A %+ Department of Psychological Sciences, Texas Tech University, Box 42051, Lubbock, TX, 79424, United States, 1 806 834 0340, sarah.victor@ttu.edu %K ecological momentary assessment %K suicidal ideation %K suicidal behavior %K actigraphy %K sleep %K cognitive control %K longitudinal %K affect %K impulsivity %D 2022 %7 30.6.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: US suicide rates have risen steadily in the past decade, and suicide risk is especially high in the months after discharge from inpatient psychiatric treatment. However, suicide research has lagged in examining dynamic within-person processes that contribute to risk over time among individuals known to be at high risk of suicide. Almost no research has examined how affective, cognitive, and physiological processes change over minutes, hours, or days to confer risk of suicidal behavior in daily life. Objective: This protocol describes a longitudinal study designed to examine real-world changes in risk of suicide across multiple assessment domains. Specifically, the study involves following adults known to be at high risk of suicide after discharge from inpatient psychiatric care using self-report, interview, actigraphy, and behavioral methods to identify proximal contributors to suicidal thoughts and behaviors. First, we hypothesize that negative affective experiences, which are featured in most major suicide theories, will comprise a latent factor indicative of psychache (emotional pain), which will predict increases in suicidal thinking over time. Second, we hypothesize that poor inhibitory control in the context of negative affective stimuli, as well as emotion-related impulsivity, will predict the transition from suicidal thinking to suicidal behavior over time. Third, we hypothesize that short sleep duration will precede within-person increases in suicidal ideation as well as increased odds of suicidal behavior among those reporting suicidal thoughts. Methods: The desired sample size is 130 adults with past-week suicidal thoughts or behaviors who are receiving inpatient psychiatric treatment. Participants will complete a battery of measures while on the inpatient unit to assess negative affective experiences, emotion-related impulsivity, inhibitory control, typical sleep patterns, and relevant covariates. After discharge from inpatient care, participants will complete 4 weeks of signal-contingent ecological momentary assessment surveys, as well as mobile behavioral measures of inhibitory control, while wearing an actigraphy device that will gather objective data on sleep. Participants will complete interviews regarding suicidal thoughts and behaviors at 4 and 8 weeks after discharge. Results: The study was funded by the National Institutes of Health in November 2020. Recruitment began in April 2021. Data analysis will begin after completion of data collection. Conclusions: This study will elucidate how affective, cognitive, and physiological risk factors contribute (or do not contribute) to within-person fluctuations in suicide risk in daily life, with important implications for extant theories of suicide. Of import, the examined risk factors are all modifiable; thus, the results will inform identification of key targets for just-in-time, flexible, personalized, digital interventions that can be used to decrease emotional distress and prevent suicide among those at highest risk. International Registered Report Identifier (IRRID): DERR1-10.2196/38582 %M 35771618 %R 10.2196/38582 %U https://www.researchprotocols.org/2022/6/e38582 %U https://doi.org/10.2196/38582 %U http://www.ncbi.nlm.nih.gov/pubmed/35771618 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e35807 %T Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study %A Mullick,Tahsin %A Radovic,Ana %A Shaaban,Sam %A Doryab,Afsaneh %+ Department of Engineering Systems and Environment, University of Virginia, Olsson Hall, 151 Engineer's Way, Charlottesville, VA, 22904, United States, 1 434 243 5823, tum7q@virginia.edu %K adolescent %K depression %K uHealth %K machine learning %K mobile phone %D 2022 %7 24.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Depression levels in adolescents have trended upward over the past several years. According to a 2020 survey by the National Survey on Drug Use and Health, 4.1 million US adolescents have experienced at least one major depressive episode. This number constitutes approximately 16% of adolescents aged 12 to 17 years. However, only 32.3% of adolescents received some form of specialized or nonspecialized treatment. Identifying worsening symptoms earlier using mobile and wearable sensors may lead to earlier intervention. Most studies on predicting depression using sensor-based data are geared toward the adult population. Very few studies look into predicting depression in adolescents. Objective: The aim of our work was to study passively sensed data from adolescents with depression and investigate the predictive capabilities of 2 machine learning approaches to predict depression scores and change in depression levels in adolescents. This work also provided an in-depth analysis of sensor features that serve as key indicators of change in depressive symptoms and the effect of variation of data samples on model accuracy levels. Methods: This study included 55 adolescents with symptoms of depression aged 12 to 17 years. Each participant was passively monitored through smartphone sensors and Fitbit wearable devices for 24 weeks. Passive sensors collected call, conversation, location, and heart rate information daily. Following data preprocessing, 67% (37/55) of the participants in the aggregated data set were analyzed. Weekly Patient Health Questionnaire-9 surveys answered by participants served as the ground truth. We applied regression-based approaches to predict the Patient Health Questionnaire-9 depression score and change in depression severity. These approaches were consolidated using universal and personalized modeling strategies. The universal strategies consisted of Leave One Participant Out and Leave Week X Out. The personalized strategy models were based on Accumulated Weeks and Leave One Week One User Instance Out. Linear and nonlinear machine learning algorithms were trained to model the data. Results: We observed that personalized approaches performed better on adolescent depression prediction compared with universal approaches. The best models were able to predict depression score and weekly change in depression level with root mean squared errors of 2.83 and 3.21, respectively, following the Accumulated Weeks personalized modeling strategy. Our feature importance investigation showed that the contribution of screen-, call-, and location-based features influenced optimal models and were predictive of adolescent depression. Conclusions: This study provides insight into the feasibility of using passively sensed data for predicting adolescent depression. We demonstrated prediction capabilities in terms of depression score and change in depression level. The prediction results revealed that personalized models performed better on adolescents than universal approaches. Feature importance provided a better understanding of depression and sensor data. Our findings can help in the development of advanced adolescent depression predictions. %M 35749157 %R 10.2196/35807 %U https://formative.jmir.org/2022/6/e35807 %U https://doi.org/10.2196/35807 %U http://www.ncbi.nlm.nih.gov/pubmed/35749157 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e36914 %T Mobile-Based and Self-Service Tool (iPed) to Collect, Manage, and Visualize Pedigree Data: Development Study %A Sun,Chen %A Xu,Jing %A Tao,Junxian %A Dong,Yu %A Chen,Haiyan %A Jia,Zhe %A Ma,Yingnan %A Zhang,Mingming %A Wei,Siyu %A Tang,Guoping %A Lyu,Hongchao %A Jiang,Yongshuai %+ College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Nangang District, Harbin, 150081, China, 86 451 86620941, jiangyongshuai@gmail.com %K pedigree %K pedigree data %K visualization %K self-service %K mobile-based %D 2022 %7 23.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Pedigree data (family history) are indispensable for genetics studies and the assessment of individuals' disease susceptibility. With the popularity of genetics testing, the collection of pedigree data is becoming more common. However, it can be time-consuming, laborious, and tedious for clinicians to investigate all pedigree data for each patient. A self-service robot could inquire about patients' family history in place of professional clinicians or genetic counselors. Objective: The aim of this study was to develop a mobile-based and self-service tool to collect and visualize pedigree data, not only for professionals but also for those who know little about genetics. Methods: There are 4 main aspects in the iPed construction, including interface building, data processing, data storage, and data visualization. The user interface was built using HTML, JavaScript libraries, and Cascading Style Sheets (version 3; Daniel Eden). Processing of the submitted data is carried out by PHP programming language. MySQL is used to document and manage the pedigree data. PHP calls the R script to accomplish the visualization. Results: iPed is freely available to all users through the iPed website. No software is required to be installed, no pedigree files need to be prepared, and no knowledge of genetics or programs is required. The users can easily complete their pedigree data collection and visualization on their own and through a dialogue with iPed. Meanwhile, iPed provides a database that stores all users’ information. Therefore, when the users need to construct new pedigree trees for other genetic traits or modify the pedigree trees that have already been created, unnecessary duplication of operations can be avoided. Conclusions: iPed is a mobile-based and self-service tool that could be used by both professionals and nonprofessionals at any time and from any place. It reduces the amount of time required to collect, manage, and visualize pedigree data. %M 35737451 %R 10.2196/36914 %U https://formative.jmir.org/2022/6/e36914 %U https://doi.org/10.2196/36914 %U http://www.ncbi.nlm.nih.gov/pubmed/35737451 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e35961 %T Digital Health Apps in the Context of Dementia: Questionnaire Study to Assess the Likelihood of Use Among Physicians %A Schinle,Markus %A Erler,Christina %A Kaliciak,Mayumi %A Milde,Christopher %A Stock,Simon %A Gerdes,Marius %A Stork,Wilhelm %+ Medical Information Technology, Embedded Systems and Sensors Engineering, FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, Karlsruhe, 76131, Germany, 49 721 9654 75, schinle@fzi.de %K digital health applications %K likelihood of use %K usability %K adherence %K dementia %K screening %K treatment %K physician %K eHealth %K questionnaire %K mobile phone %D 2022 %7 22.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Age-related diseases such as dementia are playing an increasingly important role in global population development. Thus, prevention, diagnostics, and interventions require more accessibility, which can be realized through digital health apps. With the app on prescription, Germany made history by being the first country worldwide to offer physicians the possibility to prescribe and reimburse digital health apps as of the end of the year 2020. Objective: Considering the lack of knowledge about correlations with the likelihood of use among physicians, this study aimed to address the question of what makes the use of a digital health app by physicians more likely. Methods: We developed and validated a novel measurement tool—the Digital Health Compliance Questionnaire (DHCQ)—in an interdisciplinary collaboration of experts to assess the role of proposed factors in the likelihood of using a health app. Therefore, a web-based survey was conducted to evaluate the likelihood of using a digital app called DemPredict to screen for Alzheimer dementia. Within this survey, 5 latent dimensions (acceptance, attitude toward technology, technology experience, payment for time of use, and effort of collection), the dependent variable likelihood of use, and answers to exploratory questions were recorded and tested within directed correlations. Following a non–probability-sampling strategy, the study was completed by 331 physicians from Germany in the German language, of whom 301 (90.9%) fulfilled the study criteria (eg, being in regular contact with patients with dementia). These data were analyzed using a range of statistical methods to validate the dimensions of the DHCQ. Results: The DHCQ revealed good test theoretical measures—it showed excellent fit indexes (Tucker-Lewis index=0.98; comparative fit index=0.982; standardized root mean square residual=0.073; root mean square error of approximation=0.037), good internal consistency (Cronbach α=.83), and signs of moderate to large correlations between the DHCQ dimensions and the dependent variable. The correlations between the variables acceptance, attitude toward technology, technology experience, and payment for the time of use and the dependent variable likelihood of use ranged from 0.29 to 0.79, and the correlation between effort of the collection and likelihood of use was −0.80. In addition, we found high levels of skepticism regarding data protection, and the age of the participants was found to be negatively related to their technical experience and attitude toward technology. Conclusions: In the context of the results, increased communication between the medical and technology sectors and significantly more awareness raising are recommended to make the use of digital health apps more attractive to physicians as they can be adjusted to their everyday needs. Further research could explore the connection between areas such as adherence on the patient side and its impact on the likelihood of use by physicians. %M 35731567 %R 10.2196/35961 %U https://formative.jmir.org/2022/6/e35961 %U https://doi.org/10.2196/35961 %U http://www.ncbi.nlm.nih.gov/pubmed/35731567 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e32354 %T Breast Cancer Physical Activity Mobile Intervention: Early Findings From a User Experience and Acceptability Mixed Methods Study %A Signorelli,Gabriel Ruiz %A Monteiro-Guerra,Francisco %A Rivera-Romero,Octavio %A Núñez-Benjumea,Francisco J %A Fernández-Luque,Luis %+ Adhera Health, Inc, Circuito II Bldg, Av de los Descubrimientos, s/n, Mairena del Aljarafe - Seville, Palo Alto, CA, United States, 1 656930901, luis@adherahealth.com %K breast cancer %K BC %K mobile app %K physical activity %K mHealth %K acceptability %K user experience %K mobile phone %D 2022 %7 22.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical activity (PA) is the most well-established lifestyle factor associated with breast cancer (BC) survival. Even women with advanced BC may benefit from moderate PA. However, most BC symptoms and treatment side effects are barriers to PA. Mobile health coaching systems can implement functionalities and features based on behavioral change theories to promote healthier behaviors. However, to increase its acceptability among women with BC, it is essential that these digital persuasive systems are designed considering their contextual characteristics, needs, and preferences. Objective: This study aimed to examine the potential acceptability and feasibility of a mobile-based intervention to promote PA in patients with BC; assess usability and other aspects of the user experience; and identify key considerations and aspects for future improvements, which may help increase and sustain acceptability and engagement. Methods: A mixed methods case series evaluation of usability and acceptability was conducted in this study. The study comprised 3 sessions: initial, home, and final sessions. Two standardized scales were used: the Satisfaction with Life Scale and the International Physical Activity Questionnaire–Short Form. Participants were asked to use the app at home for approximately 2 weeks. App use and PA data were collected from the app and stored on a secure server during this period. In the final session, the participants filled in 2 app evaluation scales and took part in a short individual interview. They also completed the System Usability Scale and the user version of the Mobile App Rating Scale. Participants were provided with a waist pocket, wired in-ear headphones, and a smartphone. They also received printed instructions. A content analysis of the qualitative data collected in the interviews was conducted iteratively, ensuring that no critical information was overlooked. Results: The International Physical Activity Questionnaire–Short Form found that all participants (n=4) were moderately active; however, half of them did not reach the recommended levels in the guidelines. System Usability Scale scores were all >70 out of 100 (72.5, 77.5, 95, and 80), whereas the overall user version of the Mobile App Rating Scale scores were 4, 4.3, 4.4, and 3.6 out of 5. The app was perceived to be nice, user-friendly, straightforward, and easy to understand. Recognition of achievements, the possibility of checking activity history, and the rescheduling option were positively highlighted. Technical difficulties with system data collection, particularly with the miscount of steps, could make users feel frustrated. The participants suggested improvements and indicated that the app has the potential to work well for survivors of BC. Conclusions: Early results presented in this study point to the potential of this tool concept to provide a friendly and satisfying coaching experience to users, which may help improve PA adherence in survivors of BC. %M 35731554 %R 10.2196/32354 %U https://formative.jmir.org/2022/6/e32354 %U https://doi.org/10.2196/32354 %U http://www.ncbi.nlm.nih.gov/pubmed/35731554 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e38614 %T Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management %A Lee,Peter %A Kim,Heepyung %A Kim,Yongshin %A Choi,Woohyeok %A Zitouni,M Sami %A Khandoker,Ahsan %A Jelinek,Herbert F %A Hadjileontiadis,Leontios %A Lee,Uichin %A Jeong,Yong %+ Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro Yuseong gu, Daejeon, 34141, Republic of Korea, 82 423507165, yong@kaist.ac.kr %K smart mask %K pathogen filtration %K COVID-19 %K protective equipment %K digital health %K wearable %K smart device %K wearable device %K sensor %K health monitoring %D 2022 %7 21.6.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various “smart” mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables. %M 35679029 %R 10.2196/38614 %U https://mhealth.jmir.org/2022/6/e38614 %U https://doi.org/10.2196/38614 %U http://www.ncbi.nlm.nih.gov/pubmed/35679029 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e36761 %T Assessment of Smartphone Apps for Common Neurologic Conditions (Headache, Insomnia, and Pain): Cross-sectional Study %A Minen,Mia T %A George,Alexis %A Camacho,Erica %A Yao,Leslie %A Sahu,Ananya %A Campbell,Maya %A Soviero,Mia %A Hossain,Quazi %A Verma,Deepti %A Torous,John %+ Department of Neurology, New York University Langone Health, 222 East 41st Street, New York, NY, 10017, United States, 1 2122637744, minenmd@gmail.com %K headache %K pain %K insomnia %K mobile health %K smartphone apps %K mobile phone %D 2022 %7 21.6.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There are thousands of apps for individuals struggling with headache, insomnia, and pain, but it is difficult to establish which of these apps are best suited for patients’ specific needs. If clinicians were to have access to a platform that would allow them to make an informed decision on the efficacy and feasibility of smartphone apps for patient care, they would feel confident in prescribing specific apps. Objective: We sought to evaluate the quality of apps for some of the top common, disabling neurologic conditions (headache, insomnia, and pain) based on principles derived from the American Psychiatric Association’s (APA) app evaluation model. Methods: We used the Mobile Health Index and Navigation database and expanded upon the database’s current supported conditions by adding 177 new app entries. Each app was rated for consistency with the APA’s app evaluation model, which includes 105 objective questions based on the following 5 major classes of consideration: (1) accessibility, (2) privacy and security, (3) clinical foundation, (4) engagement style, and (5) interoperability. These characteristics were evaluated to gain a broader understanding of the significant features of each app category in comparison against a control group. Results: Approximately 90% (187/201) of all apps evaluated were free to download, but only 50% (63/201) of headache- and pain-related apps were truly free. Most (87/106, 81%) sleep apps were not truly free to use. The apps had similar limitations with limited privacy, accessibility, and crisis management resources. For example, only 17% (35/201) of the apps were available in Spanish. The apps offered mostly self-help tools with little tailoring; symptom tracking was the most common feature in headache- (32/48, 67%) and pain-related apps (21/47, 45%), whereas mindfulness was the most common feature in sleep-related apps (73/106, 69%). Conclusions: Although there are many apps for headache, pain, and insomnia, all 3 types of apps have room for improvement around accessibility and privacy. Pain and headache apps share many common features, whereas insomnia apps offer mostly mindfulness-based resources. Given the many available apps to pick from, clinicians and patients should seek apps that offer the highest-quality features, such as complete privacy, remedial features, and the ability to download the app at no cost. These results suggest that there are many opportunities for the improvement of apps centered on headache, insomnia, and pain. %M 35727625 %R 10.2196/36761 %U https://mhealth.jmir.org/2022/6/e36761 %U https://doi.org/10.2196/36761 %U http://www.ncbi.nlm.nih.gov/pubmed/35727625 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e29856 %T Tailoring Mobile Data Collection for Intervention Research in a Challenging Context: Development and Implementation in the Malakit Study %A Lambert,Yann %A Galindo,Muriel %A Suárez-Mutis,Martha %A Mutricy,Louise %A Sanna,Alice %A Garancher,Laure %A Cairo,Hedley %A Hiwat,Helene %A Bordalo Miller,Jane %A Gomes,José Hermenegildo %A Marchesini,Paola %A Adenis,Antoine %A Nacher,Mathieu %A Vreden,Stephen %A Douine,Maylis %+ Centre d’Investigation Clinique Antilles-Guyane, Institut national de la santé et de la recherche médicale (Inserm 1424), Centre Hospitalier de Cayenne Andrée Rosemon, Avenue des Flamboyants, Cayenne, 97300, French Guiana, 594 594 39 48 64, yann.lambert@ch-cayenne.fr %K malaria %K Guiana Shield %K information system %K mobile data collection %K Open Data Kit %K ODK %D 2022 %7 16.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: An interventional study named Malakit was implemented between April 2018 and March 2020 to address malaria in gold mining areas in French Guiana, in collaboration with Suriname and Brazil. This innovative intervention relied on the distribution of kits for self-diagnosis and self-treatment to gold miners after training by health mediators, referred to in the project as facilitators. Objective: This paper aims to describe the process by which the information system was designed, developed, and implemented to achieve the monitoring and evaluation of the Malakit intervention. Methods: The intervention was implemented in challenging conditions at five cross-border distribution sites, which imposed strong logistical constraints for the design of the information system: isolation in the Amazon rainforest, tropical climate, and lack of reliable electricity supply and internet connection. Additional constraints originated from the interaction of the multicultural players involved in the study. The Malakit information system was developed as a patchwork of existing open-source software, commercial services, and tools developed in-house. Facilitators collected data from participants using Android tablets with ODK (Open Data Kit) Collect. A custom R package and a dashboard web app were developed to retrieve, decrypt, aggregate, monitor, and clean data according to feedback from facilitators and supervision visits on the field. Results: Between April 2018 and March 2020, nine facilitators generated a total of 4863 form records, corresponding to an average of 202 records per month. Facilitators’ feedback was essential for adapting and improving mobile data collection and monitoring. Few technical issues were reported. The median duration of data capture was 5 (IQR 3-7) minutes, suggesting that electronic data capture was not taking more time from participants, and it decreased over the course of the study as facilitators become more experienced. The quality of data collected by facilitators was satisfactory, with only 3.03% (147/4849) of form records requiring correction. Conclusions: The development of the information system for the Malakit project was a source of innovation that mirrored the inventiveness of the intervention itself. Our experience confirms that even in a challenging environment, it is possible to produce good-quality data and evaluate a complex health intervention by carefully adapting tools to field constraints and health mediators’ experience. Trial Registration: ClinicalTrials.gov NCT03695770; https://clinicaltrials.gov/ct2/show/NCT03695770 %M 35708763 %R 10.2196/29856 %U https://formative.jmir.org/2022/6/e29856 %U https://doi.org/10.2196/29856 %U http://www.ncbi.nlm.nih.gov/pubmed/35708763 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e38283 %T Assessing the Initial Validity of the PortionSize App to Estimate Dietary Intake Among Adults: Pilot and Feasibility App Validation Study %A Saha,Sanjoy %A Lozano,Chloe Panizza %A Broyles,Stephanie %A Martin,Corby K %A Apolzan,John W %+ Pennington Biomedical Research Center, Louisiana State University System, 6400 Perkins Road, Baton Rouge, LA, 70808, United States, 1 2257632827, john.apolzan@pbrc.edu %K dietary assessment %K eating %K food intake %K energy intake %K portion size %K mHealth %K digital health %K eHealth %K nutrition %K food groups %D 2022 %7 15.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Accurately assessing dietary intake can promote improved nutrition. The PortionSize app (Pennington Biomedical Research Center) was designed to quantify and provide real-time feedback on the intake of energy, food groups, saturated fat, and added sugar. Objective: This study aimed to assess the preliminary feasibility and validity of estimating food intake via the PortionSize app among adults. Methods: A total of 15 adults (aged 18-65 years) were recruited and trained to quantify the food intake from a simulated meal by using PortionSize. Trained personnel prepared 15 simulated meals and covertly weighed (weigh back) the amount of food provided to participants as well as food waste. Equivalence tests (±25% bounds) were performed to compare PortionSize to the weigh back method. Results: Participants were aged a mean of 28 (SD 12) years, and 11 were female. The mean energy intake estimated with PortionSize was 742.9 (SD 328.2) kcal, and that estimated via weigh back was 659.3 (SD 190.7) kcal (energy intake difference: mean 83.5, SD 287.5 kcal). The methods were not equivalent in estimating energy intake (P=.18), and PortionSize overestimated energy intake by 83.5 kcal (12.7%) at the meal level. Estimates of portion sizes (gram weight; P=.01), total sugar (P=.049), fruit servings (P=.01), and dairy servings (P=.047) from PortionSize were equivalent to those estimated via weigh back. PortionSize was not equivalent to weigh back with regard to estimates for carbohydrate (P=.10), fat (P=.32), vegetable (P=.37), grain (P=.31), and protein servings (P=.87). Conclusions: Due to power limitations, the equivalence tests had large equivalence bounds. Though preliminary, the results of this small pilot study warrant the further adaptation, development, and validation of PortionSize as a means to estimate energy intake and provide users with real-time and actionable dietary feedback. %M 35704355 %R 10.2196/38283 %U https://formative.jmir.org/2022/6/e38283 %U https://doi.org/10.2196/38283 %U http://www.ncbi.nlm.nih.gov/pubmed/35704355 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 6 %P e36787 %T A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set %A Elnakib,Shatha %A Vecino-Ortiz,Andres I %A Gibson,Dustin G %A Agarwal,Smisha %A Trujillo,Antonio J %A Zhu,Yifan %A Labrique,Alain B %+ Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, E8620, Baltimore, MD, 21205, United States, 1 4109554711, andres.vecino@gmail.com %K C-Score %K validation %K mortality %K predictive models %K mobile phone %D 2022 %7 14.6.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: The C-Score, which is an individual health score, is based on a predictive model validated in the UK and US populations. It was designed to serve as an individualized point-in-time health assessment tool that could be integrated into clinical counseling or consumer-facing digital health tools to encourage lifestyle modifications that reduce the risk of premature death. Objective: Our study aimed to conduct an external validation of the C-Score in the US population and expand the original score to improve its predictive capabilities in the US population. The C-Score is intended for mobile health apps on wearable devices. Methods: We conducted a literature review to identify relevant variables that were missing in the original C-Score. Subsequently, we used data from the 2005 to 2014 US National Health and Nutrition Examination Survey (NHANES; N=21,015) to test the capacity of the model to predict all-cause mortality. We used NHANES III data from 1988 to 1994 (N=1440) to conduct an external validation of the test. Only participants with complete data were included in this study. Discrimination and calibration tests were conducted to assess the operational characteristics of the adapted C-Score from receiver operating curves and a design-based goodness-of-fit test. Results: Higher C-Scores were associated with reduced odds of all-cause mortality (odds ratio 0.96, P<.001). We found a good fit of the C-Score for all-cause mortality with an area under the curve (AUC) of 0.72. Among participants aged between 40 and 69 years, C-Score models had a good fit for all-cause mortality and an AUC >0.72. A sensitivity analysis using NHANES III data (1988-1994) was performed, yielding similar results. The inclusion of sociodemographic and clinical variables in the basic C-Score increased the AUCs from 0.72 (95% CI 0.71-0.73) to 0.87 (95% CI 0.85-0.88). Conclusions: Our study shows that this digital biomarker, the C-Score, has good capabilities to predict all-cause mortality in the general US population. An expanded health score can predict 87% of the mortality in the US population. This model can be used as an instrument to assess individual mortality risk and as a counseling tool to motivate behavior changes and lifestyle modifications. %M 35483022 %R 10.2196/36787 %U https://www.jmir.org/2022/6/e36787 %U https://doi.org/10.2196/36787 %U http://www.ncbi.nlm.nih.gov/pubmed/35483022 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e34366 %T Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study %A Park,Jinkyung %A Arunachalam,Ramanathan %A Silenzio,Vincent %A Singh,Vivek K %+ School of Communication & Information, Rutgers University, 4 Huntington Street, New Brunswick, NJ, 08901, United States, 1 848 932 7588, v.singh@rutgers.edu %K algorithmic bias %K mental health %K health equity %K medical informatics %K health information systems %K gender bias %K mobile phone %D 2022 %7 14.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Approximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone–based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence–related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups. Objective: This research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy. Methods: First, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm. Results: The highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001). Conclusions: This study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future. %M 35699997 %R 10.2196/34366 %U https://formative.jmir.org/2022/6/e34366 %U https://doi.org/10.2196/34366 %U http://www.ncbi.nlm.nih.gov/pubmed/35699997 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e39198 %T Authors’ Response to: Additional Measurement Approaches for Sleep Disturbances. Comment on “Transdiagnostic Self-management Web-Based App for Sleep Disturbance in Adolescents and Young Adults: Feasibility and Acceptability Study” %A Carney,Colleen E %A Carmona,Nicole E %+ Toronto Metropolitan University, Jorgenson Hall, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada, 1 4169795000 ext 552177, ccarney@ryerson.ca %K youth %K sleep %K technology %K mHealth %K self-management %K adolescents %K young adults %K mobile phone %K smartphone %K polysomnography %D 2022 %7 13.6.2022 %9 Letter to the Editor %J JMIR Form Res %G English %X %M 35699990 %R 10.2196/39198 %U https://formative.jmir.org/2022/6/e39198 %U https://doi.org/10.2196/39198 %U http://www.ncbi.nlm.nih.gov/pubmed/35699990 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e35959 %T Additional Measurement Approaches for Sleep Disturbances. Comment on “A Transdiagnostic Self-management Web-Based App for Sleep Disturbance in Adolescents and Young Adults: Feasibility and Acceptability Study” %A Tsai,Wan-Tong %A Liu,Tzung-Liang %+ Chung Shan Medical University, No 110, Sec 1, Jianguo N Rd, South District, Taichung City, 40201, Taiwan, 886 968938360, science.tsai@gmail.com %K youth %K sleep %K technology %K mHealth %K self-management %K adolescents %K young adults %K mobile phone %K smartphone %K polysomnography %D 2022 %7 13.6.2022 %9 Letter to the Editor %J JMIR Form Res %G English %X %M 35700003 %R 10.2196/35959 %U https://formative.jmir.org/2022/6/e35959 %U https://doi.org/10.2196/35959 %U http://www.ncbi.nlm.nih.gov/pubmed/35700003 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e34080 %T Patient Onboarding and Engagement to Build a Digital Study After Enrollment in a Clinical Trial (TAILOR-PCI Digital Study): Intervention Study %A Avram,Robert %A So,Derek %A Iturriaga,Erin %A Byrne,Julia %A Lennon,Ryan %A Murthy,Vishakantha %A Geller,Nancy %A Goodman,Shaun %A Rihal,Charanjit %A Rosenberg,Yves %A Bailey,Kent %A Farkouh,Michael %A Bell,Malcolm %A Cagin,Charles %A Chavez,Ivan %A El-Hajjar,Mohammad %A Ginete,Wilson %A Lerman,Amir %A Levisay,Justin %A Marzo,Kevin %A Nazif,Tamim %A Olgin,Jeffrey %A Pereira,Naveen %+ Department of Medicine, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA, 94117, United States, 1 451 476 1325, Jeffrey.Olgin@ucsf.edu %K digital study %K clinical trial %K cardiology %K smartphone %K digital health %K mobile health %K clinical trial %K mobile phone %D 2022 %7 13.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The Tailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response After Percutaneous Coronary Intervention (TAILOR-PCI) Digital Study is a novel proof-of-concept study that evaluated the feasibility of extending the TAILOR-PCI randomized controlled trial (RCT) follow-up period by using a remote digital platform. Objective: The aim of this study is to describe patients’ onboarding, engagement, and results in a digital study after enrollment in an RCT. Methods: In this intervention study, previously enrolled TAILOR-PCI patients in the United States and Canada within 24 months of randomization were invited by letter to download the study app. Those who did not respond to the letter were contacted by phone to survey the reasons for nonparticipation. A direct-to-patient digital research platform (the Eureka Research Platform) was used to onboard patients, obtain consent, and administer activities in the digital study. The patients were asked to complete health-related surveys and digitally provide follow-up data. Our primary end points were the consent rate, the duration of participation, and the monthly activity completion rate in the digital study. The hypothesis being tested was formulated before data collection began. Results: After the parent trial was completed, letters were mailed to 907 eligible patients (representing 18.8% [907/4837] of total enrolled in the RCT) within 15.6 (SD 5.2) months of randomization across 24 sites. Among the 907 patients invited, 290 (32%) visited the study website and 110 (12.1%) consented—40.9% (45/110) after the letter, 33.6% (37/110) after the first phone call, and 25.5% (28/110) after the second call. Among the 47.4% (409/862) of patients who responded, 41.8% (171/409) declined to participate because of a lack of time, 31.2% (128/409) declined because of the lack of a smartphone, and 11.5% (47/409) declined because of difficulty understanding what was expected of them in the study. Patients who consented were older (aged 65.3 vs 62.5 years; P=.006) and had a lower prevalence of diabetes (19% vs 30%; P=.02) or tobacco use (6.4% vs 24.8%; P<.001). A greater proportion had bachelor’s degrees (47.2% vs 25.7%; P<.001) and were more computer literate (90.5% vs 62.3% of daily internet use; P<.001) than those who did not consent. The average completion rate of the 920 available monthly electronic visits was 64.9% (SD 7.6%); there was no decrease in this rate throughout the study duration. Conclusions: Extended follow-up after enrollment in an RCT by using a digital study was technically feasible but was limited because of the inability to contact most eligible patients or a lack of time or access to a smartphone. Among the enrolled patients, most completed the required electronic visits. Enhanced recruitment methods, such as the introduction of a digital study at the time of RCT consent, smartphone provision, and robust study support for onboarding, should be explored further. Trial Registration: Clinicaltrails.gov NCT01742117; https://clinicaltrials.gov/ct2/show/NCT01742117 %M 35699977 %R 10.2196/34080 %U https://formative.jmir.org/2022/6/e34080 %U https://doi.org/10.2196/34080 %U http://www.ncbi.nlm.nih.gov/pubmed/35699977 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e37743 %T Time-Varying Associations Between Device-Based and Ecological Momentary Assessment–Reported Sedentary Behaviors and the Concurrent Affective States Among Adolescents: Proof-of-Concept Study %A Zink,Jennifer %A Yang,Chih-Hsiang %A Alves,Jasmin M %A McAlister,Kelsey L %A Huh,Jimi %A Pentz,Mary Ann %A Page,Kathleen A %A Dunton,Genevieve F %A Belcher,Britni R %+ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St, Los Angeles, CA, 90032, United States, 1 323 442 8225, bbelcher@usc.edu %K accelerometry %K intensive longitudinal data %K mood %K youth %K mobile phone %D 2022 %7 10.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Previous studies on affective state–sedentary behavior (SB) associations have not accounted for their potentially time-varying nature and have used inconsistent SB measurement modalities. We investigated whether the strength of the associations between affective states and SB varied as a function of the time of day and by SB measurement modality (device-measured SB vs ecological momentary assessment–reported screen-based SB) in youth. Objective: This study aimed to establish a proof of concept that SB–affective state associations may not be static during the day. In addition, we aimed to inform the methodology of future work, which may need to model associations as functions of the time of day and carefully consider how SB is operationalized or measured. Methods: A total of 15 adolescents (age: mean 13.07, SD 1.03 years; 10/15, 67% female; 6/15, 40% Hispanic; 10/15, 67% healthy weight) wore thigh-mounted activPAL accelerometers and simultaneously reported their screen-based SBs and concurrent positive and negative affective states via ecological momentary assessment for 7 to 14 days (N=636 occasions). Time-varying effect models (varying slopes) examined how each measure of SB was associated with concurrent affective states from 7 AM to 8 PM. Results: Time-varying effect model plots revealed that these associations varied in strength throughout the day. Specifically, device-based SB was related to greater concurrent negative affect only after approximately 5 PM and was unrelated to concurrent positive affect. Screen-based SB was related to greater concurrent negative affect only from 7 AM to approximately 9 AM. This was also related to greater concurrent positive affect from 7 AM to approximately 9:30 AM and from approximately 3 PM to approximately 7 PM. Conclusions: We provide preliminary evidence to suggest that future confirmatory studies investigating the SB–affective state relationship should consider the time-varying nature of these associations and SB measurement modality. There may be critical time windows when specific types of SBs co-occur with affect, suggesting that interventions may need tailoring to the time of day and type of SB if future studies using similar methodologies can replicate our findings. %M 35687383 %R 10.2196/37743 %U https://formative.jmir.org/2022/6/e37743 %U https://doi.org/10.2196/37743 %U http://www.ncbi.nlm.nih.gov/pubmed/35687383 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e35056 %T Online Partner Seeking and Sexual Behaviors Among Men Who Have Sex With Men From Small and Midsized Towns: Cross-sectional Study %A Pravosud,Vira %A Ballard,April M %A Holloway,Ian W %A Young,April M %+ Center for Tobacco Control Research and Education, Cardiovascular Research Institute, University of California, San Francisco, 530 Parnassus Avenue, Suite 366, San Francisco, CA, 94143, United States, 1 415 514 8627, vira.pravosud@ucsf.edu %K men who have sex with men %K MSM %K sexual risk behaviors %K social networking and dating apps %K online tools %K HIV %K sexually transmitted infection %K STI prevention %K mobile phone %D 2022 %7 10.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Men who have sex with men (MSM) residing outside of large urban areas are underrepresented in research on online partner seeking and sexual behaviors related to transmission of HIV. Objective: We aimed to determine associations between the use of the internet or social networking apps (online tools) to meet partners for sex, dating, or for both purposes (online partner seeking) and sexual behaviors among MSM residing in small and midsized towns in Kentucky, United States. Methods: Using peer-referral sampling and online self-administered questionnaires, data were collected from 252 men, aged 18 to 34 years, who had recently (past 6 months) engaged in anal sex with another man and resided in Central Kentucky. Using multivariable logistic regression models, we assessed associations of online partner seeking and HIV-related sexual behaviors. Results: Most (181/252, 71.8%) of the participants reported using online tools for partner seeking. Of these 181 respondents, 166 (91.7%) had used online tools to meet partners for sex (n=45, 27.1% for sex only; and n=121, 72.9% for sex and dating) and 136 (75.1%) had used online tools to meet partners for dating (n=15, 11% for dating only; and n=121, 89% for sex and dating). Adjusted analyses revealed that MSM who had engaged in condomless insertive and receptive anal intercourse were less likely to report online partner seeking (adjusted odds ratio [aOR] 0.22, 95% CI 0.07-0.68; P=.009 and aOR 0.25, 95% CI 0.10-0.66; P=.005, respectively). Increased number of insertive and receptive anal sex partners and substance use before or during sex were associated with higher odds of online partner seeking (aOR 1.31, 95% CI 1.11-1.55; P=.001; aOR 1.20, 95% CI 1.05-1.39; P=.008; and aOR 2.50, 95% CI 1.41-4.44; P=.002, respectively). Conclusions: Among MSM who reside outside of large urban areas and practice online partner seeking, HIV risk-reduction interventions should address safer sex practices, including the risks for HIV transmission associated with alcohol or drug use before or during sex. MSM who do not practice online partner seeking are in need of continued outreach to reduce condomless anal sex. %M 35687395 %R 10.2196/35056 %U https://formative.jmir.org/2022/6/e35056 %U https://doi.org/10.2196/35056 %U http://www.ncbi.nlm.nih.gov/pubmed/35687395 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e36377 %T Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review %A Giurgiu,Marco %A Timm,Irina %A Becker,Marlissa %A Schmidt,Steffen %A Wunsch,Kathrin %A Nissen,Rebecca %A Davidovski,Denis %A Bussmann,Johannes B J %A Nigg,Claudio R %A Reichert,Markus %A Ebner-Priemer,Ulrich W %A Woll,Alexander %A von Haaren-Mack,Birte %+ Department of Sports and Sports Science, Karlsruhe Institute of Technology, Hertzstr. 16, Karlsruhe, 76187, Germany, 49 017620763557, marco.giurgiu@kit.edu %K wearables %K validation %K sedentary behavior %K physical activity %K sleep %D 2022 %7 9.6.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking. Objective: This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies. Methods: Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time). Results: Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk. Conclusions: Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity. %M 35679106 %R 10.2196/36377 %U https://mhealth.jmir.org/2022/6/e36377 %U https://doi.org/10.2196/36377 %U http://www.ncbi.nlm.nih.gov/pubmed/35679106 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e35053 %T Emerging Artificial Intelligence–Empowered mHealth: Scoping Review %A Bhatt,Paras %A Liu,Jia %A Gong,Yanmin %A Wang,Jing %A Guo,Yuanxiong %+ Department of Electrical & Computer Engineering, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 210 458 8028, yuanxiong.guo@utsa.edu %K mobile health units %K telemedicine %K machine learning %K artificial intelligence %K review literature as topic %D 2022 %7 9.6.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. Objective: Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years. Methods: Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain. Results: We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research. Conclusions: The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry. %M 35679107 %R 10.2196/35053 %U https://mhealth.jmir.org/2022/6/e35053 %U https://doi.org/10.2196/35053 %U http://www.ncbi.nlm.nih.gov/pubmed/35679107 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 6 %P e35697 %T The Surveillance of Physical Activity, Sedentary Behavior, and Sleep: Protocol for the Development and Feasibility Evaluation of a Novel Measurement System %A Crowley,Patrick %A Ikeda,Erika %A Islam,Sheikh Mohammed Shariful %A Kildedal,Rasmus %A Schade Jacobsen,Sandra %A Roslyng Larsen,Jon %A Johansson,Peter J %A Hettiarachchi,Pasan %A Aadahl,Mette %A Mork,Paul Jarle %A Straker,Leon %A Stamatakis,Emmanuel %A Holtermann,Andreas %A Gupta,Nidhi %+ The National Research Centre for the Working Environment, Lersø Parkallé 105, Copenhagen, 2100, Denmark, 45 20469173, pjc@nfa.dk %K accelerometer %K thigh-worn %K sensor-based %K system acceptability %K surveillance %K physical activity %K physical health %K physical %K sedentary %K sedentary behavior %K sleep %K surPASS %K public health %D 2022 %7 6.6.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: There is increasing recognition of the need for more comprehensive surveillance data, including information on physical activity of all intensities, sedentary behavior, and sleep. However, meeting this need poses significant challenges for current surveillance systems, which are mainly reliant on self-report. Objective: The primary objective of this project is to develop and evaluate the feasibility of a sensor-based system for use in the surveillance of physical activity, sedentary behavior, and sleep (SurPASS) at a national level in Denmark. Methods: The SurPASS project involves an international, multidisciplinary team of researchers collaborating with an industrial partner. The SurPASS system consists of (1) a thigh-worn accelerometer with Bluetooth connectivity, (2) a smartphone app, (3) an integrated back end, facilitating the automated upload, analysis, storage, and provision of individualized feedback in a manner compliant with European Union regulations on data privacy, and (4) an administrator web interface (web application) to monitor progress. The system development and evaluation will be performed in 3 phases. These phases will include gathering user input and specifications (phase 1), the iterative development, evaluation, and refinement of the system (phase 2), and the feasibility evaluation (phase 3). Results: The project started in September 2020 and completed phase 2 in February 2022. Phase 3 began in March 2022 and results will be made available in 2023. Conclusions: If feasible, the SurPASS system could be a catalyst toward large-scale, sensor-based surveillance of physical activity, sedentary behavior, and sleep. It could also be adapted for cohort and interventional research, thus contributing to the generation of evidence for both interventions and public health policies and recommendations. International Registered Report Identifier (IRRID): DERR1-10.2196/35697 %M 35666571 %R 10.2196/35697 %U https://www.researchprotocols.org/2022/6/e35697 %U https://doi.org/10.2196/35697 %U http://www.ncbi.nlm.nih.gov/pubmed/35666571 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e33458 %T Trends in Heart Rate and Heart Rate Variability During Pregnancy and the 3-Month Postpartum Period: Continuous Monitoring in a Free-living Context %A Sarhaddi,Fatemeh %A Azimi,Iman %A Axelin,Anna %A Niela-Vilen,Hannakaisa %A Liljeberg,Pasi %A Rahmani,Amir M %+ Department of Computer Science, University of California, Irvine, Donald Bren Hall, 6210, Irvine, CA, 92697, United States, 1 949 824 3590, a.rahmani@uci.edu %K heart rate %K heart rate variability %K pregnancy %K postpartum %K continuous monitoring %K PPG %K mobile phone %D 2022 %7 3.6.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV. Objective: The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period. Methods: A total of 58 pregnant women were monitored using an Internet of Things–based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters. Results: HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004). Conclusions: Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings. %M 35657667 %R 10.2196/33458 %U https://mhealth.jmir.org/2022/6/e33458 %U https://doi.org/10.2196/33458 %U http://www.ncbi.nlm.nih.gov/pubmed/35657667 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e31491 %T Validation Parameters of Patient-Generated Data for Digitally Recorded Allergic Rhinitis Symptom and Medication Scores in the @IT.2020 Project: Exploratory Study %A Dramburg,Stephanie %A Perna,Serena %A Di Fraia,Marco %A Tripodi,Salvatore %A Arasi,Stefania %A Castelli,Sveva %A Villalta,Danilo %A Buzzulini,Francesca %A Sfika,Ifigenia %A Villella,Valeria %A Potapova,Ekaterina %A Brighetti,Maria Antonia %A Travaglini,Alessandro %A Verardo,Pierluigi %A Pelosi,Simone %A Matricardi,Paolo Maria %+ Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburgerplatz, 1, Berlin, 13353, Germany, 49 30 450 566 406, paolo.matricardi@charite.de %K allergic rhinitis %K symptom scores %K patient-generated data %K patient-reported outcomes %K mHealth %K mobile health %K health applications %K allergies %K allergy monitor %K digital health %K medication scores %D 2022 %7 3.6.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health technologies enable allergists to monitor disease trends by collecting daily patient-reported outcomes of allergic rhinitis. To this end, patients with allergies are usually required to enter their symptoms and medication repetitively over long time periods, which may present a risk to data completeness and quality in the case of insufficient effort reporting. Completeness of patient’s recording is easily measured. In contrast, the intrinsic quality and accuracy of the data entered by the patients are more elusive. Objective: The aim of this study was to explore the association of adherence to digital symptom recording with a predefined set of parameters of the patient-generated symptom and medication scores and to identify parameters that may serve as proxy measure of the quality and reliability of the information recorded by the patient. Methods: The @IT.2020 project investigates the diagnostic synergy of mobile health and molecular allergology in patients with seasonal allergic rhinitis. In its pilot phase, 101 children with seasonal allergic rhinitis were recruited in Rome and instructed to record their symptoms, medication intake, and general conditions daily via a mobile app (AllergyMonitor) during the relevant pollen season. We measured adherence to daily recording as the percentage of days with data recording in the observation period. We examined the patient’s trajectories of 3 disease indices (Rhinoconjunctivitis Total Symptom Score [RTSS], Combined Symptom and Medication Score [CSMS], and Visual Analogue Scale [VAS]) as putative proxies of data quality with the following 4 parameters: (1) intravariation index, (2) percentage of zero values, (3) coefficient of variation, and (4) percentage of changes in trend. Lastly, we examined the relationship between adherence to recording and each of the 4 proxy measures. Results: Adherence to recording ranged from 20% (11/56) to 100% (56/56), with 64.4% (65/101) and 35.6% (36/101) of the patients’ values above (highly adherent patients) or below (low adherent patients) the threshold of 80%, respectively. The percentage of zero values, the coefficient of variation, and the intravariation index did not significantly change with the adherence to recording. By contrast, the proportion of changes in trend was significantly higher among highly adherent patients, independently from the analyzed score (RTSS, CSMS, and VAS). Conclusions: The percentage of changes in the trend of RTSS, CSMS, and VAS is a valuable candidate to validate the quality and accuracy of the data recorded by patients with allergic rhinitis during the pollen season. The performance of this parameter must be further investigated in real-life conditions before it can be recommended for routine use in apps and electronic diaries devoted to the management of patients with allergic rhinitis. %M 35657659 %R 10.2196/31491 %U https://mhealth.jmir.org/2022/6/e31491 %U https://doi.org/10.2196/31491 %U http://www.ncbi.nlm.nih.gov/pubmed/35657659 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 6 %P e38294 %T Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study %A Kilshaw,Robyn E %A Adamo,Colin %A Butner,Jonathan E %A Deboeck,Pascal R %A Shi,Qinxin %A Bulik,Cynthia M %A Flatt,Rachael E %A Thornton,Laura M %A Argue,Stuart %A Tregarthen,Jenna %A Baucom,Brian R W %+ Department of Psychology, University of Utah, 380 S 1530 E, Room 502, Salt Lake City, UT, 84112, United States, 1 801 581 6124, robyn.kilshaw@psych.utah.edu %K digital phenotyping %K eating disorders %K personal digital devices %K methodology %D 2022 %7 2.6.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions. Objective: We describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application. Methods: The University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes. Results: Data collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022. Conclusions: We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. Trial Registration: ClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574 International Registered Report Identifier (IRRID): DERR1-10.2196/38294 %M 35653175 %R 10.2196/38294 %U https://www.researchprotocols.org/2022/6/e38294 %U https://doi.org/10.2196/38294 %U http://www.ncbi.nlm.nih.gov/pubmed/35653175 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 5 %P e34279 %T Application of Spatial Risk Assessment Integrated With a Mobile App in Fighting Against the Introduction of African Swine Fever in Pig Farms in Thailand: Development Study %A Thanapongtharm,Weerapong %A Wongphruksasoong,Vilaiporn %A Sangrat,Waratida %A Thongsrimoung,Kittin %A Ratanavanichrojn,Nattavut %A Kasemsuwan,Suwicha %A Khamsiriwatchara,Amnat %A Kaewkungwal,Jaranit %A Leelahapongsathon,Kansuda %+ Faculty of Veterinary Medicine, Kasetsart University, Malaiman Rd, Kamphaeng Saen, Nakhon Pathom, 73140, Thailand, 66 34351901, fvetkul@ku.ac.th %K African swine fever %K multi-criteria decision analysis %K risk-based surveillance %K risk assessment %K spatial analysis %D 2022 %7 31.5.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: African swine fever (ASF), a highly contagious disease affecting both domestic and wild pigs, has been having a serious impact on the swine industry worldwide. This important transboundary animal disease can be spread by animals and ticks via direct transmission and by contaminated feed and fomites via indirect transmission because of the high environmental resistance of the ASF virus. Thus, the prevention of the introduction of ASF to areas free of ASF is essential. After an outbreak was reported in China, intensive import policies and biosecurity measures were implemented to prevent the introduction of ASF to pig farms in Thailand. Objective: Enhancing prevention and control, this study aims to identify the potential areas for ASF introduction and transmission in Thailand, develop a tool for farm assessment of ASF risk introduction focusing on smallholders, and develop a spatial analysis tool that is easily used by local officers for disease prevention and control planning. Methods: We applied a multi-criteria decision analysis approach with spatial and farm assessment and integrated the outputs with the necessary spatial layers to develop a spatial analysis on a web-based platform. Results: The map that referred to potential areas for ASF introduction and transmission was derived from 6 spatial risk factors; namely, the distance to the port, which had the highest relative importance, followed by the distance to the border, the number of pig farms using swill feeding, the density of small pig farms (<50 heads), the number of pigs moving in the area, and the distance to the slaughterhouse. The possible transmission areas were divided into 5 levels (very low, low, medium, high, and very high) at the subdistrict level, with 27 subdistricts in 10 provinces having very high suitability and 560 subdistricts in 34 provinces having high suitability. At the farm level, 17 biosecurity practices considered as useful and practical for smallholders were selected and developed on a mobile app platform. The outputs from the previous steps integrated with necessary geographic information system layers were added to a spatial analysis web-based platform. Conclusions: The tools developed in this study have been complemented with other strategies to fight against the introduction of ASF to pig farms in the country. The areas showing high and very high risk for disease introduction and transmission were applied for spatial information planning, for example, intensive surveillance, strict animal movement, and public awareness. In addition, farms with low biosecurity were improved in these areas, and the risk assessment developed on a mobile app in this study helped enhance this matter. The spatial analysis on a web-based platform helped facilitate disease prevention planning for the authorities. %M 35639455 %R 10.2196/34279 %U https://formative.jmir.org/2022/5/e34279/ %U https://doi.org/10.2196/34279 %U http://www.ncbi.nlm.nih.gov/pubmed/35639455 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 5 %P e32740 %T Pilot Testing in the Wild: Feasibility, Acceptability, Usage Patterns, and Efficacy of an Integrated Web and Smartphone Platform for Bipolar II Disorder %A Fletcher,Kathryn %A Lindblom,Katrina %A Seabrook,Elizabeth %A Foley,Fiona %A Murray,Greg %+ Centre for Mental Health, Swinburne University of Technology, PO Box 218, Hawthorn, Melbourne, 3122, Australia, 61 92148300, kfletcher@swin.edu.au %K bipolar disorder %K smartphone %K app %K web-based intervention %K ecological momentary assessment %K mobile phone %D 2022 %7 31.5.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Bipolar II disorder (BD-II) is associated with significant burden, disability, and mortality; however, there continues to be a dearth of evidence-based psychological interventions for this condition. Technology-mediated interventions incorporating self-management have untapped potential to help meet this need as an adjunct to usual clinical care. Objective: The objective of this pilot study is to assess the feasibility, acceptability, and clinical utility of a novel intervention for BD-II (Tailored Recovery-oriented Intervention for Bipolar II Experiences; TRIBE), in which mindfulness-based psychological content is delivered via an integrated web and smartphone platform. The focus of the study is evaluation of the dynamic use patterns emerging from ecological momentary assessment and intervention to assist the real-world application of mindfulness skills learned from web-delivered modules. Methods: An open trial design using pretest and posttest assessments with nested qualitative evaluation was used. Individuals (aged 18-65 years) with a diagnosis of BD-II were recruited worldwide and invited to use a prototype of the TRIBE intervention over a 3-week period. Data were collected via web-based questionnaires and phone interviews at baseline and 3-week follow-up. Results: A total of 25 participants completed baseline and follow-up assessments. Adherence rates (daily app use) were 65.6% across the 3-week study, with up to 88% (22/25) of participants using the app synergistically alongside the web-based program. Despite technical challenges with the prototype intervention (from user, hardware, and software standpoints), acceptability was adequate, and most participants rated the intervention positively in terms of concept (companion app with website: 19/25, 76%), content (19/25, 76%), and credibility and utility in supporting their management of bipolar disorder (17/25, 68%). Evaluation using behavioral archetypes identified important use pathways and a provisional model to inform platform refinement. As hypothesized, depression scores significantly decreased after the intervention (Montgomery-Asberg Depression Rating Scale baseline mean 8.60, SD 6.86, vs follow-up mean 6.16, SD 5.11; t24=2.63; P=.01; Cohen d=0.53, 95% CI 0.52-4.36). Conclusions: Our findings suggest that TRIBE is feasible and represents an appropriate and acceptable self-management program for patients with BD-II. Preliminary efficacy results are promising and support full development of TRIBE informed by the present behavioral archetype analysis. Modifications suggested by the pilot study include increasing the duration of the intervention and increasing technical support. %M 35639462 %R 10.2196/32740 %U https://formative.jmir.org/2022/5/e32740/ %U https://doi.org/10.2196/32740 %U http://www.ncbi.nlm.nih.gov/pubmed/35639462 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 5 %P e34451 %T Findings and Guidelines on Provider Technology, Fatigue, and Well-being: Scoping Review %A Hilty,Donald M %A Armstrong,Christina M %A Smout,Shelby A %A Crawford,Allison %A Maheu,Marlene M %A Drude,Kenneth P %A Chan,Steven %A Yellowlees,Peter M %A Krupinski,Elizabeth A %+ Department of Psychiatry & Behavioral Sciences, University of California Davis School of Medicine, 2230 Stockton Boulevard, Sacramento, CA, 95817, United States, 1 626 375 7857, donh032612@gmail.com %K burnout %K screen fatigue %K technology fatigue %K well-being %K videoconferencing %K Zoom fatigue %K mobile phone %D 2022 %7 25.5.2022 %9 Review %J J Med Internet Res %G English %X Background: Video and other technologies are reshaping the delivery of health care, yet barriers related to workflow and possible provider fatigue suggest that a thorough evaluation is needed for quality and process improvement. Objective: This scoping review explored the relationship among technology, fatigue, and health care to improve the conditions for providers. Methods: A 6-stage scoping review of literature (from 10 databases) published from 2000 to 2020 that focused on technology, health care, and fatigue was conducted. Technologies included synchronous video, telephone, informatics systems, asynchronous wearable sensors, and mobile health devices for health care in 4 concept areas related to provider experience: behavioral, cognitive, emotional, and physical impact; workplace at the individual, clinic, hospital, and system or organizational levels; well-being, burnout, and stress; and perceptions regarding technology. Qualitative content, discourse, and framework analyses were used to thematically analyze data for developing a spectrum of health to risk of fatigue to manifestations of burnout. Results: Of the 4221 potential literature references, 202 (4.79%) were duplicates, and our review of the titles and abstracts of 4019 (95.21%) found that 3837 (90.9%) were irrelevant. A full-text review of 182 studies revealed that 12 (6.6%) studies met all the criteria related to technology, health care, and fatigue, and these studied the behavioral, emotional, cognitive, and physical impact of workflow at the individual, hospital, and system or organizational levels. Video and electronic health record use has been associated with physical eye fatigue; neck pain; stress; tiredness; and behavioral impacts related to additional effort owing to barriers, trouble with engagement, emotional wear and tear and exhaustion, cognitive inattention, effort, expecting problems, multitasking and workload, and emotional experiences (eg, anger, irritability, stress, and concern about well-being). An additional 14 studies that evaluated behavioral, emotional, and cognitive impacts without focusing on fatigue found high user ratings on data quality, accuracy, and processing but low satisfaction with clerical tasks, the effort required in work, and interruptions costing time, resulting in more errors, stress, and frustration. Our qualitative analysis suggests a spectrum from health to risk and provides an outline of organizational approaches to human factors and technology in health care. Business, occupational health, human factors, and well-being literature have not studied technology fatigue and burnout; however, their findings help contextualize technology-based fatigue to suggest guidelines. Few studies were found to contextually evaluate differences according to health professions and practice contexts. Conclusions: Health care systems need to evaluate the impact of technology in accordance with the Quadruple Aim to support providers’ well-being and prevent workload burden, fatigue, and burnout. Implementation and effectiveness approaches and a multilevel approach with objective measures for clinical, human factors, training, professional development, and administrative workflow are suggested. This requires institutional strategies and competencies to integrate health care quality, technology and well-being outcomes. %M 35612880 %R 10.2196/34451 %U https://www.jmir.org/2022/5/e34451 %U https://doi.org/10.2196/34451 %U http://www.ncbi.nlm.nih.gov/pubmed/35612880 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 2 %P e29767 %T Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review %A Alwakeel,Lyan %A Lano,Kevin %+ Department of Informatics, King’s College London, Strand, London, WC2R 2LS, United Kingdom, 44 02078365454, lyan.alwakeel@kcl.ac.uk %K mHealth %K mobile health apps %K mobile apps %K apps %K systematic literature review %K SLR %K apps %K Mobile App Rating Scale %K MARS %K smartphone %K iOS %K Android %K mobile phone %D 2022 %7 25.5.2022 %9 Review %J JMIR Hum Factors %G English %X Background: Although the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective. Objective: This study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps. Methods: This research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub. Results: In total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges. Conclusions: Self-management mHealth apps may offer an essential means of managing users’ health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps. %M 35612887 %R 10.2196/29767 %U https://humanfactors.jmir.org/2022/2/e29767 %U https://doi.org/10.2196/29767 %U http://www.ncbi.nlm.nih.gov/pubmed/35612887 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 5 %P e23887 %T Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study %A Ramezani,Ramin %A Zhang,Wenhao %A Roberts,Pamela %A Shen,John %A Elashoff,David %A Xie,Zhuoer %A Stanton,Annette %A Eslami,Michelle %A Wenger,Neil S %A Trent,Jacqueline %A Petruse,Antonia %A Weldon,Amelia %A Ascencio,Andy %A Sarrafzadeh,Majid %A Naeim,Arash %+ Center for Smart Health, University of California, Los Angeles, 404 Westwood Plaza, Los Angeles, CA, 90095, United States, 1 4242997051, raminr@ucla.edu %K physical medicine and rehabilitation %K geriatrics %K remote sensing technology %K physical activity %K frailty %K health care delivery models %K wearable sensors %K indoor localization %K Bluetooth low energy beacons %K smartwatches %D 2022 %7 23.5.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation. Objective: The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients’ changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility. Methods: From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission. Results: Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (β=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (β=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (β=–.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (β=–.13; SE 0.06; P=.04) was associated with toileting activities. Conclusions: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients’ progress or decline. %M 35604762 %R 10.2196/23887 %U https://mhealth.jmir.org/2022/5/e23887 %U https://doi.org/10.2196/23887 %U http://www.ncbi.nlm.nih.gov/pubmed/35604762 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 5 %P e33817 %T The Association Between Sleep Disturbance and Suicidality in Psychiatric Inpatients Transitioning to the Community: Protocol for an Ecological Momentary Assessment Study %A Dewa,Lindsay H %A Pappa,Sofia %A Greene,Talya %A Cooke,James %A Mitchell,Lizzie %A Hadley,Molly %A Di Simplicio,Martina %A Woodcock,Thomas %A Aylin,Paul %+ School of Public Health, Imperial College London, Reynolds Building, St Dunstan's Road, London, W6 8RP, United Kingdom, 44 020 7594 0815, l.dewa@imperial.ac.uk %K sleep %K suicide %K psychiatric inpatient %K ecological momentary assessment %K EMA %K experience sampling %K coproduction %K sleep disturbance %K discharge %D 2022 %7 17.5.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Patients are at high risk of suicidal behavior and death by suicide immediately following discharge from inpatient psychiatric hospitals. Furthermore, there is a high prevalence of sleep problems in inpatient settings, which is associated with worse outcomes following hospitalization. However, it is unknown whether poor sleep is associated with suicidality following initial hospital discharge. Objective: Our study objective is to describe a protocol for an ecological momentary assessment (EMA) study that aims to examine the relationship between sleep and suicidality in discharged patients. Methods: Our study will use an EMA design based on a wearable device to examine the sleep-suicide relationship during the transition from acute inpatient care to the community. Prospectively discharged inpatients 18 to 35 years old with mental disorders (N=50) will be assessed for eligibility and recruited across 2 sites. Data on suicidal ideation, behavior, and imagery; nonsuicidal self-harm and imagery; defeat, entrapment, and hopelessness; affect; and sleep will be collected on the Pro-Diary V wrist-worn electronic watch for up to 14 days. Objective sleep and daytime activity will be measured using the inbuilt MotionWare software. Questionnaires will be administered face-to-face at baseline and follow up, and data will also be collected on the acceptability and feasibility of using the Pro-Diary V watch to monitor the transition following discharge. The study has been, and will continue to be, coproduced with young people with experience of being in an inpatient setting and suicidality. Results: South Birmingham Research Ethics Committee (21/WM/0128) approved the study on June 28, 2021. We expect to see a relationship between poor sleep and postdischarge suicidality. Results will be available in 2022. Conclusions: This protocol describes the first coproduced EMA study to examine the relationship between sleep and suicidality and to apply the integrated motivational volitional model in young patients transitioning from a psychiatric hospital to the community. We expect our findings will inform coproduction in suicidology research and clarify the role of digital monitoring of suicidality and sleep before and after initial hospital discharge. International Registered Report Identifier (IRRID): PRR1-10.2196/33817 %M 35579920 %R 10.2196/33817 %U https://www.researchprotocols.org/2022/5/e33817 %U https://doi.org/10.2196/33817 %U http://www.ncbi.nlm.nih.gov/pubmed/35579920 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 5 %P e37014 %T Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets %A Yamada,Yasunori %A Shinkawa,Kaoru %A Kobayashi,Masatomo %A Badal,Varsha D %A Glorioso,Danielle %A Lee,Ellen E %A Daly,Rebecca %A Nebeker,Camille %A Twamley,Elizabeth W %A Depp,Colin %A Nemoto,Miyuki %A Nemoto,Kiyotaka %A Kim,Ho-Cheol %A Arai,Tetsuaki %A Jeste,Dilip V %+ Digital Health, IBM Research, 19-21 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo, 103-8510, Japan, 81 80 6706 9381, ysnr@jp.ibm.com %K tablet %K behavior analysis %K digital biomarkers %K digital health %K motor control %K cognitive impairment %K dementia %K machine learning %K multicohort %K multination %D 2022 %7 5.5.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations. Objective: The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations. Methods: We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning–based regression analyses. Results: We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen’s horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P<.001). Conclusions: This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment. %M 35511253 %R 10.2196/37014 %U https://formative.jmir.org/2022/5/e37014 %U https://doi.org/10.2196/37014 %U http://www.ncbi.nlm.nih.gov/pubmed/35511253 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 5 %P e30517 %T Lifelog Retrieval From Daily Digital Data: Narrative Review %A Ribeiro,Ricardo %A Trifan,Alina %A Neves,António J R %+ Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-193, Portugal, 351 234370500, rfribeiro@ua.pt %K lifelog %K lifelogging %K information retrieval %K image retrieval %K computer vision %K signal processing %K event segmentation %K mobile phone %D 2022 %7 2.5.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Over the past decade, the wide availability and small size of different types of sensors, together with the decrease in pricing, have allowed the acquisition of a substantial amount of data about a person’s life in real time. These sensors can be incorporated into personal electronic devices available at a reasonable cost, such as smartphones and small wearable devices. They allow the acquisition of images, audio, location, physical activity, and physiological signals among other data. With these data, usually denoted as lifelog data, we can then analyze and understand personal experiences and behaviors. This process is called lifelogging. Objective: The objective of this paper was to present a narrative review of the existing literature about lifelogging over the past decade. To achieve this goal, we analyzed lifelogging applications used to retrieve relevant information from daily digital data, some of them with the purpose of monitoring and assisting people with memory issues and others designed for memory augmentation. We aimed for this review to be used by researchers to obtain a broad idea of the type of data used, methodologies, and applications available in this research field. Methods: We followed a narrative review methodology to conduct a comprehensive search for relevant publications in Google Scholar and Scopus databases using lifelog topic–related keywords. A total of 411 publications were retrieved and screened. Of these 411 publications, 114 (27.7%) publications were fully reviewed. In addition, 30 publications were manually included based on our bibliographical knowledge of this research field. Results: From the 144 reviewed publications, a total of 113 (78.5%) were selected and included in this narrative review based on content analysis. The findings of this narrative review suggest that lifelogs are prone to become powerful tools to retrieve memories or increase knowledge about an individual’s experiences or behaviors. Several computational tools are already available for a considerable range of applications. These tools use multimodal data of different natures, with visual lifelogs being one of the most used and rich sources of information. Different approaches and algorithms to process these data are currently in use, as this review will unravel. Moreover, we identified several open questions and possible lines of investigation in lifelogging. Conclusions: The use of personal lifelogs can be beneficial to improve the quality of our life, as they can serve as tools for memory augmentation or for providing support to people with memory issues. Through the acquisition and analysis of lifelog data, lifelogging systems can create digital memories that can be potentially used as surrogate memory. Through this narrative review, we understand that contextual information can be extracted from lifelogs, which provides an understanding of the daily life of a person based on events, experiences, and behaviors. %M 35499858 %R 10.2196/30517 %U https://mhealth.jmir.org/2022/5/e30517 %U https://doi.org/10.2196/30517 %U http://www.ncbi.nlm.nih.gov/pubmed/35499858 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 8 %N 2 %P e31815 %T The Acceptability of an Electronically Delivered Acceptance- and Mindfulness-Based Physical Activity Intervention for Survivors of Breast Cancer: One-Group Pretest-Posttest Design %A Robertson,Michael C %A Cox-Martin,Emily %A Shegog,Ross %A Markham,Christine M %A Fujimoto,Kayo %A Durand,Casey P %A Brewster,Abenaa %A Lyons,Elizabeth J %A Liao,Yue %A Flores,Sara A %A Basen-Engquist,Karen M %+ Department of Nutrition, Metabolism, and Rehabilitation Sciences, The University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, United States, 1 409 772 3030, mcrobert@utmb.edu %K cancer survivors %K exercise %K mindfulness %K Acceptance and Commitment Therapy %K behavioral sciences %D 2022 %7 29.4.2022 %9 Original Paper %J JMIR Cancer %G English %X Background: Survivors of breast cancer can face internal barriers to physical activity, such as uncertainty and frustration stemming from physical limitations, decreased physical functioning, fatigue, and pain. Interventions that draw from the principles of Acceptance and Commitment Therapy (ACT) may help survivors of breast cancer overcome some of the internal barriers associated with physical activity. Objective: The primary aim of this study was to investigate the acceptability of an electronically delivered physical activity intervention for survivors of breast cancer, centered on ACT processes. Methods: This study used a 1-group pretest-posttest design. We recruited 80 insufficiently active female survivors of breast cancer using a web-based recruitment strategy. The 8-week intervention consisted of weekly modules that featured didactic lessons and experiential exercises targeting key ACT processes in the context of physical activity promotion (namely, values, committed action, acceptance, defusion, and contacting the present moment). We determined intervention acceptability according to study retention (≥70%), adherence rates (≥75% of the participants completing ≥50% of the modules), and posttest survey scores reflecting the perceived ease of use, perceived usefulness, and interest and enjoyment of the intervention (≥5 on a 7-point Likert-type scale). We also evaluated changes in self-reported aerobic and muscle strengthening–physical activity, physical activity acceptance, physical activity regulation, and health-related outcomes. Results: The retention rate (61/80, 76%), adherence rate (60/80, 75%), average perceived ease of use (6.17, SD 1.17), perceived usefulness (5.59, SD 1.40), and interest and enjoyment scores (5.43, SD 1.40) met the acceptability criteria. Participants increased their self-reported aerobic physical activity (Cohen d=1.04), muscle strengthening–physical activity (Cohen d=1.02), physical activity acceptance (cognitive acceptance: Cohen d=0.35; behavioral commitment: Cohen d=0.51), physical activity regulation (identified regulation: Cohen d=0.37; integrated regulation: Cohen d=0.66), increased their ability to participate in social roles and activities (Cohen d=0.18), and reported less fatigue (Cohen d=0.33) and sleep disturbance (Cohen d=0.53). Conclusions: Electronically delivered acceptance- and mindfulness-based interventions may be useful for promoting physical activity in survivors of breast cancer. Further research is needed to refine these approaches and evaluate their effectiveness. %M 35486425 %R 10.2196/31815 %U https://cancer.jmir.org/2022/2/e31815 %U https://doi.org/10.2196/31815 %U http://www.ncbi.nlm.nih.gov/pubmed/35486425 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e34015 %T Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment %A Hart,Alexander %A Reis,Dorota %A Prestele,Elisabeth %A Jacobson,Nicholas C %+ Research Group Applied Statistical Modeling, Department of Psychology, Saarland University, Campus A2 4, Saarbrücken, 66123, Germany, 49 6813023130, hart.research@pm.me %K digital biomarkers %K machine learning %K ecological momentary assessment %K smartphone sensors %K internal states %K paradata %K accelerometer %K gyroscope %K mood %K mobile phone %D 2022 %7 28.4.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference. %M 35482397 %R 10.2196/34015 %U https://www.jmir.org/2022/4/e34015 %U https://doi.org/10.2196/34015 %U http://www.ncbi.nlm.nih.gov/pubmed/35482397 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e33656 %T Agreement Between Self-reports and Photos to Assess e-Cigarette Device and Liquid Characteristics in Wave 1 of the Vaping and Patterns of e-Cigarette Use Research Study: Web-Based Longitudinal Cohort Study %A Crespi,Elizabeth %A Hardesty,Jeffrey J %A Nian,Qinghua %A Sinamo,Joshua %A Welding,Kevin %A Kennedy,Ryan David %A Cohen,Joanna E %+ Institute for Global Tobacco Control, Department of Health, Behavior & Society, Johns Hopkins Bloomberg School of Public Health, 2213 McElderry Street, Baltimore, MD, 21205, United States, 1 410 614 5378, ecrespi2@jhu.edu %K tobacco %K e-cigarette %K methodology %K internet %K photo %K survey %K self-report %D 2022 %7 27.4.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: e-Cigarette device and liquid characteristics are highly customizable; these characteristics impact nicotine delivery and exposure to toxic constituents. It is critical to understand optimal methods for measuring these characteristics to accurately assess their impacts on user behavior and health. Objective: To inform future survey development, we assessed the agreement between responses from survey participants (self-reports) and photos uploaded by participants and the quantity of usable data derived from each approach. Methods: Adult regular e-cigarette users (≥5 days per week) aged ≥21 years (N=1209) were asked questions about and submitted photos of their most used e-cigarette device (1209/1209, 100%) and liquid (1132/1209, 93.63%). Device variables assessed included brand, model, reusability, refillability, display, and adjustable power. Liquid variables included brand, flavor, nicotine concentration, nicotine formulation, and bottle size. For each variable, percentage agreement was calculated where self-report and photo data were available. Krippendorff α and intraclass correlation coefficient (ICC) were calculated for categorical and continuous variables, respectively. Results were stratified by device (disposable, reusable with disposable pods or cartridges, and reusable with refillable pods, cartridges, or tanks) and liquid (customized and noncustomized) type. The sample size for each calculation ranged from 3.89% (47/1209; model of disposable devices) to 95.12% (1150/1209; device reusability). Results: Percentage agreement between photos and self-reports was substantial to very high across device and liquid types for all variables except nicotine concentration. These results are consistent with Krippendorff α calculations, except where prevalence bias was suspected. ICC results for nicotine concentration and bottle size were lower than percentage agreement, likely because ICC accounts for the level of disagreement between values. Agreement varied by device and liquid type. For example, percentage agreement for device brand was higher among users of reusable devices (94%) than among users of disposable devices (75%). Low percentage agreement may result from poor participant knowledge of characteristics, user modifications of devices inconsistent with manufacturer-intended use, inaccurate or incomplete information on websites, or photo submissions that are not a participant’s most used device or liquid. The number of excluded values (eg, self-report was “don’t know” or no photo submitted) differed between self-reports and photos; for questions asked to participants, self-reports had more usable data than photos for all variables except device model and nicotine formulation. Conclusions: Photos and self-reports yield data of similar accuracy for most variables assessed in this study: device brand, device model, reusability, adjustable power, display, refillability, liquid brand, flavor, and bottle size. Self-reports provided more data for all variables except device model and nicotine formulation. Using these approaches simultaneously may optimize data quantity and quality. Future research should examine how to assess nicotine concentration and variables not included in this study (eg, wattage and resistance) and the resource requirements of these approaches. %M 35475727 %R 10.2196/33656 %U https://www.jmir.org/2022/4/e33656 %U https://doi.org/10.2196/33656 %U http://www.ncbi.nlm.nih.gov/pubmed/35475727 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e30089 %T Exploring Wearables to Focus on the “Sweet Spot” of Physical Activity and Sleep After Hospitalization: Secondary Analysis %A Greysen,S Ryan %A Waddell,Kimberly J %A Patel,Mitesh S %+ Section of Hospital Medicine, University of Pennsylvania, 3400 Spruce Street, Maloney Suite 5040, Philadelphia, PA, 19104, United States, 1 202 664 6084, ryan.greysen@pennmedicine.upenn.edu %K sleep %K physical activity %K hospitalization %K wearables %K health care %K digital health %K patient reported outcomes %K hospital %D 2022 %7 27.4.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge. Objective: This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes (symptom burden, sleep quality, physical health, life space mobility, activities of daily living, and instrumental activities of daily living) at 13 weeks after hospital discharge. Methods: This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activité wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup. Results: For the full sample of 120 participants, we observed a “T-shaped” distribution between device-reported physical activity (steps/day) and sleep (patient-reported quality or device-recorded minutes/night) with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden (z-score 0.93, 95% CI 0.3 to 1.5; P=.02), community mobility (z-score –0.77, 95% CI –1.3 to –0.15; P=.02), and perceived physical health (z-score –0.73, 95% CI –1.3 to –0.13; P=.003), compared with those who were more physically active (≥5000 steps/day). Conclusions: Participants within the “sweet spot” that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the “sweet spot.” Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a “sweet spot” of recommended levels for both sleep and activity needed for optimal recovery. Trial Registration: ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279 %M 35476034 %R 10.2196/30089 %U https://mhealth.jmir.org/2022/4/e30089 %U https://doi.org/10.2196/30089 %U http://www.ncbi.nlm.nih.gov/pubmed/35476034 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 1 %P e32348 %T Relations Between BMI Trajectories and Habitual Physical Activity Measured by a Smartwatch in the Electronic Cohort of the Framingham Heart Study: Cohort Study %A Hammond,Michael M %A Zhang,Yuankai %A Pathiravasan,Chathurangi H. %A Lin,Honghuang %A Sardana,Mayank %A Trinquart,Ludovic %A Benjamin,Emelia J %A Borrelli,Belinda %A Manders,Emily S %A Fusco,Kelsey %A Kornej,Jelena %A Spartano,Nicole L %A Kheterpal,Vik %A Nowak,Christopher %A McManus,David D %A Liu,Chunyu %A Murabito,Joanne M %+ Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, 715 Albany St., Boston, MA, 02118, United States, 1 508 935 3461, murabito@bu.edu %K mobile health %K BMI %K smartwatch %K physical activity %K cardiovascular diseases %K cardiology %K digital health %K mHealth %K mobile health apps %D 2022 %7 27.4.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: The prevalence of obesity is rising. Most previous studies that examined the relations between BMI and physical activity (PA) measured BMI at a single timepoint. The association between BMI trajectories and habitual PA remains unclear. Objective: This study assesses the relations between BMI trajectories and habitual step-based PA among participants enrolled in the electronic cohort of the Framingham Heart Study (eFHS). Methods: We used a semiparametric group-based modeling to identify BMI trajectories from eFHS participants who attended research examinations at the Framingham Research Center over 14 years. Daily steps were recorded from the smartwatch provided at examination 3. We excluded participants with <30 days or <5 hours of smartwatch wear data. We used generalized linear models to examine the association between BMI trajectories and daily step counts. Results: We identified 3 trajectory groups for the 837 eFHS participants (mean age 53 years; 57.8% [484/837] female). Group 1 included 292 participants whose BMI was stable (slope 0.005; P=.75), group 2 included 468 participants whose BMI increased slightly (slope 0.123; P<.001), and group 3 included 77 participants whose BMI increased greatly (slope 0.318; P<.001). The median follow-up period for step count was 516 days. Adjusting for age, sex, wear time, and cohort, participants in groups 2 and 3 took 422 (95% CI –823 to –21) and 1437 (95% CI –2084 to –790) fewer average daily steps, compared with participants in group 1. After adjusting for metabolic and social risk factors, group 2 took 382 (95% CI –773 to 10) and group 3 took 1120 (95% CI –1766 to –475) fewer steps, compared with group 1. Conclusions: In this community-based eFHS, participants whose BMI trajectory increased greatly over time took significantly fewer steps, compared with participants with stable BMI trajectories. Our findings suggest that greater weight gain may correlate with lower levels of step-based physical activity. %M 35476038 %R 10.2196/32348 %U https://cardio.jmir.org/2022/1/e32348 %U https://doi.org/10.2196/32348 %U http://www.ncbi.nlm.nih.gov/pubmed/35476038 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 4 %P e35803 %T Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study %A Lindhiem,Oliver %A Goel,Mayank %A Shaaban,Sam %A Mak,Kristie J %A Chikersal,Prerna %A Feldman,Jamie %A Harris,Jordan L %+ Department of Psychiatry, School of Medicine, University of Pittsburgh, 100 N Bellefield Ave, Pittsburgh, PA, 15206, United States, 1 412 246 5909, lindhiemoj@upmc.edu %K assessment %K machine learning %K hyperactivity %K attention-deficit/hyperactivity disorder %K ADHD %K wearables %D 2022 %7 25.4.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Although hyperactivity is a core symptom of attention-deficit/hyperactivity disorder (ADHD), there are no objective measures that are widely used in clinical settings. Objective: We describe the development of a smartwatch app to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning to measure hyperactivity. The goal is to differentiate children with ADHD combined presentation (a combination of inattentive and hyperactive/impulsive presentations) or predominantly hyperactive/impulsive presentation from children with typical levels of activity. Methods: In this pilot study, we recruited 30 children, aged 6 to 11 years, to wear a smartwatch with the LemurDx app for 2 days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half of the participants had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n=15), and half were in the healthy control group (n=15). Results: The results indicated high usability scores and an overall diagnostic accuracy of 0.89 (sensitivity=0.93; specificity=0.86) when the motion sensor output was paired with the activity labels. Conclusions: State-of-the-art sensors and machine learning may provide a promising avenue for the objective measurement of hyperactivity. %M 35468089 %R 10.2196/35803 %U https://formative.jmir.org/2022/4/e35803 %U https://doi.org/10.2196/35803 %U http://www.ncbi.nlm.nih.gov/pubmed/35468089 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e32825 %T Sleep Disturbance and Quality of Life in Rheumatoid Arthritis: Prospective mHealth Study %A McBeth,John %A Dixon,William G %A Moore,Susan Mary %A Hellman,Bruce %A James,Ben %A Kyle,Simon D %A Lunt,Mark %A Cordingley,Lis %A Yimer,Belay Birlie %A Druce,Katie L %+ Centre for Epidemiology Versus Arthritis, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, United Kingdom, 44 1612755788, john.mcbeth@manchester.ac.uk %K mobile health %K sleep %K rheumatoid arthritis %K pain %K fatigue %K mood %K sleep disturbance %K HRQoL %K quality of life %K health-related quality of life %K QoL %K sleep efficiency %K WHOQoL-BREF %K mobile phone %D 2022 %7 22.4.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Sleep disturbances and poor health-related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of nonrestorative sleep, may be a driver of HRQoL. However, understanding whether these sleep disturbances reduce HRQoL has, to date, been challenging because of the need to collect complex time-varying data at high resolution. Such data collection is now made possible by the widespread availability and use of mobile health (mHealth) technologies. Objective: This mHealth study aimed to test whether sleep disturbance (both absolute values and variability) causes poor HRQoL. Methods: The quality of life, sleep, and RA study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, wore a triaxial accelerometer for 30 days to objectively assess sleep, and provided daily reports via a smartphone app that assessed sleep (Consensus Sleep Diary), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multilevel modeling tested the relationship between sleep variables and the WHOQoL-BREF domains (physical, psychological, environmental, and social). Results: Of the 268 recruited participants, 254 were included in the analysis. Across all WHOQoL-BREF domains, participants’ scores were lower than the population average. Consensus Sleep Diary sleep parameters predicted the WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points (β=1.11, 95% CI 0.07-2.15) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, anxiety levels, sleep quality, or clinical sleep disorders. However, these changes were attenuated and no longer significant when pain, fatigue, and mood were included in the model. Increased variability in total time asleep was associated with poorer physical and psychological domain scores, independent of all covariates. There was no association between actigraphy-measured sleep and WHOQoL-BREF. Conclusions: Optimizing total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing variability in total sleep time could improve HRQoL in people with RA. %M 35451978 %R 10.2196/32825 %U https://www.jmir.org/2022/4/e32825 %U https://doi.org/10.2196/32825 %U http://www.ncbi.nlm.nih.gov/pubmed/35451978 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e36977 %T Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study %A Ramachandram,Dhanesh %A Ramirez-GarciaLuna,Jose Luis %A Fraser,Robert D J %A Martínez-Jiménez,Mario Aurelio %A Arriaga-Caballero,Jesus E %A Allport,Justin %+ Swift Medical Inc, Suite 500, 1 Richmond St W, Toronto, ON, M5H 3W4, Canada, 1 888 755 2565, dhanesh@swiftmedical.io %K wound %K tissue segmentation %K automated tissue identification %K deep learning %K mobile imaging %K mobile phone %D 2022 %7 22.4.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. Objective: This study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks. Methods: A data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical’s Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices. Results: A poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff α value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. Conclusions: The interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale. %M 35451982 %R 10.2196/36977 %U https://mhealth.jmir.org/2022/4/e36977 %U https://doi.org/10.2196/36977 %U http://www.ncbi.nlm.nih.gov/pubmed/35451982 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e36762 %T SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing %A Adamowicz,Lukas %A Christakis,Yiorgos %A Czech,Matthew D %A Adamusiak,Tomasz %+ Digital Medicine and Translational Imaging, Pfizer Inc, 610 Main Street, Cambridge, MA, 02139, United States, 1 802 324 1829, lukas.adamowicz@pfizer.com %K wearable sensors %K digital medicine %K gait analysis %K human movement analysis %K digital biomarkers %K uHealth %K wearable %K sensor %K gait %K movement %K mobility %K physical activity %K sleep %K Python %K coding %K open source %K software package %K algorithm %K machine learning %K data science %K computer programming %D 2022 %7 21.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Wearable inertial sensors are providing enhanced insight into patient mobility and health. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings; however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Furthermore, few studies include code for replication or off-the-shelf software packages. In this work, we introduce SciKit Digital Health (SKDH), a Python software package (Python Software Foundation) containing various algorithms for deriving clinical features of gait, sit to stand, physical activity, and sleep, wrapped in an easily extensible framework. SKDH combines data ingestion, preprocessing, and data analysis methods geared toward modern data science workflows and streamlines the generation of digital endpoints in “good practice” environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies the construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy adult populations or those with mild impairment. SKDH is open source, as well as free to use and extend under a permissive Massachusetts Institute of Technology license, and is available from GitHub (PfizerRD/scikit-digital-health), the Python Package Index, and the conda-forge channel of Anaconda. %M 35353039 %R 10.2196/36762 %U https://mhealth.jmir.org/2022/4/e36762 %U https://doi.org/10.2196/36762 %U http://www.ncbi.nlm.nih.gov/pubmed/35353039 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 4 %P e32990 %T Design and Evaluation of a Smartphone Medical Guidance App for Outpatients of Large-Scale Medical Institutions: Retrospective Observational Study %A Teramoto,Kei %A Kuwata,Shigeki %+ Tottori University Hospital, Nishi-cho, 36-1, Yonago, 6838504, Japan, 81 859 38 7482, kei@tottori-u.ac.jp %K mHealth %K outpatient clinics %K electronic medical records %K COVID-19 %K EHR %D 2022 %7 21.4.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The greatest stressor for outpatients is the waiting time before an examination. If the patient is able to use their smartphone to check in with reception, the patient can wait for their examination at any location, and the burden of waiting can be reduced. Objective: This study aimed to report the system design and postintroductory outcomes of the Tori RinRin (TR2) system that was developed to reduce outpatient burden imposed by wait times before examination. Methods: The TR2 system was introduced at Tottori University Hospital, a large medical facility that accepts a daily average of 1500 outpatients. The system, which links the hospital’s electronic medical record database with patients’ mobile devices, has the following functions: (1) GPS-based examination check-in processing and (2) sending appointment notification messages via a cloud notification service. In order to evaluate the usefulness of the TR2 system, we surveyed the utilization rate of the TR2 system among outpatients, implemented a user questionnaire, and polled the average time required for patients to respond to call notifications about their turn. Results: The 3-month average of TR2 users 9 months after the TR 2 system introduction was 17.9% (14,536/81,066). In an investigation of 363 subjects, the mean examination call message response time using the TR2 system was 31 seconds (median 14 seconds). Among 166 subjects who responded to a user survey, 86.7% (144/166) said that the system helped reduce the burden of waiting time. Conclusions: The app allowed 17.9% of outpatients at a large medical facility to check in remotely and wait for examinations anywhere. Hence, it is effective in preventing the spread of infection, especially during pandemics such as that of coronavirus disease. The app reported in this study is beneficial for large medical facilities striving to reduce outpatient burden imposed by wait times. %M 34818208 %R 10.2196/32990 %U https://formative.jmir.org/2022/4/e32990 %U https://doi.org/10.2196/32990 %U http://www.ncbi.nlm.nih.gov/pubmed/34818208 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e32643 %T A Novel Method for Evaluating Mobile Apps (App Rating Inventory): Development Study %A Mackey,Rachel %A Gleason,Ann %A Ciulla,Robert %+ Connected Health Branch, Defense Health Agency, JBLM Box 339500 MS 34, 9933 West Hayes Street, Tacoma, WA, 98433-9500, United States, 1 253 278 1535, ann.m.gleason3.ctr@mail.mil %K mobile health apps %K app rating %K app analysis methodology %K app market research %K mobile phone %D 2022 %7 15.4.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Selecting and integrating health-related apps into patient care is impeded by the absence of objective guidelines for identifying high-quality apps from the many thousands now available. Objective: This study aimed to evaluate the App Rating Inventory, which was developed by the Defense Health Agency’s Connected Health branch, to support clinical decisions regarding app selection and evaluate medical and behavioral apps. Methods: To enhance the tool’s performance, eliminate item redundancy, reduce scoring system subjectivity, and ensure a broad application of App Rating Inventory–derived results, inventory development included 3 rounds of validation testing and 2 trial periods conducted over a 6-month interval. The development focused on content validity testing, dimensionality (ie, whether the tool’s criteria performed as operationalized), factor and commonality analysis, and interrater reliability (reliability scores improved from 0.62 to 0.95 over the course of development). Results: The development phase culminated in a review of 248 apps for a total of 6944 data points and a final 28-item, 3-category app rating system. The App Rating Inventory produces scores for the following three categories: evidence (6 items), content (11 items), and customizability (11 items). The final (fourth) metric is the total score, which constitutes the sum of the 3 categories. All 28 items are weighted equally; no item is considered more (or less) important than any other item. As the scoring system is binary (either the app contains the feature or it does not), the ratings’ results are not dependent on a rater’s nuanced assessments. Conclusions: Using predetermined search criteria, app ratings begin with an environmental scan of the App Store and Google Play. This first step in market research funnels hundreds of apps in a given disease category down to a manageable top 10 apps that are, thereafter, rated using the App Rating Inventory. The category and final scores derived from the rating system inform the clinician about whether an app is evidence informed and easy to use. Although a rating allows a clinician to make focused decisions about app selection in a context where thousands of apps are available, clinicians must weigh the following factors before integrating apps into a treatment plan: clinical presentation, patient engagement and preferences, available resources, and technology expertise. %M 35436227 %R 10.2196/32643 %U https://mhealth.jmir.org/2022/4/e32643 %U https://doi.org/10.2196/32643 %U http://www.ncbi.nlm.nih.gov/pubmed/35436227 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e33938 %T Low- and High-Intensity Physical Activity Among People with HIV: Multilevel Modeling Analysis Using Sensor- and Survey-Based Predictors %A Cook,Paul %A Jankowski,Catherine %A Erlandson,Kristine M %A Reeder,Blaine %A Starr,Whitney %A Flynn Makic,Mary Beth %+ College of Nursing, University of Colorado, 13120 E 19th Ave, Campus Box C288-04, Aurora, CO, 80045, United States, 1 3037248537, paul.cook@cuanschutz.edu %K ecological momentary assessment %K fatigue %K HIV %K physical activity %K stress %K mobile phone %D 2022 %7 14.4.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: High-intensity physical activity improves the health of people with HIV. Even when people have good intentions to engage in physical activity, they often find it difficult to maintain physical activity behavior in the long term. Two Minds Theory is a neurocognitive model that explains gaps between people’s intentions and behaviors based on the operations of 2 independent mental systems. This model predicts that everyday experiences will affect physical activity and that factors outside people’s awareness, such as sleep and stress, can have particularly strong effects on physical activity behaviors. Objective: We designed this study to test the effects of daily experiences on physical activity among people with HIV, including measures of people’s conscious experiences using daily electronic surveys and measures of nonconscious influences using sensor devices. Methods: In this study, 55 people with HIV wore a Fitbit Alta for 30 days to monitor their physical activity, sleep, and heart rate variability (HRV) as a physiological indicator of stress. Participants also used their smartphones to complete daily electronic surveys for the same 30 days about fatigue, self-efficacy, mood, stress, coping, motivation, and barriers to self-care. Time-lagged, within-person, multilevel models were used to identify the best prospective predictors of physical activity, considering the daily survey responses of people with HIV and sensor data as predictors of their physical activity the following day. We also tested baseline surveys as predictors of physical activity for comparison with daily variables. Results: Different people had different average levels of physical activity; however, physical activity also varied substantially from day to day, and daily measures were more predictive than baseline surveys. This suggests a chance to intervene based on day-to-day variations in physical activity. High-intensity physical activity was more likely when people with HIV reported less subjective fatigue on the prior day (r=−0.48) but was unrelated to actual sleep based on objective sensor data. High-intensity physical activity was also predicted by higher HRV (r=0.56), indicating less stress, lower HIV-related stigma (r=−0.21), fewer barriers to self-care (r=−0.34), and less approach coping (r=−0.34). Similar variables predicted lower-level physical activity measured based on the number of steps per day of people with HIV. Conclusions: Some predictors of physical activity, such as HRV, were only apparent based on sensor data, whereas others, such as fatigue, could be measured via self-report. Findings about coping were unexpected; however, other findings were in line with the literature. This study extends our prior knowledge on physical activity by demonstrating a prospective effect of everyday experiences on physical activity behavior, which is in line with the predictions of Two Minds Theory. Clinicians can support the physical activity of people with HIV by helping their patients reduce their daily stress, fatigue, and barriers to self-care. %M 35436236 %R 10.2196/33938 %U https://mhealth.jmir.org/2022/4/e33938 %U https://doi.org/10.2196/33938 %U http://www.ncbi.nlm.nih.gov/pubmed/35436236 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e33725 %T Japanese Version of the Mobile App Rating Scale (MARS): Development and Validation %A Yamamoto,Kazumichi %A Ito,Masami %A Sakata,Masatsugu %A Koizumi,Shiho %A Hashisako,Mizuho %A Sato,Masaaki %A Stoyanov,Stoyan R %A Furukawa,Toshi A %+ Departments of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine, School of Public Health, Yoshida-Konoe-Cho, Sakyo, Kyoto, 606-8501, Japan, 81 75 753 9492, kazumichi_yamamoto@airwaystenosis.org %K mobile health apps %K MHAs %K mHealth %K mobile application %K mobile application rating scale %K MARS %K scale development %K mental health %K mobile health applications %D 2022 %7 14.4.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The number of mobile health (mHealth) apps continues to rise each year. Widespread use of the Mobile App Rating Scale (MARS) has allowed objective and multidimensional evaluation of the quality of these apps. However, no Japanese version of MARS has been made available to date. Objective: The purposes of this study were (1) to develop a Japanese version of MARS and (2) to assess the translated version’s reliability and validity in evaluating mHealth apps. Methods: To develop the Japanese version of MARS, cross-cultural adaptation was used using a universalist approach. A total of 50 mental health apps were evaluated by 2 independent raters. Internal consistency and interrater reliability were then calculated. Convergent and divergent validity were assessed using multitrait scaling analysis and concurrent validity. Results: After cross-cultural adaptation, all 23 items from the original MARS were included in the Japanese version. Following translation, back-translation, and review by the author of the original MARS, a Japanese version of MARS was finalized. Internal consistency was acceptable by all subscales of objective and subjective quality (Cronbach α=.78-.89). Interrater reliability was deemed acceptable, with the intraclass correlation coefficient (ICC) ranging from 0.61 to 0.79 for all subscales, except for “functionality,” which had an ICC of 0.40. Convergent/divergent validity and concurrent validity were also considered acceptable. The rate of missing responses was high in several items in the “information” subscale. Conclusions: A Japanese version of MARS was developed and shown to be reliable and valid to a degree that was comparable to the original MARS. This Japanese version of MARS can be used as a standard to evaluate the quality and credibility of mHealth apps. %M 35197241 %R 10.2196/33725 %U https://mhealth.jmir.org/2022/4/e33725 %U https://doi.org/10.2196/33725 %U http://www.ncbi.nlm.nih.gov/pubmed/35197241 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e35626 %T Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis %A Chevance,Guillaume %A Golaszewski,Natalie M %A Tipton,Elizabeth %A Hekler,Eric B %A Buman,Matthew %A Welk,Gregory J %A Patrick,Kevin %A Godino,Job G %+ Laura Rodriguez Research Institute, Family Health Centers of San Diego, 823 Gateway Center Way, San Diego, CA, 92102, United States, 1 6195152344, jobg@fhcsd.org %K wearables %K activity monitors %K physical activity %K validity %K accelerometry %D 2022 %7 13.4.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. Objective: The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. Methods: We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. Results: A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of −2.99 beats per minute (k comparison=74), −2.77 kcal per minute (k comparison=29), and −3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: −23.99 to 18.01, −12.75 to 7.41, and −13.07 to 6.86, respectively). Conclusions: Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes. %M 35416777 %R 10.2196/35626 %U https://mhealth.jmir.org/2022/4/e35626 %U https://doi.org/10.2196/35626 %U http://www.ncbi.nlm.nih.gov/pubmed/35416777 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e34638 %T Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review %A Qirtas,Malik Muhammad %A Zafeiridi,Evi %A Pesch,Dirk %A White,Eleanor Bantry %+ School of Computer Science & Information Technology, University College Cork, Room 2.09, Western Gateway Building, Western Road, Cork, T23 W623, Ireland, 353 0851873544, qirtas333@gmail.com %K passive sensing %K loneliness %K social isolation %K smartphone %K sensors %K wearables %K monitoring %K scoping review %K eHealth %K mHealth %K mobile phone %D 2022 %7 12.4.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation. Objective: This scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users’ daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues. Methods: A scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems. Results: After conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation. Conclusions: Despite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants’ behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns. %M 35412465 %R 10.2196/34638 %U https://mhealth.jmir.org/2022/4/e34638 %U https://doi.org/10.2196/34638 %U http://www.ncbi.nlm.nih.gov/pubmed/35412465 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e31006 %T Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis %A Zhou,Joanne %A Lamichhane,Bishal %A Ben-Zeev,Dror %A Campbell,Andrew %A Sano,Akane %+ Department of Statistics, Rice University, 6100 Main Street, Houston, TX, 77005, United States, 1 5715194641, joanneyz98@gmail.com %K schizophrenia %K psychotic relapse %K machine learning %K clustering %K mobile phone %K routine %K Gaussian mixture models %K partition around medoids %K dynamic time warping %K balanced random forest %D 2022 %7 11.4.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse. Objective: In this study, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse. Methods: We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age. Results: The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042. Conclusions: Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions. %M 35404256 %R 10.2196/31006 %U https://mhealth.jmir.org/2022/4/e31006 %U https://doi.org/10.2196/31006 %U http://www.ncbi.nlm.nih.gov/pubmed/35404256 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e28504 %T Association Between Mobile App Use and Caregivers’ Support System, Time Spent on Caregiving, and Perceived Well-being: Survey Study From a Large Employer %A Ozluk,Pelin %A Cobb,Rebecca %A Hoots,Alyson %A Sylwestrzak,Malgorzata %+ HealthCore Inc, 123 Justison Street Suite 200, Wilmington, DE, 19801, United States, 1 302 230 2023, pozluk@healthcore.com %K caregiving %K mobile app %K mobile phone %D 2022 %7 11.4.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Mobile technology to address caregiver needs has been on the rise. There is limited evidence of effectiveness of such technologies on caregiver experiences. Objective: This study evaluates the effectiveness of ianacare, a mobile app, among employees of a large employer. ianacare mobilizes personal social circles to help with everyday tasks. Through the use of ianacare, we evaluate the associations between coordinating caregiving tasks among a caregiver’s personal support network and outcomes related to the caregiver’s support system, time use, perceived productivity, and perceived health and well-being. Caregiver tasks include tasks such as meal preparation, respite care, pet care, and transportation. Time use is the measure of a caregiver’s time spent on caregiving tasks and how much time they had to take off from work to attend planned or unplanned caregiving tasks. Methods: We conducted 2 surveys to assess within-participant changes in outcomes for the unpaid, employed, caregivers after 6 weeks of using the mobile app (n=176) between March 30, 2020, and May 11, 2020. The surveys contained questions in three domains: the caregiver’s support system, time use and perceived productivity, and perceived health and well-being. The results of the linear probability models are presented below. Results: App use was significantly associated with decreasing the probability of doing most caregiving tasks alone by 9.1% points (SE 0.04; P=.01) and increasing the probability of at least one person helping the primary caregiver by 8.0% points (SE 0.035; P=.02). App use was also associated with improving the time use of the primary caregiver who took significantly less time off work to attend to caregiving duties by 12.5% points (SE 0.04; P=.003) and decreased the probability of spending more than 30 hours weekly on caregiving by 9.1% points (SE 0.04; P=.02). Additional findings on the positive impact of the app included a decrease in the probability of reporting feeling overwhelmed by caregiving tasks by 12.5% points (SE 0.04; P=.003) and a decrease in the probability of reporting negative health effects by 6.8% points (SE 0.04; P=.07) because of caregiving. Although subjects reported that COVID-19 increased their stress attributed to caregiving and prevented them from requesting help for some caregiving tasks, using the app was still associated with improvements in receiving help and lessening of the negative effects of caregiving on the caregivers. Conclusions: App use was associated with improvements in 7 of 11 caregiver outcomes across three main categories: their support system, time spent on caregiving, and perceived health and well-being. These findings provide encouraging evidence that the mobile app can significantly reduce caregiver burden by leveraging a caregiver’s support network despite the additional challenges brought by COVID-19 on caregivers. %M 35404266 %R 10.2196/28504 %U https://www.jmir.org/2022/4/e28504 %U https://doi.org/10.2196/28504 %U http://www.ncbi.nlm.nih.gov/pubmed/35404266 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e29510 %T Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design %A Cho,Peter Jaeho %A Yi,Jaehan %A Ho,Ethan %A Shandhi,Md Mobashir Hasan %A Dinh,Yen %A Patil,Aneesh %A Martin,Leatrice %A Singh,Geetika %A Bent,Brinnae %A Ginsburg,Geoffrey %A Smuck,Matthew %A Woods,Christopher %A Shaw,Ryan %A Dunn,Jessilyn %+ Department of Biomedical Engineering, Duke University, Room 1427, Fitzpatrick Center (FCIEMAS), 101 Science Drive, Durham, NC, 27708-0281, United States, 1 919 660 5131, jessilyn.dunn@duke.edu %K bring your own device %K wearable device %K mHealth %D 2022 %7 8.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Digital health technologies, such as smartphones and wearable devices, promise to revolutionize disease prevention, detection, and treatment. Recently, there has been a surge of digital health studies where data are collected through a bring-your-own-device (BYOD) approach, in which participants who already own a specific technology may voluntarily sign up for the study and provide their digital health data. BYOD study design accelerates the collection of data from a larger number of participants than cohort design; this is possible because researchers are not limited in the study population size based on the number of devices afforded by their budget or the number of people familiar with the technology. However, the BYOD study design may not support the collection of data from a representative random sample of the target population where digital health technologies are intended to be deployed. This may result in biased study results and biased downstream technology development, as has occurred in other fields. In this viewpoint paper, we describe demographic imbalances discovered in existing BYOD studies, including our own, and we propose the Demographic Improvement Guideline to address these imbalances. %M 34913871 %R 10.2196/29510 %U https://mhealth.jmir.org/2022/4/e29510 %U https://doi.org/10.2196/29510 %U http://www.ncbi.nlm.nih.gov/pubmed/34913871 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 4 %P e23657 %T Association Between Step Count Measured With a Smartphone App (Pain-Note) and Pain Level in Patients With Chronic Pain: Observational Study %A Ogawa,Takahisa %A Castelo-Branco,Luis %A Hatta,Kotaro %A Usui,Chie %+ Department of Orthopedic Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan, 81 338136111, takahisa.o@gmail.com %K smartphone %K iPhone %K cross-sectional study %K chronic pain %K fibromyalgia %K step count %D 2022 %7 6.4.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Chronic pain is the leading cause of disability, affecting between 20% and 50% of the global population. The key recommended treatment is physical activity, which can be measured in daily life using a pedometer. However, poor adherence to pedometer use can result in incorrect measurements. Furthermore, only a few studies have investigated a possible curvilinear association between physical activity and chronic pain. Objective: In this study, we developed the Pain-Note smartphone app to collect real-world data on step count, using the smartphone’s built-in pedometer. The aims of our research are (1) to evaluate the association between daily step count and pain level among patients with chronic pain and (2) determine if the association between daily step count and pain level was curvilinear. Methods: We conducted a cross-sectional study based on step count data collected with the app and on the results of questionnaires, which measured the duration and intensity of pain, the widespread pain index, the symptom severity score, and the insomnia severity scale, including 7 questions for symptoms of depression. We analyzed the association between step count and pain level as a nonlinear relationship using a restricted cubic spline model. A prespecified subgroup analysis was also conducted based on fibromyalgia criteria. Results: Between June 1, 2018, and June 11, 2020, a total of 6138 records were identified, of which 1273 were analyzed. The mean age of the participants was 38.7 years, 81.9% (1043/1273) were female, and chronic pain was present for more than 5 years in 43.2% (550/1273) of participants. Participants in the third and fourth quartiles for step count (more than 3045 and 5668 steps a day, respectively) showed a significant positive association between higher step count and lower numerical pain rating scale (mean difference –0.43, 95% CI –0.78 to –0.08, P=.02; –0.45; 95% CI –0.8 to –0.1, P=.01, respectively) than those in the first quartile (less than or equal to 1199 steps a day). The restricted cubic spline model for the association between step count and pain scale displayed a steep decline followed by a moderate decrease as the step count increased; the inflection point was 5000 steps. However, this association was not observed among participants who met the fibromyalgia criteria (491/1273), who showed a steep positive increase below 2000 steps. Data were collected between June 1, 2018, and June 11, 2020, and were analyzed on November 18, 2021. Conclusions: Step count measured with the Pain-Note app showed a nonlinear association with pain level. Although participants with and without fibromyalgia showed a negative correlation between step count and pain level, participants who meet the criteria for fibromyalgia may present a different relationship between walking and pain perception compared to those in the general chronic pain population. %M 35384846 %R 10.2196/23657 %U https://formative.jmir.org/2022/4/e23657 %U https://doi.org/10.2196/23657 %U http://www.ncbi.nlm.nih.gov/pubmed/35384846 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 4 %P e36849 %T Adaptive Text Messaging for Postpartum Risky Drinking: Conceptual Model and Protocol for an Ecological Momentary Assessment Study %A Dauber,Sarah %A Beacham,Alexa %A Hammond,Cori %A West,Allison %A Thrul,Johannes %+ Partnership to End Addiction, 711 Third Avenue, New York, NY, 10017, United States, 1 212 841 5270, sdauber@toendaddiction.org %K postpartum %K alcohol use %K risky drinking %K mobile health %K ecologic momentary assessment %K mobile phone %D 2022 %7 4.4.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Risky drinking is prevalent among women of childbearing age. Although many women reduce their drinking during pregnancy, more than half return to prepregnancy levels during the early postpartum period. Risky drinking in new mothers may be associated with negative child and maternal health outcomes; however, new mothers are unlikely to seek treatment for risky drinking because of stigma and fear of child protective service involvement. SMS text messaging is a promising approach for reaching non–treatment-seeking new mothers at risk because of risky drinking. SMS text messaging interventions (TMIs) are empirically supported for alcohol use, but a tailored intervention for new mothers does not exist. This study aims to fill this gap by developing a just-in-time adaptive TMI for postpartum risky drinking. Objective: The objectives of this paper are to present a preliminary conceptual model of postpartum risky drinking and describe the protocol for conducting an ecological momentary assessment (EMA) study with new mothers to inform the refinement of the conceptual model and development of the TMI. Methods: This paper presents a preliminary conceptual model of postpartum risky drinking based on the motivational model of alcohol use, social cognitive theory, and temporal self-regulation theory. The model proposes three primary intervention targets: motivation, self-efficacy, and self-regulation. Theoretical and empirical literature in support of the conceptual model is described. The paper also describes procedures for a study that will collect EMA data from 30 participants recruited via social media and the perinatal Central Intake system of New Jersey. Following the baseline assessment, EMA surveys will be sent 5 times per day for 14 days. The assessment instruments and data analysis procedures are described. Results: Recruitment is scheduled to begin in January 2022 and is anticipated to conclude in March 2022. Study results are estimated to be published in July 2022. Conclusions: The study findings will enhance our understanding of daily and momentary fluctuations in risk and protective factors for risky drinking during the early postpartum period. The findings will be used to refine the conceptual model and inform the development of the TMI. The next steps for this work include the development of intervention components via an iterative participatory design process and testing of the resulting intervention in a pilot microrandomized trial. International Registered Report Identifier (IRRID): PRR1-10.2196/36849 %M 35373778 %R 10.2196/36849 %U https://www.researchprotocols.org/2022/4/e36849 %U https://doi.org/10.2196/36849 %U http://www.ncbi.nlm.nih.gov/pubmed/35373778 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e32244 %T Development of a Mobile App for Clinical Research: Challenges and Implications for Investigators %A Chettri,Shibani %A Wang,Vivian %A Balkin,Eli Asher %A Rayo,Michael F %A Lee,Clara N %+ College of Public Health, The Ohio State University, 1529 N High St, Apt 614, Columbus, OH, 43201, United States, 1 2404297749, Chettri.1@osu.edu %K mHealth %K mobile app %K patient-collected data %K data security %K mobile health %K patient data %K clinical research %K research facilities %D 2022 %7 1.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Advances in mobile app technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Collection of data via mobile devices allows for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mobile health (mHealth), but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital app technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile app development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting information technology infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data. %M 35363154 %R 10.2196/32244 %U https://mhealth.jmir.org/2022/4/e32244 %U https://doi.org/10.2196/32244 %U http://www.ncbi.nlm.nih.gov/pubmed/35363154 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e28811 %T Usability of Smart Home Thermostat to Evaluate the Impact of Weekdays and Seasons on Sleep Patterns and Indoor Stay: Observational Study %A Jalali,Niloofar %A Sahu,Kirti Sundar %A Oetomo,Arlene %A Morita,Plinio Pelegrini %+ School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 31372, plinio.morita@uwaterloo.ca %K public health %K Internet of Things (IoT) %K big data %K sleep monitoring %K health monitoring %K mobile phone %D 2022 %7 1.4.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sleep behavior and time spent at home are important determinants of human health. Research on sleep patterns has traditionally relied on self-reported data. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring. Objective: The objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations. Methods: From the 2018 ecobee Donate your Data data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ≥6 sensors, and having a maximum of 4 occupants. Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. Each household’s record was divided into different subsets based on seasonal, weekday, and seasonal weekday scales. Results: Our results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450; P<.001; odds ratio [OR] 1.8, 95% CI 1.5-3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450; P<.001; OR 1.8, 95% CI 1.4-2). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450; P<.001; OR 5.6, 95% CI 4.3-6.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445; P<.001; OR 2.06, 95% CI 1.64-2.5). Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455; P<.001; OR 1.6, 95% CI 1.3-2.3). These results are in accordance with existing literature. Conclusions: This is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change). %M 35363147 %R 10.2196/28811 %U https://mhealth.jmir.org/2022/4/e28811 %U https://doi.org/10.2196/28811 %U http://www.ncbi.nlm.nih.gov/pubmed/35363147 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e29988 %T Cost and Effort Considerations for the Development of Intervention Studies Using Mobile Health Platforms: Pragmatic Case Study %A Thorpe,Dan %A Fouyaxis,John %A Lipschitz,Jessica M %A Nielson,Amy %A Li,Wenhao %A Murphy,Susan A %A Bidargaddi,Niranjan %+ Flinders Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Tonsley Campus, 1284 South Road, Clovelly Park, 5042, Australia, 61 0872215238, dthorpe@flinders.edu.au %K health informatics %K human computer interaction %K digital health %K mobile health %K ecological momentary assessment %K ecological momentary intervention %K behavioral activation %K interventional research %K mobile health costs %D 2022 %7 31.3.2022 %9 Viewpoint %J JMIR Form Res %G English %X Background: The research marketplace has seen a flood of open-source or commercial mobile health (mHealth) platforms that can collect and use user data in real time. However, there is a lack of practical literature on how these platforms are developed, integrated into study designs, and adopted, including important information around cost and effort considerations. Objective: We intend to build critical literacy in the clinician-researcher readership into the cost, effort, and processes involved in developing and operationalizing an mHealth platform, focusing on Intui, an mHealth platform that we developed. Methods: We describe the development of the Intui mHealth platform and general principles of its operationalization across sites. Results: We provide a worked example in the form of a case study. Intui was operationalized in the design of a behavioral activation intervention in collaboration with a mental health service provider. We describe the design specifications of the study site, the developed software, and the cost and effort required to build the final product. Conclusions: Study designs, researcher needs, and technical considerations can impact effort and costs associated with the use of mHealth platforms. Greater transparency from platform developers about the impact of these factors on practical considerations relevant to end users such as clinician-researchers is crucial to increasing critical literacy around mHealth, thereby aiding in the widespread use of these potentially beneficial technologies and building clinician confidence in these tools. %M 35357313 %R 10.2196/29988 %U https://formative.jmir.org/2022/3/e29988 %U https://doi.org/10.2196/29988 %U http://www.ncbi.nlm.nih.gov/pubmed/35357313 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e29479 %T Analyzability of Photoplethysmographic Smartwatch Data by the Preventicus Heartbeats Algorithm During Everyday Life: Feasibility Study %A Merschel,Steve %A Reinhardt,Lars %+ Preventicus GmbH, Ernst-Abbe-Straße 15, Jena, 07743, Germany, 49 3641 5598450, steve.merschel@preventicus.com %K photoplethysmography %K wearable %K smartwatch %K heart rate monitoring %K cardiac arrhythmia screening %K atrial fibrillation %K signal quality %K activity profile %D 2022 %7 28.3.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Continuous heart rate monitoring via mobile health technologies based on photoplethysmography (PPG) has great potential for the early detection of sustained cardiac arrhythmias such as atrial fibrillation. However, PPG measurements are impaired by motion artifacts. Objective: The aim of this investigation was to evaluate the analyzability of smartwatch-derived PPG data during everyday life and to determine the relationship between the analyzability of the data and the activity level of the participant. Methods: A total of 41 (19 female and 22 male) adults in good cardiovascular health (aged 19-79 years) continuously wore a smartwatch equipped with a PPG sensor and a 3D accelerometer (Cardio Watch 287, Corsano Health BV) for a period of 24 hours that represented their individual daily routine. For each participant, smartwatch data were analyzed on a 1-minute basis by an algorithm designed for heart rhythm analysis (Preventicus Heartbeats, Preventicus GmbH). As outcomes, the percentage of analyzable data (PAD) and the mean acceleration (ACC) were calculated. To map changes of the ACC and PAD over the course of one day, the 24-hour period was divided into 8 subintervals comprising 3 hours each. Results: Univariate analysis of variance showed a large effect (ηp2> 0.6; P<.001) of time interval (phase) on the ACC and PAD. The PAD ranged between 34% and 100%, with an average of 71.5% for the whole day, which is equivalent to a period of 17.2 hours. Between midnight and 6 AM, the mean values were the highest for the PAD (>94%) and the lowest for the ACC (<6×10-3 m/s2). Regardless of the time of the day, the correlation between the PAD and ACC was strong (r=–0.64). A linear regression analysis for the averaged data resulted in an almost perfect coefficient of determination (r2=0.99). Conclusions: This study showed a large relationship between the activity level and the analyzability of smartwatch-derived PPG data. Given the high yield of analyzable data during the nighttime, continuous arrhythmia screening seems particularly effective during sleep phases. %M 35343902 %R 10.2196/29479 %U https://formative.jmir.org/2022/3/e29479 %U https://doi.org/10.2196/29479 %U http://www.ncbi.nlm.nih.gov/pubmed/35343902 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e34148 %T Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study %A Makhmutova,Mariko %A Kainkaryam,Raghu %A Ferreira,Marta %A Min,Jae %A Jaggi,Martin %A Clay,Ieuan %+ Digital Medicine Society, 90 Canal Street, 4th Floor, Boston, MA, 02114, United States, 1 1733095953, ieuan@dimesociety.org %K depression %K machine learning %K person-generated health data %D 2022 %7 25.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost. Objective: This study aimed to determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes. Methods: Here, we present the development of PSYCHE-D (Prediction of Severity Change-Depression), a predictive model developed using PGHD from more than 4000 individuals, which forecasts the long-term increase in depression severity. PSYCHE-D uses a 2-phase approach. The first phase supplements self-reports with intermediate generated labels, and the second phase predicts changing status over a 3-month period, up to 2 months in advance. The 2 phases are implemented as a single pipeline in order to eliminate data leakage and ensure results are generalizable. Results: PSYCHE-D is composed of 2 Light Gradient Boosting Machine (LightGBM) algorithm–based classifiers that use a range of PGHD input features, including objective activity and sleep, self-reported changes in lifestyle and medication, and generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect an increase in depression severity over a 3-month interval, with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity while maintaining specificity when compared with a random model. Conclusions: These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual’s mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals experiencing depression. %M 35333186 %R 10.2196/34148 %U https://mhealth.jmir.org/2022/3/e34148 %U https://doi.org/10.2196/34148 %U http://www.ncbi.nlm.nih.gov/pubmed/35333186 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e30789 %T Impact of Masticatory Behaviors Measured With Wearable Device on Metabolic Syndrome: Cross-sectional Study %A Uehara,Fumiko %A Hori,Kazuhiro %A Hasegawa,Yoko %A Yoshimura,Shogo %A Hori,Shoko %A Kitamura,Mari %A Akazawa,Kohei %A Ono,Takahiro %+ Division of Comprehensive Prosthodontics, Faculty of Dentistry and Graduate School of Medical and Dental Sciences, Niigata University, 2-5274 Gakkocho-dori, Niigata, 9518514, Japan, 81 25 227 2891, hori@dent.niigata-u.ac.jp %K metabolic syndrome %K mastication behaviors %K wearable device %K daily meal %K energy intake %K chew %K internet of things %D 2022 %7 24.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: It has been widely recognized that mastication behaviors are related to the health of the whole body and to lifestyle-related diseases. However, many studies were based on subjective questionnaires or were limited to small-scale research in the laboratory due to the lack of a device for measuring mastication behaviors during the daily meal objectively. Recently, a small wearable masticatory counter device, called bitescan (Sharp Co), for measuring masticatory behavior was developed. This wearable device is designed to assess objective masticatory behavior by being worn on the ear in daily life. Objective: This study aimed to investigate the relation between mastication behaviors in the laboratory and in daily meals and to clarify the difference in mastication behaviors between those with metabolic syndrome (MetS) and those without (non-MetS) measured using a wearable device. Methods: A total of 99 healthy volunteers (50 men and 49 women, mean age 36.4 [SD 11.7] years) participated in this study. The mastication behaviors (ie, number of chews and bites, number of chews per bite, and chewing rate) were measured using a wearable ear-hung device. Mastication behaviors while eating a rice ball (100 g) in the laboratory and during usual meals for an entire day were monitored, and the daily energy intake was calculated. Participants’ abdominal circumference, fasting glucose concentration, blood pressure, and serum lipids were also measured. Mastication behaviors in the laboratory and during meals for 1 entire day were compared. The participants were divided into 2 groups using the Japanese criteria for MetS (positive/negative for MetS or each MetS component), and mastication behaviors were compared. Results: Mastication behaviors in the laboratory and during daily meals were significantly correlated (number of chews r=0.36; P<.001; number of bites r=0.49; P<.001; number of chews per bite r=0.33; P=.001; and chewing rate r=0.51; P<.001). Although a positive correlation was observed between the number of chews during the 1-day meals and energy intake (r=0.26, P=.009), the number of chews per calorie ingested was negatively correlated with energy intake (r=–0.32, P=.002). Of the 99 participants, 8 fit the criteria for MetS and 14 for pre-MetS. The number of chews and bites for a rice ball in the pre-MetS(+) group was significantly lower than the pre-MetS(–) group (P=.02 and P=.04, respectively). Additionally, scores for the positive abdominal circumference and hypertension subgroups were also less than the counterpart groups (P=.004 and P=.01 for chews, P=.006 and P=.02 for bites, respectively). The number of chews and bites for an entire day in the hypertension subgroup were significantly lower than in the other groups (P=.02 and P=.006). Furthermore, the positive abdominal circumference and hypertension subgroups showed lower numbers of chews per calorie ingested for 1-day meals (P=.03 and P=.02, respectively). Conclusions: These results suggest a relationship between masticatory behaviors in the laboratory and those during daily meals and that masticatory behaviors are associated with MetS and MetS components. Trial Registration: University Hospital Medical Information Network Clinical Trials Registry R000034453; https://tinyurl.com/mwzrhrua %M 35184033 %R 10.2196/30789 %U https://mhealth.jmir.org/2022/3/e30789 %U https://doi.org/10.2196/30789 %U http://www.ncbi.nlm.nih.gov/pubmed/35184033 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e30121 %T Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study %A Brew,Bruce %A Faux,Steven G %A Blanchard,Elizabeth %+ Department of Neurology, St Vincent's Hospital, 390 Victoria Street, Darlinghurst, Sydney, 2010, Australia, 61 2 8382 4100, Bruce.Brew@svha.org.au %K falls %K smartwatch %K app fall detection %K accelerometer %K inertial sensors %K older adult %K elderly %K old age %K smart watch %K mobile health %K threshold-based algorithm %D 2022 %7 21.3.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Older adults are at an increased risk of falls with the consequent impacts on the health of the individual and health expenditure for the population. Smartwatch apps have been developed to detect a fall, but their sensitivity and specificity have not been subjected to blinded assessment nor have the factors that influence the effectiveness of fall detection been fully identified. Objective: This study aims to assess accuracy metrics for a novel fall detection smartwatch algorithm. Methods: We performed a cross-sectional study of 22 healthy adults comparing the detection of induced forward, side (left and right), and backward falls and near falls provided by a smartwatch threshold-based algorithm, with a video record of induced falls serving as the gold standard; a blinded assessor compared the two. Three different smartwatches with two different operating systems were used. There were 226 falls: 64 were backward, 51 forward, 55 left sided, and 56 right sided. Results: The overall smartwatch app sensitivity for falls was 77%, the specificity was 99%, the false-positive rate was 1.7%, and the false-negative rate was 16.4%. The positive and negative predictive values were 98% and 84%, respectively, while the accuracy was 89%. There were 249 near falls: the sensitivity was 89%, the specificity was 100%, there were no false positives, 11% were false negatives, the positive predictive value was 100%, the false-negative predictive value was 83%, and the accuracy was 93%. Conclusions: Falls were more likely to be detected if the fall was on the same side as the wrist with the smartwatch. There was a trend toward some smartwatches and operating systems having superior sensitivity, but these did not reach statistical significance. The effectiveness data and modifying factors pertaining to this smartwatch app can serve as a reference point for other similar smartwatch apps. %M 35311686 %R 10.2196/30121 %U https://formative.jmir.org/2022/3/e30121 %U https://doi.org/10.2196/30121 %U http://www.ncbi.nlm.nih.gov/pubmed/35311686 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 3 %P e25643 %T Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study %A Niemeijer,Koen %A Mestdagh,Merijn %A Kuppens,Peter %+ Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Tiensestraat 102, Post box 3717, Leuven, 3000, Belgium, 32 16372580, koen.niemeijer@kuleuven.be %K mobile sensing %K sleep %K subjective sleep quality %K negative affect %K depression %K multiverse %K multilevel modeling %K machine learning %K mood %K mood disorder %K mobile sensors %K sleep quality %K clinical applications %D 2022 %7 18.3.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Sleep influences moods and mood disorders. Existing methods for tracking the quality of people’s sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications. Objective: Our goal was to evaluate the potential of mobile sensing data to obtain information about a person’s sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model. Methods: We used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting. Results: Most models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2≤0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set. Conclusions: The approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic. %M 35302502 %R 10.2196/25643 %U https://www.jmir.org/2022/3/e25643 %U https://doi.org/10.2196/25643 %U http://www.ncbi.nlm.nih.gov/pubmed/35302502 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e31894 %T Testing Digital Methods of Patient-Reported Outcomes Data Collection: Prospective Cluster Randomized Trial to Test SMS Text Messaging and Mobile Surveys %A Agarwal,Anish K %A Ali,Zarina S %A Shofer,Frances %A Xiong,Ruiying %A Hemmons,Jessica %A Spencer,Evan %A Abdel-Rahman,Dina %A Sennett,Brian %A Delgado,Mucio K %+ Department of Emergency Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, United States, 1 6103042318, anish.agarwal@pennmedicine.upenn.edu %K patient-reported outcomes %K mobile surveys %K research methods %K text messaging %K mobile survey %K data collection %K patient engagement %K response rate %D 2022 %7 17.3.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Health care delivery continues to evolve, with an effort being made to create patient-centered care models using patient-reported outcomes (PROs) data. Collecting PROs has remained challenging and an expanding landscape of digital health offers a variety of methods to engage patients. Objective: The aim of this study is to prospectively investigate two common methods of remote PRO data collection. The study sought to compare response and engagement rates for bidirectional SMS text messaging and mobile surveys following orthopedic surgery. Methods: The study was a prospective, block randomized trial of adults undergoing elective orthopedic procedures over 6 weeks. The primary objective was to determine if the method of digital patient engagement would impact response and completion rates. The primary outcome was response rate and total completion of PRO questionnaires. Results: A total of 127 participants were block randomized into receiving a mobile survey (n=63) delivered as a hyperlink or responding to the same questions through an automated bidirectional SMS text messaging system (n=64). Gender, age, number of comorbidities, and opioid prescriptions were similar across messaging arms. Patients receiving the mobile survey were more likely to have had a knee-related surgery (n=50, 83.3% vs n=40, 62.5%; P=.02) but less likely to have had an invasive procedure (n=26, 41.3% vs n=39, 60.9%; P=.03). Overall engagement over the immediate postoperative period was similar. Prolonged engagement for patients taking opioids past postoperative day 4 was higher in the mobile survey arm at day 7 (18/19, 94.7% vs 9/16, 56.3%). Patients with more invasive procedures showed a trend toward being responsive at day 4 as compared to not responding (n=41, 59.4% vs n=24, 41.4%; P=.05). Conclusions: As mobile patient engagement becomes more common in health care, testing the various options to engage patients to gather data is crucial to inform future care and research. We found that bidirectional SMS text messaging and mobile surveys were comparable in response and engagement rates; however, mobile surveys may trend toward higher response rates over longer periods of time. Trial Registration: ClinicalTrials.gov NCT03532256; https://clinicaltrials.gov/ct2/show/NCT03532256 %M 35298394 %R 10.2196/31894 %U https://formative.jmir.org/2022/3/e31894 %U https://doi.org/10.2196/31894 %U http://www.ncbi.nlm.nih.gov/pubmed/35298394 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e35879 %T Nutrition-Related Mobile Apps in the French App Stores: Assessment of Functionality and Quality %A Martinon,Prescilla %A Saliasi,Ina %A Bourgeois,Denis %A Smentek,Colette %A Dussart,Claude %A Fraticelli,Laurie %A Carrouel,Florence %+ Health, Systemic, Process UR 4129 Research Unit, University Claude Bernard, University of Lyon, 11 Rue Guillaume Paradin, Lyon, 69008, France, 33 478785745, florence.carrouel@univ-lyon1.fr %K mobile apps %K behavior change %K diet %K healthy food %K nutrition %K prevention %K mHealth %K mobile health %K lifestyle %K French %D 2022 %7 14.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The global burden of disease attributes 20% of deaths to poor nutrition. Although hundreds of nutrition-related mobile apps have been created, and these have been downloaded by millions of users, the effectiveness of these technologies on the adoption of healthy eating has had mixed Objective: The aim of this study was to review which nutrition-related mobile apps are currently available on the French market and assess their quality. Methods: We screened apps on the Google Play Store and the French Apple App Store, from March 10 to 17, 2021, to identify those related to nutritional health. A shortlist of 15 apps was identified, and each was assessed using the French version of the Mobile App Rating Scale: 8 dietitians and nutritionists assessed 7 apps, and the remaining apps were randomly allocated to ensure 4 assessments per app. Intraclass correlation was used to evaluate interrater agreement. Means and standard deviations of scores for each section and each item were calculated. Results: The top scores for overall quality were obtained by Yazio - Régime et Calories (mean 3.84, SD 0.32), FeelEat (mean 3.71, SD 0.47), and Bonne App (mean 3.65, SD 0.09). Engagement scores ranged from a mean of 1.95 (SD 0.5) for iEatBetter: Journal alimentaire to a mean of 3.85 (SD 0.44) for FeelEat. Functionality scores ranged from a mean of 2.25 (SD 0.54) for Naor to a mean of 4.25 (SD 0.46) for Yazio. Aesthetics scores ranged from a mean of 2.17 (SD 0.34) for Naor to a mean of 3.88 (SD 0.47) for Yazio. Information scores ranged from a mean of 2.38 (SD 0.60) for iEatBetter to a mean of 3.73 (SD 0.29) for Yazio. Subjective quality scores ranged from a mean of 1.13 (SD 0.25) for iEatBetter to a mean of 2.28 (SD 0.88) for Compteur de calories FatSecret. Specificity scores ranged from a mean of 1.38 (SD 0.64) for iEatBetter to a mean of 3.50 (SD 0.91) for FeelEat. The app-specific score was always lower than the subjective quality score, which was always lower than the quality score, which was lower than the rating from the iOS or Android app stores. Conclusions: Although prevention and information messages in apps regarding nutritional habits are not scientifically verified before marketing, we found that app quality was good. Subjective quality and specificity were associated with lower ratings. Further investigations are needed to assess whether information from these apps is consistent with recommendations and to determine the long-term impacts of these apps on users. %M 35285817 %R 10.2196/35879 %U https://mhealth.jmir.org/2022/3/e35879 %U https://doi.org/10.2196/35879 %U http://www.ncbi.nlm.nih.gov/pubmed/35285817 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 3 %P e34898 %T Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study %A Zhang,Yuezhou %A Folarin,Amos A %A Sun,Shaoxiong %A Cummins,Nicholas %A Vairavan,Srinivasan %A Bendayan,Rebecca %A Ranjan,Yatharth %A Rashid,Zulqarnain %A Conde,Pauline %A Stewart,Callum %A Laiou,Petroula %A Sankesara,Heet %A Matcham,Faith %A White,Katie M %A Oetzmann,Carolin %A Ivan,Alina %A Lamers,Femke %A Siddi,Sara %A Vilella,Elisabet %A Simblett,Sara %A Rintala,Aki %A Bruce,Stuart %A Mohr,David C %A Myin-Germeys,Inez %A Wykes,Til %A Haro,Josep Maria %A Penninx,Brenda WJH %A Narayan,Vaibhav A %A Annas,Peter %A Hotopf,Matthew %A Dobson,Richard JB %A , %+ Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, United Kingdom, 44 20 7848 0473, richard.j.dobson@kcl.ac.uk %K depression %K mobile health %K location data %K mobility %K dynamic structural equation modeling %K mHealth %K mental health %K medical informatics %K modeling %D 2022 %7 11.3.2022 %9 Original Paper %J JMIR Ment Health %G English %X Background: The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. Objective: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Methods: Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. Results: This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=−0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=−0.07, P<.001) the subsequent periodicity of mobility. Conclusions: Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings. %M 35275087 %R 10.2196/34898 %U https://mental.jmir.org/2022/3/e34898 %U https://doi.org/10.2196/34898 %U http://www.ncbi.nlm.nih.gov/pubmed/35275087 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e30468 %T Data Collection Mechanisms in Health and Wellness Apps: Review and Analysis %A Philip,Ben Joseph %A Abdelrazek,Mohamed %A Bonti,Alessio %A Barnett,Scott %A Grundy,John %+ Deakin University, 221 Burwood Highway, Melbourne, 3125, Australia, 61 426824528, benjo@deakin.edu.au %K data collection %K mHealth apps %K app review %K app analysis %K mHealth %K mobile apps %K development %K data sharing %K user experience %K usability %K automation %K data reliability %K mobile phone %D 2022 %7 9.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There has been a steady rise in the availability of health wearables and built-in smartphone sensors that can be used to collect health data reliably and conveniently from end users. Given the feature overlaps and user tendency to use several apps, these are important factors impacting user experience. However, there is limited work on analyzing the data collection aspect of mobile health (mHealth) apps. Objective: This study aims to analyze what data mHealth apps across different categories usually collect from end users and how these data are collected. This information is important to guide the development of a common data model from current widely adopted apps. This will also inform what built-in sensors and wearables, a comprehensive mHealth platform should support. Methods: In our empirical investigation of mHealth apps, we identified app categories listed in a curated mHealth app library, which was then used to explore the Google Play Store for health and medical apps that were then filtered using our selection criteria. We downloaded these apps from a mirror site hosting Android apps and analyzed them using a script that we developed around the popular AndroGuard tool. We analyzed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects health data. Results: We retrieved 3251 apps meeting our criteria, and our analysis showed that 10.74% (349/3251) of these apps requested Bluetooth access. We found that 50.9% (259/509) of the Bluetooth service universally unique identifiers to be known in these apps, with the remainder being vendor specific. The most common health-related Bluetooth Low Energy services using known universally unique identifiers were Heart Rate, Glucose, and Body Composition. App permissions showed the most used device module or sensor to be the camera (669/3251, 20.57%), closely followed by location (598/3251, 18.39%), with the highest occurrence in the staying healthy app category. Conclusions: We found that not many health apps used built-in sensors or peripherals for collecting health data. The small number of the apps using Bluetooth, with an even smaller number of apps using standard Bluetooth Low Energy services, indicates a wider use of proprietary algorithms and custom services, which restrict the device use. The use of standard profiles could open this ecosystem further and could provide end users more options for apps. The relatively small proportion of apps using built-in sensors along with a high reliance on manual data entry suggests the need for more research into using sensors for data collection in health and fitness apps, which may be more desirable and improve end user experience. %M 35262499 %R 10.2196/30468 %U https://mhealth.jmir.org/2022/3/e30468 %U https://doi.org/10.2196/30468 %U http://www.ncbi.nlm.nih.gov/pubmed/35262499 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 3 %P e35984 %T Researching the Links Between Smartphone Behavior and Adolescent Well-being With the FUTURE-WP4 (Modeling the Future: Understanding the Impact of Technology on Adolescent’s Well-being Work Package 4) Project: Protocol for an Ecological Momentary Assessment Study %A Elavsky,Steriani %A Blahošová,Jana %A Lebedíková,Michaela %A Tkaczyk,Michał %A Tancos,Martin %A Plhák,Jaromír %A Sotolář,Ondřej %A Smahel,David %+ Faculty of Informatics, Masaryk University, Botanická 68A, Brno, 60200, Czech Republic, 420 549491814, elavsky@fi.muni.cz %K well-being %K adolescents %K smartphones %K intensive data %K ecological momentary assessment %D 2022 %7 8.3.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Smartphone ownership has increased among teens within the last decade, with up to 89% of adolescents owning a smartphone and engaging daily with the online world through it. Although the results of recent meta-analyses suggest that engaging digital technology plays only a small role in adolescent well-being, parents, professionals, and policymakers remain concerned about the impact that the instant connectivity of smartphones has on adolescent well-being. Objective: Herein, we introduce the protocol of a research study investigating the associations between adolescent smartphone use and different facets of well-being (social, physical, and psychological), with the aim to apply innovative methods to address the limitations of existing empirical studies. Methods: This 12-month prospective study of adolescents uses a repeated measurement-burst design with the ecological momentary assessment methodology. Adolescents (N=203; age range 13-17 years) complete baseline assessments through online questionnaires, four 14-day intensive data collection bursts, and an online questionnaire at the end of the study. As part of the 4 measurement bursts, adolescent smartphone behavior is assessed objectively by passive data collection of smartphone data logs and through self-reports in short questionnaires administered via a custom-built Android app. Results: The protocol describes the study objectives, research tools (including the development of the Android app and specialized software), and process (including pilot studies, the main study, and targets for machine learning approaches). Two of the 203 enrolled participants provided no data during the first data collection burst of the main study. Preliminary analyses of the data from the first data collection burst indicated an acceptable level of compliance (72.25%) with the daily questionnaires. The design of the study will allow for the assessment of both within- and between-person variabilities in smartphone behavior, as well as short-term variation and long-term change in smartphone behavior and how it impacts the indicators of social, physical, and psychological well-being. Conclusions: The innovative methods applied in this study (objective smartphone logs, ecological momentary assessment, and machine learning) will allow for a more nuanced assessment of the links between smartphone use and well-being, informing strategies to help adolescents navigate the online world more constructively in terms of the development of their physical, social, and psychological well-being. International Registered Report Identifier (IRRID): DERR1-10.2196/35984 %M 35258467 %R 10.2196/35984 %U https://www.researchprotocols.org/2022/3/e35984 %U https://doi.org/10.2196/35984 %U http://www.ncbi.nlm.nih.gov/pubmed/35258467 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e27677 %T Smartphone App–Based Noncontact Ecological Momentary Assessment With Experienced and Naïve Older Participants: Feasibility Study %A Burke,Louise %A Naylor,Graham %+ Hearing Sciences - Scottish Section, School of Medicine, University of Nottingham, New Lister Building Level 3, Glasgow Royal Infirmary, 10-16 Alexandra Parade, Glasgow, G31 2ER, United Kingdom, 44 0141 242 9666, graham.naylor@nottingham.ac.uk %K ecological momentary assessment %K EMA %K smartphone %K mobile phone %K mHealth %K app %K noncontact EMA %K remote EMA %K hearing %K hearing loss %K feasibility %D 2022 %7 8.3.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Smartphone app–based ecological momentary assessment (EMA) without face-to-face contact between researcher and participant (app-based noncontact EMA) potentially provides a valuable data collection tool when geographic, time, and situational factors (eg, COVID-19 restrictions) place constraints on in-person research. Nevertheless, little is known about the feasibility of this method, particularly in older and naïve EMA participants. Objective: This study aims to assess the feasibility of app-based noncontact EMA as a function of previous EMA experience, by recruiting and comparing a group of participants who had never participated in EMA before against a group of participants who had been part of an earlier in-person EMA study, and age, by recruiting middle-aged to older adults. Methods: Overall, 151 potential participants were invited via email; 46.4% (70/151) enrolled in the study by completing the baseline questionnaire set and were emailed instructions for the EMA phase. Of these participants, 67% (47/70) downloaded an EMA app and ran the survey sequence for 1 week. In total, 5 daytime surveys and 1 evening survey, each day, assessed participants’ listening environment, social activity, and conversational engagement. A semistructured exit telephone interview probed the acceptability of the method. As markers of feasibility, we assessed the enrollment rate, study completion rate, reason for noncompletion, EMA survey response rate, and likelihood of reporting an issue with survey alerts and requested assistance from researchers, family, or friends. Results: Enrollment rates among invitees (63.3% vs 38.2%; P=.004) and completion rates among enrollees (83.9% vs 53.8%; P<.001) were higher in the experienced than in the naïve EMA group. On average, experienced participants responded to 64.1% (SD 30.2%) of the daytime EMA surveys, and naïve participants responded to 54.3% (SD 29.5%) of the daytime EMA surveys (P=.27). Among participants who retrospectively reported issues with survey alerts, only 19% (3/16) requested researcher assistance during data collection. Older participants were more likely to report not being alerted to EMA surveys (P=.008), but age was unrelated to all other markers of feasibility. Post hoc analyses of the effect of the phone operating system on markers of feasibility revealed that response rates were higher among iOS users (mean 74.8%, SD 20.25%) than among Android users (mean 48.5%, SD 31.35%; P=.002). Conclusions: Smartphone app–based noncontact EMA appears to be feasible, although participants with previous EMA experience, younger participants, and iOS users performed better on certain markers of feasibility. Measures to increase feasibility may include extensive testing of the app with different phone types, encouraging participants to seek timely assistance for any issues experienced, and recruiting participants who have some previous EMA experience where possible. The limitations of this study include participants’ varying levels of existing relationship with the researcher and the implications of collecting data during the COVID-19 social restrictions. %M 35258471 %R 10.2196/27677 %U https://formative.jmir.org/2022/3/e27677 %U https://doi.org/10.2196/27677 %U http://www.ncbi.nlm.nih.gov/pubmed/35258471 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 3 %P e28332 %T mHealth Intervention to Improve Treatment Outcomes Among People With HIV Who Use Cocaine: Protocol for a Pilot Randomized Controlled Trial %A Ranjit,Yerina S %A Krishnan,Archana %A Ghosh,Debarchana %A Cravero,Claire %A Zhou,Xin %A Altice,Frederick L %+ Department of Communication, University of Missouri, 108 Switzler Hall, Columbia, MO, 65211, United States, 1 573 822 0881, ranjity@missouri.edu %K smart pillbox %K smartphone %K mHealth intervention %K people with HIV %K cocaine use %K antiretroviral therapy %K description of feasibility and acceptability %K mobile phone %D 2022 %7 7.3.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Antiretroviral therapy is effective in reducing HIV-related morbidity, mortality, and transmission among people with HIV. However, adherence and persistence to antiretroviral therapy are crucial for successful HIV treatment outcomes. People with HIV who use cocaine have poor access to HIV services and lower retention in care. Objective: The primary goal of this paper is to provide a detailed description of a mobile health intervention. This study is designed to improve medication adherence among people with HIV who use cocaine. A secondary goal is to list the important challenges and adaptations incorporated in the study design. Methods: This study, titled Project SMART, used a wireless technology–based intervention, including cellular-enabled electronic pillboxes called TowerView Health and smartphones, to provide reminders and feedback on adherence behavior. The intervention design was based on the theoretical frameworks provided by the self-determination theory and the Motivation Technology Model. The 12-week pilot randomized controlled trial with four arms provided three types of feedback: automated feedback, automated+clinician feedback, and automated feedback+social network feedback. Results: The study was funded by the National Institute of Drug Abuse (R21DA039842) on August 1, 2016. The institutional review board for the study was approved by Yale University on March 21, 2017. Data collection lasted from June 2017 to January 2020. The final enrollment was 71 participants, of whom 57 (80%) completed the study. The data are currently undergoing analysis, and the manuscript is being developed for publication in early 2022. Conclusions: Implementing complex mobile health interventions for high-risk and marginalized populations with multicomponent interventions poses certain challenges, such as finding companies with adequate technology for clients and financial stability and minimizing the research-related burden for the study population. Conducting feasibility studies is important to recognize these challenges and the opportunity to address these challenges with solutions while keeping the design of a randomized controlled trial as true as possible. Trial Registration: Clinicaltrials.gov NCT04418076; https://clinicaltrials.gov/ct2/show/NCT04418076 International Registered Report Identifier (IRRID): DERR1-10.2196/28332 %M 35254270 %R 10.2196/28332 %U https://www.researchprotocols.org/2022/3/e28332 %U https://doi.org/10.2196/28332 %U http://www.ncbi.nlm.nih.gov/pubmed/35254270 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e21959 %T Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study %A Zhang,Peng %A Fonnesbeck,Christopher %A Schmidt,Douglas C %A White,Jules %A Kleinberg,Samantha %A Mulvaney,Shelagh A %+ Department of Computer Science, School of Engineering, Vanderbilt University, 1400 18th Ave S, Rm 2023, Nashville, TN, 37212, United States, 1 615 343 8630, peng.zhang@vanderbilt.edu %K machine learning %K type 1 diabetes %K psychosocial %K self-management %K adolescents %K behavioral medicine %K ecological momentary assessment %K informatics %K mobile phone %D 2022 %7 3.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: For adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches. Objective: The goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration. Methods: We analyzed data from a randomized controlled pilot study using machine learning–based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest. Results: With 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent. Conclusions: Combining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach. %M 35238791 %R 10.2196/21959 %U https://mhealth.jmir.org/2022/3/e21959 %U https://doi.org/10.2196/21959 %U http://www.ncbi.nlm.nih.gov/pubmed/35238791 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 3 %P e27934 %T Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study %A Zhang,Bo %A Deng,Kaiwen %A Shen,Jie %A Cai,Lingrui %A Ratitch,Bohdana %A Fu,Haoda %A Guan,Yuanfang %+ University of Michigan, 2044D Palmer Commons, Ann Arbor, MI, 48109, United States, 1 7347440018, gyuanfan@umich.edu %K deep learning %K eating %K digital watch %D 2022 %7 1.3.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a mature system and large-scale, reliable training set. Objective: This study aims to fill in this gap by an integrative engineering and machine learning effort and conducting a large-scale study in terms of monitoring hours on wearable-based eating detection. Methods: This prospective, longitudinal, passively collected study, covering 3828 hours of records, was made possible by programming a digital system that streams diary, accelerometer, and gyroscope data from Apple Watches to iPhones and then transfers the data to the cloud. Results: On the basis of this data collection, we developed deep learning models leveraging spatial and time augmentation and inferring eating at an area under the curve (AUC) of 0.825 within 5 minutes in the general population. In addition, the longitudinal follow-up of the study design encouraged us to develop personalized models that detect eating behavior at an AUC of 0.872. When aggregated to individual meals, the AUC is 0.951. We then prospectively collected an independent validation cohort in a different season of the year and validated the robustness of the models (0.941 for meal-level aggregation). Conclusions: The accuracy of this model and the data streaming platform promises immediate deployment for monitoring eating in applications such as diabetic integrative care. %M 35230244 %R 10.2196/27934 %U https://www.jmir.org/2022/3/e27934 %U https://doi.org/10.2196/27934 %U http://www.ncbi.nlm.nih.gov/pubmed/35230244 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e33635 %T Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study %A Nissen,Michael %A Slim,Syrine %A Jäger,Katharina %A Flaucher,Madeleine %A Huebner,Hanna %A Danzberger,Nina %A Fasching,Peter A %A Beckmann,Matthias W %A Gradl,Stefan %A Eskofier,Bjoern M %+ Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Schlossplatz 4, Erlangen, 91054, Germany, 49 913185 28990, michael.nissen@fau.de %K wearable validation %K heart rate validation %K Fitbit Charge 4 %K Samsung Galaxy Watch Active2 %K heart rate accuracy %K fitness tracker accuracy %K wearable accuracy %K wearable %K Fitbit %K heart rate %K fitness tracker %K fitness %K cardiovascular %D 2022 %7 1.3.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research. Objective: This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Methods: Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed. Results: A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm). Conclusions: Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements. %M 35230250 %R 10.2196/33635 %U https://formative.jmir.org/2022/3/e33635 %U https://doi.org/10.2196/33635 %U http://www.ncbi.nlm.nih.gov/pubmed/35230250 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e31327 %T Personalization of Intervention Timing for Physical Activity: Scoping Review %A Chaudhari,Saurabh %A Ghanvatkar,Suparna %A Kankanhalli,Atreyi %+ Department of Information Systems and Analytics, School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore, 65 86153503, sach16795@gmail.com %K review %K physical activity %K personalized intervention %K intervention timing %K mobile apps %K fitness tracker %K mobile phone %D 2022 %7 28.2.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The use of sensors in smartphones, smartwatches, and wearable devices has facilitated the personalization of interventions to increase users’ physical activity (PA). Recent research has focused on evaluating the effects of personalized interventions in improving PA among users. However, it is critical to deliver the intervention at an appropriate time to each user to increase the likelihood of adoption of the intervention. Earlier review studies have not focused on the personalization of intervention timing for increasing PA. Objective: This review aims to examine studies of information technology–based PA interventions with personalized intervention timing (PIT); identify inputs (eg, user location) used by the system for generating the PIT, the techniques and methods used for generating the PIT, the content of the PA intervention, and delivery mode of the intervention; and identify gaps in existing literature and suggest future research directions. Methods: A scoping review was undertaken using PsycINFO, PubMed, Scopus, and Web of Science databases based on a structured search query. The main inclusion criteria were as follows: the study aimed to promote PA, included some form of PIT, and used some form of information technology for delivery of the intervention to the user. If deemed relevant, articles were included in this review after removing duplicates and examining the title, abstract, and full text of the shortlisted articles. Results: The literature search resulted in 18 eligible studies. In this review, 72% (13/18) of the studies focused on increasing PA as the primary objective, whereas it was the secondary focus in the remaining studies. The inputs used to generate the PIT were categorized as user preference, activity level, schedule, location, and predicted patterns. On the basis of the intervention technique, studies were classified as manual, semiautomated, or automated. Of these, the automated interventions were either knowledge based (based on rules or guidelines) or data driven. Of the 18 studies, only 6 (33%) evaluated the effectiveness of the intervention and reported positive outcomes. Conclusions: This work reviewed studies on PIT for PA interventions and identified several aspects of the interventions, that is, inputs, techniques, contents, and delivery mode. The reviewed studies evaluated PIT in conjunction with other personalization approaches such as activity recommendation, with no study evaluating the effectiveness of PIT alone. On the basis of the findings, several important directions for future research are also highlighted in this review. %M 35225811 %R 10.2196/31327 %U https://mhealth.jmir.org/2022/2/e31327 %U https://doi.org/10.2196/31327 %U http://www.ncbi.nlm.nih.gov/pubmed/35225811 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e24916 %T Continuous Noninvasive Remote Automated Blood Pressure Monitoring With Novel Wearable Technology: A Preliminary Validation Study %A McGillion,Michael H %A Dvirnik,Nazari %A Yang,Stephen %A Belley-Côté,Emilie %A Lamy,Andre %A Whitlock,Richard %A Marcucci,Maura %A Borges,Flavia K %A Duceppe,Emmanuelle %A Ouellette,Carley %A Bird,Marissa %A Carroll,Sandra L %A Conen,David %A Tarride,Jean-Eric %A Harsha,Prathiba %A Scott,Ted %A Good,Amber %A Gregus,Krysten %A Sanchez,Karla %A Benoit,Pamela %A Owen,Julian %A Harvey,Valerie %A Peter,Elizabeth %A Petch,Jeremy %A Vincent,Jessica %A Graham,Michelle %A Devereaux,P J %+ School of Nursing, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada, 1 905 525 9140 ext 26140, mmcgill@mcmaster.ca %K validation study %K continuous vital signs monitor %K continuous non-invasive blood pressure monitoring %K wearable %K blood pressure %K monitoring %K validation %K mHealth %K vital sign %K biosensor %K accuracy %K usability %D 2022 %7 28.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable continuous monitoring biosensor technologies have the potential to transform postoperative care with early detection of impending clinical deterioration. Objective: Our aim was to validate the accuracy of Cloud DX Vitaliti continuous vital signs monitor (CVSM) continuous noninvasive blood pressure (cNIBP) measurements in postsurgical patients. A secondary aim was to examine user acceptance of the Vitaliti CVSM with respect to comfort, ease of application, sustainability of positioning, and aesthetics. Methods: Included participants were ≥18 years old and recovering from surgery in a cardiac intensive care unit (ICU). We targeted a maximum recruitment of 80 participants for verification and acceptance testing. We also oversampled to minimize the effect of unforeseen interruptions and other challenges to the study. Validation procedures were according to the International Standards Organization (ISO) 81060-2:2018 standards for wearable, cuffless blood pressure (BP) measuring devices. Baseline BP was determined from the gold-standard ICU arterial catheter. The Vitaliti CVSM was calibrated against the reference arterial catheter. In static (seated in bed) and supine positions, 3 cNIBP measurements, each 30 seconds, were taken for each patient with the Vitaliti CVSM and an invasive arterial catheter. At the conclusion of each test session, captured cNIBP measurements were extracted using MediCollector BEDSIDE data extraction software, and Vitaliti CVSM measurements were extracted to a secure laptop through a cable connection. The errors of these determinations were calculated. Participants were interviewed about device acceptability. Results: The validation analysis included data for 20 patients. The average times from calibration to first measurement in the static position and to first measurement in the supine position were 133.85 seconds (2 minutes 14 seconds) and 535.15 seconds (8 minutes 55 seconds), respectively. The overall mean errors of determination for the static position were –0.621 (SD 4.640) mm Hg for systolic blood pressure (SBP) and 0.457 (SD 1.675) mm Hg for diastolic blood pressure (DBP). Errors of determination were slightly higher for the supine position, at 2.722 (SD 5.207) mm Hg for SBP and 2.650 (SD 3.221) mm Hg for DBP. The majority rated the Vitaliti CVSM as comfortable. This study was limited to evaluation of the device during a very short validation period after calibration (ie, that commenced within 2 minutes after calibration and lasted for a short duration of time). Conclusions: We found that the Cloud DX’s Vitaliti CVSM demonstrated cNIBP measurement in compliance with ISO 81060-2:2018 standards in the context of evaluation that commenced within 2 minutes of device calibration; this device was also well-received by patients in a postsurgical ICU setting. Future studies will examine the accuracy of the Vitaliti CVSM in ambulatory contexts, with attention to assessment over a longer duration and the impact of excessive patient motion on data artifacts and signal quality. Trial Registration: ClinicalTrials.gov NCT03493867; https://clinicaltrials.gov/ct2/show/NCT03493867 %M 34876396 %R 10.2196/24916 %U https://mhealth.jmir.org/2022/2/e24916 %U https://doi.org/10.2196/24916 %U http://www.ncbi.nlm.nih.gov/pubmed/34876396 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e33636 %T A Smartphone-Based Information Communication Technology Solution for Primary Modifiable Risk Factors for Noncommunicable Diseases: Pilot and Feasibility Study in Norway %A Gram,Inger Torhild %A Skeie,Guri %A Oyeyemi,Sunday Oluwafemi %A Borch,Kristin Benjaminsen %A Hopstock,Laila Arnesdatter %A Løchen,Maja-Lisa %+ Norwegian Centre for E-health Research, University Hospital of North Norway, Forskningsparken i Breivika, 3rd floor, Sykehusvn. 23, Tromsø, 9019, Norway, 47 92401177, inger.gram@ehealthresearch.no %K eHealth %K feasibility study %K modifiable risk factor %K noncommunicable disease %K pilot study %K smartphone-based information communication technology solution %K short text message service %K feasibility %K risk %K factor %K information communication technology %K smartphone %K development %K monitoring %D 2022 %7 25.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes are the 4 main noncommunicable diseases. These noncommunicable diseases share 4 modifiable risk factors (tobacco use, harmful use of alcohol, physical inactivity, and unhealthy diet). Short smartphone surveys have the potential to identify modifiable risk factors for individuals to monitor trends. Objective: We aimed to pilot a smartphone-based information communication technology solution to collect nationally representative data, annually, on 4 modifiable risk factors. Methods: We developed an information communication technology solution with functionalities for capturing sensitive data from smartphones, receiving, and handling data in accordance with general data protection regulations. The main survey comprised 26 questions: 8 on socioeconomic factors, 17 on the 4 risk factors, and 1 about current or previous noncommunicable diseases. For answers to the continuous questions, a keyboard was displayed for entering numbers; there were preset upper and lower limits for acceptable response values. For categorical questions, pull-down menus with response options were displayed. The second survey comprised 9 yes-or-no questions. For both surveys, we used SMS text messaging. For the main survey, we invited 11,000 individuals, aged 16 to 69 years, selected randomly from the Norwegian National Population Registry (1000 from each of the 11 counties). For the second survey, we invited a random sample of 100 individuals from each county who had not responded to the main survey. All data, except county of residence, were self-reported. We calculated the distribution for socioeconomic background, tobacco use, diet, physical activity, and health condition factors overall and by sex. Results: The response rate was 21.9% (2303/11,000; women: 1397/2263; 61.7%, men: 866/2263, 38.3%; missing: 40/2303, 1.7%). The median age for men was 52 years (IQR 40-61); the median age for women was 48 years (IQR 35-58). The main reported reason for nonparticipation in the main survey was that the sender of the initial SMS was unknown. Conclusions: We successfully developed and piloted a smartphone-based information communication technology solution for collecting data on the 4 modifiable risk factors for the 4 main noncommunicable diseases. Approximately 1 in 5 invitees responded; thus, these data may not be nationally representative. The smartphone-based information communication technology solution should be further developed with the long-term goal to reduce premature mortality from the 4 main noncommunicable diseases. %M 35212636 %R 10.2196/33636 %U https://formative.jmir.org/2022/2/e33636 %U https://doi.org/10.2196/33636 %U http://www.ncbi.nlm.nih.gov/pubmed/35212636 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e31807 %T Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study %A Óskarsdóttir,María %A Islind,Anna Sigridur %A August,Elias %A Arnardóttir,Erna Sif %A Patou,François %A Maier,Anja M %+ Department of Computer Science, Reykjavík University, Menntavegur 1, Reykjavík, 102, Iceland, 354 5996326, mariaoskars@ru.is %K wearable technology %K nearable technology %K internet of health care things %K sleep %K Withings %K study duration %K establishing standards %K seasonality %K mHealth %K digital health %D 2022 %7 22.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used. Objective: The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary. Methods: Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels. Results: Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time. Conclusions: We demonstrate that >2 months’ worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future. %M 35191850 %R 10.2196/31807 %U https://formative.jmir.org/2022/2/e31807 %U https://doi.org/10.2196/31807 %U http://www.ncbi.nlm.nih.gov/pubmed/35191850 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e28686 %T Smart Speakers: The Next Frontier in mHealth %A Sunshine,Jacob %+ Department of Anesthesiology & Pain Medicine, University of Washington, 1959 NE Pacific Street, Box 356540, Seattle, WA, 98195, United States, 1 206 543 6814, jesun@uw.edu %K digital health %K mobile health %K machine learning %K smart speaker %K smartphone %D 2022 %7 21.2.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health. Just as the introduction of the mobile phone led to considerable health innovation, smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research, particularly in out-of-hospital environments. The recent rise and ubiquity of these smart computing systems holds significant potential for enhancing chronic disease management, enabling passive identification of unwitnessed medical emergencies, detecting subtle changes in human behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact public health. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health. These include (1) as an intelligent conversational agent, (2) as a passive identifier of medically relevant diagnostic sounds, and (3) by active sensing using the device's internal hardware to measure physiologic parameters, such as with active sonar, radar, or computer vision. Each of these different modalities has specific clinical use cases, all of which need to be balanced against potential privacy concerns, equity concerns related to system access, and regulatory frameworks which have not yet been developed for this unique type of passive data collection. %M 35188467 %R 10.2196/28686 %U https://mhealth.jmir.org/2022/2/e28686 %U https://doi.org/10.2196/28686 %U http://www.ncbi.nlm.nih.gov/pubmed/35188467 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e30211 %T Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study %A Bell,Brooke Marie %A Alam,Ridwan %A Mondol,Abu Sayeed %A Ma,Meiyi %A Emi,Ifat Afrin %A Preum,Sarah Masud %A de la Haye,Kayla %A Stankovic,John A %A Lach,John %A Spruijt-Metz,Donna %+ Department of Chronic Disease Epidemiology, School of Public Health, Yale University, 60 College St, 8th Floor, New Haven, CT, 06520, United States, 1 475 235 0643, brooke.bell@yale.edu %K ecological momentary assessment %K wearable sensors %K automatic dietary assessment %K eating behavior %K eating context %K smartwatch %K mobile phone %D 2022 %7 18.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The field of dietary assessment has a long history, marked by both controversies and advances. Emerging technologies may be a potential solution to address the limitations of self-report dietary assessment methods. The Monitoring and Modeling Family Eating Dynamics (M2FED) study uses wrist-worn smartwatches to automatically detect real-time eating activity in the field. The ecological momentary assessment (EMA) methodology was also used to confirm whether eating occurred (ie, ground truth) and to measure other contextual information, including positive and negative affect, hunger, satiety, mindful eating, and social context. Objective: This study aims to report on participant compliance (feasibility) to the 2 distinct EMA protocols of the M2FED study (hourly time-triggered and eating event–triggered assessments) and on the performance (validity) of the smartwatch algorithm in automatically detecting eating events in a family-based study. Methods: In all, 20 families (58 participants) participated in the 2-week, observational, M2FED study. All participants wore a smartwatch on their dominant hand and responded to time-triggered and eating event–triggered mobile questionnaires via EMA while at home. Compliance to EMA was calculated overall, for hourly time-triggered mobile questionnaires, and for eating event–triggered mobile questionnaires. The predictors of compliance were determined using a logistic regression model. The number of true and false positive eating events was calculated, as well as the precision of the smartwatch algorithm. The Mann-Whitney U test, Kruskal-Wallis test, and Spearman rank correlation were used to determine whether there were differences in the detection of eating events by participant age, gender, family role, and height. Results: The overall compliance rate across the 20 deployments was 89.26% (3723/4171) for all EMAs, 89.7% (3328/3710) for time-triggered EMAs, and 85.7% (395/461) for eating event–triggered EMAs. Time of day (afternoon odds ratio [OR] 0.60, 95% CI 0.42-0.85; evening OR 0.53, 95% CI 0.38-0.74) and whether other family members had also answered an EMA (OR 2.07, 95% CI 1.66-2.58) were significant predictors of compliance to time-triggered EMAs. Weekend status (OR 2.40, 95% CI 1.25-4.91) and deployment day (OR 0.92, 95% CI 0.86-0.97) were significant predictors of compliance to eating event–triggered EMAs. Participants confirmed that 76.5% (302/395) of the detected events were true eating events (ie, true positives), and the precision was 0.77. The proportion of correctly detected eating events did not significantly differ by participant age, gender, family role, or height (P>.05). Conclusions: This study demonstrates that EMA is a feasible tool to collect ground-truth eating activity and thus evaluate the performance of wearable sensors in the field. The combination of a wrist-worn smartwatch to automatically detect eating and a mobile device to capture ground-truth eating activity offers key advantages for the user and makes mobile health technologies more accessible to nonengineering behavioral researchers. %M 35179508 %R 10.2196/30211 %U https://mhealth.jmir.org/2022/2/e30211 %U https://doi.org/10.2196/30211 %U http://www.ncbi.nlm.nih.gov/pubmed/35179508 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e28159 %T Characterizing and Modeling Smoking Behavior Using Automatic Smoking Event Detection and Mobile Surveys in Naturalistic Environments: Observational Study %A Zhai,DongHui %A van Stiphout,Ruud %A Schiavone,Giuseppina %A De Raedt,Walter %A Van Hoof,Chris %+ imec at OnePlanet Research Center, Bronland 10, Wageningen, 6708WH, Netherlands, 31 317 745 801, ruud.vanstiphout@imec.nl %K smoking behavior modeling %K ambulatory study %K wearable sensors %K temporal patterns of smoking %K Poisson mixed-effects model %K mobile phone %D 2022 %7 18.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There are 1.1 billion smokers worldwide, and each year, more than 8 million die prematurely because of cigarette smoking. More than half of current smokers make a serious quit every year. Nonetheless, 90% of unaided quitters relapse within the first 4 weeks of quitting due to the lack of limited access to cost-effective and efficient smoking cessation tools in their daily lives. Objective: This study aims to enable quantified monitoring of ambulatory smoking behavior 24/7 in real life by using continuous and automatic measurement techniques and identifying and characterizing smoking patterns using longitudinal contextual signals. This work also intends to provide guidance and insights into the design and deployment of technology-enabled smoking cessation applications in naturalistic environments. Methods: A 4-week observational study consisting of 46 smokers was conducted in both working and personal life environments. An electric lighter and a smartphone with an experimental app were used to track smoking events and acquire concurrent contextual signals. In addition, the app was used to prompt smoking-contingent ecological momentary assessment (EMA) surveys. The smoking rate was assessed based on the timestamps of smoking and linked statistically to demographics, time, and EMA surveys. A Poisson mixed-effects model to predict smoking rate in 1-hour windows was developed to assess the contribution of each predictor. Results: In total, 8639 cigarettes and 1839 EMA surveys were tracked over 902 participant days. Most smokers were found to have an inaccurate and often biased estimate of their daily smoking rate compared with the measured smoking rate. Specifically, 74% (34/46) of the smokers made more than one (mean 4.7, SD 4.2 cigarettes per day) wrong estimate, and 70% (32/46) of the smokers overestimated it. On the basis of the timestamp of the tracked smoking events, smoking rates were visualized at different hours and were found to gradually increase and peak at 6 PM in the day. In addition, a 1- to 2-hour shift in smoking patterns was observed between weekdays and weekends. When moderate and heavy smokers were compared with light smokers, their ages (P<.05), Fagerström Test of Nicotine Dependence (P=.01), craving level (P<.001), enjoyment of cigarettes (P<.001), difficulty resisting smoking (P<.001), emotional valence (P<.001), and arousal (P<.001) were all found to be significantly different. In the Poisson mixed-effects model, the number of cigarettes smoked in a 1-hour time window was highly dependent on the smoking status of an individual (P<.001) and was explained by hour (P=.02) and age (P=.005). Conclusions: This study reported the high potential and challenges of using an electronic lighter for smoking annotation and smoking-triggered EMAs in an ambulant environment. These results also validate the techniques for smoking behavior monitoring and pave the way for the design and deployment of technology-enabled smoking cessation applications. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2018-028284 %M 35179512 %R 10.2196/28159 %U https://mhealth.jmir.org/2022/2/e28159 %U https://doi.org/10.2196/28159 %U http://www.ncbi.nlm.nih.gov/pubmed/35179512 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e27337 %T Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review %A Thornton,Louise %A Osman,Bridie %A Champion,Katrina %A Green,Olivia %A Wescott,Annie B %A Gardner,Lauren A %A Stewart,Courtney %A Visontay,Rachel %A Whife,Jesse %A Parmenter,Belinda %A Birrell,Louise %A Bryant,Zachary %A Chapman,Cath %A Lubans,David %A Slade,Tim %A Torous,John %A Teesson,Maree %A Van de Ven,Pepijn %+ The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Level 6, Jane Foss Russel Building, Camperdown, Sydney, 2006, Australia, 61 0403744089, louise.thornton@sydney.edu.au %K smartphone %K app %K alcohol %K smoking %K diet %K measurement %K mobile phone %D 2022 %7 17.2.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Poor diet, alcohol use, and tobacco smoking have been identified as strong determinants of chronic diseases, such as cardiovascular disease, diabetes, and cancer. Smartphones have the potential to provide a real-time, pervasive, unobtrusive, and cost-effective way to measure these health behaviors and deliver instant feedback to users. Despite this, the validity of using smartphones to measure these behaviors is largely unknown. Objective: The aim of our review is to identify existing smartphone-based approaches to measure these health behaviors and critically appraise the quality of their measurement properties. Methods: We conducted a systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsycINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library databases in March 2020. Articles that were written in English; reported measuring diet, alcohol use, or tobacco use via a smartphone; and reported on at least one measurement property (eg, validity, reliability, and responsiveness) were eligible. The methodological quality of the included studies was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instruments Risk of Bias checklist. Outcomes were summarized in a narrative synthesis. This systematic review was registered with PROSPERO, identifier CRD42019122242. Results: Of 12,261 records, 72 studies describing the measurement properties of smartphone-based approaches to measure diet (48/72, 67%), alcohol use (16/72, 22%), and tobacco use (8/72, 11%) were identified and included in this review. Across the health behaviors, 18 different measurement techniques were used in smartphones. The measurement properties most commonly examined were construct validity, measurement error, and criterion validity. The results varied by behavior and measurement approach, and the methodological quality of the studies varied widely. Most studies investigating the measurement of diet and alcohol received very good or adequate methodological quality ratings, that is, 73% (35/48) and 69% (11/16), respectively, whereas only 13% (1/8) investigating the measurement of tobacco use received a very good or adequate rating. Conclusions: This review is the first to provide evidence regarding the different types of smartphone-based approaches currently used to measure key behavioral risk factors for chronic diseases (diet, alcohol use, and tobacco use) and the quality of their measurement properties. A total of 19 measurement techniques were identified, most of which assessed dietary behaviors (48/72, 67%). Some evidence exists to support the reliability and validity of using smartphones to assess these behaviors; however, the results varied by behavior and measurement approach. The methodological quality of the included studies also varied. Overall, more high-quality studies validating smartphone-based approaches against criterion measures are needed. Further research investigating the use of smartphones to assess alcohol and tobacco use and objective measurement approaches is also needed. International Registered Report Identifier (IRRID): RR2-10.1186/s13643-020-01375-w %M 35175212 %R 10.2196/27337 %U https://mhealth.jmir.org/2022/2/e27337 %U https://doi.org/10.2196/27337 %U http://www.ncbi.nlm.nih.gov/pubmed/35175212 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e31537 %T The Content, Quality, and Behavior Change Techniques in Nutrition-Themed Mobile Apps for Children in Canada: App Review and Evaluation Study %A Brown,Jacqueline Marie %A Franco-Arellano,Beatriz %A Froome,Hannah %A Siddiqi,Amina %A Mahmood,Amina %A Arcand,JoAnne %+ Faculty of Health Sciences, Ontario Tech University, 2000 Simcoe Street North, Oshawa, ON, L1H 7K4, Canada, 1 905 721 8668, joanne.arcand@ontariotechu.ca %K mHealth %K children %K app quality %K behavior change techniques %K child nutrition %K mobile apps %K Canada %K mobile phone %D 2022 %7 16.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Children increasingly use mobile apps. Strategies to increase child engagement with apps include the use of gamification and images that incite fun and interaction, such as food. However, the foods and beverages that children are exposed to while using apps are unknown and may vary by app type. Objective: The aim of this study is to identify the app content (ie, types of foods and beverages) included in nutrition-themed apps intended for children, to assess the use of game-like features, and to examine app characteristics such as overall quality and behavior change techniques (BCTs). Methods: This analysis used a cross-sectional database of nutrition-themed apps intended for children (≤12 years), collected between May 2018 and June 2019 from the Apple App Store and Google Play Store (n=259). Apps were classified into four types: food games or nongames that included didactic nutrition guides, habit trackers, and other. Food and beverages were identified in apps and classified into 16 food categories, as recommended (8/16, 50%) and as not recommended (8/16, 50%) by dietary guidelines, and quantified by app type. Binomial logistic regression assessed whether game apps were associated with foods and beverages not recommended by guidelines. App quality, overall and by subscales, was determined using the Mobile App Rating Scale. The BCT Taxonomy was used to classify the different behavioral techniques that were identified in a subsample of apps (124/259, 47.9%). Results: A total of 259 apps displayed a median of 6 (IQR 3) foods and beverages. Moreover, 62.5% (162/259) of apps were classified as food games, 27.4% (71/259) as didactic nutrition guides, 6.6% (17/259) as habit trackers, and 3.5% (9/259) as other. Most apps (198/259, 76.4%) displayed at least one food or beverage that was not recommended by the dietary guidelines. Food game apps were almost 3 times more likely to display food and beverages not recommended by the guidelines compared with nongame apps (β=2.8; P<.001). The overall app quality was moderate, with a median Mobile App Rating Scale score of 3.6 (IQR 0.7). Functionality was the subscale with the highest score (median 4, IQR 0.3). Nutrition guides were more likely to be educational and contain informative content on healthy eating (score 3.7), compared with the other app types, although they also scored significantly lower in engagement (score 2.3). Most apps (105/124, 84.7%) displayed at least one BCT, with the most common BCT being information about health consequences. Conclusions: Findings suggest nutrition-themed apps intended for children displayed food and beverage content not recommended by dietary guidelines, with gaming apps more likely to display not recommended foods than their nongame counterparts. Many apps have a moderate app quality, and the use of consequences (instead of rewards) was the most common BCT. %M 35171100 %R 10.2196/31537 %U https://mhealth.jmir.org/2022/2/e31537 %U https://doi.org/10.2196/31537 %U http://www.ncbi.nlm.nih.gov/pubmed/35171100 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 2 %P e33063 %T Panic Attack Prediction Using Wearable Devices and Machine Learning: Development and Cohort Study %A Tsai,Chan-Hen %A Chen,Pei-Chen %A Liu,Ding-Shan %A Kuo,Ying-Ying %A Hsieh,Tsung-Ting %A Chiang,Dai-Lun %A Lai,Feipei %A Wu,Chia-Tung %+ Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei City, 106319, Taiwan, 886 978006469, tony006469@gmail.com %K panic disorder %K panic attack %K prediction %K wearable device %K machine learning %K lifestyle %D 2022 %7 15.2.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: A panic attack (PA) is an intense form of anxiety accompanied by multiple somatic presentations, leading to frequent emergency department visits and impairing the quality of life. A prediction model for PAs could help clinicians and patients monitor, control, and carry out early intervention for recurrent PAs, enabling more personalized treatment for panic disorder (PD). Objective: This study aims to provide a 7-day PA prediction model and determine the relationship between a future PA and various features, including physiological factors, anxiety and depressive factors, and the air quality index (AQI). Methods: We enrolled 59 participants with PD (Diagnostic and Statistical Manual of Mental Disorders, 5th edition, and the Mini International Neuropsychiatric Interview). Participants used smartwatches (Garmin Vívosmart 4) and mobile apps to collect their sleep, heart rate (HR), activity level, anxiety, and depression scores (Beck Depression Inventory [BDI], Beck Anxiety Inventory [BAI], State-Trait Anxiety Inventory state anxiety [STAI-S], State-Trait Anxiety Inventory trait anxiety [STAI-T], and Panic Disorder Severity Scale Self-Report) in their real life for a duration of 1 year. We also included AQIs from open data. To analyze these data, our team used 6 machine learning methods: random forests, decision trees, linear discriminant analysis, adaptive boosting, extreme gradient boosting, and regularized greedy forests. Results: For 7-day PA predictions, the random forest produced the best prediction rate. Overall, the accuracy of the test set was 67.4%-81.3% for different machine learning algorithms. The most critical variables in the model were questionnaire and physiological features, such as the BAI, BDI, STAI, MINI, average HR, resting HR, and deep sleep duration. Conclusions: It is possible to predict PAs using a combination of data from questionnaires and physiological and environmental data. %M 35166679 %R 10.2196/33063 %U https://medinform.jmir.org/2022/2/e33063 %U https://doi.org/10.2196/33063 %U http://www.ncbi.nlm.nih.gov/pubmed/35166679 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e31830 %T Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods %A Varma,Maya %A Washington,Peter %A Chrisman,Brianna %A Kline,Aaron %A Leblanc,Emilie %A Paskov,Kelley %A Stockham,Nate %A Jung,Jae-Yoon %A Sun,Min Woo %A Wall,Dennis P %+ Department of Pediatrics and Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA, 94304, United States, 1 650 497 9214, dpwall@stanford.edu %K mobile health %K autism spectrum disorder %K social phenotyping %K computer vision %K gaze %K mobile diagnostics %K pattern recognition %K autism %K diagnostic %K pattern %K engagement %K gaming %K app %K insight %K vision %K video %D 2022 %7 15.2.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires and clinical observation, often result in long waiting times for results. Recent advances in computer vision and mobile technology hold potential for speeding up the diagnostic process by enabling computational analysis of behavioral and social impairments from home videos. Such techniques can improve objectivity and contribute quantitatively to the diagnostic process. Objective: In this work, we evaluate whether home videos collected from a game-based mobile app can be used to provide diagnostic insights into ASD. To the best of our knowledge, this is the first study attempting to identify potential social indicators of ASD from mobile phone videos without the use of eye-tracking hardware, manual annotations, and structured scenarios or clinical environments. Methods: Here, we used a mobile health app to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We used automated data set annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns, which represent regions of an individual’s visual focus and (2) visual scanning methods, which refer to the ways in which individuals scan their surrounding environment. We compared the gaze fixation and visual scanning methods used by children during a 90-second gameplay video to identify statistically significant differences between the 2 cohorts; we then trained a long short-term memory (LSTM) neural network to determine if gaze indicators could be predictive of ASD. Results: Our results show that gaze fixation patterns differ between the 2 cohorts; specifically, we could identify 1 statistically significant region of fixation (P<.001). In addition, we also demonstrate that there are unique visual scanning patterns that exist for individuals with ASD when compared to NT children (P<.001). A deep learning model trained on coarse gaze fixation annotations demonstrates mild predictive power in identifying ASD. Conclusions: Ultimately, our study demonstrates that heterogeneous video data sets collected from mobile devices hold potential for quantifying visual patterns and providing insights into ASD. We show the importance of automated labeling techniques in generating large-scale data sets while simultaneously preserving the privacy of participants, and we demonstrate that specific social engagement indicators associated with ASD can be identified and characterized using such data. %M 35166683 %R 10.2196/31830 %U https://www.jmir.org/2022/2/e31830 %U https://doi.org/10.2196/31830 %U http://www.ncbi.nlm.nih.gov/pubmed/35166683 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e26275 %T Use of Mobile Apps for Visual Acuity Assessment: Systematic Review and Meta-analysis %A Suo,Lingge %A Ke,Xianghan %A Zhang,Di %A Qin,Xuejiao %A Chen,Xuhao %A Hong,Ying %A Dai,Wanwei %A Wu,Defu %A Zhang,Chun %A Zhang,Dongsong %+ Department of Business Information Systems and Operations Management, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223-0001, United States, 1 704 687 1893, dzhang15@uncc.edu %K smartphone %K iPad %K eye screening %K visual acuity %K app %K meta-analysis %D 2022 %7 14.2.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Vision impairments (VIs) and blindness are major global public health issues. A visual acuity (VA) test is one of the most crucial standard psychophysical tests of visual function and has been widely used in a broad range of health care domains, especially in many clinical settings. In recent years, there has been increasing research on mobile app–based VA assessment designed to allow people to test their VA at any time and any location. Objective: The goal of the review was to assess the accuracy and reliability of using mobile VA measurement apps. Methods: We searched PubMed, Embase, Cochrane Library, and Google Scholar for relevant articles on mobile apps for VA assessment published between January 1, 2008, and July 1, 2020. Two researchers independently inspected and selected relevant studies. Eventually, we included 22 studies that assessed tablet or smartphone apps for VA measurement. We then analyzed sensitivity, specificity, and accuracy in the 6 papers we found through a meta-analysis. Results: Most of the 22 selected studies can be considered of high quality based on the Quality Assessment of Diagnostic Accuracy Studies–2. In a meta-analysis of 6 studies involving 24,284 participants, we categorized the studies based on the age groups of the study participants (ie, aged 3-5 years, aged 6-22 years, and aged 55 years and older), examiner (ie, professional and nonprofessional examiners), and the type of mobile devices (ie, smartphone, iPad). In the group aged 3 to 5 years, the pooled sensitivity for VA app tests versus clinical VA tests was 0.87 (95% CI 0.79-0.93; P=.39), and the pooled specificity was 0.78 (95% CI 0.70-0.85; P=.37). In the group aged 6 to 22 years, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86 (95% CI 0.84-0.87; P<.001), and the pooled specificity for VA app tests versus clinical VA tests was 0.91 (95% CI 0.90-0.91; P=.27). In the group aged 55 years and older, the pooled sensitivity for VA app tests versus clinical VA tests was 0.85 (95% CI 0.55-0.98), and the pooled specificity for VA app tests versus clinical VA tests was 0.98 (95% CI 0.95-0.99). We found that the nonprofessional examiner group (AUC 0.93) had higher accuracy than the professional examiner group (AUC 0.87). In the iPad-based group, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86, and the pooled specificity was 0.79. In the smartphone-based group, the pooled sensitivity for VA app tests versus clinical VA tests was 0.86 (P<.001), and the pooled specificity for VA app tests versus clinical VA tests was 0.91 (P<.001). Conclusions: In this study, we conducted a comprehensive review of the research on existing mobile apps for VA tests to investigate their diagnostic value and limitations. Evidence gained from this study suggests that mobile app–based VA tests can be useful for on-demand VI detection. %M 35156935 %R 10.2196/26275 %U https://mhealth.jmir.org/2022/2/e26275 %U https://doi.org/10.2196/26275 %U http://www.ncbi.nlm.nih.gov/pubmed/35156935 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 2 %P e31724 %T In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study %A Carlier,Chiara %A Niemeijer,Koen %A Mestdagh,Merijn %A Bauwens,Michael %A Vanbrabant,Peter %A Geurts,Luc %A van Waterschoot,Toon %A Kuppens,Peter %+ Department of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Tiensestraat 102, Leuven, 3000, Belgium, 32 16 37 44 85, chiara.carlier@student.kuleuven.be %K depression %K emotions %K mobile sensing %K language %K LIWC %K openSMILE %K speech %K writing %K mobile phone %D 2022 %7 11.2.2022 %9 Original Paper %J JMIR Ment Health %G English %X Background: Emotions and mood are important for overall well-being. Therefore, the search for continuous, effortless emotion prediction methods is an important field of study. Mobile sensing provides a promising tool and can capture one of the most telling signs of emotion: language. Objective: The aim of this study is to examine the separate and combined predictive value of mobile-sensed language data sources for detecting both momentary emotional experience as well as global individual differences in emotional traits and depression. Methods: In a 2-week experience sampling method study, we collected self-reported emotion ratings and voice recordings 10 times a day, continuous keyboard activity, and trait depression severity. We correlated state and trait emotions and depression and language, distinguishing between speech content (spoken words), speech form (voice acoustics), writing content (written words), and writing form (typing dynamics). We also investigated how well these features predicted state and trait emotions using cross-validation to select features and a hold-out set for validation. Results: Overall, the reported emotions and mobile-sensed language demonstrated weak correlations. The most significant correlations were found between speech content and state emotions and between speech form and state emotions, ranging up to 0.25. Speech content provided the best predictions for state emotions. None of the trait emotion–language correlations remained significant after correction. Among the emotions studied, valence and happiness displayed the most significant correlations and the highest predictive performance. Conclusions: Although using mobile-sensed language as an emotion marker shows some promise, correlations and predictive R2 values are low. %M 35147507 %R 10.2196/31724 %U https://mental.jmir.org/2022/2/e31724 %U https://doi.org/10.2196/31724 %U http://www.ncbi.nlm.nih.gov/pubmed/35147507 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e31131 %T An Association of Influenza Epidemics in Children With Mobile App Data: Population-Based Observational Study in Osaka, Japan %A Katayama,Yusuke %A Kiyohara,Kosuke %A Hirose,Tomoya %A Ishida,Kenichiro %A Tachino,Jotaro %A Nakao,Shunichiro %A Noda,Tomohiro %A Ojima,Masahiro %A Kiguchi,Takeyuki %A Matsuyama,Tasuku %A Kitamura,Tetsuhisa %+ Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, 2-15 Yamada-oka, Suita, Japan, 81 6 6879 5707, orion13@hp-emerg.med.osaka-u.ac.jp %K syndromic surveillance %K mobile app %K influenza %K epidemic %K children %D 2022 %7 10.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Early surveillance to prevent the spread of influenza is a major public health concern. If there is an association of influenza epidemics with mobile app data, it may be possible to forecast influenza earlier and more easily. Objective: We aimed to assess the relationship between seasonal influenza and the frequency of mobile app use among children in Osaka Prefecture, Japan. Methods: This was a retrospective observational study that was performed over a three-year period from January 2017 to December 2019. Using a linear regression model, we calculated the R2 value of the regression model to evaluate the relationship between the number of “fever” events selected in the mobile app and the number of influenza patients ≤14 years of age. We conducted three-fold cross-validation using data from two years as the training data set and the data of the remaining year as the test data set to evaluate the validity of the regression model. And we calculated Spearman correlation coefficients between the calculated number of influenza patients estimated using the regression model and the number of influenza patients, limited to the period from December to April when influenza is prevalent in Japan. Results: We included 29,392 mobile app users. The R2 value for the linear regression model was 0.944, and the adjusted R2 value was 0.915. The mean Spearman correlation coefficient for the three regression models was 0.804. During the influenza season (December–April), the Spearman correlation coefficient between the number of influenza patients and the calculated number estimated using the linear regression model was 0.946 (P<.001). Conclusions: In this study, the number of times that mobile apps were used was positively associated with the number of influenza patients. In particular, there was a good association of the number of influenza patients with the number of “fever” events selected in the mobile app during the influenza epidemic season. %M 35142628 %R 10.2196/31131 %U https://formative.jmir.org/2022/2/e31131 %U https://doi.org/10.2196/31131 %U http://www.ncbi.nlm.nih.gov/pubmed/35142628 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e32772 %T Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results %A Ponnada,Aditya %A Wang,Shirlene %A Chu,Daniel %A Do,Bridgette %A Dunton,Genevieve %A Intille,Stephen %+ Khoury College of Computer Sciences, Northeastern University, 360 Huntington Avenue, Boston, MA, 02130, United States, 1 6173061610, ponnada.a@northeastern.edu %K intensive longitudinal data %K ecological momentary assessment %K experience sampling %K microinteractions %K smartwatch %K health behavior research %K mobile phone %D 2022 %7 9.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Ecological momentary assessment (EMA) uses mobile technology to enable in situ self-report data collection on behaviors and states. In a typical EMA study, participants are prompted several times a day to answer sets of multiple-choice questions. Although the repeated nature of EMA reduces recall bias, it may induce participation burden. There is a need to explore complementary approaches to collecting in situ self-report data that are less burdensome yet provide comprehensive information on an individual’s behaviors and states. A new approach, microinteraction EMA (μEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap assessments using a quick, glanceable microinteraction. However, the viability of using μEMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated. Objective: This paper describes the μEMA protocol currently used in the Temporal Influences on Movement & Exercise (TIME) Study conducted with young adults, the interface of the μEMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the μEMA app, changes made to the main TIME Study μEMA protocol and app based on the pilot feedback, and preliminary μEMA results from a subset of active participants in the TIME Study. Methods: The TIME Study involves data collection on behaviors and states from 246 individuals; measurements include passive sensing from a smartwatch and smartphone and intensive smartphone-based hourly EMA, with 4-day EMA bursts every 2 weeks. Every day, participants also answer a nightly EMA survey. On non–EMA burst days, participants answer μEMA questions on the smartwatch, assessing momentary states such as physical activity, sedentary behavior, and affect. At the end of the study, participants describe their experience with EMA and μEMA in a semistructured interview. A pilot study was used to test and refine the μEMA protocol before the main study. Results: Changes made to the μEMA study protocol based on pilot feedback included adjusting the single-question selection method and smartwatch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, a total of 81 participants had completed at least 6 months of data collection in the main study. For 662,397 μEMA questions delivered, the compliance rate was 67.6% (SD 24.4%) and the completion rate was 79% (SD 22.2%). Conclusions: The TIME Study provides opportunities to explore a novel approach for collecting temporally dense intensive longitudinal self-report data in a sustainable manner. Data suggest that μEMA may be valuable for understanding behaviors and states at the individual level, thus possibly supporting future longitudinal interventions that require within-day, temporally dense self-report data as people go about their lives. %M 35138253 %R 10.2196/32772 %U https://formative.jmir.org/2022/2/e32772 %U https://doi.org/10.2196/32772 %U http://www.ncbi.nlm.nih.gov/pubmed/35138253 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e31877 %T Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies %A Beukenhorst,Anna L %A Burke,Katherine M %A Scheier,Zoe %A Miller,Timothy M %A Paganoni,Sabrina %A Keegan,Mackenzie %A Collins,Ella %A Connaghan,Kathryn P %A Tay,Anna %A Chan,James %A Berry,James D %A Onnela,Jukka-Pekka %+ Department of Biostatistics, Harvard T.H. Chan School of Public Health, 4th Floor, 677 Huntington Avenue, Boston, MA, MA 02115, United States, 1 (617) 4951000, beuk@hsph.harvard.edu %K digital phenotyping %K mobile health %K trial %K smartphones %K attrition %K mobile phone %D 2022 %7 4.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients’ cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term. Objective: The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis. Methods: We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan–Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer. Results: Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%). Conclusions: These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients’ progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful. Trial Registration: ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711 %M 35119373 %R 10.2196/31877 %U https://mhealth.jmir.org/2022/2/e31877 %U https://doi.org/10.2196/31877 %U http://www.ncbi.nlm.nih.gov/pubmed/35119373 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e30410 %T Remote Assessment of Cardiovascular Risk Factors and Cognition in Middle-Aged and Older Adults: Proof-of-Concept Study %A Eastman,Jennifer A %A Kaup,Allison R %A Bahorik,Amber L %A Butcher,Xochitl %A Attarha,Mouna %A Marcus,Gregory M %A Pletcher,Mark J %A Olgin,Jeffrey E %A Barnes,Deborah E %A Yaffe,Kristine %+ San Francisco VA Medical Center, 4150 Clement St., San Francisco, CA, 94121, United States, 1 951 760 6711, jennifer.eastman@ucsf.edu %K mHealth %K internet %K mobile health %K digital health %K eHealth %K cardiovascular %K risk factors %K cognition %K cognitive impairment %K remote cognitive assessment %K aging %D 2022 %7 2.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Adults with cardiovascular disease risk factors (CVRFs) are also at increased risk of developing cognitive decline and dementia. However, it is often difficult to study the relationships between CVRFs and cognitive function because cognitive assessment typically requires time-consuming in-person neuropsychological evaluations that may not be feasible for real-world situations. Objective: We conducted a proof-of-concept study to determine if the association between CVRFs and cognitive function could be detected using web-based, self-administered cognitive tasks and CVRF assessment. Methods: We recruited 239 participants aged ≥50 years (mean age 62.7 years, SD 8.8; 42.7% [n=102] female, 88.7% [n=212] White) who were enrolled in the Health eHeart Study, a web-based platform focused on cardiac disease. The participants self-reported CVRFs (hypertension, high cholesterol, diabetes, and atrial fibrillation) using web-based health surveys between August 2016 and July 2018. After an average of 3 years of follow-up, we remotely evaluated episodic memory, working memory, and executive function via the web-based Posit Science platform, BrainHQ. Raw data were normalized and averaged into 3 domain scores. We used linear regression models to examine the association between CVRFs and cognitive function. Results: CVRF prevalence was 62.8% (n=150) for high cholesterol, 45.2% (n=108) for hypertension, 10.9% (n=26) for atrial fibrillation, and 7.5% (n=18) for diabetes. In multivariable models, atrial fibrillation was associated with worse working memory (β=-.51, 95% CI -0.91 to -0.11) and worse episodic memory (β=-.31, 95% CI -0.59 to -0.04); hypertension was associated with worse episodic memory (β=-.27, 95% CI -0.44 to -0.11). Diabetes and high cholesterol were not associated with cognitive performance. Conclusions: Self-administered web-based tools can be used to detect both CVRFs and cognitive health. We observed that atrial fibrillation and hypertension were associated with worse cognitive function even in those in their 60s and 70s. The potential of mobile assessments to detect risk factors for cognitive aging merits further investigation. %M 35107430 %R 10.2196/30410 %U https://formative.jmir.org/2022/2/e30410 %U https://doi.org/10.2196/30410 %U http://www.ncbi.nlm.nih.gov/pubmed/35107430 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 1 %P e33747 %T Daily Level Association of Physical Activity and Performance on Ecological Momentary Cognitive Tests in Free-living Environments: A Mobile Health Observational Study %A Zlatar,Zvinka Z %A Campbell,Laura M %A Tang,Bin %A Gabin,Spenser %A Heaton,Anne %A Higgins,Michael %A Swendsen,Joel %A Moore,David J %A Moore,Raeanne C %+ Department of Psychiatry, University of California, San Diego, 9500 Gilman Dr, MC 0811, La Jolla, CA, 92093, United States, 1 858 822 7737, zzlatar@health.ucsd.edu %K smartphones %K neuropsychology %K ecological momentary assessment %K digital health %K exercise %K people living with HIV %K aging %K wearables %K mobile cognition %K mobile phone %D 2022 %7 31.1.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Research suggests that physical activity (PA) has both acute and chronic beneficial effects on cognitive function in laboratory settings and under supervised conditions. Mobile health technologies make it possible to reliably measure PA and cognition in free-living environments, thus increasing generalizability and reach. Research is needed to determine whether the benefits of PA on cognitive function extend from the laboratory to real-world contexts. Objective: This observational study aims to examine the association between daily fluctuations in PA and cognitive performance using mobile health technologies in free-living environments. Methods: A total of 90 adults (mean age 59, SD 6.3 years; 65/90, 72% men) with various comorbidities (eg, cardiovascular risk and HIV) and different levels of baseline cognition (ranging from cognitively normal to impaired) completed ecological momentary cognitive tests (EMCTs) on a smartphone twice daily while wearing an accelerometer to capture PA levels for 14 days. Linear mixed-effects models examined the daily associations of PA with executive function and verbal learning EMCTs. Moderation analyses investigated whether the relationship between daily PA and daily performance on EMCTs changed as a function of baseline cognition, cardiovascular risk, and functional status (independent vs dependent). Results: Days with greater PA were associated with better (faster) performance on an executive function EMCT after covariate adjustment (estimate −0.013; β=−.16; P=.04). Moderation analyses (estimate 0.048; β=.58; P=.001) indicated that days with greater PA were associated with better (faster) executive function performance in individuals who were functionally dependent (effect size −0.53; P<.001) and not in functionally independent adults (effect size −0.01; P=.91). Conclusions: EMCTs may be a sensitive tool for capturing daily-level PA-related fluctuations in cognitive performance in real-world contexts and could be a promising candidate for tracking cognitive performance in digital health interventions aimed at increasing PA. Further research is needed to determine individual characteristics that may moderate the association between daily PA and EMCT performance in free-living environments. %M 35099402 %R 10.2196/33747 %U https://mhealth.jmir.org/2022/1/e33747 %U https://doi.org/10.2196/33747 %U http://www.ncbi.nlm.nih.gov/pubmed/35099402 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 1 %P e30583 %T Investigating When, Which, and Why Users Stop Using a Digital Health Intervention to Promote an Active Lifestyle: Secondary Analysis With A Focus on Health Action Process Approach–Based Psychological Determinants %A Schroé,Helene %A Crombez,Geert %A De Bourdeaudhuij,Ilse %A Van Dyck,Delfien %+ Department of Movement and Sports Sciences, Faculty of Medicine and Health, Ghent University, Watersportlaan 2, Ghent, 9000, Belgium, 32 9264 63 63, helene.schroe@ugent.be %K digital health %K psychosocial determinants %K health action process approach %K physical activity %K sedentary behavior %K attrition %K dropout %K mobile health %K healthy life style %K health behaviors %D 2022 %7 31.1.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Digital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored. Objective: The aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition. Methods: The sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used. Results: The study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%). Conclusions: This study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance. Trial Registration: ClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-019-3456-7 %M 35099400 %R 10.2196/30583 %U https://mhealth.jmir.org/2022/1/e30583 %U https://doi.org/10.2196/30583 %U http://www.ncbi.nlm.nih.gov/pubmed/35099400 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 1 %P e28095 %T The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones %A Laiou,Petroula %A Kaliukhovich,Dzmitry A %A Folarin,Amos A %A Ranjan,Yatharth %A Rashid,Zulqarnain %A Conde,Pauline %A Stewart,Callum %A Sun,Shaoxiong %A Zhang,Yuezhou %A Matcham,Faith %A Ivan,Alina %A Lavelle,Grace %A Siddi,Sara %A Lamers,Femke %A Penninx,Brenda WJH %A Haro,Josep Maria %A Annas,Peter %A Cummins,Nicholas %A Vairavan,Srinivasan %A Manyakov,Nikolay V %A Narayan,Vaibhav A %A Dobson,Richard JB %A Hotopf,Matthew %A , %+ Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, Memory Lane, London, SE5 8AF, United Kingdom, 44 20 7848 0002, petroula.laiou@kcl.ac.uk %K major depressive disorder %K PHQ-8 %K smartphone %K GPS %K home stay %K mobile phone %D 2022 %7 28.1.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms. Objective: The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD. Methods: We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse–Major Depressive Disorder study. The participants were recruited from three study sites: King’s College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables. Results: Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI −0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day). Conclusions: Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD. %M 35089148 %R 10.2196/28095 %U https://mhealth.jmir.org/2022/1/e28095 %U https://doi.org/10.2196/28095 %U http://www.ncbi.nlm.nih.gov/pubmed/35089148 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 1 %P e30360 %T Feasibility, Acceptability, and Design of a Mobile Ecological Momentary Assessment for High-Risk Men Who Have Sex With Men in Hanoi, Vietnam: Qualitative Study %A Trang,Kathy %A Le,Lam X %A Brown,Carolyn A %A To,Margaret Q %A Sullivan,Patrick S %A Jovanovic,Tanja %A Worthman,Carol M %A Giang,Le Minh %+ Global TIES for Children, New York University, 627 Broadway, New York City, NY, 10012, United States, 1 212 998 1212, kathytrang.kt@gmail.com %K men who have sex with men %K HIV %K mental disorder %K ecological momentary assessment %K mobile phone %K mHealth %K sexual minorities %K pilot projects %D 2022 %7 27.1.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Men who have sex with men (MSM) are at a disproportionate risk for HIV infection and common mental disorders worldwide. In the context of HIV, common mental disorders are important and are frequent drivers of suboptimal prevention and treatment outcomes. Mobile ecological momentary assessments (EMAs), or the repeated sampling of people’s behaviors and psychological states in their daily lives using mobile phones, can clarify the triggers and HIV-related sequelae of depressive-anxious symptoms and contribute toward the design of ecological momentary interventions (EMIs) that cater to the contextually varying needs of individuals to optimize prevention and treatment outcomes. Objective: This study aims to characterize the feasibility and acceptability of mobile EMA among high-risk MSM in Hanoi, Vietnam. It aims to evaluate the perceived relevance, usability, and concerns of this group with regard to the content and delivery of mobile EMA and the potential of leveraging such platforms in the future to deliver EMIs. Methods: Between January and April 2018, a total of 46 participants were recruited. The participants completed 6 to 8 mobile EMA surveys daily for 7 days. Surveys occurred once upon waking, 4 to 6 times throughout the day, and once before sleeping. All surveys queried participants’ perceived safety, social interactions, psychological state, and mental health symptoms. The morning survey further queried on sleep and medication use within the past 24 hours, whereas the night survey queried on sexual activity and substance use and allowed participants to share an audio recording of a stressful experience they had that day. At the end of the week, participants were interviewed about their experiences with using the app. Results: Participants completed an average of 21.7 (SD 12.7) prompts over the 7-day period. Excluding nonresponders, the average compliance rate was 61.8% (SD 26.6%). A thematic analysis of qualitative interviews suggested an overall positive reception of the app and 5 recurring themes, which were centered on the relevance of psychological and behavioral items to daily experiences (eg, mental health symptoms and audio recording), benefits of using the app (eg, increased self-understanding), worries and concerns (eg, privacy), usability (eg, confusion about the interface), and recommendations for future design (eg, integrating more open-ended questions). Conclusions: Mobile EMA is feasible and acceptable among young MSM in Vietnam; however, more research is needed to adapt EMA protocols to this context and enhance compliance. Most participants eagerly provided information about their mental health status and daily activities. As several participants looked toward the app for further mental health and psychosocial support, EMIs have the potential to reduce HIV and mental health comorbidity among MSM. %M 35084340 %R 10.2196/30360 %U https://formative.jmir.org/2022/1/e30360 %U https://doi.org/10.2196/30360 %U http://www.ncbi.nlm.nih.gov/pubmed/35084340 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 1 %P e31857 %T Attitudes Toward Mobile Apps for Pandemic Research Among Smartphone Users in Germany: National Survey %A Buhr,Lorina %A Schicktanz,Silke %A Nordmeyer,Eike %+ Department of Medical Ethics and History of Medicine, University Medical Center Göttingen, Humboldtallee 36, Göttingen, 37073, Germany, 49 5513969009, sschick@gwdg.de %K user %K pandemic %K smartphone apps %K mobile apps %K telephone-based survey %K Germany %K data sharing %K data donation %K ethics %K trust %K COVID-19 %K mHealth %K mobile applications %K digital health %K health applications %D 2022 %7 24.1.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: During the COVID-19 pandemic, but also in the context of previous epidemic diseases, mobile apps for smartphones were developed with different goals and functions, such as digital contact tracing, test management, symptom monitoring, quarantine compliance, and epidemiological and public health research. Objective: The aim of this study was to explore the potential for the acceptance of research-orientated apps (ROAs) in the German population. To this end, we identified distinctive attitudes toward pandemic apps and data sharing for research purposes among smartphone users in general and with a focus on differences in attitudes between app users and nonusers in particular. Methods: We conducted a cross-sectional, national, telephone-based survey of 1003 adults in Germany, of which 924 were useable for statistical analysis. The 17-item survey assessed current usage of pandemic apps, motivations for using or not using pandemic apps, trust in app distributors and attitudes toward data handling (data storage and transmission), willingness to share coded data with researchers using a pandemic app, social attitudes toward app use, and demographic and personal characteristics. Results: A vast majority stated that they used a smartphone (778/924, 84.2%), but less than half of the smartphone users stated that they used a pandemic app (326/778, 41.9%). The study focused on the subsample of smartphone users. Interestingly, when asked about preferred organizations for data storage and app distribution, trust in governmental (federal or state government, regional health office), public-appointed (statutory health insurance), or government-funded organizations (research institutes) was much higher than in private organizations (private research institutions, clinics, health insurances, information technology [IT] companies). Having a university degree significantly (P<.001) increased the likelihood of using a pandemic app, while having a migration background significantly (P<.001) decreased it. The overwhelming majority (653/778, 83.9%) of smartphone users were willing to provide their app data for state-funded research. Regarding attitudes toward app usage, striking differences between users and nonusers were found. Almost all app users (317/327, 96.9%) stated they would be willing to share data, whereas only 74.3% (336/452) of nonusers supported data sharing via an app. Two-thirds (216/326, 66.3%) of app users fully or rather agreed with the statement that using a pandemic app is a social duty, whereas almost the same proportion of nonusers entirely or rather disagreed with that statement (273/451, 60.5%). Conclusions: These findings indicate a high potential for the adoption of ROAs among smartphone users in Germany as long as organizational providers engaged in development, operation, and distribution are state-funded or governmental institutions and transparency about data-using research institutions is provided. %M 35072646 %R 10.2196/31857 %U https://mhealth.jmir.org/2022/1/e31857 %U https://doi.org/10.2196/31857 %U http://www.ncbi.nlm.nih.gov/pubmed/35072646 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 1 %P e26276 %T Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study %A Abbas,Anzar %A Hansen,Bryan J %A Koesmahargyo,Vidya %A Yadav,Vijay %A Rosenfield,Paul J %A Patil,Omkar %A Dockendorf,Marissa F %A Moyer,Matthew %A Shipley,Lisa A %A Perez-Rodriguez,M Mercedez %A Galatzer-Levy,Isaac R %+ AiCure, 214 Sullivan Street 6C, New York, NY, 10012, United States, 1 8005700448, anzar@anzarabbas.com %K digital biomarkers %K phenotyping %K computer vision %K facial expressivity %K negative symptoms %K vocal acoustics %D 2022 %7 21.1.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Machine learning–based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage. Objective: This study aimed to determine the accuracy of machine learning–based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones. Methods: Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale. Results: Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity. Conclusions: Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed. %M 35060906 %R 10.2196/26276 %U https://formative.jmir.org/2022/1/e26276 %U https://doi.org/10.2196/26276 %U http://www.ncbi.nlm.nih.gov/pubmed/35060906 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 1 %P e32404 %T Exploring Children’s Engagement in Monitoring Indoor Air Quality: Longitudinal Study %A Kim,Sunyoung %A Sohanchyk,Gregory %+ School of Communication and Information, Rutgers University, 4 Huntington Street, New Brunswick, NJ, 08901, United States, 1 8489327585, sunyoung.kim@rutgers.edu %K children %K indoor air quality %K mobile app %K awareness %K longitudinal deployment %D 2022 %7 21.1.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Indoor air pollution is harmful to everyone, but children are of particular concern, as they are more vulnerable to its adverse health effects from air pollutants. Although mobile technology is increasingly being designed to support monitoring and improving air quality indoors, little attention has been paid to its use by and for children. Previously, we created inAirKids, a child-friendly device to promote children’s engagement with monitoring indoor air quality through a participatory design process. The next step is to evaluate its usability in the real world. Objective: The aim of this study is to investigate how inAirKids affects children’s understanding of and engagement with indoor air quality through a longitudinal field deployment study. Methods: We deployed inAirKids in the homes of 9 children aged between 6 and 7 years, and investigated their use for up to 16 weeks by conducting semistructured, biweekly interviews. Results: The results show that participants promptly engaged with inAirKids but quickly lost interest in it owing to the lack of engaging factors to sustain engagement. In addition, we identified 2 design considerations that can foster sustained engagement of children with monitoring indoor air quality: design interactivity for engaging in continuity and corporate hands-on activities as part of indoor air quality monitoring for experiential learning. Conclusions: Our findings shed light on the potential to promote the engagement of children in indoor air quality as well as considerations for designing a child-friendly digital device. To the best of our knowledge, this is the first longitudinal field deployment to investigate how to engage children in monitoring indoor air quality. %M 35060916 %R 10.2196/32404 %U https://formative.jmir.org/2022/1/e32404 %U https://doi.org/10.2196/32404 %U http://www.ncbi.nlm.nih.gov/pubmed/35060916 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 1 %P e32104 %T User Control of Personal mHealth Data Using a Mobile Blockchain App: Design Science Perspective %A Sengupta,Arijit %A Subramanian,Hemang %+ Department of Information Systems and Business Analytics, College of Business, Florida International University, 11200 Southwest 8th Street, Miami, FL, 33199, United States, 1 3053481427, arijit.sengupta@fiu.edu %K blockchain %K mobile apps %K mining %K HIPAA %K personal health data %K data privacy preservation %K security %K accuracy %K transaction safety %D 2022 %7 20.1.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Integrating pervasive computing with blockchain’s ability to store privacy-protected mobile health (mHealth) data while providing Health Insurance Portability and Accountability Act (HIPAA) compliance is a challenge. Patients use a multitude of devices, apps, and services to collect and store mHealth data. We present the design of an internet of things (IoT)–based configurable blockchain with different mHealth apps on iOS and Android, which collect the same user’s data. We discuss the advantages of using such a blockchain architecture and demonstrate 2 things: the ease with which users can retain full control of their pervasive mHealth data and the ease with which HIPAA compliance can be accomplished by providers who choose to access user data. Objective: The purpose of this paper is to design, evaluate, and test IoT-based mHealth data using wearable devices and an efficient, configurable blockchain, which has been designed and implemented from the first principles to store such data. The purpose of this paper is also to demonstrate the privacy-preserving and HIPAA-compliant nature of pervasive computing-based personalized health care systems that provide users with total control of their own data. Methods: This paper followed the methodical design science approach adapted in information systems, wherein we evaluated prior designs, proposed enhancements with a blockchain design pattern published by the same authors, and used the design to support IoT transactions. We prototyped both the blockchain and IoT-based mHealth apps in different devices and tested all use cases that formed the design goals for such a system. Specifically, we validated the design goals for our system using the HIPAA checklist for businesses and proved the compliance of our architecture for mHealth data on pervasive computing devices. Results: Blockchain-based personalized health care systems provide several advantages over traditional systems. They provide and support extreme privacy protection, provide the ability to share personalized data and delete data upon request, and support the ability to analyze such data. Conclusions: We conclude that blockchains, specifically the consensus, hasher, storer, miner architecture presented in this paper, with configurable modules and software as a service model, provide many advantages for patients using pervasive devices that store mHealth data on the blockchain. Among them is the ability to store, retrieve, and modify ones generated health care data with a single private key across devices. These data are transparent, stored perennially, and provide patients with privacy and pseudoanonymity, in addition to very strong encryption for data access. Firms and device manufacturers would benefit from such an approach wherein they relinquish user data control while giving users the ability to select and offer their own mHealth data on data marketplaces. We show that such an architecture complies with the stringent requirements of HIPAA for patient data access. %M 35049504 %R 10.2196/32104 %U https://mhealth.jmir.org/2022/1/e32104 %U https://doi.org/10.2196/32104 %U http://www.ncbi.nlm.nih.gov/pubmed/35049504 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 1 %P e25444 %T An Open-Source Privacy-Preserving Large-Scale Mobile Framework for Cardiovascular Health Monitoring and Intervention Planning With an Urban African American Population of Young Adults: User-Centered Design Approach %A Clifford,Gari %A Nguyen,Tony %A Shaw,Corey %A Newton,Brittney %A Francis,Sherilyn %A Salari,Mohsen %A Evans,Chad %A Jones,Camara %A Akintobi,Tabia Henry %A Taylor Jr,Herman %+ Cardiovascular Research Institute, Morehouse School of Medicine, 720 Westview Drive, Atlanta, GA, 30310, United States, 1 404 752 1545, htaylor@msm.edu %K agile design %K cardiovascular disease %K community-based participatory research %K exposome %K user-centered design %K minority health %K African American %K mobile phone %D 2022 %7 11.1.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are increasingly affecting younger populations, particularly African Americans in the southern United States. Access to preventive and therapeutic services, biological factors, and social determinants of health (ie, structural racism, resource limitation, residential segregation, and discriminatory practices) all combine to exacerbate health inequities and their resultant disparities in morbidity and mortality. These factors manifest early in life and have been shown to impact health trajectories into adulthood. Early detection of and intervention in emerging risk offers the best hope for preventing race-based differences in adult diseases. However, young-adult populations are notoriously difficult to recruit and retain, often because of a lack of knowledge of personal risk and a low level of concern for long-term health outcomes. Objective: This study aims to develop a system design for the MOYO mobile platform. Further, we seek to addresses the challenge of primordial prevention in a young, at-risk population (ie, Southern-urban African Americans). Methods: Urban African Americans, aged 18 to 29 years (n=505), participated in a series of co-design sessions to develop MOYO prototypes (ie, HealthTech Events). During the sessions, participants were orientated to the issues of CVD risk health disparities and then tasked with wireframing prototype screens depicting app features that they considered desirable. All 297 prototype screens were subsequently analyzed using NVivo 12 (QSR International), a qualitative analysis software. Using the grounded theory approach, an open-coding method was applied to a subset of data, approximately 20% (5/25), or 5 complete prototypes, to identify the dominant themes among the prototypes. To ensure intercoder reliability, 2 research team members analyzed the same subset of data. Results: Overall, 9 dominant design requirements emerged from the qualitative analysis: customization, incentive motivation, social engagement, awareness, education, or recommendations, behavior tracking, location services, access to health professionals, data user agreements, and health assessment. This led to the development of a cross-platform app through an agile design process to collect standardized health surveys, narratives, geolocated pollution, weather, food desert exposure data, physical activity, social networks, and physiology through point-of-care devices. A Health Insurance Portability and Accountability Act–compliant cloud infrastructure was developed to collect, process, and review data, as well as generate alerts to allow automated signal processing and machine learning on the data to produce critical alerts. Integration with wearables and electronic health records via fast health care interoperability resources was implemented. Conclusions: The MOYO mobile platform provides a comprehensive health and exposure monitoring system that allows for a broad range of compliance, from passive background monitoring to active self-reporting. These study findings support the notion that African Americans should be meaningfully involved in designing technologies that are developed to improve CVD outcomes in African American communities. %M 35014970 %R 10.2196/25444 %U https://formative.jmir.org/2022/1/e25444 %U https://doi.org/10.2196/25444 %U http://www.ncbi.nlm.nih.gov/pubmed/35014970 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 1 %P e30557 %T Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study %A Vaidyam,Aditya %A Halamka,John %A Torous,John %+ Beth Israel Deaconess Medical Center, 330 Brrokline Avenue, Boston, MA, 02215, United States, 1 6176676700, jtorous@bidmc.harvard.edu %K digital phenotyping %K mHealth %K apps %K FHIR %K digital health %K health data %K patient-generated health data %K mobile health %K smartphones %K wearables %K mobile apps %K mental health, mobile phone %D 2022 %7 7.1.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables. Objective: This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP. Methods: The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code. Results: With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources–based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques. Conclusions: The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions. %M 34994710 %R 10.2196/30557 %U https://mhealth.jmir.org/2022/1/e30557 %U https://doi.org/10.2196/30557 %U http://www.ncbi.nlm.nih.gov/pubmed/34994710 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 7 %N 1 %P e29107 %T Comparison of Daily Routines Between Middle-aged and Older Participants With and Those Without Diabetes in the Electronic Framingham Heart Study: Cohort Study %A Zhang,Yuankai %A Pathiravasan,Chathurangi H %A Hammond,Michael M %A Liu,Hongshan %A Lin,Honghuang %A Sardana,Mayank %A Trinquart,Ludovic %A Borrelli,Belinda %A Manders,Emily S %A Kornej,Jelena %A Spartano,Nicole L %A Nowak,Christopher %A Kheterpal,Vik %A Benjamin,Emelia J %A McManus,David D %A Murabito,Joanne M %A Liu,Chunyu %+ Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, MA, 02118, United States, 1 (617) 638 5104, liuc@bu.edu %K diabetes %K mobile health %K smartwatch %K daily physical activities %K daily routine pattern %K sleep %K step counts %K diabetes self-management %K mobile phone %D 2022 %7 7.1.2022 %9 Original Paper %J JMIR Diabetes %G English %X Background: Daily routines (eg, physical activity and sleep patterns) are important for diabetes self-management. Traditional research methods are not optimal for documenting long-term daily routine patterns in participants with glycemic conditions. Mobile health offers an effective approach for collecting users’ long-term daily activities and analyzing their daily routine patterns in relation to diabetes status. Objective: This study aims to understand how routines function in diabetes self-management. We evaluate the associations of daily routine variables derived from a smartwatch with diabetes status in the electronic Framingham Heart Study (eFHS). Methods: The eFHS enrolled the Framingham Heart Study participants at health examination 3 between 2016 and 2019. At baseline, diabetes was defined as fasting blood glucose level ≥126 mg/dL or as a self-report of taking a glucose-lowering medication; prediabetes was defined as fasting blood glucose level of 100-125 mg/dL. Using smartwatch data, we calculated the average daily step counts and estimated the wake-up times and bedtimes for the eFHS participants on a given day. We compared the average daily step counts and the intraindividual variability of the wake-up times and bedtimes of the participants with diabetes and prediabetes with those of the referents who were neither diabetic nor prediabetic, adjusting for age, sex, and race or ethnicity. Results: We included 796 participants (494/796, 62.1% women; mean age 52.8, SD 8.7 years) who wore a smartwatch for at least 10 hours/day and remained in the study for at least 30 days after enrollment. On average, participants with diabetes (41/796, 5.2%) took 1611 fewer daily steps (95% CI 863-2360; P<.001) and had 12 more minutes (95% CI 6-18; P<.001) in the variation of their estimated wake-up times, 6 more minutes (95% CI 2-9; P=.005) in the variation of their estimated bedtimes compared with the referents (546/796, 68.6%) without diabetes or prediabetes. Participants with prediabetes (209/796, 26.2%) also walked fewer daily steps (P=.04) and had a larger variation in their estimated wake-up times (P=.04) compared with the referents. Conclusions: On average, participants with diabetes at baseline walked significantly fewer daily steps and had larger variations in their wake-up times and bedtimes than the referent group. These findings suggest that modifying the routines of participants with poor glycemic health may be an important approach to the self-management of diabetes. Future studies should be designed to improve the remote monitoring and self-management of diabetes. %M 34994694 %R 10.2196/29107 %U https://diabetes.jmir.org/2022/1/e29107 %U https://doi.org/10.2196/29107 %U http://www.ncbi.nlm.nih.gov/pubmed/34994694 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 1 %P e30863 %T Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study %A Haveman,Marjolein E %A van Rossum,Mathilde C %A Vaseur,Roswita M E %A van der Riet,Claire %A Schuurmann,Richte C L %A Hermens,Hermie J %A de Vries,Jean-Paul P M %A Tabak,Monique %+ Department of Surgery, University Medical Center Groningen, University of Groningen, BA60, Hanzeplein 1, Groningen, 9713 GZ, Netherlands, 31 625646832, m.e.haveman@umcg.nl %K wearable sensors %K telemonitoring %K continuous monitoring %K vital signs %K mHealth %K wearable %K biosensor %K validity %K accuracy %D 2022 %7 7.1.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients’ baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. Objective: The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. Methods: Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). Results: A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. Conclusions: Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring. %M 34994703 %R 10.2196/30863 %U https://formative.jmir.org/2022/1/e30863 %U https://doi.org/10.2196/30863 %U http://www.ncbi.nlm.nih.gov/pubmed/34994703 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 1 %P e25375 %T Ecological Momentary Assessment of Physical Activity and Wellness Behaviors in College Students Throughout a School Year: Longitudinal Naturalistic Study %A Bai,Yang %A Copeland,William E %A Burns,Ryan %A Nardone,Hilary %A Devadanam,Vinay %A Rettew,Jeffrey %A Hudziak,James %+ Department of Health and Kinesiology, College of Health, University of Utah, HPER-North, Room 204, Salt Lake City, UT, 84102, United States, 1 8015870482, yang.bai@utah.edu %K young adulthood %K wellness %K substance use %K Apple Watch %D 2022 %7 4.1.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Wellness Environment app study is a longitudinal study focused on promoting health in college students. Objective: The two aims of this study were (1) to assess physical activity (PA) variation across the days of the week and throughout the academic year and (2) to explore the correlates that were associated with PA, concurrently and longitudinally. Methods: The participants were asked to report their wellness and risk behaviors on a 14-item daily survey through a smartphone app. Each student was provided an Apple Watch to track their real time PA. Data were collected from 805 college students from Sept 2017 to early May 2018. PA patterns across the days of the week and throughout the academic year were summarized. Concurrent associations of daily steps with wellness or risk behavior were tested in the general linear mixed-effects model. The longitudinal, reciprocal association between daily steps and health or risk behaviors were tested with cross-lagged analysis. Results: Female college students were significantly more active than male ones. The students were significantly more active during the weekday than weekend. Temporal patterns also revealed that the students were less active during Thanksgiving, winter, and spring breaks. Strong concurrent positive correlations were found between higher PA and self-reported happy mood, 8+ hours of sleep, ≥1 fruit and vegetable consumption, ≥4 bottles of water intake, and ≤2 hours of screen time (P<.001). Similar longitudinal associations found that the previous day’s wellness behaviors independently predicted the following day’s higher PA except for mood. Conversely, the higher previous-day PA levels were associated with better mood, more fruit and vegetable consumption, and playing less music, but with higher liquor consumption the next day. Conclusions: This study provides a comprehensive surveillance of longitudinal PA patterns and their independent association with a variety of wellness and risk behaviors in college students. %M 34982721 %R 10.2196/25375 %U https://publichealth.jmir.org/2022/1/e25375 %U https://doi.org/10.2196/25375 %U http://www.ncbi.nlm.nih.gov/pubmed/34982721 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e25748 %T Assessing Cognitive Function in Multiple Sclerosis With Digital Tools: Observational Study %A Hsu,Wan-Yu %A Rowles,William %A Anguera,Joaquin A %A Anderson,Annika %A Younger,Jessica W %A Friedman,Samuel %A Gazzaley,Adam %A Bove,Riley %+ Department of Neurology, Weill Institute for Neurosciences, University of California, 675 Nelson Rising Lane, San Francisco, CA, 94158, United States, 1 415 595 2795, Riley.Bove@ucsf.edu %K cognition %K digital health %K mHealth %K multiple sclerosis %K cognitive assessment %D 2021 %7 30.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Cognitive impairment (CI) is one of the most prevalent symptoms of multiple sclerosis (MS). However, it is difficult to include cognitive assessment as part of MS standard care since the comprehensive neuropsychological examinations are usually time-consuming and extensive. Objective: To improve access to CI assessment, we evaluated the feasibility and potential assessment sensitivity of a tablet-based cognitive battery in patients with MS. Methods: In total, 53 participants with MS (24 [45%] with CI and 29 [55%] without CI) and 24 non-MS participants were assessed with a tablet-based cognitive battery (Adaptive Cognitive Evaluation [ACE]) and standard cognitive measures, including the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). Associations between performance in ACE and the SDMT/PASAT were explored, with group comparisons to evaluate whether ACE modules can capture group-level differences. Results: Correlations between performance in ACE and the SDMT (R=–0.57, P<.001), as well as PASAT (R=–0.39, P=.01), were observed. Compared to non-MS and non-CI MS groups, the CI MS group showed a slower reaction time (CI MS vs non-MS: P<.001; CI MS vs non-CI MS: P=.004) and a higher attention cost (CI MS vs non-MS: P=.02; CI MS vs non-CI MS: P<.001). Conclusions: These results provide preliminary evidence that ACE, a tablet-based cognitive assessment battery, provides modules that could potentially serve as a digital cognitive assessment for people with MS. Trial Registration: ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618 %M 34967751 %R 10.2196/25748 %U https://www.jmir.org/2021/12/e25748 %U https://doi.org/10.2196/25748 %U http://www.ncbi.nlm.nih.gov/pubmed/34967751 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e31316 %T Effects of Urban Green Space on Cardiovascular and Respiratory Biomarkers in Chinese Adults: Panel Study Using Digital Tracking Devices %A Yang,Lin %A Chan,Ka Long %A Yuen,John W M %A Wong,Frances K Y %A Han,Lefei %A Ho,Hung Chak %A Chang,Katherine K P %A Ho,Yuen Shan %A Siu,Judy Yuen-Man %A Tian,Linwei %A Wong,Man Sing %+ School of Nursing, The Hong Kong Polytechnic University, Hung Hom Campus, GH519, Hong Kong, Hong Kong, 852 2766 6419, frances.wong@polyu.edu.hk %K green space %K biomarker %K cardiovascular disease %K respiratory disease %D 2021 %7 30.12.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: The health benefits of urban green space have been widely reported in the literature; however, the biological mechanisms remain unexplored, and a causal relationship cannot be established between green space exposure and cardiorespiratory health. Objective: Our aim was to conduct a panel study using personal tracking devices to continuously collect individual exposure data from healthy Chinese adults aged 50 to 64 years living in Hong Kong. Methods: A panel of cardiorespiratory biomarkers was tested each week for a period of 5 consecutive weeks. Data on weekly exposure to green space, air pollution, and the physical activities of individual participants were collected by personal tracking devices. The effects of green space exposure measured by the normalized difference vegetation index (NDVI) at buffer zones of 100, 250, and 500 meters on a panel of cardiorespiratory biomarkers were estimated by a generalized linear mixed-effects model, with adjustment for confounding variables of sociodemographic characteristics, exposure to air pollutants and noise, exercise, and nutrient intake. Results: A total of 39 participants (mean age 56.4 years, range 50-63 years) were recruited and followed up for 5 consecutive weeks. After adjustment for sex, income, occupation, physical activities, dietary intake, noise, and air pollution, significant negative associations with the NDVI for the 250-meter buffer zone were found in total cholesterol (–21.6% per IQR increase in NDVI, 95% CI –32.7% to –10.6%), low-density lipoprotein (–14.9%, 95% CI –23.4% to –6.4%), glucose (–11.2%, 95% CI –21.9% to –0.5%), and high-sensitivity C-reactive protein (–41.3%, 95% CI –81.7% to –0.9%). Similar effect estimates were found for the 100-meter and 250-meter buffer zones. After adjustment for multiple testing, the effect estimates of glucose and high-sensitivity C-reactive protein were no longer significant. Conclusions: The health benefits of green space can be found in some metabolic and inflammatory biomarkers. Further studies are warranted to establish the causal relationship between green space and cardiorespiratory health. %M 34967754 %R 10.2196/31316 %U https://cardio.jmir.org/2021/2/e31316 %U https://doi.org/10.2196/31316 %U http://www.ncbi.nlm.nih.gov/pubmed/34967754 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 12 %P e31890 %T Detection of Clenbuterol-Induced Changes in Heart Rate Using At-Home Recorded Smartwatch Data: Randomized Controlled Trial %A Elzinga,Willem O %A Prins,Samantha %A Borghans,Laura G J M %A Gal,Pim %A Vargas,Gabriel A %A Groeneveld,Geert J %A Doll,Robert J %+ Centre for Human Drug Research, Zernikedreef 8, Leiden, 2333CL, Netherlands, 31 715246400, rjdoll@chdr.nl %K photoplethysmography %K smartwatch %K wearable %K at-home %K heart rate %K RCT %K wearable device %K digital health %K cardiovascular %K cardiology %K sensors %K heart rate sensor %K smart technology %D 2021 %7 30.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Although electrocardiography is the gold standard for heart rate (HR) recording in clinical trials, the increasing availability of smartwatch-based HR monitors opens up possibilities for drug development studies. Smartwatches allow for inexpensive, unobtrusive, and continuous HR estimation for potential detection of treatment effects outside the clinic, during daily life. Objective: The aim of this study is to evaluate the repeatability and sensitivity of smartwatch-based HR estimates collected during a randomized clinical trial. Methods: The data were collected as part of a multiple-dose, investigator-blinded, randomized, placebo-controlled, parallel-group study of 12 patients with Parkinson disease. After a 6-day baseline period, 4 and 8 patients were treated for 7 days with an ascending dose of placebo and clenbuterol, respectively. Throughout the study, the smartwatch provided HR and sleep state estimates. The HR estimates were quantified as the 2.5th, 50th, and 97.5th percentiles within awake and asleep segments. Linear mixed models were used to calculate the following: (1) the intraclass correlation coefficient (ICC) of estimated sleep durations, (2) the ICC and minimum detectable effect (MDE) of the HR estimates, and (3) the effect sizes of the HR estimates. Results: Sleep duration was moderately repeatable (ICC=0.64) and was not significantly affected by study day (P=.83), clenbuterol (P=.43), and study day by clenbuterol (P=.73). Clenbuterol-induced changes were detected in the asleep HR as of the first night (+3.79 beats per minute [bpm], P=.04) and in the awake HR as of the third day (+8.79 bpm, P=.001). The median HR while asleep had the highest repeatability (ICC=0.70). The MDE (N=12) was found to be smaller when patients were asleep (6.8 bpm to 11.7 bpm) than while awake (10.7 bpm to 22.1 bpm). Overall, the effect sizes for clenbuterol-induced changes were higher while asleep (0.49 to 2.75) than while awake (0.08 to 1.94). Conclusions: We demonstrated the feasibility of using smartwatch-based HR estimates to detect clenbuterol-induced changes during clinical trials. The asleep HR estimates were most repeatable and sensitive to treatment effects. We conclude that smartwatch-based HR estimates obtained during daily living in a clinical trial can be used to detect and track treatment effects. Trial Registration: Netherlands Trials Register NL8002; https://www.trialregister.nl/trial/8002 %M 34967757 %R 10.2196/31890 %U https://formative.jmir.org/2021/12/e31890 %U https://doi.org/10.2196/31890 %U http://www.ncbi.nlm.nih.gov/pubmed/34967757 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e26763 %T Assessing Physicians’ Recall Bias of Work Hours With a Mobile App: Interview and App-Recorded Data Comparison %A Wang,Hsiao-Han %A Lin,Yu-Hsuan %+ Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Township, Miaoli County, 35053, Taiwan, 886 37 206 166 ext 36383, yuhsuanlin@nhri.edu.tw %K smartphone %K mobile app %K work hours %K recall bias %K time perception %K physicians %K labor policy %D 2021 %7 24.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Previous studies have shown inconsistencies in the accuracy of self-reported work hours. However, accurate documentation of work hours is fundamental for the formation of labor policies. Strict work-hour policies decrease medical errors, improve patient safety, and promote physicians’ well-being. Objective: The aim of this study was to estimate physicians’ recall bias of work hours with a mobile app, and to examine the association between the recall bias and physicians’ work hours. Methods: We quantified recall bias by calculating the differences between the app-recorded and self-reported work hours of the previous week and the penultimate week. We recruited 18 physicians to install the “Staff Hours” app, which automatically recorded GPS-defined work hours for 2 months, contributing 1068 person-days. We examined the association between work hours and two recall bias indicators: (1) the difference between self-reported and app-recorded work hours and (2) the percentage of days for which work hours were not precisely recalled during interviews. Results: App-recorded work hours highly correlated with self-reported counterparts (r=0.86-0.88, P<.001). Self-reported work hours were consistently significantly lower than app-recorded hours by –8.97 (SD 8.60) hours and –6.48 (SD 8.29) hours for the previous week and the penultimate week, respectively (both P<.001). The difference for the previous week was significantly correlated with work hours in the previous week (r=–0.410, P=.01), whereas the correlation of the difference with the hours in the penultimate week was not significant (r=–0.119, P=.48). The percentage of hours not recalled (38.6%) was significantly higher for the penultimate week (38.6%) than for the first week (16.0%), and the former was significantly correlated with work hours of the penultimate week (r=0.489, P=.002) Conclusions: Our study identified the existence of recall bias of work hours, the extent to which the recall was biased, and the influence of work hours on recall bias. %M 34951600 %R 10.2196/26763 %U https://www.jmir.org/2021/12/e26763 %U https://doi.org/10.2196/26763 %U http://www.ncbi.nlm.nih.gov/pubmed/34951600 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 4 %P e30767 %T Perceptions of Older Men Using a Mobile Health App to Monitor Lower Urinary Tract Symptoms and Tamsulosin Side Effects: Mixed Methods Study %A Wang,Elizabeth Y %A Breyer,Benjamin N %A Lee,Austin W %A Rios,Natalie %A Oni-Orisan,Akinyemi %A Steinman,Michael A %A Sim,Ida %A Kenfield,Stacey A %A Bauer,Scott R %+ University of California San Francisco, 550 16th St, 6th floor, Box 1695, San Francisco, CA, 94121, United States, 1 4152214810 ext 24322, Scott.Bauer@ucsf.edu %K BPH %K mobile health %K mHealth %K telehealth %K telemedicine %D 2021 %7 24.12.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Mobile health (mHealth) apps may provide an efficient way for patients with lower urinary tract symptoms (LUTS) to log and communicate symptoms and medication side effects with their clinicians. Objective: The aim of this study was to explore the perceptions of older men with LUTS after using an mHealth app to track their symptoms and tamsulosin side effects. Methods: Structured phone interviews were conducted after a 2-week study piloting the daily use of a mobile app to track the severity of patient-selected LUTS and tamsulosin side effects. Quantitative and qualitative data were considered. Results: All 19 (100%) pilot study participants completed the poststudy interviews. Most of the men (n=13, 68%) reported that the daily questionnaires were the right length, with 32% (n=6) reporting that the questionnaires were too short. Men with more severe symptoms were less likely to report changes in perception of health or changes in self-management; 47% (n=9) of the men reported improved awareness of symptoms and 5% (n=1) adjusted fluid intake based on the questionnaire. All of the men were willing to share app data with their clinicians. Thematic analysis of qualitative data yielded eight themes: (1) orientation (setting up app, format, symptom selection, and side-effect selection), (2) triggers (routine or habit and symptom timing), (3) daily questionnaire (reporting symptoms, reporting side effects, and tailoring), (4) technology literacy, (5) perceptions (awareness, causation or relevance, data quality, convenience, usefulness, and other apps), (6) self-management, (7) clinician engagement (communication and efficiency), and (8) improvement (reference materials, flexibility, language, management recommendations, and optimize clinician engagement). Conclusions: We assessed the perceptions of men using an mHealth app to monitor and improve management of LUTS and medication side effects. LUTS management may be further optimized by tailoring the mobile app experience to meet patients’ individual needs, such as tracking a greater number of symptoms and integrating the app with clinicians’ visits. mHealth apps are likely a scalable modality to monitor symptoms and improve care of older men with LUTS. Further study is required to determine the best ways to tailor the mobile app and to communicate data to clinicians or incorporate data into the electronical medical record meaningfully. %M 34951599 %R 10.2196/30767 %U https://humanfactors.jmir.org/2021/4/e30767 %U https://doi.org/10.2196/30767 %U http://www.ncbi.nlm.nih.gov/pubmed/34951599 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 12 %P e31618 %T Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study %A Cho,Sylvia %A Weng,Chunhua %A Kahn,Michael G %A Natarajan,Karthik %+ Department of Biomedical Informatics, Columbia University, 622 West 168th Street PH20, New York, NY, 10032, United States, 1 212 305 5334, sc3901@cumc.columbia.edu %K patient-generated health data %K data accuracy %K data quality %K wearable device %K fitness trackers %K qualitative research %D 2021 %7 23.12.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying the data quality dimensions. Objective: This study aims to identify data quality dimensions for person-generated wearable device data for research purposes. Methods: This study was conducted in 3 phases: literature review, survey, and focus group discussion. The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers’ perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions. Results: In total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance–related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes. Conclusions: In this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed. %M 34941540 %R 10.2196/31618 %U https://mhealth.jmir.org/2021/12/e31618 %U https://doi.org/10.2196/31618 %U http://www.ncbi.nlm.nih.gov/pubmed/34941540 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 12 %P e32891 %T Accuracy and Cost-effectiveness of Technology-Assisted Dietary Assessment Comparing the Automated Self-administered Dietary Assessment Tool, Intake24, and an Image-Assisted Mobile Food Record 24-Hour Recall Relative to Observed Intake: Protocol for a Randomized Crossover Feeding Study %A Whitton,Clare %A Healy,Janelle D %A Collins,Clare E %A Mullan,Barbara %A Rollo,Megan E %A Dhaliwal,Satvinder S %A Norman,Richard %A Boushey,Carol J %A Delp,Edward J %A Zhu,Fengqing %A McCaffrey,Tracy A %A Kirkpatrick,Sharon I %A Atyeo,Paul %A Mukhtar,Syed Aqif %A Wright,Janine L %A Ramos-García,César %A Pollard,Christina M %A Kerr,Deborah A %+ School of Population Health, Faculty of Health Sciences, Curtin University, Kent Street, Perth, 6102, Australia, 61 892664122, D.Kerr@curtin.edu.au %K 24-hour recall %K Automated Self-Administered Dietary Assessment Tool %K Intake24 %K mobile food record %K image-assisted dietary assessment %K validation %K controlled feeding %K accuracy %K dietary measurement error %K self-report %K energy intake %K adult %K cost-effectiveness %K acceptability %K mobile technology %K diet surveys %K mobile phone %D 2021 %7 16.12.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The assessment of dietary intake underpins population nutrition surveillance and nutritional epidemiology and is essential to inform effective public health policies and programs. Technological advances in dietary assessment that use images and automated methods have the potential to improve accuracy, respondent burden, and cost; however, they need to be evaluated to inform large-scale use. Objective: The aim of this study is to compare the accuracy, acceptability, and cost-effectiveness of 3 technology-assisted 24-hour dietary recall (24HR) methods relative to observed intake across 3 meals. Methods: Using a controlled feeding study design, 24HR data collected using 3 methods will be obtained for comparison with observed intake. A total of 150 healthy adults, aged 18 to 70 years, will be recruited and will complete web-based demographic and psychosocial questionnaires and cognitive tests. Participants will attend a university study center on 3 separate days to consume breakfast, lunch, and dinner, with unobtrusive documentation of the foods and beverages consumed and their amounts. Following each feeding day, participants will complete a 24HR process using 1 of 3 methods: the Automated Self-Administered Dietary Assessment Tool, Intake24, or the Image-Assisted mobile Food Record 24-Hour Recall. The sequence of the 3 methods will be randomized, with each participant exposed to each method approximately 1 week apart. Acceptability and the preferred 24HR method will be assessed using a questionnaire. Estimates of energy, nutrient, and food group intake and portion sizes from each 24HR method will be compared with the observed intake for each day. Linear mixed models will be used, with 24HR method and method order as fixed effects, to assess differences in the 24HR methods. Reporting bias will be assessed by examining the ratios of reported 24HR intake to observed intake. Food and beverage omission and intrusion rates will be calculated, and differences by 24HR method will be assessed using chi-square tests. Psychosocial, demographic, and cognitive factors associated with energy misestimation will be evaluated using chi-square tests and multivariable logistic regression. The financial costs, time costs, and cost-effectiveness of each 24HR method will be assessed and compared using repeated measures analysis of variance tests. Results: Participant recruitment commenced in March 2021 and is planned to be completed by the end of 2021. Conclusions: This protocol outlines the methodology of a study that will evaluate the accuracy, acceptability, and cost-effectiveness of 3 technology-enabled dietary assessment methods. This will inform the selection of dietary assessment methods in future studies on nutrition surveillance and epidemiology. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621000209897; https://tinyurl.com/2p9fpf2s International Registered Report Identifier (IRRID): DERR1-10.2196/32891 %M 34924357 %R 10.2196/32891 %U https://www.researchprotocols.org/2021/12/e32891 %U https://doi.org/10.2196/32891 %U http://www.ncbi.nlm.nih.gov/pubmed/34924357 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 12 %P e29190 %T Willingness to Share Data From Wearable Health and Activity Trackers: Analysis of the 2019 Health Information National Trends Survey Data %A Rising,Camella J %A Gaysynsky,Anna %A Blake,Kelly D %A Jensen,Roxanne E %A Oh,April %+ Behavioral Research Program, Division of Cancer Control and Population Sciences, US National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20850, United States, 1 240 276 5262, camella.rising@nih.gov %K mobile health %K population health %K health communication %K survey methodology %K mobile apps %K devices %K online social networking %K mobile phone %D 2021 %7 13.12.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sharing data from wearable health and activity trackers (wearables) with others may improve the health and behavioral outcomes of wearable users by generating social support and improving their ability to manage their health. Investigating individual factors that influence US adults’ willingness to share wearable data with different types of individuals may provide insights about the population subgroups that are most or least likely to benefit from wearable interventions. Specifically, it is necessary to identify digital health behaviors potentially associated with willingness to share wearable data given that the use of and engagement with various technologies may broadly influence web-based health information–sharing behaviors. Objective: This study aims to identify sociodemographic, health, and digital health behavior correlates of US adults’ willingness to share wearable data with health care providers and family or friends. Methods: Data for the analytic sample (N=1300) were obtained from the 2019 Health Information National Trends Survey of the National Cancer Institute. Digital health behavior measures included frequency of wearable device use, use of smartphones or tablets to help communicate with providers, use of social networking sites to share health information, and participation in a web-based health community. Multivariable logistic regression analysis of weighted data examined the associations between digital health behaviors and willingness to share wearable device data, controlling for sociodemographics and health-related characteristics. Results: Most US adults reported willingness to share wearable data with providers (81.86%) and with family or friends (69.51%). Those who reported higher health self-efficacy (odds ratio [OR] 1.97, 95% CI 1.11-3.51), higher level of trust in providers as a source of health information (OR 1.98, 95% CI 1.12-3.49), and higher level of physical activity (OR 2.00, 95% CI 1.21-3.31) had greater odds of willingness to share data with providers. In addition, those with a higher frequency of wearable use (OR 2.15, 95% CI 1.35-3.43) and those who reported use of smartphones or tablets to help communicate with providers (OR 1.99, 95% CI 1.09-3.63) had greater odds of willingness to share data with providers. Only higher level of physical activity was associated with greater odds of willingness to share wearable data with family or friends (OR 1.70, 95% CI 1.02-2.84). Sociodemographic factors were not significantly associated with willingness to share wearable data. Conclusions: The findings of this study suggest that, among US adult wearable users, behavior-related factors, rather than sociodemographic characteristics, are key drivers of willingness to share health information obtained from wearables with others. Moreover, behavioral correlates of willingness to share wearable data are unique to the type of recipient (ie, providers vs family or friends). Future studies could use these findings to inform the development of interventions that aim to improve the use of patient-generated data from wearable devices in health care settings. %M 34898448 %R 10.2196/29190 %U https://mhealth.jmir.org/2021/12/e29190 %U https://doi.org/10.2196/29190 %U http://www.ncbi.nlm.nih.gov/pubmed/34898448 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 12 %P e29916 %T Adoption and Appropriateness of mHealth for Weight Management in the Real World: A Qualitative Investigation of Patient Perspectives %A Breland,Jessica Y %A Agha,Khizran %A Mohankumar,Rakshitha %+ Center for Innovation to Implementation, VA Palo Alto Health Care System, 795 Willow Road (MPD-152), Menlo Park, CA, 94025, United States, 1 650 493 5000, jessica.breland@va.gov %K mHealth %K implementation %K adoption %K engagement %K weight management %K obesity %K weight loss %K mobile health %K veterans %K barriers %D 2021 %7 8.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Mobile health (mHealth) interventions for weight management can result in weight loss outcomes comparable to in-person treatments. However, there is little information on implementing these treatments in real-world settings. Objective: This work aimed to answer two implementation research questions related to mHealth for weight management: (1) what are barriers and facilitators to mHealth adoption (initial use) and engagement (continued use)? and (2) what are patient beliefs about the appropriateness (ie, perceived fit, relevance, or compatibility) of mHealth for weight management? Methods: We conducted semistructured interviews with patients with obesity at a single facility in an integrated health care system (the Veterans Health Administration). All participants had been referred to a new mHealth program, which included access to a live coach. We performed a rapid qualitative analysis of interviews to identify themes related to the adoption of, engagement with, and appropriateness of mHealth for weight management. Results: We interviewed 24 veterans, seven of whom used the mHealth program. Almost all participants were ≥45 years of age and two-thirds were White. Rapid analysis identified three themes: (1) coaching both facilitates and prevents mHealth adoption and engagement by promoting accountability but leading to guilt among those not meeting goals; (2) preferences regarding the mode of treatment delivery, usability, and treatment content were barriers to mHealth appropriateness and adoption, including preferences for in-person care and a dislike of self-monitoring; and (3) a single invitation was not sufficient to facilitate adoption of a new mHealth program. Themes were unrelated to participants’ age, race, or ethnicity. Conclusions: In a study assessing real-world use of mHealth in a group of middle-aged and older adults, we found that—despite free access to mHealth with a live coach—most did not complete the registration process. Our findings suggest that implementing mHealth for weight management requires more than one information session. Findings also suggest that focusing on the coaching relationship and how users’ lives and goals change over time may be an important way to facilitate engagement and improved health. Most participants thought mHealth was appropriate for weight management, with some nevertheless preferring in-person care. Therefore, the best way to guarantee equitable care will be to ensure multiple routes to achieving the same behavioral health goals. Veterans Health Administration patients have the option of using mHealth for weight management, but can also attend group weight management programs or single-session nutrition classes or access fitness facilities. Health care policy does not allow such access for most people in the United States; however, expanded access to behavioral weight management is an important long-term goal to ensure health for all. %M 34889761 %R 10.2196/29916 %U https://formative.jmir.org/2021/12/e29916 %U https://doi.org/10.2196/29916 %U http://www.ncbi.nlm.nih.gov/pubmed/34889761 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 12 %P e28040 %T Test-Retest Reliability of Home-Based Fitness Assessments Using a Mobile App (R Plus Health) in Healthy Adults: Prospective Quantitative Study %A Lin,I-I %A Chen,You-Lin %A Chuang,Li-Ling %+ School of Physical Therapy & Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, No. 259 Wen-hua 1st Rd, Guishan Dist, Taoyuan, 33302, Taiwan, 886 3 2118800 ext 3177, lchuang@gap.cgu.edu.tw %K mobile health app %K reliability %K home-based fitness assessments %K healthy adults %K mobile phone %K digital health %D 2021 %7 8.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Poor physical fitness has a negative impact on overall health status. An increasing number of health-related mobile apps have emerged to reduce the burden of medical care and the inconvenience of long-distance travel. However, few studies have been conducted on home-based fitness tests using apps. Insufficient monitoring of physiological signals during fitness assessments have been noted. Therefore, we developed R Plus Health, a digital health app that incorporates all the components of a fitness assessment with concomitant physiological signal monitoring. Objective: The aim of this study is to investigate the test-retest reliability of home-based fitness assessments using the R Plus Health app in healthy adults. Methods: A total of 31 healthy young adults self-executed 2 fitness assessments using the R Plus Health app, with a 2- to 3-day interval between assessments. The fitness assessments included cardiorespiratory endurance, strength, flexibility, mobility, and balance tests. The intraclass correlation coefficient was computed as a measure of the relative reliability of the fitness assessments and determined their consistency. The SE of measurement, smallest real difference at a 90% CI, and Bland–Altman analyses were used to assess agreement, sensitivity to real change, and systematic bias detection, respectively. Results: The relative reliability of the fitness assessments using R Plus Health was moderate to good (intraclass correlation coefficient 0.8-0.99 for raw scores, 0.69-0.99 for converted scores). The SE of measurement and smallest real difference at a 90% CI were 1.44-6.91 and 3.36-16.11, respectively, in all fitness assessments. The 95% CI of the mean difference indicated no significant systematic error between the assessments for the strength and balance tests. The Bland–Altman analyses revealed no significant systematic bias between the assessments for all tests, with a few outliers. The Bland–Altman plots illustrated narrow limits of agreement for upper extremity strength, abdominal strength, and right leg stance tests, indicating good agreement between the 2 assessments. Conclusions: Home-based fitness assessments using the R Plus Health app were reliable and feasible in young, healthy adults. The results of the fitness assessments can offer a comprehensive understanding of general health status and help prescribe safe and suitable exercise training regimens. In future work, the app will be tested in different populations (eg, patients with chronic diseases or users with poor fitness), and the results will be compared with clinical test results. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000030905; http://www.chictr.org.cn/showproj.aspx?proj=50229 %M 34657835 %R 10.2196/28040 %U https://formative.jmir.org/2021/12/e28040 %U https://doi.org/10.2196/28040 %U http://www.ncbi.nlm.nih.gov/pubmed/34657835 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e26988 %T Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study %A Taylor,Salima %A Korpusik,Mandy %A Das,Sai %A Gilhooly,Cheryl %A Simpson,Ryan %A Glass,James %A Roberts,Susan %+ Jean Mayer United States Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, 711 Washington Street, Boston, MA, 02111, United States, 1 617 556 3238, susan.roberts@tufts.edu %K energy intake %K macronutrient intakes %K 24-hour recall %K machine learning %K convolutional neural networks %K nutrition %K diet %K app %K natural language processing %D 2021 %7 6.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Self-monitoring food intake is a cornerstone of national recommendations for health, but existing apps for this purpose are burdensome for users and researchers, which limits use. Objective: We developed and pilot tested a new app (COCO Nutritionist) that combines speech understanding technology with technologies for mapping foods to appropriate food composition codes in national databases, for lower-burden and automated nutritional analysis of self-reported dietary intake. Methods: COCO was compared with the multiple-pass, interviewer-administered 24-hour recall method for assessment of energy intake. COCO was used for 5 consecutive days, and 24-hour dietary recalls were obtained for two of the days. Participants were 35 women and men with a mean age of 28 (range 20-58) years and mean BMI of 24 (range 17-48) kg/m2. Results: There was no significant difference in energy intake between values obtained by COCO and 24-hour recall for days when both methods were used (mean 2092, SD 1044 kcal versus mean 2030, SD 687 kcal, P=.70). There were also no significant differences between the methods for percent of energy from protein, carbohydrate, and fat (P=.27-.89), and no trend in energy intake obtained with COCO over the entire 5-day study period (P=.19). Conclusions: This first demonstration of a dietary assessment method using natural spoken language to map reported foods to food composition codes demonstrates a promising new approach to automate assessments of dietary intake. %M 34874885 %R 10.2196/26988 %U https://www.jmir.org/2021/12/e26988 %U https://doi.org/10.2196/26988 %U http://www.ncbi.nlm.nih.gov/pubmed/34874885 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 12 %P e32007 %T Examining the Theoretical Framework of Behavioral Activation for Major Depressive Disorder: Smartphone-Based Ecological Momentary Assessment Study %A van Genugten,Claire Rosalie %A Schuurmans,Josien %A Hoogendoorn,Adriaan W %A Araya,Ricardo %A Andersson,Gerhard %A Baños,Rosa %A Botella,Cristina %A Cerga Pashoja,Arlinda %A Cieslak,Roman %A Ebert,David Daniel %A García-Palacios,Azucena %A Hazo,Jean-Baptiste %A Herrero,Rocío %A Holtzmann,Jérôme %A Kemmeren,Lise %A Kleiboer,Annet %A Krieger,Tobias %A Smoktunowicz,Ewelina %A Titzler,Ingrid %A Topooco,Naira %A Urech,Antoine %A Smit,Johannes H %A Riper,Heleen %+ Department of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Oldenaller 1, Amsterdam, 1081HJ, Netherlands, 31 0207884666, c.genugten@ggzingeest.nl %K depression %K behavioral activation %K theoretical framework %K ecological momentary assessment %K random-intercept cross-lagged panel model %K behavior %K framework %K EMA %K smartphone %K mental health %K treatment %K engagement %K mood %D 2021 %7 6.12.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Behavioral activation (BA), either as a stand-alone treatment or as part of cognitive behavioral therapy, has been shown to be effective for treating depression. The theoretical underpinnings of BA derive from Lewinsohn et al’s theory of depression. The central premise of BA is that having patients engage in more pleasant activities leads to them experiencing more pleasure and elevates their mood, which, in turn, leads to further (behavioral) activation. However, there is a dearth of empirical evidence about the theoretical framework of BA. Objective: This study aims to examine the assumed (temporal) associations of the 3 constructs in the theoretical framework of BA. Methods: Data were collected as part of the “European Comparative Effectiveness Research on Internet-based Depression Treatment versus treatment-as-usual” trial among patients who were randomly assigned to receive blended cognitive behavioral therapy (bCBT). As part of bCBT, patients completed weekly assessments of their level of engagement in pleasant activities, the pleasure they experienced as a result of these activities, and their mood over the course of the treatment using a smartphone-based ecological momentary assessment (EMA) application. Longitudinal cross-lagged and cross-sectional associations of 240 patients were examined using random intercept cross-lagged panel models. Results: The analyses did not reveal any statistically significant cross-lagged coefficients (all P>.05). Statistically significant cross-sectional positive associations between activities, pleasure, and mood levels were identified. Moreover, the levels of engagement in activities, pleasure, and mood slightly increased over the duration of the treatment. In addition, mood seemed to carry over, over time, while both levels of engagement in activities and pleasurable experiences did not. Conclusions: The results were partially in accordance with the theoretical framework of BA, insofar as the analyses revealed cross-sectional relationships between levels of engagement in activities, pleasurable experiences deriving from these activities, and enhanced mood. However, given that no statistically significant temporal relationships were revealed, no conclusions could be drawn about potential causality. A shorter measurement interval (eg, daily rather than weekly EMA reports) might be more attuned to detecting potential underlying temporal pathways. Future research should use an EMA methodology to further investigate temporal associations, based on theory and how treatments are presented to patients. Trial Registration: ClinicalTrials.gov, NCT02542891, https://clinicaltrials.gov/ct2/show/NCT02542891; German Clinical Trials Register, DRKS00006866, https://tinyurl.com/ybja3xz7; Netherlands Trials Register, NTR4962, https://www.trialregister.nl/trial/4838; ClinicalTrials.Gov, NCT02389660, https://clinicaltrials.gov/ct2/show/NCT02389660; ClinicalTrials.gov, NCT02361684, https://clinicaltrials.gov/ct2/show/NCT02361684; ClinicalTrials.gov, NCT02449447, https://clinicaltrials.gov/ct2/show/NCT02449447; ClinicalTrials.gov, NCT02410616, https://clinicaltrials.gov/ct2/show/NCT02410616; ISRCTN registry, ISRCTN12388725, https://www.isrctn.com/ISRCTN12388725 %M 34874888 %R 10.2196/32007 %U https://mental.jmir.org/2021/12/e32007 %U https://doi.org/10.2196/32007 %U http://www.ncbi.nlm.nih.gov/pubmed/34874888 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e29071 %T Assessing the Implementation and Effectiveness of the Electronic Patient-Reported Outcome Tool for Older Adults With Complex Care Needs: Mixed Methods Study %A Steele Gray,Carolyn %A Chau,Edward %A Tahsin,Farah %A Harvey,Sarah %A Loganathan,Mayura %A McKinstry,Brian %A Mercer,Stewart W %A Nie,Jason Xin %A Palen,Ted E %A Ramsay,Tim %A Thavorn,Kednapa %A Upshur,Ross %A Wodchis,Walter P %+ Bridgepoint Collaboratory for Research and Innovation, Lunenfeld-Tanenebaum Research Institute, Sinai Health, 1 Bridgepoint Drive, Toronto, ON, M4M 2B5, Canada, 1 4168047100, Carolyn.SteeleGray@sinaihealth.ca %K older adults %K goal-oriented care %K quality of life %K self-management %K primary care %K eHealth %K pragmatic trial %K mobile phone %D 2021 %7 2.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Goal-oriented care is being adopted to deliver person-centered primary care to older adults with multimorbidity and complex care needs. Although this model holds promise, its implementation remains a challenge. Digital health solutions may enable processes to improve adoption; however, they require evaluation to determine feasibility and impact. Objective: This study aims to evaluate the implementation and effectiveness of the electronic Patient-Reported Outcome (ePRO) mobile app and portal system, designed to enable goal-oriented care delivery in interprofessional primary care practices. The research questions driving this study are as follows: Does ePRO improve quality of life and self-management in older adults with complex needs? What mechanisms are likely driving observed outcomes? Methods: A multimethod, pragmatic randomized controlled trial using a stepped-wedge design and ethnographic case studies was conducted over a 15-month period in 6 comprehensive primary care practices across Ontario with a target enrollment of 176 patients. The 6 practices were randomized into either early (3-month control period; 12-month intervention) or late (6-month control period; 9-month intervention) groups. The primary outcome measure of interest was the Assessment of Quality of Life-4D (AQoL-4D). Data were collected at baseline and at 3 monthly intervals for the duration of the trial. Ethnographic data included observations and interviews with patients and providers at the midpoint and end of the intervention. Outcome data were analyzed using linear models conducted at the individual level, accounting for cluster effects at the practice level, and ethnographic data were analyzed using qualitative description and framework analysis methods. Results: Recruitment challenges resulted in fewer sites and participants than expected; of the 176 target, only 142 (80.6%) patients were identified as eligible to participate because of lower-than-expected provider participation and fewer-than-expected patients willing to participate or perceived as ready to engage in goal-setting. Of the 142 patients approached, 45 (32%) participated. Patients set a variety of goals related to self-management, mental health, social health, and overall well-being. Owing to underpowering, the impact of ePRO on quality of life could not be definitively assessed; however, the intervention group, ePRO plus usual care (mean 15.28, SD 18.60) demonstrated a nonsignificant decrease in quality of life (t24=−1.20; P=.24) when compared with usual care only (mean 21.76, SD 2.17). The ethnographic data reveal a complex implementation process in which the meaningfulness (or coherence) of the technology to individuals’ lives and work acted as a key driver of adoption and tool appraisal. Conclusions: This trial experienced many unexpected and significant implementation challenges related to recruitment and engagement. Future studies could be improved through better alignment of the research methods and intervention to the complex and diverse clinical settings, dynamic goal-oriented care process, and readiness of provider and patient participants. Trial Registration: ClinicalTrials.gov NCT02917954; https://clinicaltrials.gov/ct2/show/NCT02917954 %M 34860675 %R 10.2196/29071 %U https://www.jmir.org/2021/12/e29071 %U https://doi.org/10.2196/29071 %U http://www.ncbi.nlm.nih.gov/pubmed/34860675 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 12 %P e27533 %T Selecting and Evaluating Mobile Health Apps for the Healthy Life Trajectories Initiative: Development of the eHealth Resource Checklist %A Vanderloo,Leigh M %A Carsley,Sarah %A Agarwal,Payal %A Marini,Flavia %A Dennis,Cindy-Lee %A Birken,Catherine %+ Child Health Evaluative Sciences, The Hospital for Sick Children, 686 Bay St, Toronto, ON, M5G 0A4, Canada, 1 5194956306, lvande32@uwo.ca %K eHealth resources %K applications %K quality assessment %K preconception health %D 2021 %7 2.12.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The ubiquity of smartphones and mobile devices in the general population presents an unprecedented opportunity for preventative health. Not surprisingly, the use of electronic health (eHealth) resources accessed through mobile devices in clinical trials is becoming more prevalent; the selection, screening, and collation of quality eHealth resources is necessary to clinical trials using these technologies. However, the constant creation and turnover of new eHealth resources can make this task difficult. Although syntheses of eHealth resources are becoming more common, their methodological and reporting quality require improvement so as to be more accessible to nonexperts. Further, there continues to be significant variation in quality criteria employed for assessment, with no clear method for developing the included criteria. There is currently no single existing framework that addresses all six dimensions of mobile health app quality identified in Agarwal et al’s recent scoping review (ie, basic descriptions of the design and usage of the resource; technical features and accessibility; health information quality; usability; evidence of impact; and user engagement and behavior change). In instances where highly systematic tactics are not possible (due to time constraints, cost, or lack of expertise), there may be value in adopting practical and pragmatic approaches to helping researchers and clinicians identify and disseminate e-resources. Objective: The study aimed to create a set of guidelines (ie, a checklist) to aid the members of the Healthy Life Trajectories Initiative (HeLTI) Canada trial—a preconception randomized controlled clinical trial to prevent child obesity—to assist their efforts in searching, identifying, screening, and including selected eHealth resources for participant use in the study intervention. Methods: A framework for searching, screening, and selecting eHealth resources was adapted from the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) checklist for systematic and scoping reviews to optimize the rigor, clarity, and transparency of the process. Details regarding searching, selecting, extracting, and assessing quality of eHealth resources are described. Results: This study resulted in the systematic development of a checklist consisting of 12 guiding principles, organized in a chronological versus priority sequence to aid researchers in searching, screening, and assessing the quality of various eHealth resources. Conclusions: The eHealth Resource Checklist will assist researchers in navigating the eHealth resource space by providing a mechanism to detail their process of developing inclusion criteria, identifying search location, selecting and reviewing evidence, extracting information, evaluating the quality of the evidence, and synthesizing the extracted evidence. The overarching goal of this checklist is to provide researchers or generalists new to the eHealth field with a tool that balances pragmatism with rigor and that helps standardize the process of searching and critiquing digital material—a particularly important aspect given the recent explosion of and reliance on eHealth resources. Moreover, this checklist may be useful to other researchers and practitioners developing similar health interventions. %M 34860681 %R 10.2196/27533 %U https://mhealth.jmir.org/2021/12/e27533 %U https://doi.org/10.2196/27533 %U http://www.ncbi.nlm.nih.gov/pubmed/34860681 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 12 %P e27512 %T The Use of Food Images and Crowdsourcing to Capture Real-time Eating Behaviors: Acceptability and Usability Study %A Harrington,Katharine %A Zenk,Shannon N %A Van Horn,Linda %A Giurini,Lauren %A Mahakala,Nithya %A Kershaw,Kiarri N %+ Northwestern University Feinberg School of Medicine, 680 N Lake Shore, Suite 1400, Chicago, IL, 60611, United States, 1 312 503 4014, k-kershaw@northwestern.edu %K ecological momentary assessment %K eating behaviors %K crowdsourcing %K food consumption images %K food image processing %K mobile phone %D 2021 %7 2.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: As poor diet quality is a significant risk factor for multiple noncommunicable diseases prevalent in the United States, it is important that methods be developed to accurately capture eating behavior data. There is growing interest in the use of ecological momentary assessments to collect data on health behaviors and their predictors on a micro timescale (at different points within or across days); however, documenting eating behaviors remains a challenge. Objective: This pilot study (N=48) aims to examine the feasibility—usability and acceptability—of using smartphone-captured and crowdsource-labeled images to document eating behaviors in real time. Methods: Participants completed the Block Fat/Sugar/Fruit/Vegetable Screener to provide a measure of their typical eating behavior, then took pictures of their meals and snacks and answered brief survey questions for 7 consecutive days using a commercially available smartphone app. Participant acceptability was determined through a questionnaire regarding their experiences administered at the end of the study. The images of meals and snacks were uploaded to Amazon Mechanical Turk (MTurk), a crowdsourcing distributed human intelligence platform, where 2 Workers assigned a count of food categories to the images (fruits, vegetables, salty snacks, and sweet snacks). The agreement among MTurk Workers was assessed, and weekly food counts were calculated and compared with the Screener responses. Results: Participants reported little difficulty in uploading photographs and remembered to take photographs most of the time. Crowdsource-labeled images (n=1014) showed moderate agreement between the MTurk Worker responses for vegetables (688/1014, 67.85%) and high agreement for all other food categories (871/1014, 85.89% for fruits; 847/1014, 83.53% for salty snacks, and 833/1014, 81.15% for sweet snacks). There were no significant differences in weekly food consumption between the food images and the Block Screener, suggesting that this approach may measure typical eating behaviors as accurately as traditional methods, with lesser burden on participants. Conclusions: Our approach offers a potentially time-efficient and cost-effective strategy for capturing eating events in real time. %M 34860666 %R 10.2196/27512 %U https://formative.jmir.org/2021/12/e27512 %U https://doi.org/10.2196/27512 %U http://www.ncbi.nlm.nih.gov/pubmed/34860666 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 12 %P e30304 %T Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation %A Majam,Mohammed %A Phatsoane,Mothepane %A Hanna,Keith %A Faul,Charles %A Arora,Lovkesh %A Makthal,Sarvesh %A Kumar,Akhil %A Jois,Kashyap %A Lalla-Edward,Samanta Tresha %+ Ezintsha, Faculty of Health Sciences, University of Witswatersrand, Sunnyside Office Park, 31 Princess of Wales Terrace, Johannesburg, 2193, South Africa, 27 826172490, slallaedward@ezintsha.org %K machine learning %K predictive risk %K modeling %K algorithm %K HIV status %K HIV %K risk assessment %K South Africa %D 2021 %7 2.12.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score. Objective: The primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV. Methods: In total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); (2) two HIV tests (one per study visit) being performed by a nurse/counselor according to South African national guidelines (to evaluate the prediction accuracy of the tool); and (3) communicating test results and completing a user experience survey questionnaire. The output metrics for this study will be computed by using the participants’ risk assessment scores as “predictions” and the test results as the “ground truth.” Analyses will be completed after visit 1 and then again after visit 2. All risk assessment scores will be used to calculate the reliability of the machine-guided tool. Results: Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool’s performance and usability, together with user satisfaction and recommendations for improvement. Conclusions: Machine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV. Trial Registration: South African National Clinical Trial Registry DOH-27-042021-679; https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545 International Registered Report Identifier (IRRID): DERR1-10.2196/30304 %M 34860679 %R 10.2196/30304 %U https://www.researchprotocols.org/2021/12/e30304 %U https://doi.org/10.2196/30304 %U http://www.ncbi.nlm.nih.gov/pubmed/34860679 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 12 %P e27208 %T Walking and Daily Affect Among Sedentary Older Adults Measured Using the StepMATE App: Pilot Randomized Controlled Trial %A Bisson,Alycia N %A Sorrentino,Victoria %A Lachman,Margie E %+ Psychiatry Department, Brigham and Women’s Hospital, Room 394, 221 Longwood Ave, Boston, MA, 02115, United States, 1 5082598151, alyciansullivan@brandeis.edu %K physical activity %K fitness technology %K intervention %K behavioral science %K aging %K mobile phone %D 2021 %7 1.12.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Although fitness technology can track and encourage increases in physical activity, few smartphone apps are based on behavior change theories. Apps that do include behavioral components tend to be costly and often do not include strategies to help those who are unsure of how to increase their physical activity. Objective: The aim of this pilot study is to test the efficacy of a new app, StepMATE, for increasing daily walking in a sample of inactive adults and to examine daily relationships between walking and self-reported mood and energy. Methods: The participants were middle-aged and older adults aged ≥50 years (mean 61.64, SD 7.67 years). They were randomly assigned to receive either a basic, pedometer-like version of the app or a version with supports to help them determine where, when, and with whom to walk. Of the 96 participants randomized to 1 of 2 conditions, 87 (91%) completed pretest assessments and 81 (84%) successfully downloaded the app. Upon downloading the app, step data from the week prior were automatically recorded. The participants in both groups were asked to set a daily walking goal, which they could change at any point during the intervention. They were asked to use the app as much as possible over the next 4 weeks. Twice per day, pop-up notifications assessed mood and energy levels. Results: Although one group had access to additional app features, both groups used the app in a similar way, mainly using just the walk-tracking feature. Multilevel models revealed that both groups took significantly more steps during the 4-week study than during the week before downloading the app (γ=0.24; P<.001). During the study, the participants in both groups averaged 5248 steps per day compared with an average of 3753 steps per day during the baseline week. Contrary to predictions, there were no differences in step increases between the two conditions. Cognition significantly improved from pre- to posttest (γ=0.17; P=.02). Across conditions, on days in which the participants took more steps than average, they reported better mood and higher energy levels on the same day and better mood on the subsequent day. Daily associations among walking, mood, and energy were significant for women but not for men and were stronger for older participants (those aged ≥62 years) than for the younger participants. Conclusions: Both groups increased their steps to a similar extent, suggesting that setting and monitoring daily walking goals was sufficient for an initial increase and maintenance of steps. Across conditions, walking had benefits for positive mood and energy levels, particularly for women and older participants. Further investigations should identify other motivating factors that could lead to greater and more sustained increases in physical activity. Trial Registration: ClinicalTrials.gov NCT03124537; https://clinicaltrials.gov/ct2/show/NCT03124537 %M 34855609 %R 10.2196/27208 %U https://mhealth.jmir.org/2021/12/e27208 %U https://doi.org/10.2196/27208 %U http://www.ncbi.nlm.nih.gov/pubmed/34855609 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 12 %P e15433 %T Tools for Evaluating the Content, Efficacy, and Usability of Mobile Health Apps According to the Consensus-Based Standards for the Selection of Health Measurement Instruments: Systematic Review %A Muro-Culebras,Antonio %A Escriche-Escuder,Adrian %A Martin-Martin,Jaime %A Roldán-Jiménez,Cristina %A De-Torres,Irene %A Ruiz-Muñoz,Maria %A Gonzalez-Sanchez,Manuel %A Mayoral-Cleries,Fermin %A Biró,Attila %A Tang,Wen %A Nikolova,Borjanka %A Salvatore,Alfredo %A Cuesta-Vargas,Antonio Ignacio %+ Grupo Clinimetría (F-14), University of Málaga, C/ Arquitecto Francisco Peñalosa 3, Málaga, 29071, Spain, 34 951952852, acuesta@uma.es %K mobile health %K mHealth %K eHealth %K mobile apps %K assessment %K rating %K smartphone %K questionnaire design %K mobile phone %D 2021 %7 1.12.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: There are several mobile health (mHealth) apps in mobile app stores. These apps enter the business-to-customer market with limited controls. Both, apps that users use autonomously and those designed to be recommended by practitioners require an end-user validation to minimize the risk of using apps that are ineffective or harmful. Prior studies have reviewed the most relevant aspects in a tool designed for assessing mHealth app quality, and different options have been developed for this purpose. However, the psychometric properties of the mHealth quality measurement tools, that is, the validity and reliability of the tools for their purpose, also need to be studied. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) initiative has developed tools for selecting the most suitable measurement instrument for health outcomes, and one of the main fields of study was their psychometric properties. Objective: This study aims to address and psychometrically analyze, following the COSMIN guideline, the quality of the tools that are used to measure the quality of mHealth apps. Methods: From February 1, 2019, to December 31, 2019, 2 reviewers searched PubMed and Embase databases, identifying mHealth app quality measurement tools and all the validation studies associated with each of them. For inclusion, the studies had to be meant to validate a tool designed to assess mHealth apps. Studies that used these tools for the assessment of mHealth apps but did not include any psychometric validation were excluded. The measurement tools were analyzed according to the 10 psychometric properties described in the COSMIN guideline. The dimensions and items analyzed in each tool were also analyzed. Results: The initial search showed 3372 articles. Only 10 finally met the inclusion criteria and were chosen for analysis in this review, analyzing 8 measurement tools. Of these tools, 4 validated ≥5 psychometric properties defined in the COSMIN guideline. Although some of the tools only measure the usability dimension, other tools provide information such as engagement, esthetics, or functionality. Furthermore, 2 measurement tools, Mobile App Rating Scale and mHealth Apps Usability Questionnaire, have a user version, as well as a professional version. Conclusions: The Health Information Technology Usability Evaluation Scale and the Measurement Scales for Perceived Usefulness and Perceived Ease of Use were the most validated tools, but they were very focused on usability. The Mobile App Rating Scale showed a moderate number of validated psychometric properties, measures a significant number of quality dimensions, and has been validated in a large number of mHealth apps, and its use is widespread. It is suggested that the continuation of the validation of this tool in other psychometric properties could provide an appropriate option for evaluating the quality of mHealth apps. %M 34855618 %R 10.2196/15433 %U https://mhealth.jmir.org/2021/12/e15433 %U https://doi.org/10.2196/15433 %U http://www.ncbi.nlm.nih.gov/pubmed/34855618 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 12 %P e25071 %T An Electronic Patient-Reported Outcome Mobile App for Data Collection in Type A Hemophilia: Design and Usability Study %A Petracca,Francesco %A Tempre,Rosaria %A Cucciniello,Maria %A Ciani,Oriana %A Pompeo,Elena %A Sannino,Luigi %A Lovato,Valeria %A Castaman,Giancarlo %A Ghirardini,Alessandra %A Tarricone,Rosanna %+ Centre for Research in Health and Social Care Management (CERGAS), Government, Health and Non Profit Division, SDA Bocconi, Via Sarfatti, 10, Milan, 20136, Italy, 39 02 58365257, francesco.petracca@unibocconi.it %K mobile apps %K mHealth %K hemophilia A %K rare diseases %K usability %K user-centered design %K design science %K mobile phone %D 2021 %7 1.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: There is currently limited evidence on the level and intensity of physical activity in individuals with hemophilia A. Mobile technologies can offer a rigorous and reliable alternative to support data collection processes but they are often associated with poor user retention. The lack of longitudinal continuity in their use can be partly attributed to the insufficient consideration of stakeholder inputs in the development process of mobile apps. Several user-centered models have been proposed to guarantee that a thorough knowledge of the end user needs is considered in the development process of mobile apps. Objective: The aim of this study is to design and validate an electronic patient-reported outcome mobile app that requires sustained active input by individuals during POWER, an observational study that aims at evaluating the relationship between physical activity levels and bleeding in patients with hemophilia A. Methods: We adopted a user-centered design and engaged several stakeholders in the development and usability testing of this mobile app. During the concept generation and ideation phase, we organized a need-assessment focus group (FG) with patient representatives to elicit specific design requirements for the end users. We then conducted 2 exploratory FGs to seek additional inputs for the app’s improvement and 2 confirmatory FGs to validate the app and test its usability in the field through the mobile health app usability questionnaire. Results: The findings from the thematic analysis of the need-assessment FG revealed that there was a demand for sense making, for simplification of app functionalities, for maximizing integration, and for minimizing the feeling of external control. Participants involved in the later stages of the design refinement contributed to improving the design further by upgrading the app’s layout and making the experience with the app more efficient through functions such as chatbots and visual feedback on the number of hours a wearable device had been worn, to ensure that the observed data were actually registered. The end users rated the app highly during the quantitative assessment, with an average mobile health app usability questionnaire score of 5.32 (SD 0.66; range 4.44-6.23) and 6.20 (SD 0.43; range 5.72-6.88) out of 7 in the 2 iterative usability testing cycles. Conclusions: The results of the usability test indicated a high, growing satisfaction with the electronic patient-reported outcome app. The adoption of a thorough user-centered design process using several types of FGs helped maximize the likelihood of sustained retention of the app’s users and made it fit for data collection of relevant outcomes in the observational POWER study. The continuous use of the app and the actual level of engagement will be evaluated during the ongoing trial. Trial Registration: ClinicalTrials.gov NCT04165135; https://clinicaltrials.gov/ct2/show/NCT04165135 %M 34855619 %R 10.2196/25071 %U https://formative.jmir.org/2021/12/e25071 %U https://doi.org/10.2196/25071 %U http://www.ncbi.nlm.nih.gov/pubmed/34855619 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 11 %P e30308 %T The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation %A Stöhr,Mark R %A Günther,Andreas %A Majeed,Raphael W %+ Justus-Liebig-University Giessen, Universities of Giessen and Marburg Lung Center (UGMLC), German Center for Lung Research (DZL), Klinikstraße 36, Gießen, 35392, Germany, 49 641 985 42117, mark.stoehr@innere.med.uni-giessen.de %K usability %K metadata %K data visualization %K semantic web %K data management %K data warehousing %K communication barriers %K quality improvement %K biological ontologies %K data curation %D 2021 %7 29.11.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: In the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data. Objective: This study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues. Methods: We evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories. Results: A total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app. Conclusions: A usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond. %M 34847059 %R 10.2196/30308 %U https://medinform.jmir.org/2021/11/e30308 %U https://doi.org/10.2196/30308 %U http://www.ncbi.nlm.nih.gov/pubmed/34847059 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e26743 %T Using Ecological Momentary Assessment to Study the Development of COVID-19 Worries in Sweden: Longitudinal Study %A Schulz,Peter Johannes %A Andersson,Elin M %A Bizzotto,Nicole %A Norberg,Margareta %+ Institute of Communication and Health, Università della Svizzera italiana, Via Giuseppe Buffi 13, Lugano, 6900, Switzerland, 41 58666 ext 4724, schulzp@usi.ch %K COVID-19 %K coronavirus %K longitudinal studies %K EMA %K worry %K fear %K pandemics %D 2021 %7 29.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The foray of COVID-19 around the globe has certainly instigated worries in many people, and lockdown measures may well have triggered more specific worries. Sweden, more than other countries, relied on voluntary measures to fight the pandemic. This provides a particularly interesting context to assess people’s reactions to the threat of the pandemic. Objective: The general aim of this study was to better understand the worried reactions to the virus and the associated lockdown measures. As there have been very few longitudinal studies in this area published to date, development of feelings of worry over time was analyzed over a longer range than in previous research. Affective variables, worry in particular, were included because most of the research in this field has focused on cognitive variables. To employ new methodology, ecological momentary assessment was used for data collection and a multilevel modeling approach was adopted for data analysis. Methods: Results were based on an unbalanced panel sample of 260 Swedish participants filling in 3226 interview questionnaires by smartphone over a 7-week period in 2020 during the rapid rise of cases in the early phase of the pandemic. Causal factors considered in this study included the perceived severity of an infection, susceptibility of a person to the threat posed by the virus, perceived efficacy of safeguarding measures, and assessment of government action against the spread of COVID-19. The effect of these factors on worries was traced in two analytical steps: the effects at the beginning of the study and the effect on the trend during the study. Results: The level of general worry related to COVID-19 was modest (mean 6.67, SD 2.54 on an 11-point Likert scale); the increase during the study period was small, but the interindividual variation of both the worry level and its increase over time was large. Findings confirmed that the hypothesized causal factors (severity of infection, susceptibility to the threat of the virus, efficacy of safeguarding, and assessment of government preventive action) did indeed affect the level of worry. Conclusions: The results confirmed earlier research in a very special case and demonstrated the usefulness of a different study design, which takes a longitudinal perspective, and a new type of data analysis borrowed from multilevel study design. %M 34847065 %R 10.2196/26743 %U https://www.jmir.org/2021/11/e26743 %U https://doi.org/10.2196/26743 %U http://www.ncbi.nlm.nih.gov/pubmed/34847065 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 11 %P e25618 %T Mobile Text Messaging for Tobacco Risk Communication Among Young Adult Community College Students: Randomized Trial of Project Debunk %A Prokhorov,Alexander V %A Calabro,Karen Sue %A Arya,Ashish %A Russell,Sophia %A Czerniak,Katarzyna W %A Botello,Gabrielle C %A Chen,Minxing %A Yuan,Ying %A Perez,Adriana %A Vidrine,Damon J %A Perry,Cheryl L %A Khalil,Georges Elias %+ Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Science Building, 2004 Mowry Road Office 2252, Gainesville, FL, 32610, United States, 1 3522948415, gkhalil@ufl.edu %K tobacco use %K risk communication %K text messaging %K message framing %K regulatory science %K young adults %K vaping %K mobile phone %D 2021 %7 24.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of new and emerging tobacco products (NETPs) and conventional tobacco products (CTPs) has been linked to several alarming medical conditions among young adults (YAs). Considering that 96% of YAs own mobile phones, SMS text messaging may be an effective strategy for tobacco risk communication. Objective: Project Debunk is a community-based randomized trial aiming to identify specific types of messages that effectively improve perceived NETP and CTP risk among YAs in community colleges. Methods: With YAs recruited offline from 3 campuses at the Houston Community College (September 2016 to July 2017), we conducted a 6-month randomized trial with 8 arms based on the combination of 3 message categories: framing (gain-framed vs loss-framed), depth (simple vs complex), and appeal (emotional vs rational). Participants received fully automated web-based SMS text messages in two 30-day campaigns (2 messages per day). We conducted repeated-measures mixed-effect models stratified by message type received, predicting perceived CTP and NETP risks. Owing to multiple testing with 7 models, an association was deemed significant for P<.007 (.05 divided by 7). Results: A total of 636 participants completed the baseline survey, were randomized to 1 of 8 conditions (between 73 and 86 participants per condition), and received messages from both campaigns. By the 2-month post campaign 2 assessment point, 70.1% (446/636) completed all outcome measures. By the end of both campaigns, participants had a significant increase in perceived NETP risk over time (P<.001); however, participants had a marginal increase in perceived CTP risk (P=.008). Separately for each group, there was a significant increase in perceived NETP risk among participants who received rational messages (P=.005), those who received emotional messages (P=.006), those who received simple messages (P=.003), and those who received gain-framed messages (P=.003). Conclusions: In this trial, YAs had an increase in perceived NETP risk. However, with stratification, we observed a significant increase in perceived NETP risk upon exposure to rational, emotional, simple, and gain-framed messages. In addition, YAs generally had an increase in perceived CTP risk and presented nonsignificant but observable improvement upon exposure to emotional, complex, and loss-framed messages. With the results of this study, researchers and practitioners implementing mobile health programs may take advantage of our tailored messages through larger technology-based programs such as smartphone apps and social media campaigns. Trial Registration: ClinicalTrials.gov NCT03457480; https://clinicaltrials.gov/ct2/show/NCT03457480 International Registered Report Identifier (IRRID): RR2-10.2196/10977 %M 34822339 %R 10.2196/25618 %U https://mhealth.jmir.org/2021/11/e25618 %U https://doi.org/10.2196/25618 %U http://www.ncbi.nlm.nih.gov/pubmed/34822339 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e31678 %T Digital Instruments for Reporting of Gastrointestinal Symptoms in Clinical Trials: Comparison of End-of-Day Diaries Versus the Experience Sampling Method %A Beckers,Abraham B %A Snijkers,Johanna T W %A Weerts,Zsa Zsa R M %A Vork,Lisa %A Klaassen,Tim %A Smeets,Fabienne G M %A Masclee,Ad A M %A Keszthelyi,Daniel %+ Division of Gastroenterology-Hepatology, Department of Internal Medicine, Maastricht University Medical Center, Universiteitssingel 50, Maastricht, 6229 ER, Netherlands, 31 0433881844, ab.beckers@maastrichtuniversity.nl %K irritable bowel syndrome %K functional dyspepsia %K digital diary %K experience sampling method %K smartphone app %K mobile phone application %K mHealth %K eHealth %K compliance %K patient-reported outcome measures %D 2021 %7 24.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Questionnaires are necessary tools for assessing symptoms of disorders of the brain-gut interaction in clinical trials. We previously reported on the excellent adherence to a smartphone app used as symptom diary in a randomized clinical trial on irritable bowel syndrome (IBS). Other sampling methods, such as the experience sampling method (ESM), are better equipped to measure symptom variability over time and provide useful information regarding possible symptom triggers, and they are free of ecological and recall bias. The high frequency of measurements, however, could limit the feasibility of ESM in clinical trials. Objective: This study aimed to compare the adherence rates of a smartphone-based end-of-day diary and ESM for symptom assessment in IBS and functional dyspepsia (FD). Methods: Data from 4 separate studies were included. Patients with IBS participated in a randomized controlled trial, which involved a smartphone end-of-day diary for a 2+8-week (pretreatment + treatment) period, and an observational study in which patients completed ESM assessments using a smartphone app for 1 week. Patients with FD participated in a randomized controlled trial, which involved a smartphone end-of-day diary for a 2+12-week (pretreatment + treatment) period, and an observational study in which patients completed ESM assessments using a smartphone app for 1 week. Adherence rates were compared between these 2 symptom sampling methods. Results: In total, 25 patients with IBS and 15 patients with FD were included. Overall adherence rates for the end-of-day diaries were significantly higher than those for ESM (IBS: 92.7% vs 69.8%, FD: 90.1% vs 61.4%, respectively). Conclusions: This study demonstrates excellent adherence rates for smartphone app–based end-of-day diaries as used in 2 separate clinical trials. Overall adherence rates for ESM were significantly lower, rendering it more suitable for intermittent sampling periods rather than continuous sampling during longer clinical trials. %M 34821561 %R 10.2196/31678 %U https://formative.jmir.org/2021/11/e31678 %U https://doi.org/10.2196/31678 %U http://www.ncbi.nlm.nih.gov/pubmed/34821561 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 11 %P e28204 %T Outcomes of Digital Biomarker–Based Interventions: Protocol for a Systematic Review of Systematic Reviews %A Motahari-Nezhad,Hossein %A Péntek,Márta %A Gulácsi,László %A Zrubka,Zsombor %+ Health Economics Research Center, University Research and Innovation Center, Obuda University, Bécsi út 96/b, Budapest, 1034, Hungary, 36 30 202 9415, zsombor.zrubka@uni-corvinus.hu %K digital biomarker %K outcome %K systematic review %K meta-analysis %K digital health %K mobile health %K Grading of Recommendations, Assessment, Development and Evaluation %K AMSTAR-2 %K review %K biomarkers %K clinical outcome %K interventions %K wearables %K portables %K digestables %K implants %D 2021 %7 24.11.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Digital biomarkers are defined as objective, quantifiable, physiological, and behavioral data that are collected and measured using digital devices such as portables, wearables, implantables, or digestibles. For their widespread adoption in publicly financed health care systems, it is important to understand how their benefits translate into improved patient outcomes, which is essential for demonstrating their value. Objective: The paper presents the protocol for a systematic review that aims to assess the quality and strength of the evidence reported in systematic reviews regarding the impact of digital biomarkers on clinical outcomes compared to interventions without digital biomarkers. Methods: A comprehensive search for reviews from 2019 to 2020 will be conducted in PubMed and the Cochrane Library using keywords related to digital biomarkers and a filter for systematic reviews. Original full-text English publications of systematic reviews comparing clinical outcomes of interventions with and without digital biomarkers via meta-analysis will be included. The AMSTAR-2 tool will be used to assess the methodological quality of these reviews. To assess the quality of evidence, we will evaluate the systematic reviews using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) tool. To detect the possible presence of reporting bias, we will determine whether a protocol was published prior to the start of the studies. A qualitative summary of the results by digital biomarker technology and outcomes will be provided. Results: This protocol was submitted before data collection. Search, screening, and data extraction will commence in December 2021 in accordance with the published protocol. Conclusions: Our study will provide a comprehensive summary of the highest level of evidence available on digital biomarker interventions, providing practical guidance for health care providers. Our results will help identify clinical areas in which the use of digital biomarkers has led to favorable clinical outcomes. In addition, our findings will highlight areas of evidence gaps where the clinical benefits of digital biomarkers have not yet been demonstrated. International Registered Report Identifier (IRRID): PRR1-10.2196/28204 %M 34821568 %R 10.2196/28204 %U https://www.researchprotocols.org/2021/11/e28204 %U https://doi.org/10.2196/28204 %U http://www.ncbi.nlm.nih.gov/pubmed/34821568 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 11 %P e26397 %T A Mobile Intervention for Self-Efficacious and Goal-Directed Smartphone Use in the General Population: Randomized Controlled Trial %A Keller,Jan %A Roitzheim,Christina %A Radtke,Theda %A Schenkel,Konstantin %A Schwarzer,Ralf %+ Department of Education and Psychology, Freie Universität Berlin, Habelschwerdter Allee 45, Berlin, 14195, Germany, 49 30 8385 4906, jan.keller@fu-berlin.de %K problematic smartphone use %K smartphone unlocks %K smartphone time %K behavior change %K self-efficacy %K action planning %K digital detox %K time-out %K randomized controlled trial %D 2021 %7 23.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: People spend large parts of their everyday life using their smartphones. Despite various advantages of the smartphone for daily life, problematic forms of smartphone use exist that are related to negative psychological and physiological consequences. To reduce problematic smartphone use, existing interventions are oftentimes app-based and include components that help users to monitor and restrict their smartphone use by setting timers and blockers. These kinds of digital detox interventions, however, fail to exploit psychological resources, such as through promoting self-efficacious and goal-directed smartphone use. Objective: The aim of this study is to evaluate the theory-based smartphone app “Not Less But Better” that was developed to make people aware of psychological processes while using the smartphone and to support them in using their smartphone in accordance with their goals and values. Methods: In a randomized controlled trial, effects of a 20-day intervention app consisting of five 4-day training modules to foster a goal-directed smartphone use were evaluated. In the active control condition (treatment as usual), participants received a digital detox treatment and planned daily time-outs of at least 1 hour per day. Up to a 3-week follow-up, self-reported problematic smartphone use, objectively measured daily smartphone unlocks, time of smartphone use, self-efficacy, and planning towards goal-directed smartphone use were assessed repeatedly. Linear 2-level models tested intervention effects. Mediation models served to analyze self-efficacy and planning as potential mechanisms of the intervention. Results: Out of 232 enrolled participants, 110 (47.4%; 55 participants in each condition) provided data at postintervention and 88 (37.9%; 44 participants in each condition) at 3-week follow-up. Both conditions manifested substantial reductions in problematic smartphone use and in the amount of time spent with the smartphone. The number of daily unlocks did not change over time. Further, modelling changes in self-efficacy as a mediator between the intervention and problematic smartphone use at follow-up fit well to the data and showed an indirect effect (b=–0.09; 95% bias-corrected bootstrap CI –0.26 to –0.01), indicating that self-efficacy was an important intervention mechanism. Another mediation model revealed an indirect effect from changes in planning via smartphone unlocks at postintervention on problematic smartphone use at follow-up (b=–0.029, 95% bias-corrected bootstrap CI –0.078 to –0.003). Conclusions: An innovative, theory-based intervention app on goal-directed smartphone use has been found useful in lowering problematic smartphone use and time spent with the smartphone. However, observed reductions in both outcomes were not superior to the active control condition (ie, digital detox treatment). Nonetheless, the present findings highlight the importance in promoting self-efficacy and planning goal-directed smartphone use to achieve improvements in problematic smartphone use. This scalable intervention app appears suitable for practical use and as an alternative to common digital detox apps. Future studies should address issues of high attrition by adding just-in-time procedures matched to smartphone users’ needs. Trial Registration: German Clinical Trials Register DRKS00017606; https://tinyurl.com/27c9kmwy %M 34817388 %R 10.2196/26397 %U https://mhealth.jmir.org/2021/11/e26397 %U https://doi.org/10.2196/26397 %U http://www.ncbi.nlm.nih.gov/pubmed/34817388 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 11 %P e30309 %T Momentary Manifestations of Negative Symptoms as Predictors of Clinical Outcomes in People at High Risk for Psychosis: Experience Sampling Study %A Paetzold,Isabell %A Hermans,Karlijn S F M %A Schick,Anita %A Nelson,Barnaby %A Velthorst,Eva %A Schirmbeck,Frederike %A , %A van Os,Jim %A Morgan,Craig %A van der Gaag,Mark %A de Haan,Lieuwe %A Valmaggia,Lucia %A McGuire,Philip %A Kempton,Matthew %A Myin-Germeys,Inez %A Reininghaus,Ulrich %+ Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J 5 1, Mannheim, 68159, Germany, 49 62117031931, ulrich.reininghaus@zi-mannheim.de %K ecological momentary assessment %K psychotic disorder %K psychopathology %D 2021 %7 19.11.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Negative symptoms occur in individuals at ultrahigh risk (UHR) for psychosis. Although there is evidence that observer ratings of negative symptoms are associated with level of functioning, the predictive value of subjective experience in daily life for individuals at UHR has not been studied yet. Objective: This study therefore aims to investigate the predictive value of momentary manifestations of negative symptoms for clinical outcomes in individuals at UHR. Methods: Experience sampling methodology was used to measure momentary manifestations of negative symptoms (blunted affective experience, lack of social drive, anhedonia, and social anhedonia) in the daily lives of 79 individuals at UHR. Clinical outcomes (level of functioning, illness severity, UHR status, and transition status) were assessed at baseline and at 1- and 2-year follow-ups. Results: Lack of social drive, operationalized as greater experienced pleasantness of being alone, was associated with poorer functioning at the 2-year follow-up (b=−4.62, P=.01). Higher levels of anhedonia were associated with poorer functioning at the 1-year follow-up (b=5.61, P=.02). Higher levels of social anhedonia were associated with poorer functioning (eg, disability subscale: b=6.36, P=.006) and greater illness severity (b=−0.38, P=.045) at the 1-year follow-up. In exploratory analyses, there was evidence that individuals with greater variability of positive affect (used as a measure of blunted affective experience) experienced a shorter time to remission from UHR status at follow-up (hazard ratio=4.93, P=.005). Conclusions: Targeting negative symptoms in individuals at UHR may help to predict clinical outcomes and may be a promising target for interventions in the early stages of psychosis. %M 34807831 %R 10.2196/30309 %U https://mental.jmir.org/2021/11/e30309 %U https://doi.org/10.2196/30309 %U http://www.ncbi.nlm.nih.gov/pubmed/34807831 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 11 %P e30915 %T Opening the Black Box of Daily Life in Nonsuicidal Self-injury Research: With Great Opportunity Comes Great Responsibility %A Kiekens,Glenn %A Robinson,Kealagh %A Tatnell,Ruth %A Kirtley,Olivia J %+ Faculty of Psychology and Educational Sciences, Clinical Psychology, KU Leuven, Tiensestraat 102, Leuven, 3720, Belgium, 32 16372852, glenn.kiekens@kuleuven.be %K real-time monitoring %K nonsuicidal self-injury %K NSSI %K experience sampling %K ecological momentary assessment %K digital psychiatry %D 2021 %7 19.11.2021 %9 Viewpoint %J JMIR Ment Health %G English %X Although nonsuicidal self-injury (NSSI)—deliberate damaging of body tissue without suicidal intent—is a behavior that occurs in interaction with real-world contexts, studying NSSI in the natural environment has historically been impossible. Recent advances in real-time monitoring technologies have revolutionized our ability to do exactly that, providing myriad research and clinical practice opportunities. In this viewpoint paper, we review new research pathways to improve our ability to understand, predict, and prevent NSSI, and provide critical perspectives on the responsibilities inherent to conducting real-time monitoring studies on NSSI. Real-time monitoring brings unique opportunities to advance scientific understanding about (1) the dynamic course of NSSI, (2) the real-time predictors thereof and ability to detect acute risk, (3) the ecological validity of theoretical models, (4) the functional mechanisms and outcomes of NSSI, and (5) the promotion of person-centered care and novel technology-based interventions. By considering the opportunities of real-time monitoring research in the context of the accompanying responsibilities (eg, inclusive recruitment, sound and transparent research practices, participant safety and engagement, measurement reactivity, researcher well-being and training), we provide novel insights and resources to open the black box of daily life in the next decade(s) of NSSI research. %M 34807835 %R 10.2196/30915 %U https://mental.jmir.org/2021/11/e30915 %U https://doi.org/10.2196/30915 %U http://www.ncbi.nlm.nih.gov/pubmed/34807835 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 11 %P e29176 %T An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC): Design and Evaluation Study %A Greulich,Leonard %A Hegselmann,Stefan %A Dugas,Martin %+ Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Building A11, Münster, 48149, Germany, 49 15905368729, leonard.greulich@uni-muenster.de %K electronic data capture %K open science %K data interoperability %K metadata reuse %K mobile health %K data standard %K mobile phone %D 2021 %7 19.11.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. Objective: The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. Methods: We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. Results: We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. Conclusions: Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians. %M 34806987 %R 10.2196/29176 %U https://medinform.jmir.org/2021/11/e29176 %U https://doi.org/10.2196/29176 %U http://www.ncbi.nlm.nih.gov/pubmed/34806987 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 11 %P e27674 %T Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation %A Böttcher,Sebastian %A Bruno,Elisa %A Manyakov,Nikolay V %A Epitashvili,Nino %A Claes,Kasper %A Glasstetter,Martin %A Thorpe,Sarah %A Lees,Simon %A Dümpelmann,Matthias %A Van Laerhoven,Kristof %A Richardson,Mark P %A Schulze-Bonhage,Andreas %A , %+ Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Breisacher Str. 64, Freiburg im Breisgau, 79106, Germany, 49 76127052410, sebastian.boettcher@uniklinik-freiburg.de %K wearables %K epilepsy %K seizure detection %K multimodal data %K mHealth %K mobile health %K digital health %K eHealth %D 2021 %7 19.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. Objective: Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. Methods: An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. Results: In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. Conclusions: We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection. %M 34806993 %R 10.2196/27674 %U https://mhealth.jmir.org/2021/11/e27674 %U https://doi.org/10.2196/27674 %U http://www.ncbi.nlm.nih.gov/pubmed/34806993 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e30991 %T A Technology-Based Pregnancy Health and Wellness Intervention (Two Happy Hearts): Case Study %A Jimah,Tamara %A Borg,Holly %A Kehoe,Priscilla %A Pimentel,Pamela %A Turner,Arlene %A Labbaf,Sina %A Asgari Mehrabadi,Milad %A Rahmani,Amir M. %A Dutt,Nikil %A Guo,Yuqing %+ Sue & Bill Gross School of Nursing, University of California, Irvine, 299D Berk Hall, Irvine, CA, 92697, United States, 1 949 824 9057, tjimah@hs.uci.edu %K ecological momentary assessment %K heart rate %K mHealth %K physical activity %K pregnancy %K sleep %K wearable electronic device %D 2021 %7 17.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement. Objective: The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user’s engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components. Methods: A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotional health using the Two Happy Hearts mobile app–based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data. Results: Decreased resting heart rate was significantly correlated with increased heart rate variability (r=–0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery. Conclusions: The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women. %M 34787576 %R 10.2196/30991 %U https://formative.jmir.org/2021/11/e30991 %U https://doi.org/10.2196/30991 %U http://www.ncbi.nlm.nih.gov/pubmed/34787576 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e29128 %T A Two-Minute Walking Test With a Smartphone App for Persons With Multiple Sclerosis: Validation Study %A van Oirschot,Pim %A Heerings,Marco %A Wendrich,Karine %A den Teuling,Bram %A Dorssers,Frank %A van Ee,René %A Martens,Marijn Bart %A Jongen,Peter Joseph %+ Orikami Digital Health Products, Ridderstraat 29, Nijmegen, 6511 TM, Netherlands, 31 24 301 0100, pim@mssherpa.nl %K multiple sclerosis %K relapsing remitting %K mobility %K mobile phone %K 2-Minute Walking Test %D 2021 %7 17.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Walking disturbances are a common dysfunction in persons with multiple sclerosis (MS). The 2-Minute Walking Test (2MWT) is widely used to quantify walking speed. We implemented a smartphone-based 2MWT (s2MWT) in MS sherpa, an app for persons with MS. When performing the s2MWT, users of the app are instructed to walk as fast as safely possible for 2 minutes in the open air, while the app records their movement and calculates the distance walked. Objective: The aim of this study is to investigate the concurrent validity and test-retest reliability of the MS sherpa s2MWT. Methods: We performed a validation study on 25 persons with relapsing-remitting MS and 79 healthy control (HC) participants. In the HC group, 21 participants were matched to the persons with MS based on age, gender, and education and these followed the same assessment schedule as the persons with MS (the HC-matched group), whereas 58 participants had a less intense assessment schedule to determine reference values (the HC-normative group). Intraclass correlation coefficients (ICCs) were determined between the distance measured by the s2MWT and the distance measured using distance markers on the pavement during these s2MWT assessments. ICCs were also determined for test-retest reliability and derived from 10 smartphone tests per study participant, with 3 days in between each test. We interviewed 7 study participants with MS regarding their experiences with the s2MWT. Results: In total, 755 s2MWTs were completed. The adherence rate for the persons with MS and the participants in the HC-matched group was 92.4% (425/460). The calculated distance walked on the s2MWT was, on average, 8.43 m or 5% (SD 18.9 m or 11%) higher than the distance measured using distance markers (n=43). An ICC of 0.817 was found for the concurrent validity of the s2MWT in the combined analysis of persons with MS and HC participants. Average ICCs of 9 test-retest reliability analyses of the s2MWT for persons with MS and the participants in the HC-matched group were 0.648 (SD 0.150) and 0.600 (SD 0.090), respectively, whereas the average ICC of 2 test-retest reliability analyses of the s2MWT for the participants in the HC-normative group was 0.700 (SD 0.029). The interviewed study participants found the s2MWT easy to perform, but they also expressed that the test results can be confronting and that a pressure to reach a certain distance can be experienced. Conclusions: The high correlation between s2MWT distance and the conventional 2MWT distance indicates a good concurrent validity. Similarly, high correlations underpin a good test-retest reliability of the s2MWT. We conclude that the s2MWT can be used to measure the distance that the persons with MS walk in 2 minutes outdoors near their home, from which both clinical studies and clinical practice can benefit. %M 34787581 %R 10.2196/29128 %U https://formative.jmir.org/2021/11/e29128 %U https://doi.org/10.2196/29128 %U http://www.ncbi.nlm.nih.gov/pubmed/34787581 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 11 %P e28857 %T Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study %A Beukenhorst,Anna L %A Sergeant,Jamie C %A Schultz,David M %A McBeth,John %A Yimer,Belay B %A Dixon,Will G %+ Centre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 2622757506, beuk@hsph.harvard.edu %K geolocation %K global positioning system %K smartphones %K mobile phone %K mobile health %K environmental exposures %K data analysis %K digital epidemiology %K missing data %K location data %K mobile application %D 2021 %7 16.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective: The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods: We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants’ time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results: For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions: The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants. %M 34783661 %R 10.2196/28857 %U https://mhealth.jmir.org/2021/11/e28857 %U https://doi.org/10.2196/28857 %U http://www.ncbi.nlm.nih.gov/pubmed/34783661 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e21462 %T The Online Patient Satisfaction Index for Patients With Low Back Pain: Development, Reliability, and Validation Study %A Afzali,Tamana %A Lauridsen,Henrik Hein %A Thomsen,Janus Laust %A Hartvigsen,Jan %A Jensen,Martin Bach %A Riis,Allan %+ Research Unit for General Practice in Aalborg, Department of Clinical Medicine, Aalborg University, Fyrkildevej 7, Aalborg, 9220, Denmark, 45 20823660, ariis@dcm.aau.dk %K data accuracy %K patient satisfaction %K rehabilitation %K low back pain %K internet-based intervention %K mobile phone %D 2021 %7 15.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Low back pain is highly prevalent, and most often, a specific causative factor cannot be identified. Therefore, for most patients, their low back pain is labeled as nonspecific. Patient education and information are recommended for all these patients. The internet is an accessible source of medical information on low back pain. Approximately 50% of patients with low back pain search the internet for health and medical advice. Patient satisfaction with education and information is important in relation to patients’ levels of inclination to use web-based information and their trust in the information they find. Although patients who are satisfied with the information they retrieve use the internet as a supplementary source of information, dissatisfied patients tend to avoid using the internet. Consumers’ loyalty to a product is often applied to evaluate their satisfaction. Consumers have been shown to be good ambassadors for a service when they are willing to recommend the service to a friend or colleague. When consumers are willing to recommend a service to a friend or colleague, they are also likely to be future users of the service. To the best of our knowledge, no multi-item instrument exists to specifically evaluate satisfaction with information delivered on the web for people with low back pain. Objective: This study aims to report on the development, reliability testing, and construct validity testing of the Online Patient Satisfaction Index to measure patients’ satisfaction with web-based information for low back pain. Methods: This is a cross-sectional validation study of the Online Patient Satisfaction Index. The index was developed with experts and assessed for face validity. It was subsequently administered to 150 adults with nonspecific low back pain. Of these, 46% (70/150) were randomly assigned to participate in a reliability test using an intraclass correlation coefficient of agreement. Construct validity was evaluated by hypothesis testing based on a web app (MyBack) and Wikipedia on low back pain. Results: The index includes 8 items. The median score (range 0-24) based on the MyBack website was 20 (IQR 18-22), and the median score for Wikipedia was 12 (IQR 8-15). The entire score range was used. Overall, 53 participants completed a retest, of which 39 (74%) were stable in their satisfaction with the home page and were included in the analysis for reliability. Intraclass correlation coefficient of agreement was estimated to be 0.82 (95% CI 0.68-0.90). Two hypothesized correlations for construct validity were confirmed through an analysis using complete data. Conclusions: The index had good face validity, excellent reliability, and good construct validity and can be used to measure satisfaction with the provision of web-based information regarding nonspecific low back pain among people willing to access the internet to obtain health information. Trial Registration: ClinicalTrials.gov NCT03449004; https://clinicaltrials.gov/ct2/show/NCT03449004 %M 34779785 %R 10.2196/21462 %U https://formative.jmir.org/2021/11/e21462 %U https://doi.org/10.2196/21462 %U http://www.ncbi.nlm.nih.gov/pubmed/34779785 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 11 %P e22218 %T Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study %A Martinez,Gonzalo J %A Mattingly,Stephen M %A Robles-Granda,Pablo %A Saha,Koustuv %A Sirigiri,Anusha %A Young,Jessica %A Chawla,Nitesh %A De Choudhury,Munmun %A D'Mello,Sidney %A Mark,Gloria %A Striegel,Aaron %+ Computer Science and Engineering, University of Notre Dame, 400 Main Building, Notre Dame, IN, 46556, United States, 1 574 631 5000, gmarti11@nd.edu %K adherence %K compliance %K wearables %K smartphones %K research design %K ecological momentary assessment %K mobile sensing %K mobile phone %D 2021 %7 12.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Studies that use ecological momentary assessments (EMAs) or wearable sensors to track numerous attributes, such as physical activity, sleep, and heart rate, can benefit from reductions in missing data. Maximizing compliance is one method of reducing missing data to increase the return on the heavy investment of time and money into large-scale studies. Objective: This paper aims to identify the extent to which compliance can be prospectively predicted from individual attributes and initial compliance. Methods: We instrumented 757 information workers with fitness trackers for 1 year and conducted EMAs in the first 56 days of study participation as part of an observational study. Their compliance with the EMA and fitness tracker wearing protocols was analyzed. Overall, 31 individual characteristics (eg, demographics and personalities) and behavioral variables (eg, early compliance and study portal use) were considered, and 14 variables were selected to create beta regression models for predicting compliance with EMAs 56 days out and wearable compliance 1 year out. We surveyed study participation and correlated the results with compliance. Results: Our modeling indicates that 16% and 25% of the variance in EMA compliance and wearable compliance, respectively, could be explained through a survey of demographics and personality in a held-out sample. The likelihood of higher EMA and wearable compliance was associated with being older (EMA: odds ratio [OR] 1.02, 95% CI 1.00-1.03; wearable: OR 1.02, 95% CI 1.01-1.04), speaking English as a first language (EMA: OR 1.38, 95% CI 1.05-1.80; wearable: OR 1.39, 95% CI 1.05-1.85), having had a wearable before joining the study (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.50, 95% CI 1.23-1.83), and exhibiting conscientiousness (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.34, 95% CI 1.14-1.58). Compliance was negatively associated with exhibiting extraversion (EMA: OR 0.74, 95% CI 0.64-0.85; wearable: OR 0.67, 95% CI 0.57-0.78) and having a supervisory role (EMA: OR 0.65, 95% CI 0.54-0.79; wearable: OR 0.66, 95% CI 0.54-0.81). Furthermore, higher wearable compliance was negatively associated with agreeableness (OR 0.68, 95% CI 0.56-0.83) and neuroticism (OR 0.85, 95% CI 0.73-0.98). Compliance in the second week of the study could help explain more variance; 62% and 66% of the variance in EMA compliance and wearable compliance, respectively, was explained. Finally, compliance correlated with participants’ self-reflection on the ease of participation, usefulness of our compliance portal, timely resolution of issues, and compensation adequacy, suggesting that these are avenues for improving compliance. Conclusions: We recommend conducting an initial 2-week pilot to measure trait-like compliance and identify participants at risk of long-term noncompliance, performing oversampling based on participants’ individual characteristics to avoid introducing bias in the sample when excluding data based on noncompliance, using an issue tracking portal, and providing special care in troubleshooting to help participants maintain compliance. %M 34766911 %R 10.2196/22218 %U https://mhealth.jmir.org/2021/11/e22218 %U https://doi.org/10.2196/22218 %U http://www.ncbi.nlm.nih.gov/pubmed/34766911 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e30462 %T The Influence of Normative Perceptions on the Uptake of the COVID-19 TraceTogether Digital Contact Tracing System: Cross-sectional Study %A Lee,Jeong Kyu %A Lin,Lavinia %A Kang,Hyunjin %+ Saw Swee Hock School of Public Health, National University of Singapore, Tahir Foundation Building, 12 Science Drive 2, 10-01, Singapore, 117549, Singapore, 65 66015838, lee.jeongkyu@gmail.com %K COVID-19 %K social norms %K TraceTogether %K Singapore %K contact tracing %K mobile app %K token %D 2021 %7 12.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In 2020, the Singapore government rolled out the TraceTogether program, a digital system to facilitate contact tracing efforts in response to the COVID-19 pandemic. This system is available as a smartphone app and Bluetooth-enabled token to help identify close contacts. As of February 1, 2021, more than 80% of the population has either downloaded the mobile app or received the token in Singapore. Despite the high adoption rate of the TraceTogether mobile app and token (ie, device), it is crucial to understand the role of social and normative perceptions in uptake and usage by the public, given the collective efforts for contact tracing. Objective: This study aimed to examine normative influences (descriptive and injunctive norms) on TraceTogether device use for contact tracing purposes, informed by the theory of normative social behavior, a theoretical framework to explain how perceived social norms are related to behaviors. Methods: From January to February 2021, cross-sectional data were collected by a local research company through emailing their panel members who were (1) Singapore citizens or permanent residents aged 21 years or above; (2) able to read English; and (3) internet users with access to a personal email account. The study sample (n=1137) was restricted to those who had either downloaded the TraceTogether mobile app or received the token. Results: Multivariate (linear and ordinal logistic) regression analyses were carried out to assess the relationships of the behavioral outcome variables (TraceTogether device usage and intention of TraceTogether device usage) with potential correlates, including perceived social norms, perceived community, and interpersonal communication. Multivariate regression analyses indicated that descriptive norms (unstandardized regression coefficient β=0.31, SE=0.05; P<.001) and injunctive norms (unstandardized regression coefficient β=0.16, SE=0.04; P<.001) were significantly positively associated with the intention to use the TraceTogether device. It was also found that descriptive norms were a significant correlate of TraceTogether device use frequency (adjusted odds ratio [aOR] 2.08, 95% CI 1.66-2.61; P<.001). Though not significantly related to TraceTogether device use frequency, injunctive norms moderated the relationship between descriptive norms and the outcome variable (aOR 1.12, 95% CI 1.03-1.21; P=.005). Conclusions: This study provides useful implications for the design of effective intervention strategies to promote the uptake and usage of digital methods for contact tracing in a multiethnic Asian population. Our findings highlight that influence from social networks plays an important role in developing normative perceptions in relation to TraceTogether device use for contact tracing. To promote the uptake of the TraceTogether device and other preventive behaviors for COVID-19, it would be useful to devise norm-based interventions that address these normative perceptions by presenting high prevalence and approval of important social referents, such as family and close friends. %M 34623956 %R 10.2196/30462 %U https://publichealth.jmir.org/2021/11/e30462 %U https://doi.org/10.2196/30462 %U http://www.ncbi.nlm.nih.gov/pubmed/34623956 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e29020 %T Population Health Surveillance Using Mobile Phone Surveys in Low- and Middle-Income Countries: Methodology and Sample Representativeness of a Cross-sectional Survey of Live Poultry Exposure in Bangladesh %A Berry,Isha %A Mangtani,Punam %A Rahman,Mahbubur %A Khan,Iqbal Ansary %A Sarkar,Sudipta %A Naureen,Tanzila %A Greer,Amy L %A Morris,Shaun K %A Fisman,David N %A Flora,Meerjady Sabrina %+ Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada, 1 416 978 0901, isha.berry@mail.utoronto.ca %K mobile telephone survey %K health surveillance %K survey methodology %K Bangladesh %D 2021 %7 12.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Population-based health surveys are typically conducted using face-to-face household interviews in low- and middle-income countries (LMICs). However, telephone-based surveys are cheaper, faster, and can provide greater access to hard-to-reach or remote populations. The rapid growth in mobile phone ownership in LMICs provides a unique opportunity to implement novel data collection methods for population health surveys. Objective: This study aims to describe the development and population representativeness of a mobile phone survey measuring live poultry exposure in urban Bangladesh. Methods: A population-based, cross-sectional, mobile phone survey was conducted between September and November 2019 in North and South Dhaka City Corporations (DCC), Bangladesh, to measure live poultry exposure using a stratified probability sampling design. Data were collected using a computer-assisted telephone interview platform. The call operational data were summarized, and the participant data were weighted by age, sex, and education to the 2011 census. The demographic distribution of the weighted sample was compared with external sources to assess population representativeness. Results: A total of 5486 unique mobile phone numbers were dialed, with 1047 respondents completing the survey. The survey had an overall response rate of 52.2% (1047/2006) and a co-operation rate of 89.0% (1047/1176). Initial results comparing the sociodemographic profile of the survey sample to the census population showed that mobile phone sampling slightly underrepresented older individuals and overrepresented those with higher secondary education. After weighting, the demographic profile of the sample population matched well with the latest DCC census population profile. Conclusions: Probability-based mobile phone survey sampling and data collection methods produced a population-representative sample with minimal adjustment in DCC, Bangladesh. Mobile phone–based surveys can offer an efficient, economic, and robust way to conduct surveillance for population health outcomes, which has important implications for improving population health surveillance in LMICs. %M 34766914 %R 10.2196/29020 %U https://publichealth.jmir.org/2021/11/e29020 %U https://doi.org/10.2196/29020 %U http://www.ncbi.nlm.nih.gov/pubmed/34766914 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e23874 %T Formative Study of Mobile Phone Use for Family Planning Among Young People in Sierra Leone: Global Systematic Survey %A Chukwu,Emeka %A Gilroy,Sonia %A Addaquay,Kojo %A Jones,Nki Nafisa %A Karimu,Victor Gbadia %A Garg,Lalit %A Dickson,Kim Eva %+ Department of Computer Information System, Faculty of Information and Communications Technology (ICT), University of Malta, Msida, MSD 2080, Malta, 356 99330888, nnaemeka_ec@hotmail.com %K young people %K short message service %K SMS %K chatbot %K text message %K interactive voice response %K IVR %K WhatsApp %K Facebook %K family planning %K contraceptives %K Sierra Leone %D 2021 %7 12.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Teenage pregnancy remains high with low contraceptive prevalence among adolescents (aged 15-19 years) in Sierra Leone. Stakeholders leverage multiple strategies to address the challenge. Mobile technology is pervasive and presents an opportunity to reach young people with critical sexual reproductive health and family planning messages. Objective: The objectives of this research study are to understand how mobile health (mHealth) is used for family planning, understand phone use habits among young people in Sierra Leone, and recommend strategies for mobile-enabled dissemination of family planning information at scale. Methods: This formative research study was conducted using a systematic literature review and focus group discussions (FGDs). The literature survey assessed similar but existing interventions through a systematic search of 6 scholarly databases. Cross-sections of young people of both sexes and their support groups were engaged in 9 FGDs in an urban and a rural district in Sierra Leone. The FGD data were qualitatively analyzed using MAXQDA software (VERBI Software GmbH) to determine appropriate technology channels, content, and format for different user segments. Results: Our systematic search results were categorized using Grading of Recommended Assessment and Evaluation (GRADE) into communication channels, audiovisual messaging format, purpose of the intervention, and message direction. The majority of reviewed articles report on SMS-based interventions. At the same time, most intervention purposes are for awareness and as helpful resources. Our survey did not find documented use of custom mHealth apps for family planning information dissemination. From the FGDs, more young people in Sierra Leone own basic mobile phones than those that have feature capablilities or are smartphone. Young people with smartphones use them mostly for WhatsApp and Facebook. Young people widely subscribe to the social media–only internet bundle, with the cost ranging from 1000 leones (US $0.11) to 1500 leones (US $0.16) daily. Pupils in both districts top-up their voice call and SMS credit every day between 1000 leones (US $0.11) and 5000 leones (US $0.52). Conclusions: mHealth has facilitated family planning information dissemination for demand creation around the world. Despite the widespread use of social and new media, SMS is the scalable channel to reach literate and semiliterate young people. We have cataloged mHealth for contraceptive research to show SMS followed by call center as widely used channels. Jingles are popular for audiovisual message formats, mostly delivered as either push or pull only message directions (not both). Interactive voice response and automated calls are best suited to reach nonliterate young people at scale. %M 34766908 %R 10.2196/23874 %U https://formative.jmir.org/2021/11/e23874 %U https://doi.org/10.2196/23874 %U http://www.ncbi.nlm.nih.gov/pubmed/34766908 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e22369 %T Remote Digital Psychiatry for Mobile Mental Health Assessment and Therapy: MindLogger Platform Development Study %A Klein,Arno %A Clucas,Jon %A Krishnakumar,Anirudh %A Ghosh,Satrajit S %A Van Auken,Wilhelm %A Thonet,Benjamin %A Sabram,Ihor %A Acuna,Nino %A Keshavan,Anisha %A Rossiter,Henry %A Xiao,Yao %A Semenuta,Sergey %A Badioli,Alessandra %A Konishcheva,Kseniia %A Abraham,Sanu Ann %A Alexander,Lindsay M %A Merikangas,Kathleen R %A Swendsen,Joel %A Lindner,Ariel B %A Milham,Michael P %+ MATTER Lab, Child Mind Institute, 101 East 56th Street, New York, NY, 10022, United States, 1 347 577 2091, arno@childmind.org %K mental health %K mHealth %K mobile health %K digital health %K eHealth %K digital psychiatry %K digital phenotyping %K teletherapy %K mobile device %K mobile phone %K smartphone %K ecological momentary assessment %K ecological momentary intervention %K EMA %K EMI %K ESM %K experience sampling %K experience sampling methods %D 2021 %7 11.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Universal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There are many people without access to care because of economic, geographic, and cultural barriers, as well as the limited availability of clinical experts who could help advance our understanding and treatment of mental health. Objective: This study aims to create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has the potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. Methods: We searched for products that satisfy these requirements. We designed and developed a new software platform called MindLogger, which exceeds the requirements. To demonstrate the platform’s configurability, we built multiple applets (collections of activities) within the MindLogger mobile app and deployed several of them, including a comprehensive set of assessments underway in a large-scale, longitudinal mental health study. Results: Of the hundreds of products we researched, we found 10 that met our primary requirements with 4 that support end-to-end encryption, 2 that enable restricted access to individual users’ data, 1 that provides open-source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities; privacy and security; and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser–based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. Conclusions: We demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study and by building a variety of other mental health–related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing the availability of applets designed to assess and administer interventions will facilitate access to health care in the general population. %M 34762054 %R 10.2196/22369 %U https://www.jmir.org/2021/11/e22369 %U https://doi.org/10.2196/22369 %U http://www.ncbi.nlm.nih.gov/pubmed/34762054 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 4 %P e15220 %T Feasibility of Real-time Behavior Monitoring Via Mobile Technology in Czech Adults Aged 50 Years and Above: 12-Week Study With Ecological Momentary Assessment %A Elavsky,Steriani %A Klocek,Adam %A Knapova,Lenka %A Smahelova,Martina %A Smahel,David %A Cimler,Richard %A Kuhnova,Jitka %+ Department of Human Movement Studies, University of Ostrava, Varenska 40a, Ostrava, 70200, Czech Republic, 420 553462588, steriani.elavsky@osu.cz %K mHealth %K mobile phone %K older adults %K health behavior %K physical activity %K Fitbit %D 2021 %7 10.11.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Czech older adults have lower rates of physical activity than the average population and lag behind in the use of digital technologies, compared with their peers from other European countries. Objective: This study aims to assess the feasibility of intensive behavior monitoring through technology in Czech adults aged ≥50 years. Methods: Participants (N=30; mean age 61.2 years, SD 6.8 years, range 50-74 years; 16/30, 53% male; 7/30, 23% retired) were monitored for 12 weeks while wearing a Fitbit Charge 2 monitor and completed three 8-day bursts of intensive data collection through surveys presented on a custom-made mobile app. Web-based surveys were also completed before and at the end of the 12-week period (along with poststudy focus groups) to evaluate participants’ perceptions of their experience in the study. Results: All 30 participants completed the study. Across the three 8-day bursts, participants completed 1454 out of 1744 (83% compliance rate) surveys administered 3 times per day on a pseudorandom schedule, 451 out of 559 (81% compliance rate) end-of-day surveys, and 736 episodes of self-reported planned physical activity (with 29/736, 3.9% of the reports initiated but returned without data). The overall rating of using the mobile app and Fitbit was above average (74.5 out of 100 on the System Usability Scale). The majority reported that the Fitbit (27/30, 90%) and mobile app (25/30, 83%) were easy to use and rated their experience positively (25/30, 83%). Focus groups revealed that some surveys were missed owing to notifications not being noticed or that participants needed a longer time window for survey completion. Some found wearing the monitor in hot weather or at night uncomfortable, but overall, participants were highly motivated to complete the surveys and be compliant with the study procedures. Conclusions: The use of a mobile survey app coupled with a wearable device appears feasible for use among Czech older adults. Participants in this study tolerated the intensive assessment schedule well, but lower compliance may be expected in studies of more diverse groups of older adults. Some difficulties were noted with the pairing and synchronization of devices on some types of smartphones, posing challenges for large-scale studies. %M 34757317 %R 10.2196/15220 %U https://aging.jmir.org/2021/4/e15220 %U https://doi.org/10.2196/15220 %U http://www.ncbi.nlm.nih.gov/pubmed/34757317 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 11 %P e27087 %T Gait and Axial Spondyloarthritis: Comparative Gait Analysis Study Using Foot-Worn Inertial Sensors %A Soulard,Julie %A Vaillant,Jacques %A Baillet,Athan %A Gaudin,Philippe %A Vuillerme,Nicolas %+ University Grenoble Alpes, AGEIS, Faculty of Medicine, Jean Roget Building, Place du commandant Nal, La Tronche, 38700, France, 33 476637104, juliesoulard.physio@gmail.com %K ankylosing spondylitis %K spondyloarthritis %K gait %K locomotion %K pain %K mobility %K spatiotemporal %K digital health %K sensors %D 2021 %7 9.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Axial spondyloarthritis (axSpA) can lead to spinal mobility restrictions associated with restricted lower limb ranges of motion, thoracic kyphosis, spinopelvic ankylosis, or decrease in muscle strength. It is well known that these factors can have consequences on spatiotemporal gait parameters during walking. However, no study has assessed spatiotemporal gait parameters in patients with axSpA. Divergent results have been obtained in the studies assessing spatiotemporal gait parameters in ankylosing spondylitis, a subgroup of axSpA, which could be partly explained by self-reported pain intensity scores at time of assessment. Inertial measurement units (IMUs) are increasingly popular and may facilitate gait assessment in clinical practice. Objective: This study compared spatiotemporal gait parameters assessed with foot-worn IMUs in patients with axSpA and matched healthy individuals without and with pain intensity score as a covariate. Methods: A total of 30 patients with axSpA and 30 age- and sex-matched healthy controls performed a 10-m walk test at comfortable speed. Various spatiotemporal gait parameters were computed from foot-worn inertial sensors including gait speed in ms–1 (mean walking velocity), cadence in steps/minute (number of steps in a minute), stride length in m (distance between 2 consecutive footprints of the same foot on the ground), swing time in percentage (portion of the cycle during which the foot is in the air), stance time in percentage (portion of the cycle during which part of the foot touches the ground), and double support time in percentage (portion of the cycle where both feet touch the ground). Results: Age, height, and weight were not significantly different between groups. Self-reported pain intensity was significantly higher in patients with axSpA than healthy controls (P<.001). Independent sample t tests indicated that patients with axSpA presented lower gait speed (P<.001) and cadence (P=.004), shorter stride length (P<.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than healthy controls. When using pain intensity as a covariate, spatiotemporal gait parameters were still significant with patients with axSpA exhibiting lower gait speed (P<.001), shorter stride length (P=.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than matched healthy controls. Interestingly, there were no longer statistically significant between-group differences observed for the cadence (P=.17). Conclusions: Gait was significantly altered in patients with axSpA with reduced speed, cadence, stride length, and swing time and increased double support and stance time. Taken together, these changes in spatiotemporal gait parameters could be interpreted as the adoption of a so-called cautious gait pattern in patients with axSpA. Among factors that may influence gait in patients with axSpA, patient self-reported pain intensity could play a role. Finally, IMUs allowed computation of spatiotemporal gait parameters and are usable to assess gait in patients with axSpA in clinical routine. Trial Registration: ClinicalTrials.gov NCT03761212; https://clinicaltrials.gov/ct2/show/NCT03761212 International Registered Report Identifier (IRRID): RR2-10.1007/s00296-019-04396-4 %M 34751663 %R 10.2196/27087 %U https://mhealth.jmir.org/2021/11/e27087 %U https://doi.org/10.2196/27087 %U http://www.ncbi.nlm.nih.gov/pubmed/34751663 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e27765 %T Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study %A Mestrom,Eveline %A Deneer,Ruben %A Bonomi,Alberto G %A Margarito,Jenny %A Gelissen,Jos %A Haakma,Reinder %A Korsten,Hendrikus H M %A Scharnhorst,Volkher %A Bouwman,R Arthur %+ Department of Anesthesiology, Catharina Hospital Eindhoven, Michelangelolaan 2, Eindhoven, 5623 EJ, Netherlands, 31 646067638, eveline.mestrom@catharinaziekenhuis.nl %K validation %K heart rate %K photoplethysmography %K perioperative patients %K unobtrusive sensing %D 2021 %7 4.11.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. Objective: The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. Methods: In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor’s ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient’s wrist. The agreement between the HR reported by the patient’s monitor and the HR extracted from the OHRM’s PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. Results: A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was –0.14 bpm (LoA between –3.08 bpm and 2.79 bpm) and –0.19% (LoA between –5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was –0.11 bpm (LoA between –2.79 bpm and 2.59 bpm) and –0.15% (LoA between –3.92% and 3.64%). Conclusions: This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of –5 bpm to 5 bpm or –10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients. %M 34734834 %R 10.2196/27765 %U https://cardio.jmir.org/2021/2/e27765 %U https://doi.org/10.2196/27765 %U http://www.ncbi.nlm.nih.gov/pubmed/34734834 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e31649 %T Usability Evaluation of an Offline Electronic Data Capture App in a Prospective Multicenter Dementia Registry (digiDEM Bayern): Mixed Method Study %A Reichold,Michael %A Heß,Miriam %A Kolominsky-Rabas,Peter %A Gräßel,Elmar %A Prokosch,Hans-Ulrich %+ Department of Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, Erlangen, 91058, Germany, 49 91318526720, michael.reichold@fau.de %K dementia %K usability %K evaluation %K mobile device %K registry %K electronic data collection %K offline %K mobile app %K digital health %K usability testing %D 2021 %7 3.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Digital registries have been shown to provide an efficient way of gaining a better understanding of the clinical complexity and long-term progression of diseases. The paperless method of electronic data capture (EDC) during a patient interview saves both time and resources. In the prospective multicenter project “Digital Dementia Registry Bavaria (digiDEM Bayern),” interviews are also performed on site in rural areas with unreliable internet connectivity. It must be ensured that EDC can still be performed in such a context and that there is no need to fall back on paper-based questionnaires. In addition to a web-based data collection solution, the EDC system REDCap (Research Electronic Data Capture) offers the option to collect data offline via an app and to synchronize it afterward. Objective: The aim of this study was to evaluate the usability of the REDCap app as an offline EDC option for a lay user group and to examine the necessary technology acceptance of using mobile devices for data collection. The feasibility of the app-based offline data collection in the digiDEM Bayern dementia registry project was then evaluated before going live. Methods: An exploratory mixed method design was employed in the form of an on-site usability test with the “Thinking Aloud” method combined with an online questionnaire including the System Usability Scale (SUS). The acceptance of mobile devices for data collection was surveyed based on five categories of the technology acceptance model. Results: Using the “Thinking Aloud” method, usability issues were identified and solutions were accordingly derived. Evaluation of the REDCap app resulted in a SUS score of 74, which represents “good” usability. After evaluating the technology acceptance questionnaire, it can be concluded that the lay user group is open to mobile devices as interview tools. Conclusions: The usability evaluation results show that a lay user group generally agree that data collecting partners in the digiDEM project can handle the REDCap app well. The usability evaluation provided statements about positive aspects and could also identify usability issues relating to the REDCap app. In addition, the current technology acceptance in the sample showed that heterogeneous groups of different ages with diverse experiences in handling mobile devices are also ready for the use of app-based EDC systems. Based on these results, it can be assumed that the offline use of an app-based EDC system on mobile devices is a viable solution for collecting data in a decentralized registry–based research project. %M 34730543 %R 10.2196/31649 %U https://formative.jmir.org/2021/11/e31649 %U https://doi.org/10.2196/31649 %U http://www.ncbi.nlm.nih.gov/pubmed/34730543 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e28929 %T A Case Study of an SMS Text Message Community Panel Survey and Its Potential for Use During the COVID-19 Pandemic %A Chan,Lilian %A El-Haddad,Nouhad %A Freeman,Becky %A O'Hara,Blythe J %A Woodland,Lisa %A Harris-Roxas,Ben %+ Prevention Research Collaboration, Sydney School of Public Health and Charles Perkins Centre, The University of Sydney, John Hopkins Drive, Camperdown, 2006, Australia, 61 286277554, lilian.chan@sydney.edu.au %K data collection %K mobile phone %K short message service %K tobacco %K COVID-19 %K survey %D 2021 %7 3.11.2021 %9 Viewpoint %J JMIR Form Res %G English %X During the COVID-19 pandemic many traditional methods of data collection, such as intercept surveys or focus groups, are not feasible. This paper proposes that establishing community panels through SMS text messages may be a useful method during the pandemic, by describing a case study of how an innovative SMS text message community panel was used for the “Shisha No Thanks” project to collect data from young adults of Arabic-speaking background about their attitudes on the harms of waterpipe smoking. Participants were asked to complete an initial recruitment survey, and then subsequently sent 1 survey question per week. The study recruited 133 participants to the SMS text message community panel and the mean response rate for each question was 73.0% (97.1/133) (range 76/133 [57.1%] to 112/133 [84.2%]). The SMS text message community panel approach is not suited for all populations, nor for all types of inquiry, particularly due to limitations of the type of responses that it allows and the required access to mobile devices. However, it is a rapid method for data collection, and therefore during the COVID-19 pandemic, it can provide service providers and policymakers with timely information to inform public health responses. In addition, this method negates the need for in-person interactions and allows for longitudinal data collection. It may be useful in supplementing other community needs assessment activities, and may be particularly relevant for people who are considered to be more difficult to reach, particularly young people, culturally and linguistically diverse communities, and other groups that might otherwise be missed by traditional methods. %M 34612824 %R 10.2196/28929 %U https://formative.jmir.org/2021/11/e28929 %U https://doi.org/10.2196/28929 %U http://www.ncbi.nlm.nih.gov/pubmed/34612824 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e25392 %T A Transdiagnostic Self-management Web-Based App for Sleep Disturbance in Adolescents and Young Adults: Feasibility and Acceptability Study %A Carmona,Nicole E %A Usyatynsky,Aleksandra %A Kutana,Samlau %A Corkum,Penny %A Henderson,Joanna %A McShane,Kelly %A Shapiro,Colin %A Sidani,Souraya %A Stinson,Jennifer %A Carney,Colleen E %+ Department of Psychology, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada, 1 416 979 5000 ext 552177, ccarney@ryerson.ca %K youth %K sleep %K technology %K mHealth %K self-management %K adolescents %K young adults %K mobile phone %D 2021 %7 1.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Sleep disturbance and its daytime sequelae, which comprise complex, transdiagnostic sleep problems, are pervasive problems in adolescents and young adults (AYAs) and are associated with negative outcomes. Effective interventions must be both evidence based and individually tailored. Some AYAs prefer self-management and digital approaches. Leveraging these preferences is helpful, given the dearth of AYA treatment providers trained in behavioral sleep medicine. We involved AYAs in the co-design of a behavioral, self-management, transdiagnostic sleep app called DOZE (Delivering Online Zzz’s with Empirical Support). Objective: This study tests the feasibility and acceptability of DOZE in a community AYA sample aged 15-24 years. The secondary objective is to evaluate sleep and related outcomes in this nonclinical sample. Methods: Participants used DOZE for 4 weeks (2 periods of 2 weeks). They completed sleep diaries, received feedback on their sleep, set goals in identified target areas, and accessed tips to help them achieve their goals. Measures of acceptability and credibility were completed at baseline and end point. Google Analytics was used to understand the patterns of app use to assess feasibility. Participants completed questionnaires assessing fatigue, sleepiness, chronotype, depression, anxiety, and quality of life at baseline and end point. Results: In total, 83 participants created a DOZE account, and 51 completed the study. During the study, 2659 app sessions took place with an average duration of 3:02 minutes. AYAs tracked most days in period 1 (mean 10.52, SD 4.87) and period 2 (mean 9.81, SD 6.65), with a modal time of 9 AM (within 2 hours of waking). DOZE was appraised as highly acceptable (mode≥4) on the items “easy to use,” “easy to understand,” “time commitment,” and “overall satisfaction” and was rated as credible (mode≥4) at baseline and end point across all items (logic, confident it would work, confident recommending it to a friend, willingness to undergo, and perceived success in treating others). The most common goals set were decreasing schedule variability (34/83, 41% of participants), naps (17/83, 20%), and morning lingering in bed (16/83, 19%). AYAs accessed tips on difficulty winding down (24/83, 29% of participants), being a night owl (17/83, 20%), difficulty getting up (13/83, 16%), and fatigue (13/83, 16%). There were significant improvements in morning lingering in bed (P=.03); total wake time (P=.02); sleep efficiency (P=.002); total sleep time (P=.03); and self-reported insomnia severity (P=.001), anxiety (P=.002), depression (P=.004), and energy (P=.01). Conclusions: Our results support the feasibility, acceptability, credibility, and preliminary efficacy of DOZE. AYAs are able to set and achieve goals based on tailored feedback on their sleep habits, which is consistent with research suggesting that AYAs prefer autonomy in their health care choices and produce good results when given tools that support their autonomy. Trial Registration: ClinicalTrials.gov NCT03960294; https://clinicaltrials.gov/ct2/show/NCT03960294 %M 34723820 %R 10.2196/25392 %U https://formative.jmir.org/2021/11/e25392 %U https://doi.org/10.2196/25392 %U http://www.ncbi.nlm.nih.gov/pubmed/34723820 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e29755 %T Multiparameter Continuous Physiological Monitoring Technologies in Neonates Among Health Care Providers and Caregivers at a Private Tertiary Hospital in Nairobi, Kenya: Feasibility, Usability, and Acceptability Study %A Ginsburg,Amy Sarah %A Kinshella,Mai-Lei Woo %A Naanyu,Violet %A Rigg,Jessica %A Chomba,Dorothy %A Coleman,Jesse %A Hwang,Bella %A Ochieng,Roseline %A Ansermino,J Mark %A Macharia,William M %+ Department of Obstetrics and Gynecology, British Columbia Children’s and Women’s Hospital and The University of British Columbia, V344 - 950 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada, 1 6048752253, maggie.kinshella@cw.bc.ca %K infants %K Africa %K medical technology design %K user perspectives %K in-depth interviews %K direct observations %D 2021 %7 28.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Continuous physiological monitoring technologies are important for strengthening hospital care for neonates, particularly in resource-constrained settings, and understanding user perspectives is critical for informing medical technology design, development, and optimization. Objective: This study aims to assess the feasibility, usability, and acceptability of 2 noninvasive, multiparameter, continuous physiological monitoring technologies for use in neonates in an African health care setting. Methods: We assessed 2 investigational technologies from EarlySense and Sibel, compared with the reference Masimo Rad-97 technology through in-depth interviews and direct observations. A purposive sample of health care administrators, health care providers, and caregivers at Aga Khan University Hospital, a tertiary, private hospital in Nairobi, Kenya, were included. Data were analyzed using a thematic approach in NVivo 12 software. Results: Between July and August 2020, we interviewed 12 health care providers, 5 health care administrators, and 10 caregivers and observed the monitoring of 12 neonates. Staffing and maintenance of training in neonatal units are important feasibility considerations, and simple training requirements support the feasibility of the investigational technologies. Key usability characteristics included ease of use, wireless features, and reduced number of attachments connecting the neonate to the monitoring technology, which health care providers considered to increase the efficiency of care. The main factors supporting acceptability included caregiver-highlighted perceptions of neonate comfort and health care respondent technology familiarity. Concerns about the side effects of wireless connections, electromagnetic fields, and mistrust of unfamiliar technologies have emerged as possible acceptability barriers to investigational technologies. Conclusions: Overall, respondents considered the investigational technologies feasible, usable, and acceptable for the care of neonates at this health care facility. Our findings highlight the potential of different multiparameter continuous physiological monitoring technologies for use in different neonatal care settings. Simple and user-friendly technologies may help to bridge gaps in current care where there are many neonates; however, challenges in maintaining training and ensuring feasibility within resource-constrained health care settings warrant further research. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-035184 %M 34709194 %R 10.2196/29755 %U https://www.jmir.org/2021/10/e29755 %U https://doi.org/10.2196/29755 %U http://www.ncbi.nlm.nih.gov/pubmed/34709194 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 10 %P e20739 %T Use of a Mobile App for the Process Evaluation of an Intervention in Health Care: Development and Usability Study %A Chin,Winnie Szu Yun %A Kurowski,Alicia %A Gore,Rebecca %A Chen,Guanling %A Punnett,Laura %A , %+ Division of Population Sciences, Dana-Farber Cancer Institute, 450 Brookline Ave, Room LW711, Boston, MA, 02215, United States, 1 617 632 5602, winnies_chin@dfci.harvard.edu %K mobile apps %K usability testing %K user experience design %K mobile phone %K mhealth %K iterative testing %K participatory research %K user demographics %K worker participation %D 2021 %7 28.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Process evaluation measures the context in which an outcome was or was not achieved through the ongoing monitoring of operations. Mobile apps are a potentially less burdensome tool for collecting these metrics in real time from participants. Research-driven apps are not always developed while paying attention to their usability for target users. Usability testing uncovers gaps in researchers’, developers’, and users’ mental models of what an efficient, effective, and satisfying product looks like and facilitates design improvement. Models may vary by user demographics. Objective: This study describes the development of a mobile app for collecting process evaluation metrics in an intervention study with health care workers that uses feedback at multiple stages to refine the app design, quantify usage based on workers' overall adoption of the app and the app's specific function, and compare the demographic and job characteristics of end users. Methods: An app was developed to evaluate the Center for Promotion of Health in the New England Workplace Healthy Workplace Participatory Program, which trains teams to develop solutions for workforce health obstacles. Labor-management health and safety committee members, program champions, and managers were invited to use the app. An accompanying website was available for team facilitators. The app’s 4 functions were meeting creation, postmeeting surveys, project time logs, and chat messages. Google Analytics recorded screen time. Two stages of pilot tests assessed functionality and usability across different device software, hardware, and platforms. In stage 1, student testers assessed the first functional prototype by performing task scenarios expected from end users. Feedback was used to fix issues and inform further development. In stage 2, the app was offered to all study participants; volunteers completed task scenarios and provided feedback at deployment. End user data for 18 months after deployment were summarized and compared by user characteristics. Results: In stage 1, functionality problems were documented and fixed. The System Usability Scale scores from 7 student testers corresponded to good usability (mobile app=72.9; website=72.5), whereas 15 end users rated usability as ok (mobile app=64.7; website=62.5). Predominant usability themes from student testers were flexibility and efficiency and visibility of system status; end users prioritized flexibility andefficiency and recognition rather than recall. Both student testers and end users suggested useful features that would have resulted in the large-scale restructuring of the back end; these were considered for their benefits versus cost. In stage 2, the median total use time over 18 months was 10.9 minutes (IQR 23.8) and 14.5 visits (IQR 12.5). There were no observable patterns in use by demographic characteristics. Conclusions: Occupational health researchers developing a mobile app should budget for early and iterative testing to find and fix problems or usability issues, which can increase eventual product use and prevent potential gaps in data. %M 34709186 %R 10.2196/20739 %U https://formative.jmir.org/2021/10/e20739 %U https://doi.org/10.2196/20739 %U http://www.ncbi.nlm.nih.gov/pubmed/34709186 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e25730 %T The Use of Cannabinoids for Insomnia in Daily Life: Naturalistic Study %A Kuhathasan,Nirushi %A Minuzzi,Luciano %A MacKillop,James %A Frey,Benicio N %+ Mood Disorders Program and Women’s Health Concerns Clinic, St. Joseph’s Healthcare Hamilton, 100 West 5th Street, Hamilton, ON, L8N 3K7, Canada, 1 905 522 1155, freybn@mcmaster.ca %K medicinal cannabis %K insomnia %K symptom management %K linear mixed-effects %D 2021 %7 27.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Insomnia is a prevalent condition that presents itself at both the symptom and diagnostic levels. Although insomnia is one of the main reasons individuals seek medicinal cannabis, little is known about the profile of cannabinoid use or the perceived benefit of the use of cannabinoids in daily life. Objective: We conducted a retrospective study of medicinal cannabis users to investigate the use profile and perceived efficacy of cannabinoids for the management of insomnia. Methods: Data were collected using the Strainprint app, which allows medicinal cannabis users to log conditions and symptoms, track cannabis use, and monitor symptom severity pre- and postcannabis use. Our analyses examined 991 medicinal cannabis users with insomnia across 24,189 tracked cannabis use sessions. Sessions were analyzed, and both descriptive statistics and linear mixed-effects modeling were completed to examine use patterns and perceived efficacy. Results: Overall, cannabinoids were perceived to be efficacious across all genders and ages, and no significant differences were found among product forms, ingestion methods, or gender groups. Although all strain categories were perceived as efficacious, predominant indica strains were found to reduce insomnia symptomology more than cannabidiol (CBD) strains (estimated mean difference 0.59, SE 0.11; 95% CI 0.36-0.81; adjusted P<.001) and predominant sativa strains (estimated mean difference 0.74, SE 0.16; 95% CI 0.43-1.06; adjusted P<.001). Indica hybrid strains also presented a greater reduction in insomnia symptomology than CBD strains (mean difference 0.52, SE 0.12; 95% CI 0.29-0.74; adjusted P<.001) and predominant sativa strains (mean difference 0.67, SE 0.16; 95% CI 0.34-1.00; adjusted P=.002). Conclusions: Medicinal cannabis users perceive a significant improvement in insomnia with cannabinoid use, and this study suggests a possible advantage with the use of predominant indica strains compared with predominant sativa strains and exclusively CBD in this population. This study emphasizes the need for randomized placebo-controlled trials assessing the efficacy and safety profile of cannabinoids for the treatment of insomnia. %M 34704957 %R 10.2196/25730 %U https://www.jmir.org/2021/10/e25730 %U https://doi.org/10.2196/25730 %U http://www.ncbi.nlm.nih.gov/pubmed/34704957 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 10 %P e20638 %T A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study %A MacLeod,Lucy %A Suruliraj,Banuchitra %A Gall,Dominik %A Bessenyei,Kitti %A Hamm,Sara %A Romkey,Isaac %A Bagnell,Alexa %A Mattheisen,Manuel %A Muthukumaraswamy,Viswanath %A Orji,Rita %A Meier,Sandra %+ Department of Psychiatry, Dalhousie University, 5850/5980 University Avenue, PO Box 9700, Halifax, NS, B3K 6R8, Canada, 1 782414 ext 8054, sandra.m.meier@gmail.com %K mobile sensing %K youth %K psychiatry %K feasibility %K mobile phone %D 2021 %7 26.10.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Internalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier. Objective: This study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada. Methods: In this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants. Results: More severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=−0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=−0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit. Conclusions: Passively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on. %M 34698650 %R 10.2196/20638 %U https://mhealth.jmir.org/2021/10/e20638 %U https://doi.org/10.2196/20638 %U http://www.ncbi.nlm.nih.gov/pubmed/34698650 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 10 %P e29933 %T Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study %A Santala,Onni E %A Halonen,Jari %A Martikainen,Susanna %A Jäntti,Helena %A Rissanen,Tuomas T %A Tarvainen,Mika P %A Laitinen,Tomi P %A Laitinen,Tiina M %A Väliaho,Eemu-Samuli %A Hartikainen,Juha E K %A Martikainen,Tero J %A Lipponen,Jukka A %+ School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Yliopistonranta 1, P O BOX 1627, Kuopio, FI-70211, Finland, 358 503010879, elmeris@uef.fi %K atrial fibrillation %K ECG %K algorithm %K stroke %K mHealth %K user experience %K Awario analysis Service %K Suunto Movesense %K cardiology %K digital health %K mobile health %K wearable device %K heart belt %K arrhythmia monitor %K heart monitor %D 2021 %7 22.10.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. Objective: We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. Methods: Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). Results: The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient’s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). Conclusions: A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 %M 34677135 %R 10.2196/29933 %U https://mhealth.jmir.org/2021/10/e29933 %U https://doi.org/10.2196/29933 %U http://www.ncbi.nlm.nih.gov/pubmed/34677135 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e19789 %T Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study %A Taylor,Casey Overby %A Flaks-Manov,Natalie %A Ramesh,Shankar %A Choe,Eun Kyoung %+ Departments of Medicine and Biomedical Engineering, Johns Hopkins University School of Medicine, 217D Hackerman Hall, 3101 Wyman Park Dr, Baltimore, MD, 21218, United States, 1 4432876657, cot@jhu.edu %K wearables %K personal data %K research participation %K crowdsourcing %D 2021 %7 21.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices. Objective: This study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data. Methods: This was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses. Results:  A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income. Conclusions: Most wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02). %M 34673528 %R 10.2196/19789 %U https://www.jmir.org/2021/10/e19789 %U https://doi.org/10.2196/19789 %U http://www.ncbi.nlm.nih.gov/pubmed/34673528 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 10 %P e28622 %T Reach Outcomes and Costs of Different Physician Referral Strategies for a Weight Management Program Among Rural Primary Care Patients: Type 3 Hybrid Effectiveness-Implementation Trial %A Porter,Gwenndolyn %A Michaud,Tzeyu L %A Schwab,Robert J %A Hill,Jennie L %A Estabrooks,Paul A %+ Department of Health Promotion, University of Nebraska Medical Center, 984365 Nebraska Medical Center, Omaha, NE, 68198, United States, 1 4025591082, gwenndolyn.porter@unmc.edu %K weight management %K rural %K RE-AIM %K hybrid effectiveness-implementation %K primary care %K obesity %K physicians %K digital health %K health technology %K mobile phone %D 2021 %7 20.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Rural residents are at high risk for obesity; however, little resources exist to address this disproportional burden of disease. Primary care may provide an opportunity to connect primary care patients with overweight and obesity to effective weight management programming. Objective: The purpose of this study is to examine the utility of different physician referral and engagement processes for improving the reach of an evidence-based and technology-delivered weight management program with counseling support for rural primary care patients. Methods: A total of 5 rural primary care physicians were randomly assigned a sequence of four referral strategies: point-of-care (POC) referral with active telephone follow-up (ATF); POC referral, no ATF; a population health registry–derived letter referral with ATF; and letter referral, no ATF. For registry-derived referrals, physicians screened a list of patients with BMI ≥25 and approved patients for participation to receive a personalized referral letter via mail. Results: Out of a potential 991 referrals, 573 (57.8%) referrals were made over 16 weeks, and 98 (9.9%) patients were enrolled in the program (58/98, 59.2% female). Differences based on letter (485/991, 48.9%) versus POC (506/991, 51.1%) referrals were identified for completion (100% vs 7%; P<.001) and for proportion screened (36% vs 12%; P<.001) but not for proportion enrolled (12% vs 8%; P=.10). Patients receiving ATF were more likely to be screened (47% vs 7%; P<.001) and enrolled (15% vs 7%; P<.001) than those not receiving ATF. On the basis of the number of referrals made in each condition, we found variations in the proportion and number of enrollees (POC with ATF: 27/190, 50%; POC no ATF: 14/316, 41%; letter ATF: 30/199; 15.1%; letter no ATF: 27/286, 9.4%). Across all conditions, participants were representative of the racial and ethnic characteristics of the region (60% female, P=.15; 94% White individuals, P=.60; 94% non-Hispanic, P=.19). Recruitment costs totaled US $6192, and the overall recruitment cost per enrolled participant was US $63. Cost per enrolled participant ranged from POC with ATF (US $47), registry-derived letter without ATF (US $52), and POC without ATF (US $56) to registry-derived letter with ATF (US $91). Conclusions: Letter referral with ATF appears to be the best option for enrolling a large number of patients in a digitally delivered weight management program; however, POC with ATF and letters without ATF yielded similar numbers at a lower cost. The best referral option is likely dependent on the best fit with clinical resources. Trial Registration: ClinicalTrials.gov NCT03690557; http://clinicaltrials.gov/ct2/show/NCT03690557 %M 34668873 %R 10.2196/28622 %U https://formative.jmir.org/2021/10/e28622 %U https://doi.org/10.2196/28622 %U http://www.ncbi.nlm.nih.gov/pubmed/34668873 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 10 %P e31241 %T Assessing the Real-time Influence of Racism-Related Stress and Suicidality Among Black Men: Protocol for an Ecological Momentary Assessment Study %A Adams,Leslie %A Igbinedion,Godwin %A DeVinney,Aubrey %A Azasu,Enoch %A Nestadt,Paul %A Thrul,Johannes %A Joe,Sean %+ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 624 N Broadway, Baltimore, MD, 21205-1900, United States, 1 410 955 1906, ladams36@jhu.edu %K Black men %K suicide %K racism %K ecological momentary assessment %D 2021 %7 20.10.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Suicide is the third leading cause of death among Black adults aged 18-35 years. Although men represent a majority of suicide deaths among Black adults, less is known regarding the extent to which unique cultural stressors, such as racism-related stress (eg, racial discrimination), are salient in exacerbating suicide risk among Black men. Moreover, few studies examine the daily influence of racism-related stressors on suicide outcomes using real-time smartphone-based approaches. Smartphone-based mobile health approaches using ecological momentary assessments (EMA) provide an opportunity to assess and characterize racism-related stressors as a culturally sensitive suicide risk factor among Black young adult men. Objective: The goal of this study is to describe a protocol development process that aims to capture real-time racism-related stressors and suicide outcomes using a smartphone-based EMA platform (MetricWire). Methods: Guided by the Interpersonal Theory of Suicide (ITS), we developed a brief EMA protocol using a multiphased approach. First, we conducted a literature review to identify brief measures previously used in EMA studies, with special emphasis on studies including Black participants. The identified measures were then shortened to items with the highest construct validity (eg, factor loadings) and revised to reflect momentary or daily frequency. Feasibility and acceptability of the study protocol will be assessed using self-report survey and qualitative responses. To protect participants from harm, a three-tier safety protocol was developed to identify participants with moderate, elevated, and acute risk based on EMA survey response to trigger outreach by the study coordinator. Results: The final EMA protocol, which will be completed over a 7-day period, is comprised of 15 questions administered 4 times per day and a daily questionnaire of 22 items related to sleep-related impairment and disruption, as well as racism-related stress. Study recruitment is currently underway. We anticipate the study will be completed in February 2023. Dissemination will be conducted through peer-reviewed publications and conference presentations. Conclusions: This protocol will address gaps in our understanding of Black men’s suicide outcomes in the social contexts that they regularly navigate and will clarify the temporal role of racism-related stressors that influence suicidal outcomes. International Registered Report Identifier (IRRID): PRR1-10.2196/31241 %M 34668869 %R 10.2196/31241 %U https://www.researchprotocols.org/2021/10/e31241 %U https://doi.org/10.2196/31241 %U http://www.ncbi.nlm.nih.gov/pubmed/34668869 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 10 %P e29426 %T Passive Sensing of Preteens’ Smartphone Use: An Adolescent Brain Cognitive Development (ABCD) Cohort Substudy %A Wade,Natasha E %A Ortigara,Joseph M %A Sullivan,Ryan M %A Tomko,Rachel L %A Breslin,Florence J %A Baker,Fiona C %A Fuemmeler,Bernard F %A Delrahim Howlett,Katia %A Lisdahl,Krista M %A Marshall,Andrew T %A Mason,Michael J %A Neale,Michael C %A Squeglia,Lindsay M %A Wolff-Hughes,Dana L %A Tapert,Susan F %A Bagot,Kara S %A , %+ University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, United States, 1 (858) 280 6398, nwade@health.ucsd.edu %K preadolescents %K smartphone use %K passive sensing %K screen use %K screen time %K mobile phone %D 2021 %7 18.10.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Concerns abound regarding childhood smartphone use, but studies to date have largely relied on self-reported screen use. Self-reporting of screen use is known to be misreported by pediatric samples and their parents, limiting the accurate determination of the impact of screen use on social, emotional, and cognitive development. Thus, a more passive, objective measurement of smartphone screen use among children is needed. Objective: This study aims to passively sense smartphone screen use by time and types of apps used in a pilot sample of children and to assess the feasibility of passive sensing in a larger longitudinal sample. Methods: The Adolescent Brain Cognitive Development (ABCD) study used passive, objective phone app methods for assessing smartphone screen use over 4 weeks in 2019-2020 in a subsample of 67 participants (aged 11-12 years; 31/67, 46% female; 23/67, 34% White). Children and their parents both reported average smartphone screen use before and after the study period, and they completed a questionnaire regarding the acceptability of the study protocol. Descriptive statistics for smartphone screen use, app use, and protocol feasibility and acceptability were reviewed. Analyses of variance were run to assess differences in categorical app use by demographics. Self-report and parent report were correlated with passive sensing data. Results: Self-report of smartphone screen use was partly consistent with objective measurement (r=0.49), although objective data indicated that children used their phones more than they reported. Passive sensing revealed the most common types of apps used were for streaming (mean 1 hour 57 minutes per day, SD 1 hour 32 minutes), communication (mean 48 minutes per day, SD 1 hour 17 minutes), gaming (mean 41 minutes per day, SD 41 minutes), and social media (mean 36 minutes per day, SD 1 hour 7 minutes). Passive sensing of smartphone screen use was generally acceptable to children (43/62, 69%) and parents (53/62, 85%). Conclusions: The results of passive, objective sensing suggest that children use their phones more than they self-report. Therefore, use of more robust methods for objective data collection is necessary and feasible in pediatric samples. These data may then more accurately reflect the impact of smartphone screen use on behavioral and emotional functioning. Accordingly, the ABCD study is implementing a passive sensing protocol in the full ABCD cohort. Taken together, passive assessment with a phone app provided objective, low-burden, novel, informative data about preteen smartphone screen use. %M 34661541 %R 10.2196/29426 %U https://mental.jmir.org/2021/10/e29426 %U https://doi.org/10.2196/29426 %U http://www.ncbi.nlm.nih.gov/pubmed/34661541 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 10 %P e30916 %T The Accuracy of Tidal Volume Measured With a Smart Shirt During Tasks of Daily Living in Healthy Subjects: Cross-sectional Study %A Mannée,Denise %A de Jongh,Frans %A van Helvoort,Hanneke %+ Department of Pulmonary Disease, Radboud University Medical Centre, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands, 31 24 361 1111, denise.mannee@radboudumc.nl %K telemonitoring %K Hexoskin smart shirt %K smart textiles %K textile sensors %K accuracy %K healthy subjects %K calibration %K tidal volume %D 2021 %7 18.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The Hexoskin is a smart shirt that can take continuous and objective measurements and could be part of a potential telemonitoring system. Objective: The aim of this study was to determine the accuracy of the calibrated Hexoskin in measuring tidal volumes (TVs) in comparison to spirometry during various tasks. Methods: In a cross-sectional study, the TV of 15 healthy subjects was measured while performing seven tasks using spirometry and the Hexoskin. These tasks were performed during two sessions; between sessions, all equipment was removed. A one-time spirometer-based calibration per task was determined in session 1 and applied to the corresponding task in both sessions. Bland-Altman analysis was used to determine the agreement between TV that was measured with the Hexoskin and that measured with spirometry. A priori, we determined that the bias had to be less than ±5%, with limits of agreement (LOA) of less than ±15%. Lung volumes were measured and had to have LOA of less than ±0.150 L. Results: In the first session, all tasks had a median bias within the criteria (±0.6%). In the second session, biases were ±8.9%; only two tasks met the criteria. In both sessions, LOA were within the criteria in six out of seven tasks (±14.7%). LOA of lung volumes were greater than 0.150 L. Conclusions: The Hexoskin was able to correctly measure TV in healthy subjects during various tasks. However, after reapplication of the equipment, calibration factors were not able to be reused to obtain results within the determined boundaries. Trial Registration: Netherlands Trial Register NL6934; https://www.trialregister.nl/trial/6934 %M 34661546 %R 10.2196/30916 %U https://formative.jmir.org/2021/10/e30916 %U https://doi.org/10.2196/30916 %U http://www.ncbi.nlm.nih.gov/pubmed/34661546 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e18403 %T Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach %A Li,Xinyue %A Kane,Michael %A Zhang,Yunting %A Sun,Wanqi %A Song,Yuanjin %A Dong,Shumei %A Lin,Qingmin %A Zhu,Qi %A Jiang,Fan %A Zhao,Hongyu %+ Department of Biostatistics, Yale School of Public Health, 300 George Street, Suite 503, New Haven, CT, 06511, United States, 1 203 785 3613, hongyu.zhao@yale.edu %K wearable device %K actigraphy %K circadian rhythm %K physical activity %K early childhood development %D 2021 %7 14.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable devices have been widely used in clinical studies to study daily activity patterns, but the analysis remains a major obstacle for researchers. Objective: This study proposes a novel method to characterize sleep-activity rhythms using actigraphy and further use it to describe early childhood daily rhythm formation and examine its association with physical development. Methods: We developed a machine learning–based Penalized Multiband Learning (PML) algorithm to sequentially infer dominant periodicities based on the Fast Fourier Transform (FFT) algorithm and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a cohort of 262 healthy infants at ages 6, 12, 18, and 24 months, with 159, 101, 111, and 141 participants at each time point, respectively. Autocorrelation analysis and Fisher test in harmonic analysis with Bonferroni correction were applied for comparison with the PML. The association between activity rhythm features and early childhood motor development, assessed using the Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression analysis. Results: The PML results showed that 1-day periodicity was most dominant at 6 and 12 months, whereas one-day, one-third–day, and half-day periodicities were most dominant at 18 and 24 months. These periodicities were all significant in the Fisher test, with one-fourth–day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not the other periodicities. At 6 months, PDMS-2 was associated with the assessment seasons. At 12 months, PDMS-2 was associated with the assessment seasons and FFT signals at one-third–day periodicity (P<.001) and half-day periodicity (P=.04), respectively. In particular, the subcategories of stationary, locomotion, and gross motor were associated with the FFT signals at one-third–day periodicity (P<.001). Conclusions: The proposed PML algorithm can effectively conduct circadian rhythm analysis using time-series wearable device data. The application of the method effectively characterized sleep-wake rhythm development and identified the association between daily rhythm formation and motor development during early childhood. %M 34647895 %R 10.2196/18403 %U https://www.jmir.org/2021/10/e18403 %U https://doi.org/10.2196/18403 %U http://www.ncbi.nlm.nih.gov/pubmed/34647895 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 10 %P e32656 %T Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study %A Shaukat-Jali,Ruksana %A van Zalk,Nejra %A Boyle,David Edward %+ Dyson School of Design Engineering, Imperial College London, 25 Exhibition Road, London, SW7 2AZ, United Kingdom, 44 2083318091, n.van-zalk@imperial.ac.uk %K social anxiety %K wearable sensors %K physiological measurement %K machine learning %K young adults %K mental health %K mHealth %K new methods %K anxiety %K wearable %K sensor %K digital phenotyping %K digital biomarkers %D 2021 %7 7.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Subclinical (ie, threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection. Objective: This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA). Methods: Young adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index. Results: With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity. Conclusions: The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors. %M 34617905 %R 10.2196/32656 %U https://formative.jmir.org/2021/10/e32656 %U https://doi.org/10.2196/32656 %U http://www.ncbi.nlm.nih.gov/pubmed/34617905 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 10 %P e29849 %T Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation %A Rahimi-Eichi,Habiballah %A Coombs III,Garth %A Vidal Bustamante,Constanza M %A Onnela,Jukka-Pekka %A Baker,Justin T %A Buckner,Randy L %+ Department of Psychology, Harvard University, 52 Oxford Street, Northwest Building, East Wing, Room 280, Cambridge, MA, 02138, United States, 1 3057337293, hrahimi@fas.harvard.edu %K actigraphy %K accelerometer %K sleep %K deep-phenotyping %K smartphone %K mobile phone %D 2021 %7 6.10.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Objective: This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest. Methods: The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward–sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each. Results: Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. Conclusions: We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments. %M 34612831 %R 10.2196/29849 %U https://mhealth.jmir.org/2021/10/e29849 %U https://doi.org/10.2196/29849 %U http://www.ncbi.nlm.nih.gov/pubmed/34612831 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e26476 %T Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study %A Stucky,Benjamin %A Clark,Ian %A Azza,Yasmine %A Karlen,Walter %A Achermann,Peter %A Kleim,Birgit %A Landolt,Hans-Peter %+ Institute of Pharmacology and Toxicology, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland, 41 44 635 59 53, landolt@pharma.uzh.ch %K wearables %K actigraphy %K polysomnography %K validation %K multisensory %K mobile phone %D 2021 %7 5.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. Objective: This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. Methods: A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. Results: Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non–rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. Conclusions: We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data. %M 34609317 %R 10.2196/26476 %U https://www.jmir.org/2021/10/e26476 %U https://doi.org/10.2196/26476 %U http://www.ncbi.nlm.nih.gov/pubmed/34609317 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e28731 %T Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring %A de Vries,Herman %A Kamphuis,Wim %A Oldenhuis,Hilbrand %A van der Schans,Cees %A Sanderman,Robbert %+ Professorship Personalized Digital Health, Hanze University of Applied Sciences, Zernikeplein 11, Groningen, 9747 AS, Netherlands, 31 0031 50 5953572, h.j.de.vries@pl.hanze.nl %K stress %K strain %K burnout %K resilience %K heart rate variability %K sleep %K wearables %K digital health %K sensors %K ecological momentary assessment %K mobile phone %D 2021 %7 4.10.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes. %M 34319877 %R 10.2196/28731 %U https://cardio.jmir.org/2021/2/e28731 %U https://doi.org/10.2196/28731 %U http://www.ncbi.nlm.nih.gov/pubmed/34319877 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 9 %P e31236 %T Participatory Surveillance of COVID-19 in Lesotho via Weekly Calls: Protocol for Cell Phone Data Collection %A Greenleaf,Abigail R %A Mwima,Gerald %A Lethoko,Molibeli %A Conkling,Martha %A Keefer,George %A Chang,Christiana %A McLeod,Natasha %A Maruyama,Haruka %A Chen,Qixuan %A Farley,Shannon M %A Low,Andrea %+ ICAP at Columbia University, Mailman School of Public Health, Columbia University, 60 Haven Avenue, New York, NY, United States, 1 2123050398, arg2177@cumc.columbia.edu %K COVID-19 %K cell phones %K mHealth %K Africa south of the Sahara %K surveillance %D 2021 %7 27.9.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The increase in cell phone ownership in low- and middle-income countries (LMIC) has created an opportunity for low-cost, rapid data collection by calling participants on their cell phones. Cell phones can be mobilized for a myriad of data collection purposes, including surveillance. In LMIC, cell phone–based surveillance has been used to track Ebola, measles, acute flaccid paralysis, and diarrheal disease, as well as noncommunicable diseases. Phone-based surveillance in LMIC is a particularly pertinent, burgeoning approach in the context of the COVID-19 pandemic. Participatory surveillance via cell phone could allow governments to assess burden of disease and complements existing surveillance systems. Objective: We describe the protocol for the LeCellPHIA (Lesotho Cell Phone PHIA) project, a cell phone surveillance system that collects weekly population-based data on influenza-like illness (ILI) in Lesotho by calling a representative sample of a recent face-to-face survey. Methods: We established a phone-based surveillance system to collect ILI symptoms from approximately 1700 participants who had participated in a recent face-to-face survey in Lesotho, the Population-based HIV Impact Assessment (PHIA) Survey. Of the 15,267 PHIA participants who were over 18 years old, 11,975 (78.44%) consented to future research and provided a valid phone number. We followed the PHIA sample design and included 342 primary sampling units from 10 districts. We randomly selected 5 households from each primary sampling unit that had an eligible participant and sampled 1 person per household. We oversampled the elderly, as they are more likely to be affected by COVID-19. A 3-day Zoom training was conducted in June 2020 to train LeCellPHIA interviewers. Results: The surveillance system launched July 1, 2020, beginning with a 2-week enrollment period followed by weekly calls that will continue until September 30, 2022. Of the 11,975 phone numbers that were in the sample frame, 3020 were sampled, and 1778 were enrolled. Conclusions: The surveillance system will track COVID-19 in a resource-limited setting. The novel approach of a weekly cell phone–based surveillance system can be used to track other health outcomes, and this protocol provides information about how to implement such a system. International Registered Report Identifier (IRRID): DERR1-10.2196/31236 %M 34351866 %R 10.2196/31236 %U https://www.researchprotocols.org/2021/9/e31236 %U https://doi.org/10.2196/31236 %U http://www.ncbi.nlm.nih.gov/pubmed/34351866 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 8 %N 3 %P e22818 %T Testing of a Self-administered 6-Minute Walk Test Using Technology: Usability, Reliability and Validity Study %A Smith-Turchyn,Jenna %A Adams,Scott C %A Sabiston,Catherine M %+ School of Rehabilitation Science, McMaster University, 1400 Main Street W., Hamilton, ON, L8S 1C7, Canada, 1 9055259140, smithjf@mcmaster.ca %K exercise %K physical activity %K usability testing %K applications %K mobile phone %D 2021 %7 23.9.2021 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: The need to attend a medically supervised hospital- or clinic-based appointment is a well-recognized barrier to exercise participation. The development of reliable and accurate home-based functional tests has the potential to decrease the burden on the health care system while enabling support, information, and assessment. Objective: This study aims to explore the usability (ie, acceptability, satisfaction, accuracy, and practicality) of the EasyMeasure app to self-administer the 6-minute walk test (6MWT) in young, healthy adults and determine parallel form reliability and construct validity of conducting a self-administered 6MWT using technology. Methods: We used a usability study design. English-speaking, undergraduate university students who had access to an iPhone or iPad device running iOS 10 or later and self-reported ability to walk for 6 minutes were recruited for this study. Consenting participants were randomized to either a standard 6MWT group (ie, supervised without the use of the app) or a technology 6MWT group (ie, unsupervised with the app to mimic independent implementation of the test). All participants performed a maximal treadmill test. Participants in the 6MWT group completed the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire and a satisfaction questionnaire after completing the assessment. Parallel form reliability of the 6MWT using technology was analyzed by comparing participant self-administered scores and assessor scores using Pearson correlation coefficients across and between trials. Construct validity was assessed by comparing participant 6MWT scores (both standard and using technology) with maximum treadmill test variables (peak oxygen uptake and ventilatory threshold [VT]). Results: In total, 20 university students consented to participate in the study. All but 2 participants (8/10, 80%) in the technology 6MWT group had deviations that prevented them from accurately conducting the 6MWT using the app, and none of the participants were able to successfully score the 6MWT. However, a significantly strong correlation was found (r=.834; P=.003) when comparing participants’ scores for the 6MWT using technology with the assessors’ scores. No significant correlations were found between maximal treadmill test peak oxygen uptake scores and 6MWT prediction equations using standard 6MWT scores (equation 1: r=0.119; P=.78; equation 2: r=0.095; P=.82; equation 3: r=0.119; P=.78); however, standard 6MWT scores were significantly correlated with VT values (r=0.810; P=.02). The calculated submaximal treadmill scores and assessor 6MWT scores using technology also demonstrated a significant correlation (r=0.661; P=.04). Conclusions: This study demonstrated significant usability concerns regarding the accuracy of a self-administered 6MWT using the EasyMeasure app. However, the strong and significant correlation between the 6MWT and VT values demonstrates the potential of the 6MWT to measure functional capacity for community-based exercise screening and patient monitoring. %M 34554105 %R 10.2196/22818 %U https://rehab.jmir.org/2021/3/e22818 %U https://doi.org/10.2196/22818 %U http://www.ncbi.nlm.nih.gov/pubmed/34554105 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e25472 %T Determinants of the Use of Health and Fitness Mobile Apps by Patients With Asthma: Secondary Analysis of Observational Studies %A Neves,Ana Luísa %A Jácome,Cristina %A Taveira-Gomes,Tiago %A Pereira,Ana Margarida %A Almeida,Rute %A Amaral,Rita %A Alves-Correia,Magna %A Mendes,Sandra %A Chaves-Loureiro,Cláudia %A Valério,Margarida %A Lopes,Cristina %A Carvalho,Joana %A Mendes,Ana %A Ribeiro,Carmelita %A Prates,Sara %A Ferreira,José Alberto %A Teixeira,Maria Fernanda %A Branco,Joana %A Santalha,Marta %A Vasconcelos,Maria João %A Lozoya,Carlos %A Santos,Natacha %A Cardia,Francisca %A Moreira,Ana Sofia %A Taborda-Barata,Luís %A Pinto,Cláudia Sofia %A Ferreira,Rosário %A Morais Silva,Pedro %A Monteiro Ferreira,Tania %A Câmara,Raquel %A Lobo,Rui %A Bordalo,Diana %A Guimarães,Cristina %A Espírito Santo,Maria %A Ferraz de Oliveira,José %A Cálix Augusto,Maria José %A Gomes,Ricardo %A Vieira,Inês %A da Silva,Sofia %A Marques,Maria %A Cardoso,João %A Morete,Ana %A Aroso,Margarida %A Cruz,Ana Margarida %A Nunes,Carlos %A Câmara,Rita %A Rodrigues,Natalina %A Abreu,Carmo %A Albuquerque,Ana Luísa %A Vieira,Claúdia %A Santos,Carlos %A Páscoa,Rosália %A Chaves-Loureiro,Carla %A Alves,Adelaide %A Neves,Ângela %A Varanda Marques,José %A Reis,Bruno %A Ferreira-Magalhães,Manuel %A Almeida Fonseca,João %+ Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Praça de Gomes Teixeira, Porto, 4099-002, Portugal, 351 225513622, cristinajacome.ft@gmail.com %K mobile apps %K smartphone %K patient participation %K self-management %K asthma %D 2021 %7 22.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Health and fitness apps have potential benefits to improve self-management and disease control among patients with asthma. However, inconsistent use rates have been reported across studies, regions, and health systems. A better understanding of the characteristics of users and nonusers is critical to design solutions that are effectively integrated in patients’ daily lives, and to ensure that these equitably reach out to different groups of patients, thus improving rather than entrenching health inequities. Objective: This study aimed to evaluate the use of general health and fitness apps by patients with asthma and to identify determinants of usage. Methods: A secondary analysis of the INSPIRERS observational studies was conducted using data from face-to-face visits. Patients with a diagnosis of asthma were included between November 2017 and August 2020. Individual-level data were collected, including age, gender, marital status, educational level, health status, presence of anxiety and depression, postcode, socioeconomic level, digital literacy, use of health services, and use of health and fitness apps. Multivariate logistic regression was used to model the probability of being a health and fitness app user. Statistical analysis was performed in R. Results: A total of 526 patients attended a face-to-face visit in the 49 recruiting centers and 514 had complete data. Most participants were ≤40 years old (66.4%), had at least 10 years of education (57.4%), and were in the 3 higher quintiles of the socioeconomic deprivation index (70.1%). The majority reported an overall good health status (visual analogue scale [VAS] score>70 in 93.1%) and the prevalence of anxiety and depression was 34.3% and 11.9%, respectively. The proportion of participants who reported using health and fitness mobile apps was 41.1% (n=211). Multivariate models revealed that single individuals and those with more than 10 years of education are more likely to use health and fitness mobile apps (adjusted odds ratio [aOR] 2.22, 95%CI 1.05-4.75 and aOR 1.95, 95%CI 1.12-3.45, respectively). Higher digital literacy scores were also associated with higher odds of being a user of health and fitness apps, with participants in the second, third, and fourth quartiles reporting aORs of 6.74 (95%CI 2.90-17.40), 10.30 (95%CI 4.28-27.56), and 11.52 (95%CI 4.78-30.87), respectively. Participants with depression symptoms had lower odds of using health and fitness apps (aOR 0.32, 95%CI 0.12-0.83). Conclusions: A better understanding of the barriers and enhancers of app use among patients with lower education, lower digital literacy, or depressive symptoms is key to design tailored interventions to ensure a sustained and equitable use of these technologies. Future studies should also assess users’ general health-seeking behavior and their interest and concerns specifically about digital tools. These factors may impact both initial engagement and sustained use. %M 34550077 %R 10.2196/25472 %U https://www.jmir.org/2021/9/e25472 %U https://doi.org/10.2196/25472 %U http://www.ncbi.nlm.nih.gov/pubmed/34550077 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 9 %P e30422 %T Trends in Stress Throughout Pregnancy and Postpartum Period During the COVID-19 Pandemic: Longitudinal Study Using Ecological Momentary Assessment and Data From the Postpartum Mothers Mobile Study %A Omowale,Serwaa S %A Casas,Andrea %A Lai,Yu-Hsuan %A Sanders,Sarah A %A Hill,Ashley V %A Wallace,Meredith L %A Rathbun,Stephen L %A Gary-Webb,Tiffany L %A Burke,Lora E %A Davis,Esa M %A Mendez,Dara D %+ Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA, 15261, United States, 1 4126243001, ddm11@pitt.edu %K COVID-19 %K ecological momentary assessment %K health status disparities %K pandemics %K postpartum %K pregnancy %K psychological stress %D 2021 %7 21.9.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Stress is associated with adverse birth and postpartum health outcomes. Few studies have longitudinally explored racial differences in maternal stress in a birthing population in the United States during the ongoing COVID-19 pandemic. Objective: This study aimed to do the following: (1) assess changes in reported stress before, during, and after initial emergency declarations (eg, stay-at-home orders) were in place due to the COVID-19 pandemic, and (2) assess Black-White differences in reported stress in a pregnant and postpartum population from Southwestern Pennsylvania. Methods: We leveraged data from the ongoing Postpartum Mothers Mobile Study (PMOMS), which surveys participants in real time throughout the pregnancy and postpartum periods via ecological momentary assessment (EMA) and smartphone technology. We analyzed data from a subset of PMOMS participants (n=85) who were either Black or White, and who submitted EMA responses regarding stress between November 1, 2019, and August 31, 2020, the time frame of this study. We divided data into four phases based on significant events during the COVID-19 pandemic: “pre” phase (baseline), “early” phase (first case of COVID-19 reported in United States), “during” phase (stay-at-home orders), and “post” phase (stay-at-home orders eased). We assessed mean stress levels at each phase using linear mixed-effects models and post hoc contrasts based on the models. Results: Overall mean stress (0=not at all to 4=a lot) during the pre phase was 0.8 for Black and White participants (range for Black participants: 0-3.9; range for White participants: 0-2.8). There was an increase of 0.3 points (t5649=5.2, P<.001) in the during phase as compared with the pre phase, and an increase of 0.2 points (t5649=3.1, P=.002) in the post phase compared with the pre phase (n=85). No difference was found between Black and White participants in the change in mean stress from the pre phase to the during phase (overall change predicted for the regression coefficient=–0.02, P=.87). There was a significant difference between Black and White participants in the change in mean stress from the during phase to the post phase (overall change predicted for the regression coefficient=0.4, P<.001). Conclusions: There was an overall increase in mean stress levels in this subset of pregnant and postpartum participants during the same time as the emergency declarations/stay-at-home orders in the United States. Compared to baseline, mean stress levels remained elevated when stay-at-home orders eased. We found no significant difference in the mean stress levels by race. Given that stress is associated with adverse birth outcomes and postpartum health, stress induced by the ongoing COVID-19 pandemic may have adverse implications for birthing populations in the United States. International Registered Report Identifier (IRRID): RR2-10.2196/13569 %M 34328420 %R 10.2196/30422 %U https://mental.jmir.org/2021/9/e30422 %U https://doi.org/10.2196/30422 %U http://www.ncbi.nlm.nih.gov/pubmed/34328420 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e27576 %T A Smartphone-Based App to Improve Adjuvant Treatment Adherence to Multidisciplinary Decisions in Patients With Early-Stage Breast Cancer: Observational Study %A Yu,Jing %A Wu,Jiayi %A Huang,Ou %A Chen,Xiaosong %A Shen,Kunwei %+ Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025, China, 86 13564497086, chenxiaosong0156@hotmail.com %K breast cancer %K adherence %K multidisciplinary treatment %K adjuvant treatment %K smartphone-based app %K mobile phone %D 2021 %7 16.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Multidisciplinary treatment (MDT) and adjuvant therapy are associated with improved survival rates in breast cancer. However, nonadherence to MDT decisions is common in patients. We developed a smartphone-based app that can facilitate the full-course management of patients after surgery. Objective: This study aims to investigate the influence factors of treatment nonadherence and to determine whether this smartphone-based app can improve the compliance rate with MDTs. Methods: Patients who had received a diagnosis of invasive breast cancer and had undergone MDT between March 2013 and May 2019 were included. Patients were classified into 3 groups: Pre-App cohort (November 2017, before the launch of the app); App nonused, cohort (after November 2017 but not using the app); and App used cohort (after November 2017 and using the app). Univariate and multivariate analyses were performed to identify the factors related to MDT adherence. Compliance with specific adjuvant treatments, including chemotherapy, radiotherapy, endocrine therapy, and targeted therapy, was also evaluated. Results: A total of 4475 patients were included, with Pre-App, App nonused, and App used cohorts comprising 2966 (66.28%), 861 (19.24%), and 648 (14.48%) patients, respectively. Overall, 15.53% (695/4475) patients did not receive MDT recommendations; the noncompliance rate ranged from 27.4% (75/273) in 2013 to 8.8% (44/500) in 2019. Multivariate analysis demonstrated that app use was independently associated with adherence to adjuvant treatment. Compared with the patients in the Pre-App cohort, patients in the App used cohort were less likely to deviate from MDT recommendations (odds ratio [OR] 0.61, 95% CI 0.43-0.87; P=.007); no significant difference was found in the App nonused cohort (P=.77). Moreover, app use decreased the noncompliance rate for adjuvant chemotherapy (OR 0.41, 95% CI 0.27-0.65; P<.001) and radiotherapy (OR 0.49, 95% CI 0.25-0.96; P=.04), but not for anti-HER2 therapy (P=.76) or endocrine therapy (P=.39). Conclusions: This smartphone-based app can increase MDT adherence in patients undergoing adjuvant therapy; this was more obvious for adjuvant chemotherapy and radiotherapy. %M 34528890 %R 10.2196/27576 %U https://www.jmir.org/2021/9/e27576 %U https://doi.org/10.2196/27576 %U http://www.ncbi.nlm.nih.gov/pubmed/34528890 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 9 %P e30833 %T Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study %A Ryu,Jihan %A Sükei,Emese %A Norbury,Agnes %A H Liu,Shelley %A Campaña-Montes,Juan José %A Baca-Garcia,Enrique %A Artés,Antonio %A Perez-Rodriguez,M Mercedes %+ Department of Psychiatry, Icahn School of Medicine at Mount Sinai, Icahn (East) Bldg, 4th Floor, L4-53, 1425 Madison Ave, New York, NY, 10029, United States, 1 241 9775, mercedes.perez@mssm.edu %K anxiety disorder %K COVID-19 %K social media %K public health %K digital phenotype %K ecological momentary assessment %K smartphone %K machine learning %K hidden Markov model %D 2021 %7 15.9.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions: Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae. %M 34524091 %R 10.2196/30833 %U https://mental.jmir.org/2021/9/e30833 %U https://doi.org/10.2196/30833 %U http://www.ncbi.nlm.nih.gov/pubmed/34524091 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e31295 %T Factors Associated With Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data %A Hirten,Robert P %A Danieletto,Matteo %A Tomalin,Lewis %A Choi,Katie Hyewon %A Zweig,Micol %A Golden,Eddye %A Kaur,Sparshdeep %A Helmus,Drew %A Biello,Anthony %A Pyzik,Renata %A Calcagno,Claudia %A Freeman,Robert %A Sands,Bruce E %A Charney,Dennis %A Bottinger,Erwin P %A Murrough,James W %A Keefer,Laurie %A Suarez-Farinas,Mayte %A Nadkarni,Girish N %A Fayad,Zahi A %+ The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY, 10029, United States, 1 212 241 0150, robert.hirten@mountsinai.org %K wearable device %K COVID-19 %K stress %K heart rate variability %K psychological %K psychology %K physiology %K mental health %K health care worker %K observational %K app %K heart rate %K nervous system %K resilience %K emotion %K support %K quality of life %D 2021 %7 13.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact. Objective: This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress. Methods: HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally. Results: A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City’s COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial. Conclusions: High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic. %M 34379602 %R 10.2196/31295 %U https://www.jmir.org/2021/9/e31295 %U https://doi.org/10.2196/31295 %U http://www.ncbi.nlm.nih.gov/pubmed/34379602 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e26608 %T Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study %A Sahandi Far,Mehran %A Eickhoff,Simon B %A Goni,Maria %A Dukart,Juergen %+ Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich, 52425, Germany, 49 1632874330, juergen.dukart@gmail.com %K health sciences %K medical research %K biomarkers %K diagnostic markers %K neurological disorders %K Parkinson disease %K mobile phone %D 2021 %7 13.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD). Objective: Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC). Methods: Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects. Results: Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task. Conclusions: These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications. %M 34515645 %R 10.2196/26608 %U https://www.jmir.org/2021/9/e26608 %U https://doi.org/10.2196/26608 %U http://www.ncbi.nlm.nih.gov/pubmed/34515645 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e22844 %T Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study %A Meyerhoff,Jonah %A Liu,Tony %A Kording,Konrad P %A Ungar,Lyle H %A Kaiser,Susan M %A Karr,Chris J %A Mohr,David C %+ Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, 750 N Lake Shore Dr, 10th Floor, Chicago, IL, 60611, United States, 1 312 503 1403, d-mohr@northwestern.edu %K mHealth %K personal sensing %K digital phenotyping %K passive sensing %K ecological momentary assessment %K depression %K anxiety %K digital biomarkers %K digital phenotyping %K mental health assessment %K mobile device %K mobile phone %K internet technology %K psychiatric disorders %K mobile phone %D 2021 %7 3.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. Objective: This study aims to evaluate whether changes in phone sensor–derived behavioral features were associated with subsequent changes in mental health symptoms. Methods: This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. Results: A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=−0.23, P=.02; Locations: r=−0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=−0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=−0.20; P=.03) and Transitions (r=−0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety. Conclusions: Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms. %M 34477562 %R 10.2196/22844 %U https://www.jmir.org/2021/9/e22844 %U https://doi.org/10.2196/22844 %U http://www.ncbi.nlm.nih.gov/pubmed/34477562 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e25364 %T Presentation, Treatment, and Natural Course of Severe Symptoms of Urinary Tract Infections Measured by a Smartphone App: Observational and Feasibility Study %A Vellinga,Akke %A Farrell,Karen %A Fallon,Roisin %A Hare,Daniel %A Sutton-Fitzpatrick,Una %A Cormican,Martin %+ School of Medicine, National University of Ireland Galway, 1 Distillery Road, Galway, H91TK33, Ireland, 353 91495194, akke.vellinga@nuigalway.ie %K urinary tract infections %K general practice %K smartphone application %K mobile phone %D 2021 %7 3.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Urinary tract infections (UTIs) are one of the most common conditions in women. Current information on the presentation, management, and natural course of the infection is based on paper diaries filled out and subsequently posted by patients. Objective: The aim of this study is to explore the feasibility of a smartphone app to assess the natural course and management of UTIs. Methods: A smartphone app was developed to collect data from study participants presenting with symptoms of UTI in general practice. After initial demographic and treatment information, symptom severity was recorded by the patient after a reminder on their smartphone, which occurred twice daily for a period of 7 days or until symptom resolution. Results: A total of 181 women aged 18-76 years downloaded the smartphone app. The duration of symptoms was determined from the results of 178 participants. All patients submitted a urine sample, most patients were prescribed an antibiotic (163/181, 90.1%), and 38.7% (70/181) of the patients had a positive culture. Moderately bad or worse symptoms lasted a mean of 3.8 (SD 3.2; median 4) days, and 70.2% (125/178) of the patients indicated that they were cured on day 4 after consultation. This compares with other research assessing symptom duration and management of UTIs using paper diaries. Patients were very positive about the usability of the smartphone app and often found the reminders supportive. On the basis of the feedback and the analysis of the data, some suggestions for improvement were made. Conclusions: Smartphone diaries for symptom scores over the course of infections are an efficient and acceptable means of collecting data in research. %M 34477567 %R 10.2196/25364 %U https://www.jmir.org/2021/9/e25364 %U https://doi.org/10.2196/25364 %U http://www.ncbi.nlm.nih.gov/pubmed/34477567 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 9 %P e24402 %T Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation %A Li,Qiaoqin %A Liu,Yongguo %A Zhu,Jiajing %A Chen,Zhi %A Liu,Lang %A Yang,Shangming %A Zhu,Guanyi %A Zhu,Bin %A Li,Juan %A Jin,Rongjiang %A Tao,Jing %A Chen,Lidian %+ Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, No 4, Section 2, Jianshe North Road, Chengdu, 610054, China, 86 13980786625, liuyg@uestc.edu.cn %K feature selection %K inertial measurement unit %K motion recognition %K rehabilitation exercises %K machine learning %D 2021 %7 2.9.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is usually optimized only for the specific learning algorithm. Objective: This study aimed to provide a feature selection method for motion recognition of upper-limb exercises and improve the recognition performance. Methods: Motion data from 5 types of upper-limb exercises performed by 21 participants were collected by a customized inertial measurement unit (IMU) node. A total of 60 time-domain and frequency-domain features were extracted from the original sensor data. A hybrid feature selection method by combining filter and wrapper methods (FESCOM) was proposed to eliminate irrelevant features for motion recognition of upper-limb exercises. In the filter stage, candidate features were first selected from the original feature set according to the significance for motion recognition. In the wrapper stage, k-nearest neighbors (kNN), Naïve Bayes (NB), and random forest (RF) were evaluated as the wrapping components to further refine the features from the candidate feature set. The performance of the proposed FESCOM method was verified using experiments on motion recognition of upper-limb exercises and compared with the traditional wrapper method. Results: Using kNN, NB, and RF as the wrapping components, the classification error rates of the proposed FESCOM method were 1.7%, 8.9%, and 7.4%, respectively, and the feature selection time in each iteration was 13 seconds, 71 seconds, and 541 seconds, respectively. Conclusions: The experimental results demonstrated that, in the case of 5 motion types performed by 21 healthy participants, the proposed FESCOM method using kNN and NB as the wrapping components achieved better recognition performance than the traditional wrapper method. The FESCOM method dramatically reduces the search time in the feature selection process. The results also demonstrated that the optimal number of features depends on the classifier. This approach serves to improve feature selection and classification algorithm selection for upper-limb motion recognition based on wearable sensor data, which can be extended to motion recognition of more motion types and participants. %M 34473067 %R 10.2196/24402 %U https://mhealth.jmir.org/2021/9/e24402 %U https://doi.org/10.2196/24402 %U http://www.ncbi.nlm.nih.gov/pubmed/34473067 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e30472 %T Ecological Momentary Assessment of Bipolar Disorder Symptoms and Partner Affect: Longitudinal Pilot Study %A Yerushalmi,Mor %A Sixsmith,Andrew %A Pollock Star,Ariel %A King,David B %A O'Rourke,Norm %+ Department of Public Health and Multidisciplinary Center for Research on Aging, Ben-Gurion University of the Negev, P O Box 653, Be'er Sheva, 8410501, Israel, 972 549901808, ORourke@bgu.ac.il %K bipolar disorder %K couples %K dyadic analyses %K ecological momentary assessment %K EMA %K bipolar disorder %K partner %K relationships %K mHealth %K mobile apps %K mental health %K depression %K BPD %K mood %D 2021 %7 2.9.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The World Health Organization ranks bipolar disorder (BD) as the 7th leading cause of disability. Although the effects on those with BD are well described, less is reported on the impact of BD on cohabiting partners or any interactions between the two; this requires in vivo data collection measured each day over several months. Objective: We set out to demonstrate the utility of ecological momentary assessment with BD couples measured using yoked smartphone apps. When randomly prompted over time, we assumed distinct patterns of association would emerge between BD symptoms (both depression and hypo/mania) and partner mood (positive and negative affect). Methods: For this pilot study, we recruited an international sample of young and older adults with BD and their cohabiting partners where available. Both participants and partners downloaded separate apps onto their respective smartphones. Within self-specified “windows of general availability,” participants with BD were randomly prompted to briefly report symptoms of depression and hypo/mania (ie, BDSx), positive and negative mood (ie, POMS-15; partners), and any important events of the day (both). The partner app was yoked to the participant app so that the former was prompted roughly 30 minutes after the participant with BD or the next morning if outside the partner’s specified availability. Results: Four couples provided 312 matched BD symptom and partner mood responses over an average of 123 days (range 65-221 days). Both were GPS- and time-stamped (mean 3:11 hrs between questionnaires, SD 4:51 hrs). Total depression had a small but significant association with positive (r=–.14; P=.02) and negative partner affect (r=.15; P=.01]. Yet total hypo/mania appeared to have no association with positive partner affect (r=–.01; P=.87); instead, negative partner affect was significantly correlated with total hypo/mania (r=.26; P=.01). However, when we look specifically at BD factors, we see that negative partner affect is associated only with affrontive symptoms of hypo/mania (r=.38; P=.01); elation or loss of insight appears unrelated to either positive (r=.10; P=.09) or negative partner affect (r=.02; P=.71). Yet affrontive symptoms of hypo/mania were significantly correlated with negative affect, but only when couples were together (r=.41; P=.01), not when apart (r=.22; P=.12). That is, these angry interpersonal symptoms of hypo/mania appear to be experienced most negatively by spouses when couples are together. Conclusions: These initial findings demonstrate the utility of in vivo ambulatory data collection in longitudinal mental health research. Preliminary analyses suggest different BD symptoms are associated with negative and positive partner mood. These negative effects appear greater for hypo/mania than depressive symptoms, but proximity to the person with BD is important. %M 34473069 %R 10.2196/30472 %U https://formative.jmir.org/2021/9/e30472 %U https://doi.org/10.2196/30472 %U http://www.ncbi.nlm.nih.gov/pubmed/34473069 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 8 %P e29957 %T Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis %A Kishore,Kamal %A Jaswal,Vidushi %A Verma,Madhur %A Koushal,Vipin %+ All India Institute of Medical Sciences, Jodhpur Romana Road, Bathinda, 151001, India, 91 9466445513, drmadhurverma@gmail.com %K COVID-19 %K lockdown %K nonpharmaceutical Interventions %K social distancing %K digital surveillance %K Google Community Mobility Reports %K community mobility %D 2021 %7 30.8.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices. Objective: The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India. Methods: In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity. Results: At the national level, mobility decreased from –38% to –77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a –60% reduction in mobility depicted more mobility compared to –82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (–0.05 to –0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase. Conclusions: Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance. %M 34174780 %R 10.2196/29957 %U https://publichealth.jmir.org/2021/8/e29957 %U https://doi.org/10.2196/29957 %U http://www.ncbi.nlm.nih.gov/pubmed/34174780 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e27709 %T A Machine Learning Approach to Passively Informed Prediction of Mental Health Risk in People with Diabetes: Retrospective Case-Control Analysis %A Yu,Jessica %A Chiu,Carter %A Wang,Yajuan %A Dzubur,Eldin %A Lu,Wei %A Hoffman,Julia %+ Livongo Health, Inc, 150 W Evelyn Ave, Ste 150, Mountain View, CA, 94041, United States, 1 6508048434, jessica.yu@livongo.com %K diabetes mellitus %K mental health %K risk detection %K passive sensing %K ecological momentary assessment %K machine learning %D 2021 %7 27.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Proactive detection of mental health needs among people with diabetes mellitus could facilitate early intervention, improve overall health and quality of life, and reduce individual and societal health and economic burdens. Passive sensing and ecological momentary assessment are relatively newer methods that may be leveraged for such proactive detection. Objective: The primary aim of this study was to conceptualize, develop, and evaluate a novel machine learning approach for predicting mental health risk in people with diabetes mellitus. Methods: A retrospective study was designed to develop and evaluate a machine learning model, utilizing data collected from 142,432 individuals with diabetes enrolled in the Livongo for Diabetes program. First, participants’ mental health statuses were verified using prescription and medical and pharmacy claims data. Next, four categories of passive sensing signals were extracted from the participants’ behavior in the program, including demographics and glucometer, coaching, and event data. Data sets were then assembled to create participant-period instances, and descriptive analyses were conducted to understand the correlation between mental health status and passive sensing signals. Passive sensing signals were then entered into the model to train and test its performance. The model was evaluated based on seven measures: sensitivity, specificity, precision, area under the curve, F1 score, accuracy, and confusion matrix. SHapley Additive exPlanations (SHAP) values were computed to determine the importance of individual signals. Results: In the training (and validation) and three subsequent test sets, the model achieved a confidence score greater than 0.5 for sensitivity, specificity, area under the curve, and accuracy. Signals identified as important by SHAP values included demographics such as race and gender, participant’s emotional state during blood glucose checks, time of day of blood glucose checks, blood glucose values, and interaction with the Livongo mobile app and web platform. Conclusions: Results of this study demonstrate the utility of a passively informed mental health risk algorithm and invite further exploration to identify additional signals and determine when and where such algorithms should be deployed. %M 34448707 %R 10.2196/27709 %U https://www.jmir.org/2021/8/e27709 %U https://doi.org/10.2196/27709 %U http://www.ncbi.nlm.nih.gov/pubmed/34448707 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e28918 %T Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study %A Di Matteo,Daniel %A Fotinos,Kathryn %A Lokuge,Sachinthya %A Mason,Geneva %A Sternat,Tia %A Katzman,Martin A %A Rose,Jonathan %+ The Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, ON, Canada, 1 416 978 6992, daniel.dimatteo@utoronto.ca %K mobile sensing %K passive EMA %K passive sensing %K psychiatric assessment %K mood and anxiety disorders %K mobile apps %K mhealth %K mobile phone %K digital health %K digital phenotyping %D 2021 %7 13.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression. Objective: The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression. Methods: An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated. Results: Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression. Conclusions: We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries. %M 34397386 %R 10.2196/28918 %U https://www.jmir.org/2021/8/e28918 %U https://doi.org/10.2196/28918 %U http://www.ncbi.nlm.nih.gov/pubmed/34397386 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e25415 %T Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study %A Xu,Haoran %A Yan,Wei %A Lan,Ke %A Ma,Chenbin %A Wu,Di %A Wu,Anshuo %A Yang,Zhicheng %A Wang,Jiachen %A Zang,Yaning %A Yan,Muyang %A Zhang,Zhengbo %+ Centre for Artificial Intelligence in Medicine, Medical Innovation Research Department, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, Beijing, 100853, China, 86 13693321644, zhengbozhang301@gmail.com %K signal quality %K electrocardiogram %K respiratory signal %K isolation forest %K machine learning %K mobile health %D 2021 %7 12.8.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. Objective: The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. Methods: Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. Results: The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. Conclusions: This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research. %M 34387554 %R 10.2196/25415 %U https://mhealth.jmir.org/2021/8/e25415 %U https://doi.org/10.2196/25415 %U http://www.ncbi.nlm.nih.gov/pubmed/34387554 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 3 %P e29021 %T Ecological Momentary Assessment of Depression in People With Advanced Dementia: Longitudinal Pilot Study %A Niculescu,Iulia %A Quirt,Hannah %A Arora,Twinkle %A Borsook,Terry %A Green,Robin %A Ford,Brett %A Iaboni,Andrea %+ KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada, 1 (416) 597 3422 ext 3027, andrea.iaboni@uhn.ca %K dementia %K depression %K ecological momentary assessment %K tool performance %D 2021 %7 4.8.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Barriers to assessing depression in advanced dementia include the presence of informant and patient recall biases. Ecological momentary assessment provides an improved approach for mood assessment by collecting observations in intervals throughout the day, decreasing recall bias, and increasing ecological validity. Objective: This study aims to evaluate the feasibility, reliability, and validity of the modified 4-item Cornell Scale for Depression in Dementia for Momentary Assessment (mCSDD4-MA) tool to assess depression in patients with advanced dementia. Methods: A intensive longitudinal pilot study design was used. A total of 12 participants with advanced dementia were enrolled from an inpatient psychogeriatric unit. Participants were assessed using clinical depression assessments at admission and discharge. Research staff recorded observations four times a day for 6 weeks on phones with access to the mCSDD4-MA tool. Descriptive data related to feasibility were reported (ie, completion rates). Statistical models were used to examine the interrater reliability and construct and predictive validity of the data. Results: Overall, 1923 observations were completed, representing 55.06% (1923/3496) of all rating opportunities with 2 raters and 66.01% (1923/2913) with at least one rater. Moderate interrater reliability was demonstrated for all items, except for lack of interest. Moderate correlations were observed between observers and patient-reported outcomes, where observers reported fewer symptoms relative to participants’ self-reports. Several items were associated with and able to predict depression. Conclusions: The mCSDD4-MA tool was feasible to use, and most items in the tool showed moderate reliability and validity for assessing depression in dementia. Repeated and real-time depression assessment in advanced dementia holds promise for the identification of clinical depression and depressive symptoms. %M 34346884 %R 10.2196/29021 %U https://aging.jmir.org/2021/3/e29021 %U https://doi.org/10.2196/29021 %U http://www.ncbi.nlm.nih.gov/pubmed/34346884 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e23938 %T Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study %A O'Driscoll,Ruairi %A Turicchi,Jake %A Hopkins,Mark %A Duarte,Cristiana %A Horgan,Graham W %A Finlayson,Graham %A Stubbs,R James %+ Appetite Control and Energy Balance Group, School of Psychology, University of Leeds, Woodhouse, Leeds, United Kingdom, 44 113 343 2846, psrod@leeds.ac.uk %K bioenergetics %K energy balance %K accelerometers %K machine learning %K validation %D 2021 %7 4.8.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices. Objective: This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction of energy expenditure in several wearables (ie, Fitbit Charge 2, ActiGraph GT3-x, SenseWear Armband Mini, and Polar H7) using two laboratory data sets comprising different activities. Methods: Two laboratory studies (study 1: n=59, age 44.4 years, weight 75.7 kg; study 2: n=30, age=31.9 years, weight=70.6 kg), in which adult participants performed a sequential lab-based activity protocol consisting of resting, household, ambulatory, and nonambulatory tasks, were combined in this study. In both studies, accelerometer and physiological data were collected from the wearables alongside energy expenditure using indirect calorimetry. Three regression algorithms were used to predict metabolic equivalents (METs; ie, random forest, gradient boosting, and neural networks), and five classification algorithms (ie, k-nearest neighbor, support vector machine, random forest, gradient boosting, and neural networks) were used for physical activity intensity classification as sedentary, light, or moderate to vigorous. Algorithms were evaluated using leave-one-subject-out cross-validations and out-of-sample validations. Results: The root mean square error (RMSE) was lowest for gradient boosting applied to SenseWear and Polar H7 data (0.91 METs), and in the classification task, gradient boost applied to SenseWear and Polar H7 was the most accurate (85.5%). Fitbit models achieved an RMSE of 1.36 METs and 78.2% accuracy for classification. Errors tended to increase in out-of-sample validations with the SenseWear neural network achieving RMSE values of 1.22 METs in the regression tasks and the SenseWear gradient boost and random forest achieving an accuracy of 80% in classification tasks. Conclusions: Algorithms trained on combined data sets demonstrated high predictive accuracy, with a tendency for superior performance of random forests and gradient boosting for most but not all wearable devices. Predictions were poorer in the between-study validations, which creates uncertainty regarding the generalizability of the tested algorithms. %M 34346890 %R 10.2196/23938 %U https://mhealth.jmir.org/2021/8/e23938 %U https://doi.org/10.2196/23938 %U http://www.ncbi.nlm.nih.gov/pubmed/34346890 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e27751 %T Ecological Momentary Assessment and mHealth Interventions Among Men Who Have Sex With Men: Scoping Review %A Clark,Viktor %A Kim,Sunny Jung %+ Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, 23219, United States, 1 8046283443, nowackv@mymail.vcu.edu %K mHealth %K men who have sex with men %K mobile health %K interventions %K mental health %K sexual health %K ecological momentary assessment %K behavior %D 2021 %7 3.8.2021 %9 Review %J J Med Internet Res %G English %X Background: Ecological momentary assessment (EMA) is a research design that allows for the measurement of nearly instantaneous experiences within the participant’s natural environment. Using EMA can help improve recall bias, ecological validity, and patient engagement while enhancing personalization and the ubiquity of interventions. People that can benefit from the use of EMA are men who have sex with men (MSM). Previous EMA studies have been successful in capturing patterns of depression, anxiety, substance use, and risky sexual behavior. These findings are directly relevant to MSM, who have high rates of each of these psychological and behavioral outcomes. Although there is a driving force behind the growing literature surrounding EMAs among MSM, no synthesizing reviews yet exist. Objective: The aims of this study were to (1) synthesize the literature across fields on how EMA methods have been used among MSM, (2) better understand the feasibility and acceptability of EMA interventions among MSM, and (3) inform designs for future research studies on best evidence-based practices for EMA interventions. Methods: Based on 4 library databases, we conducted a scoping review of EMAs used within interventions among MSM. The eligibility criteria included peer-reviewed studies conducted in the United States and the use of EMA methodology in an intervention for MSM. Modeling after the Centers for Disease Control and Prevention’s Compendium of Evidence-Based Interventions as the framework, we applied a typology that used 8 distinct review criteria, for example, sample size, design of the intervention, random assignment, design of the follow-up investigation, rate of retention, and rate of engagement. Results: Our results (k=15, N=952) indicated a range of sample sizes; the smallest sample size was 12, while the largest sample size was 120. Of the 15 studies, 7 (47%) focused on outcomes related to substance use or outcomes related to psychological experiences. Of the 15 studies, 5 (33%) implemented an EMA intervention across 30 days. Of the 15 studies, 2 studies (13%) used random assignment, and 2 studies (13%) had quasi-experimental designs. Of the 15 studies, 10 studies (67%) reported acceptable retention rates greater than 70%. The outcomes that had event-contingent prompts (ie, prompts after engaging in substance use) were not as effective in engaging participants, with overall engagement rates as low as 37%. Conclusions: Our systematic scoping review indicates strong evidence that the EMA methodology is both feasible and acceptable at high rates among MSM, especially, when examining psychological and behavioral outcomes such as negative or positive affect, risky sexual behavior, or substance use. Further research on optimal designs of EMA interventions for MSM is warranted. %M 34342585 %R 10.2196/27751 %U https://www.jmir.org/2021/8/e27751 %U https://doi.org/10.2196/27751 %U http://www.ncbi.nlm.nih.gov/pubmed/34342585 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e27466 %T Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study %A Ganti,Venu %A Carek,Andrew M %A Jung,Hewon %A Srivatsa,Adith V %A Cherry,Deborah %A Johnson,Levather Neicey %A Inan,Omer T %+ School of Electrical and Computer Engineering, Georgia Institute of Technology, 85 5th St NW, Atlanta, GA, 30308, United States, 1 2406434250, vganti6@gatech.edu %K wearable sensing %K pulse transit time %K cuffless blood pressure %K noninvasive blood pressure estimation %K health equity %K mobile phone %D 2021 %7 2.8.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Noninvasive and cuffless approaches to monitor blood pressure (BP), in light of their convenience and accuracy, have paved the way toward remote screening and management of hypertension. However, existing noninvasive methodologies, which operate on mechanical, electrical, and optical sensing modalities, have not been thoroughly evaluated in demographically and racially diverse populations. Thus, the potential accuracy of these technologies in populations where they could have the greatest impact has not been sufficiently addressed. This presents challenges in clinical translation due to concerns about perpetuating existing health disparities. Objective: In this paper, we aim to present findings on the feasibility of a cuffless, wrist-worn, pulse transit time (PTT)–based device for monitoring BP in a diverse population. Methods: We recruited a diverse population through a collaborative effort with a nonprofit organization working with medically underserved areas in Georgia. We used our custom, multimodal, wrist-worn device to measure the PTT through seismocardiography, as the proximal timing reference, and photoplethysmography, as the distal timing reference. In addition, we created a novel data-driven beat-selection algorithm to reduce noise and improve the robustness of the method. We compared the wearable PTT measurements with those from a finger-cuff continuous BP device over the course of several perturbations used to modulate BP. Results: Our PTT-based wrist-worn device accurately monitored diastolic blood pressure (DBP) and mean arterial pressure (MAP) in a diverse population (N=44 participants) with a mean absolute difference of 2.90 mm Hg and 3.39 mm Hg for DBP and MAP, respectively, after calibration. Meanwhile, the mean absolute difference of our systolic BP estimation was 5.36 mm Hg, a grade B classification based on the Institute for Electronics and Electrical Engineers standard. We have further demonstrated the ability of our device to capture the commonly observed demographic differences in underlying arterial stiffness. Conclusions: Accurate DBP and MAP estimation, along with grade B systolic BP estimation, using a convenient wearable device can empower users and facilitate remote BP monitoring in medically underserved areas, thus providing widespread hypertension screening and management for health equity. %M 34338646 %R 10.2196/27466 %U https://mhealth.jmir.org/2021/8/e27466 %U https://doi.org/10.2196/27466 %U http://www.ncbi.nlm.nih.gov/pubmed/34338646 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e29840 %T Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study %A Zhang,Yuezhou %A Folarin,Amos A %A Sun,Shaoxiong %A Cummins,Nicholas %A Ranjan,Yatharth %A Rashid,Zulqarnain %A Conde,Pauline %A Stewart,Callum %A Laiou,Petroula %A Matcham,Faith %A Oetzmann,Carolin %A Lamers,Femke %A Siddi,Sara %A Simblett,Sara %A Rintala,Aki %A Mohr,David C %A Myin-Germeys,Inez %A Wykes,Til %A Haro,Josep Maria %A Penninx,Brenda W J H %A Narayan,Vaibhav A %A Annas,Peter %A Hotopf,Matthew %A Dobson,Richard J B %A , %+ Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SGDP Centre, IoPPN, Box PO 80, De Crespigny Park, Denmark Hill, London, SE5 8AF, United Kingdom, 44 20 7848 0473, richard.j.dobson@kcl.ac.uk %K mental health %K depression %K digital biomarkers %K digital phenotyping %K digital health %K Bluetooth %K hierarchical Bayesian model %K mobile health %K mHealth %K monitoring %D 2021 %7 30.7.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Research in mental health has found associations between depression and individuals’ behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. Objective: This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). Methods: The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. Results: A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Conclusions: Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals’ behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings. %M 34328441 %R 10.2196/29840 %U https://mhealth.jmir.org/2021/7/e29840 %U https://doi.org/10.2196/29840 %U http://www.ncbi.nlm.nih.gov/pubmed/34328441 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e23109 %T The Utility of Real-Time Remote Auscultation Using a Bluetooth-Connected Electronic Stethoscope: Open-Label Randomized Controlled Pilot Trial %A Hirosawa,Takanobu %A Harada,Yukinori %A Ikenoya,Kohei %A Kakimoto,Shintaro %A Aizawa,Yuki %A Shimizu,Taro %+ Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu-cho, Shimotsuga, Tochigi, 321-0293, Japan, 81 282 87 2498, shimizutaro7@gmail.com %K telemedicine %K electronic stethoscope %K simulator %K remote auscultation %K lung auscultation %K cardiac auscultation %K physical examination %D 2021 %7 27.7.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The urgent need for telemedicine has become clear in the COVID-19 pandemic. To facilitate telemedicine, the development and improvement of remote examination systems are required. A system combining an electronic stethoscope and Bluetooth connectivity is a promising option for remote auscultation in clinics and hospitals. However, the utility of such systems remains unknown. Objective: This study was conducted to assess the utility of real-time auscultation using a Bluetooth-connected electronic stethoscope compared to that of classical auscultation, using lung and cardiology patient simulators. Methods: This was an open-label, randomized controlled trial including senior residents and faculty in the department of general internal medicine of a university hospital. The only exclusion criterion was a refusal to participate. This study consisted of 2 parts: lung auscultation and cardiac auscultation. Each part contained a tutorial session and a test session. All participants attended a tutorial session, in which they listened to 15 sounds on the simulator using a classic stethoscope and were told the correct classification. Thereafter, participants were randomly assigned to either the real-time remote auscultation group (intervention group) or the classical auscultation group (control group) for test sessions. In the test sessions, participants had to classify a series of 10 lung sounds and 10 cardiac sounds, depending on the study part. The intervention group listened to the sounds remotely using the electronic stethoscope, a Bluetooth transmitter, and a wireless, noise-canceling, stereo headset. The control group listened to the sounds directly using a traditional stethoscope. The primary outcome was the test score, and the secondary outcomes were the rates of correct answers for each sound. Results: In total, 20 participants were included. There were no differences in age, sex, and years from graduation between the 2 groups in each part. The overall test score of lung auscultation in the intervention group (80/110, 72.7%) was not different from that in the control group (71/90, 78.9%; P=.32). The only lung sound for which the correct answer rate differed between groups was that of pleural friction rubs (P=.03); it was lower in the intervention group (3/11, 27%) than in the control group (7/9, 78%). The overall test score for cardiac auscultation in the intervention group (50/60, 83.3%) was not different from that in the control group (119/140, 85.0%; P=.77). There was no cardiac sound for which the correct answer rate differed between groups. Conclusions: The utility of a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope was comparable to that of direct auscultation using a classic stethoscope, except for classification of pleural friction rubs. This means that most of the real world’s essential cardiopulmonary sounds could be classified by a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope. Trial Registration: UMIN-CTR UMIN000040828; https://tinyurl.com/r24j2p6s and UMIN-CTR UMIN000041601; https://tinyurl.com/bsax3j5f %M 34313598 %R 10.2196/23109 %U https://mhealth.jmir.org/2021/7/e23109 %U https://doi.org/10.2196/23109 %U http://www.ncbi.nlm.nih.gov/pubmed/34313598 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 7 %P e26297 %T Development of a Mobile App for Ecological Momentary Assessment of Circadian Data: Design Considerations and Usability Testing %A Woolf,Thomas B %A Goheer,Attia %A Holzhauer,Katherine %A Martinez,Jonathan %A Coughlin,Janelle W %A Martin,Lindsay %A Zhao,Di %A Song,Shanshan %A Ahmad,Yanif %A Sokolinskyi,Kostiantyn %A Remayeva,Tetyana %A Clark,Jeanne M %A Bennett,Wendy %A Lehmann,Harold %+ Department of Physiology, Johns Hopkins University School of Medicine, 725 N Wolfe St, Baltimore, MD, 21205, United States, 1 410 416 2643, twoolf@jhu.edu %K mhealth %K circadian %K sleep %K ecological momentary assessment %K timing of eating %K mobile applications %K habits %K body weight %K surveys and questionnaires %D 2021 %7 23.7.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Collecting data on daily habits across a population of individuals is challenging. Mobile-based circadian ecological momentary assessment (cEMA) is a powerful frame for observing the impact of daily living on long-term health. Objective: In this paper, we (1) describe the design, testing, and rationale for specifications of a mobile-based cEMA app to collect timing of eating and sleeping data and (2) compare cEMA and survey data collected as part of a 6-month observational cohort study. The ultimate goal of this paper is to summarize our experience and lessons learned with the Daily24 mobile app and to highlight the pros and cons of this data collection modality. Methods: Design specifications for the Daily24 app were drafted by the study team based on the research questions and target audience for the cohort study. The associated backend was optimized to provide real-time data to the study team for participant monitoring and engagement. An external 8-member advisory board was consulted throughout the development process, and additional test users recruited as part of a qualitative study provided feedback through in-depth interviews. Results: After ≥4 days of at-home use, 37 qualitative study participants provided feedback on the app. The app generally received positive feedback from test users for being fast and easy to use. Test users identified several bugs and areas where modifications were necessary to in-app text and instructions and also provided feedback on the engagement strategy. Data collected through the mobile app captured more variability in eating windows than data collected through a one-time survey, though at a significant cost. Conclusions: Researchers should consider the potential uses of a mobile app beyond the initial data collection when deciding whether the time and monetary expenditure are advisable for their situation and goals. %M 34296999 %R 10.2196/26297 %U https://formative.jmir.org/2021/7/e26297 %U https://doi.org/10.2196/26297 %U http://www.ncbi.nlm.nih.gov/pubmed/34296999 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e17780 %T Measuring the Healthiness of Ready-to-Eat Child-Targeted Cereals: Evaluation of the FoodSwitch Platform in Sweden %A Mottas,Antoine %A Lappi,Veli-Matti %A Sundström,Johan %A Neal,Bruce %A Mhurchu,Cliona Ni %A Löf,Marie %A Rådholm,Karin %+ Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, SE-58183, Sweden, 46 13 281756, karin.radholm@liu.se %K breakfast cereals %K child-targeted cereals %K front-of-pack labels %K Keyhole symbol %K Health Star Rating %K FoodSwitch %K diet %K food intake %D 2021 %7 22.7.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Childhood obesity is a major public health issue. The increase in the consumption of foods with poor nutritional value, such as processed foods, contributes to this. Breakfast cereals are often advertised as a healthy way to start the day, but the healthiness of these products varies greatly. Objective: Our main objective was to gather information about the nutritional characteristics of ready-to-eat breakfast cereals in Sweden and to investigate the healthiness of products targeted at children compared to other cereals by use of the FoodSwitch platform. A secondary objective was to evaluate the alignment between the Keyhole symbol and the Health Star Rating. Methods: The FoodSwitch app is a mobile health (mHealth) tool used to present nutrition data and healthier alternative products to consumers. Ready-to-eat breakfast cereals from the largest Swedish grocery retailers were collected using the FoodSwitch platform. Products were defined as targeting children if they presented features addressing children on the package. Results: Overall, information on 261 ready-to-eat cereals was examined. Of this total, 8% (n=21) were targeted at children. Child-targeted cereals were higher in sugar (22.3 g/100 g vs 12.8 g/100 g, P<.001) and lower in fiber (6.2 g/100 g vs 9.8 g/100 g, P<.001) and protein (8.1 g/100 g vs 10.5 g/100 g, P<.001). Total fat (3 g/100 g vs 10.5 g/100 g, P<.001) and saturated fat (0.8 g/100 g vs 2.6 g/100 g, P<.001) were also lower. No difference was found in salt content (P=.61). Fewer child-targeted breakfast cereals displayed an on-pack Keyhole label (n=1, 5% vs n=53, 22%; P=.06), and the mean Health Star Rating value was 3.5 for child-targeted cereals compared to others (mean 3.8, P=.07). A correlation was found between the Keyhole symbol and the Health Star Rating. Conclusions: Ready-to-eat breakfast cereals targeted at children were less healthy in terms of sugar and fiber content compared to products not targeted at children. There is a need to improve the nutritional quality of child-targeted cereals. %M 34292165 %R 10.2196/17780 %U https://mhealth.jmir.org/2021/7/e17780 %U https://doi.org/10.2196/17780 %U http://www.ncbi.nlm.nih.gov/pubmed/34292165 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 7 %P e29191 %T Geospatial Analysis of Neighborhood Environmental Stress in Relation to Biological Markers of Cardiovascular Health and Health Behaviors in Women: Protocol for a Pilot Study %A Tamura,Kosuke %A Curlin,Kaveri %A Neally,Sam J %A Vijayakumar,Nithya P %A Mitchell,Valerie M %A Collins,Billy S %A Gutierrez-Huerta,Cristhian %A Troendle,James F %A Baumer,Yvonne %A Osei Baah,Foster %A Turner,Briana S %A Gray,Veronica %A Tirado,Brian A %A Ortiz-Chaparro,Erika %A Berrigan,David %A Mehta,Nehal N %A Vaccarino,Viola %A Zenk,Shannon N %A Powell-Wiley,Tiffany M %+ Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Drive, Bldg 10-CRC, 5-5330, Bethesda, MD, 20892, United States, 1 3018278660, kosuke.tamura@nih.gov %K wearables %K global positioning system %K ecological momentary assessment %K accelerometer %K biomarkers of stress %K mobile phone %D 2021 %7 22.7.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Innovative analyses of cardiovascular (CV) risk markers and health behaviors linked to neighborhood stressors are essential to further elucidate the mechanisms by which adverse neighborhood social conditions lead to poor CV outcomes. We propose to objectively measure physical activity (PA), sedentary behavior, and neighborhood stress using accelerometers, GPS, and real-time perceived ecological momentary assessment via smartphone apps and to link these to biological measures in a sample of White and African American women in Washington, DC, neighborhoods. Objective: The primary aim of this study is to test the hypothesis that living in adverse neighborhood social conditions is associated with higher stress-related neural activity among 60 healthy women living in high or low socioeconomic status neighborhoods in Washington, DC. Sub-aim 1 of this study is to test the hypothesis that the association is moderated by objectively measured PA using an accelerometer. A secondary objective is to test the hypothesis that residing in adverse neighborhood social environment conditions is related to differences in vascular function. Sub-aim 2 of this study is to test the hypothesis that the association is moderated by objectively measured PA. The third aim of this study is to test the hypothesis that adverse neighborhood social environment conditions are related to differences in immune system activation. Methods: The proposed study will be cross-sectional, with a sample of at least 60 women (30 healthy White women and 30 healthy Black women) from Wards 3 and 5 in Washington, DC. A sample of the women (n=30) will be recruited from high-income areas in Ward 3 from census tracts within a 15% of Ward 3’s range for median household income. The other participants (n=30) will be recruited from low-income areas in Wards 5 from census tracts within a 15% of Ward 5’s range for median household income. Finally, participants from Wards 3 and 5 will be matched based on age, race, and BMI. Participants will wear a GPS unit and accelerometer and report their stress and mood in real time using a smartphone. We will then examine the associations between GPS-derived neighborhood variables, stress-related neural activity measures, and adverse biological markers. Results: The National Institutes of Health Institutional Review Board has approved this study. Recruitment will begin in the summer of 2021. Conclusions: Findings from this research could inform the development of multilevel behavioral interventions and policies to better manage environmental factors that promote immune system activation or psychosocial stress while concurrently working to increase PA, thereby influencing CV health. International Registered Report Identifier (IRRID): PRR1-10.2196/29191 %M 34292168 %R 10.2196/29191 %U https://www.researchprotocols.org/2021/7/e29191 %U https://doi.org/10.2196/29191 %U http://www.ncbi.nlm.nih.gov/pubmed/34292168 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e24308 %T Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years %A Domin,Alex %A Spruijt-Metz,Donna %A Theisen,Daniel %A Ouzzahra,Yacine %A Vögele,Claus %+ Research Group: Self-Regulation and Health, Department of Behavioural and Cognitive Sciences, University of Luxembourg, Maison des Sciences Humaines, 11, Porte des Sciences, Esch-sur-Alzette, L-4366, Luxembourg, 352 46 66 44 9389, alex.domin@uni.lu %K scoping review %K smartphone application %K physical activity %K behavior change %K mobile health %K research design %K mHealth %K adolescents %K adults %K BCT %K mobile phonescoping review %K smartphone application %K physical activity %K behavior change %K mobile health %K research design %K mHealth %K adolescents %K adults %K BCT %K mobile phone %D 2021 %7 21.7.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions. Objective: The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume. Methods: We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy. Results: Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines. Conclusions: Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions. %M 34287209 %R 10.2196/24308 %U https://mhealth.jmir.org/2021/7/e24308 %U https://doi.org/10.2196/24308 %U http://www.ncbi.nlm.nih.gov/pubmed/34287209 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 7 %P e27891 %T Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study %A Alinia,Parastoo %A Sah,Ramesh Kumar %A McDonell,Michael %A Pendry,Patricia %A Parent,Sara %A Ghasemzadeh,Hassan %A Cleveland,Michael John %+ Department of Human Development, Washington State University, 501 Johnson Tower, Pullman, WA, 99164, United States, 1 509 335 2870, michael.cleveland@wsu.edu %K alcohol relapse prevention %K stress markers %K alcohol consumption %K electrodermal activity %K heart rate variability %K emotion %K mobile phone %D 2021 %7 21.7.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Previous research has highlighted the role of stress in substance misuse and addiction, particularly for relapse risk. Mobile health interventions that incorporate real-time monitoring of physiological markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress to prevent alcohol relapse. Before such interventions can be developed, measurements of these processes in ambulatory, real-world settings are needed. Objective: This research is a proof-of-concept study to establish the feasibility of using a wearable sensor device to continuously monitor stress in an ambulatory setting. Toward that end, we first aimed to examine the quality of 2 continuously monitored physiological signals—electrodermal activity (EDA) and heart rate variability (HRV)—and show that the data follow standard quality measures according to the literature. Next, we examined the associations between the statistical features extracted from the EDA and HRV signals and self-reported outcomes. Methods: Participants (N=11; female: n=10) were asked to wear an Empatica E4 wearable sensor for continuous unobtrusive physiological signal collection for up to 14 days. During the same time frame, participants responded to a daily diary study using ecological momentary assessment of self-reported stress, emotions, alcohol-related cravings, pain, and discomfort via a web-based survey, which was conducted 4 times daily. Participants also participated in structured interviews throughout the study to assess daily alcohol use and to validate self-reported and physiological stress markers. In the analysis, we first used existing artifact detection methods and physiological signal processing approaches to assess the quality of the physiological data. Next, we examined the descriptive statistics for self-reported outcomes. Finally, we investigated the associations between the features of physiological signals and self-reported outcomes. Results: We determined that 87.86% (1,032,265/1,174,898) of the EDA signals were clean. A comparison of the frequency of skin conductance responses per minute with previous research confirmed that the physiological signals collected in the ambulatory setting were successful. The results also indicated that the statistical features of the EDA and HRV measures were significantly correlated with the self-reported outcomes, including the number of stressful events marked on the sensor device, positive and negative emotions, and experienced pain and discomfort. Conclusions: The results demonstrated that the physiological data collected via an Empatica E4 wearable sensor device were consistent with previous literature in terms of the quality of the data and that features of these physiological signals were significantly associated with several self-reported outcomes among a sample of adults diagnosed with alcohol use disorder. These results suggest that ambulatory assessment of stress is feasible and can be used to develop tailored mobile health interventions to enhance sustained recovery from alcohol use disorder. %M 34287205 %R 10.2196/27891 %U https://formative.jmir.org/2021/7/e27891 %U https://doi.org/10.2196/27891 %U http://www.ncbi.nlm.nih.gov/pubmed/34287205 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 7 %P e22693 %T Mobile Ecological Momentary Assessment and Intervention and Health Behavior Change Among Adults in Rakai, Uganda: Pilot Randomized Controlled Trial %A Beres,Laura K %A Mbabali,Ismail %A Anok,Aggrey %A Katabalwa,Charles %A Mulamba,Jeremiah %A Thomas,Alvin G %A Bugos,Eva %A Nakigozi,Gertrude %A Grabowski,Mary K %A Chang,Larry W %+ Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, E5031, Baltimore, MD, 21205, United States, 1 410 955 7159, laura.beres@jhu.edu %K ecological momentary assessment %K ecological momentary intervention %K mHealth %K digital health %K smartphone %K mobile phone %K randomized trial %K Uganda %K Africa %D 2021 %7 20.7.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: An extraordinary increase in mobile phone ownership has revolutionized the opportunities to use mobile health approaches in lower- and middle-income countries (LMICs). Ecological momentary assessment and intervention (EMAI) uses mobile technology to gather data and deliver timely, personalized behavior change interventions in an individual’s natural setting. To our knowledge, there have been no previous trials of EMAI in sub-Saharan Africa. Objective: To advance the evidence base for mobile health (mHealth) interventions in LMICs, we conduct a pilot randomized trial to assess the feasibility of EMAI and establish estimates of the potential effect of EMAI on a range of health-related behaviors in Rakai, Uganda. Methods: This prospective, parallel-group, randomized pilot trial compared health behaviors between adult participants submitting ecological momentary assessment (EMA) data and receiving behaviorally responsive interventional health messaging (EMAI) with those submitting EMA data alone. Using a fully automated mobile phone app, participants submitted daily reports on 5 different health behaviors (fruit consumption, vegetable consumption, alcohol intake, cigarette smoking, and condomless sex with a non–long-term partner) during a 30-day period before randomization (P1). Participants were then block randomized to the control arm, continuing EMA reporting through exit, or the intervention arm, EMA reporting and behavioral health messaging receipt. Participants exited after 90 days of follow-up, divided into study periods 2 (P2: randomization + 29 days) and 3 (P3: 30 days postrandomization to exit). We used descriptive statistics to assess the feasibility of EMAI through the completeness of data and differences in reported behaviors between periods and study arms. Results: The study included 48 participants (24 per arm; 23/48, 48% women; median age 31 years). EMA data collection was feasible, with 85.5% (3777/4418) of the combined days reporting behavioral data. There was a decrease in the mean proportion of days when alcohol was consumed in both arms over time (control: P1, 9.6% of days to P2, 4.3% of days; intervention: P1, 7.2% of days to P3, 2.4% of days). Decreases in sex with a non–long-term partner without a condom were also reported in both arms (P1 to P3 control: 1.9% of days to 1% of days; intervention: 6.6% of days to 1.3% of days). An increase in vegetable consumption was found in the intervention (vegetable: 65.6% of days to 76.6% of days) but not in the control arm. Between arms, there was a significant difference in the change in reported vegetable consumption between P1 and P3 (control: 8% decrease in the mean proportion of days vegetables consumed; intervention: 11.1% increase; P=.01). Conclusions: Preliminary estimates suggest that EMAI may be a promising strategy for promoting behavior change across a range of behaviors. Larger trials examining the effectiveness of EMAI in LMICs are warranted. Trial Registration: ClinicalTrials.gov NCT04375423; https://www.clinicaltrials.gov/ct2/show/NCT04375423 %M 34283027 %R 10.2196/22693 %U https://formative.jmir.org/2021/7/e22693 %U https://doi.org/10.2196/22693 %U http://www.ncbi.nlm.nih.gov/pubmed/34283027 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 3 %P e24553 %T Satisfaction, Usability, and Compliance With the Use of Smartwatches for Ecological Momentary Assessment of Knee Osteoarthritis Symptoms in Older Adults: Usability Study %A Rouzaud Laborde,Charlotte %A Cenko,Erta %A Mardini,Mamoun T %A Nerella,Subhash %A Kheirkhahan,Matin %A Ranka,Sanjay %A Fillingim,Roger B %A Corbett,Duane B %A Weber,Eric %A Rashidi,Parisa %A Manini,Todd %+ Department of Pharmacy, University of Toulouse, Hopital Paule de Viguier, 330 Avenue de Grande Bretagne, TSA 70034, Toulouse, 31059, France, 33 625088692, charlotte.laborde@yahoo.fr %K ehealth %K mobile health %K ecological momentary assessment %K real-time online assessment and mobility monitor %K ROAMM %K older adults %K compliance %K personal satisfaction %K usability %K smartwatch %K knee osteoarthritis %K pain %K fatigue %K wearable electronic device %K mobile application %D 2021 %7 14.7.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Smartwatches enable physicians to monitor symptoms in patients with knee osteoarthritis, their behavior, and their environment. Older adults experience fluctuations in their pain and related symptoms (mood, fatigue, and sleep quality) that smartwatches are ideally suited to capture remotely in a convenient manner. Objective: The aim of this study was to evaluate satisfaction, usability, and compliance using the real-time, online assessment and mobility monitoring (ROAMM) mobile app designed for smartwatches for individuals with knee osteoarthritis. Methods: Participants (N=28; mean age 73.2, SD 5.5 years; 70% female) with reported knee osteoarthritis were asked to wear a smartwatch with the ROAMM app installed. They were prompted to report their prior night’s sleep quality in the morning, followed by ecological momentary assessments (EMAs) of their pain, fatigue, mood, and activity in the morning, afternoon, and evening. Satisfaction, comfort, and usability were evaluated using a standardized questionnaire. Compliance with regard to answering EMAs was calculated after excluding time when the watch was not being worn for technical reasons (eg, while charging). Results: A majority of participants reported that the text displayed was large enough to read (22/26, 85%), and all participants found it easy to enter ratings using the smartwatch. Approximately half of the participants found the smartwatch to be comfortable (14/26, 54%) and would consider wearing it as their personal watch (11/24, 46%). Most participants were satisfied with its battery charging system (20/26, 77%). A majority of participants (19/26, 73%) expressed their willingness to use the ROAMM app for a 1-year research study. The overall EMA compliance rate was 83% (2505/3036 responses). The compliance rate was lower among those not regularly wearing a wristwatch (10/26, 88% vs 16/26, 71%) and among those who found the text too small to read (4/26, 86% vs 22/26, 60%). Conclusions: Older adults with knee osteoarthritis positively rated the ROAMM smartwatch app and were generally satisfied with the device. The high compliance rates coupled with the willingness to participate in a long-term study suggest that the ROAMM app is a viable approach to remotely collecting health symptoms and behaviors for both research and clinical endeavors. %M 34259638 %R 10.2196/24553 %U https://aging.jmir.org/2021/3/e24553 %U https://doi.org/10.2196/24553 %U http://www.ncbi.nlm.nih.gov/pubmed/34259638 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e26290 %T Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard %A Filos,Dimitris %A Lekka,Irini %A Kilintzis,Vasileios %A Stefanopoulos,Leandros %A Karavidopoulou,Youla %A Maramis,Christos %A Diou,Christos %A Sarafis,Ioannis %A Papapanagiotou,Vasileios %A Alagialoglou,Leonidas %A Ioakeimidis,Ioannis %A Hassapidou,Maria %A Charmandari,Evangelia %A Heimeier,Rachel %A O'Malley,Grace %A O’Donnell,Shane %A Doyle,Gerardine %A Delopoulos,Anastasios %A Maglaveras,Nicos %+ Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, Aristotle University, University Campus, Box 323, Thessaloniki, 54124, Greece, 30 2310999281, nicmag@auth.gr %K public health authorities %K childhood obesity %K children’s behavior %K environment %K COVID-19 %K big data %K mHealth %K uHealth %K intervention %D 2021 %7 9.7.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project “BigO: Big Data Against Childhood Obesity” aims to tackle childhood obesity by creating new sources of evidence based on big data. Objective: This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app. Methods: The OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children’s obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard’s functionality was evaluated during a focus group discussion with public health experts. Results: The preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children’s eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children’s physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard’s potential to aid in a better understanding of the interplay between children’s obesogenic behaviors and the environment, and improvements were suggested. Conclusions: Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment. %M 34048353 %R 10.2196/26290 %U https://mhealth.jmir.org/2021/7/e26290 %U https://doi.org/10.2196/26290 %U http://www.ncbi.nlm.nih.gov/pubmed/34048353 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e26401 %T Determining the Evolution of Headache Among Regular Users of a Daily Electronic Diary via a Smartphone App: Observational Study %A Raffaelli,Bianca %A Mecklenburg,Jasper %A Overeem,Lucas Hendrik %A Scholler,Simon %A Dahlem,Markus A %A Kurth,Tobias %A Oliveira Gonçalves,Ana Sofia %A Reuter,Uwe %A Neeb,Lars %+ Department of Neurology, Charité – Universitätsmedizin Berlin, 1 Charitéplatz, Berlin, 10117, Germany, 49 30 450 660888, bianca.raffaelli@charite.de %K headache %K migraine %K mobile app %K headache app %K electronic diary %K app %K pain %K frequency %K intensity %D 2021 %7 7.7.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone-based apps represent a major development in health care management. Specifically in headache care, the use of electronic headache diaries via apps has become increasingly popular. In contrast to the soaring volume of available data, scientific use of these data resources is sparse. Objective: In this analysis, we aimed to assess changes in headache and migraine frequency, headache and migraine intensity, and use of acute medication among people who showed daily use of the headache diary as implemented in the freely available basic version of the German commercial app, M-sense. Methods: The basic version of M-sense comprises an electronic headache diary, documentation of lifestyle factors with a possible impact on headaches, and evaluation of headache patterns. This analysis included all M-sense users who had entered data into the app on a daily basis for at least 7 months. Results: We analyzed data from 1545 users. Mean MHD decreased from 9.42 (SD 5.81) at baseline to 6.39 (SD 5.09) after 6 months (P<.001; 95% CI 2.80-3.25). MMD, AMD, and migraine intensity were also significantly reduced. Similar results were found in 985 users with episodic migraine and in 126 users with chronic migraine. Conclusions: Among regular users of an electronic headache diary, headache and migraine frequency, in addition to other headache characteristics, improved over time. The use of an electronic headache diary may support standard headache care. %M 34255716 %R 10.2196/26401 %U https://mhealth.jmir.org/2021/7/e26401 %U https://doi.org/10.2196/26401 %U http://www.ncbi.nlm.nih.gov/pubmed/34255716 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 7 %P e25850 %T Studying Microtemporal, Within-Person Processes of Diet, Physical Activity, and Related Factors Using the APPetite-Mobile-App: Feasibility, Usability, and Validation Study %A Ruf,Alea %A Koch,Elena Doris %A Ebner-Priemer,Ulrich %A Knopf,Monika %A Reif,Andreas %A Matura,Silke %+ Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Heinrich-Hoffmann-Straße 10, Frankfurt, 60528, Germany, 49 69 6301 83348, alea.ruf@kgu.de %K diet %K physical activity %K microtemporal processes %K within-person factors %K ecological momentary assessment %K smartphone-app %K mobile phone %K mHealth %K dietary assessment %K feasibility %K usability %K validity %D 2021 %7 5.7.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Diet and physical activity (PA) have a major impact on physical and mental health. However, there is a lack of effective strategies for sustaining these health-protective behaviors. A shift to a microtemporal, within-person approach is needed to capture dynamic processes underlying eating behavior and PA, as they change rapidly across minutes or hours and differ among individuals. However, a tool that captures these microtemporal, within-person processes in daily life is currently not present. Objective: The APPetite-mobile-app is developed for the ecological momentary assessment of microtemporal, within-person processes of complex dietary intake, objectively recorded PA, and related factors. This study aims to evaluate the feasibility and usability of the APPetite-mobile-app and the validity of the incorporated APPetite-food record. Methods: The APPetite-mobile-app captures dietary intake event-contingently through a food record, captures PA continuously through accelerometers, and captures related factors (eg, stress) signal-contingently through 8 prompts per day. Empirical data on feasibility (n=157), usability (n=84), and validity (n=44) were collected within the Eat2beNICE-APPetite-study. Feasibility and usability were examined in healthy participants and psychiatric patients. The relative validity of the APPetite-food record was assessed with a subgroup of healthy participants by using a counterbalanced crossover design. The reference method was a 24-hour recall. In addition, the energy intake was compared with the total energy expenditure estimated from accelerometry. Results: Good feasibility, with compliance rates above 80% for prompts and the accelerometer, as well as reasonable average response and recording durations (prompt: 2.04 min; food record per day: 17.66 min) and latencies (prompts: 3.16 min; food record: 58.35 min) were found. Usability was rated as moderate, with a score of 61.9 of 100 on the System Usability Scale. The evaluation of validity identified large differences in energy and macronutrient intake between the two methods at the group and individual levels. The APPetite-food record captured higher dietary intakes, indicating a lower level of underreporting, compared with the 24-hour recall. Energy intake was assessed fairly accurately by the APPetite-food record at the group level on 2 of 3 days when compared with total energy expenditure. The comparison with mean total energy expenditure (2417.8 kcal, SD 410) showed that the 24-hour recall (1909.2 kcal, SD 478.8) underestimated habitual energy intake to a larger degree than the APPetite-food record (2146.4 kcal, SD 574.5). Conclusions: The APPetite-mobile-app is a promising tool for capturing microtemporal, within-person processes of diet, PA, and related factors in real time or near real time and is, to the best of our knowledge, the first of its kind. First evidence supports the good feasibility and moderate usability of the APPetite-mobile-app and the validity of the APPetite-food record. Future findings in this context will build the foundation for the development of personalized lifestyle modification interventions, such as just-in-time adaptive interventions. %M 34342268 %R 10.2196/25850 %U https://www.jmir.org/2021/7/e25850 %U https://doi.org/10.2196/25850 %U http://www.ncbi.nlm.nih.gov/pubmed/34342268 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e25310 %T Development of an Android-Based Self-Report Assessment for Elderly Driving Risk (SAFE-DR) App: Mixed Methods Study %A Hwang,Ho Sung %A Choi,Seong-Youl %+ Department of Occupational Therapy, Gwangju Women’s University, 201, Yeodae-gil, Gwangsan-gu, Gwangju, , Republic of Korea, 82 10 3209 ext 9146, ckshrj6@hanmail.net %K Android driving app %K driving safety %K reliability %K self-assessment %K validity %K mHealth %K driving %D 2021 %7 17.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Self-report assessments for elderly drivers are used in various countries for accessible, widespread self-monitoring of driving ability in the elderly population. Likewise, in South Korea, a paper-based Self-Report Assessment for Elderly Driving Risk (SAFE-DR) has been developed. Here, we implemented the SAFE-DR through an Android app, which provides the advantages of accessibility, convenience, and provision of diverse information, and verified its reliability and validity. Objective: This study tested the validity and reliability of a mobile app-based version of a self-report assessment for elderly persons contextualized to the South Korean culture and compared it with a paper-based test. Methods: In this mixed methods study, we recruited and interviewed 567 elderly drivers (aged 65 years and older) between August 2018 and May 2019. For participants who provided consent, the app-based test was repeated after 2 weeks and an additional paper-based test (Driver 65 Plus test) was administered. Using the collected data, we analyzed the reliability and validity of the app-based SAFE-DR. The internal consistency of provisional items in each subdomain of the SAFE-DR and the test-retest stability were analyzed to examine reliability. Exploratory factor analysis was performed to examine the validity of the subdomain configuration. To verify the appropriateness of using an app-based test for older drivers possibly unfamiliar with mobile technology, the correlation between the results of the SAFE-DR app and the paper-based offline test was also analyzed. Results: In the reliability analysis, Cronbach α for all items was 0.975 and the correlation of each item with the overall score ranged from r=0.520 to r=0.823; 4 items with low correlations were removed from each of the subdomains. In the retest after 2 weeks, the mean correlation coefficient across all items was r=0.951, showing very high reliability. Exploratory factor analysis on 40 of the 44 items established 5 subdomains: on-road (8 items), coping (16 items), cognitive functions (5 items), general conditions (8 items), and medical health (3 items). A very strong negative correlation of –0.864 was observed between the total score for the app-based SAFE-DR and the paper-based Driver 65 Plus with decorrelation scales. The app-based test was found to be reliable. Conclusions: In this study, we developed an app-based self-report assessment tool for elderly drivers and tested its reliability and validity. This app can help elderly individuals easily assess their own driving skills. Therefore, this assessment can be used to educate drivers and for preventive screening for elderly drivers who want to renew their driver’s licenses in South Korea. In addition, the app can contribute to safe driving among elderly drivers. %M 33934068 %R 10.2196/25310 %U https://mhealth.jmir.org/2021/6/e25310 %U https://doi.org/10.2196/25310 %U http://www.ncbi.nlm.nih.gov/pubmed/33934068 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e24666 %T Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study %A Schütz,Narayan %A Saner,Hugo %A Botros,Angela %A Pais,Bruno %A Santschi,Valérie %A Buluschek,Philipp %A Gatica-Perez,Daniel %A Urwyler,Prabitha %A Müri,René M %A Nef,Tobias %+ Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern, Switzerland, 41 31 632 75 79, tobias.nef@artorg.unibe.ch %K sleep restlessness %K telemonitoring %K digital biomarkers %K contactless sensing %K pervasive computing %K home-monitoring %K older adults %K toss and turns %K sleep monitoring %K body movements in bed %D 2021 %7 11.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective: In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods: We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results: Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions: Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options. %M 34114966 %R 10.2196/24666 %U https://mhealth.jmir.org/2021/6/e24666 %U https://doi.org/10.2196/24666 %U http://www.ncbi.nlm.nih.gov/pubmed/34114966 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e16304 %T The SleepFit Tablet Application for Home-Based Clinical Data Collection in Parkinson Disease: User-Centric Development and Usability Study %A Mascheroni,Alessandro %A Choe,Eun Kyoung %A Luo,Yuhan %A Marazza,Michele %A Ferlito,Clara %A Caverzasio,Serena %A Mezzanotte,Francesco %A Kaelin-Lang,Alain %A Faraci,Francesca %A Puiatti,Alessandro %A Ratti,Pietro Luca %+ Neurocenter of Southern Switzerland, Regional Hospital of Lugano, EOC, via Tesserete 46, Lugano, CH-6903, Switzerland, 41 353 412 71 91, pietroluca.ratti@gmail.com %K Parkinson disease %K ecological momentary assessment %K finger-tapping test %K subjective scales %K sleep diaries %K tablet application %K home-based system %D 2021 %7 8.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Parkinson disease (PD) is a common, multifaceted neurodegenerative disorder profoundly impacting patients' autonomy and quality of life. Assessment in real-life conditions of subjective symptoms and objective metrics of mobility and nonmotor symptoms such as sleep disturbance is strongly advocated. This information would critically guide the adaptation of antiparkinsonian medications and nonpharmacological interventions. Moreover, since the spread of the COVID-19 pandemic, health care practices are being reshaped toward a more home-based care. New technologies could play a pivotal role in this new approach to clinical care. Nevertheless, devices and information technology tools might be unhandy for PD patients, thus dramatically limiting their widespread employment. Objective: The goals of the research were development and usability evaluation of an application, SleepFit, for ecological momentary assessment of objective and subjective clinical metrics at PD patients’ homes, and as a remote tool for researchers to monitor patients and integrate and manage data. Methods: An iterative and user-centric strategy was employed for the development of SleepFit. The core structure of SleepFit consists of (1) an electronic finger-tapping test; (2) motor, sleepiness, and emotional subjective scales; and (3) a sleep diary. Applicable design, ergonomic, and navigation principles have been applied while tailoring the application to the specific patient population. Three progressively enhanced versions of the application (alpha, v1.0, v2.0) were tested by a total of 56 patients with PD who were asked to perform multiple home assessments 4 times per day for 2 weeks. Patient compliance was calculated as the proportion of completed tasks out of the total number of expected tasks. Satisfaction on the latest version (v2.0) was evaluated as potential willingness to use SleepFit again after the end of the study. Results: From alpha to v1.0, SleepFit was improved in graphics, ergonomics, and navigation, with automated flows guiding the patients in performing tasks throughout the 24 hours, and real-time data collection and consultation were made possible thanks to a remote web portal. In v2.0, the kiosk-mode feature restricts the use of the tablet to the SleepFit application only, thus preventing users from accidentally exiting the application. A total of 52 (4 dropouts) patients were included in the analyses. Overall compliance (all versions) was 88.89% (5707/6420). SleepFit was progressively enhanced and compliance increased from 87.86% (2070/2356) to 89.92% (2899/3224; P=.04). Among the patients who used v2.0, 96% (25/26) declared they would use SleepFit again. Conclusions: SleepFit can be considered a state-of-the-art home-based system that increases compliance in PD patients, ensures high-quality data collection, and works as a handy tool for remote monitoring and data management in clinical research. Thanks to its user-friendliness and modular structure, it could be employed in other clinical studies with minimum adaptation efforts. Trial Registration: ClinicalTrials.gov NCT02723396; https://clinicaltrials.gov/ct2/show/NCT02723396 %M 34100767 %R 10.2196/16304 %U https://mhealth.jmir.org/2021/6/e16304 %U https://doi.org/10.2196/16304 %U http://www.ncbi.nlm.nih.gov/pubmed/34100767 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 2 %P e28902 %T A Transcranial Magnetic Stimulation Trigger System for Suppressing Motor-Evoked Potential Fluctuation Using Electroencephalogram Coherence Analysis: Algorithm Development and Validation Study %A Sasaki,Keisuke %A Fujishige,Yuki %A Kikuchi,Yutaka %A Odagaki,Masato %+ Department of Systems Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadorimachi, Maebashi, 371-0816, Japan, 81 27 265 7337, odagaki@maebashi-it.ac.jp %K motor-evoked potential %K transcranial magnetic stimulation %K electroencephalogram %K coherence %K variability %K fluctuation %K trigger %K threshold %K coefficient of variation %K primary motor cortex %D 2021 %7 7.6.2021 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Transcranial magnetic stimulation (TMS), when applied over the primary motor cortex, elicits a motor-evoked potential (MEP) in electromyograms measured from peripheral muscles. MEP amplitude has often been observed to fluctuate trial to trial, even with a constant stimulus. Many factors cause MEP fluctuations in TMS. One of the primary factors is the weak stationarity and instability of cortical activity in the brain, from which we assumed MEP fluctuations originate. We hypothesized that MEP fluctuations are suppressed when TMS is delivered to the primary motor cortex at a time when several electroencephalogram (EEG) channels measured on the scalp are highly similar in the frequency domain. Objective: We developed a TMS triggering system to suppress MEP fluctuations using EEG coherence analysis, which was performed to detect the EEG signal similarity between the 2 channels in the frequency domain. Methods: Seven healthy adults participated in the experiment to confirm whether the TMS trigger system works adequately, and the mean amplitude and coefficient of the MEP variation were recorded and compared with the values obtained during the control task. We also determined the experimental time under each condition and verified whether it was within the predicted time. Results: The coefficient of variation of MEP amplitude decreased in 5 of the 7 participants, and significant differences (P=.02) were confirmed in 2 of the participants according to an F test. The coefficient of variation of the experimental time required for each stimulus after threshold modification was less than that without threshold modification, and a significant difference (P<.001) was confirmed by performing an F test. Conclusions: We found that MEP could be suppressed using the system developed in this study and that the TMS trigger system could also stabilize the experimental time by changing the triggering threshold automatically. %M 38907381 %R 10.2196/28902 %U https://biomedeng.jmir.org/2021/2/e28902 %U https://doi.org/10.2196/28902 %U http://www.ncbi.nlm.nih.gov/pubmed/38907381 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 8 %N 2 %P e28020 %T Integrating Behavior of Children with Profound Intellectual, Multiple, or Severe Motor Disabilities With Location and Environment Data Sensors for Independent Communication and Mobility: App Development and Pilot Testing %A Herbuela,Von Ralph Dane Marquez %A Karita,Tomonori %A Furukawa,Yoshiya %A Wada,Yoshinori %A Yagi,Yoshihiro %A Senba,Shuichiro %A Onishi,Eiko %A Saeki,Tatsuo %+ Department of Special Needs Education, Graduate School of Education, Ehime University, 3 Bunkyo-cho, Matsuyama, Ehime, 790-8577, Japan, 81 89 927 9517, karita.tomonori.mh@ehime-u.ac.jp %K profound intellectual and multiple disabilities %K severe motor and intellectual disabilities %K mobile app development %K augmentative and alternative communication %K AAC %K smartphone-based data collection %K behavior %K child %K sensor %K communication %K mobility %K development %K pilot %K app %D 2021 %7 7.6.2021 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Children with profound intellectual and multiple disabilities (PIMD) or severe motor and intellectual disabilities (SMID) only communicate through movements, vocalizations, body postures, muscle tensions, or facial expressions on a pre- or protosymbolic level. Yet, to the best of our knowledge, there are few systems developed to specifically aid in categorizing and interpreting behaviors of children with PIMD or SMID to facilitate independent communication and mobility. Further, environmental data such as weather variables were found to have associations with human affects and behaviors among typically developing children; however, studies involving children with neurological functioning impairments that affect communication or those who have physical and/or motor disabilities are unexpectedly scarce. Objective: This paper describes the design and development of the ChildSIDE app, which collects and transmits data associated with children’s behaviors, and linked location and environment information collected from data sources (GPS, iBeacon device, ALPS Sensor, and OpenWeatherMap application programming interface [API]) to the database. The aims of this study were to measure and compare the server/API performance of the app in detecting and transmitting environment data from the data sources to the database, and to categorize the movements associated with each behavior data as the basis for future development and analyses. Methods: This study utilized a cross-sectional observational design by performing multiple single-subject face-to-face and video-recorded sessions among purposively sampled child-caregiver dyads (children diagnosed with PIMD/SMID, or severe or profound intellectual disability and their primary caregivers) from September 2019 to February 2020. To measure the server/API performance of the app in detecting and transmitting data from data sources to the database, frequency distribution and percentages of 31 location and environment data parameters were computed and compared. To categorize which body parts or movements were involved in each behavior, the interrater agreement κ statistic was used. Results: The study comprised 150 sessions involving 20 child-caregiver dyads. The app collected 371 individual behavior data, 327 of which had associated location and environment data from data collection sources. The analyses revealed that ChildSIDE had a server/API performance >93% in detecting and transmitting outdoor location (GPS) and environment data (ALPS sensors, OpenWeatherMap API), whereas the performance with iBeacon data was lower (82.3%). Behaviors were manifested mainly through hand (22.8%) and body movements (27.7%), and vocalizations (21.6%). Conclusions: The ChildSIDE app is an effective tool in collecting the behavior data of children with PIMD/SMID. The app showed high server/API performance in detecting outdoor location and environment data from sensors and an online API to the database with a performance rate above 93%. The results of the analysis and categorization of behaviors suggest a need for a system that uses motion capture and trajectory analyses for developing machine- or deep-learning algorithms to predict the needs of children with PIMD/SMID in the future. %M 34096878 %R 10.2196/28020 %U https://rehab.jmir.org/2021/2/e28020 %U https://doi.org/10.2196/28020 %U http://www.ncbi.nlm.nih.gov/pubmed/34096878 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e22748 %T App-Based Versus Standard Six-Minute Walk Test in Pulmonary Hypertension: Mixed Methods Study %A Salvi,Dario %A Poffley,Emma %A Tarassenko,Lionel %A Orchard,Elizabeth %+ Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom, 44 1865 617675, dario.salvi.work@gmail.com %K cardiology %K exercise test %K pulmonary hypertension %K mobile apps %K GPS %D 2021 %7 7.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Pulmonary arterial hypertension (PAH) is a chronic disease of the pulmonary vasculature that can lead to heart failure and premature death. Assessment of patients with PAH includes performing a 6-minute walk test (6MWT) in clinics. We developed a smartphone app to compute the walked distance (6MWD) indoors, by counting U-turns, and outdoors, by using satellite positioning. Objective: The goal of the research was to assess (1) accuracy of the indoor 6MWTs in clinical settings, (2) validity and test-retest reliability of outdoor 6MWTs in the community, (3) compliance, usability, and acceptance of the app, and (4) feasibility of pulse oximetry during 6MWTs. Methods: We tested the app on 30 PAH patients over 6 months. Patients were asked to perform 3 conventional 6MWTs in clinic while using the app in the indoor mode and one or more app-based 6MWTs in outdoor mode in the community per month. Results: Bland-Altman analysis of 70 pairs of conventional versus app-based indoor 6MWDs suggests that the app is sometimes inaccurate (14.6 m mean difference, lower and upper limit of agreement: –133.35 m to 162.55 m). The comparison of 69 pairs of conventional 6MWDs and community-based outdoor 6MWDs within 7 days shows that community tests are strongly related to those performed in clinic (correlation 0.89), but the interpretation of the distance should consider that differences above the clinically significant threshold are not uncommon. Analysis of 89 pairs of outdoor tests performed by the same patient within 7 days shows that community-based tests are repeatable (intraclass correlation 0.91, standard error of measurement 36.97 m, mean coefficient of variation 12.45%). Questionnaires and semistructured interviews indicate that the app is usable and well accepted, but motivation to use it could be affected if the data are not used for clinical decision, which may explain low compliance in 52% of our cohort. Analysis of pulse oximetry data indicates that conventional pulse oximeters are unreliable if used during a walk. Conclusions: App-based outdoor 6MWTs in community settings are valid, repeatable, and well accepted by patients. More studies would be needed to assess the benefits of using the app in clinical practice. Trial Registration: ClinicalTrials.gov NCT04633538; https://clinicaltrials.gov/ct2/show/NCT04633538 %M 34096876 %R 10.2196/22748 %U https://mhealth.jmir.org/2021/6/e22748 %U https://doi.org/10.2196/22748 %U http://www.ncbi.nlm.nih.gov/pubmed/34096876 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 6 %P e25687 %T Racial Discrimination, Sedentary Time, and Physical Activity in African Americans: Quantitative Study Combining Ecological Momentary Assessment and Accelerometers %A Nam,Soohyun %A Jeon,Sangchoon %A Ash,Garrett %A Whittemore,Robin %A Vlahov,David %+ School of Nursing, Yale University, 400 West Campus Dr, West Haven, CT, 06516, United States, 1 203 737 2822, soohyun.nam@yale.edu %K racial discrimination %K physical activity %K ecological momentary assessment %K African American %K pilot study %K mobile phone %D 2021 %7 7.6.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: A growing number of studies indicate that exposure to social stress, such as perceived racial discrimination, may contribute to poor health, health behaviors, and health disparities. Increased physical activity (PA) may buffer the impact of social stress resulting from racial discrimination. However, to date, data on the relationship between racial discrimination and PA have been mixed. Part of the reason is that the effect of perceived racial discrimination on PA has primarily been examined in cross-sectional studies that captured retrospective measures of perceived racial discrimination associated with individuals’ current PA outcomes. The association between real-time perceived racial discrimination and PA among African Americans remains unclear. Objective: The purpose of this study is to examine the relationship among demographic, anthropometric and clinical, and psychological factors with lifetime racial discrimination and examine the within- and between-person associations between daily real-time racial discrimination and PA outcomes (total energy expenditure, sedentary time, and moderate-to-vigorous PA patterns) measured by ecological momentary assessment (EMA) and accelerometers in healthy African Americans. Methods: This pilot study used an intensive, observational, case-crossover design of African Americans (n=12) recruited from the community. After participants completed baseline surveys, they were asked to wear an accelerometer for 7 days to measure their PA levels. EMA was sent to participants 5 times per day for 7 days to assess daily real-time racial discrimination. Multilevel models were used to examine the within- and between-person associations of daily racial discrimination on PA. Results: More EMA-reported daily racial discrimination was associated with younger age (r=0.75; P=.02). Daily EMA-reported microaggression was associated with depressive symptoms (r=0.66; P=.05), past race-related events (r=0.82; P=.004), and lifetime discrimination (r=0.78; P=.01). In the within-person analyses, the day-level association of racial discrimination and sedentary time was significant (β=.30, SE 0.14; P=.03), indicating that on occasions when participants reported more racial discrimination than usual, more sedentary time was observed. Between-person associations of racial discrimination (β=−.30, SE 0.28; P=.29) or microaggression (β=−.34, SE 0.36; P=.34) with total energy expenditure were suggestive but inconclusive. Conclusions: Concurrent use of EMA and accelerometers is a feasible method to examine the relationship between racial discrimination and PA in real time. Examining daily processes at the within-person level has the potential to elucidate the mechanisms of which racial discrimination may have on health and health behaviors and to guide the development of personalized interventions for increasing PA in racial ethnic minorities. Future studies with a precision health approach, incorporating within- and between-person associations, are warranted to further elucidate the effects of racial discrimination and PA. International Registered Report Identifier (IRRID): RR2-10.1002/nur.22068 %M 34096870 %R 10.2196/25687 %U https://formative.jmir.org/2021/6/e25687 %U https://doi.org/10.2196/25687 %U http://www.ncbi.nlm.nih.gov/pubmed/34096870 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e28892 %T Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II %A Mack,Dante L %A DaSilva,Alex W %A Rogers,Courtney %A Hedlund,Elin %A Murphy,Eilis I %A Vojdanovski,Vlado %A Plomp,Jane %A Wang,Weichen %A Nepal,Subigya K %A Holtzheimer,Paul E %A Wagner,Dylan D %A Jacobson,Nicholas C %A Meyer,Meghan L %A Campbell,Andrew T %A Huckins,Jeremy F %+ Department of Psychological and Brain Sciences, Dartmouth College, Moore Hall, 3 Maynard St, Hanover, NH, 03755, United States, 1 603 646 3181, f002vhk@dartmouth.edu %K anxiety %K college %K COVID-19 %K COVID fatigue %K depression %K George Floyd %K mobile sensing %K phone usage %K sleep %K digital phenotyping %D 2021 %7 4.6.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. Objective: By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. Methods: Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. Results: Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020. Conclusions: In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic. %M 33900935 %R 10.2196/28892 %U https://www.jmir.org/2021/6/e28892 %U https://doi.org/10.2196/28892 %U http://www.ncbi.nlm.nih.gov/pubmed/33900935 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e26006 %T Smartphone-Based VO2max Measurement With Heart Snapshot in Clinical and Real-world Settings With a Diverse Population: Validation Study %A Webster,Dan E %A Tummalacherla,Meghasyam %A Higgins,Michael %A Wing,David %A Ashley,Euan %A Kelly,Valerie E %A McConnell,Michael V %A Muse,Evan D %A Olgin,Jeffrey E %A Mangravite,Lara M %A Godino,Job %A Kellen,Michael R %A Omberg,Larsson %+ Sage Bionetworks, 2901 3rd Ave #330, Seattle, WA, United States, 1 206 928 8250, larsson.omberg@sagebionetworks.org %K VO2max %K heart rate %K digital health %K real-world data %K cardiorespiratory fitness %K remote monitoring %K mobile phone %K smartphone %K validation %D 2021 %7 4.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Maximal oxygen consumption (VO2max) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VO2max is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff. Objective: To overcome the limitations of clinical VO2max measurement, we aim to develop a digital VO2max estimation protocol that can be self-administered remotely using only the sensors within a smartphone. We also aim to validate this measure within a broadly representative population across a spectrum of smartphone devices. Methods: Two smartphone-based VO2max estimation protocols were developed: a 12-minute run test (12-MRT) based on distance measured by GPS and a 3-minute step test (3-MST) based on heart rate recovery measured by a camera. In a 101-person cohort, balanced across age deciles and sex, participants completed a gold standard treadmill-based VO2max measurement, two silver standard clinical protocols, and the smartphone-based 12-MRT and 3-MST protocols in the clinic and at home. In a separate 120-participant cohort, the video-based heart rate measurement underlying the 3-MST was measured for accuracy in individuals across the spectrum skin tones while using 8 different smartphones ranging in cost from US $99 to US $999. Results: When compared with gold standard VO2max testing, Lin concordance was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. However, in remote settings, the 12-MRT was significantly less concordant with the gold standard (pc=0.25) compared with the 3-MST (pc=0.61), although both had high test-retest reliability (12-MRT intraclass correlation coefficient=0.88; 3-MST intraclass correlation coefficient=0.86). On the basis of the finding that 3-MST concordance was generalizable to remote settings whereas 12-MRT was not, the video-based heart rate measure within the 3-MST was selected for further investigation. Heart rate measurements in any of the combinations of the six Fitzpatrick skin tones and 8 smartphones resulted in a concordance of pc≥0.81. Performance did not correlate with device cost, with all phones selling under US $200 performing better than pc>0.92. Conclusions: These findings demonstrate the importance of validating mobile health measures in the real world across a diverse cohort and spectrum of hardware. The 3-MST protocol, termed as heart snapshot, measured VO2max with similar accuracy to supervised in-clinic tests such as the Tecumseh (pc=0.94) protocol, while also generalizing to remote and unsupervised measurements. Heart snapshot measurements demonstrated fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between various iOS and Android phone configurations. This software is freely available for all validation data and analysis code. %M 34085945 %R 10.2196/26006 %U https://mhealth.jmir.org/2021/6/e26006 %U https://doi.org/10.2196/26006 %U http://www.ncbi.nlm.nih.gov/pubmed/34085945 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 6 %N 2 %P e27027 %T Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies %A Ray,Mary Katherine %A McMichael,Alana %A Rivera-Santana,Maria %A Noel,Jacob %A Hershey,Tamara %+ Department of Psychiatry, Washington University in St. Louis, 4525 Scott Ave, East Bldg, St. Louis, MO, 63110, United States, 1 314 362 5041, m.ray@wustl.edu %K ecological momentary assessment %K continuous glucose monitoring %K actigraphy %K accelerometer %K ambulatory blood pressure monitoring %K personal digital assistant %K mobile phone %K smartphone %K mHealth %D 2021 %7 3.6.2021 %9 Viewpoint %J JMIR Diabetes %G English %X Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use. %M 34081017 %R 10.2196/27027 %U https://diabetes.jmir.org/2021/2/e27027 %U https://doi.org/10.2196/27027 %U http://www.ncbi.nlm.nih.gov/pubmed/34081017 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e26462 %T Sleep Detection for Younger Adults, Healthy Older Adults, and Older Adults Living With Dementia Using Wrist Temperature and Actigraphy: Prototype Testing and Case Study Analysis %A Wei,Jing %A Boger,Jennifer %+ Department of Systems Design Engineering, University of Waterloo, Engineering 5, 6th Floor, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 38328, jboger@uwaterloo.ca %K sleep monitoring %K wearables %K accelerometer %K wrist temperature %K circadian rhythm %K younger adults %K older adults %K dementia %K mobile phone %D 2021 %7 1.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sleep is essential for one’s health and quality of life. Wearable technologies that use motion and temperature sensors have made it possible to self-monitor sleep. Although there is a growing body of research on sleep monitoring using wearable devices for healthy young-to-middle-aged adults, few studies have focused on older adults, including those living with dementia. Objective: This study aims to investigate the impact of age and dementia on sleep detection through movement and wrist temperature. Methods: A total of 10 younger adults, 10 healthy older adults, and 8 older adults living with dementia (OAWD) were recruited. Each participant wore a Mi Band 2 (accemetry-based sleep detection) and our custom-built wristband (actigraphy and wrist temperature) 24 hours a day for 2 weeks and was asked to keep a daily sleep journal. Sleep parameters detected by the Mi Band 2 were compared with sleep journals, and visual analysis of actigraphy and temperature data was performed. Results: The absolute differences in sleep onset and offset between the sleep journals and Mi Band 2 were 39 (SD 51) minutes and 31 (SD 52) minutes for younger adults, 49 (SD 58) minutes and 33 (SD 58) minutes for older adults, and 253 (SD 104) minutes and 161 (SD 94) minutes for OAWD. The Mi Band 2 was unable to accurately detect sleep in 3 healthy older adults and all OAWDs. The average sleep and wake temperature difference of OAWD (1.26 °C, SD 0.82 °C) was significantly lower than that of healthy older adults (2.04 °C, SD 0.70 °C) and healthy younger adults (2.48 °C, SD 0.88 °C). Actigraphy data showed that older adults had more movement during sleep compared with younger adults and that this trend appears to increase for those with dementia. Conclusions: The Mi Band 2 did not accurately detect sleep in older adults who had greater levels of nighttime movement. As more nighttime movement appears to be a phenomenon that increases in prevalence with age and even more so with dementia, further research needs to be conducted with a larger sample size and greater diversity of commercially available wearable devices to explore these trends more conclusively. All participants, including older adults and OAWD, had a distinct sleep and wake wrist temperature contrast, which suggests that wrist temperature could be leveraged to create more robust and broadly applicable sleep detection algorithms. %M 34061038 %R 10.2196/26462 %U https://mhealth.jmir.org/2021/6/e26462 %U https://doi.org/10.2196/26462 %U http://www.ncbi.nlm.nih.gov/pubmed/34061038 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e19536 %T Factors That Influence the Use of Electronic Diaries in Health Care: Scoping Review %A Daniëls,Naomi E M %A Hochstenbach,Laura M J %A van Zelst,Catherine %A van Bokhoven,Marloes A %A Delespaul,Philippe A E G %A Beurskens,Anna J H M %+ Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Vijverdalseweg 1, Maastricht, 6226NB, Netherlands, 31 43 3883820, naomi.daniels@maastrichtuniversity.nl %K compliance %K delivery of health care %K diary %K ecological momentary assessment %K intention %K motivation %K scoping review %D 2021 %7 1.6.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: A large number of people suffer from psychosocial or physical problems. Adequate strategies to alleviate needs are scarce or lacking. Symptom variation can offer insights into personal profiles of coping and resilience (detailed functional analyses). Hence, diaries are used to report mood and behavior occurring in daily life. To reduce inaccuracies, biases, and noncompliance with paper diaries, a shift to electronic diaries has occurred. Although these diaries are increasingly used in health care, information is lacking about what determines their use. Objective: The aim of this study was to map the existing empirical knowledge and gaps concerning factors that influence the use of electronic diaries, defined as repeated recording of psychosocial or physical data lasting at least one week using a smartphone or a computer, in health care. Methods: A scoping review of the literature published between January 2000 and December 2018 was conducted using queries in PubMed and PsycInfo databases. English or Dutch publications based on empirical data about factors that influence the use of electronic diaries for psychosocial or physical purposes in health care were included. Both databases were screened, and findings were summarized using a directed content analysis organized by the Consolidated Framework for Implementation Research (CFIR). Results: Out of 3170 articles, 22 studies were selected for qualitative synthesis. Eleven themes were determined in the CFIR categories of intervention, user characteristics, and process. No information was found for the CFIR categories inner (eg, organizational resources, innovation climate) and outer (eg, external policies and incentives, pressure from competitors) settings. Reminders, attractive designs, tailored and clear data visualizations (intervention), smartphone experience, and intrinsic motivation to change behavior (user characteristics) could influence the use of electronic diaries. During the implementation process, attention should be paid to both theoretical and practical training. Conclusions: Design aspects, user characteristics, and training and instructions determine the use of electronic diaries in health care. It is remarkable that there were no empirical data about factors related to embedding electronic diaries in daily clinical practice. More research is needed to better understand influencing factors for optimal electronic diary use. %M 34061036 %R 10.2196/19536 %U https://mhealth.jmir.org/2021/6/e19536 %U https://doi.org/10.2196/19536 %U http://www.ncbi.nlm.nih.gov/pubmed/34061036 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e25316 %T User-Centered Development of a Mobile App for Biopsychosocial Pain Assessment in Adults: Usability, Reliability, and Validity Study %A Lopes,Filipa %A Rodrigues,Mário %A Silva,Anabela G %+ Center for Health Technology and Services Research (CINTESIS.UA), School of Health Sciences, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-193, Portugal, 351 234247119 ext 27120, asilva@ua.pt %K pain assessment %K mobile app %K validity %K reliability %K usability %K mHealth %K pain %K user-centered design %D 2021 %7 14.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Pain-related mobile apps targeting pain assessment commonly limit pain assessment to pain behaviors and physiological aspects. However, current guidelines state that pain assessment should follow the biopsychosocial model, clearly addressing biological, psychological, and social aspects of the pain experience. Existing reviews also highlight that pain specialists and end users are not commonly involved in the development process of mobile apps for pain assessment, negatively affecting the quality of the available apps. Objective: This study aimed to develop a mobile app for pain assessment (AvaliaDor) and assess its usability, validity, reliability, and measurement error in a sample of real patients with chronic pain recruited from a physiotherapy clinic. Methods: This study was divided into 2 phases: phase 1—development of the AvaliaDor app; and phase 2—assessment of the apps’ usability, reliability, measurement error, and validity. AvaliaDor was developed (phase 1) based on the literature and the recommendations of physiotherapists and patients with pain in cycles of evaluation, inclusion of recommendations, and reevaluation until no further changes were required. The final version of the app was then tested in patients with musculoskeletal pain attending a private physiotherapy practice (phase 2) who were asked to use the app twice on 2 consecutive days for reliability purposes. In addition, participants had to complete a set of paper-based scales (Brief Pain Inventory, painDETECT, Pain Catastrophizing Scale, and Tampa Scale for Kinesiophobia), which were used to assess the validity (criterion validity and hypothesis testing) of the app, and the Post-Study System Usability Questionnaire was used to assess its usability. Results: The development process (phase 1) included 5 physiotherapists external to the research team and 5 patients with musculoskeletal pain, and it resulted in the creation of an app named AvaliaDor, which includes an assessment of pain intensity, location, and phenotype; associated disability; and the issues of pain catastrophizing and fear of movement. A total of 52 patients with pain (mean age 50.12 years, SD 11.71 years; 39 females) participated in phase 2 and used the app twice. The Pearson correlation coefficient between the scores on the paper-based scales and the app ranged between 0.81 and 0.93 for criterion validity and between 0.41 and 0.59 for hypothesis testing. Test-retest reliability was moderate to good (intraclass correlation coefficient between 0.67 and 0.90) and the score for usability was 1.16 (SD 0.27), indicating good usability. Conclusions: A mobile app named AvaliaDor was developed to assess the intensity, location, and phenotype of pain; associated disability; and the issues of pain catastrophizing and fear of movement in a user-centered design process. The app was shown to be usable, valid, and reliable for assessing pain from a biopsychosocial perspective in a heterogeneous group of patients with pain. Future work can explore the long-term use of AvaliaDor in clinical contexts and its advantages for the assessment and management of patients with pain. %M 33988515 %R 10.2196/25316 %U https://mhealth.jmir.org/2021/5/e25316 %U https://doi.org/10.2196/25316 %U http://www.ncbi.nlm.nih.gov/pubmed/33988515 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e21763 %T Smartphone and Tablet Software Apps to Collect Data in Sport and Exercise Settings: Cross-sectional International Survey %A Shaw,Matthew Peter %A Satchell,Liam Paul %A Thompson,Steve %A Harper,Ed Thomas %A Balsalobre-Fernández,Carlos %A Peart,Daniel James %+ Sports, Physical Activity and Food, Western Norway University of Applied Sciences, Røyrgata 6, Sogndal, 6856, Norway, 47 57676391, matthew.shaw@hvl.no %K mobile apps %K sports %K smartphone %K mobile phone %K questionnaire %K survey %D 2021 %7 13.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Advances in smartphone technology have facilitated an increase in the number of commercially available smartphone and tablet apps that enable the collection of physiological and biomechanical variables typically monitored in sport and exercise settings. Currently, it is not fully understood whether individuals collect data using mobile devices and tablets, independent of additional hardware, in their practice. Objective: This study aims to explore the use of smartphone and tablet software apps to collect data by individuals working in various sport and exercise settings, such as sports coaching, strength and conditioning, and personal training. Methods: A total of 335 practitioners completed an electronic questionnaire that surveyed their current training practices, with a focus on 2 areas: type of data collection and perceptions of reliability and validity regarding app use. An 18-item questionnaire, using a 5-point Likert scale, evaluated the perception of app use. Results: A total of 204 respondents reported using apps to directly collect data, with most of them (196/335, 58.5%) collecting biomechanical data, and 41.2% (138/335) respondents reported using at least one evidence-based app. A binomial general linear model determined that evidence accessibility (β=.35, 95% CI 0.04-0.67; P=.03) was significantly related to evidence-based app use. Age (β=−.03, 95% CI −0.06 to 0.00; P=.03) had a significant negative effect on evidence-based app use. Conclusions: This study demonstrates that practitioners show a greater preference for using smartphones and tablet devices to collect biomechanical data such as sprint velocity and jump performance variables. When it is easier to access information on the quality of apps, practitioners are more likely to use evidence-based apps. App developers should seek independent research to validate their apps. In addition, app developers should seek to provide clear signposting to the scientific support of their software in alternative ways. %M 33983122 %R 10.2196/21763 %U https://mhealth.jmir.org/2021/5/e21763 %U https://doi.org/10.2196/21763 %U http://www.ncbi.nlm.nih.gov/pubmed/33983122 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 4 %N 1 %P e24644 %T Utilization of the iOS Shortcuts App to Generate a Surgical Logbook Tool: Feasibility Study %A Thompson,Daniel %+ Department of Vascular Surgery, St Vincent's Hospital Melbourne, 41 Victoria Pde, Fitzroy, 3065, Australia, 61 92312211, Daniel.thompson@svha.org.au %K app %K audit %K data collection %K data %K feasibility %K medical education %K mHealth %K surgery %K surgical audit %K surgical education %K utility %D 2021 %7 13.5.2021 %9 Original Paper %J JMIR Perioper Med %G English %X Background: Surgical audit is an essential aspect of modern reflective surgical practice and is key to improving surgical outcomes. The surgical logbook is an important method of data collection for both personal and unit audits; however, current electronic data collection tools, especially mobile apps, lack the minimum recommended data fields. Objective: This feasibility study details the creation of a free, effective surgical logbook tool with the iOS Shortcuts app and investigates the time investment required to maintain a surgical logbook with this tool. In addition, we investigate the potential utility of the Shortcuts app in creating medical data collection tools. Methods: Using the iOS Shortcuts app, we created a shortcut “Operation Note,” which collects surgical logbook data by using the minimum and extended audit data sets recommended by the Royal Australasian College of Surgeons. We practically assessed the feasibility of the tool, assessing the time requirement for entry, accuracy, and completeness of the entered data. Results: The shortcut collected accurate and useful data for a surgical audit. Data entry took on average 65 seconds per case for the minimum data set, and 135 seconds per case for the extended data set, with a mean difference of 68 seconds (P<.001; 95% CI 61.6-77.7). Conclusions: This feasibility study demonstrates the utility of the iOS Shortcuts app in the creation of a surgical logbook and the time-consuming nature of data collection for surgical audit. Our iOS Operation Note shortcut is a free, rapid, and customizable alternative to currently available logbook apps and offers surgical trainees and consultants a method for recording surgical operations, complications, and demographic data. %M 33983132 %R 10.2196/24644 %U https://periop.jmir.org/2021/1/e24644 %U https://doi.org/10.2196/24644 %U http://www.ncbi.nlm.nih.gov/pubmed/33983132 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 5 %P e29562 %T Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study %A Clingan,Caroline A %A Dittakavi,Manasa %A Rozwadowski,Michelle %A Gilley,Kristen N %A Cislo,Christine R %A Barabas,Jenny %A Sandford,Erin %A Olesnavich,Mary %A Flora,Christopher %A Tyler,Jonathan %A Mayer,Caleb %A Stoneman,Emily %A Braun,Thomas %A Forger,Daniel B %A Tewari,Muneesh %A Choi,Sung Won %+ Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, 1500 E Medical Center Dr, D4118 Medical Professional Building, Ann Arbor, MI, 48109, United States, 1 734 615 5707, sungchoi@med.umich.edu %K mobile health %K app %K mHealth %K wearable %K sensor %K COVID-19 %K health care worker %K frontline worker %K smartphone %K digital health %D 2021 %7 12.5.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Health care workers (HCWs) have been working on the front lines of the COVID-19 pandemic with high risks of viral exposure, infection, and transmission. Standard COVID-19 testing is insufficient to protect HCWs from these risks and prevent the spread of disease. Continuous monitoring of physiological data with wearable sensors, self-monitoring of symptoms, and asymptomatic COVID-19 testing may aid in the early detection of COVID-19 in HCWs and may help reduce further transmission among HCWs, patients, and families. Objective: By using wearable sensors, smartphone-based symptom logging, and biospecimens, this project aims to assist HCWs in self-monitoring COVID-19. Methods: We conducted a prospective, longitudinal study of HCWs at a single institution. The study duration was 1 year, wherein participants were instructed on the continuous use of two wearable sensors (Fitbit Charge 3 smartwatch and TempTraq temperature patches) for up to 30 days. Participants consented to provide biospecimens (ie, nasal swabs, saliva swabs, and blood) for up to 1 year from study entry. Using a smartphone app called Roadmap 2.0, participants entered a daily mood score, submitted daily COVID-19 symptoms, and completed demographic and health-related quality of life surveys at study entry and 30 days later. Semistructured qualitative interviews were also conducted at the end of the 30-day period, following completion of daily mood and symptoms reporting as well as continuous wearable sensor use. Results: A total of 226 HCWs were enrolled between April 28 and December 7, 2020. The last participant completed the 30-day study procedures on January 16, 2021. Data collection will continue through January 2023, and data analyses are ongoing. Conclusions: Using wearable sensors, smartphone-based symptom logging and survey completion, and biospecimen collections, this study will potentially provide data on the prevalence of COVID-19 infection among HCWs at a single institution. The study will also assess the feasibility of leveraging wearable sensors and self-monitoring of symptoms in an HCW population. Trial Registration: ClinicalTrials.gov NCT04756869; https://clinicaltrials.gov/ct2/show/NCT04756869 International Registered Report Identifier (IRRID): DERR1-10.2196/29562 %M 33945497 %R 10.2196/29562 %U https://www.researchprotocols.org/2021/5/e29562 %U https://doi.org/10.2196/29562 %U http://www.ncbi.nlm.nih.gov/pubmed/33945497 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e27342 %T On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data %A Nakanishi,Miharu %A Shibasaki,Ryosuke %A Yamasaki,Syudo %A Miyazawa,Satoshi %A Usami,Satoshi %A Nishiura,Hiroshi %A Nishida,Atsushi %+ Research Center for Social Science & Medicine, Tokyo Metopolitan Institute of Medical Science, 2-1-6 Kamikitazawa, Setagaya-ku, Tokyo, 156-8506, Japan, 81 3 6834 2292, mnakanishi-tky@umin.ac.jp %K COVID-19 %K mobility data %K on-site dining %K public health and social measures %K public health %K mobile phone %K mobility %K protection %K time series %K location %K infectious disease %K transmission %D 2021 %7 11.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: During the second wave of COVID-19 in August 2020, the Tokyo Metropolitan Government implemented public health and social measures to reduce on-site dining. Assessing the associations between human behavior, infection, and social measures is essential to understand achievable reductions in cases and identify the factors driving changes in social dynamics. Objective: The aim of this study was to investigate the association between nighttime population volumes, the COVID-19 epidemic, and the implementation of public health and social measures in Tokyo. Methods: We used mobile phone location data to estimate populations between 10 PM and midnight in seven Tokyo metropolitan areas. Mobile phone trajectories were used to distinguish and extract on-site dining from stay-at-work and stay-at-home behaviors. Numbers of new cases and symptom onsets were obtained. Weekly mobility and infection data from March 1 to November 14, 2020, were analyzed using a vector autoregression model. Results: An increase in the number of symptom onsets was observed 1 week after the nighttime population volume increased (coefficient=0.60, 95% CI 0.28 to 0.92). The effective reproduction number significantly increased 3 weeks after the nighttime population volume increased (coefficient=1.30, 95% CI 0.72 to 1.89). The nighttime population volume increased significantly following reports of decreasing numbers of confirmed cases (coefficient=–0.44, 95% CI –0.73 to –0.15). Implementation of social measures to restaurants and bars was not significantly associated with nighttime population volume (coefficient=0.004, 95% CI –0.07 to 0.08). Conclusions: The nighttime population started to increase after decreasing incidence of COVID-19 was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of an epidemic, sufficiently informed by mobility data. %M 33886486 %R 10.2196/27342 %U https://mhealth.jmir.org/2021/5/e27342 %U https://doi.org/10.2196/27342 %U http://www.ncbi.nlm.nih.gov/pubmed/33886486 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 2 %P e15417 %T Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study %A Tonti,Simone %A Marzolini,Brunella %A Bulgheroni,Maria %+ Ab.Acus srl, Via Francesco Caracciolo 77, Milano, 20155, Italy, 39 02 89693979, mariabulgheroni@ab-acus.com %K telemonitoring %K data integrity %K technical validation %K cloud computing %K ubiquitous computing %K behavioral analysis %K mHealth %D 2021 %7 11.5.2021 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Smartphone use is widely spreading in society. Their embedded functions and sensors may play an important role in therapy monitoring and planning. However, the use of smartphones for intrapersonal behavioral and physical monitoring is not yet fully supported by adequate studies addressing technical reliability and acceptance. Objective: The objective of this paper is to identify and discuss technical issues that may impact on the wide use of smartphones as clinical monitoring tools. The focus is on the quality of the data and transparency of the acquisition process. Methods: QuantifyMyPerson is a platform for continuous monitoring of smartphone use and embedded sensors data. The platform consists of an app for data acquisition, a backend cloud server for data storage and processing, and a web-based dashboard for data management and visualization. The data processing aims to extract meaningful features for the description of daily life such as phone status, calls, app use, GPS, and accelerometer data. A total of health subjects installed the app on their smartphones, running it for 7 months. The acquired data were analyzed to assess impact on smartphone performance (ie, battery consumption and anomalies in functioning) and data integrity. Relevance of the selected features in describing changes in daily life was assessed through the computation of a k-nearest neighbors global anomaly score to detect days that differ from others. Results: The effectiveness of smartphone-based monitoring depends on the acceptability and interoperability of the system as user retention and data integrity are key aspects. Acceptability was confirmed by the full transparency of the app and the absence of any conflicts with daily smartphone use. The only perceived issue was the battery consumption even though the trend of battery drain with and without the app running was comparable. Regarding interoperability, the app was successfully installed and run on several Android brands. The study shows that some smartphone manufacturers implement power-saving policies not allowing continuous sensor data acquisition and impacting integrity. Data integrity was 96% on smartphones whose power-saving policies do not impact the embedded sensor management and 84% overall. Conclusions: The main technological barriers to continuous behavioral and physical monitoring (ie, battery consumption and power-saving policies of manufacturers) may be overcome. Battery consumption increase is mainly due to GPS triangulation and may be limited, while data missing because of power-saving policies are related only to periods of nonuse of the phone since the embedded sensors are reactivated by any smartphone event. Overall, smartphone-based passive sensing is fully feasible and scalable despite the Android market fragmentation. %M 38907377 %R 10.2196/15417 %U https://biomedeng.jmir.org/2021/2/e15417 %U https://doi.org/10.2196/15417 %U http://www.ncbi.nlm.nih.gov/pubmed/38907377 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 5 %P e26186 %T Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study %A Lai,Jocelyn %A Rahmani,Amir %A Yunusova,Asal %A Rivera,Alexander P %A Labbaf,Sina %A Hu,Sirui %A Dutt,Nikil %A Jain,Ramesh %A Borelli,Jessica L %+ UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, 4201 Social and Behavioral Sciences Gateway, Irvine, CA, 92697, United States, 1 4086565508, jocelyn.lai@uci.edu %K COVID-19 %K emerging adulthood %K multimodal personal chronicles %K case study %K wearable internet of things %K individualized mHealth %K college students %K mental health %D 2021 %7 11.5.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The year 2020 has been challenging for many, particularly for young adults who have been adversely affected by the COVID-19 pandemic. Emerging adulthood is a developmental phase with significant changes in the patterns of daily living; it is a risky phase for the onset of major mental illness. College students during the pandemic face significant risk, potentially losing several protective factors (eg, housing, routine, social support, job, and financial security) that are stabilizing for mental health and physical well-being. Individualized multiple assessments of mental health, referred to as multimodal personal chronicles, present an opportunity to examine indicators of health in an ongoing and personalized way using mobile sensing devices and wearable internet of things. Objective: To assess the feasibility and provide an in-depth examination of the impact of the COVID-19 pandemic on college students through multimodal personal chronicles, we present a case study of an individual monitored using a longitudinal subjective and objective assessment approach over a 9-month period throughout 2020, spanning the prepandemic period of January through September. Methods: The individual, referred to as Lee, completed psychological assessments measuring depression, anxiety, and loneliness across 4 time points in January, April, June, and September. We used the data emerging from the multimodal personal chronicles (ie, heart rate, sleep, physical activity, affect, behaviors) in relation to psychological assessments to understand patterns that help to explicate changes in the individual’s psychological well-being across the pandemic. Results: Over the course of the pandemic, Lee’s depression severity was highest in April, shortly after shelter-in-place orders were mandated. His depression severity remained mildly severe throughout the rest of the months. Associations in positive and negative affect, physiology, sleep, and physical activity patterns varied across time periods. Lee’s positive affect and negative affect were positively correlated in April (r=0.53, P=.04) whereas they were negatively correlated in September (r=–0.57, P=.03). Only in the month of January was sleep negatively associated with negative affect (r=–0.58, P=.03) and diurnal beats per minute (r=–0.54, P=.04), and then positively associated with heart rate variability (resting root mean square of successive differences between normal heartbeats) (r=0.54, P=.04). When looking at his available contextual data, Lee noted certain situations as supportive coping factors and other situations as potential stressors. Conclusions: We observed more pandemic concerns in April and noticed other contextual events relating to this individual’s well-being, reflecting how college students continue to experience life events during the pandemic. The rich monitoring data alongside contextual data may be beneficial for clinicians to understand client experiences and offer personalized treatment plans. We discuss benefits as well as future directions of this system, and the conclusions we can draw regarding the links between the COVID-19 pandemic and college student mental health. %M 33882022 %R 10.2196/26186 %U https://formative.jmir.org/2021/5/e26186 %U https://doi.org/10.2196/26186 %U http://www.ncbi.nlm.nih.gov/pubmed/33882022 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 5 %P e18096 %T Usability and Acceptance of Wearable Biosensors in Forensic Psychiatry: Cross-sectional Questionnaire Study %A de Looff,Pieter Christiaan %A Nijman,Henk %A Didden,Robert %A Noordzij,Matthijs L %+ Behavioural Science Institute, Radboud University, Postbus 9104, Nijmegen, Netherlands, 31 030 2256405, peterdelooff@gmail.com %K forensic psychiatry %K wearable biosensors %K intellectual disabilities %K usability %K acceptance %K continuous use %K emotion regulation %K behavior regulation %D 2021 %7 10.5.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The use of wearable biosensor devices for monitoring and coaching in forensic psychiatric settings yields high expectations for improved self-regulation of emotions and behavior in clients and staff members. More so, if clients have mild intellectual disabilities (IQ 50-85), they might benefit from these biosensors as they are easy to use in everyday life, which ensures that clients can practice with the devices in multiple stress and arousal-inducing situations. However, research on (continuous) use and acceptance of biosensors in forensic psychiatry for clients with mild intellectual disabilities and their caretakers is scarce. Although wearable biosensors show promise for health care, recent research showed that the acceptance and continuous use of wearable devices in consumers is not as was anticipated, probably due to low expectations. Objective: The main goal of this study was to investigate the associations between and determinants of the expectation of usability, the actual experienced usability, and the intention for continuous use of biosensors. Methods: A total of 77 participants (31 forensic clients with mild intellectual disabilities and 46 forensic staff members) participated in a 1-week trial. Preceding the study, we selected 4 devices thought to benefit the participants in domains of self-regulation, physical health, or sleep. Qualitative and quantitative questionnaires were used that explored the determinants of usability, acceptance, and continuous use of biosensors. Questionnaires consisted of the System Usability Scale, the Technology Acceptance Model questionnaire, and the extended expectation confirmation model questionnaire. Results: Only the experienced usability of the devices was associated with intended continuous use. Forensic clients scored higher on acceptance and intention for continuous use than staff members. Moderate associations were found between usability with acceptance and continuous use. Staff members showed stronger associations between usability and acceptance (r=.80, P<.001) and usability and continuous use (r=.79, P<.001) than clients, who showed more moderate correlations between usability and acceptance (r=.46, P=.01) and usability and continuous use (r=.52, P=.003). The qualitative questionnaires in general indicated that the devices were easy to use and gave clear information. Conclusions: Contrary to expectations, it was the actual perceived usability of wearing a biosensor that was associated with continuous use and to a much lesser extent the expectancy of usability. Clients scored higher on acceptance and intention for continuous use, but associations between usability and both acceptance and continuous use were markedly stronger in staff members. This study provides clear directions on how to further investigate these associations. For example, whether this is a true effect or due to a social desirability bias in the client group must be investigated. Clients with mild intellectual disabilities might benefit from the ease of use of these devices and their continuing monitoring and coaching apps. For these clients, it is especially important to develop easy-to-use biosensors with a minimum requirement on cognitive capacity to increase usability, acceptance, and continuous use. %M 33970115 %R 10.2196/18096 %U https://formative.jmir.org/2021/5/e18096 %U https://doi.org/10.2196/18096 %U http://www.ncbi.nlm.nih.gov/pubmed/33970115 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e23681 %T The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study %A Davoudi,Anis %A Mardini,Mamoun T %A Nelson,David %A Albinali,Fahd %A Ranka,Sanjay %A Rashidi,Parisa %A Manini,Todd M %+ Department of Biomedical Engineering, University of Florida, M542, Stetson Medical Science Building, 1345 Center Dr, Gainesville, FL, 32610, United States, 1 352 294 5086, anisdavoudi@ufl.edu %K human activity recognition %K machine learning %K wearable accelerometers %K mobile phone %D 2021 %7 3.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Research has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors. Objective: This study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. Methods: In total, 93 participants (mean age 72.2 years, SD 7.1) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary versus nonsedentary, locomotion versus nonlocomotion, and lifestyle versus nonlifestyle activities (eg, leisure walk vs computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on 5 different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used to develop random forest models to assess activity category recognition accuracy and MET estimation. Results: Model performance for both MET estimation and activity category recognition were strengthened with the use of additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03-0.09 MET increase in prediction error compared with wearing all 5 devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for the detection of locomotion (balanced accuracy decrease range 0-0.01), sedentary (balanced accuracy decrease range 0.05-0.13), and lifestyle activities (balanced accuracy decrease range 0.04-0.08) compared with all 5 placements. The accuracy of recognizing activity categories increased with additional placements (accuracy decrease range 0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. Conclusions: Additional accelerometer devices slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults. %M 33938809 %R 10.2196/23681 %U https://mhealth.jmir.org/2021/5/e23681 %U https://doi.org/10.2196/23681 %U http://www.ncbi.nlm.nih.gov/pubmed/33938809 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e17581 %T Using a Mobile Phone App to Analyze the Relationship Between Planned and Performed Physical Activity in University Students: Observational Study %A Stewart,Matthew T %A Nezich,Taylor %A Lee,Joyce M %A Hasson,Rebecca E %A Colabianchi,Natalie %+ School of Kinesiology, University of Michigan, 1402 Washington Heights, Ann Arbor, MI, 48109, United States, 1 (734) 647 3543, colabian@umich.edu %K mobile phone application %K physical activity %K intention-behavior relationship %D 2021 %7 29.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The relationship between intention and behavior has been well researched, but most studies fail to capture dynamic, time-varying contextual factors. Ecological momentary assessment through mobile phone technology is an innovative method for collecting data in real time, including time-use data. However, only a limited number of studies have examined day-level plans to be physically active and subsequent physical activity behavior using real-time time-use data to better understand this relationship. Objective: This study aims to examine whether plans to be physically active (recorded in advance on an electronic calendar) were associated with objectively assessed physical activity (accelerometry), to identify activities that replaced planned periods of physical activity by using the mobile app Life in a Day (LIAD), and to test the feasibility and acceptability of LIAD for collecting real-time time-use data. Methods: The study included 48 university students who were randomly assigned to 1 of 3 protocols, which were defined by 1, 3, or 5 days of data collection. Participants were asked to record their planned activities on a Google Calendar and were provided with mobile phones with LIAD to complete time-use entries in real time for a set of categories (eg, exercise or sports, eating or cooking, school, or personal care). Participants were instructed to wear an accelerometer on their nondominant wrist during the protocol period. A total of 144 days of protocol data were collected from the 48 participants. Results: Protocol data for 123 days were eligible for analysis. A Fisher exact test showed a statistically significant association between plans and physical activity behavior (P=.02). The congruence between plans and behavior was fair (Cohen κ=0.220; 95% CI 0.028-0.411). Most participants did not plan to be active, which occurred on 75.6% (93/123) of days. Of these 93 days, no physical activity occurred on 76 (81.7%) days, whereas some physical activity occurred on 17 (18.3%) days. On the remaining 24.4% (30/123) of days, some physical activity was planned. Of these 30 days, no physical activity occurred on 18 (60%) days, whereas some physical activity occurred on 12 (40%) days. LIAD data indicated that activities related to screen time most often replaced planned physical activity, whereas unplanned physical activity was often related to active transport. Feasibility analyses indicated little difficulty in using LIAD, and there were no significant differences in feasibility by protocol length. Conclusions: Consistent with previous literature, physical activity plans and physical activity behaviors were linked, but not strongly linked. LIAD offers insight into the relationship between plans and behavior, highlighting the importance of active transport for physical activity and the influence of screen-related behaviors on insufficient physical activity. LIAD is a feasible and practical method for collecting time-use data in real time. %M 33913812 %R 10.2196/17581 %U https://mhealth.jmir.org/2021/4/e17581 %U https://doi.org/10.2196/17581 %U http://www.ncbi.nlm.nih.gov/pubmed/33913812 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 7 %N 2 %P e27975 %T Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study %A Low,Carissa A %A Li,Meng %A Vega,Julio %A Durica,Krina C %A Ferreira,Denzil %A Tam,Vernissia %A Hogg,Melissa %A Zeh III,Herbert %A Doryab,Afsaneh %A Dey,Anind K %+ Mobile Sensing + Health Institute, Center for Behavioral Health, Media, and Technology, University of Pittsburgh, 3347 Forbes Ave, Suite 200, Pittsburgh, PA, 15213, United States, 1 4126235973, lowca@upmc.edu %K mobile sensing %K symptom %K cancer %K surgery %K wearable device %K smartphone %K mobile phone %D 2021 %7 27.4.2021 %9 Original Paper %J JMIR Cancer %G English %X Background: Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms Objective: The aim of this study was to examine whether smartphone and Fitbit data could be used to estimate daily symptom burden before and after pancreatic surgery. Methods: A total of 44 patients scheduled for pancreatic surgery participated in this prospective longitudinal study and provided sufficient sensor and self-reported symptom data for analyses. Participants collected smartphone sensor and Fitbit data and completed daily symptom ratings starting at least two weeks before surgery, throughout their inpatient recovery, and for up to 60 days after postoperative discharge. Day-level behavioral features reflecting mobility and activity patterns, sleep, screen time, heart rate, and communication were extracted from raw smartphone and Fitbit data and used to classify the next day as high or low symptom burden, adjusted for each individual’s typical level of reported symptoms. In addition to the overall symptom burden, we examined pain, fatigue, and diarrhea specifically. Results: Models using light gradient boosting machine (LightGBM) were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy, surpassing baseline models. The most important sensor features for discriminating high symptom days were related to physical activity bouts, sleep, heart rate, and location. LightGBM models predicting next-day diarrhea (79.0% accuracy), fatigue (75.8% accuracy), and pain (79.6% accuracy) performed similarly. Conclusions: Results suggest that digital biomarkers may be useful in predicting patient-reported symptom burden before and after cancer surgery. Although model performance in this small sample may not be adequate for clinical implementation, findings support the feasibility of collecting mobile sensor data from older patients who are acutely ill as well as the potential clinical value of mobile sensing for passive monitoring of patients with cancer and suggest that data from devices that many patients already own and use may be useful in detecting worsening perioperative symptoms and triggering just-in-time symptom management interventions. %M 33904822 %R 10.2196/27975 %U https://cancer.jmir.org/2021/2/e27975 %U https://doi.org/10.2196/27975 %U http://www.ncbi.nlm.nih.gov/pubmed/33904822 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 4 %P e24482 %T Youth and Provider Perspectives on Behavior-Tracking Mobile Apps: Qualitative Analysis %A Armstrong,Courtney C %A Odukoya,Erica J %A Sundaramurthy,Keerthi %A Darrow,Sabrina M %+ Department of Psychology, University of California, Berkeley, 2121 Berkeley Way, University of California, Berkeley, CA, 94720, United States, 1 9178417015, courtney.armstrong@berkeley.edu %K qualitative %K mHealth %K mobile phone %K behavior monitoring %K youth %D 2021 %7 22.4.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Mobile health apps stand as one possible means of improving evidence-based mental health interventions for youth. However, a better understanding of youth and provider perspectives is necessary to support widespread implementation. Objective: The objective of this research was to explore both youth and provider perspectives on using mobile apps to enhance evidence-based clinical care, with an emphasis on gathering perspectives on behavior-tracking apps. Methods: Inductive qualitative analysis was conducted on data obtained from semistructured interviews held with 10 youths who received psychotherapy and 12 mental health care providers who conducted therapy with youths aged 13-26 years. Interviews were independently coded by multiple coders and consensus meetings were held to establish reliability. Results: During the interviews, the youths and providers broadly agreed on the benefits of behavior tracking and believed that tracking via app could be more enjoyable and accessible. Providers and youths also shared similar concerns that negative emotions and user burden could limit app usage. Participants also suggested potential app features that, if implemented, would help meet the clinical needs of providers and support long-term use among youth. Such features included having a pleasant user interface, reminders for clients, and graphical output of data to clients and providers. Conclusions: Youths and providers explained that the integration of mobile health into psychotherapy has the potential to make treatment, particularly behavior tracking, easy and more accessible. However, both groups had concerns about the increased burden that could be placed on the clients and providers. %M 33885364 %R 10.2196/24482 %U https://mental.jmir.org/2021/4/e24482 %U https://doi.org/10.2196/24482 %U http://www.ncbi.nlm.nih.gov/pubmed/33885364 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e19564 %T Usability of a Mobile App for Real-Time Assessment of Fatigue and Related Symptoms in Patients With Multiple Sclerosis: Observational Study %A Palotai,Miklos %A Wallack,Max %A Kujbus,Gergo %A Dalnoki,Adam %A Guttmann,Charles %+ Center for Neurological Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA, 02215, United States, 1 617 278 0613, palotai@bwh.harvard.edu %K multiple sclerosis %K fatigue %K depression %K mobile application %K mobile phone %K real-time assessment %D 2021 %7 16.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Although fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS), its pathogenesis is not well understood. Neurogenic, inflammatory, endocrine, and metabolic mechanisms have been proposed. Taking into account the temporal dynamics and comorbid mood symptoms of fatigue may help differentiate fatigue phenotypes. These phenotypes may reflect different pathogeneses and may respond to different mechanism-specific treatments. Although several tools have been developed to assess various symptoms (including fatigue), monitor clinical status, or improve the perceived level of fatigue in patients with MS, options for a detailed, real-time assessment of MS-related fatigue and relevant comorbidities are still limited. Objective: This study aims to present a novel mobile app specifically designed to differentiate fatigue phenotypes using circadian symptom monitoring and state-of-the-art characterization of MS-related fatigue and its related symptoms. We also aim to report the first findings regarding patient compliance and the relationship between compliance and patient characteristics, including MS disease severity. Methods: After developing the app, we used it in a prospective study designed to investigate the brain magnetic resonance imaging correlates of MS-related fatigue. In total, 64 patients with MS were recruited into this study and asked to use the app over a 2-week period. The app features the following modules: Visual Analogue Scales (VASs) to assess circadian changes in fatigue, depression, anxiety, and pain; daily sleep diaries (SLDs) to assess sleep habits and quality; and 10 one-time questionnaires to assess fatigue, depression, anxiety, sleepiness, physical activity, and motivation, as well as several other one-time questionnaires that were created to assess those relevant aspects of fatigue that were not captured by existing fatigue questionnaires. The app prompts subjects to assess their symptoms multiple times a day and enables real-time symptom monitoring through a web-accessible portal. Results: Of 64 patients, 56 (88%) used the app, of which 51 (91%) completed all one-time questionnaires and 47 (84%) completed all one-time questionnaires, VASs, and SLDs. Patients reported no issues with the usage of the app, and there were no technical issues with our web-based data collection system. The relapsing-remitting MS to secondary-progressive MS ratio was significantly higher in patients who completed all one-time questionnaires, VASs, and SLDs than in those who completed all one-time questionnaires but not all VASs and SLDs (P=.01). No other significant differences in demographics, fatigue, or disease severity were observed between the degrees of compliance. Conclusions: The app can be used with reasonable compliance across patients with relapsing-remitting and secondary-progressive MS irrespective of demographics, fatigue, or disease severity. %M 33861208 %R 10.2196/19564 %U https://mhealth.jmir.org/2021/4/e19564 %U https://doi.org/10.2196/19564 %U http://www.ncbi.nlm.nih.gov/pubmed/33861208 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 4 %P e17503 %T A User-Centered Chatbot (Wakamola) to Collect Linked Data in Population Networks to Support Studies of Overweight and Obesity Causes: Design and Pilot Study %A Asensio-Cuesta,Sabina %A Blanes-Selva,Vicent %A Conejero,J Alberto %A Frigola,Ana %A Portolés,Manuel G %A Merino-Torres,Juan Francisco %A Rubio Almanza,Matilde %A Syed-Abdul,Shabbir %A Li,Yu-Chuan (Jack) %A Vilar-Mateo,Ruth %A Fernandez-Luque,Luis %A García-Gómez,Juan M %+ Instituto de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain, 34 96 387 70 07 ext 71846, sasensio@dpi.upv.es %K mHealth %K obesity %K overweight %K chatbot %K assessment %K public health %K Telegram %K user-centered design %K Social Network Analysis %D 2021 %7 14.4.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps. Objective: This study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations. Methods: We first studied the users’ needs and gathered users’ graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility. Results: We collected 452 answers to the survey and interviewed 4 specialists. Based on this research, we developed a Telegram chatbot named Wakamola structured in six sections: personal, diet, physical activity, social network, user's status score, and project information. We defined a user's status score as a normalized sum (0-100) of scores about diet (frequency of eating 50 foods), physical activity, BMI, and social network. We performed a pilot to evaluate the chatbot implementation among 85 healthy volunteers. Of 74 participants who completed all sections, we found 8 underweight people (11%), 5 overweight people (7%), and no obesity cases. The mean BMI was 21.4 kg/m2 (normal weight). The most consumed foods were olive oil, milk and derivatives, cereals, vegetables, and fruits. People walked 10 minutes on 5.8 days per week, slept 7.02 hours per day, and were sitting 30.57 hours per week. Moreover, we were able to create a social network with 74 users, 178 relations, and 12 communities. Conclusions: The Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives. %M 33851934 %R 10.2196/17503 %U https://medinform.jmir.org/2021/4/e17503 %U https://doi.org/10.2196/17503 %U http://www.ncbi.nlm.nih.gov/pubmed/33851934 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e24604 %T Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study %A Zhang,Yuezhou %A Folarin,Amos A %A Sun,Shaoxiong %A Cummins,Nicholas %A Bendayan,Rebecca %A Ranjan,Yatharth %A Rashid,Zulqarnain %A Conde,Pauline %A Stewart,Callum %A Laiou,Petroula %A Matcham,Faith %A White,Katie M %A Lamers,Femke %A Siddi,Sara %A Simblett,Sara %A Myin-Germeys,Inez %A Rintala,Aki %A Wykes,Til %A Haro,Josep Maria %A Penninx,Brenda WJH %A Narayan,Vaibhav A %A Hotopf,Matthew %A Dobson,Richard JB %A , %+ Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SGDP Centre, IoPPN, Box PO 80, De Crespigny Park, Denmark Hill, London, United Kingdom, 44 20 7848 0473, richard.j.dobson@kcl.ac.uk %K mobile health (mHealth) %K mental health %K depression %K sleep %K wearable device %K monitoring %D 2021 %7 12.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. Objective: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Methods: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. Results: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. Conclusions: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant. %M 33843591 %R 10.2196/24604 %U https://mhealth.jmir.org/2021/4/e24604 %U https://doi.org/10.2196/24604 %U http://www.ncbi.nlm.nih.gov/pubmed/33843591 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e27336 %T Force-Sensitive Mat for Vertical Jump Measurement to Assess Lower Limb Strength: Validity and Reliability Study %A Vanegas,Erik %A Salazar,Yolocuauhtli %A Igual,Raúl %A Plaza,Inmaculada %+ Electrical/Electronics Engineering and Communications Department, EUP Teruel, Universidad de Zaragoza, Atarazana 2, Teruel, 44003, Spain, 34 978618102, erikvanegas599@gmail.com %K vertical jump %K mHealth %K mobile health %K force-sensitive resistor %K lower limb strength %K leg strength %D 2021 %7 9.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Vertical jump height is widely used in health care and sports fields to assess muscle strength and power from lower limb muscle groups. Different approaches have been proposed for vertical jump height measurement. Some commonly used approaches need no sensor at all; however, these methods tend to overestimate the height reached by the subjects. There are also novel systems using different kind of sensors like force-sensitive resistors, capacitive sensors, and inertial measurement units, among others, to achieve more accurate measurements. Objective: The objective of this study is twofold. The first objective is to validate the functioning of a developed low-cost system able to measure vertical jump height. The second objective is to assess the effects on obtained measurements when the sampling frequency of the system is modified. Methods: The system developed in this study consists of a matrix of force-sensitive resistor sensors embedded in a mat with electronics that allow a full scan of the mat. This mat detects pressure exerted on it. The system calculates the jump height by using the flight-time formula, and the result is sent through Bluetooth to any mobile device or PC. Two different experiments were performed. In the first experiment, a total of 38 volunteers participated with the objective of validating the performance of the system against a high-speed camera used as reference (120 fps). In the second experiment, a total of 15 volunteers participated. Raw data were obtained in order to assess the effects of different sampling frequencies on the performance of the system with the same reference device. Different sampling frequencies were obtained by performing offline downsampling of the raw data. In both experiments, countermovement jump and countermovement jump with arm swing techniques were performed. Results: In the first experiment an overall mean relative error (MRE) of 1.98% and a mean absolute error of 0.38 cm were obtained. Bland-Altman and correlation analyses were performed, obtaining a coefficient of determination equal to R2=.996. In the second experiment, sampling frequencies of 200 Hz, 100 Hz, and 66.6 Hz show similar performance with MRE below 3%. Slower sampling frequencies show an exponential increase in MRE. On both experiments, when dividing jump trials in different heights reached, a decrease in MRE with higher height trials suggests that the precision of the proposed system increases as height reached increases. Conclusions: In the first experiment, we concluded that results between the proposed system and the reference are systematically the same. In the second experiment, the relevance of a sufficiently high sampling frequency is emphasized, especially for jump trials whose height is below 10 cm. For trials with heights above 30 cm, MRE decreases in general for all sampling frequencies, suggesting that at higher heights reached, the impact of high sampling frequencies is lesser. %M 33835040 %R 10.2196/27336 %U https://mhealth.jmir.org/2021/4/e27336 %U https://doi.org/10.2196/27336 %U http://www.ncbi.nlm.nih.gov/pubmed/33835040 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e27667 %T Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study %A Yamada,Yasunori %A Shinkawa,Kaoru %A Kobayashi,Masatomo %A Takagi,Hironobu %A Nemoto,Miyuki %A Nemoto,Kiyotaka %A Arai,Tetsuaki %+ IBM Research, Nihonbashi, Hakozaki-cho, Chuo-ku, Tokyo, 103-8510, Japan, 81 80 6706 9381, ysnr@jp.ibm.com %K cognitive impairment %K smart speaker %K speech analysis %K accident %K prevention %K older adults %K prediction %K risk %K assistant %D 2021 %7 8.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. Objective: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. Methods: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data—neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)—from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. Results: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. Conclusions: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function. %M 33830066 %R 10.2196/27667 %U https://www.jmir.org/2021/4/e27667 %U https://doi.org/10.2196/27667 %U http://www.ncbi.nlm.nih.gov/pubmed/33830066 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e16806 %T Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations %A Hasan,Md Kamrul %A Aziz,Md Hasanul %A Zarif,Md Ishrak Islam %A Hasan,Mahmudul %A Hashem,MMA %A Guha,Shion %A Love,Richard R %A Ahamed,Sheikh %+ Department of Electrical Engineering and Computer Science, Vanderbilt University, 334 Featheringill Hall, Nashville, TN, United States, 1 6153435032, kamrul.hasan@Vanderbilt.Edu %K noninvasive hemoglobin %K smartphone-based hemoglobin %K hemoglobin level from image and video %D 2021 %7 8.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device. Objective: In this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone. Methods: We explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner. Results: The fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot. Conclusions: We addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone’s camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal’s features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone. %M 33830065 %R 10.2196/16806 %U https://mhealth.jmir.org/2021/4/e16806 %U https://doi.org/10.2196/16806 %U http://www.ncbi.nlm.nih.gov/pubmed/33830065 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e22759 %T Respondent Characteristics and Dietary Intake Data Collected Using Web-Based and Traditional Nutrition Surveillance Approaches: Comparison and Usability Study %A Timon,Claire M %A Walton,Janette %A Flynn,Albert %A Gibney,Eileen R %+ Institute of Food and Health, University College Dublin, Institute of Food and Health, University College Dublin, Belfield, Dublin, 4, Ireland, 353 17162819, eileen.gibney@ucd.ie %K diet %K survey and questionnaire %K technology %K nutrition surveillance %D 2021 %7 7.4.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: There are many constraints to conducting national food consumption surveys for national nutrition surveillance, including cost, time, and participant burden. Validated web-based dietary assessment technologies offer a potential solution to many of these constraints. Objective: This study aims to investigate the feasibility of using a previously validated, web-based, 24-hour recall dietary assessment tool (Foodbook24) for nutrition surveillance by comparing the demographic characteristics and the quality of dietary intake data collected from a web-based cohort of participants in Ireland to those collected from the most recent Irish National Adult Nutrition Survey (NANS). Methods: Irish adult participants (aged ≥18 years) were recruited to use Foodbook24 (a web-based tool) between March and October 2016. Demographic and dietary intake (assessed by means of 2 nonconsecutive, self-administered, 24-hour recalls) data were collected using Foodbook24. Following the completion of the study, the dietary intake data collected from the web-based study were statistically weighted to represent the age-gender distribution of intakes reported in the NANS (2008-2010) to facilitate the controlled comparison of intake data. The demographic characteristics of the survey respondents were investigated using descriptive statistics. The controlled comparison of weighted mean daily nutrient intake data collected from the Foodbook24 web-based study (329 plausible reporters of a total of 545 reporters) and the mean daily nutrient intake data collected from the NANS (1051 plausible reporters from 1500 reporters) was completed using the Wilcoxon–Mann-Whitney U test in Creme Nutrition software. Results: Differences between the demographic characteristics of the survey participants across the 2 surveys were observed. Notable differences included a lower proportion of adults aged ≥65 years and a higher proportion of females who participated in the web-based Foodbook24 study relative to the NANS study (P<.001). Similar ranges of mean daily intake for the majority of nutrients and food groups were observed (eg, energy [kilocalorie per day] and carbohydrate [gram per day]), although significant differences for some nutrients (eg, riboflavin [mg/10 MJ], P<.001 and vitamin B12 [µg/10 MJ], P<.001) and food groups were identified. A high proportion of participants (200/425, 47.1%) reported a willingness to continue using Foodbook24 for an additional 6 months. Conclusions: These findings suggest that by using targeted recruitment strategies in the future to ensure the recruitment of a more representative sample, there is potential for web-based methodologies such as Foodbook24 to be used for nutrition surveillance efforts in Ireland. %M 33825694 %R 10.2196/22759 %U https://publichealth.jmir.org/2021/4/e22759 %U https://doi.org/10.2196/22759 %U http://www.ncbi.nlm.nih.gov/pubmed/33825694 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 4 %P e21638 %T Perceived Impacts, Acceptability, and Recommendations for Ecological Momentary Assessment Among Youth Experiencing Homelessness: Qualitative Study %A Acorda,Darlene %A Businelle,Michael %A Santa Maria,Diane %+ Cizik School of Nursing, The University of Texas Health Science Center at Houston, 6901 Bertner Avenue, Houston, TX, 77030, United States, 1 832 824 1179, darlene.e.acorda@uth.tmc.edu %K youth experiencing homelessness %K ecological momentary assessment %K mobile apps %K behavior change %D 2021 %7 6.4.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: The use of ecological momentary assessment (EMA) to study youth experiencing homelessness (YEH) behaviors is an emerging area of research. Despite high rates of participation and potential clinical utility, few studies have investigated the acceptability and recommendations for EMA from the YEH perspective. Objective: This study aimed to describe the perceived benefits, usability, acceptability, and barriers to the use of EMA from the homeless youth perspective. Methods: YEH were recruited from a larger EMA study. Semistructured exit interviews were performed using an interview guide that focused on the YEH experience with the EMA app, and included perceived barriers and recommendations for future studies. Data analyses used an inductive approach with thematic analysis to identify major themes and subthemes. Results: A total of 18 YEH aged 19-24 years participated in individual and group exit interviews. The EMA was highly acceptable to YEH and they found the app and EMA surveys easy to navigate. Perceived benefits included increased behavioral and emotional awareness with some YEH reporting a decrease in their high-risk behaviors as a result of participation. Another significant perceived benefit was the ability to use the phones for social support and make connections to family, friends, and potential employers. Barriers were primarily survey and technology related. Survey-related barriers included the redundancy of questions, the lack of customizable responses, and the timing of survey prompts. Technology-related barriers included the “freezing” of the app, battery charge, and connectivity issues. Recommendations for future studies included the need to provide real-time mental health support for symptomatic youth, to create individually customized questions, and to test the use of personalized motivational messages that respond to the EMA data in real time. Conclusions: YEH are highly receptive to the use of EMA in studies. Further studies are warranted to understand the impact of EMA on YEH behaviors. Incorporating the YEH perspective into the design and implementation of EMA studies may help minimize barriers, increase acceptability, and improve participation rates in this hard-to-reach, disconnected population. %M 33821805 %R 10.2196/21638 %U https://formative.jmir.org/2021/4/e21638 %U https://doi.org/10.2196/21638 %U http://www.ncbi.nlm.nih.gov/pubmed/33821805 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e23728 %T Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data %A Liu,Jessica Chia %A Goetz,Jack %A Sen,Srijan %A Tewari,Ambuj %+ Department of Statistics, University of Michigan, 1085 South University Ave, Ann Arbor, MI, 48109, United States, 1 7346474820, liujess@umich.edu %K privacy %K data protection %K machine learning %K mobile health %K wearable electronic devices %D 2021 %7 30.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective: We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods: We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results: In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions: Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods. %M 33783362 %R 10.2196/23728 %U https://mhealth.jmir.org/2021/3/e23728 %U https://doi.org/10.2196/23728 %U http://www.ncbi.nlm.nih.gov/pubmed/33783362 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e25313 %T Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study %A Muggeridge,David Joseph %A Hickson,Kirsty %A Davies,Aimie Victoria %A Giggins,Oonagh M %A Megson,Ian L %A Gorely,Trish %A Crabtree,Daniel R %+ Edinburgh Napier University, Sighthill Campus, Edinburgh, EH11 4BN, United Kingdom, 44 7979086243, d.muggeridge@napier.ac.uk %K heart rate %K photoplethysmography %K wearable electronic devices %K validation study %K exercise %K mobile phone %D 2021 %7 25.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Accurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities. Objective: The primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise. Methods: A total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error. Results: Overall, there was a very good correlation between the Polar OH1 and Polar H10 devices (r=0.95), with a mean bias of −1 beats·min-1 and limits of agreement of −20 to 19 beats·min-1. The Fitbit Charge 3 device underestimated heart rate by 7 beats·min-1 compared with Polar H10, with a limit of agreement of −46 to 33 beats·min-1 and poor correlation (r=0.8). The mean absolute percentage error for both devices was deemed acceptable (<5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities. Conclusions: Compared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises. %M 33764310 %R 10.2196/25313 %U https://mhealth.jmir.org/2021/3/e25313 %U https://doi.org/10.2196/25313 %U http://www.ncbi.nlm.nih.gov/pubmed/33764310 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e24465 %T Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach %A Sükei,Emese %A Norbury,Agnes %A Perez-Rodriguez,M Mercedes %A Olmos,Pablo M %A Artés,Antonio %+ Signal Theory and Communications Department, Universidad Carlos III de Madrid, Torres Quevedo Bldg, Av de la Universidad, 30, Leganés, 28911, Spain, 34 916248839, esukei@tsc.uc3m.es %K mental health %K affect %K mobile health %K mobile phone %K digital phenotype %K machine learning %K Bayesian analysis %K probabilistic models %K personalized models %D 2021 %7 22.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states. %M 33749612 %R 10.2196/24465 %U https://mhealth.jmir.org/2021/3/e24465 %U https://doi.org/10.2196/24465 %U http://www.ncbi.nlm.nih.gov/pubmed/33749612 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 3 %P e25309 %T Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study %A Biondi,Andrea %A Laiou,Petroula %A Bruno,Elisa %A Viana,Pedro F %A Schreuder,Martijn %A Hart,William %A Nurse,Ewan %A Pal,Deb K %A Richardson,Mark P %+ Institute of Psychiatry, Psychology & Neuroscience, King's College London, 5 Cutcombe Rd, London, SE5 9RT, United Kingdom, 44 7753986485, andrea.2.biondi@kcl.ac.uk %K epilepsy %K EEG %K electroencephalography %K brain ictogenicity %K wearables %K seizure prediction %K brain %K seizures %K mobile technology %D 2021 %7 19.3.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy. Objective: EEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk. Methods: A total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King’s College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk. Results: The EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022. Conclusions: With the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence. International Registered Report Identifier (IRRID): PRR1-10.2196/25309 %M 33739290 %R 10.2196/25309 %U https://www.researchprotocols.org/2021/3/e25309 %U https://doi.org/10.2196/25309 %U http://www.ncbi.nlm.nih.gov/pubmed/33739290 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e20738 %T Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review %A Cho,Sylvia %A Ensari,Ipek %A Weng,Chunhua %A Kahn,Michael G %A Natarajan,Karthik %+ Department of Biomedical informatics, Columbia University, 622 West 168th Street PH20, New York, NY, 10032, United States, 1 212 305 5334, sc3901@cumc.columbia.edu %K patient generated health data %K data accuracy %K data quality %K wearable device %K fitness trackers %K mobile phone %D 2021 %7 19.3.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse. Objective: This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research. Methods: The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well. Results: A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy. Conclusions: Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices. %M 33739294 %R 10.2196/20738 %U https://mhealth.jmir.org/2021/3/e20738 %U https://doi.org/10.2196/20738 %U http://www.ncbi.nlm.nih.gov/pubmed/33739294 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 3 %P e24589 %T Mobile Health Crowdsensing (MHCS) Intervention on Chronic Disease Awareness: Protocol for a Systematic Review %A Tokosi,Temitope Oluwaseyi %A Twum-Darko,Michael %+ Graduate Centre for Management, Faculty of Business and Management Sciences, Cape Peninsula University of Technology, Keizersgracht Street, District Six Campus, Cape Town, 8000, South Africa, 27 76 047 1328, toksymoore@gmail.com %K mHealth %K crowdsensing %K chronic diseases %K awareness %K mobile phone %D 2021 %7 19.3.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Mobile health crowdsensing (MHCS) involves the use of mobile communication technologies to promote health by supporting health care practices (eg, health data collection, delivery of health care information, or patient observation and provision of care). MHCS technologies (eg, smartphones) have sensory capabilities, such as GPS, voice, light, and camera, to collect, analyze, and share user-centered data (explicit and implicit). The current literature indicates no scientific study related to MHCS interventions for chronic diseases. The proposed systematic review will examine the impact of MHCS interventions on chronic disease awareness. Objective: The objectives of this study are to identify and describe various MHCS intervention strategies applied to chronic disease awareness. Methods: Literature from various databases, such as MEDLINE, Embase, PsycINFO, CINAHL, and Cochrane Central Register of Controlled Trials, will be examined. Trial registers, reports, grey literature, and unpublished academic theses will also be included. All mobile technologies, such as cell phones, personal digital assistants, and tablets that have short message service, multimedia message service, video, and audio capabilities, will be included. MHCS will be the primary intervention strategy. The search strategy will include keywords such as mHealth, crowdsensing, and awareness among other medical subject heading terms. Articles published from January 1, 1945, to December 31, 2019, will be eligible for inclusion. The authors will independently screen and select studies, extract data, and assess the risk of bias, with discrepancies resolved by an independent party not involved in the study. The authors will assess statistical heterogeneity by examining the types of participants, interventions, study designs, and outcomes in each study, and pool studies judged to be statistically homogeneous. In the assessment of heterogeneity, a sensitivity analysis will be considered to explore statistical heterogeneity. Statistical heterogeneity will be investigated using the chi-square test of homogeneity on Cochrane Q test, and quantified using the I2 statistic. Results: The preliminary search query found 1 paper. Further literature search commenced in mid-March 2021 and is to be concluded in April 2021. The proposed systematic review protocol has been registered in PROSPERO (The International Prospective Register of Systematic Reviews; no. CRD42020161435). Furthermore, the use of search data extraction and capturing in Review Manager version 5.3 (Cochrane) commenced in January 2021 and ended in February 2021. Further literature search will begin in mid-March 2021 and will be concluded in April 2021. The final stages will include analyses and writing, which are anticipated to start and be completed in May 2021. Conclusions: The knowledge derived from this study will inform health care stakeholders—including researchers, policy makers, investors, health professionals, technologists, and engineers—of the impact of MHCS interventions on chronic disease awareness. International Registered Report Identifier (IRRID): PRR1-10.2196/24589 %M 33739288 %R 10.2196/24589 %U https://www.researchprotocols.org/2021/3/e24589 %U https://doi.org/10.2196/24589 %U http://www.ncbi.nlm.nih.gov/pubmed/33739288 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e26320 %T A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study %A Koyama,Takafumi %A Sato,Shusuke %A Toriumi,Madoka %A Watanabe,Takuro %A Nimura,Akimoto %A Okawa,Atsushi %A Sugiura,Yuta %A Fujita,Koji %+ Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 1138519, Japan, 81 3 5803 5279, fujiorth@tmd.ac.jp %K carpal tunnel syndrome %K anomaly detection %K machine learning %K smartphone %K screening %K thumb %K diagnostic %K data collection %K app %K algorithm %D 2021 %7 14.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Carpal tunnel syndrome (CTS) is a medical condition caused by compression of the median nerve in the carpal tunnel due to aging or overuse of the hand. The symptoms include numbness of the fingers and atrophy of the thenar muscle. Thenar atrophy recovers slowly postoperatively; therefore, early diagnosis and surgery are important. While physical examinations and nerve conduction studies are used to diagnose CTS, problems with the diagnostic ability and equipment, respectively, exist. Despite research on a CTS-screening app that uses a tablet and machine learning, problems with the usage rate of tablets and data collection for machine learning remain. Objective: To make data collection for machine learning easier and more available, we developed a screening app for CTS using a smartphone and an anomaly detection algorithm, aiming to examine our system as a useful screening tool for CTS. Methods: In total, 36 participants were recruited, comprising 36 hands with CTS and 27 hands without CTS. Participants controlled the character in our app using their thumbs. We recorded the position of the thumbs and time; generated screening models that classified CTS and non-CTS using anomaly detection and an autoencoder; and calculated the sensitivity, specificity, and area under the curve (AUC). Results: Participants with and without CTS were classified with 94% sensitivity, 67% specificity, and an AUC of 0.86. When dividing the data by direction, the model with data in the same direction as the thumb opposition had the highest AUC of 0.99, 92% sensitivity, and 100% specificity. Conclusions: Our app could reveal the difficulty of thumb opposition for patients with CTS and screen for CTS with high sensitivity and specificity. The app is highly accessible because of the use of smartphones and can be easily enhanced by anomaly detection. %M 33714936 %R 10.2196/26320 %U https://mhealth.jmir.org/2021/3/e26320 %U https://doi.org/10.2196/26320 %U http://www.ncbi.nlm.nih.gov/pubmed/33714936 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e23391 %T Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): Exploratory Pilot Study With Physical Activity Measurement %A Ponnada,Aditya %A Thapa-Chhetry,Binod %A Manjourides,Justin %A Intille,Stephen %+ Khoury College of Computer Sciences, Bouve College of Health Sciences, Northeastern University, 360 Huntington Avenue, Boston, MA, , United States, 1 617 306 1610, ponnada.a@northeastern.edu %K ecological momentary assessment (EMA) %K experience sampling %K physical activity %K smartwatch %K microinteractions %K criterion validity %K activity monitor %K μEMA %D 2021 %7 10.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Ecological momentary assessment (EMA) is an in situ method of gathering self-report on behaviors using mobile devices. In typical phone-based EMAs, participants are prompted repeatedly with multiple-choice questions, often causing participation burden. Alternatively, microinteraction EMA (micro-EMA or μEMA) is a type of EMA where all the self-report prompts are single-question surveys that can be answered using a 1-tap glanceable microinteraction conveniently on a smartwatch. Prior work suggests that μEMA may permit a substantially higher prompting rate than EMA, yielding higher response rates and lower participation burden. This is achieved by ensuring μEMA prompt questions are quick and cognitively simple to answer. However, the validity of participant responses from μEMA self-report has not yet been formally assessed. Objective: In this pilot study, we explored the criterion validity of μEMA self-report on a smartwatch, using physical activity (PA) assessment as an example behavior of interest. Methods: A total of 17 participants answered 72 μEMA prompts each day for 1 week using a custom-built μEMA smartwatch app. At each prompt, they self-reported whether they were doing sedentary, light/standing, moderate/walking, or vigorous activities by tapping on the smartwatch screen. Responses were compared with a research-grade activity monitor worn on the dominant ankle simultaneously (and continuously) measuring PA. Results: Participants had an 87.01% (5226/6006) μEMA completion rate and a 74.00% (5226/7062) compliance rate taking an average of only 5.4 (SD 1.5) seconds to answer a prompt. When comparing μEMA responses with the activity monitor, we observed significantly higher (P<.001) momentary PA levels on the activity monitor when participants self-reported engaging in moderate+vigorous activities compared with sedentary or light/standing activities. The same comparison did not yield any significant differences in momentary PA levels as recorded by the activity monitor when the μEMA responses were randomly generated (ie, simulating careless taps on the smartwatch). Conclusions: For PA measurement, high-frequency μEMA self-report could be used to capture information that appears consistent with that of a research-grade continuous sensor for sedentary, light, and moderate+vigorous activity, suggesting criterion validity. The preliminary results show that participants were not carelessly answering μEMA prompts by randomly tapping on the smartwatch but were reporting their true behavior at that moment. However, more research is needed to examine the criterion validity of μEMA when measuring vigorous activities. %M 33688843 %R 10.2196/23391 %U https://mhealth.jmir.org/2021/3/e23391 %U https://doi.org/10.2196/23391 %U http://www.ncbi.nlm.nih.gov/pubmed/33688843 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 3 %P e14837 %T A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study %A Ahn,Euijoon %A Liu,Na %A Parekh,Tej %A Patel,Ronak %A Baldacchino,Tanya %A Mullavey,Tracy %A Robinson,Amanda %A Kim,Jinman %+ School of Computer Science, The University of Sydney, Rm 340, Level 3, J12, 1 Cleveland St, Darlington, 2006, Australia, 61 290369804, jinman.kim@sydney.edu.au %K public health %K infectious disease reporting %K mobile app %K disease notification %K mobile phone %D 2021 %7 9.3.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Outbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks. Objective: This study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making. Methods: We developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia. Results: We identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases. Conclusions: We demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases. %M 33687334 %R 10.2196/14837 %U https://publichealth.jmir.org/2021/3/e14837 %U https://doi.org/10.2196/14837 %U http://www.ncbi.nlm.nih.gov/pubmed/33687334 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e24365 %T Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study %A Bai,Ran %A Xiao,Le %A Guo,Yu %A Zhu,Xuequan %A Li,Nanxi %A Wang,Yashen %A Chen,Qinqin %A Feng,Lei %A Wang,Yinghua %A Yu,Xiangyi %A Wang,Chunxue %A Hu,Yongdong %A Liu,Zhandong %A Xie,Haiyong %A Wang,Gang %+ The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, No 5 Ankang Lane, Xicheng District, Beijing, 100088, China, 86 13466604224, gangwangdoc@ccmu.edu.cn %K digital phenotype %K major depressive disorder %K machine learning %K mobile phone %D 2021 %7 8.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Major depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor the mental condition of patients with MDD has been examined in several studies. However, few studies have used passively collected data to monitor mood changes over time. Objective: The aim of this study is to examine the feasibility of monitoring mood status and stability of patients with MDD using machine learning models trained by passively collected data, including phone use data, sleep data, and step count data. Methods: We constructed 950 data samples representing time spans during three consecutive Patient Health Questionnaire-9 assessments. Each data sample was labeled as Steady or Mood Swing, with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, and Mood Swing-moderate based on patients’ Patient Health Questionnaire-9 scores from three visits. A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models. Results: A total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67% (SD 8.47%) and that of recall was 90.44% (SD 6.93%), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate). Conclusions: Our proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173 %M 33683207 %R 10.2196/24365 %U https://mhealth.jmir.org/2021/3/e24365 %U https://doi.org/10.2196/24365 %U http://www.ncbi.nlm.nih.gov/pubmed/33683207 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e24194 %T Measurement of Human Walking Movements by Using a Mobile Health App: Motion Sensor Data Analysis %A Lee,Sungchul %A Walker,Ryan M %A Kim,Yoohwan %A Lee,Hyunhwa %+ School of Computing and Information Systems, Grand Valley State University, D-2-206 Mackinac Hall, Allendale, MI, 49401, United States, 1 616 331 4372, lees2@gvsu.edu %K mobile health %K mHealth %K walking balance %K smartphone %K motion sensor %K sensor %K walking %K walking balance %K mobile phone %D 2021 %7 5.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: This study presents a new approach to measure and analyze the walking balance of humans by collecting motion sensor data in a smartphone. Objective: We aimed to develop a mobile health (mHealth) app that can measure the walking movements of human individuals and analyze the differences in the walking movements of different individuals based on their health conditions. A smartphone’s motion sensors were used to measure the walking movements and analyze the rotation matrix data by calculating the variation of each xyz rotation, which shows the variables in walking-related movement data over time. Methods: Data were collected from 3 participants, that is, 2 healthy individuals (1 female and 1 male) and 1 male with back pain. The participant with back pain injured his back during strenuous exercise but he did not have any issues in walking. The participants wore the smartphone in the middle of their waistline (as the center of gravity) while walking. They were instructed to walk straight at their own pace in an indoor hallway of a building. The walked a distance of approximately 400 feet. They walked for 2-3 minutes in a straight line and then returned to the starting location. A rotation vector in the smartphone, calculated by the rotation matrix, was used to measure the pitch, roll, and yaw angles of the human body while walking. Each xyz-rotation vector datum was recalculated to find the variation in each participant’s walking movement. Results: The male participant with back pain showed a diminished level of walking balance with a wider range of xyz-axis variations in the rotations compared to those of the healthy participants. The standard deviation in the xyz-axis of the male participant with back pain was larger than that of the healthy male participant. Moreover, the participant with back pain had the widest combined range of right-to-left and forward-to-backward motions. The healthy male participant showed smaller standard deviation while walking than the male participant with back pain and the female healthy participant, indicating that the healthy male participant had a well-balanced walking movement. The walking movement of the female healthy participant showed symmetry in the left-to-right (x-axis) and up-to-down (y-axis) motions in the x-y variations of rotation vectors, indicating that she had lesser bias in gait than the others. Conclusions: This study shows that our mHealth app based on smartphone sensors and rotation vectors can measure the variations in the walking movements of different individuals. Further studies are needed to measure and compare walking movements by age, gender, as well as types of health problems or disease. This app can help in finding differences in gait in people with diseases that affect gait. %M 33666557 %R 10.2196/24194 %U https://mhealth.jmir.org/2021/3/e24194 %U https://doi.org/10.2196/24194 %U http://www.ncbi.nlm.nih.gov/pubmed/33666557 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e26702 %T Going Remote—Demonstration and Evaluation of Remote Technology Delivery and Usability Assessment With Older Adults: Survey Study %A Hill,Jordan R %A Harrington,Addison B %A Adeoye,Philip %A Campbell,Noll L %A Holden,Richard J %+ Department of Medicine, Indiana University School of Medicine, 1101 W 10th St, Indianapolis, IN, 46202, United States, 1 7655438559, jrh6@iu.edu %K COVID-19 %K mobile usability testing %K usability inspection %K methods %K aging %K agile %K mobile phone %D 2021 %7 4.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The COVID-19 pandemic necessitated “going remote” with the delivery, support, and assessment of a study intervention targeting older adults enrolled in a clinical trial. While remotely delivering and assessing technology is not new, there are few methods available in the literature that are proven to be effective with diverse populations, and none for older adults specifically. Older adults comprise a diverse population, including in terms of their experience with and access to technology, making this a challenging endeavor. Objective: Our objective was to remotely deliver and conduct usability testing for a mobile health (mHealth) technology intervention for older adult participants enrolled in a clinical trial of the technology. This paper describes the methodology used, its successes, and its limitations. Methods: We developed a conceptual model for remote operations, called the Framework for Agile and Remote Operations (FAR Ops), that combined the general requirements for spaceflight operations with Agile project management processes to quickly respond to this challenge. Using this framework, we iteratively created care packages that differed in their contents based on participant needs and were sent to study participants to deliver the study intervention—a medication management app—and assess its usability. Usability data were collected using the System Usability Scale (SUS) and a novel usability questionnaire developed to collect more in-depth data. Results: In the first 6 months of the project, we successfully delivered 21 care packages. We successfully designed and deployed a minimum viable product in less than 6 weeks, generally maintained a 2-week sprint cycle, and achieved a 40% to 50% return rate for both usability assessment instruments. We hypothesize that lack of engagement due to the pandemic and our use of asynchronous communication channels contributed to the return rate of usability assessments being lower than desired. We also provide general recommendations for performing remote usability testing with diverse populations based on the results of our work, including implementing screen sharing capabilities when possible, and determining participant preference for phone or email communications. Conclusions: The FAR Ops model allowed our team to adopt remote operations for our mHealth trial in response to interruptions from the COVID-19 pandemic. This approach can be useful for other research or practice-based projects under similar circumstances or to improve efficiency, cost, effectiveness, and participant diversity in general. In addition to offering a replicable approach, this paper tells the often-untold story of practical challenges faced by mHealth projects and practical strategies used to address them. Trial Registration: ClinicalTrials.gov NCT04121858; https://clinicaltrials.gov/ct2/show/NCT04121858 %M 33606655 %R 10.2196/26702 %U https://mhealth.jmir.org/2021/3/e26702 %U https://doi.org/10.2196/26702 %U http://www.ncbi.nlm.nih.gov/pubmed/33606655 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e24202 %T Mobile Apps for Foot Measurement in Pedorthic Practice: Scoping Review %A Kabir,Muhammad Ashad %A Rahman,Sheikh Sowmen %A Islam,Mohammad Mainul %A Ahmed,Sayed %A Laird,Craig %+ School of Computing and Mathematics, Charles Sturt University, Panorama Ave, Bathurst, NSW, 2795, Australia, 61 263386259, akabir@csu.edu.au %K foot measurement %K foot scanning %K mobile app %K custom shoes making %K apps review %K diabetic foot %K pedorthics %K footcare %D 2021 %7 4.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As the use of smartphones increases globally across various fields of research and technology, significant contributions to the sectors related to health, specifically foot health, can be observed. Numerous smartphone apps are now being used for providing accurate information about various foot-related properties. Corresponding to this abundance of foot scanning and measuring apps available in app stores, there is a need for evaluating these apps, as limited information regarding their evidence-based quality is available. Objective: The aim of this review was to assess the measurement techniques and essential software quality characteristics of mobile foot measurement apps, and to determine their potential as commercial tools used by foot care health professionals, to assist in measuring feet for custom shoes, and for individuals to enhance their awareness of foot health and hygiene to ultimately prevent foot-related problems. Methods: An electronic search across Android and iOS app stores was performed between July and August 2020 to identify apps related to foot measurement and general foot health. The selected apps were rated by three independent raters, and all discrepancies were resolved by discussion among raters and other investigators. Based on previous work on app rating tools, a modified rating scale tool was devised to rate the selected apps. The internal consistency of the rating tool was tested with a group of three people who rated the selected apps over 2-3 weeks. This scale was then used to produce evaluation scores for the selected foot measurement apps and to assess the interrater reliability. Results: Evaluation inferences showed that all apps failed to meet even half of the measurement-specific criteria required for the proper manufacturing of custom-made footwear. Only 23% (6/26) of the apps reportedly used external scanners or advanced algorithms to reconstruct 3D models of a user’s foot that could possibly be used for ordering custom-made footwear (shoes, insoles/orthoses), and medical casts to fit irregular foot sizes and shapes. The apps had varying levels of performance and usability, although the overall measurement functionality was subpar with a mean of 1.93 out of 5. Apps linked to online shops and stores (shoe recommendation) were assessed to be more usable than other apps but lacked some features (eg, custom shoe sizes and shapes). Overall, the current apps available for foot measurement do not follow any specific guidelines for measurement purposes. Conclusions: Most commercial apps currently available in app stores are not viable for use as tools in assisting foot care health professionals or individuals to measure their feet for custom-made footwear. Current apps lack software quality characteristics and need significant improvements to facilitate proper measurement, enhance awareness of foot health, and induce motivation to prevent and cure foot-related problems. Guidelines similar to the essential criteria items introduced in this study need to be developed for future apps aimed at foot measurement for custom-made or individually fitted footwear and to create awareness of foot health. %M 33661124 %R 10.2196/24202 %U https://mhealth.jmir.org/2021/3/e24202 %U https://doi.org/10.2196/24202 %U http://www.ncbi.nlm.nih.gov/pubmed/33661124 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e22732 %T Text Messaging and Web-Based Survey System to Recruit Patients With Low Back Pain and Collect Outcomes in the Emergency Department: Observational Study %A Amorim,Anita Barros %A Coombs,Danielle %A Richards,Bethan %A Maher,Chris G %A Machado,Gustavo C %+ Discipline of Physiotherapy, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Level 7 East, Susan Wakil Health Building D18, Camperdown, Sydney, 2006, Australia, 61 0401399572, anita.amorim@sydney.edu.au %K emergency department %K clinical trial %K low back pain %K acute pain %K data collection %K patient recruitment %K short message service %K patient reported outcome measures %K mobile phone %D 2021 %7 4.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Low back pain (LBP) is a frequent reason for emergency department (ED) presentations, with a global prevalence of 4.4%. Despite being common, the number of clinical trials investigating LBP in the ED is low. Recruitment of patients in EDs can be challenging because of the fast-paced and demanding ED environment. Objective: The aim of this study is to describe the recruitment and response rates using an SMS text messaging and web-based survey system supplemented by telephone calls to recruit patients with LBP and collect health outcomes in the ED. Methods: An automated SMS text messaging system was integrated into Research Electronic Data Capture and used to collect patient-reported outcomes for an implementation trial in Sydney, Australia. We invited patients with nonserious LBP who presented to participating EDs at 1, 2, and 4 weeks after ED discharge. Patients who did not respond to the initial SMS text message invitation were sent a reminder SMS text message or contacted via telephone. The recruitment rate was measured as the proportion of patients who agreed to participate, and the response rate was measured as the proportion of participants completing the follow-up surveys at weeks 2 and 4. Regression analyses were used to explore factors associated with response rates. Results: In total, 807 patients with nonserious LBP were invited to participate and 425 (53.0%) agreed to participate. The week 1 survey was completed by 51.5% (416/807) of participants. At week 2, the response rate was 86.5% (360/416), and at week 4, it was 84.4% (351/416). Overall, 60% of the surveys were completed via SMS text messaging and on the web and 40% were completed via telephone. Younger participants and those from less socioeconomically disadvantaged areas were more likely to respond to the survey via the SMS text messaging and web-based system. Conclusions: Using an SMS text messaging and web-based survey system supplemented by telephone calls is a viable method for recruiting patients with LBP and collecting health outcomes in the ED. This hybrid system could potentially reduce the costs of using traditional recruitment and data collection methods (eg, face-to-face, telephone calls only). International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2017-019052 %M 33661125 %R 10.2196/22732 %U https://mhealth.jmir.org/2021/3/e22732 %U https://doi.org/10.2196/22732 %U http://www.ncbi.nlm.nih.gov/pubmed/33661125 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 3 %P e20175 %T Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study %A Roux de Bézieux,Hector %A Bullard,James %A Kolterman,Orville %A Souza,Michael %A Perraudeau,Fanny %+ Pendulum Therapeutics, Inc, 933 20th Street, San Francisco, CA, 94107, United States, 1 650 276 6517, fanny.perraudeau@pendulum.co %K clinical trials %K continuous glucose monitoring %K lifestyle modification %K mobile app %K telemedicine %K diabetes %D 2021 %7 4.3.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1 diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps to monitor and manage care. Objective: We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits. Methods: Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual diet log. Results: All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction with sensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10. Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future. Conclusions: We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits. CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely. Trial Registration: ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888. %M 33661120 %R 10.2196/20175 %U https://formative.jmir.org/2021/3/e20175 %U https://doi.org/10.2196/20175 %U http://www.ncbi.nlm.nih.gov/pubmed/33661120 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 1 %P e23527 %T Point-of-Care Quantification of Serum Alpha-Fetoprotein for Screening Birth Defects in Resource-Limited Settings: Proof-of-Concept Study %A Srinivasan,Balaji %A Finkelstein,Julia L %A Erickson,David %A Mehta,Saurabh %+ Division of Nutritional Sciences, Cornell University, 314 Savage Hall, Ithaca, NY, 14850, United States, 1 607 255 2640, smehta@cornell.edu %K alpha-fetoprotein %K point-of-care testing %K screening %K neural tube defects %K mobile phone %D 2021 %7 3.3.2021 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Maternal serum alpha-fetoprotein (MSAFP) concentration typically increases during pregnancy and is routinely measured during the second trimester as a part of screening for fetal neural tube defects and Down syndrome. However, most pregnancy screening tests are not available in the settings they are needed the most. A mobile device–enabled technology based on MSAFP for screening birth defects could enable the rapid screening and triage of high-risk pregnancies, especially where maternal serum screening and fetal ultrasound scan facilities are not easily accessible. Shifting the approach from clinic- and laboratory-dependent care to a mobile platform based on our point-of-care approach will enable translation to resource-limited settings and the global health care market. Objective: The objective of this study is to develop and perform proof-of-concept testing of a lateral flow immunoassay on a mobile platform for rapid, point-of-care quantification of serum alpha-fetoprotein (AFP) levels, from a drop of human serum, within a few minutes. Methods: The development of the immunoassay involved the selection of commercially available antibodies and optimization of their concentrations by an iterative method to achieve the required detection limits. We compared the performance of our method with that of commercially obtained human serum samples, with known AFP concentrations quantified by the Abbott ARCHITECT chemiluminescent magnetic microparticle immunoassay (CMIA). Results: We tested commercially obtained serum samples (N=20) with concentrations ranging from 2.2 to 446 ng/mL to compare the results of our point-of-care assay with results from the Abbott ARCHITECT CMIA. A correlation of 0.98 (P<.001) was observed on preliminary testing and comparison with the CMIA. The detection range of our point-of-care assay covers the range of maternal serum AFP levels observed during pregnancy. Conclusions: The preliminary test results from the AFP test on the mobile platform performed in this study represent a proof of concept that will pave the way for our future work focused on developing a mobile device–enabled quad-screen point-of-care testing with the potential to enable the screening of high-risk pregnancies in various settings. The AFP test on the mobile platform can be applied to enable screening for high-risk pregnancies, within a few minutes, at the point of care even in remote areas where maternal serum tests and fetal ultrasound scans are not easily accessible; assessment of whether clinical follow-up and diagnostic testing may be needed after a positive initial screening evaluation; and development of surveillance tools for birth defects. %M 34746648 %R 10.2196/23527 %U https://biomedeng.jmir.org/2021/1/e23527 %U https://doi.org/10.2196/23527 %U http://www.ncbi.nlm.nih.gov/pubmed/34746648 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e17023 %T Compliance With Mobile Ecological Momentary Assessment of Self-Reported Health-Related Behaviors and Psychological Constructs in Adults: Systematic Review and Meta-analysis %A Williams,Marie T %A Lewthwaite,Hayley %A Fraysse,François %A Gajewska,Alexandra %A Ignatavicius,Jordan %A Ferrar,Katia %+ Innovation, Implementation And Clinical Translation in Health, Allied Health and Human Performance, University of South Australia, City East Campus, North Terrace, Adelaide, 5000, Australia, 61 8 8302 1153, Marie.Williams@unisa.edu.au %K mobile momentary ecological assessment %K adult %K compliance %K systematic review %K meta-analysis %K mobile phone %D 2021 %7 3.3.2021 %9 Review %J J Med Internet Res %G English %X Background: Mobile ecological momentary assessment (mEMA) permits real-time capture of self-reported participant behaviors and perceptual experiences. Reporting of mEMA protocols and compliance has been identified as problematic within systematic reviews of children, youth, and specific clinical populations of adults. Objective: This study aimed to describe the use of mEMA for self-reported behaviors and psychological constructs, mEMA protocol and compliance reporting, and associations between key components of mEMA protocols and compliance in studies of nonclinical and clinical samples of adults. Methods: In total, 9 electronic databases were searched (2006-2016) for observational studies reporting compliance to mEMA for health-related data from adults (>18 years) in nonclinical and clinical settings. Screening and data extraction were undertaken by independent reviewers, with discrepancies resolved by consensus. Narrative synthesis described participants, mEMA target, protocol, and compliance. Random effects meta-analysis explored factors associated with cohort compliance (monitoring duration, daily prompt frequency or schedule, device type, training, incentives, and burden score). Random effects analysis of variance (P≤.05) assessed differences between nonclinical and clinical data sets. Results: Of the 168 eligible studies, 97/105 (57.7%) reported compliance in unique data sets (nonclinical=64/105 [61%], clinical=41/105 [39%]). The most common self-reported mEMA target was affect (primary target: 31/105, 29.5% data sets; secondary target: 50/105, 47.6% data sets). The median duration of the mEMA protocol was 7 days (nonclinical=7, clinical=12). Most protocols used a single time-based (random or interval) prompt type (69/105, 65.7%); median prompt frequency was 5 per day. The median number of items per prompt was similar for nonclinical (8) and clinical data sets (10). More than half of the data sets reported mEMA training (84/105, 80%) and provision of participant incentives (66/105, 62.9%). Less than half of the data sets reported number of prompts delivered (22/105, 21%), answered (43/105, 41%), criterion for valid mEMA data (37/105, 35.2%), or response latency (38/105, 36.2%). Meta-analysis (nonclinical=41, clinical=27) estimated an overall compliance of 81.9% (95% CI 79.1-84.4), with no significant difference between nonclinical and clinical data sets or estimates before or after data exclusions. Compliance was associated with prompts per day and items per prompt for nonclinical data sets. Although widespread heterogeneity existed across analysis (I2>90%), no compelling relationship was identified between key features of mEMA protocols representing burden and mEMA compliance. Conclusions: In this 10-year sample of studies using the mEMA of self-reported health-related behaviors and psychological constructs in adult nonclinical and clinical populations, mEMA was applied across contexts and health conditions and to collect a range of health-related data. There was inconsistent reporting of compliance and key features within protocols, which limited the ability to confidently identify components of mEMA schedules likely to have a specific impact on compliance. %M 33656451 %R 10.2196/17023 %U https://www.jmir.org/2021/3/e17023 %U https://doi.org/10.2196/17023 %U http://www.ncbi.nlm.nih.gov/pubmed/33656451 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e23362 %T Barriers to the Large-Scale Adoption of a COVID-19 Contact Tracing App in Germany: Survey Study %A Blom,Annelies G %A Wenz,Alexander %A Cornesse,Carina %A Rettig,Tobias %A Fikel,Marina %A Friedel,Sabine %A Möhring,Katja %A Naumann,Elias %A Reifenscheid,Maximiliane %A Krieger,Ulrich %+ School of Social Sciences, University of Mannheim, A5, 6, Mannheim, 68131, Germany, 49 621 181 2298, a.wenz@uni-mannheim.de %K digital health %K mobile health %K smartphone %K mobile phone %K app %K digital technology %K contact tracing %K coronavirus %K COVID-19 %K survey %D 2021 %7 2.3.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: During the COVID-19 pandemic, one way to reduce further transmissions of SARS-CoV-2 is the widespread use of contact tracing apps. Such apps keep track of proximity contacts and warn contacts of persons who tested positive for an infection. Objective: In this study, we analyzed potential barriers to the large-scale adoption of the official contact tracing app that was introduced in Germany on June 16, 2020. Methods: Survey data were collected from 3276 adults during the week the app was introduced using an offline-recruited, probability-based online panel of the general adult population in Germany. Results: We estimate that 81% of the population aged 18 to 77 years possess the devices and ability to install the official app and that 35% are also willing to install and use it. Potential spreaders show high access to devices required to install the app (92%) and high ability to install the app (91%) but low willingness (31%) to correctly adopt the app, whereas for vulnerable groups, the main barrier is access (62%). Conclusions: The findings suggest a pessimistic view on the effectiveness of app-based contact tracing to contain the COVID-19 pandemic. We recommend targeting information campaigns at groups with a high potential to spread the virus but who are unwilling to install and correctly use the app, in particular men and those aged between 30 and 59 years. In addition, vulnerable groups, in particular older individuals and those in lower-income households, may be provided with equipment and support to overcome their barriers to app adoption. %M 33577466 %R 10.2196/23362 %U https://www.jmir.org/2021/3/e23362 %U https://doi.org/10.2196/23362 %U http://www.ncbi.nlm.nih.gov/pubmed/33577466 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e20989 %T The Reliability of Remote Patient-Reported Outcome Measures via Mobile Apps to Replace Outpatient Visits After Rotator Cuff Repair Surgery: Repetitive Test-Retest Comparison Study for 1-Year Follow-up %A Hong,Taek Ho %A Kim,Myung Ku %A Ryu,Dong Jin %A Park,Jun Sung %A Bae,Gi Cheol %A Jeon,Yoon Sang %+ Department of Orthopedic Surgery, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon, 22332, Republic of Korea, 82 010 8353 3695, ysjeon80@hanmail.net %K patient-reported outcome measures (PROMs) %K location %K remote PROMs using mobile application %K smartphone %K mobile phone %K follow-up loss %D 2021 %7 1.3.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: With the development of health care–related mobile apps, attempts have been made to implement remote patient-reported outcome measures (PROMs). In order for remote PROMs to be widely used by mobile apps, the results should not be different depending on the location; that is, remote PROM results performed in locations other than hospitals should be able to obtain reliable results equivalent to those performed in hospitals, and this is very important. However, to our knowledge, there are no studies that have assessed the reliability of PROMs using mobile apps according to the location by comparing the results performed remotely from the hospital and performed at the outpatient visits. Objective: The purpose of this study was to evaluate the reliability of remote PROMs using mobile apps compared to PROMs performed during outpatient follow-up visits after arthroscopic shoulder surgery. Methods: A total of 174 patients who underwent arthroscopic rotator cuff repair completed questionnaires 2 days before visiting the clinic for the 1-, 2-, 3-, 6-, and 12-month follow-ups (test A). The patients completed the questionnaires at the clinic (test B) using the same mobile app and device for the 1-, 2-, 3-, 6-, and 12-month follow-ups. Test-retest comparisons were performed to analyze the differences and reliability of the PROMs according to the period. Results: Comparisons of tests A and B showed statistically significant differences at 1, 2, and 3 months (all Ps<.05 except for the ASES function scale at 3-months) but not 6 or 12 months after surgery (all Ps>.05). The intraclass correlation values between the two groups were relatively low at the 1-, 2-, and 3-month follow-ups but were within the reliable range at 6 and 12 months after surgery. The rate of completion of tests A and B using the mobile app was significantly lower in the group older than 70 years than in the other groups for all postoperative periods (P<.001). Conclusions: PROMs using mobile apps with different locations differed soon after surgery but were reliably similar after 6 months. The remote PROMs using mobile apps could be used reliably for the patient more than 6 months after surgery. However, it is to be expected that the use of mobile app–based questionnaires is not as useful in the group older than 70 years as in other age groups. %M 33646133 %R 10.2196/20989 %U https://www.jmir.org/2021/3/e20989 %U https://doi.org/10.2196/20989 %U http://www.ncbi.nlm.nih.gov/pubmed/33646133 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e25289 %T Using Fitbit as an mHealth Intervention Tool to Promote Physical Activity: Potential Challenges and Solutions %A Balbim,Guilherme M %A Marques,Isabela G %A Marquez,David X %A Patel,Darshilmukesh %A Sharp,Lisa K %A Kitsiou,Spyros %A Nyenhuis,Sharmilee M %+ Department of Biomedical and Health Information Sciences, College of Applied Health Sciences, University of Illinois at Chicago, 1919 W Taylor St (MC 530), Chicago, IL, 60612, United States, 1 312 355 3519, skitsiou@uic.edu %K physical activity %K fitness trackers %K Fitbit %K smartphones %K interventional studies %K adults %K older adults %K wearable %K intervention %D 2021 %7 1.3.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Consumer-based physical activity (PA) trackers, also known as wearables, are increasingly being used in research studies as intervention or measurement tools. One of the most popular and widely used brands of PA trackers is Fitbit. Since the release of the first Fitbit in 2009, hundreds of experimental studies have used Fitbit devices to facilitate PA self-monitoring and behavior change via goal setting and feedback tools. Fitbit’s ability to capture large volumes of PA and physiological data in real time creates enormous opportunities for researchers. At the same time, however, it introduces a number of challenges (eg, technological, operational, logistical), most of which are not sufficiently described in study publications. Currently, there are no technical reports, guidelines, nor other types of publications discussing some of these challenges and offering guidance to researchers on how to best incorporate Fitbit devices in their study design and intervention to achieve their research goals. As a result, researchers are often left alone to discover and address some of these issues during the study through “trial and error.” This paper aims to address this gap. Drawing on our cumulative experience of conducting multiple studies with various Fitbit PA trackers over the years, we present and discuss various key challenges associated with the use of Fitbit PA trackers in research studies. Difficulties with the use of Fitbit PA trackers are encountered throughout the entire research process. Challenges and solutions are categorized in 4 main categories: study preparation, intervention delivery, data collection and analysis, and study closeout. Subsequently, we describe a number of empirically tested strategies used in 4 of our interventional studies involving participants from a broad range of demographic characteristics, racial/ethnic backgrounds, and literacy levels. Researchers should be prepared to address challenges and issues in a timely fashion to ensure that the Fitbit effectively assists participants and researchers in achieving research and outcome goals. %M 33646135 %R 10.2196/25289 %U https://mhealth.jmir.org/2021/3/e25289 %U https://doi.org/10.2196/25289 %U http://www.ncbi.nlm.nih.gov/pubmed/33646135 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e17590 %T Understanding Gaps Between Daily Living and Clinical Settings in Chronic Disease Management: Qualitative Study %A Ozkaynak,Mustafa %A Valdez,Rupa %A Hannah,Katia %A Woodhouse,Gina %A Klem,Patrick %+ College of Nursing, University of Colorado | Anschutz Medical Campus, Campus Box 288-18 Education 2 North Building, 13120 E. 19th Avenue Room 4121, Aurora, CO, 80045, United States, 1 303 724 8273, mustafa.ozkaynak@cuanschutz.edu %K health information systems %K workflow %K self-management %K activities of daily living %K mobile phone %D 2021 %7 25.2.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Management of chronic conditions entails numerous activities in both clinical and daily living settings. Activities across these settings interact, creating a high potential for a gap to occur if there is an inconsistency or disconnect between controlled clinical settings and complex daily living environments. Objective: The aim of this study is to characterize gaps (from the patient’s perspective) between health-related activities across home-based and clinical settings using anticoagulation treatment as an example. The causes, consequences, and mitigation strategies (reported by patients) were identified to understand these gaps. We conceptualized gaps as latent phenomena (ie, a break in continuity). Methods: Patients (n=39) and providers (n=4) from the anticoagulation clinic of an urban, western mountain health care system were recruited. Data were collected through primary interviews with patients, patient journaling with tablet computers, exit interviews with patients, and provider interviews. Data were analyzed qualitatively using a theory-driven approach and framework method of analysis. Results: The causes of gaps included clinician recommendations not fitting into patients’ daily routines, recommendations not fitting into patients’ living contexts, and information not transferred across settings. The consequences of these gaps included increased cognitive and physical workload on the patient, poor patient satisfaction, and compromised adherence to the therapy plan. We identified resources and strategies used to overcome these consequences as patient-generated strategies, routines, collaborative management, social environment, and tools and technologies. Conclusions: Understanding gaps, their consequences, and mitigating strategies can lead to the development of interventions that help narrow these gaps. Such interventions could take the form of collaborative health information technologies, novel patient and clinician education initiatives, and programs that strongly integrate health systems and community resources. Current technologies are insufficient to narrow the gaps between clinical and daily living settings due to the limited number and types of routines that are tracked. %M 33629657 %R 10.2196/17590 %U https://www.jmir.org/2021/2/e17590 %U https://doi.org/10.2196/17590 %U http://www.ncbi.nlm.nih.gov/pubmed/33629657 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 2 %P e14179 %T Ecological Momentary Assessment Using Smartphones in Patients With Depression: Feasibility Study %A Maatoug,Redwan %A Peiffer-Smadja,Nathan %A Delval,Guillaume %A Brochu,Térence %A Pitrat,Benjamin %A Millet,Bruno %+ Sorbonne Université, AP-HP, Service de psychiatrie adulte de la Pitié-Salpêtrière, Institut du Cerveau, ICM, F-75013, 47-83 Boulevard de l'hôpital, Paris, 75013, France, 33 682476484, redwanmaatoug@gmail.com %K ecological momentary assessment %K depression %K smartphone %K feasibility study %K user experience %D 2021 %7 24.2.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Ecological momentary assessment (EMA) is a promising tool in the management of psychiatric disorders and particularly depression. It allows for a real-time evaluation of symptoms and an earlier detection of relapse or treatment efficacy. The generalization of the smartphone in the modern world offers a new, large-scale support for EMA. Objective: The main objective of this study was twofold: (1) to assess patients’ compliance with an EMA smartphone app defined by the number of EMAs completed, and (2) to estimate the external validity of the EMA using a correlation between self-esteem/guilt/mood variables and Hamilton Depression Rating Scale (HDRS) score. Methods: Eleven patients at the Pitié-Salpêtrière Hospital, Paris, France, were monitored for 28 days by means of a smartphone app. Every patient enrolled in the study had two types of assessment: (1) three outpatient consultations with a psychiatrist at three different time points (days 1, 15, and 28), and (2) real-time data collection using an EMA smartphone app with a single, fixed notification per day at 3 pm for 28 days. The results of the real-time data collected were reviewed during the three outpatient consultations by a psychiatrist using a dashboard that aggregated all of the patients’ data into a user-friendly format. Results: Of the 11 patients in the study, 6 patients attended the 3 outpatient consultations with the psychiatrist and completed the HDRS at each consultation. We found a positive correlation between the HDRS score and the variables of self-esteem, guilt, and mood (Spearman correlation coefficient 0.57). Seven patients completed the daily EMAs for 28 days or longer, with an average response rate to the EMAs of 62.5% (175/280). Furthermore, we observed a positive correlation between the number of responses to EMAs and the duration of follow-up (Spearman correlation coefficient 0.63). Conclusions: This preliminary study with a prolonged follow-up demonstrates significant patient compliance with the smartphone app. In addition, the self-assessments performed by patients seemed faithful to the standardized measurements performed by the psychiatrist. The results also suggest that for some patients it is more convenient to use the smartphone app than to attend outpatient consultations. %M 33625367 %R 10.2196/14179 %U https://formative.jmir.org/2021/2/e14179 %U https://doi.org/10.2196/14179 %U http://www.ncbi.nlm.nih.gov/pubmed/33625367 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e26107 %T Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study %A Hirten,Robert P %A Danieletto,Matteo %A Tomalin,Lewis %A Choi,Katie Hyewon %A Zweig,Micol %A Golden,Eddye %A Kaur,Sparshdeep %A Helmus,Drew %A Biello,Anthony %A Pyzik,Renata %A Charney,Alexander %A Miotto,Riccardo %A Glicksberg,Benjamin S %A Levin,Matthew %A Nabeel,Ismail %A Aberg,Judith %A Reich,David %A Charney,Dennis %A Bottinger,Erwin P %A Keefer,Laurie %A Suarez-Farinas,Mayte %A Nadkarni,Girish N %A Fayad,Zahi A %+ The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building RM 5-12, New York, NY, 10029, United States, 1 212 241 0150, robert.hirten@mountsinai.org %K wearable device %K COVID-19 %K identification %K prediction %K heart rate variability %K physiological %K wearable %K app %K data %K infectious disease %K symptom %K prediction %K diagnosis %K observational %D 2021 %7 22.2.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection. %M 33529156 %R 10.2196/26107 %U https://www.jmir.org/2021/2/e26107 %U https://doi.org/10.2196/26107 %U http://www.ncbi.nlm.nih.gov/pubmed/33529156 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e25451 %T Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study %A Su,Dongning %A Liu,Zhu %A Jiang,Xin %A Zhang,Fangzhao %A Yu,Wanting %A Ma,Huizi %A Wang,Chunxue %A Wang,Zhan %A Wang,Xuemei %A Hu,Wanli %A Manor,Brad %A Feng,Tao %A Zhou,Junhong %+ Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, 1200 Centre Street, Roslindale, MA, 02131, United States, 1 6179715346, junhongzhou@hsl.harvard.edu %K smartphone %K gait %K stride time (variability) %K validity %K Parkinson disease %D 2021 %7 19.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD. Objective: We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD. Methods: Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system. Results: High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F1,103=14.1, P<.001) and stride time variability (F1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: β=.39, P<.001; dual task: β=.37, P=.01), HAM-A (single-task: β=.49, P=.007; dual-task: β=.48, P=.009), and HAM-D (single task: β=.44, P=.01; dual task: β=.49, P=.009). Moreover, those with greater dual-task stride time variability (β=.48, P=.001) or dual-task cost of stride time variability (β=.44, P=.004) exhibited lower MoCA scores. Conclusions: A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD. %M 33605894 %R 10.2196/25451 %U http://mhealth.jmir.org/2021/2/e25451/ %U https://doi.org/10.2196/25451 %U http://www.ncbi.nlm.nih.gov/pubmed/33605894 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 2 %P e18385 %T Improving Efficiency of Clinical Studies Using a Total Digital Approach: Prospective Observational Study %A Schenck-Gustafsson,Karin %A Carnlöf,Carina %A Jensen-Urstad,Mats %A Insulander,Per %+ Heart and Vascular Theme, Karolinska University Hospital, Karolinska Institutet, Norrbacka S1:02, Stockholm, S 17176, Sweden, 46 707686487, karin.schenck-gustafsson@ki.se %K ECG recordings %K women %K palpitations %K full digitalization %K eAuthentication %K BankID %K clinical trial %K mHealth %K electrocardiogram %D 2021 %7 18.2.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: In general, most clinical studies have long recruitment periods. Signing the informed consent is particularly time-consuming when the participant must meet physically with the researchers. Therefore, introducing fully web-based techniques with the use of eAuthentication (BankID) and new digital electrocardiogram (ECG) monitoring could speed up inclusion time, increase adherence, and also reach out to more remote regions. Objective: The objectives of this study were to explore whether inclusion of a large number of participants could be realized quickly by using a total digital approach both for information and signing of informed consent, along with ECG monitoring and instant feedback on a mobile device. We also explored whether this approach can increase adherence in registration of ECG recordings and answering questionnaires, and if it would result in a more geographically uniform distribution of participants covering a wide age span. Methods: Women with palpitations were intensively studied over 2 months by means of a handheld ECG monitoring device (Coala Heart Monitor). The device connects to a smartphone or tablet, which allows the participants to obtain the results immediately. Recruitment, study information, and signing the informed consent form with the help of BankID were performed in a completely digital manner. Results: Between March and May 2018, 2424 women indicated their interest in participating in the study. On June 19, 2018, presumptive participants were invited to log in and register. After 25 days, 1082 women were included in the study; among these, 1020 women fulfilled the inclusion criteria, 913 of whom completed all phases of the study: recording ECG using the handheld device, completion of the prestudy questionnaires, and completion of the poststudy questionnaires 2 months after the ECG recordings. The dropout rate was 9%. In total, 101,804 ECG recordings were made. The mean age was 56 (SD 11) years (range 21-88 years) and 35 participants were 75 years or older. The participants were evenly distributed between living in the countryside and in cities. Conclusions: Total digital inclusion recruitment of 1082 participants was achieved in only 25 days, and resulted in a good geographical distribution, excellent adherence, and ability to reach a vast age span, including elderly women. Studies using a total digital design would be particularly appealing during a pandemic since physical contact should be avoided as much as possible. Trial Registration: ISRCTN Registry ISRCTN22495299; http://www.isrctn.com/ISRCTN22495299 %M 33599617 %R 10.2196/18385 %U http://formative.jmir.org/2021/2/e18385/ %U https://doi.org/10.2196/18385 %U http://www.ncbi.nlm.nih.gov/pubmed/33599617 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e20700 %T Sociodemographic, Health and Lifestyle, Sampling, and Mental Health Determinants of 24-Hour Motor Activity Patterns: Observational Study %A Difrancesco,Sonia %A Riese,Harriëtte %A Merikangas,Kathleen R %A Shou,Haochang %A Zipunnikov,Vadim %A Antypa,Niki %A van Hemert,Albert M %A Schoevers,Robert A %A Penninx,Brenda W J H %A Lamers,Femke %+ Amsterdam Public Health Research Institute, Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Oldenaller 1, Amsterdam, 1078XL, Netherlands, 31 643193730, s.difrancesco@ggzingeest.nl %K actigraphy %K functional data analysis %K mental health %K well-being %K activity %D 2021 %7 17.2.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Analyzing actigraphy data using standard circadian parametric models and aggregated nonparametric indices may obscure temporal information that may be a hallmark of the circadian impairment in psychiatric disorders. Functional data analysis (FDA) may overcome such limitations by fully exploiting the richness of actigraphy data and revealing important relationships with mental health outcomes. To our knowledge, no studies have extensively used FDA to study the relationship between sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics and daily motor activity patterns assessed with actigraphy in a sample of individuals with and without depression/anxiety. Objective: We aimed to study the association between daily motor activity patterns assessed via actigraphy and (1) sociodemographic, health and lifestyle, and sampling factors, and (2) psychiatric clinical characteristics (ie, presence and severity of depression/anxiety disorders). Methods: We obtained 14-day continuous actigraphy data from 359 participants from the Netherlands Study of Depression and Anxiety with current (n=93), remitted (n=176), or no (n=90) depression/anxiety diagnosis, based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition. Associations between patterns of daily motor activity, quantified via functional principal component analysis (fPCA), and sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics were assessed using generalized estimating equation regressions. For exploratory purposes, function-on-scalar regression (FoSR) was applied to quantify the time-varying association of sociodemographic, health and lifestyle, sampling, and psychiatric clinical characteristics on daily motor activity. Results: Four components of daily activity patterns captured 77.4% of the variability in the data: overall daily activity level (fPCA1, 34.3% variability), early versus late morning activity (fPCA2, 16.5% variability), biphasic versus monophasic activity (fPCA3, 14.8% variability), and early versus late biphasic activity (fPCA4, 11.8% variability). A low overall daily activity level was associated with a number of sociodemographic, health and lifestyle, and psychopathology variables: older age (P<.001), higher education level (P=.005), higher BMI (P=.009), greater number of chronic diseases (P=.02), greater number of cigarettes smoked per day (P=.02), current depressive and/or anxiety disorders (P=.05), and greater severity of depressive symptoms (P<.001). A high overall daily activity level was associated with work/school days (P=.02) and summer (reference: winter; P=.03). Earlier morning activity was associated with older age (P=.02), having a partner (P=.009), work/school days (P<.001), and autumn and spring (reference: winter; P=.02 and P<.001, respectively). Monophasic activity was associated with older age (P=.005). Biphasic activity was associated with work/school days (P<.001) and summer (reference: winter; P<.001). Earlier biphasic activity was associated with older age (P=.005), work/school days (P<.001), and spring and summer (reference: winter; P<.001 and P=.005, respectively). In FoSR analyses, age, work/school days, and season were the main determinants having a time-varying association with daily motor activity (all P<.05). Conclusions: Features of daily motor activity extracted with fPCA reflect commonly studied factors such as the intensity of daily activity and preference for morningness/eveningness. The presence and severity of depression/anxiety disorders were found to be associated mainly with a lower overall activity pattern but not with the time of the activity. Age, work/school days, and season were the variables most strongly associated with patterns and time of activity, and thus future epidemiological studies on motor activity in depression/anxiety should take these variables into account. %M 33595445 %R 10.2196/20700 %U http://www.jmir.org/2021/2/e20700/ %U https://doi.org/10.2196/20700 %U http://www.ncbi.nlm.nih.gov/pubmed/33595445 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e25655 %T Development and Validation of Risk Scores for All-Cause Mortality for a Smartphone-Based “General Health Score” App: Prospective Cohort Study Using the UK Biobank %A Clift,Ashley K %A Le Lannou,Erwann %A Tighe,Christian P %A Shah,Sachin S %A Beatty,Matthew %A Hyvärinen,Arsi %A Lane,Stephen J %A Strauss,Tamir %A Dunn,Devin D %A Lu,Jiahe %A Aral,Mert %A Vahdat,Dan %A Ponzo,Sonia %A Plans,David %+ Huma Therapeutics, 13th Floor Millbank Tower, 21-24 Millbank, London, United Kingdom, 44 7527 016574, david.plans@huma.com %K C-Score %K mortality %K risk score %K smartphone %K health score %K medical informatics %K public health %K mobile health %K development %K validation %K app %K prospective %K cohort %K machine learning %D 2021 %7 16.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Given the established links between an individual’s behaviors and lifestyle factors and potentially adverse health outcomes, univariate or simple multivariate health metrics and scores have been developed to quantify general health at a given point in time and estimate risk of negative future outcomes. However, these health metrics may be challenging for widespread use and are unlikely to be successful at capturing the broader determinants of health in the general population. Hence, there is a need for a multidimensional yet widely employable and accessible way to obtain a comprehensive health metric. Objective: The objective of the study was to develop and validate a novel, easily interpretable, points-based health score (“C-Score”) derived from metrics measurable using smartphone components and iterations thereof that utilize statistical modeling and machine learning (ML) approaches. Methods: A literature review was conducted to identify relevant predictor variables for inclusion in the first iteration of a points-based model. This was followed by a prospective cohort study in a UK Biobank population for the purposes of validating the C-Score and developing and comparatively validating variations of the score using statistical and ML models to assess the balance between expediency and ease of interpretability and model complexity. Primary and secondary outcome measures were discrimination of a points-based score for all-cause mortality within 10 years (Harrell c-statistic) and discrimination and calibration of Cox proportional hazards models and ML models that incorporate C-Score values (or raw data inputs) and other predictors to predict the risk of all-cause mortality within 10 years. Results: The study cohort comprised 420,560 individuals. During a cohort follow-up of 4,526,452 person-years, there were 16,188 deaths from any cause (3.85%). The points-based model had good discrimination (c-statistic=0.66). There was a 31% relative reduction in risk of all-cause mortality per decile of increasing C-Score (hazard ratio of 0.69, 95% CI 0.663-0.675). A Cox model integrating age and C-Score had improved discrimination (8 percentage points; c-statistic=0.74) and good calibration. ML approaches did not offer improved discrimination over statistical modeling. Conclusions: The novel health metric (“C-Score”) has good predictive capabilities for all-cause mortality within 10 years. Embedding the C-Score within a smartphone app may represent a useful tool for democratized, individualized health risk prediction. A simple Cox model using C-Score and age balances parsimony and accuracy of risk predictions and could be used to produce absolute risk estimations for app users. %M 33591285 %R 10.2196/25655 %U http://mhealth.jmir.org/2021/2/e25655/ %U https://doi.org/10.2196/25655 %U http://www.ncbi.nlm.nih.gov/pubmed/33591285 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 2 %P e20335 %T Evaluating Apple Inc Mobility Trend Data Related to the COVID-19 Outbreak in Japan: Statistical Analysis %A Kurita,Junko %A Sugishita,Yoshiyuki %A Sugawara,Tamie %A Ohkusa,Yasushi %+ Department of Nursing, Tokiwa University, 1-430-1 Miwa, Mito, Ibraki, 3108585, Japan, 81 29 232 2511, kuritaj@tokiwa.ac.jp %K peak %K COVID-19 %K effective reproduction number %K mobility trend data %K Apple %K countermeasure %D 2021 %7 15.2.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In Japan, as a countermeasure against the COVID-19 outbreak, both the national and local governments issued voluntary restrictions against going out from residences at the end of March 2020 in preference to the lockdowns instituted in European and North American countries. The effect of such measures can be studied with mobility data, such as data which is generated by counting the number of requests made to Apple Maps for directions in select countries/regions, sub-regions, and cities. Objective: We investigate the associations of mobility data provided by Apple Inc and an estimate an an effective reproduction number R(t). Methods: We regressed R(t) on a polynomial function of daily Apple data, estimated using the whole period, and analyzed subperiods delimited by March 10, 2020. Results: In the estimation results, R(t) was 1.72 when voluntary restrictions against going out ceased and mobility reverted to a normal level. However, the critical level of reducing R(t) to <1 was obtained at 89.3% of normal mobility. Conclusions: We demonstrated that Apple mobility data are useful for short-term prediction of R(t). The results indicate that the number of trips should decrease by 10% until herd immunity is achieved and that higher voluntary restrictions against going out might not be necessary for avoiding a re-emergence of the outbreak. %M 33481755 %R 10.2196/20335 %U http://publichealth.jmir.org/2021/2/e20335/ %U https://doi.org/10.2196/20335 %U http://www.ncbi.nlm.nih.gov/pubmed/33481755 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e19242 %T Investigator Experiences Using Mobile Technologies in Clinical Research: Qualitative Descriptive Study %A McKenna,Kevin Christopher %A Geoghegan,Cindy %A Swezey,Teresa %A Perry,Brian %A Wood,William A %A Nido,Virginia %A Morin,Steve L %A Grabert,Brigid K %A Hallinan,Zachary P %A Corneli,Amy L %+ Department of Population Health Sciences, Duke University, 215 Morris Street, Suite 210, Durham, NC, 27705, United States, 1 9196688274, kevin.mckenna@duke.edu %K mHealth %K mobile technology %K mobile clinical trials %K digital health %K clinical research %K mobile devices %K digital health technology %K mobile applications %K clinical trial %D 2021 %7 12.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The successful adoption of mobile technology for use in clinical trials relies on positive reception from key stakeholders, including clinical investigators; however, little information is known about the perspectives of investigators using mobile technologies in clinical trials. Objective: The aim of this study was to seek investigators’ insights on the advantages and challenges of mobile clinical trials (MCTs); site-level budgetary, training, and other support needs necessary to adequately prepare for and implement MCTs; and the advantages and disadvantages for trial participants using mobile technologies in clinical trials. Methods: Using a qualitative descriptive study design, we conducted in-depth interviews with investigators involved in the conduct of MCTs. Data were analyzed using applied thematic analysis. Results: We interviewed 12 investigators who represented a wide variety of clinical specialties and reported using a wide range of mobile technologies. Investigators most commonly cited 3 advantages of MCTs over traditional clinical trials: more streamlined study operations, remote data capture, and improvement in the quality of studies and data collected. Investigators also reported that MCTs can be designed around the convenience of trial participants, and individuals may be more willing to participate in MCTs because they can take part from their homes. In addition, investigators recognized that MCTs can also involve additional burden for participants and described that operational challenges, technology adoption barriers, uncertainties about data quality, and time burden made MCTs more challenging than traditional clinical trials. Investigators stressed that additional training and dedicated staff effort may be needed to select a particular technology for use in a trial, helping trial participants learn and use the technology, and for staff troubleshooting the technology. Investigators also expressed that sharing data collected in real time with investigators and trial participants is an important aspect of MCTs that warrants consideration and potentially additional training and education. Conclusions: Investigator perspectives can inform the use of mobile technologies in future clinical trials by proactively identifying and addressing potential challenges. %M 33576742 %R 10.2196/19242 %U http://mhealth.jmir.org/2021/2/e19242/ %U https://doi.org/10.2196/19242 %U http://www.ncbi.nlm.nih.gov/pubmed/33576742 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e25118 %T Efficiency and Quality of Data Collection Among Public Mental Health Surveys Conducted During the COVID-19 Pandemic: Systematic Review %A Lin,Yu-Hsuan %A Chen,Chung-Yen %A Wu,Shiow-Ing %+ Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 35053, Taiwan, 886 37 206166 ext 36383, yuhsuanlin@nhri.edu.tw %K COVID-19 %K mental health %K Newcastle-Ottawa Scale %K review %K data collection %K survey %K surveillance %K literature %K research %D 2021 %7 10.2.2021 %9 Review %J J Med Internet Res %G English %X Background: The World Health Organization has recognized the importance of assessing population-level mental health during the COVID-19 pandemic. During a global crisis such as the COVID-19 pandemic, a timely surveillance method is urgently needed to track the impact on public mental health. Objective: This brief systematic review focused on the efficiency and quality of data collection of studies conducted during the COVID-19 pandemic. Methods: We searched the PubMed database using the following search strings: ((COVID-19) OR (SARS-CoV-2)) AND ((Mental health) OR (psychological) OR (psychiatry)). We screened the titles, abstracts, and texts of the published papers to exclude irrelevant studies. We used the Newcastle-Ottawa Scale to evaluate the quality of each research paper. Results: Our search yielded 37 relevant mental health surveys of the general public that were conducted during the COVID-19 pandemic, as of July 10, 2020. All these public mental health surveys were cross-sectional in design, and the journals efficiently made these articles available online in an average of 18.7 (range 1-64) days from the date they were received. The average duration of recruitment periods was 9.2 (range 2-35) days, and the average sample size was 5137 (range 100-56,679). However, 73% (27/37) of the selected studies had Newcastle-Ottawa Scale scores of <3 points, which suggests that these studies are of very low quality for inclusion in a meta-analysis. Conclusions: The studies examined in this systematic review used an efficient data collection method, but there was a high risk of bias, in general, among the existing public mental health surveys. Therefore, following recommendations to avoid selection bias, or employing novel methodologies considering both a longitudinal design and high temporal resolution, would help provide a strong basis for the formation of national mental health policies. %M 33481754 %R 10.2196/25118 %U http://www.jmir.org/2021/2/e25118/ %U https://doi.org/10.2196/25118 %U http://www.ncbi.nlm.nih.gov/pubmed/33481754 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e24472 %T Acceptability, Validity, and Engagement With a Mobile App for Frequent, Continuous Multiyear Assessment of Youth Health Behaviors (mNCANDA): Mixed Methods Study %A Cummins,Kevin M %A Brumback,Ty %A Chung,Tammy %A Moore,Raeanne C %A Henthorn,Trevor %A Eberson,Sonja %A Lopez,Alyssa %A Sarkissyan,Tatev %A Nooner,Kate B %A Brown,Sandra A %A Tapert,Susan F %+ Department of Psychology, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, United States, 1 8582952763, kcummins@ucsd.edu %K mobile applications %K young adults %K smartphone %K health behavior %K underage drinking %K alcohol drinking %K self-report %K illicit drugs %K mobile phone %D 2021 %7 10.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Longitudinal studies of many health behaviors often rely on infrequent self-report assessments. The measurement of psychoactive substance use among youth is expected to improve with more frequent mobile assessments, which can reduce recall bias. Researchers have used mobile devices for longitudinal research, but studies that last years and assess youth continuously at a fine-grained, temporal level (eg, weekly) are rare. A tailored mobile app (mNCANDA [mobile National Consortium on Alcohol and Neurodevelopment in Adolescence]) and a brief assessment protocol were designed specifically to provide a feasible platform to elicit responses to health behavior assessments in longitudinal studies, including NCANDA (National Consortium on Alcohol and Neurodevelopment in Adolescence). Objective: This study aimed to determine whether an acceptable mobile app system could provide repeatable and valid assessment of youth’s health behaviors in different developmental stages over extended follow-up. Methods: Participants were recruited (n=534; aged 17-28 years) from a larger longitudinal study of neurodevelopment. Participants used mNCANDA to register reports of their behaviors for up to 18 months. Response rates as a function of time measured using mNCANDA and participant age were modeled using generalized estimating equations to evaluate response rate stability and age effects. Substance use reports captured using mNCANDA were compared with responses from standardized interviews to assess concurrent validity. Reactivity was assessed by evaluating patterns of change in substance use after participants initiated weekly reports via mNCANDA. Quantitative feedback about the app was obtained from the participants. Qualitative interviews were conducted with a subset of participants who used the app for at least one month to obtain feedback on user experience, user-derived explanations of some quantitative results, and suggestions for system improvements. Results: The mNCANDA protocol adherence was high (mean response rate 82%, SD 27%) and stable over time across all age groups. The median time to complete each assessment was 51 s (mean response time 1.14, SD 1.03 min). Comparisons between mNCANDA and interview self-reports on recent (previous 30 days) alcohol and cannabis use days demonstrate close agreement (eg, within 1 day of reported use) for most observations. Models used to identify reactivity failed to detect changes in substance use patterns subsequent to enrolling in mNCANDA app assessments (P>.39). Most participants (64/76, 84%) across the age range reported finding the mNCANDA system acceptable. Participants provided recommendations for improving the system (eg, tailoring signaling times). Conclusions: mNCANDA provides a feasible, multi-year, continuous, fine-grained (eg, weekly) assessment of health behaviors designed to minimize respondent burden and provides acceptable regimes for long-term self-reporting of health behaviors. Fine-grained characterization of variability in behaviors over relatively long periods (eg, up to 18 months) may, through the use of mNCANDA, improve our understanding of the relationship between substance use exposure and neurocognitive development. %M 33565988 %R 10.2196/24472 %U http://mhealth.jmir.org/2021/2/e24472/ %U https://doi.org/10.2196/24472 %U http://www.ncbi.nlm.nih.gov/pubmed/33565988 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e19210 %T An Innovative Wearable Device For Monitoring Continuous Body Surface Temperature (HEARThermo): Instrument Validation Study %A Yeh,Chun-Yin %A Chung,Yi-Ting %A Chuang,Kun-Ta %A Shu,Yu-Chen %A Kao,Hung-Yu %A Chen,Po-Lin %A Ko,Wen-Chien %A Ko,Nai-Ying %+ Department of Nursing, National Cheng Kung University, No 1, University Road, Tainan, 701, Taiwan, 886 6 2353535 ext 5838, nyko@mail.ncku.edu.tw %K body surface temperature %K wearable device %K validation %K continuous monitoring %D 2021 %7 10.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Variations in body temperature are highly informative during an illness. To date, there are not many adequate studies that have investigated the feasibility of a wearable wrist device for the continuous monitoring of body surface temperatures in humans. Objective: The objective of this study was to validate the performance of HEARThermo, an innovative wearable device, which was developed to continuously monitor the body surface temperature in humans. Methods: We implemented a multi-method research design in this study, which included 2 validation studies—one in the laboratory and one with human subjects. In validation study I, we evaluated the test-retest reliability of HEARThermo in the laboratory to measure the temperature and to correct the values recorded by each HEARThermo by using linear regression models. We conducted validation study II on human subjects who wore HEARThermo for the measurement of their body surface temperatures. Additionally, we compared the HEARThermo temperature recordings with those recorded by the infrared skin thermometer simultaneously. We used intraclass correlation coefficients (ICCs) and Bland-Altman plots to analyze the criterion validity and agreement between the 2 measurement tools. Results: A total of 66 participants (age range, 10-77 years) were recruited, and 152,881 completed data were analyzed in this study. The 2 validation studies in the laboratory and on human skin indicated that HEARThermo showed a good test-retest reliability (ICC 0.96-0.98) and adequate criterion validity with the infrared skin thermometer at room temperatures of 20°C-27.9°C (ICC 0.72, P<.001). The corrected measurement bias averaged –0.02°C, which was calibrated using a water bath ranging in temperature from 16°C to 40°C. The values of each HEARThermo improved by the regression models were not significantly different from the temperature of the water bath (P=.19). Bland-Altman plots showed no visualized systematic bias. HEARThermo had a bias of 1.51°C with a 95% limit of agreement between –1.34°C and 4.35°C. Conclusions: The findings of our study show the validation of HEARThermo for the continuous monitoring of body surface temperatures in humans. %M 33565990 %R 10.2196/19210 %U http://mhealth.jmir.org/2021/2/e19210/ %U https://doi.org/10.2196/19210 %U http://www.ncbi.nlm.nih.gov/pubmed/33565990 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 2 %P e19430 %T App-Based Salt Reduction Intervention in School Children and Their Families (AppSalt) in China: Protocol for a Mixed Methods Process Evaluation %A Sun,Yuewen %A Luo,Rong %A Li,Yuan %A He,Feng J %A Tan,Monique %A MacGregor,Graham A %A Liu,Hueiming %A Zhang,Puhong %+ The George Institute for Global Health, Peking University Health Science Center, Room 011, Unit 2, Tayuan Diplomatic Office Building No. 14 Liangmahe Nan Lu, Beijing, 100600, China, 86 10 8280 0177, zpuhong@georgeinstitute.org.cn %K mobile health %K mobile phone %K process evaluation %K salt reduction %K health education %D 2021 %7 10.2.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The app-based salt reduction intervention program in school children and their families (AppSalt) is a multicomponent mobile health (mHealth) intervention program, which involves multiple stakeholders, including students, parents, teachers, school heads, and local health and education authorities. The complexity of the AppSalt program highlights the need for process evaluation to investigate how the implementation will be achieved at different sites. Objective: This paper presents a process evaluation protocol of the AppSalt program, which aims to monitor the implementation of the program, explain its causal mechanisms, and provide evidence for scaling up the program nationwide. Methods: A mixed methods approach will be used to collect data relating to five process evaluation dimensions: fidelity, dose delivered, dose received, reach, and context. Quantitative data, including app use logs, activity logs, and routine monitoring data, will be collected alongside the intervention process to evaluate the quantity and quality of intervention activities. The quantitative data will be summarized as medians, means, and proportions as appropriate. Qualitative data will be collected through semistructured interviews of purposely selected intervention participants and key stakeholders from local health and education authorities. The thematic analysis technique will be used for analyzing the qualitative data with the support of NVivo 12. The qualitative data will be triangulated with the quantitative data during the interpretation phase to explain the 5 process evaluation dimensions. Results: The intervention activities of the AppSalt program were initiated at 27 primary schools in three cities since October 2018. We have completed the 1-year intervention of this program. The quantitative data for this study, including app use log, activity logs, and the routine monitoring data, were collected and organized during the intervention process. After completing the intervention, we conducted semistructured interviews with 32 students, 32 parents, 9 teachers, 9 school heads, and 8 stakeholders from local health and education departments. Data analysis is currently underway. Conclusions: Using mHealth technology for salt reduction among primary school students is an innovation in China. The findings of this study will help researchers understand the implementation of the AppSalt program and similar mHealth interventions in real-world settings. Furthermore, this process evaluation will be informative for other researchers and policy makers interested in replicating the AppSalt program and designing their salt reduction intervention. International Registered Report Identifier (IRRID): DERR1-10.2196/19430 %M 33565991 %R 10.2196/19430 %U http://www.researchprotocols.org/2021/2/e19430/ %U https://doi.org/10.2196/19430 %U http://www.ncbi.nlm.nih.gov/pubmed/33565991 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 2 %P e20464 %T Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology %A Cole,Casey Anne %A Powers,Shannon %A Tomko,Rachel L %A Froeliger,Brett %A Valafar,Homayoun %+ Department of Computer Science and Engineering, University of South Carolina, 1400 Assembly Street, Columbia, SC, 29208, United States, 1 9372067968, coleca@email.sc.edu %K smartwatch %K CReSS %K smoking topography %K ASPIRE %K automated %K wearable technology %K wearable computing %K smoking %D 2021 %7 5.2.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: While there have been many technological advances in studying the neurobiological and clinical basis of tobacco use disorder and nicotine addiction, there have been relatively minor advances in technologies for monitoring, characterizing, and intervening to prevent smoking in real time. Better understanding of real-time smoking behavior can be helpful in numerous applications without the burden and recall bias associated with self-report. Objective: The goal of this study was to test the validity of using a smartwatch to advance the study of temporal patterns and characteristics of smoking in a controlled laboratory setting prior to its implementation in situ. Specifically, the aim was to compare smoking characteristics recorded by Automated Smoking PerceptIon and REcording (ASPIRE) on a smartwatch with the pocket Clinical Research Support System (CReSS) topography device, using video observation as the gold standard. Methods: Adult smokers (N=27) engaged in a video-recorded laboratory smoking task using the pocket CReSS while also wearing a Polar M600 smartwatch. In-house software, ASPIRE, was used to record accelerometer data to identify the duration of puffs and interpuff intervals (IPIs). The recorded sessions from CReSS and ASPIRE were manually annotated to assess smoking topography. Agreement between CReSS-recorded and ASPIRE-recorded smoking behavior was compared. Results: ASPIRE produced more consistent number of puffs and IPI durations relative to CReSS, when comparing both methods to visual puff count. In addition, CReSS recordings reported many implausible measurements in the order of milliseconds. After filtering implausible data recorded from CReSS, ASPIRE and CReSS produced consistent results for puff duration (R2=.79) and IPIs (R2=.73). Conclusions: Agreement between ASPIRE and other indicators of smoking characteristics was high, suggesting that the use of ASPIRE is a viable method of passively characterizing smoking behavior. Moreover, ASPIRE was more accurate than CReSS for measuring puffs and IPIs. Results from this study provide the foundation for future utilization of ASPIRE to passively and accurately monitor and quantify smoking behavior in situ. %M 33544083 %R 10.2196/20464 %U https://formative.jmir.org/2021/2/e20464 %U https://doi.org/10.2196/20464 %U http://www.ncbi.nlm.nih.gov/pubmed/33544083 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e24704 %T Heart Rate Variability and Firstbeat Method for Detecting Sleep Stages in Healthy Young Adults: Feasibility Study %A Kuula,Liisa %A Pesonen,Anu-Katriina %+ SleepWell Research Program, University of Helsinki, Haartmaninkatu 3 (PL 21), Helsinki, 00014, Finland, 358 02941 911, liisa.kuula@helsinki.fi %K electroencephalogram %K actigraphy %K polysomnography %K sleep %K heart rate %K rapid eye movements %D 2021 %7 3.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Polysomnography (PSG) is considered the only reliable way to distinguish between different sleep stages. Wearable devices provide objective markers of sleep; however, these devices often rely only on accelerometer data, which do not enable reliable sleep stage detection. The alteration between sleep stages correlates with changes in physiological measures such as heart rate variability (HRV). Utilizing HRV measures may thus increase accuracy in wearable algorithms. Objective: We examined the validity of the Firstbeat sleep analysis method, which is based on HRV and accelerometer measurements. The Firstbeat method was compared against PSG in a sample of healthy adults. Our aim was to evaluate how well Firstbeat distinguishes sleep stages, and which stages are most accurately detected with this method. Methods: Twenty healthy adults (mean age 24.5 years, SD 3.5, range 20-37 years; 50% women) wore a Firstbeat Bodyguard 2 measurement device and a Geneactiv actigraph, along with taking ambulatory SomnoMedics PSG measurements for two consecutive nights, resulting in 40 nights of sleep comparisons. We compared the measures of sleep onset, wake, combined stage 1 and stage 2 (light sleep), stage 3 (slow wave sleep), and rapid eye movement (REM) sleep between Firstbeat and PSG. We calculated the sensitivity, specificity, and accuracy from the 30-second epoch-by-epoch data. Results: In detecting wake, Firstbeat yielded good specificity (0.77), and excellent sensitivity (0.95) and accuracy (0.93) against PSG. Light sleep was detected with 0.69 specificity, 0.67 sensitivity, and 0.69 accuracy. Slow wave sleep was detected with 0.91 specificity, 0.72 sensitivity, and 0.87 accuracy. REM sleep was detected with 0.92 specificity, 0.60 sensitivity, and 0.84 accuracy. There were two measures that differed significantly between Firstbeat and PSG: Firstbeat underestimated REM sleep (mean 18 minutes, P=.03) and overestimated wake time (mean 14 minutes, P<.001). Conclusions: This study supports utilizing HRV alongside an accelerometer as a means for distinguishing sleep from wake and for identifying sleep stages. The Firstbeat method was able to detect light sleep and slow wave sleep with no statistically significant difference to PSG. Firstbeat underestimated REM sleep and overestimated wake time. This study suggests that Firstbeat is a feasible method with sufficient validity to measure nocturnal sleep stage variation. %M 33533726 %R 10.2196/24704 %U http://mhealth.jmir.org/2021/2/e24704/ %U https://doi.org/10.2196/24704 %U http://www.ncbi.nlm.nih.gov/pubmed/33533726 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 1 %P e14326 %T Developing the Accuracy of Vital Sign Measurements Using the Lifelight Software Application in Comparison to Standard of Care Methods: Observational Study Protocol %A Jones,Thomas L %A Heiden,Emily %A Mitchell,Felicity %A Fogg,Carole %A McCready,Sharon %A Pearce,Laurence %A Kapoor,Melissa %A Bassett,Paul %A Chauhan,Anoop J %+ Portsmouth Hospitals NHS Trust, Southwick Hill Road, Portsmouth, United Kingdom, 1 02392286236 ext 77006236, anoop.chauhan@porthosp.nhs.uk %K health technology %K remote monitoring %K vital signs %K patient deterioration %D 2021 %7 28.1.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Vital sign measurements are an integral component of clinical care, but current challenges with the accuracy and timeliness of patient observations can impact appropriate clinical decision making. Advanced technologies using techniques such as photoplethysmography have the potential to automate noncontact physiological monitoring and recording, improving the quality and accessibility of this essential clinical information. Objective: In this study, we aim to develop the algorithm used in the Lifelight software application and improve the accuracy of its estimated heart rate, respiratory rate, oxygen saturation, and blood pressure measurements. Methods: This preliminary study will compare measurements predicted by the Lifelight software with standard of care measurements for an estimated population sample of 2000 inpatients, outpatients, and healthy people attending a large acute hospital. Both training datasets and validation datasets will be analyzed to assess the degree of correspondence between the vital sign measurements predicted by the Lifelight software and the direct physiological measurements taken using standard of care methods. Subgroup analyses will explore how the performance of the algorithm varies with particular patient characteristics, including age, sex, health condition, and medication. Results: Recruitment of participants to this study began in July 2018, and data collection will continue for a planned study period of 12 months. Conclusions: Digital health technology is a rapidly evolving area for health and social care. Following this initial exploratory study to develop and refine the Lifelight software application, subsequent work will evaluate its performance across a range of health characteristics, and extended validation trials will support its pathway to registration as a medical device. Innovations in health technology such as this may provide valuable opportunities for increasing the efficiency and accessibility of vital sign measurements and improve health care services on a large scale across multiple health and care settings. International Registered Report Identifier (IRRID): DERR1-10.2196/14326 %M 33507157 %R 10.2196/14326 %U http://www.researchprotocols.org/2021/1/e14326/ %U https://doi.org/10.2196/14326 %U http://www.ncbi.nlm.nih.gov/pubmed/33507157 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e19346 %T Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study %A Taeger,Johannes %A Bischoff,Stefanie %A Hagen,Rudolf %A Rak,Kristen %+ Department of Otorhinolaryngology, Plastic, Aesthetic and Reconstructive Head and Neck Surgery, University Hospital Würzburg, Josef-Schneider-Straße 11, Würzburg, 97080, Germany, 49 931 201 21489, taeger_j@ukw.de %K facial nerve %K facial palsy %K app development %K medical informatics %K eHealth %K mHealth %K Stennert’s index %K depth mapping camera %K smartphone sensors %D 2021 %7 26.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: For the classification of facial paresis, various systems of description and evaluation in the form of clinician-graded or software-based scoring systems are available. They serve the purpose of scientific and clinical assessment of the spontaneous course of the disease or monitoring therapeutic interventions. Nevertheless, none have been able to achieve universal acceptance in everyday clinical practice. Hence, a quick and precise tool for assessing the functional status of the facial nerve would be desirable. In this context, the possibilities that the TrueDepth camera of recent iPhone models offer have sparked our interest. Objective: This paper describes the utilization of the iPhone’s TrueDepth camera via a specially developed app prototype for quick, objective, and reproducible quantification of facial asymmetries. Methods: After conceptual and user interface design, a native app prototype for iOS was programmed that accesses and processes the data of the TrueDepth camera. Using a special algorithm, a new index for the grading of unilateral facial paresis ranging from 0% to 100% was developed. The algorithm was adapted to the well-established Stennert index by weighting the individual facial regions based on functional and cosmetic aspects. Test measurements with healthy subjects using the app were performed in order to prove the reliability of the system. Results: After the development process, the app prototype had no runtime or buildtime errors and also worked under suboptimal conditions such as different measurement angles, so it met our criteria for a safe and reliable app. The newly defined index expresses the result of the measurements as a generally understandable percentage value for each half of the face. The measurements that correctly rated the facial expressions of healthy individuals as symmetrical in all cases were reproducible and showed no statistically significant intertest variability. Conclusions: Based on the experience with the app prototype assessing healthy subjects, the use of the TrueDepth camera should have considerable potential for app-based grading of facial movement disorders. The app and its algorithm, which is based on theoretical considerations, should be evaluated in a prospective clinical study and correlated with common facial scores. %M 33496670 %R 10.2196/19346 %U http://mhealth.jmir.org/2021/1/e19346/ %U https://doi.org/10.2196/19346 %U http://www.ncbi.nlm.nih.gov/pubmed/33496670 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 4 %N 1 %P e25413 %T Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs %A Tiase,Victoria L %A Sward,Katherine A %A Del Fiol,Guilherme %A Staes,Catherine %A Weir,Charlene %A Cummins,Mollie R %+ The Value Institute, New York–Presbyterian Hospital, 525 East 68th Street, New York, NY, United States, 1 212 305 8865, vtiase@nyp.org %K information needs %K asthma %K symptom management %K mobile health %K patient-generated health data %K pediatrics %K adolescents %D 2021 %7 26.1.2021 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD. Objective: In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration. Methods: We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers. Results: We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit. Conclusions: We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts. %M 33496674 %R 10.2196/25413 %U http://pediatrics.jmir.org/2021/1/e25413/ %U https://doi.org/10.2196/25413 %U http://www.ncbi.nlm.nih.gov/pubmed/33496674 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e25018 %T The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study %A Sobolev,Michael %A Vitale,Rachel %A Wen,Hongyi %A Kizer,James %A Leeman,Robert %A Pollak,J P %A Baumel,Amit %A Vadhan,Nehal P %A Estrin,Deborah %A Muench,Frederick %+ The Partnership to End Addiction, 485 Lexington Avenue, 3rd Floor, New York, NY, 10017, United States, 1 9175320623, fmuench@toendaddiction.org %K impulse control %K impulsivity %K self-regulation %K self-control %K mobile health %K mHealth %K ecological momentary assessment %K active task %K ResearchKit %D 2021 %7 22.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. Objective: The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. Methods: The DMT app was developed using Apple’s ResearchKit (iOS) and Android’s ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app. Results: We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research. Conclusions: This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions. Trial Registration: ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653 %M 33480854 %R 10.2196/25018 %U http://mhealth.jmir.org/2021/1/e25018/ %U https://doi.org/10.2196/25018 %U http://www.ncbi.nlm.nih.gov/pubmed/33480854 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e17691 %T Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review %A Fischer,Florian %A Kleen,Sina %+ Institut of Public Health, Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany, 49 751 501 9441, florian.fischer1@charite.de %K apps %K questionnaire %K survey %K epidemiology %K healthcare %D 2021 %7 22.1.2021 %9 Review %J J Med Internet Res %G English %X Background: The broad availability of smartphones and the number of health apps in app stores have risen in recent years. Health apps have benefits for individuals (eg, the ability to monitor one’s health) as well as for researchers (eg, the ability to collect data in population-based, clinical, and observational studies). Although the number of health apps on the global app market is huge and the associated potential seems to be great, app-based questionnaires for collecting patient-related data have not played an important role in epidemiological studies so far. Objective: This study aims to provide an overview of studies that have collected patient data using an app-based approach, with a particular focus on longitudinal studies. This literature review describes the current extent to which smartphones have been used for collecting (patient) data for research purposes, and the potential benefits and challenges associated with this approach. Methods: We conducted a scoping review of studies that used data collection via apps. PubMed was used to identify studies describing the use of smartphone app questionnaires for collecting data over time. Overall, 17 articles were included in the summary. Results: Based on the results of this scoping review, there are only a few studies that integrate smartphone apps into data-collection approaches. Studies dealing with the collection of health-related data via smartphone apps have mainly been developed with regard to psychosomatic, neurodegenerative, respiratory, and cardiovascular diseases, as well as malign neoplasm. Among the identified studies, the duration of data collection ranged from 4 weeks to 12 months, and the participants’ mean ages ranged from 7 to 69 years. Potential can be seen for real-time information transfer, fast data synchronization (which saves time and increases effectivity), and the possibility of tracking responses longitudinally. Furthermore, smartphone-based data-collection techniques might prevent biases, such as reminder bias or mistakes occurring during manual data transfers. In chronic diseases, real-time communication with physicians and early detection of symptoms enables rapid modifications in disease management. Conclusions: The results indicate that using mobile technologies can help to overcome challenges linked with data collection in epidemiological research. However, further feasibility studies need to be conducted in the near future to test the applicability and acceptance of these mobile apps for epidemiological research in various subpopulations. %M 33480850 %R 10.2196/17691 %U http://www.jmir.org/2021/1/e17691/ %U https://doi.org/10.2196/17691 %U http://www.ncbi.nlm.nih.gov/pubmed/33480850 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e26836 %T Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study %A Mao,Zijun %A Yao,Hong %A Zou,Qi %A Zhang,Weiting %A Dong,Ying %+ College of Public Administration, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China, 86 15871410683, maozijun@hust.edu.cn %K COVID-19 %K digital contact tracing %K emergency management %K graph database %K big data %K visualization %K China %D 2021 %7 22.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. Objective: Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. Methods: A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. Results: An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. Conclusions: During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing. %M 33460389 %R 10.2196/26836 %U http://mhealth.jmir.org/2021/1/e26836/ %U https://doi.org/10.2196/26836 %U http://www.ncbi.nlm.nih.gov/pubmed/33460389 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e24773 %T Adherence of Mobile App-Based Surveys and Comparison With Traditional Surveys: eCohort Study %A Pathiravasan,Chathurangi H %A Zhang,Yuankai %A Trinquart,Ludovic %A Benjamin,Emelia J %A Borrelli,Belinda %A McManus,David D %A Kheterpal,Vik %A Lin,Honghuang %A Sardana,Mayank %A Hammond,Michael M %A Spartano,Nicole L %A Dunn,Amy L %A Schramm,Eric %A Nowak,Christopher %A Manders,Emily S %A Liu,Hongshan %A Kornej,Jelena %A Liu,Chunyu %A Murabito,Joanne M %+ Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Crosstown 2, 201 Massachusetts Ave, Boston, MA, 02118, United States, 1 508 935 3461, murabito@bu.edu %K eCohort %K mobile health %K mHealth %K smartphone %K survey %K app %K Framingham Heart Study %K adherence %K agreement %K cardiovascular disease %D 2021 %7 20.1.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: eCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection. Objective: The aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center. Methods: We defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC). Results: Among the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77). Conclusions: We observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors. %M 33470944 %R 10.2196/24773 %U http://www.jmir.org/2021/1/e24773/ %U https://doi.org/10.2196/24773 %U http://www.ncbi.nlm.nih.gov/pubmed/33470944 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e24467 %T The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App %A Vasiloglou,Maria F %A van der Horst,Klazine %A Stathopoulou,Thomai %A Jaeggi,Michael P %A Tedde,Giulia S %A Lu,Ya %A Mougiakakou,Stavroula %+ ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern, 3008, Switzerland, 41 6327592, stavroula.mougiakakou@artorg.unibe.ch %K mHealth %K dietary assessment %K smartphone %K apps %K human mistakes %K mobile phone %D 2021 %7 13.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Technological advancements have enabled nutrient estimation by smartphone apps such as goFOOD. This is an artificial intelligence–based smartphone system, which uses food images or video captured by the user as input and then translates these into estimates of nutrient content. The quality of the data is highly dependent on the images the user records. This can lead to a major loss of data and impaired quality. Instead of removing these data from the study, in-depth analysis is needed to explore common mistakes and to use them for further improvement of automated apps for nutrition assessment. Objective: The aim of this study is to analyze common mistakes made by participants using the goFOOD Lite app, a version of goFOOD, which was designed for food-logging, but without providing results to the users, to improve both the instructions provided and the automated functionalities of the app. Methods: The 48 study participants were given face-to-face instructions for goFOOD Lite and were asked to record 2 pictures (1 recording) before and 2 pictures (1 recording) after the daily consumption of each food or beverage, using a reference card as a fiducial marker. All pictures that were discarded for processing due to mistakes were analyzed to record the main mistakes made by users. Results: Of the 468 recordings of nonpackaged food items captured by the app, 60 (12.8%) had to be discarded due to errors in the capturing procedure. The principal problems were as follows: wrong fiducial marker or improper marker use (19 recordings), plate issues such as a noncompatible or nonvisible plate (8 recordings), a combination of various issues (17 recordings), and other reasons such as obstacles (hand) in front of the camera or matching recording pairs (16 recordings). Conclusions: No other study has focused on the principal problems in the use of automatic apps for assessing nutritional intake. This study shows that it is important to provide study participants with detailed instructions if high-quality data are to be obtained. Future developments could focus on making it easier to recognize food on various plates from its color or shape and on exploring alternatives to using fiducial markers. It is also essential for future studies to understand the training needed by the participants as well as to enhance the app’s user-friendliness and to develop automatic image checks based on participant feedback. %M 33439139 %R 10.2196/24467 %U http://mhealth.jmir.org/2021/1/e24467/ %U https://doi.org/10.2196/24467 %U http://www.ncbi.nlm.nih.gov/pubmed/33439139 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e19609 %T The Temporal Relationship Between Ecological Pain and Life-Space Mobility in Older Adults With Knee Osteoarthritis: A Smartwatch-Based Demonstration Study %A Mardini,Mamoun T %A Nerella,Subhash %A Kheirkhahan,Matin %A Ranka,Sanjay %A Fillingim,Roger B %A Hu,Yujie %A Corbett,Duane B %A Cenko,Erta %A Weber,Eric %A Rashidi,Parisa %A Manini,Todd M %+ Department of Aging and Geriatric Research, University of Florida, 2004 Mowry Road, PO Box 112610, Gainesville, FL, 32610, United States, 1 352 273 8962, malmardini@ufl.edu %K ecological momentary assessment %K smartwatch app %K life-space mobility %K pain %K knee osteoarthritis %K global positioning system %D 2021 %7 13.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Older adults who experience pain are more likely to reduce their community and life-space mobility (ie, the usual range of places in an environment in which a person engages). However, there is significant day-to-day variability in pain experiences that offer unique insights into the consequences on life-space mobility, which are not well understood. This variability is complex and cannot be captured with traditional recall-based pain surveys. As a solution, ecological momentary assessments record repeated pain experiences throughout the day in the natural environment. Objective: The aim of this study was to examine the temporal association between ecological momentary assessments of pain and GPS metrics in older adults with symptomatic knee osteoarthritis by using a smartwatch platform called Real-time Online Assessment and Mobility Monitor. Methods: Participants (n=19, mean 73.1 years, SD 4.8; female: 13/19, 68%; male: 6/19, 32%) wore a smartwatch for a mean period of 13.16 days (SD 2.94). Participants were prompted in their natural environment about their pain intensity (range 0-10) at random time windows in the morning, afternoon, and evening. GPS coordinates were collected at 15-minute intervals and aggregated each day into excursion, ellipsoid, clustering, and trip frequency features. Pain intensity ratings were averaged across time windows for each day. A random effects model was used to investigate the within and between-person effects. Results: The daily mean pain intensities reported by participants ranged between 0 and 8 with 40% reporting intensities ≥2. The within-person associations between pain intensity and GPS features were more likely to be statistically significant than those observed between persons. Within-person pain intensity was significantly associated with excursion size, and others (excursion span, total distance, and ellipse major axis) showed a statistical trend (excursion span: P=.08; total distance: P=.07; ellipse major axis: P=.07). Each point increase in the mean pain intensity was associated with a 3.06 km decrease in excursion size, 2.89 km decrease in excursion span, 5.71 km decrease total distance travelled per day, 31.4 km2 decrease in ellipse area, 0.47 km decrease ellipse minor axis, and 3.64 km decrease in ellipse major axis. While not statistically significant, the point estimates for number of clusters (P=.73), frequency of trips (P=.81), and homestay (P=.15) were positively associated with pain intensity, and entropy (P=.99) was negatively associated with pain intensity. Conclusions: In this demonstration study, higher intensity knee pain in older adults was associated with lower life-space mobility. Results demonstrate that a custom-designed smartwatch platform is effective at simultaneously collecting rich information about ecological pain and life-space mobility. Such smart tools are expected to be important for remote health interventions that harness the variability in pain symptoms while understanding their impact on life-space mobility. %M 33439135 %R 10.2196/19609 %U http://mhealth.jmir.org/2021/1/e19609/ %U https://doi.org/10.2196/19609 %U http://www.ncbi.nlm.nih.gov/pubmed/33439135 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 8 %N 1 %P e24333 %T Smartphone-Based Self-Reports of Depressive Symptoms Using the Remote Monitoring Application in Psychiatry (ReMAP): Interformat Validation Study %A Goltermann,Janik %A Emden,Daniel %A Leehr,Elisabeth Johanna %A Dohm,Katharina %A Redlich,Ronny %A Dannlowski,Udo %A Hahn,Tim %A Opel,Nils %+ Department of Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, Münster, 48149, Germany, 49 251 8356610, n_opel01@uni-muenster.de %K mobile monitoring %K smartphone %K digital biomarkers %K digital phenotyping %K course of illness %K psychometric quality %K mood disorders %K depression %K affective disorders %K mobile phone %D 2021 %7 12.1.2021 %9 Original Paper %J JMIR Ment Health %G English %X Background: Smartphone-based symptom monitoring has gained increased attention in psychiatric research as a cost-efficient tool for prospective and ecologically valid assessments based on participants’ self-reports. However, a meaningful interpretation of smartphone-based assessments requires knowledge about their psychometric properties, especially their validity. Objective: The goal of this study is to systematically investigate the validity of smartphone-administered assessments of self-reported affective symptoms using the Remote Monitoring Application in Psychiatry (ReMAP). Methods: The ReMAP app was distributed to 173 adult participants of ongoing, longitudinal psychiatric phenotyping studies, including healthy control participants, as well as patients with affective disorders and anxiety disorders; the mean age of the sample was 30.14 years (SD 11.92). The Beck Depression Inventory (BDI) and single-item mood and sleep information were assessed via the ReMAP app and validated with non–smartphone-based BDI scores and clinician-rated depression severity using the Hamilton Depression Rating Scale (HDRS). Results: We found overall high comparability between smartphone-based and non–smartphone-based BDI scores (intraclass correlation coefficient=0.921; P<.001). Smartphone-based BDI scores further correlated with non–smartphone-based HDRS ratings of depression severity in a subsample (r=0.783; P<.001; n=51). Higher agreement between smartphone-based and non–smartphone-based assessments was found among affective disorder patients as compared to healthy controls and anxiety disorder patients. Highly comparable agreement between delivery formats was found across age and gender groups. Similarly, smartphone-based single-item self-ratings of mood correlated with BDI sum scores (r=–0.538; P<.001; n=168), while smartphone-based single-item sleep duration correlated with the sleep item of the BDI (r=–0.310; P<.001; n=166). Conclusions: These findings demonstrate that smartphone-based monitoring of depressive symptoms via the ReMAP app provides valid assessments of depressive symptomatology and, therefore, represents a useful tool for prospective digital phenotyping in affective disorder patients in clinical and research applications. %M 33433392 %R 10.2196/24333 %U https://mental.jmir.org/2021/1/e24333 %U https://doi.org/10.2196/24333 %U http://www.ncbi.nlm.nih.gov/pubmed/33433392 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e18320 %T Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study %A Hao,Yingying %A Ma,Xiao-Kai %A Zhu,Zheng %A Cao,Zhen-Bo %+ School of Kinesiology, Shanghai University of Sport, 399 Changhai Road, Shanghai, 200438, China, 86 2165508160, caozb_edu@yahoo.co.jp %K wrist-wearable activity devices %K accelerometer %K energy expenditure %K step counts %K free-living %D 2021 %7 7.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. Objective: The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. Methods: Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). Results: Pearson correlation coefficients r for 24-hour TEE ranged from .78 (Shine 2, Amazfit) to .96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. Conclusions: Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions. %M 33410757 %R 10.2196/18320 %U http://mhealth.jmir.org/2021/1/e18320/ %U https://doi.org/10.2196/18320 %U http://www.ncbi.nlm.nih.gov/pubmed/33410757 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 12 %P e20642 %T COVID-19–Related Disruptions and Increased mHealth Emergency Use Intention: Experience Sampling Method Study %A Zhang,Zhenduo %A Zhang,Li %A Zheng,Junwei %A Xiao,Huan %A Li,Zhigang %+ School of Economics and Management, Beijing Polytechnic, No 9, Liangshuihe 1st Street, Beijing Economic and Technological Development Zone, Beijing, China, 86 13651205747, lizhigang@bpi.edu.cn %K mobile health services %K emergency use intention %K event disruption %K COVID-19–induced strain %K promotion regulatory focus %K mHealth %K COVID-19 %D 2020 %7 30.12.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The COVID-19 pandemic has become a global public health event, which has raised concerns regarding individuals’ health. Individuals need to cope with the COVID-19 pandemic with guidelines on symptom recognition, home isolation, and maintain mental health. Besides routine use of mobile health (mHealth) such as accessing information to keep healthy, individuals can use mHealth services in situations requiring urgent medical care, which is defined as mHealth emergency use. It is not known whether individuals have increased their daily mHealth services emergency use as a result of disruptions caused by the COVID-19 pandemic. Objective: The purpose of this diary analysis study is to assess the influences of daily disruptions related to the COVID-19 pandemic on individuals’ mHealth emergency use. The secondary purpose of this study is to explore the mediating role of COVID-19–induced strain and the moderating role of promotion regulatory focus in the relationship between daily disruptions of COVID-19 and mHealth emergency use. Drawing from the cognitive activation theory of stress, we investigated the underlying mechanism and boundary condition of the influence of COVID-19–related disruptions on daily mHealth emergency use. Methods: To test the proposed model, this study adopts the experience sampling method to collect daily data. The experience sampling method helps researchers to capture participants’ fluctuations in emotions, mental engagement in an activity, and experienced stress. This study collected 550 cases nested in 110 samples in mainland China to test the conceptual model. In addition, we employed hierarchical linear modeling analysis to test the effect of COVID-19–related disruptions on mHealth emergency use. Results: We found that COVID-19–related disruptions increased COVID-19–induced strain (γ=0.24, P<.001) and mHealth emergency use on a daily basis (γ=0.28, P<.001). COVID-19–induced daily strain mediated this relationship (effect=0.09, 95% CI 0.05-0.14). Promotion regulatory focus moderated the relationship between COVID-19–induced strain and mHealth emergency use (γ=0.35, P=.02). In addition, the indirect relationship between disruptions and mHealth emergency use intentions through COVID-19–induced strain is contingent upon promotion regulatory focus: this relationship was stronger in those with high promotion regulatory focus (effect=0.12, 95% CI 0.06-0.19) than in those with low promotion regulatory focus (effect=0.06, 95% CI 0.02-0.11). Conclusions: Event disruption of the COVID-19 pandemic induced mHealth emergency use intention through increased psychological strain. Furthermore, individuals’ promotion regulatory focus amplified this indirect relationship. Our findings extend our understanding of the factors underlying mHealth emergency use intention and illustrate the potential contingent role of promotion regulatory focus in the cognitive activation theory of stress. This study also opens avenues for future research on mHealth emergency use intention in other countries and cultural settings. %M 33315579 %R 10.2196/20642 %U http://mhealth.jmir.org/2020/12/e20642/ %U https://doi.org/10.2196/20642 %U http://www.ncbi.nlm.nih.gov/pubmed/33315579 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 12 %P e19712 %T Effectiveness of a Mobile-Based Influenza-Like Illness Surveillance System (FluMob) Among Health Care Workers: Longitudinal Study %A Lwin,May Oo %A Lu,Jiahui %A Sheldenkar,Anita %A Panchapakesan,Chitra %A Tan,Yi-Roe %A Yap,Peiling %A Chen,Mark I %A Chow,Vincent TK %A Thoon,Koh Cheng %A Yung,Chee Fu %A Ang,Li Wei %A Ang,Brenda SP %+ School of New Media and Communication, Tianjin University, No. 92 Weijin Road, Tianjin, 300072, China, 86 18222418810, lujiahui@tju.edu.cn %K participatory surveillance %K syndromic surveillance %K mobile phone %K influenza-like illness %K health care workers %D 2020 %7 7.12.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Existing studies have suggested that internet-based participatory surveillance systems are a valid sentinel for influenza-like illness (ILI) surveillance. However, there is limited scientific knowledge on the effectiveness of mobile-based ILI surveillance systems. Previous studies also adopted a passive surveillance approach and have not fully investigated the effectiveness of the systems and their determinants. Objective: The aim of this study was to assess the efficiency of a mobile-based surveillance system of ILI, termed FluMob, among health care workers using a targeted surveillance approach. Specifically, this study evaluated the effectiveness of the system for ILI surveillance pertaining to its participation engagement and surveillance power. In addition, we aimed to identify the factors that can moderate the effectiveness of the system. Methods: The FluMob system was launched in two large hospitals in Singapore from April 2016 to March 2018. A total of 690 clinical and nonclinical hospital staff participated in the study for 18 months and were prompted via app notifications to submit a survey listing 18 acute respiratory symptoms (eg, fever, cough, sore throat) on a weekly basis. There was a period of study disruption due to maintenance of the system and the end of the participation incentive between May and July of 2017. Results: On average, the individual submission rate was 41.4% (SD 24.3%), with a rate of 51.8% (SD 26.4%) before the study disruption and of 21.5% (SD 30.6%) after the disruption. Multivariable regression analysis showed that the adjusted individual submission rates were higher for participants who were older (<30 years, 31.4% vs 31-40 years, 40.2% [P<.001]; 41-50 years, 46.0% [P<.001]; >50 years, 39.9% [P=.01]), ethnic Chinese (Chinese, 44.4% vs non-Chinese, 34.7%; P<.001), and vaccinated against flu in the past year (vaccinated, 44.6% vs nonvaccinated, 34.4%; P<.001). In addition, the weekly ILI incidence was 1.07% on average. The Pearson correlation coefficient between ILI incidence estimated by FluMob and that reported by Singapore Ministry of Health was 0.04 (P=.75) with all data and was 0.38 (P=.006) including only data collected before the study disruption. Health care workers with higher risks of ILI and influenza such as women, non-Chinese, allied health staff, those who had children in their households, not vaccinated against influenza, and reported allergy demonstrated higher surveillance correlations. Conclusions: Mobile-based ILI surveillance systems among health care workers can be effective. However, proper operation of the mobile system without major disruptions is vital for the engagement of participants and the persistence of surveillance power. Moreover, the effectiveness of the mobile surveillance system can be moderated by participants’ characteristics, which highlights the importance of targeted disease surveillance that can reduce the cost of recruitment and engagement. %M 33284126 %R 10.2196/19712 %U https://mhealth.jmir.org/2020/12/e19712 %U https://doi.org/10.2196/19712 %U http://www.ncbi.nlm.nih.gov/pubmed/33284126 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 12 %P e16746 %T Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation %A Wang,Chi-Te %A Han,Ji-Yan %A Fang,Shih-Hau %A Lai,Ying-Hui %+ Department of Biomedical Engineering, National Yang-Ming University, No155, Sec 2, Linong Street, Taipei, 112, Taiwan, 886 228267021, yh.lai@gm.ym.edu.tw %K voice disorder %K speech envelope %K phonation habits %K background noise %K noise reduction %K adaptive threshold %K dosimetry %K phonotrauma %D 2020 %7 3.12.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. Objective: This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. Methods: We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. Results: The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. Conclusions: This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users. %M 33270033 %R 10.2196/16746 %U https://mhealth.jmir.org/2020/12/e16746 %U https://doi.org/10.2196/16746 %U http://www.ncbi.nlm.nih.gov/pubmed/33270033 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 12 %P e21671 %T User Perceptions and Experiences of an Interactive Voice Response Mobile Phone Survey Pilot in Uganda: Qualitative Study %A Tweheyo,Raymond %A Selig,Hannah %A Gibson,Dustin G %A Pariyo,George William %A Rutebemberwa,Elizeus %+ Department of Health Policy Planning and Management, Makerere University School of Public Health, Mulago Hill Road, P O Box, 7072, Kampala, 256, Uganda, 256 772466695, rtweheyo@musph.ac.ug %K interactive voice response %K noncommunicable diseases %K qualitative %K Uganda %D 2020 %7 3.12.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: With the growing burden of noncommunicable diseases in low- and middle- income countries, the World Health Organization recommended a stepwise approach of surveillance for noncommunicable diseases. This is expensive to conduct on a frequent basis and using interactive voice response mobile phone surveys has been put forth as an alternative. However, there is limited evidence on how to design and deliver interactive voice response calls that are robust and acceptable to respondents. Objective: This study aimed to explore user perceptions and experiences of receiving and responding to an interactive voice response call in Uganda in order to adapt and refine the instrument prior to national deployment. Methods: A qualitative study design was used and comprised a locally translated audiorecorded interactive voice response survey delivered in 4 languages to 59 purposively selected participants' mobile phones in 5 survey rounds guided by data saturation. The interactive voice response survey had modules on sociodemographic characteristics, physical activity, fruit and vegetable consumption, diabetes, and hypertension. After the interactive voice response survey, study staff called participants back and used a semistructured interview to collect information on the participant’s perceptions of interactive voice response call audibility, instruction clarity, interview pace, language courtesy and appropriateness, the validity of questions, and the lottery incentive. Descriptive statistics were used for the interactive voice response survey, while a framework analysis was used to analyze qualitative data. Results: Key findings that favored interactive voice response survey participation or completion included preference for brief surveys of 10 minutes or shorter, preference for evening calls between 6 PM and 10 PM, preference for courteous language, and favorable perceptions of the lottery-type incentive. While key findings curtailing participation were suspicion about the caller’s identity, unclear voice, confusing skip patterns, difficulty with the phone interface such as for selecting inappropriate digits for both ordinary and smartphones, and poor network connectivity for remote and rural participants. Conclusions: Interactive voice response surveys should be as brief as possible and considerate of local preferences to increase completion rates. Caller credibility needs to be enhanced through either masking the caller or prior community mobilization. There is need to evaluate the preferred timing of interactive voice response calls, as the finding of evening call preference is inconclusive and might be contextual. %M 33270037 %R 10.2196/21671 %U https://formative.jmir.org/2020/12/e21671 %U https://doi.org/10.2196/21671 %U http://www.ncbi.nlm.nih.gov/pubmed/33270037 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 12 %P e24066 %T Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study %A Richter,Maike Frederike %A Storck,Michael %A Blitz,Rogério %A Goltermann,Janik %A Seipp,Juliana %A Dannlowski,Udo %A Baune,Bernhard T %A Dugas,Martin %A Opel,Nils %+ Department of Psychiatry, University of Münster, Albert-Schweitzer-Str. 11, Münster, 48149, Germany, 49 2518358160, n_opel01@uni-muenster.de %K affective disorders %K digital data collection %K psychiatry %K P4 medicine %D 2020 %7 1.12.2020 %9 Original Paper %J JMIR Ment Health %G English %X Background: Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. Objective: The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. Methods: We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. Results: Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Conclusions: Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care. %M 33258791 %R 10.2196/24066 %U https://mental.jmir.org/2020/12/e24066 %U https://doi.org/10.2196/24066 %U http://www.ncbi.nlm.nih.gov/pubmed/33258791 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 9 %N 4 %P e16376 %T Leveraging Walking Performance to Understand Work Fatigue Among Young Adults: Mixed-Methods Study %A Yan,Xinghui %A Rau,Pei-Luen Patrick %A Zhong,Runting %+ Department of Industrial Engineering, Shunde Building, Tsinghua University, Beijing, 100084, China, 86 010 6277 6664, rpl@tsinghua.edu.cn %K work fatigue %K fatigability %K walking performance %K 6MWT %K mobile health %D 2020 %7 13.11.2020 %9 Original Paper %J Interact J Med Res %G English %X Background: Work fatigue negatively impacts personal health in the long term. Prior research has indicated the possibility of leveraging both walking parameters and perceptual measures to assess a person’s fatigue status. However, an effective and ubiquitous approach to assessing work fatigue in young adults remains unexplored. Objective: The goals of this paper were to (1) explore how walking rhythms and multiple streams of data, including reaction time, self-reports, and an activity diary, reflect work-induced fatigue in the lab setting; (2) identify the relationship between objective performance and subjective perception in indicating fatigue status and fatigability; and (3) propose a mobile-based assessment for work-induced fatigue that uses multiple measurements. Methods: We conducted a 2-day in-lab study to measure participants’ fatigue status using multiple measurements, including the stair climb test (SCT), the 6-minute walk test (6MWT), and the reaction time test. Both the SCT and the 6MWT were conducted at different points in time and under 2 conditions (measurement time, including prior to and after work, and pace, including normal and fast). Participants reported their fatigue perception through questionnaires completed before conducting walking tests and in an activity diary recorded over a week. Walking performance data were collected by a smartphone with a built-in 3-axis accelerometer. To examine the effect of fatigability on walking performance, we first clustered participants into 2 groups based on their reported mental fatigue level in the entry surveys and then compared their walking performance using a generalized linear model (GLM). The reaction time was examined using a 2-way repeated-measures GLM. We conducted semistructured interviews to understand participants’ fatigue perception after each day’s walking tests. Results: All participants (N=26; mean age 24.68 years) were divided into 2 groups—the fatigue-sensitive group (11/26, 42%) and the fatigue-nonsensitive group (15/26, 58%)—based on their mental subscores from 3 entry surveys: Fatigue Scale-14, Three-Dimensional Work Fatigue Inventory, and Fatigue Self-Assessment Scale (FSAS). The fatigue-sensitive group reported a significantly higher FSAS score in the before-work setting (t50=–3.361; P=.001). The fatigue-sensitive group covered fewer steps than the fatigue-nonsensitive group (β1=–0.099; SE 0.019; t1=–5.323; P<.001) and had a higher step-to-step time variability in the 6MWT (β1=9.61 × 10–4; t1=2.329; P=.02). No strong correlation between subjective and objective measurements was observed in the study. Conclusions: Walking parameters, including step counts and step-to-step time variability, and some selected scales (eg, FSAS) were found to reflect participants’ work-induced fatigue. Overall, our work suggests the opportunity of employing mobile-based walking measurements to indicate work fatigue among young adults. %M 33185557 %R 10.2196/16376 %U http://www.i-jmr.org/2020/4/e16376/ %U https://doi.org/10.2196/16376 %U http://www.ncbi.nlm.nih.gov/pubmed/33185557 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e20723 %T Using a Mobile App-Based International Classification of Functioning, Disability, and Health Set to Assess the Functioning of Spinal Cord Injury Patients: Rasch Analysis %A Jia,Mengmeng %A Tang,Jie %A Xie,Sumei %A He,Xiaokuo %A Wang,Yingmin %A Liu,Ting %A Yan,Tiebin %A Li,Kun %+ School of Nursing, Sun Yat-sen University, No. 74 Zhong Shan Second Road, Guangzhou, , China, 86 138 22206519, likun22@mail.sysu.edu.cn %K International Classification of Functioning, Disability and Health %K spinal cord injuries %K mobile health app %K Rasch analysis %D 2020 %7 11.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The International Classification of Functioning, Disability, and Health (ICF) is a unified system of functioning terminology that has been used to develop electronic health records and assessment instruments. Its application has been limited, however, by its complex terminology, numerous categories, uncertain operationalization, and the training required to use it well. Together is a mobile health app designed to extend medical support to the families of spinal cord injury (SCI) patients in China. The app’s core framework is a set of only 31 ICF categories. The app also provides rating guidelines and automatically transforms routine assessment results to the terms of the ICF qualifiers. Objective: The goal of the research is to examine the suitability of the ICF set used in the app Together for use as an instrument for assessing the functioning of SCI patients. Methods: A cross-sectional study was conducted including 112 SCI patients recruited before discharge from four rehabilitation centers in China between May 2018 and October 2019. Nurses used the app to assess patient functioning in face-to-face interviews. The resulting data were then subjected to Rasch analysis. Results: After deleting two categories (family relationships and socializing) and one personal factor (knowledge about spinal cord injury) that did not fit the Rasch model, the body functions and body structures, activities and participation, and contextual factors components of the ICF exhibited adequate fit to the Rasch model. All three demonstrated acceptable person separation indices. The 28 categories retained in the set were free of differential item functioning by gender, age, education level, or etiology. Conclusions: Together overcomes some of the obstacles to practical application of the ICF. The app is a reliable assessment tool for assessing functioning after spinal cord injury. %M 33174860 %R 10.2196/20723 %U https://mhealth.jmir.org/2020/11/e20723 %U https://doi.org/10.2196/20723 %U http://www.ncbi.nlm.nih.gov/pubmed/33174860 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e22006 %T The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review %A Beauchamp,Ulrikke Lyng %A Pappot,Helle %A Holländer-Mieritz,Cecilie %+ Department of Oncology, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, Copenhagen, 2100, Denmark, 45 35453545, cecilie.hollaender-mieritz@regionh.dk %K cancer treatment %K wearables %K adherence %K sensor technology %D 2020 %7 11.11.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Interest in the use of wearables in medical care is increasing. Wearables can be used to monitor different variables, such as vital signs and physical activity. A crucial point for using wearables in oncology is if patients already under the burden of severe disease and oncological treatment can accept and adhere to the device. At present, there are no specific recommendations for the use of wearables in oncology, and little research has examined the purpose of using wearables in oncology. Objective: The purpose of this review is to explore the use of wearables in clinical trials during cancer treatment, with a special focus on adherence. Methods: PubMed and EMBASE databases were searched prior and up to October 3, 2019, with no limitation in the date of publication. The search strategy was aimed at studies using wearables for monitoring adult patients with cancer during active antineoplastic treatment. Studies were screened independently by 2 reviewers by title and abstract, selected for inclusion and exclusion, and the full-text was assessed for eligibility. Data on study design, type of wearable used, primary outcome, adherence, and device outcome were extracted. Results were presented descriptively. Results: Our systematic search identified 1269 studies, of which 25 studies met our inclusion criteria. The types of cancer represented in the studies were breast (7/25), gastrointestinal (4/25), lung (4/25), and gynecologic (1/25); 9 studies had multiple types of cancer. Oncologic treatment was primarily chemotherapy (17/25). The study-type distribution was pilot/feasibility study (12/25), observational study (10/25), and randomized controlled trial (3/25). The median sample size was 40 patients (range 7-180). All studies used a wearable with an accelerometer. Adherence varied across studies, from 60%-100% for patients wearing the wearable/evaluable sensor data and 45%-94% for evaluable days, but was differently measured and reported. Of the 25 studies, the most frequent duration for planned monitoring with a wearable was 8-30 days (13/25). Topics for wearable outcomes were physical activity (19/25), circadian rhythm (8/25), sleep (6/25), and skin temperature (1/25). Patient-reported outcomes (PRO) were used in 17 studies; of the 17 PRO studies, only 9 studies reported correlations between the wearable outcome and the PRO. Conclusions: We found that definitions of outcome measures and adherence varied across studies, and limited consensus among studies existed on which variables to monitor during treatment. Less heterogeneity, better consensus in terms of the use of wearables, and established standards for the definitions of wearable outcomes and adherence would improve comparisons of outcomes from studies using wearables. Adherence, and the definition of such, seems crucial to conclude on data from wearable studies in oncology. Additionally, research using advanced wearable devices and active use of the data are encouraged to further explore the potential of wearables in oncology during treatment. Particularly, randomized clinical studies are warranted to create consensus on when and how to implement in oncological practice. %M 33174852 %R 10.2196/22006 %U http://mhealth.jmir.org/2020/11/e22006/ %U https://doi.org/10.2196/22006 %U http://www.ncbi.nlm.nih.gov/pubmed/33174852 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e19696 %T Smart Data Collection for the Assessment of Treatment Effects in Irritable Bowel Syndrome: Observational Study %A Weerts,Zsa Zsa R M %A Heinen,Koert G E %A Masclee,Ad A M %A Quanjel,Amber B A %A Winkens,Bjorn %A Vork,Lisa %A Rinkens,Paula E L M %A Jonkers,Daisy M A E %A Keszthelyi,Daniel %+ Division Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Universiteitssingel 50, Maastricht, 6202 AZ, Netherlands, z.weerts@maastrichtuniversity.nl %K irritable bowel syndrome %K digital diary %K smartphone application %K mobile phone application %K mhealth %K e-health %K compliance %K electronic case report file %K patient reported outcome measures %K peppermint oil %K PERSUADE study. %D 2020 %7 2.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: End-of-day symptom diaries are recommended by drug regulatory authorities to assess treatment response in patients with irritable bowel syndrome. We developed a smartphone app to measure treatment response. Objective: Because the employment of an app to measure treatment response in irritable bowel syndrome is relatively new, we aimed to explore patients’ adherence to diary use and characteristics associated with adherence. Methods: A smartphone app was developed to serve as a symptom diary. Patients with irritable bowel syndrome (based on Rome IV criteria) were instructed to fill out end-of-day diary questionnaires during an 8-week treatment. Additional online questionnaires assessed demographics, irritable bowel syndrome symptom severity, and psychosocial comorbidities. Adherence rate to the diary was defined as the percentage of days completed out of total days. Adherence to the additional web-based questionnaires was also assessed. Results: Overall, 189 patients were included (age: mean 34.0 years, SD 13.3 years; female: 147/189, 77.8%; male: 42/189, 22.2%). The mean adherence rate was 87.9% (SD 9.4%). However, adherence to the diary decreased over time (P<.001). No significant association was found between adherence and gender (P=.84), age (P=.22), or education level (lower education level: P=.58, middle education level: P=.46, versus high education level), while higher anxiety scores were associated with lower adherence (P=.03). Adherence to the online questionnaires was also high (>99%). Missing data due to technical issues were limited. Conclusions: The use of a smartphone app as a symptom diary to assess treatment response resulted in high patient adherence. The data-collection framework described led to standardized data collection with excellent completeness and can be used for future randomized controlled trials. Due to the slight decrease in adherence to diary use throughout the study, this method might be less suitable for longer trials. %M 33030150 %R 10.2196/19696 %U https://mhealth.jmir.org/2020/11/e19696 %U https://doi.org/10.2196/19696 %U http://www.ncbi.nlm.nih.gov/pubmed/33030150 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e19874 %T Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation %A Marini,Gabriele %A Tag,Benjamin %A Goncalves,Jorge %A Velloso,Eduardo %A Jurdak,Raja %A Capurro,Daniel %A McCarthy,Clare %A Shearer,William %A Kostakos,Vassilis %+ University of Melbourne, Grattan Street,, Melbourne, 3052, Australia, 61 390358966, marinig@student.unimelb.edu.au %K localization %K indoor %K efficiency %K Bluetooth %K occupancy %K mobility %K metrics %K smartphone %K mobile phone %D 2020 %7 27.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. Objective: The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. Methods: We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. Results: Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. Conclusions: Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness. %M 33107838 %R 10.2196/19874 %U http://mhealth.jmir.org/2020/10/e19874/ %U https://doi.org/10.2196/19874 %U http://www.ncbi.nlm.nih.gov/pubmed/33107838 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 8 %N 4 %P e20126 %T A Tablet App for Handwriting Skill Screening at the Preliteracy Stage: Instrument Validation Study %A Dui,Linda Greta %A Lunardini,Francesca %A Termine,Cristiano %A Matteucci,Matteo %A Stucchi,Natale Adolfo %A Borghese,Nunzio Alberto %A Ferrante,Simona %+ Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Colombo 40, Milan, 20133, Italy, 39 0223999690, lindagreta.dui@polimi.it %K serious game %K tablet %K isochrony %K homothety %K speed-accuracy tradeoff %K steering law %K writing %K prevention %D 2020 %7 22.10.2020 %9 Original Paper %J JMIR Serious Games %G English %X Background: Difficulties in handwriting, such as dysgraphia, impact several aspects of a child’s everyday life. Current methodologies for the detection of such difficulties in children have the following three main weaknesses: (1) they are prone to subjective evaluation; (2) they can be administered only when handwriting is mastered, thus delaying the diagnosis and the possible adoption of countermeasures; and (3) they are not always easily accessible to the entire community. Objective: This work aims at developing a solution able to: (1) quantitatively measure handwriting features whose alteration is typically seen in children with dysgraphia; (2) enable their study in a preliteracy population; and (3) leverage a standard consumer technology to increase the accessibility of both early screening and longitudinal monitoring of handwriting difficulties. Methods: We designed and developed a novel tablet-based app Play Draw Write to assess potential markers of dysgraphia through the quantification of the following three key handwriting laws: isochrony, homothety, and speed-accuracy tradeoff. To extend such an approach to a preliteracy age, the app includes the study of the laws in terms of both word writing and symbol drawing. The app was tested among healthy children with mastered handwriting (third graders) and those at a preliterate age (kindergartners). Results: App testing in 15 primary school children confirmed that the three laws hold on the tablet surface when both writing words and drawing symbols. We found significant speed modulation according to size (P<.001), no relevant changes to fraction time for 67 out of 70 comparisons, and significant regression between movement time and index of difficulty for 44 out of 45 comparisons (P<.05, R2>0.28, 12 degrees of freedom). Importantly, the three laws were verified on symbols among 19 kindergartners. Results from the speed-accuracy exercise showed a significant evolution with age of the global movement time (circle: P=.003, square: P<.001, word: P=.001), the goodness of fit of the regression between movement time and accuracy constraints (square: P<.001, circle: P=.02), and the index of performance (square: P<.001). Our findings show that homothety, isochrony, and speed-accuracy tradeoff principles are present in children even before handwriting acquisition; however, some handwriting-related skills are partially refined with age. Conclusions: The designed app represents a promising solution for the screening of handwriting difficulties, since it allows (1) anticipation of the detection of alteration of handwriting principles at a preliteracy age and (2) provision of broader access to the monitoring of handwriting principles. Such a solution potentially enables the selective strengthening of lacking abilities before they exacerbate and affect the child’s whole life. %M 33090110 %R 10.2196/20126 %U http://games.jmir.org/2020/4/e20126/ %U https://doi.org/10.2196/20126 %U http://www.ncbi.nlm.nih.gov/pubmed/33090110 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e16485 %T Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App: Digital Perinatal Outcome Audit %A Crehan,Caroline %A Kesler,Erin %A Chikomoni,Indira Angela %A Sun,Kristi %A Dube,Queen %A Lakhanpaul,Monica %A Heys,Michelle %+ UCL-Great Ormond Street Hospital Institute of Child Health, University College London, 30 Guilford Street, Holborn, London, WC1N 1EH, United Kingdom, 44 (0)2079052212, m.heys@ucl.ac.uk %K infant, newborn %K mHealth %K data collection %K clinical audit %K digital health %K low income population %K mobile phone %D 2020 %7 21.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) is showing increasing potential to address health outcomes in underresourced settings as smartphone coverage increases. The NeoTree is an mHealth app codeveloped in Malawi to improve the quality of newborn care at the point of admission to neonatal units. When collecting vital demographic and clinical data, this interactive platform provides clinical decision support and training for the end users (health care professionals [HCPs]), according to evidence-based national and international guidelines. Objective: This study aims to examine 1 month’s data collected using NeoTree in an outcome audit of babies admitted to a district-level neonatal nursery in Malawi and to demonstrate proof of concept of digital outcome audit data in this setting. Methods: Using a phased approach over 1 month (November 21-December 19, 2016), frontline HCPs were trained and supported to use NeoTree to admit newborns. Discharge data were collected by the research team using a discharge form within NeoTree, called NeoDischarge. We conducted a descriptive analysis of the exported pseudoanonymized data and presented it to the newborn care department as a digital outcome audit. Results: Of 191 total admissions, 134 (70.2%) admissions were completed using NeoTree, and 129 (67.5%) were exported and analyzed. Of 121 patients for whom outcome data were available, 102 (84.3%) were discharged alive. The overall case fatality rate was 93 per 1000 admitted babies. Prematurity with respiratory distress syndrome, birth asphyxia, and neonatal sepsis contributed to 25% (3/12), 58% (7/12), and 8% (1/12) of deaths, respectively. Data were more than 90% complete for all fields. Deaths may have been underreported because of phased implementation and some families of babies with imminent deaths self-discharging home. Detailed characterization of the data enabled departmental discussion of modifiable factors for quality improvement, for example, improved thermoregulation of infants. Conclusions: This digital outcome audit demonstrates that data can be captured digitally at the bedside by HCPs in underresourced newborn facilities, and these data can contribute to a meaningful review of the quality of care, outcomes, and potential modifiable factors. Coverage may be improved during future implementation by streamlining the admission process to be solely via digital format. Our results present a new methodology for newborn audits in low-resource settings and are a proof of concept for a novel newborn data system in these settings. %M 33084581 %R 10.2196/16485 %U https://mhealth.jmir.org/2020/10/e16485 %U https://doi.org/10.2196/16485 %U http://www.ncbi.nlm.nih.gov/pubmed/33084581 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 7 %N 2 %P e17986 %T The Impact of Reducing the Number of Wearable Devices on Measuring Gait in Parkinson Disease: Noninterventional Exploratory Study %A Czech,Matthew %A Demanuele,Charmaine %A Erb,Michael Kelley %A Ramos,Vesper %A Zhang,Hao %A Ho,Bryan %A Patel,Shyamal %+ Digital Medicine & Translational Imaging, Early Clinical Development, Pfizer Inc, 610 Main Street, Cambridge, MA, 02139, United States, 1 617 512 7885, shyamal.patel@pfizer.com %K gait %K Parkinson disease %K wearable sensors %K digital medicine %D 2020 %7 21.10.2020 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Measuring free-living gait using wearable devices may offer higher granularity and temporal resolution than the current clinical assessments for patients with Parkinson disease (PD). However, increasing the number of devices worn on the body adds to the patient burden and impacts the compliance. Objective: This study aimed to investigate the impact of reducing the number of wearable devices on the ability to assess gait impairments in patients with PD. Methods: A total of 35 volunteers with PD and 60 healthy volunteers performed a gait task during 2 clinic visits. Participants with PD were assessed in the On and Off medication state using the Movement Disorder Society version of the Unified Parkinson Disease Rating Scale (MDS-UPDRS). Gait features derived from a single lumbar-mounted accelerometer were compared with those derived using 3 and 6 wearable devices for both participants with PD and healthy participants. Results: A comparable performance was observed for predicting the MDS-UPDRS gait score using longitudinal mixed-effects model fit with gait features derived from a single (root mean square error [RMSE]=0.64; R2=0.53), 3 (RMSE=0.64; R2=0.54), and 6 devices (RMSE=0.54; R2=0.65). In addition, MDS-UPDRS gait scores predicted using all 3 models differed significantly between On and Off motor states (single device, P=.004; 3 devices, P=.004; 6 devices, P=.045). Conclusions: We observed a marginal benefit in using multiple devices for assessing gait impairments in patients with PD when compared with gait features derived using a single lumbar-mounted accelerometer. The wearability burden associated with the use of multiple devices may offset gains in accuracy for monitoring gait under free-living conditions. %M 33084585 %R 10.2196/17986 %U http://rehab.jmir.org/2020/2/e17986/ %U https://doi.org/10.2196/17986 %U http://www.ncbi.nlm.nih.gov/pubmed/33084585 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e23197 %T Characteristics and Symptoms of App Users Seeking COVID-19–Related Digital Health Information and Remote Services: Retrospective Cohort Study %A Perlman,Amichai %A Vodonos Zilberg,Alina %A Bak,Peter %A Dreyfuss,Michael %A Leventer-Roberts,Maya %A Vurembrand,Yael %A Jeffries,Howard E %A Fisher,Eyal %A Steuerman,Yael %A Namir,Yinat %A Goldschmidt,Yaara %A Souroujon,Daniel %+ K Health Inc, 298 Fifth Avenue, 7th Floor, New York, NY, 10001, United States, 1 616 304 4654, daniel@khealth.ai %K digital health %K remote care %K symptom checker %K telemedicine %K COVID-19 %K symptom %K cohort study %K self-reported %K online tool %D 2020 %7 20.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice. Objective: The aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19–related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools. Methods: This study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)–driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19–related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician. Results: During the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department. Conclusions: Our results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19. %M 32961527 %R 10.2196/23197 %U http://www.jmir.org/2020/10/e23197/ %U https://doi.org/10.2196/23197 %U http://www.ncbi.nlm.nih.gov/pubmed/32961527 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e22080 %T Implementation of a Home-Based mHealth App Intervention Program With Human Mediation for Swallowing Tongue Pressure Strengthening Exercises in Older Adults: Longitudinal Observational Study %A Kim,HyangHee %A Cho,Nam-Bin %A Kim,Jinwon %A Kim,Kyung Min %A Kang,Minji %A Choi,Younggeun %A Kim,Minjae %A You,Heecheon %A Nam,Seok In %A Shin,Soyeon %+ Graduate Program in Speech-Language Pathology, Yonsei University College of Medicine, 11-12 Yondaedongmun-gil, Seodaemun-gu, Seoul, 03721, Republic of Korea, 82 2 2228 3900, h.kim@yonsei.ac.kr %K mHealth %K older adults %K swallowing tongue pressure %K Iowa Oral Performance Instrument %K app %K swallowing disorders %K swallowing maneuver %K effortful prolonged swallowing %K effortful pitch glide %K effortful tongue rotation %D 2020 %7 16.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Tongue pressure is an effective index of swallowing function, and it decreases with aging and disease progression. Previous research has shown beneficial effects of swallowing exercises combined with myofunctional tongue-strengthening therapy on tongue function. Tongue exercises delivered through mobile health (mHealth) technologies have the potential to advance health care in the digital age to be more efficient for people with limited resources, especially older adults. Objective: The purpose of this study is to explore the immediate and long-term maintenance effects of an 8-week home-based mHealth app intervention with biweekly (ie, every 2 weeks) human mediation aimed at improving the swallowing tongue pressure in older adults. Methods: We developed an mHealth app intervention that was used for 8 weeks (3 times/day, 5 days/week, for a total of 120 sessions) by 11 community-dwelling older adults (10 women; mean age 75.7 years) who complained of swallowing difficulties. The app included a swallowing monitoring and intervention protocol with 3 therapy maneuvers: effortful prolonged swallowing, effortful pitch glide, and effortful tongue rotation. The 8-week intervention was mediated by biweekly face-to-face meetings to monitor each participant’s progress and ability to implement the training sessions according to the given protocol. Preintervention and postintervention isometric and swallowing tongue pressures were measured using the Iowa Oral Performance Instrument. We also investigated the maintenance effects of the intervention on swallowing tongue pressure at 12 weeks postintervention. Results: Of the 11 participants, 8 adhered to the home-based 8-week app therapy program with the optimal intervention dosage. At the main trial end point (ie, 8 weeks) of the intervention program, the participants demonstrated a significant increase in swallowing tongue pressure (median 17.5 kPa before the intervention and 26.5 kPa after the intervention; P=.046). However, long-term maintenance effects of the training program on swallowing tongue pressure at 12 weeks postintervention were not observed. Conclusions: Swallowing tongue pressure is known to be closely related to dysphagia symptoms. This is the first study to demonstrate the effectiveness of the combined methods of effortful prolonged swallowing, effortful pitch glide, and effortful tongue rotation using mobile app training accompanied by biweekly human mediation in improving swallowing tongue pressure in older adults. The mHealth app is a promising platform that can be used to deliver effective and convenient therapeutic service to vulnerable older adults. To investigate the therapeutic efficacy with a larger sample size and observe the long-term effects of the intervention program, further studies are warranted. International Registered Report Identifier (IRRID): RR2-10.2196/19585 %M 33012704 %R 10.2196/22080 %U http://mhealth.jmir.org/2020/10/e22080/ %U https://doi.org/10.2196/22080 %U http://www.ncbi.nlm.nih.gov/pubmed/33012704 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e19589 %T Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study %A Wang,Wenjun %A Wang,Yikai %A Zhang,Xin %A Jia,Xiaoli %A Li,Yaping %A Dang,Shuangsuo %+ Second Affiliated Hospital of Xi'an Jiaotong University, 157 Xiwu Road, Xi'an, 710004, China, 86 02987679688, dangshuangsuo123@xjtu.edu.cn %K novel coronavirus %K SARS %K SARS-CoV-2 %K COVID-19 %K social media %K WeChat %K early detection %K surveillance %K infodemiology %K infoveillance %D 2020 %7 5.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future. Objective: This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media. Methods: WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020. Results: WeChat Index hits for “Feidian” (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority’s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords “SARS,” “coronavirus,” “novel coronavirus,” “shortness of breath,” “dyspnea,” and “diarrhea,” but these terms were not as meaningful for the early detection of the outbreak as the term “Feidian”. Conclusions: By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks. %M 32931439 %R 10.2196/19589 %U https://mhealth.jmir.org/2020/10/e19589 %U https://doi.org/10.2196/19589 %U http://www.ncbi.nlm.nih.gov/pubmed/32931439 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e21364 %T Syndromic Surveillance Insights from a Symptom Assessment App Before and During COVID-19 Measures in Germany and the United Kingdom: Results From Repeated Cross-Sectional Analyses %A Mehl,Alicia %A Bergey,Francois %A Cawley,Caoimhe %A Gilsdorf,Andreas %+ Department of Epidemiology & Public Health, Ada Health GmbH, Karl-Liebknecht-Str 1, Berlin, 10178, Germany, 49 30 403 67 390, alicia.mehl@ada.com %K epidemiology %K participatory epidemiology %K participatory surveillance %K COVID-19 symptom assessment apps %K symptom checker apps %K syndromic surveillance %K COVID-19 measures %K COVID-19 lockdown %K digital public health %K health effects of COVID-19 measures, infoveillance %D 2020 %7 9.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Unprecedented lockdown measures have been introduced in countries worldwide to mitigate the spread and consequences of COVID-19. Although attention has been focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to the limitations of existing syndromic surveillance data and tools. Objective: The aim of this study is to explore the added value of mobile phone app–based symptom assessment tools as real-time health insight providers to inform public health policy makers. Methods: A comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an assessment within the Ada app in Germany and the United Kingdom was conducted between two periods, namely before and after the implementation of “Phase One” COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analyzed using a Pearson chi-square test and reported as log2 fold changes. Results: Overall, 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Overall, 34,200-37,400 symptomatic users in the United Kingdom reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany: 68,600/103,200, 66.52%; United Kingdom: 51,200/71,600, 72.74%). The majority were aged 10-29 years (Germany: 68,500/100,000, 68.45%; United Kingdom: 50,900/68,800, 73.91%), and about one-quarter were aged 30-59 years (Germany: 26,200/100,000, 26.15%; United Kingdom: 14,900/68,800, 21.65%). Overall, 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures period as compared to the Baseline period, and 34 of these were reported in both countries. The following mental health symptoms (log2 fold change, P value) were reported less often during the Measures period: inability to manage constant stress and demands at work (–1.07, P<.001), memory difficulty (–0.56, P<.001), depressed mood (–0.42, P<.001), and impaired concentration (–0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. In total, 14 of the 34 symptoms had statistically significant associations with weather variables. Conclusions: Symptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system. %M 32997640 %R 10.2196/21364 %U http://mhealth.jmir.org/2020/10/e21364/ %U https://doi.org/10.2196/21364 %U http://www.ncbi.nlm.nih.gov/pubmed/32997640 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e20741 %T COVID-19 Contact Tracing Apps: Predicted Uptake in the Netherlands Based on a Discrete Choice Experiment %A Jonker,Marcel %A de Bekker-Grob,Esther %A Veldwijk,Jorien %A Goossens,Lucas %A Bour,Sterre %A Rutten-Van Mölken,Maureen %+ Erasmus School of Health Policy & Management, Erasmus University Rotterdam, , Rotterdam, Netherlands, 31 10 408 8555, marcel@mfjonker.com %K COVID-19 %K discrete choice experiment %K contact tracing %K participatory epidemiology %K participatory surveillance %K app %K uptake %K prediction %K smartphone %K transmission %K privacy %K mobile phone %D 2020 %7 9.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone-based contact tracing apps can contribute to reducing COVID-19 transmission rates and thereby support countries emerging from lockdowns as restrictions are gradually eased. Objective: The primary objective of our study is to determine the potential uptake of a contact tracing app in the Dutch population, depending on the characteristics of the app. Methods: A discrete choice experiment was conducted in a nationally representative sample of 900 Dutch respondents. Simulated maximum likelihood methods were used to estimate population average and individual-level preferences using a mixed logit model specification. Individual-level uptake probabilities were calculated based on the individual-level preference estimates and subsequently aggregated into the sample as well as subgroup-specific contact tracing app adoption rates. Results: The predicted app adoption rates ranged from 59.3% to 65.7% for the worst and best possible contact tracing app, respectively. The most realistic contact tracing app had a predicted adoption of 64.1%. The predicted adoption rates strongly varied by age group. For example, the adoption rates of the most realistic app ranged from 45.6% to 79.4% for people in the oldest and youngest age groups (ie, ≥75 years vs 15-34 years), respectively. Educational attainment, the presence of serious underlying health conditions, and the respondents’ stance on COVID-19 infection risks were also correlated with the predicted adoption rates but to a lesser extent. Conclusions: A secure and privacy-respecting contact tracing app with the most realistic characteristics can obtain an adoption rate as high as 64% in the Netherlands. This exceeds the target uptake of 60% that has been formulated by the Dutch government. The main challenge will be to increase the uptake among older adults, who are least inclined to install and use a COVID-19 contact tracing app. %M 32795998 %R 10.2196/20741 %U https://mhealth.jmir.org/2020/10/e20741 %U https://doi.org/10.2196/20741 %U http://www.ncbi.nlm.nih.gov/pubmed/32795998 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e19474 %T Barriers and Enablers to Using a Patient-Facing Electronic Questionnaire: A Qualitative Theoretical Domains Framework Analysis %A Yamada,Janet %A Kouri,Andrew %A Simard,Sarah-Nicole %A Segovia,Stephanie A %A Gupta,Samir %+ Department of Medicine, University of Toronto, Division of Respirology, St. Michael's Hospital, Unity Health Toronto, Bond Wing, Suite 6042, 30 Bond St, Toronto, ON, M5B 1W8, Canada, 1 416 864 6026, samir.gupta@unityhealth.to %K asthma %K electronic questionnaire %K patients %K barriers %K enablers %K mobile phone %D 2020 %7 8.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic patient questionnaires are becoming ubiquitous in health care. To address care gaps that contribute to poor asthma management, we developed the Electronic Asthma Management System, which includes a previsit electronic patient questionnaire linked to a computerized clinical decision support system. Objective: This study aims to identify the determinants (barriers and enablers) of patient uptake and completion of a previsit mobile health questionnaire. Methods: We conducted semistructured interviews with adult patients with asthma in Toronto, Canada. After demonstrating the questionnaire, participants completed the questionnaire using their smartphones and were then interviewed regarding perceived barriers and enablers to using and completing the questionnaire. Interview questions were based on the Theoretical Domains Framework to identify the determinants of health-related behavior. We generated themes that addressed the enablers and barriers to the uptake and completion of the questionnaire. Results: In total, 12 participants were interviewed for saturation. Key enablers were as follows: the questionnaire was easy to complete without additional knowledge or skills and was perceived as a priority and responsibility for patients, use could lead to more efficient and personalized care, completion on one’s own time would be convenient, and uptake and completion could be optimized through patient reminders. Concerns about data security, the usefulness of questionnaire data, the stress of completing it accurately and on time, competing priorities, and preferences to complete the questionnaire on other devices were the main barriers. Conclusions: The barriers and enablers identified by patients should be addressed by developing implementation strategies to enhance e-questionnaire use and completion by patients. As the use of e-questionnaires grows, our findings will contribute to implementation efforts across settings and diseases. %M 33030437 %R 10.2196/19474 %U http://www.jmir.org/2020/10/e19474/ %U https://doi.org/10.2196/19474 %U http://www.ncbi.nlm.nih.gov/pubmed/33030437 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e21676 %T Development and Acceptability of a Method to Investigate Prescription Drug Misuse in Daily Life: Ecological Momentary Assessment Study %A Papp,Lauren M %A Barringer,Alexandra %A Blumenstock,Shari M %A Gu,Pamela %A Blaydes,Madison %A Lam,Jaime %A Kouros,Chrystyna D %+ Department of Human Development and Family Studies, University of Wisconsin-Madison, 1300 Linden Drive, Madison, WI, 53706, United States, 1 608 262 8611, papp@wisc.edu %K compliance %K ecological momentary assessment %K prescription drug misuse %K young adult %D 2020 %7 1.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Prescription drug misuse and abuse is an established public health challenge, and young adults are particularly affected. There is a striking lack of real-time, naturalistic data collection assessing intentions to misuse and other precipitating factors at the time of actual misuse, leaving the conditions under which individuals are most likely to misuse prescription medications unknown. Ecological momentary assessment (EMA) apps and protocols designed to capture this information would accelerate and expand the knowledge base and could directly contribute to prevention and treatment efforts. Objective: The objectives of this study are to describe the development and administration of a mobile app and the EMA protocol designed to collect real-time factors associated with college students’ prescription drug misuse intentions and behaviors in daily life; present completion rates, compliance, acceptability, and reactivity associated with the EMA protocol for participants who endorsed recent prescription drug misuse at screening (ie, risk group; n=300) and those who did not (ie, nonrisk group; n=55); and establish initial construct validity by linking the reports of misuse behaviors in daily life collected via the EMA app to prescription drug misuse reported on a standard survey. Methods: An EMA data collection app and protocol were designed specifically to capture hypothesized contextual factors along with prescription drug misuse intentions and behaviors in daily life. Using this protocol, young adult college students (N=352) completed signal- and event-contingent reports over a 28-day period. When the intention to misuse a prescription drug was endorsed, a brief follow-up prompt was sent 15 min later to collect participants’ indications of whether or not misuse had occurred. Results: Risk-group participants were significantly more likely than nonrisk counterparts to endorse any prescription drug misuse intentions in daily life (P<.001), to complete one or more follow-up reports (P<.001), and to endorse any prescription drug misuse behavior in daily life on the follow-ups (P<.001). Overall, participants demonstrated consistent engagement with the EMA procedures and returned an average of 74.5 (SD 23.82; range 10-122) reports. Participants in the risk and nonrisk groups did not differ in the number of reports they completed (P=.12), the number of their reporting days (P=.32), or their average completion rates (P=.14). The results indicated some evidence of reactivity to the momentary reporting procedure. Participants reported uniformly positive experiences and remained highly engaged throughout the reporting protocol and broader study. Conclusions: The novel EMA app and protocol provide an effective way to assess real-time factors associated with prescription drug misuse intentions and behaviors in daily life. The resulting investigations offer the potential to provide highly translatable information for research and prevention efforts. %M 32877351 %R 10.2196/21676 %U https://mhealth.jmir.org/2020/10/e21676 %U https://doi.org/10.2196/21676 %U http://www.ncbi.nlm.nih.gov/pubmed/32877351 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e18556 %T Research Participants’ Perspectives on Using an Electronic Portal for Engagement and Data Collection: Focus Group Results From a Large Epidemiologic Cohort %A Rees-Punia,Erika %A Patel,Alpa V %A Beckwitt,Asher %A Leach,Corinne R %A Gapstur,Susan M %A Smith,Tenbroeck G %+ American Cancer Society, 250 Williams St, Atlanta, GA, 30303, United States, 1 4049823684, erika.rees-punia@cancer.org %K focus groups %K health information technology %K epidemiologic studies %D 2020 %7 1.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Epidemiologic cohort studies have begun to leverage electronic research participant portals to facilitate data collection, integrate wearable technologies, lower costs, and engage participants. However, little is known about the acceptability of portal use by research participants. Objective: The aim of this study is to conduct focus groups among a sample of Cancer Prevention Study-3 (CPS-3) participants to better understand their preferences and concerns about research portals. Methods: CPS-3 participants were stratified based on sex, race and ethnicity, age, and cancer status, and randomly invited to participate. Focus groups used an exploratory case design with semistructured guides to prompt discussion. Using a constant comparison technique, transcripts were assigned codes to identify themes. Results: Participants (31/59, 52% women; 52/59, 88% White/non-Latinx) were favorably disposed toward using a research participant portal to take surveys, communicate with the study staff, and upload data. Most participants indicated that a portal would be beneficial and convenient but expressed concerns over data safety. Participants stressed the importance of an easy-to-use and trustworthy portal that is compatible with mobile devices. Conclusions: In addition to being beneficial to researchers, portals may also benefit participants as long as the portals are secure and simple. Participants believe that portals can provide convenient ways to report data and remain connected to the study. %M 33001033 %R 10.2196/18556 %U https://www.jmir.org/2020/10/e18556 %U https://doi.org/10.2196/18556 %U http://www.ncbi.nlm.nih.gov/pubmed/33001033 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 9 %P e18086 %T Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study %A Thota,Darshan %+ Madigan Army Medical Center, 9040A Jackson Ave, Joint Base Lewis-McChord, WA, 98431, United States, 1 253 968 5958, thota1@gmail.com %K Fitbit %K sleep %K healthy %K mood %K context %K waking %D 2020 %7 29.9.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Mental health disorders can disrupt a person’s sleep, resulting in lower quality of life. Early identification and referral to mental health services are critical for active duty service members returning from forward-deployed missions. Although technologies like wearable computing devices have the potential to help address this problem, research on the role of technologies like Fitbit in mental health services is in its infancy. Objective: If Fitbit proves to be an appropriate clinical tool in a military setting, it could provide potential cost savings, improve clinician access to patient data, and create real-time treatment options for the greater active duty service member population. The purpose of this study was to determine if the Fitbit device can be used to identify indicators of mental health disorders by measuring the relationship between Fitbit sleep data, self-reported mood, and environmental contextual factors that may disrupt sleep. Methods: This observational cohort study was conducted at the Madigan Army Medical Center. The study included 17 healthy adults who wore a Fitbit Flex for 2 weeks and completed a daily self-reported mood and sleep log. Daily Fitbit data were obtained for each participant. Contextual factors were collected with interim and postintervention surveys. This study had 3 specific aims: (1) Determine the correlation between daily Fitbit sleep data and daily self-reported sleep, (2) Determine the correlation between number of waking events and self-reported mood, and (3) Explore the qualitative relationships between Fitbit waking events and self-reported contextual factors for sleep. Results: There was no significant difference in the scores for the pre-intevention Pittsburg Sleep Quality Index (PSQI; mean 5.88 points, SD 3.71 points) and postintervention PSQI (mean 5.33 points, SD 2.83 points). The Wilcoxon signed-ranks test showed that the difference between the pre-intervention PSQI and postintervention PSQI survey data was not statistically significant (Z=0.751, P=.05). The Spearman correlation between Fitbit sleep time and self-reported sleep time was moderate (r=0.643, P=.005). The Spearman correlation between number of waking events and self-reported mood was weak (r=0.354, P=.163). Top contextual factors disrupting sleep were “pain,” “noises,” and “worries.” A subanalysis of participants reporting “worries” found evidence of potential stress resilience and outliers in waking events. Conclusions: Findings contribute valuable evidence on the strength of the Fitbit Flex device as a proxy that is consistent with self-reported sleep data. Mood data alone do not predict number of waking events. Mood and Fitbit data combined with further screening tools may be able to identify markers of underlying mental health disease. %M 32990631 %R 10.2196/18086 %U http://formative.jmir.org/2020/9/e18086/ %U https://doi.org/10.2196/18086 %U http://www.ncbi.nlm.nih.gov/pubmed/32990631 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e17818 %T Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study %A Sultana,Madeena %A Al-Jefri,Majed %A Lee,Joon %+ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Teaching Research & Wellness 5E17, 3280 Hospital Dr. NW, Calgary, AB, T2N 4Z6, Canada, 1 403 220 2968, joonwu.lee@ucalgary.ca %K mHealth %K mental health %K emotion detection %K emotional transition detection %K spatiotemporal context %K supervised machine learning %K artificial intelligence %K mobile phone %K digital biomarkers %K digital phenotyping %D 2020 %7 29.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Emotional state in everyday life is an essential indicator of health and well-being. However, daily assessment of emotional states largely depends on active self-reports, which are often inconvenient and prone to incomplete information. Automated detection of emotional states and transitions on a daily basis could be an effective solution to this problem. However, the relationship between emotional transitions and everyday context remains to be unexplored. Objective: This study aims to explore the relationship between contextual information and emotional transitions and states to evaluate the feasibility of detecting emotional transitions and states from daily contextual information using machine learning (ML) techniques. Methods: This study was conducted on the data of 18 individuals from a publicly available data set called ExtraSensory. Contextual and sensor data were collected using smartphone and smartwatch sensors in a free-living condition, where the number of days for each person varied from 3 to 9. Sensors included an accelerometer, a gyroscope, a compass, location services, a microphone, a phone state indicator, light, temperature, and a barometer. The users self-reported approximately 49 discrete emotions at different intervals via a smartphone app throughout the data collection period. We mapped the 49 reported discrete emotions to the 3 dimensions of the pleasure, arousal, and dominance model and considered 6 emotional states: discordant, pleased, dissuaded, aroused, submissive, and dominant. We built general and personalized models for detecting emotional transitions and states every 5 min. The transition detection problem is a binary classification problem that detects whether a person’s emotional state has changed over time, whereas state detection is a multiclass classification problem. In both cases, a wide range of supervised ML algorithms were leveraged, in addition to data preprocessing, feature selection, and data imbalance handling techniques. Finally, an assessment was conducted to shed light on the association between everyday context and emotional states. Results: This study obtained promising results for emotional state and transition detection. The best area under the receiver operating characteristic (AUROC) curve for emotional state detection reached 60.55% in the general models and an average of 96.33% across personalized models. Despite the highly imbalanced data, the best AUROC curve for emotional transition detection reached 90.5% in the general models and an average of 88.73% across personalized models. In general, feature analyses show that spatiotemporal context, phone state, and motion-related information are the most informative factors for emotional state and transition detection. Our assessment showed that lifestyle has an impact on the predictability of emotion. Conclusions: Our results demonstrate a strong association of daily context with emotional states and transitions as well as the feasibility of detecting emotional states and transitions using data from smartphone and smartwatch sensors. %M 32990638 %R 10.2196/17818 %U http://mhealth.jmir.org/2020/9/e17818/ %U https://doi.org/10.2196/17818 %U http://www.ncbi.nlm.nih.gov/pubmed/32990638 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e19716 %T Mobile Insight in Risk, Resilience, and Online Referral (MIRROR): Psychometric Evaluation of an Online Self-Help Test %A van Herpen,Merel Marjolein %A Boeschoten,Manon A %A te Brake,Hans %A van der Aa,Niels %A Olff,Miranda %+ ARQ Centre of Expertise for the Impact of Disasters and Crises, Nienoord 5, Diemen, , Netherlands, 31 610082023, m.van.herpen@impact.arq.org %K potentially traumatic events %K mobile mental health %K self-help %K online %K resilience %K posttraumatic stress disorder %D 2020 %7 25.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Most people who experience a potentially traumatic event (PTE) recover on their own. A small group of individuals develops psychological complaints, but this is often not detected in time or guidance to care is suboptimal. To identify these individuals and encourage them to seek help, a web-based self-help test called Mobile Insight in Risk, Resilience, and Online Referral (MIRROR) was developed. MIRROR takes an innovative approach since it integrates both negative and positive outcomes of PTEs and time since the event and provides direct feedback to the user. Objective: The goal of this study was to assess MIRROR’s use, examine its psychometric properties (factor structure, internal consistency, and convergent and divergent validity), and evaluate how well it classifies respondents into different outcome categories compared with reference measures. Methods: MIRROR was embedded in the website of Victim Support Netherlands so visitors could use it. We compared MIRROR’s outcomes to reference measures of PTSD symptoms (PTSD Checklist for DSM-5), depression, anxiety, stress (Depression Anxiety Stress Scale–21), psychological resilience (Resilience Evaluation Scale), and positive mental health (Mental Health Continuum Short Form). Results: In 6 months, 1112 respondents completed MIRROR, of whom 663 also completed the reference measures. Results showed good internal consistency (interitem correlations range .24 to .55, corrected item-total correlations range .30 to .54, and Cronbach alpha coefficient range .62 to .68), and convergent and divergent validity (Pearson correlations range –.259 to .665). Exploratory and confirmatory factor analyses (EFA+CFA) yielded a 2-factor model with good model fit (CFA model fit indices: χ219=107.8, P<.001, CFI=.965, TLI=.948, RMSEA=.065), conceptual meaning, and parsimony. MIRROR correctly classified respondents into different outcome categories compared with the reference measures. Conclusions: MIRROR is a valid and reliable self-help test to identify negative (PTSD complaints) and positive outcomes (psychosocial functioning and resilience) of PTEs. MIRROR is an easily accessible online tool that can help people who have experienced a PTE to timely identify psychological complaints and find appropriate support, a tool that might be highly needed in times like the coronavirus pandemic. %M 32975521 %R 10.2196/19716 %U http://www.jmir.org/2020/9/e19716/ %U https://doi.org/10.2196/19716 %U http://www.ncbi.nlm.nih.gov/pubmed/32975521 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e18491 %T Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study %A Crane,Tracy E %A Skiba,Meghan B %A Miller,Austin %A Garcia,David O %A Thomson,Cynthia A %+ Department of Biobehavioral Health Sciences, College of Nursing, University of Arizona, 1305 N Martin Ave, Tucson, AZ, 85721, United States, 1 5203310120, tecrane@email.arizona.edu %K wearable electronic devices %K physical activity %K cancer survivors %K activity trackers %K mobile phone %D 2020 %7 24.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The collection of self-reported physical activity using validated questionnaires has known bias and measurement error. Objective: Accelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control. Methods: From July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns. Results: We evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer. Conclusions: The high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area. %M 32969828 %R 10.2196/18491 %U http://mhealth.jmir.org/2020/9/e18491/ %U https://doi.org/10.2196/18491 %U http://www.ncbi.nlm.nih.gov/pubmed/32969828 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e20488 %T Digital Cardiovascular Biomarker Responses to Transcutaneous Cervical Vagus Nerve Stimulation: State-Space Modeling, Prediction, and Simulation %A Gazi,Asim H %A Gurel,Nil Z %A Richardson,Kristine L S %A Wittbrodt,Matthew T %A Shah,Amit J %A Vaccarino,Viola %A Bremner,J Douglas %A Inan,Omer T %+ School of Electrical and Computer Engineering, Georgia Institute of Technology, North Ave NW, Atlanta, GA, 30332, United States, 1 4693608083, asim.gazi@gatech.edu %K vagus nerve stimulation %K noninvasive %K wearable sensing %K digital biomarkers %K dynamic models %K state space %K biomarker %K cardiovascular %K neuromodulation %K bioelectronic medicine %D 2020 %7 22.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Transcutaneous cervical vagus nerve stimulation (tcVNS) is a promising alternative to implantable stimulation of the vagus nerve. With demonstrated potential in myriad applications, ranging from systemic inflammation reduction to traumatic stress attenuation, closed-loop tcVNS during periods of risk could improve treatment efficacy and reduce ineffective delivery. However, achieving this requires a deeper understanding of biomarker changes over time. Objective: The aim of the present study was to reveal the dynamics of relevant cardiovascular biomarkers, extracted from wearable sensing modalities, in response to tcVNS. Methods: Twenty-four human subjects were recruited for a randomized double-blind clinical trial, for whom electrocardiography and photoplethysmography were used to measure heart rate and photoplethysmogram amplitude responses to tcVNS, respectively. Modeling these responses in state-space, we (1) compared the biomarkers in terms of their predictability and active vs sham differentiation, (2) studied the latency between stimulation onset and measurable effects, and (3) visualized the true and model-simulated biomarker responses to tcVNS. Results: The models accurately predicted future heart rate and photoplethysmogram amplitude values with root mean square errors of approximately one-fifth the standard deviations of the data. Moreover, (1) the photoplethysmogram amplitude showed superior predictability (P=.03) and active vs sham separation compared to heart rate; (2) a consistent delay of greater than 5 seconds was found between tcVNS onset and cardiovascular effects; and (3) dynamic characteristics differentiated responses to tcVNS from the sham stimulation. Conclusions: This work furthers the state of the art by modeling pertinent biomarker responses to tcVNS. Through subsequent analysis, we discovered three key findings with implications related to (1) wearable sensing devices for bioelectronic medicine, (2) the dominant mechanism of action for tcVNS-induced effects on cardiovascular physiology, and (3) the existence of dynamic biomarker signatures that can be leveraged when titrating therapy in closed loop. Trial Registration: ClinicalTrials.gov NCT02992899; https://clinicaltrials.gov/ct2/show/NCT02992899 International Registered Report Identifier (IRRID): RR2-10.1016/j.brs.2019.08.002 %M 32960179 %R 10.2196/20488 %U http://mhealth.jmir.org/2020/9/e20488/ %U https://doi.org/10.2196/20488 %U http://www.ncbi.nlm.nih.gov/pubmed/32960179 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e18284 %T Comparison of a Mobile Health Electronic Visual Analog Scale App With a Traditional Paper Visual Analog Scale for Pain Evaluation: Cross-Sectional Observational Study %A Turnbull,Alexandra %A Sculley,Dean %A Escalona-Marfil,Carles %A Riu-Gispert,Lluís %A Ruiz-Moreno,Jorge %A Gironès,Xavier %A Coda,Andrea %+ School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Health Precinct, BE154, PO Box 127, Ourimbah, 2258, Australia, 61 0243484507, andrea.coda@newcastle.edu.au %K pain %K mobile app %K mHealth %K digital health %K electronic visual analog scale %K visual analog scale %K symptom %K eHealth %K reliability %D 2020 %7 17.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Accurate quantification of pain in a clinical setting is vital. The use of an electronic pain scale enables data to be collected, analyzed, and utilized much faster compared with traditional paper-based scales. The advancement of smart technology in pediatric and adult pain evaluation may offer opportunities to introduce easy-to-use and reliable pain assessment methods within different clinical settings. If promptly introduced within different pediatric and adult pain clinic services, validated and easily accessible mobile health pain apps may lead to early pain detection, promoting improvement in patient’s quality of life and leading to potentially less time off from school or work. Objective: This cross-sectional observational study aimed to investigate the interchangeability of an electronic visual analog scale (eVAS) app with a traditional paper visual analog scale (pVAS) among Australian children, adolescents, and adults for pain evaluation. Methods: Healthy participants (age range 10-75 years) were recruited from a sporting club and a secondary school in Melbourne (Australia). The data collection process involved application of pressure (8.5 kg/cm2) from a Wagner Force Dial FDK 20 to the midpoint of the thumb. The pressure was applied twice with a 5-minute interval. At each pressure application, participants were asked to randomly record their pain perception using the “eVAS” accessible via the “Interactive Clinics” app and the traditional pVAS. Statistical analysis was conducted to determine intermethod and intramethod reliabilities. Results: Overall, 109 healthy participants were recruited. Adults (mean age 42.43 years, SD 14.50 years) had excellent reliability, with an intraclass correlation coefficient (ICC) of 0.94 (95% CI 0.91-0.96). Children and adolescents (mean age 13.91 years, SD 2.89 years) had moderate-to-good intermethod and intramethod reliabilities, with an ICC of 0.80 (95% CI 0.70-0.87) and average ICC of 0.80 (95% CI 0.69-0.87), respectively. Conclusions: The eVAS app appears to be interchangeable compared with the traditional pVAS among children, adolescents, and adults. This pain evaluation method may offer new opportunities to introduce user-friendly and validated pain assessment apps for patients, clinicians, and allied health professionals. %M 32940621 %R 10.2196/18284 %U http://www.jmir.org/2020/9/e18284/ %U https://doi.org/10.2196/18284 %U http://www.ncbi.nlm.nih.gov/pubmed/32940621 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e17852 %T Accuracy of Sedentary Behavior–Triggered Ecological Momentary Assessment for Collecting Contextual Information: Development and Feasibility Study %A Giurgiu,Marco %A Niermann,Christina %A Ebner-Priemer,Ulrich %A Kanning,Martina %+ Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Hertzstr 16, Karlsruhe, 76131, Germany, 49 176203557, marco.giurgiu@kit.edu %K sedentariness %K Ecological Momentary Assessment %K accelerometry %K mHealth %K context %D 2020 %7 15.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sedentary behavior has received much attention in the scientific community over the past decade. There is growing evidence that sedentary behavior is negatively associated with physical and mental health. However, an in-depth understanding of the social and environmental context of sedentary behavior is missing. Information about sedentary behavior, such as how everyday sedentary behavior occurs throughout the day (eg, number and length of sedentary bouts), where, when, and with whom it takes place, and what people are doing while being sedentary, is useful to inform the development of interventions aimed at reducing sedentary time. However, examining everyday sedentary behavior requires specific methods. Objective: The purpose of this paper is (1) to introduce sedentary behavior–triggered Ecological Momentary Assessment (EMA) as a methodological advancement in the field of sedentary behavior research and (2) to examine the accuracy of sedentary behavior–triggered EMA in 3 different studies in healthy adults. Moreover, we compare the accuracy of sedentary behavior–triggered EMA to simulations of random-trigger designs. Methods: Sedentary behavior–triggered EMA comprises a continuous assessment of sedentary behavior via accelerometers and repeated contextual assessments via electronic diaries (ie, an application on a smartphone). More specifically, the accelerometer analyzes and transfers data regarding body position (a sitting or lying position, or an upright position) via Bluetooth Low Energy (BLE) to a smartphone in real time and triggers the deployment of questionnaires. Each time a participant spends a specified time (eg, 20 minutes) in a sedentary position, the e-diary triggers contextual assessments. To test the accuracy of this method, we calculated a percentage score for all triggered prompts in relation to the total number of bouts that could trigger a prompt. Results: Based on the accelerometer recordings, 29.3% (5062/17278) of all sedentary bouts were classified as moderate-to-long (20-40 minutes) and long bouts (≥ 41 minutes). On average, the accuracy by participant was 82.77% (3339/4034; SD 21.01%, range 71.00-88.22%) on the study level. Compared to simulations of random prompts (every 120 minutes), the number of triggered prompts was up to 47.9% (n=704) higher through the sedentary behavior–triggered EMA approach. Nearly 40% (799/2001) of all prolonged sedentary bouts (≥ 20 minutes) occurred during work, and in 57% (1140/2001) of all bouts, the participants were not alone. Conclusions: Sedentary behavior–triggered EMA is an accurate method for collecting contextual information on sedentary behavior in daily life. Given the growing interest in sedentary behavior research, this sophisticated approach offers a real advancement as it can be used to collect social and environmental contextual information or to unravel dynamic associations. Furthermore, it can be modified to develop sedentary behavior–triggered mHealth interventions. %M 32930668 %R 10.2196/17852 %U http://mhealth.jmir.org/2020/9/e17852/ %U https://doi.org/10.2196/17852 %U http://www.ncbi.nlm.nih.gov/pubmed/32930668 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e17977 %T Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study %A Turicchi,Jake %A O'Driscoll,Ruairi %A Finlayson,Graham %A Duarte,Cristiana %A Palmeira,A L %A Larsen,Sofus C %A Heitmann,Berit L %A Stubbs,R James %+ School of Psychology, The University of Leeds, 2 Lifton Place, Leeds, LS2 9JS, United Kingdom, 44 7718300764, psjt@leeds.ac.uk %K weight variability %K weight fluctuation %K weight cycling %K weight instability %K imputation %K validation %K digital tracking %K smart scales %K body weight %K energy balance %D 2020 %7 11.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. Objective: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches Methods: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. Results: Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. Conclusions: The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data. %M 32915155 %R 10.2196/17977 %U http://mhealth.jmir.org/2020/9/e17977/ %U https://doi.org/10.2196/17977 %U http://www.ncbi.nlm.nih.gov/pubmed/32915155 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e22208 %T Clinometric Gait Analysis Using Smart Insoles in Patients With Hemiplegia After Stroke: Pilot Study %A Seo,Minseok %A Shin,Myung-Jun %A Park,Tae Sung %A Park,Jong-Hwan %+ Department of Rehabilitation Medicine, School of Medicine, Pusan National University, 305 Gudeok-Ro Seo-Gu, Busan, 49241, Republic of Korea, 82 1085130907, drshinmj@gmail.com %K stroke %K hemiplegia %K gait %K smart insole %K medical informatics %K rehabilitation %K observational %K wearable %K assessment %D 2020 %7 10.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: For effective rehabilitation after stroke, it is essential to conduct an objective assessment of the patient’s functional status. Several stroke severity scales have been used for this purpose, but such scales have various limitations. Objective: Gait analysis using smart insole technology can be applied continuously, objectively, and quantitatively, thereby overcoming the shortcomings of other assessment tools. Methods: To confirm the reliability of gait analysis using smart insole technology, normal healthy controls wore insoles in their shoes during the Timed Up and Go (TUG) test. The gait parameters were compared with the manually collected data. To determine the gait characteristics of patients with hemiplegia due to stroke, they were asked to wear insoles and take the TUG test; gait parameters were calculated and compared with those of control subjects. To investigate whether the gait analysis accurately reflected the patients’ clinical condition, we analyzed the relationships of 22 gait parameters on 4 stroke severity scales. Results: The smart insole gait parameter data were similar to those calculated manually. Among the 18 gait parameters tested, 14 were significantly effective at distinguishing patients from healthy controls. The smart insole data revealed that the stance duration on both sides was longer in patients than controls, which has proven difficult to show using other methods. Furthermore, the sound side in patients showed a markedly longer stance duration. Regarding swing duration, that of the sound side was shorter in patients than controls, whereas that of the hemiplegic side was longer. We identified 10 significantly correlated gait parameters on the stroke severity scales. Notably, the difference in stance duration between the sound and hemiplegic sides was significantly correlated with the Fugl-Meyer Assessment (FMA) lower extremity score. Conclusions: This study confirmed the feasibility and applicability of the smart insole as a device to assess the gait of patients with hemiplegia due to stroke. In addition, we demonstrated that the FMA score was significantly correlated with the smart insole data. Providing an environment where stroke patients can easily measure walking ability helps to maintain chronic functions as well as acute rehabilitation. Trial Registration: UMIN Clinical Trials Registry UMIN000041646, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047538 %M 32909949 %R 10.2196/22208 %U http://mhealth.jmir.org/2020/9/e22208/ %U https://doi.org/10.2196/22208 %U http://www.ncbi.nlm.nih.gov/pubmed/32909949 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e15284 %T Comparing a Mobile Phone Automated System With a Paper and Email Data Collection System: Substudy Within a Randomized Controlled Trial %A Bond,Diana M %A Hammond,Jeremy %A Shand,Antonia W %A Nassar,Natasha %+ Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Level 2, Charles Perkins Centre D17, Sydney, 2006, Australia, 61 2 9036 7006, diana.bond@sydney.edu.au %K mobile phones %K text messaging %K data collection methods %K clinical trial %K breastfeeding %K maternal health %D 2020 %7 25.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Traditional data collection methods using paper and email are increasingly being replaced by data collection using mobile phones, although there is limited evidence evaluating the impact of mobile phone technology as part of an automated research management system on data collection and health outcomes. Objective: The aim of this study is to compare a web-based mobile phone automated system (MPAS) with a more traditional delivery and data collection system combining paper and email data collection (PEDC) in a cohort of breastfeeding women. Methods: We conducted a substudy of a randomized controlled trial in Sydney, Australia, which included women with uncomplicated term births who intended to breastfeed. Women were recruited within 72 hours of giving birth. A quasi-randomized number of women were recruited using the PEDC system, and the remainder were recruited using the MPAS. The outcomes assessed included the effectiveness of data collection, impact on study outcomes, response rate, acceptability, and cost analysis between the MPAS and PEDC methods. Results: Women were recruited between April 2015 and December 2016. The analysis included 555 women: 471 using the MPAS and 84 using the PEDC. There were no differences in clinical outcomes between the 2 groups. At the end of the 8-week treatment phase, the MPAS group showed an increased response rate compared with the PEDC group (56% vs 37%; P<.001), which was also seen at the 2-, 6-, and 12-month follow-ups. At the 2-month follow-up, the MPAS participants also showed an increased rate of self-reported treatment compliance (70% vs 56%; P<.001) and a higher recommendation rate for future use (95% vs 64%; P<.001) as compared with the PEDC group. The cost analysis between the 2 groups was comparable. Conclusions: MPAS is an effective and acceptable method for improving the overall management, treatment compliance, and methodological quality of clinical research to ensure the validity and reliability of findings. %M 32763873 %R 10.2196/15284 %U http://mhealth.jmir.org/2020/8/e15284/ %U https://doi.org/10.2196/15284 %U http://www.ncbi.nlm.nih.gov/pubmed/32763873 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e17754 %T Sensory-Discriminative Three-Dimensional Body Pain Mobile App Measures Versus Traditional Pain Measurement With a Visual Analog Scale: Validation Study %A Kaciroti,Niko %A DosSantos,Marcos Fabio %A Moura,Brenda %A Bellile,Emily Light %A Nascimento,Thiago Dias %A Maslowski,Eric %A Danciu,Theodora E %A Donnell,Adam %A DaSilva,Alexandre F %+ Headache & Orofacial Pain Effort (H.O.P.E.), Department of Biologic and Materials Sciences, School of Dentistry, University of Michigan, 1011 N. University Ave., Room 1014A,, Ann Arbor, MI, 48109-1078, United States, 1 (734) 615 3807, adasilva@umich.edu %K pain measurement %K chronic pain %K migraine %K visual analog scale %K facial pain %D 2020 %7 19.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: To quantify pain severity in patients and the efficacy treatments, researchers and clinicians apply tools such as the traditional visual analog scale (VAS) that leads to inaccurate interpretation of the main sensory pain. Objective: This study aimed to validate the pain measurements of a neuroscience-based 3D body pain mobile app called GeoPain. Methods: Patients with temporomandibular disorder (TMD) were assessed using GeoPain measures in comparison to VAS and positive and negative affect schedule (PANAS), pain and mood scales, respectively. Principal component analysis (PCA), scatter score analysis, Pearson methods, and effect size were used to determine the correlation between GeoPain and VAS measures. Results: The PCA resulted in two main orthogonal components: first principal component (PC1) and second principal component (PC2). PC1 comprises a combination score of all GeoPain measures, which had a high internal consistency and clustered together in TMD pain. PC2 included VAS and PANAS. All loading coefficients for GeoPain measures in PC1 were above 0.70, with low loadings for VAS and PANAS. Meanwhile, PC2 was dominated by a VAS and PANAS coefficient >0.4. Repeated measure analysis revealed a strong correlation between the VAS and mood scores from PANAS over time, which might be related to the subjectivity of the VAS measure, whereas sensory-discriminative GeoPain measures, not VAS, demonstrated an association between chronicity and TMD pain in locations spread away from the most commonly reported area or pain epicenter (P=.01). Analysis using VAS did not detect an association at baseline between TMD and chronic pain. The long-term reliability (lag >1 day) was consistently high for the pain area and intensity number summation (PAINS) with lag autocorrelations averaging between 0.7 and 0.8, and greater than the autocorrelations for VAS averaging between 0.3 and 0.6. The combination of higher reliability for PAINS and its objectivity, displayed by the lack of association with PANAS as compared with VAS, indicated that PAINS has better sensitivity and reliability for measuring treatment effect over time for sensory-discriminative pain. The effect sizes for PAINS were larger than those for VAS, consequently requiring smaller sample sizes to assess the analgesic efficacy of treatment if PAINS was used versus VAS. The PAINS effect size was 0.51 SD for both facial sides and 0.60 SD for the right side versus 0.35 SD for VAS. Therefore, the sample size required to detect such effect sizes with 80% power would be n=125 per group for VAS, but as low as n=44 per group for PAINS, which is almost a third of the sample size needed by VAS. Conclusions: GeoPain demonstrates precision and reliability as a 3D mobile interface for measuring and analyzing sensory-discriminative aspects of subregional pain in terms of its severity and response to treatment, without being influenced by mood variations from patients. %M 32124732 %R 10.2196/17754 %U https://mhealth.jmir.org/2020/8/e17754 %U https://doi.org/10.2196/17754 %U http://www.ncbi.nlm.nih.gov/pubmed/32124732 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e18580 %T An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation %A Ruth,Caleb J %A Huey,Samantha Lee %A Krisher,Jesse T %A Fothergill,Amy %A Gannon,Bryan M %A Jones,Camille Elyse %A Centeno-Tablante,Elizabeth %A Hackl,Laura S %A Colt,Susannah %A Finkelstein,Julia Leigh %A Mehta,Saurabh %+ Division of Nutritional Sciences, Cornell University, 314 Savage Hall, Ithaca, NY, United States, 1 (607) 255 2640, smehta@cornell.edu %K data science %K data collection %K database management systems %K global health %K public health %K data management %K health information management %K population surveillance %K longitudinal studies %K randomized controlled trial %K Electronic Data Capture (EDC) %D 2020 %7 13.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. Objective: This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. Methods: We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. Results: ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). Conclusions: ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement. %M 32788154 %R 10.2196/18580 %U https://www.jmir.org/2020/8/e18580 %U https://doi.org/10.2196/18580 %U http://www.ncbi.nlm.nih.gov/pubmed/32788154 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e15947 %T Complementing Human Behavior Assessment by Leveraging Personal Ubiquitous Devices and Social Links: An Evaluation of the Peer-Ceived Momentary Assessment Method %A Berrocal,Allan %A Concepcion,Waldo %A De Dominicis,Stefano %A Wac,Katarzyna %+ Quality of Life Technologies Lab, Department of Computer Science, University of Geneva, Route de Drize 7, Carouge, 1227, Switzerland, 41 222790242, wac@stanford.edu %K peer-ceived momentary assessment %K PeerMA %K ecological momentary assessment %K EMA %K human state assessment %K behavior modeling %K human-smartphone interaction %K digital health %K well-being %K mobile phone %D 2020 %7 7.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Ecological momentary assessment (EMA) enables individuals to self-report their subjective momentary physical and emotional states. However, certain conditions, including routine observable behaviors (eg, moods, medication adherence) as well as behaviors that may suggest declines in physical or mental health (eg, memory losses, compulsive disorders) cannot be easily and reliably measured via self-reports. Objective: This study aims to examine a method complementary to EMA, denoted as peer-ceived momentary assessment (PeerMA), which enables the involvement of peers (eg, family members, friends) to report their perception of the individual’s subjective physical and emotional states. In this paper, we aim to report the feasibility results and identified human factors influencing the acceptance and reliability of the PeerMA Methods: We conducted two studies of 4 weeks each, collecting self-reports from 20 participants about their stress, fatigue, anxiety, and well-being, in addition to collecting peer-reported perceptions from 27 of their peers. Results: Preliminary results showed that some of the peers reported daily assessments for stress, fatigue, anxiety, and well-being statistically equal to those reported by the participant. We also showed how pairing assessments of participants and peers in time enables a qualitative and quantitative exploration of unique research questions not possible with EMA-only based assessments. We reported on the usability and implementation aspects based on the participants’ experience to guide the use of the PeerMA to complement the information obtained via self-reports for observable behaviors and physical and emotional states among healthy individuals. Conclusions: It is possible to leverage the PeerMA method as a complement to EMA to assess constructs that fall in the realm of observable behaviors and states in healthy individuals. %M 32763876 %R 10.2196/15947 %U https://mhealth.jmir.org/2020/8/e15947 %U https://doi.org/10.2196/15947 %U http://www.ncbi.nlm.nih.gov/pubmed/32763876 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e18761 %T A Novel Smartphone App for the Measurement of Ultra–Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study %A Chen,Yung-Sheng %A Lu,Wan-An %A Pagaduan,Jeffrey C %A Kuo,Cheng-Deng %+ Department of Medical Research, Taipei Veterans General Hospital, Number 201, Section 2, Shipai Rd, Beitou District, Taipei, 112, Taiwan, 886 932981776, cdkuo23@gmail.com %K heart rate variability %K smartphone %K reproducibility %K limits of agreement %K autonomic nervous function %D 2020 %7 31.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined. Objective: The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP. Methods: In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis. Results: Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF. Conclusions: Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement. %M 32735219 %R 10.2196/18761 %U https://mhealth.jmir.org/2020/7/e18761 %U https://doi.org/10.2196/18761 %U http://www.ncbi.nlm.nih.gov/pubmed/32735219 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e13737 %T Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology %A Prinable,Joseph %A Jones,Peter %A Boland,David %A Thamrin,Cindy %A McEwan,Alistair %+ School of Electrical and Information Engineering, The University of Sydney, Room 402, Building J03, Maze Crescent, Darlington, 2006, Australia, 61 404035701, joseph.prinable@sydney.edu.au %K photoplethysmogram %K respiration %K asthma monitoring %K LSTM %D 2020 %7 31.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. Objective: In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. Methods: A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. Results: Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). Conclusions: A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation. %M 32735229 %R 10.2196/13737 %U http://mhealth.jmir.org/2020/7/e13737/ %U https://doi.org/10.2196/13737 %U http://www.ncbi.nlm.nih.gov/pubmed/32735229 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e18413 %T Evaluating Network Readiness for mHealth Interventions Using the Beacon Mobile Phone App: Application Development and Validation Study %A Scherr,Thomas Foster %A Moore,Carson Paige %A Thuma,Philip %A Wright,David Wilson %+ Department of Chemistry, Vanderbilt University, 7300 Stevenson Center, 1234 Stevenson Center Lane, Nashville, TN, 37235, United States, 1 615 322 5516, Thomas.f.scherr@vanderbilt.edu %K mHealth %K network readiness %K network assessment %K mobile network %D 2020 %7 28.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) interventions have the potential to transform the global health care landscape. The processing power of mobile devices continues to increase, and growth of mobile phone use has been observed worldwide. Uncertainty remains among key stakeholders and decision makers as to whether global health interventions can successfully tap into this trend. However, when correctly implemented, mHealth can reduce geographic, financial, and social barriers to quality health care. Objective: The aim of this study was to design and test Beacon, a mobile phone–based tool for evaluating mHealth readiness in global health interventions. Here, we present the results of an application validation study designed to understand the mobile network landscape in and around Macha, Zambia, in 2019. Methods: Beacon was developed as an automated mobile phone app that continually collects spatiotemporal data and measures indicators of network performance. Beacon was used in and around Macha, Zambia, in 2019. Results were collected, even in the absence of network connectivity, and asynchronously uploaded to a database for further analysis. Results: Beacon was used to evaluate three mobile phone networks around Macha. Carriers A and B completed 6820/7034 (97.0%) and 6701/7034 (95.3%) downloads and 1349/1608 (83.9%) and 1431/1608 (89.0%) uploads, respectively, while Carrier C completed only 62/1373 (4.5%) file downloads and 0/1373 (0.0%) file uploads. File downloads generally occurred within 4 to 12 seconds, and their maximum download speeds occurred between 2 AM and 5 AM. A decrease in network performance, demonstrated by increases in upload and download durations, was observed beginning at 5 PM and continued throughout the evening. Conclusions: Beacon was able to compare the performance of different cellular networks, show times of day when cellular networks experience heavy loads and slow down, and identify geographic “dead zones” with limited or no cellular service. Beacon is a ready-to-use tool that could be used by organizations that are considering implementing mHealth interventions in low- and middle-income countries but are questioning the feasibility of the interventions, including infrastructure and cost. It could also be used by organizations that are looking to optimize the delivery of an existing mHealth intervention with improved logistics management. %M 32720909 %R 10.2196/18413 %U http://mhealth.jmir.org/2020/7/e18413/ %U https://doi.org/10.2196/18413 %U http://www.ncbi.nlm.nih.gov/pubmed/32720909 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 3 %N 2 %P e18008 %T Inferring Destinations and Activity Types of Older Adults From GPS Data: Algorithm Development and Validation %A Bayat,Sayeh %A Naglie,Gary %A Rapoport,Mark J %A Stasiulis,Elaine %A Chikhaoui,Belkacem %A Mihailidis,Alex %+ Institute of Biomaterials and Biomedical Engineering, University of Toronto, 550 University Avenue, Toronto, ON, Canada, 1 416 597 3422 ext 7345, sayeh.bayat@mail.utoronto.ca %K outdoor mobility %K older adults %K GPS %K life space %K activity types %K machine learning %D 2020 %7 28.7.2020 %9 Original Paper %J JMIR Aging %G English %X Background: Outdoor mobility is an important aspect of older adults’ functional status. GPS has been used to create indicators reflecting the spatiotemporal dimensions of outdoor mobility for applications in health and aging. However, outdoor mobility is a multidimensional construct. There is, as of yet, no classification algorithm that groups and characterizes older adults’ outdoor mobility based on its semantic aspects (ie, mobility intentions and motivations) by integrating geographic and domain knowledge. Objective: This study assesses the feasibility of using GPS to determine semantic dimensions of older adults’ outdoor mobility, including destinations and activity types. Methods: A total of 5 healthy individuals, aged 65 years or older, carried a GPS device when traveling outside their homes for 4 weeks. The participants were also given a travel diary to record details of all excursions from their homes, including date, time, and destination information. We first designed and implemented an algorithm to extract destinations and infer activity types (eg, food, shopping, and sport) from the GPS data. We then evaluated the performance of the GPS-derived destination and activity information against the traditional diary method. Results: Our results detected the stop locations of older adults from their GPS data with an F1 score of 87%. On average, the extracted home locations were within a 40.18-meter (SD 1.18) distance of the actual home locations. For the activity-inference algorithm, our results reached an F1 score of 86% for all participants, suggesting a reasonable accuracy against the travel diary recordings. Our results also suggest that the activity inference’s accuracy measure differed by neighborhood characteristics (ie, Walk Score). Conclusions: We conclude that GPS technology is accurate for determining semantic dimensions of outdoor mobility. However, further improvements may be needed to develop a robust application of this system that can be adopted in clinical practice. %M 32720647 %R 10.2196/18008 %U http://aging.jmir.org/2020/2/e18008/ %U https://doi.org/10.2196/18008 %U http://www.ncbi.nlm.nih.gov/pubmed/32720647 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 7 %P e12417 %T Continuous 7-Month Internet of Things–Based Monitoring of Health Parameters of Pregnant and Postpartum Women: Prospective Observational Feasibility Study %A Saarikko,Johanna %A Niela-Vilen,Hannakaisa %A Ekholm,Eeva %A Hamari,Lotta %A Azimi,Iman %A Liljeberg,Pasi %A Rahmani,Amir M %A Löyttyniemi,Eliisa %A Axelin,Anna %+ School of Nursing and Department of Computer Science, University of California, 106D Berk Hall, Irvine, CA, 92697-3959, United States, 1 949 824 3590, a.rahmani@uci.edu %K prenatal care %K postnatal care %K wearable electronics %K biosensing %K cloud computing %K mHealth %K physical activity %K sleep %K heart rate %D 2020 %7 24.7.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Monitoring during pregnancy is vital to ensure the mother’s and infant’s health. Remote continuous monitoring provides health care professionals with significant opportunities to observe health-related parameters in their patients and to detect any pathological signs at an early stage of pregnancy, and may thus partially replace traditional appointments. Objective: This study aimed to evaluate the feasibility of continuously monitoring the health parameters (physical activity, sleep, and heart rate) of nulliparous women throughout pregnancy and until 1 month postpartum, with a smart wristband and an Internet of Things (IoT)–based monitoring system. Methods: This prospective observational feasibility study used a convenience sample of 20 nulliparous women from the Hospital District of Southwest Finland. Continuous monitoring of physical activity/step counts, sleep, and heart rate was performed with a smart wristband for 24 hours a day, 7 days a week over 7 months (6 months during pregnancy and 1 month postpartum). The smart wristband was connected to a cloud server. The total number of possible monitoring days during pregnancy weeks 13 to 42 was 203 days and 28 days in the postpartum period. Results: Valid physical activity data were available for a median of 144 (range 13-188) days (75% of possible monitoring days), and valid sleep data were available for a median of 137 (range 0-184) days (72% of possible monitoring days) per participant during pregnancy. During the postpartum period, a median of 15 (range 0-25) days (54% of possible monitoring days) of valid physical activity data and 16 (range 0-27) days (57% of possible monitoring days) of valid sleep data were available. Physical activity decreased from the second trimester to the third trimester by a mean of 1793 (95% CI 1039-2548) steps per day (P<.001). The decrease continued by a mean of 1339 (95% CI 474-2205) steps to the postpartum period (P=.004). Sleep during pregnancy also decreased from the second trimester to the third trimester by a mean of 20 minutes (95% CI –0.7 to 42 minutes; P=.06) and sleep time shortened an additional 1 hour (95% CI 39 minutes to 1.5 hours) after delivery (P<.001). The mean resting heart rate increased toward the third trimester and returned to the early pregnancy level during the postpartum period. Conclusions: The smart wristband with IoT technology was a feasible system for collecting representative data on continuous variables of health parameters during pregnancy. Continuous monitoring provides real-time information between scheduled appointments and thus may help target and tailor pregnancy follow-up. %M 32706696 %R 10.2196/12417 %U http://formative.jmir.org/2020/7/e12417/ %U https://doi.org/10.2196/12417 %U http://www.ncbi.nlm.nih.gov/pubmed/32706696 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e17238 %T Worldwide Prevalence of Hearing Loss Among Smartphone Users: Cross-Sectional Study Using a Mobile-Based App %A Masalski,Marcin %A Morawski,Krzysztof %+ Department of Otolaryngology Head and Neck Surgery, Faculty of Medicine, Wroclaw Medical University, Wybrzeze Ludwika Pasteura 1, Wroclaw, 50-367, Poland, 48 515086252, marcin.masalski@pwr.edu.pl %K hearing loss %K epidemiology %K mobile-based %K hearing test %K pure-tone audiometry %D 2020 %7 23.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: In addition to the aging process, risk factors for hearing loss in adults include, among others, exposure to noise, use of ototoxic drugs, genetics, and limited access to medical care. Differences in exposure to these factors are bound to be reflected in the prevalence of hearing loss. Assessment of hearing loss can easily be carried out on a large scale and at low cost using mobile apps. Objective: This study aimed to conduct a worldwide assessment of the differences in hearing loss prevalence between countries in a group of mobile device users. Methods: Hearing tests were conducted using the open-access Android-based mobile app Hearing Test. The app is available free of charge in the Google Play store, provided that consent to the use of the results for scientific purposes is given. This study included hearing tests carried out on device models supported by the app with bundled headphones in the set. Calibration factors for supported models were determined using the biological method. The tests consisted of self-determining the quietest audible tone in the frequency range from 250 Hz to 8 kHz by adjusting its intensity using the buttons. The ambient noise level was optionally monitored using a built-in microphone. Following the test, the user could compare his hearing threshold against age norms by providing his or her age. The user's location was identified based on the phone’s IP address. Results: From November 23, 2016 to November 22, 2019, 733,716 hearing tests were conducted on 236,716 mobile devices across 212 countries. After rejecting the tests that were incomplete, performed with disconnected headphones, not meeting the time criterion, repeated by the same user, or carried out regularly on one device, 116,733 of 733,716 tests (15.9%) were qualified for further analysis. The prevalence of hearing loss, defined as the average threshold at frequencies 0.5 kHz, 1 kHz, 2 kHz, and 4 kHz above 25 dB HL in the better ear, was calculated at 15.6% (95% CI 15.4-15.8). Statistically significant differences were found between countries (P<.001), with the highest prevalences for Bangladesh, Pakistan, and India (>28%) and the lowest prevalences for Taiwan, Finland, and South Korea (<11%). Conclusions: Hearing thresholds measured by means of mobile devices were congruent with the literature data on worldwide hearing loss prevalence. Uniform recruitment criteria simplify the comparison of the hearing loss prevalence across countries. Hearing testing on mobile devices may be a valid tool in epidemiological studies carried out on a large scale. %M 32706700 %R 10.2196/17238 %U http://www.jmir.org/2020/7/e17238/ %U https://doi.org/10.2196/17238 %U http://www.ncbi.nlm.nih.gov/pubmed/32706700 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e16113 %T Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study %A Jang,Jong-Hwan %A Choi,Junggu %A Roh,Hyun Woong %A Son,Sang Joon %A Hong,Chang Hyung %A Kim,Eun Young %A Kim,Tae Young %A Yoon,Dukyong %+ Department of Biomedical Informatics, School of Medicine, Ajou University, World cup-ro 206, Yeongtong-gu, Suwon, Gyeonggi-do, 16499, Republic of Korea, 82 031 219 4476, yoon8302@gmail.com %K accelerometer %K actigraphy %K imputation %K autoencoder %K deep learning %D 2020 %7 23.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks. Objective: The aim of this study was to impute missing values in data using a deep learning approach. Methods: To develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning–based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning–based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression). Results: The zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts. Conclusions: Our deep learning–based imputation model performed better than the other methods when imputing missing values in actigraphy data. %M 32445459 %R 10.2196/16113 %U http://mhealth.jmir.org/2020/7/e16113/ %U https://doi.org/10.2196/16113 %U http://www.ncbi.nlm.nih.gov/pubmed/32445459 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e14328 %T Cognition in Context: Understanding the Everyday Predictors of Cognitive Performance in a New Era of Measurement %A Weizenbaum,Emma %A Torous,John %A Fulford,Daniel %+ Department of Psychological and Brain Sciences, Boston University, 2nd Floor, 900 Commonwealth Avenue, Boston, MA, 02215, United States, 1 512 217 2825, eweizen@bu.edu %K smartphone %K mobile phone %K neuropsychology %K individualized medicine %D 2020 %7 23.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Research suggests that variability in attention and working memory scores, as seen across time points, may be a sensitive indicator of impairment compared with a singular score at one point in time. Given that fluctuation in cognitive performance is a meaningful metric of real-world function and trajectory, it is valuable to understand the internal state-based and environmental factors that could be driving these fluctuations in performance. Objective: In this viewpoint, we argue for the use of repeated mobile assessment as a way to better understand how context shapes moment-to-moment cognitive performance. To elucidate potential factors that give rise to intraindividual variability, we highlight existing literature that has linked both internal and external modifying variables to a number of cognitive domains. We identify ways in which these variables could be measured using mobile assessment to capture them in ecologically meaningful settings (ie, in daily life). Finally, we describe a number of studies that have already begun to use mobile assessment to measure changes in real time cognitive performance in people’s daily environments and the ways in which this burgeoning methodology may continue to advance the field. Methods: This paper describes selected literature on contextual factors that examined how experimentally induced or self-reported contextual variables (ie, affect, motivation, time of day, environmental noise, physical activity, and social activity) related to tests of cognitive performance. We also selected papers that used mobile assessment of cognition; these papers were chosen for their use of high-frequency time-series measurement of cognition using a mobile device. Results: Upon review of the relevant literature, it is evident that contextual factors have the potential to meaningfully impact cognitive performance when measured in laboratory and daily life environments. Although this research has shed light on the question of what gives rise to real-life variability in cognitive function (eg, affect and activity), many of the studies were limited by traditional methods of data collection (eg, involving retrospective recall). Furthermore, cognition has often been measured in one domain or in one age group, which does not allow us to extrapolate results to other cognitive domains and across the life span. On the basis of the literature reviewed, mobile assessment of cognition shows high levels of feasibility and validity and could be a useful method for capturing individual cognitive variability in real-world contexts via passive and active measures. Conclusions: We propose that, through the use of mobile assessment, there is an opportunity to combine multiple sources of contextual and cognitive data. These data have the potential to provide individualized digital signatures that could improve diagnostic precision and lead to meaningful clinical outcomes in a wide range of psychiatric and neurological disorders. %M 32706680 %R 10.2196/14328 %U https://mhealth.jmir.org/2020/7/e14328 %U https://doi.org/10.2196/14328 %U http://www.ncbi.nlm.nih.gov/pubmed/32706680 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e16405 %T Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study %A Dimitratos,Sarah M %A German,J Bruce %A Schaefer,Sara E %+ Foods for Health Institute, University of California, 2141 Robert Mondavi Institute, North Building, 1 Shields Ave, Davis, CA, 95616, United States, 1 530 574 0797, seschaefer@ucdavis.edu %K wearable technology %K mobile health %K mobile phone %K food intake %K validation study %D 2020 %7 22.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice, but few are reliable for obtaining accurate and precise measurements of diet and nutrition. Objective: This study aimed to assess the ability of wearable technology to monitor the nutritional intake of adult participants. This paper describes the development of a reference method to validate the wristband’s estimation of daily nutritional intake of 25 free-living study participants and to evaluate the accuracy (kcal/day) and practical utility of the technology. Methods: Participants were asked to use a nutrition tracking wristband and an accompanying mobile app consistently for two 14-day test periods. A reference method was developed to validate the estimation of daily nutritional intake of participants by the wristband. The research team collaborated with a university dining facility to prepare and serve calibrated study meals and record the energy and macronutrient intake of each participant. A continuous glucose monitoring system was used to measure adherence with dietary reporting protocols, but these findings are not reported. Bland-Altman tests were used to compare the reference and test method outputs (kcal/day). Results: A total of 304 input cases were collected of daily dietary intake of participants (kcal/day) measured by both reference and test methods. The Bland-Altman analysis had a mean bias of −105 kcal/day (SD 660), with 95% limits of agreement between −1400 and 1189. The regression equation of the plot was Y=−0.3401X+1963, which was significant (P<.001), indicating a tendency for the wristband to overestimate for lower calorie intake and underestimate for higher intake. Researchers observed transient signal loss from the sensor technology of the wristband to be a major source of error in computing dietary intake among participants. Conclusions: This study documents high variability in the accuracy and utility of a wristband sensor to track nutritional intake, highlighting the need for reliable, effective measurement tools to facilitate accurate, precision-based technologies for personal dietary guidance and intervention. %M 32706729 %R 10.2196/16405 %U https://mhealth.jmir.org/2020/7/e16405 %U https://doi.org/10.2196/16405 %U http://www.ncbi.nlm.nih.gov/pubmed/32706729 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e15948 %T Comparison of Geographic Information System and Subjective Assessments of Momentary Food Environments as Predictors of Food Intake: An Ecological Momentary Assessment Study %A Elliston,Katherine G %A Schüz,Benjamin %A Albion,Tim %A Ferguson,Stuart G %+ College of Health and Medicine, University of Tasmania, 17 Liverpool Street, Hobart, 7000, Australia, 61 362264259, katherine.elliston@utas.edu.au %K ecological momentary assessment %K mHealth %K geographic information systems %K food intake %K mobile phone %D 2020 %7 22.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: It has been observed that eating is influenced by the presence and availability of food. Being aware of the presence of food in the environment may enable mobile health (mHealth) apps to use geofencing techniques to determine the most appropriate time to proactively deliver interventions. To date, however, studies on eating typically rely on self-reports of environmental contexts, which may not be accurate or feasible for issuing mHealth interventions. Objective: This study aimed to compare the subjective and geographic information system (GIS) assessments of the momentary food environment to explore the feasibility of using GIS data to predict eating behavior and inform geofenced interventions. Methods: In total, 72 participants recorded their food intake in real-time for 14 days using an ecological momentary assessment approach. Participants logged their food intake and responded to approximately 5 randomly timed assessments each day. During each assessment, the participants reported the number and type of food outlets nearby. Their electronic diaries simultaneously recorded their GPS coordinates. The GPS data were later overlaid with a GIS map of food outlets to produce an objective count of the number of food outlets within 50 m of the participant. Results: Correlations between self-reported and GIS counts of food outlets within 50 m were only of a small size (r=0.17; P<.001). Logistic regression analyses revealed that the GIS count significantly predicted eating similar to the self-reported counts (area under the curve for the receiver operating characteristic curve [AUC-ROC] self-report=0.53, SE 0.00 versus AUC-ROC 50 m GIS=0.53, SE 0.00; P=.41). However, there was a significant difference between the GIS-derived and self-reported counts of food outlets and the self-reported type of food outlets (AUC-ROC self-reported outlet type=0.56, SE 0.01; P<.001). Conclusions: The subjective food environment appears to predict eating better than objectively measured food environments via GIS. mHealth apps may need to consider the type of food outlets rather than the raw number of outlets in an individual’s environment. %M 32706728 %R 10.2196/15948 %U https://mhealth.jmir.org/2020/7/e15948 %U https://doi.org/10.2196/15948 %U http://www.ncbi.nlm.nih.gov/pubmed/32706728 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e18082 %T Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study %A Barata,Filipe %A Tinschert,Peter %A Rassouli,Frank %A Steurer-Stey,Claudia %A Fleisch,Elgar %A Puhan,Milo Alan %A Brutsche,Martin %A Kotz,David %A Kowatsch,Tobias %+ Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Weinbergstrasse 56/57, Zurich, 8092, Switzerland, 41 446323509, fbarata@ethz.ch %K asthma %K cough recognition %K cough segmentation %K sex assignment %K deep learning %K smartphone %K mobile phone %D 2020 %7 14.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma. %M 32459641 %R 10.2196/18082 %U https://www.jmir.org/2020/7/e18082 %U https://doi.org/10.2196/18082 %U http://www.ncbi.nlm.nih.gov/pubmed/32459641 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e17891 %T Automated and Interviewer-Administered Mobile Phone Surveys in Burkina Faso: Sociodemographic Differences Among Female Mobile Phone Survey Respondents and Nonrespondents %A Greenleaf,Abigail R %A Gadiaga,Aliou %A Choi,Yoonjoung %A Guiella,Georges %A Turke,Shani %A Battle,Noelle %A Ahmed,Saifuddin %A Moreau,Caroline %+ ICAP at Columbia University, 722 W 168th St, New York, NY, 10032, United States, 1 4439553694, arg2177@cumc.columbia.edu %K cell phone %K mHealth %K Africa South of the Sahara %K Burkina Faso %K methodology, survey, nonrespondents, survey methods, interviews, telephone %D 2020 %7 14.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The remarkable growth of cell phone ownership in low- and middle-income countries has generated significant interest in using cell phones for conducting surveys through computer-assisted telephone interviews, live interviewer-administered surveys, or automated surveys (ie, interactive voice response). Objective: This study aimed to compare, by mode, the sociodemographic characteristics of cell phone owners who completed a follow-up phone survey with those who did not complete the survey. Methods: The study was based on a nationally representative sample of women aged 15 to 49 years who reported cell phone ownership during a household survey in Burkina Faso in 2016. Female cell phone owners were randomized to participate in a computer-assisted telephone interview or hybrid interactive voice response follow-up phone survey 11 months after baseline interviews. Completion of the phone survey was defined as participants responding to more than 50% of questions in the phone survey. We investigated sociodemographic characteristics associated with cell phone survey completion using multivariable logistic regression models, stratifying the analysis by survey mode and by directly comparing computer-assisted telephone interview and hybrid interactive voice response respondents. Results: A total of 1766 women were called for the phone survey between November 5 and 17, 2017. In both the computer-assisted telephone interview and hybrid interactive voice response samples, women in urban communities and women with secondary education or higher were more likely to complete the survey than their rural and less-educated counterparts. Compared directly, women who completed the hybrid interactive voice response survey had higher odds of having a secondary education than those who completed computer-assisted telephone interviews (odds ratio 1.7, 95% CI 1.1-2.6). Conclusions: In Burkina Faso, computer-assisted telephone interviews are the preferred method of conducting cell phone surveys owing to less sample distortion and a higher response rate compared with a hybrid interactive voice response survey. %M 32673250 %R 10.2196/17891 %U https://mhealth.jmir.org/2020/7/e17891 %U https://doi.org/10.2196/17891 %U http://www.ncbi.nlm.nih.gov/pubmed/32673250 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e16018 %T Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study %A Wang,Zhi Chun %A Zhang,Shi Ping %A Yuen,Pong Chi %A Chan,Kam Wa %A Chan,Yi Yi %A Cheung,Chun Hoi %A Chow,Chi Ho %A Chua,Ka Kit %A Hu,Jun %A Hu,Zhichao %A Lao,Beini %A Leung,Chun Chuen %A Li,Hong %A Zhong,Linda %A Liu,Xusheng %A Liu,Yulong %A Liu,Zhenjie %A Lun,Xin %A Mo,Wei %A Siu,Sheung Yuen %A Xiong,Zhoujian %A Yeung,Wing Fai %A Zhang,Run Yun %A Zhang,Xuebin %+ School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, , China (Hong Kong), 852 3411 2466, spzhang@hkbu.edu.hk %K mobile health %K smartphone %K traditional Chinese medicine %K telemedicine %K tongue image %K machine learning %K oral disease %K Gwet AC2 %K COVID-19 %D 2020 %7 9.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated. Objective: The first objective of this study was to develop an operating classification scheme for tongue coating diagnosis. The second and main objective of this study was to determine the intra-rater and inter-rater reliability of tongue coating diagnosis using the operating classification scheme. Methods: An operating classification scheme for tongue coating was developed using a stepwise approach and a quasi-Delphi method. First, tongue images (n=2023) were analyzed by 2 groups of assessors to develop the operating classification scheme for tongue coating diagnosis. Based on clinicians’ (n=17) own interpretations as well as their use of the operating classification scheme, the results of tongue diagnosis on a representative tongue image set (n=24) were compared. After gathering consensus for the operating classification scheme, the clinicians were instructed to use the scheme to assess tongue features of their patients under direct visual inspection. At the same time, the clinicians took tongue images of the patients with smartphones and assessed tongue features observed in the smartphone image using the same classification scheme. The intra-rater agreements of these two assessments were calculated to determine which features of tongue coating were better retained by the image. Using the finalized operating classification scheme, clinicians in the study group assessed representative tongue images (n=24) that they had taken, and the intra-rater and inter-rater reliability of their assessments was evaluated. Results: Intra-rater agreement between direct subject inspection and tongue image inspection was good to very good (Cohen κ range 0.69-1.0). Additionally, when comparing the assessment of tongue images on different days, intra-rater reliability was good to very good (κ range 0.7-1.0), except for the color of the tongue body (κ=0.22) and slippery tongue fur (κ=0.1). Inter-rater reliability was moderate for tongue coating (Gwet AC2 range 0.49-0.55), and fair for color and other features of the tongue body (Gwet AC2=0.34). Conclusions: Taken together, our study has shown that tongue images collected via smartphone contain some reliable features, including tongue coating, that can be used in mHealth analysis. Our findings thus support the use of smartphones in telemedicine for detecting changes in tongue coating. %M 32459647 %R 10.2196/16018 %U https://mhealth.jmir.org/2020/7/e16018 %U https://doi.org/10.2196/16018 %U http://www.ncbi.nlm.nih.gov/pubmed/32459647 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e17451 %T The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study %A Lee,Edmund WJ %A Bekalu,Mesfin Awoke %A McCloud,Rachel %A Vallone,Donna %A Arya,Monisha %A Osgood,Nathaniel %A Li,Xiaoyan %A Minsky,Sara %A Viswanath,Kasisomayajula %+ Dana-Farber Cancer Institute, 375 Longwood Avenue, Boston, MA, 02215, United States, 1 6178587988, Edmund_Lee@dfci.harvard.edu %K mobile health %K mobile phone %K tobacco use %K big data %K spatial analysis %K data science %D 2020 %7 7.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: People from underserved communities such as those from lower socioeconomic positions or racial and ethnic minority groups are often disproportionately targeted by the tobacco industry, through the relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or protobacco marketing and promotional strategies. It is difficult to capture the smoking behaviors of individuals in actual locations as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the United States—when used alongside data sources on TRO locations—apps could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing, particularly in relation to the locations of TROs as well as locations where they were most likely to smoke. To date, it remains unclear how health apps could be used practically by health promotion organizations to better reach underserved communities in their tobacco control efforts. Objective: This study aimed to demonstrate how smartphone apps could augment existing data on locations of TROs within underserved communities in Massachusetts and Texas to help inform tobacco control efforts. Methods: Data for this study were collected from 2 sources: (1) geolocations of TROs from the North American Industry Classification System 2016 and (2) 95 participants (aged 18 to 34 years) from underserved communities who resided in Massachusetts and Texas and took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hot spot analysis, and fitted power-law distribution to identify the TROs that attracted the most human traffic using mobility data. Results: Participants reported encountering protobacco messages mostly from store signs and displays and antitobacco messages predominantly through television. In Massachusetts, clusters of TROs (Dorchester Center and Jamaica Plain) and reported smoking behaviors (Dorchester Center, Roxbury Crossing, Lawrence) were found in economically disadvantaged neighborhoods. Despite the widespread distribution of TROs throughout the communities, participants overwhelmingly visited a relatively small number of TROs in Roxbury and Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land, and Missouri City) were found primarily in Houston, whereas clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown. Conclusions: Smartphone apps could be used to pair geolocation data with self-reported smoking behavior in order to gain a better understanding of how tobacco product marketing and promotion influence smoking behavior within vulnerable communities. Public health officials could take advantage of smartphone data collection capabilities to implement targeted tobacco control efforts in these strategic locations to reach underserved communities in their built environment. %M 32673252 %R 10.2196/17451 %U https://www.jmir.org/2020/7/e17451 %U https://doi.org/10.2196/17451 %U http://www.ncbi.nlm.nih.gov/pubmed/32673252 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e17120 %T Ecological Momentary Assessment Within a Digital Health Intervention for Reminiscence in Persons With Dementia and Caregivers: User Engagement Study %A Potts,Courtney %A Bond,Raymond %A Ryan,Assumpta %A Mulvenna,Maurice %A McCauley,Claire %A Laird,Elizabeth %A Goode,Deborah %+ Ulster University, School of Computing, Shore Road, Jordanstown, United Kingdom, 44 28 9036 8602, md.mulvenna@ulster.ac.uk %K ecological momentary assessment %K EMA %K app %K behaviour analytics %K event logging %K dementia %K carers %K reminiscence %K reminiscing %K mHealth %D 2020 %7 6.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: User-interaction event logs provide rich and large data sets that can provide valuable insights into how people engage with technology. Approaches such as ecological momentary assessment (EMA) can be used to gather accurate real-time data in an individual’s natural environment by asking questions at any given instant. Objective: The purpose of this study was to evaluate user engagement and responses to EMA questions using InspireD, an app used for reminiscence by persons with dementia and their caregivers. Research findings can be used to inform EMA use within digital health interventions. Methods: A feasibility trial was conducted in which participants (n=56) used the InspireD app over a 12-week period. Participants were a mean age of 73 (SD 13) and were either persons with dementia (n=28) or their caregivers (n=28). Questions, which they could either answer or choose to dismiss, were presented to participants at various instants after reminiscence with personal or generic photos, videos, and music. Presentation and dismissal rates for questions were compared by hour of the day and by trial week to investigate user engagement. Results: Overall engagement was high, with 69.1% of questions answered when presented. Questions that were presented in the evening had the lowest dismissal rate; the dismissal rate for questions presented at 9 PM was significantly lower than the dismissal rate for questions presented at 11 AM (9 PM: 10%; 11 AM: 50%; χ21=21.4, P<.001). Questions asked following reminiscence with personal media, especially those asked after personal photos, were less likely to be answered compared to those asked after other media. In contrast, questions asked after the user had listened to generic media, in particular those asked after generic music, were much more likely to be answered. Conclusions: The main limitation of our study was the lack of generalizability of results to a larger population given the quasi-experimental design and older demographic where half of participants were persons with dementia; however, this study shows that older people are willing to participate and engage in EMA. Based on this study, we propose a series of recommendations for app design to increase user engagement with EMA. These include presenting questions no more than once per day, after 8 PM in the evening, and only if the user is not trying to complete a task within the app. %M 32420890 %R 10.2196/17120 %U https://mhealth.jmir.org/2020/7/e17120 %U https://doi.org/10.2196/17120 %U http://www.ncbi.nlm.nih.gov/pubmed/32420890 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e18781 %T A Web-Based Mobile App (INTERACCT App) for Adolescents Undergoing Cancer and Hematopoietic Stem Cell Transplantation Aftercare to Improve the Quality of Medical Information for Clinicians: Observational Study %A Lawitschka,Anita %A Buehrer,Stephanie %A Bauer,Dorothea %A Peters,Konrad %A Silbernagl,Marisa %A Zubarovskaya,Natalia %A Brunmair,Barbara %A Kayali,Fares %A Hlavacs,Helmut %A Mateus-Berr,Ruth %A Riedl,David %A Rumpold,Gerhard %A Peters,Christina %+ Stem Cell Transplantation-Outpatient and Aftercare Clinic, St. Anna Children’s Hospital, Medical University Vienna, Kinderspitalgasse 15, 1090 Vienna, Vienna, Austria, 43 1 40 170 ext 2900, anita.lawitschka@stanna.at %K mobile app %K adolescents %K cancer %K stem cell transplant %K self-reported heath status %K medical information exchange %K mobile phone %D 2020 %7 30.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A growing number of cancer and hematopoietic stem cell transplant (HSCT) survivors require long-term follow-up with optimal communication schemes, and patients' compliance is crucial. Adolescents have various unmet needs. Regarding self-report of symptoms and health status, users of mobile apps showed enhanced compliance. Currently, HSCT aftercare at the HSCT outpatient clinic of the St. Anna Children’s Hospital in Vienna, Austria, is based on handwritten diaries, carrying various disadvantages. Recently, we developed the prototype of a web-based, self-monitoring gamified mobile app tailored for adolescents: the INTERACCT (Integrating Entertainment and Reaction Assessment into Child Cancer Therapy) app. Objective: This observational, prospective study evaluated the usability of the INTERACCT app for tracking real-time self-reported symptoms and health status data in adolescent HSCT patients and a healthy matched control group. The primary outcome of the study was the quality of the self-reported medical information. We hypothesized that the mobile app would provide superior medical information for the clinicians than would the handwritten diaries. Methods: Health data were reported via paper diary and mobile app for 5 consecutive days each. The quality of medical information was rated on a 5-point scale independently and blinded by two HSCT clinicians, and the duration of use was evaluated. A total of 52 participant questionnaires were assessed for gaming patterns and device preferences, self-efficacy, users’ satisfaction, acceptability, and suggestions for improvement of the mobile app. Interrater reliability was calculated with the intraclass correlation coefficient, based on a two-way mixed model; one-way repeated-measures analysis of variance and t tests were conducted post hoc. Descriptive methods were used for correlation with participants’ demographics. For users’ satisfaction and acceptability of the mobile app, the median and the IQR were calculated. Results: Data from 42 participants—15 patients and 27 healthy students—with comparable demographics were evaluated. The results of our study indicated a superiority of the quality of self-reported medical data in the INTERACCT app over traditional paper-and-pencil assessment (mobile app: 4.14 points, vs paper-based diary: 3.77 points, P=.02). The mobile app outperformed paper-and-pencil assessments mainly among the patients, in particular among patients with treatment-associated complications (mobile app: 4.43 points, vs paper-based diary: 3.73 points, P=.01). The mobile app was used significantly longer by adolescents (≥14 years: 4.57 days, vs ≤13 years: 3.14 days, P=.03) and females (4.76 days for females vs 2.95 days for males, P=.004). This corresponds with a longer duration of use among impaired patients with comorbidities. User satisfaction and acceptability ratings for the mobile app were high across all groups, but adherence to entering a large amount of data decreased over time. Based on our results, we developed a case vignette of the target group. Conclusions: Our study was the first to show that the quality of patient-reported medical information submitted via the INTERACCT app embedded in a serious game is superior to that submitted via a handwritten diary. In light of these results, a refinement of the mobile app supported by a machine learning approach is planned within an international research project. %M 32602847 %R 10.2196/18781 %U http://mhealth.jmir.org/2020/6/e18781/ %U https://doi.org/10.2196/18781 %U http://www.ncbi.nlm.nih.gov/pubmed/32602847 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e15547 %T Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study %A Pryss,Rüdiger %A Schlee,Winfried %A Hoppenstedt,Burkhard %A Reichert,Manfred %A Spiliopoulou,Myra %A Langguth,Berthold %A Breitmayer,Marius %A Probst,Thomas %+ Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Str 2, Würzburg, 97080, Germany, 49 931 20146471, ruediger.pryss@uni-wuerzburg.de %K mHealth %K crowdsensing %K tinnitus %K machine learning %K mobile operating system differences %K ecological momentary assessment %K mobile phone %D 2020 %7 30.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective: In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods: TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results: Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions: In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder. %M 32602842 %R 10.2196/15547 %U http://www.jmir.org/2020/6/e15547/ %U https://doi.org/10.2196/15547 %U http://www.ncbi.nlm.nih.gov/pubmed/32602842 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 6 %P e15609 %T Using Electronic Data Collection Platforms to Assess Complementary and Integrative Health Patient-Reported Outcomes: Feasibility Project %A Haun,Jolie N %A Alman,Amy C %A Melillo,Christine %A Standifer,Maisha %A McMahon-Grenz,Julie %A Shin,Marlena %A Lapcevic,W A %A Patel,Nitin %A Elwy,A Rani %+ Research Service, James A. Haley VA Medical Center, 8900 Grand Oak Circle, Tampa, FL, 33637, United States, 1 813 558 7622, Jolie.Haun@va.gov %K integrative medicine %K health information technology %K health services research %K mobile phone %K patient-reported outcomes %K veteran %D 2020 %7 26.6.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: The Veteran Administration (VA) Office of Patient-Centered Care and Cultural Transformation is invested in improving veteran health through a whole-person approach while taking advantage of the electronic resources suite available through the VA. Currently, there is no standardized process to collect and integrate electronic patient-reported outcomes (ePROs) of complementary and integrative health (CIH) into clinical care using a web-based survey platform. This quality improvement project enrolled veterans attending CIH appointments within a VA facility and used web-based technologies to collect ePROs. Objective: This study aimed to (1) determine a practical process for collecting ePROs using patient email services and a web-based survey platform and (2) conduct analyses of survey data using repeated measures to estimate the effects of CIH on patient outcomes. Methods: In total, 100 veterans from one VA facility, comprising 11 cohorts, agreed to participate. The VA patient email services (Secure Messaging) were used to manually send links to a 16-item web-based survey stored on a secure web-based survey storage platform (Qualtrics). Each survey included questions about patient outcomes from CIH programs. Each cohort was sent survey links via Secure Messaging (SM) at 6 time points: weeks 1 through 4, week 8, and week 12. Process evaluation interviews were conducted with five primary care providers to assess barriers and facilitators to using the patient-reported outcome survey in usual care. Results: This quality improvement project demonstrated the usability of SM and Qualtrics for ePRO collection. However, SM for ePROs was labor intensive for providers. Descriptive statistics on health competence (2-item Perceived Health Competence Scale), physical and mental health (Patient-Reported Outcomes Measurement Information System Global-10), and stress (4-item Perceived Stress Scale) indicated that scores did not significantly change over time. Survey response rates varied (18/100, 18.0%-42/100, 42.0%) across each of the 12 weekly survey periods. In total, 74 of 100 participants provided ≥1 survey, and 90% (66/74) were female. The majority, 62% (33/53) of participants, who reported the use of any CIH modality, reported the use of two or more unique modalities. Primary care providers highlighted specific challenges with SM and offered solutions regarding staff involvement in survey implementation. Conclusions: This quality improvement project informs our understanding of the processes currently available for using SM and web-based data platforms to collect ePROs. The study results indicate that although it is possible to use SM and web-based survey platforms for ePROs, automating scheduled administration will be necessary to reduce provider burden. The lack of significant change in ePROs may be due to standard measures taking a biomedical approach to wellness. Future work should focus on identifying ideal ePRO processes that would include standardized, whole-person measures of wellness. %M 32589163 %R 10.2196/15609 %U http://medinform.jmir.org/2020/6/e15609/ %U https://doi.org/10.2196/15609 %U http://www.ncbi.nlm.nih.gov/pubmed/32589163 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 6 %P e15777 %T Smartphone Self-Monitoring by Young Adolescents and Parents to Assess and Improve Family Functioning: Qualitative Feasibility Study %A Swendeman,Dallas %A Sumstine,Stephanie %A Brink,Amber %A Mindry,Deborah %A Medich,Melissa %A Russell,Michael %+ Global Center for Children and Families, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, 10920 Wilshire Blvd, Suite 350, Los Angeles, CA, 90024, United States, 1 3107948158, dswendeman@mednet.ucla.edu %K adolescents %K parenting %K conflict %K self-monitoring %K smartphones %K mHealth %K ecological momentary assessment %K mobile phone %D 2020 %7 23.6.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: The natural integration of mobile phones into the daily routines of families provides novel opportunities to study and support family functioning and the quality of interactions between family members in real time. Objective: This study aimed to examine user experiences of feasibility, acceptability, and reactivity (ie, changes in awareness and behaviors) of using a smartphone app for self-monitoring of family functioning with 36 participants across 15 family dyads and triads of young adolescents aged 10 to 14 years and their parents. Methods: Participants were recruited from 2 family wellness centers in a middle-to-upper income shopping area and a low-income school site. Participants were instructed and prompted by alarms to complete ecological momentary assessments (EMAs) by using a smartphone app over 2 weeks 4 times daily (upon waking in the morning, afternoon, early evening, and end of day at bedtime). The domains assessed included parental monitoring and positive parenting, parent involvement and discipline, parent-child conflict and resolution, positive interactions and support, positive and negative affect, sleep, stress, family meals, and general child and family functioning. Qualitative interviews assessed user experiences generally and with prompts for positive and negative feedback. Results: The participants were primarily white and Latino of mixed-income- and education levels. Children were aged 10 to 14 years, and parents had a mean age of 45 years (range 37-50). EMA response rates were high (95% to over 100%), likely because of cash incentives for EMA completion, engaging content per user feedback, and motivated sample from recruitment sites focused on social-emotional programs for family wellness. Some participants responded for up to 19 days, consistent with some user experience interview feedback of desires to continue participation for up to 3 or 4 weeks. Over 80% (25/31) of participants reported increased awareness of their families’ daily routines and functioning of their families. Most also reported positive behavior changes in the following domains: decision making, parental monitoring, quantity and quality of time together, communication, self-regulation of stress and conflict, discipline, and sleep. Conclusions: The results of this study support the feasibility and acceptability of using smartphone EMA by young adolescents and parents for assessing and self-monitoring family daily routines and interactions. The findings also suggest that smartphone self-monitoring may be a useful tool to support improvement in family functioning through functions of reflection on antecedents and consequences of situations, prompting positive and negative alternatives, seeding goals, and reinforcement by self-tracking for self-correction and self-rewards. Future studies should include larger samples with more diverse and higher-risk populations, longer study durations, the inclusion of passive phone sensors and peripheral biometric devices, and integration with counseling and parenting interventions and programs. %M 32574148 %R 10.2196/15777 %U http://formative.jmir.org/2020/6/e15777/ %U https://doi.org/10.2196/15777 %U http://www.ncbi.nlm.nih.gov/pubmed/32574148 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e19787 %T Wearable Activity Trackers for Monitoring Adherence to Home Confinement During the COVID-19 Pandemic Worldwide: Data Aggregation and Analysis %A Pépin,Jean Louis %A Bruno,Rosa Maria %A Yang,Rui-Yi %A Vercamer,Vincent %A Jouhaud,Paul %A Escourrou,Pierre %A Boutouyrie,Pierre %+ HP2 (Hypoxia and Physio-Pathologies) Laboratory, Inserm (French National Institute of Health and Medical Research) U1042, University Grenoble Alpes, CHU (University Hospital) Michallon, Grenoble, 38043, France, 33 476768473, JPepin@chu-grenoble.fr %K wearable activity trackers %K pandemic %K COVID-19 %K home confinement %K lockdown %K monitoring %K wearables %K tracking %D 2020 %7 19.6.2020 %9 Short Paper %J J Med Internet Res %G English %X Background: In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations’ adherence to home confinement to adapt policies and control measures accordingly. Objective: The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals’ natural activity patterns regardless of their location. Methods: We analyzed big data from individuals using activity trackers (Withings) that count the wearer’s average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens’ activities. Results: Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people’s activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders. Conclusions: Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies. %M 32501803 %R 10.2196/19787 %U http://www.jmir.org/2020/6/e19787/ %U https://doi.org/10.2196/19787 %U http://www.ncbi.nlm.nih.gov/pubmed/32501803 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e15619 %T Rethinking the Use of Mobile Apps for Dietary Assessment in Medical Research %A Khazen,Wael %A Jeanne,Jean-François %A Demaretz,Laëtitia %A Schäfer,Florent %A Fagherazzi,Guy %+ Innovation Science and Nutrition, Danone Nutricia Research, RD 128 Avenue de la Vauve, Palaiseau, 91767, France, 33 622256853, florent.schafer@danone.com %K diet %K dietary assessment %K epidemiology %K clinical research %K mobile diet app %K academic apps %K consumer-grade apps %D 2020 %7 18.6.2020 %9 Viewpoint %J J Med Internet Res %G English %X Food intake and usual dietary intake are among the key determinants of health to be assessed in medical research and important confounding factors to be accounted for in clinical studies. Although various methods are available for gathering dietary data, those based on innovative technologies are particularly promising. With combined cost-effectiveness and ease of use, it is safe to assume that mobile technologies can now optimize tracking of eating occasions and dietary behaviors. Yet, choosing a dietary assessment tool that meets research objectives and data quality standards remains challenging. In this paper, we describe the purposes of collecting dietary data in medical research and outline the main considerations for using mobile dietary assessment tools based on participant and researcher expectations. %M 32554383 %R 10.2196/15619 %U https://www.jmir.org/2020/6/e15619 %U https://doi.org/10.2196/15619 %U http://www.ncbi.nlm.nih.gov/pubmed/32554383 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e20185 %T Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study %A Huckins,Jeremy F %A daSilva,Alex W %A Wang,Weichen %A Hedlund,Elin %A Rogers,Courtney %A Nepal,Subigya K %A Wu,Jialing %A Obuchi,Mikio %A Murphy,Eilis I %A Meyer,Meghan L %A Wagner,Dylan D %A Holtzheimer,Paul E %A Campbell,Andrew T %+ Department of Psychological and Brain Science, Dartmouth College, HB6207, Hanover, NH, 03755, United States, 1 508 657 4825, jeremy.f.huckins@dartmouth.edu %K COVID-19 %K depression %K anxiety %K mobile sensing %K sedentary %K phone usage %K mental health %K behavior %K pandemic %K app %D 2020 %7 17.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The vast majority of people worldwide have been impacted by coronavirus disease (COVID-19). In addition to the millions of individuals who have been infected with the disease, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggests that this can lead to profound behavioral and mental health changes; however, researchers are rarely able to track these changes with frequent, near-real-time sampling or compare their findings to previous years of data for the same individuals. Objective: By combining mobile phone sensing and self-reported mental health data among college students who have been participating in a longitudinal study for the past 2 years, we sought to answer two overarching questions. First, have the behaviors and mental health of the participants changed in response to the COVID-19 pandemic compared to previous time periods? Second, are these behavior and mental health changes associated with the relative news coverage of COVID-19 in the US media? Methods: Behaviors such as the number of locations visited, distance traveled, duration of phone usage, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments of the Patient Health Questionnaire-4. The participants were 217 undergraduate students, with 178 (82.0%) students providing data during the Winter 2020 term. Differences in behaviors and self-reported mental health collected during the Winter 2020 term compared to previous terms in the same cohort were modeled using mixed linear models. Results: During the first academic term impacted by COVID-19 (Winter 2020), individuals were more sedentary and reported increased anxiety and depression symptoms (P<.001) relative to previous academic terms and subsequent academic breaks. Interactions between the Winter 2020 term and the week of the academic term (linear and quadratic) were significant. In a mixed linear model, phone usage, number of locations visited, and week of the term were strongly associated with increased amount of COVID-19–related news. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, and phone usage), both anxiety (P<.001) and depression (P=.03) were significantly associated with COVID-19–related news. Conclusions: Compared with prior academic terms, individuals in the Winter 2020 term were more sedentary, anxious, and depressed. A wide variety of behaviors, including increased phone usage, decreased physical activity, and fewer locations visited, were associated with fluctuations in COVID-19 news reporting. While this large-scale shift in mental health and behavior is unsurprising, its characterization is particularly important to help guide the development of methods to reduce the impact of future catastrophic events on the mental health of the population. %M 32519963 %R 10.2196/20185 %U http://www.jmir.org/2020/6/e20185/ %U https://doi.org/10.2196/20185 %U http://www.ncbi.nlm.nih.gov/pubmed/32519963 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 6 %P e13247 %T Possible Application of Ecological Momentary Assessment to Older Adults’ Daily Depressive Mood: Integrative Literature Review %A Kim,Heejung %A Kim,Sunah %A Kong,Seong Sook %A Jeong,Yi-Rang %A Kim,Hyein %A Kim,Namhee %+ College of Nursing, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 10 9267 3611, namheekim0316@gmail.com %K ecological momentary assessment %K depression %K aged %K review %D 2020 %7 2.6.2020 %9 Review %J JMIR Ment Health %G English %X Background: Ecological momentary assessment is a method of investigating individuals’ real-time experiences, behaviors, and moods in their natural environment over time. Despite its general usability and clinical value for evaluating daily depressive mood, there are several methodological challenges when applying ecological momentary assessment to older adults. Objective: The aims of this integrative literature review were to examine possible uses of the ecological momentary assessment methodology with older adults and to suggest strategies to increase the feasibility of its application in geriatric depression research and practice. Methods: We searched 4 electronic databases (MEDLINE, CINAHL, PsycINFO, and EMBASE) and gray literature; we also hand searched the retrieved articles’ references. We limited all database searches to articles published in peer-reviewed journals from 2009 to 2019. Search terms were “ecological momentary assessment,” “smartphone assessment,” “real time assessment,” “electronic daily diary,” “mHealth momentary assessment,” “mobile-based app,” and “experience sampling method,” combined with the relevant terms of depression. We included any studies that enrolled older adults even as a subgroup and that reported depressive mood at least once a day for more than 2 days. Results: Of the 38 studies that met the inclusion criteria, only 1 study enrolled adults aged 65 years or older as the entire sample; the remainder of the reviewed studies used mixed samples of both younger and older adults. Most of the analyzed studies (18/38, 47%) were quantitative, exploratory (descriptive, correlational, and predictive), and cohort in design. Ecological momentary assessment was used to describe the fluctuating pattern of participants’ depressive moods primarily and to examine the correlation between mood patterns and other health outcomes as a concurrent symptom. We found 3 key methodological issues: (1) heterogeneity in study design and protocol, (2) issues with definitions of dropout and adherence, and (3) variation in how depressive symptoms were measured with ecological momentary assessment. Some studies (8/38, 21%) examined the age difference of participants with respect to dropout or poor compliance rate. Detailed participant burden was reported, such as technical problems, aging-related health problems, or discomfort while using the device. Conclusions: Ecological momentary assessment has been used for comprehensive assessment of multiple mental health indicators in relation to depressive mood. Our findings provide methodological considerations for further studies that may be implemented using ecological momentary assessment to assess daily depressive mood in older adults. Conducting more feasibility studies focusing on older adults with standardized data collection protocols and mixed-methods research is required to reflect users’ experiences. Further telepsychiatric evaluation and diagnosis based on ecological momentary assessment data should involve standardized and sophisticated strategies to maximize the potential of ecological momentary assessment for older adults with depression in the community setting. %M 32484442 %R 10.2196/13247 %U https://mental.jmir.org/2020/6/e13247 %U https://doi.org/10.2196/13247 %U http://www.ncbi.nlm.nih.gov/pubmed/32484442 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 5 %P e16875 %T Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors %A Jacobson,Nicholas C %A Summers,Berta %A Wilhelm,Sabine %+ Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 300, Office # 333S, Lebanon, NH, 03766, United States, 1 6036467037, Nicholas.C.Jacobson@dartmouth.edu %K biomarkers %K machine learning %K technology assessment, biomedical %K social anxiety %K social anxiety disorder %K mobile phone %D 2020 %7 29.5.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. Objective: This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. Methods: In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. Results: The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. Conclusions: These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect. %M 32348284 %R 10.2196/16875 %U http://www.jmir.org/2020/5/e16875/ %U https://doi.org/10.2196/16875 %U http://www.ncbi.nlm.nih.gov/pubmed/32348284 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e15458 %T Associations Between Parent Self-Reported and Accelerometer-Measured Physical Activity and Sedentary Time in Children: Ecological Momentary Assessment Study %A de Brito,Junia N %A Loth,Katie A %A Tate,Allan %A Berge,Jerica M %+ Department of Family Medicine and Community Health, University of Minnesota, 717 Delaware Street SE, Suite 400, Minneapolis, MN, 55414, United States, 1 612 625 0931, nogue013@umn.edu %K ecological momentary assessment %K accelerometry %K mobile devices %K physical activity %K sedentary behavior %K children %D 2020 %7 19.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Retrospective self-report questionnaires are the most common method for assessing physical activity (PA) and sedentary behavior (SB) in children when the use of objective assessment methods (eg, accelerometry) is cost prohibitive. However, self-report measures have limitations (eg, recall bias). The use of real-time, mobile ecological momentary assessment (EMA) has been proposed to address these shortcomings. The study findings will provide useful information for researchers interested in using EMA surveys for measuring PA and SB in children, particularly when reported by a parent or caregiver. Objective: This study aimed to examine the associations between the parent’s EMA report of their child’s PA and SB and accelerometer-measured sedentary time (ST), light-intensity PA (LPA), and moderate-to-vigorous–intensity PA (MVPA) and to examine if these associations differed by day of week, sex, and season. Methods: A total of 140 parent-child dyads (mean child age 6.4 years, SD 0.8; n=66 girls; n=21 African American; n=24 American Indian; n=25 Hispanic/Latino; n=24 Hmong; n=22 Somali; and n=24 white) participated in this study. During an 8-day period, parents reported child PA and SB via multiple daily signal contingent EMA surveys, and children wore a hip-mounted accelerometer to objectively measure ST, LPA, and MVPA. Accelerometer data was matched to the time period occurring before parent EMA-report of child PA and SB. Generalized estimating equations with interaction-term analyses were performed to determine whether the relationship between parent-EMA report of child PA and SB and accelerometer-measured ST and LPA and MVPA outcomes differed by day of the week, sex and season. Results: The parent’s EMA report of their child’s PA and SB was strongly associated with accelerometer-measured ST, LPA, and MVPA. The parent’s EMA report of their child’s PA was stronger during the weekend than on weekdays for accelerometer-measured ST (P≤.001) and LPA (P<.001). For the parent’s EMA report of their child’s SB, strong associations were observed with accelerometer-measured ST (P<.001), LPA (P=.005), and MVPA (P=.008). The findings related to sex-interaction terms indicated that the association between the parent-reported child’s PA via EMA and the accelerometer-measured MVPA was stronger for boys than girls (P=.02). The association between the parent’s EMA report of their child’s PA and SB and accelerometer-measured ST and PA was similar across seasons in this sample (all P values >.31). Conclusions: When the use of accelerometry-based methods is not feasible and in contexts where the parent is able to spend more proximate time observing the child’s PA and SB, the parent’s EMA report might be a superior method for measuring PA and SB in young children relative to self-report, given the EMA’s strong associations with accelerometer-measured PA and ST. %M 32348283 %R 10.2196/15458 %U http://mhealth.jmir.org/2020/5/e15458/ %U https://doi.org/10.2196/15458 %U http://www.ncbi.nlm.nih.gov/pubmed/32348283 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e17219 %T A Mobile Health App for the Collection of Functional Outcomes After Inpatient Stroke Rehabilitation: Pilot Randomized Controlled Trial %A Li,Li %A Huang,Jia %A Wu,Jingsong %A Jiang,Cai %A Chen,Shanjia %A Xie,Guanli %A Ren,Jinxin %A Tao,Jing %A Chan,Chetwyn C H %A Chen,Lidian %A Wong,Alex W K %+ Program in Occupational Therapy, Washington University School of Medicine, 600 South Taylor Avenue, MSC 8505-94-01, St. Louis, MO, United States, 1 314 286 0278, wongal@wustl.edu %K telemedicine %K cell phone %K stroke %K rehabilitation %K activities of daily living %K outcome and process assessment %K health care %D 2020 %7 13.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Monitoring the functional status of poststroke patients after they transition home is significant for rehabilitation. Mobile health (mHealth) technologies may provide an opportunity to reach and follow patients post discharge. However, the feasibility and validity of functional assessments administered by mHealth technologies are unknown. Objective: This study aimed to evaluate the feasibility, validity, and reliability of functional assessments administered through the videoconference function of a mobile phone–based app compared with administration through the telephone function in poststroke patients after rehabilitation hospitalization. Methods: A randomized controlled trial was conducted in a rehabilitation hospital in Southeast China. Participants were randomly assigned to either a videoconference follow-up (n=60) or a telephone follow-up (n=60) group. We measured the functional status of participants in each group at 2-week and 3-month follow-up periods. Half the participants in each group were followed by face-to-face home visit assessments as the gold standard. Validity was assessed by comparing any score differences between videoconference follow-up and home visit assessments, as well as telephone follow-up and home visit assessments. Reliability was assessed by computing agreements between videoconference follow-up and home visit assessments, as well as telephone follow-up and home visit assessments. Feasibility was evaluated by the levels of completion, satisfaction, comfort, and confidence in the 2 groups. Results: Scores obtained from the videoconference follow-up were similar to those of the home visit assessment. However, most scores collected from telephone administration were higher than those of the home visit assessment. The agreement between videoconference follow-up and home visit assessments was higher than that between telephone follow-up and home visit assessments at all follow-up periods. In the telephone follow-up group, completion rates were 95% and 82% at 2-week and 3-month follow-up points, respectively. In the videoconference follow-up group, completion rates were 95% and 80% at 2-week and 3-month follow-up points, respectively. There were no differences in the completion rates between the 2 groups at all follow-up periods (X21=1.6, P=.21 for 2-week follow-up; X21=1.9, P=.17 for 3-month follow-up). Patients in the videoconference follow-up group perceived higher confidence than those in the telephone follow-up group at both 2-week and 3-month follow-up periods (X23=6.7, P=.04 for 2-week follow-up; X23=8.0, P=.04 for 3-month follow-up). The videoconference follow-up group demonstrated higher satisfaction than the telephone follow-up group at 3-month follow-up (X23=13.9; P=.03). Conclusions: The videoconference follow-up assessment of functional status demonstrates higher validity and reliability, as well as higher confidence and satisfaction perceived by patients, than the telephone assessment. The videoconference assessment provides an efficient means of assessing functional outcomes of patients after hospital discharge. This method provides a novel solution for clinical trials requiring longitudinal assessments. Trial Registration: chictr.org.cn: ChiCTR1900027626; http://www.chictr.org.cn/edit.aspx?pid=44831&htm=4. %M 32401221 %R 10.2196/17219 %U https://mhealth.jmir.org/2020/5/e17219 %U https://doi.org/10.2196/17219 %U http://www.ncbi.nlm.nih.gov/pubmed/32401221 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e15628 %T Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments %A Habets,Jeroen %A Heijmans,Margot %A Herff,Christian %A Simons,Claudia %A Leentjens,Albert FG %A Temel,Yasin %A Kuijf,Mark %A Kubben,Pieter %+ Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Universiteitssingel 50, Maastricht, 6229 ER, Netherlands, 31 43 3881348, j.habets@maastrichtuniversity.nl %K ecological momentary assessment %K experience sampling method %K electronic diary %K Parkinson’s disease monitoring %D 2020 %7 11.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Parkinson disease monitoring is currently transitioning from periodic clinical assessments to continuous daily life monitoring in free-living conditions. Traditional Parkinson disease monitoring methods lack intraday fluctuation detection. Electronic diaries (eDiaries) hold the potential to collect subjective experiences on the severity and burden of motor and nonmotor symptoms in free-living conditions. Objective: This study aimed to develop a Parkinson disease–specific eDiary based on ecological momentary assessments (EMAs) and to explore its validation. Methods: An observational cohort of 20 patients with Parkinson disease used the smartphone-based EMA eDiary for 14 consecutive days without adjusting free-living routines. The eDiary app presented an identical questionnaire consisting of questions regarding affect, context, motor and nonmotor symptoms, and motor performance 7 times daily at semirandomized moments. In addition, patients were asked to complete a morning and an evening questionnaire. Results: Mean affect correlated moderate-to-strong and moderate with motor performance (R=0.38 to 0.75; P<.001) and motor symptom (R=0.34 to 0.50; P<.001) items, respectively. The motor performance showed a weak-to-moderate negative correlation with motor symptoms (R=−0.31 to −0.48; P<.001). Mean group answers given for on-medication conditions vs wearing-off-medication conditions differed significantly (P<.05); however, not enough questionnaires were completed for the wearing-off-medication condition to reproduce these findings on individual levels. Conclusions: We presented a Parkinson disease–specific EMA eDiary. Correlations between given answers support the internal validity of the eDiary and underline EMA’s potential in free-living Parkinson disease monitoring. Careful patient selection and EMA design adjustment to this targeted population and their fluctuations are necessary to generate robust proof of EMA validation in future work. Combining clinical Parkinson disease knowledge with practical EMA experience is inevitable to design and perform studies, which will lead to the successful integration of eDiaries in free-living Parkinson disease monitoring. %M 32339999 %R 10.2196/15628 %U https://mhealth.jmir.org/2020/5/e15628 %U https://doi.org/10.2196/15628 %U http://www.ncbi.nlm.nih.gov/pubmed/32339999 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e17320 %T A Mobile Technology for Collecting Patient-Reported Physical Activity and Distress Outcomes: Cross-Sectional Cohort Study %A Jung,Miyeon %A Lee,SaeByul %A Kim,Jisun %A Kim,HeeJeong %A Ko,BeomSeok %A Son,Byung Ho %A Ahn,Sei-Hyun %A Park,Yu Rang %A Cho,Daegon %A Chung,Haekwon %A Park,Hye Jin %A Lee,Minsun %A Lee,Jong Won %A Chung,Seockhoon %A Chung,Il Yong %+ Division of Breast Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, , Republic of Korea, 82 2 3010 3998, doorkeeper1@gmail.com %K telemedicine %K breast neoplasms %K mobile apps %K quality of life %K validation %K patient-reported outcome measures (PROMs) %K questionnaire %D 2020 %7 4.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Electronic patient-reported outcome (PROs) provides a fast and reliable assessment of a patient’s health-related quality of life. Nevertheless, using PRO in the traditional paper format is not practical for clinical practice due to the limitations associated with data analysis and management. A questionnaire app was developed to address the need for a practical way to group and use distress and physical activity assessment tools. Objective: The purpose of this study was to assess the level of agreement between electronic (mobile) and paper-and-pencil questionnaire responses. Methods: We validated the app version of the distress thermometer (DT), International Physical Activity Questionnaire (IPAQ), and Patient Health Questionnaire–9 (PHQ-9). A total of 102 participants answered the paper and app versions of the DT and IPAQ, and 96 people completed the PHQ-9. The study outcomes were the correlation of the data between the paper-and-pencil and app versions. Results: A total of 106 consecutive breast cancer patients were enrolled and analyzed for validation of paper and electronic (app) versions. The Spearman correlation values of paper and app surveys for patients who responded to the DT questionnaire within 7 days, within 3 days, and on the same day were .415 (P<.001), .437 (P<.001), and .603 (P<.001), respectively. Similarly, the paper and app survey correlation values of the IPAQ total physical activity metabolic equivalent of task (MET; Q2-6) were .291 (P=.003), .324 (P=.005), and .427 (P=.01), respectively. The correlation of the sum of the Patient Health Questionnaire–9 (Q1-9) according to the time interval between the paper-based questionnaire and the app-based questionnaire was .469 for 14 days (P<.001), .574 for 7 days (P<.001), .593 for 3 days (P<.001), and .512 for the same day (P=.03). These were all statistically significant. Similarly, the correlation of the PHQ (Q10) value according to the time interval between the paper-based questionnaire and the app-based questionnaire was .283 for 14 days (P=.005), .409 for 7 days (P=.001), .415 for 3 days (P=.009), and .736 for the same day (P=.001). These were all statistically significant. In the overall trend, the shorter the interval between the paper-and-pencil questionnaire and the app-based questionnaire, the higher the correlation value. Conclusions: The app version of the distress and physical activity questionnaires has shown validity and a high level of association with the paper-based DT, IPAQ (Q2-6), and PHQ-9. The app-based questionnaires were not inferior to their respective paper versions and confirm the feasibility for their use in clinical practice. The high correlation between paper and mobile app data allows the use of new mobile apps to benefit the overall health care system. Trial Registration: ClinicalTrials.gov NCT03072966; https://clinicaltrials.gov/ct2/show/NCT03072966 %M 32364508 %R 10.2196/17320 %U https://mhealth.jmir.org/2020/5/e17320 %U https://doi.org/10.2196/17320 %U http://www.ncbi.nlm.nih.gov/pubmed/32364508 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e16507 %T App-based Self-administrable Clinical Tests of Physical Function: Development and Usability Study %A Bergquist,Ronny %A Vereijken,Beatrix %A Mellone,Sabato %A Corzani,Mattia %A Helbostad,Jorunn L %A Taraldsen,Kristin %+ Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Edvard Griegs gate 8, Trondheim, 7030, Norway, 47 47634462, ronny.bergquist@ntnu.no %K physical function %K mHealth app %K usability %K older people %K seniors %D 2020 %7 27.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Objective measures of physical function in older adults are widely used to predict health outcomes such as disability, institutionalization, and mortality. App-based clinical tests allow users to assess their own physical function and have objective tracking of changes over time by use of their smartphones. Such tests can potentially guide interventions remotely and provide more detailed prognostic information about the participant’s physical performance for the users, therapists, and other health care personnel. We developed 3 smartphone apps with instrumented versions of the Timed Up and Go (Self-TUG), tandem stance (Self-Tandem), and Five Times Sit-to-Stand (Self-STS) tests. Objective: This study aimed to test the usability of 3 smartphone app–based self-tests of physical function using an iterative design. Methods: The apps were tested in 3 iterations: the first (n=189) and second (n=134) in a lab setting and the third (n=20) in a separate home-based study. Participants were healthy adults between 60 and 80 years of age. Assessors observed while participants self-administered the tests without any guidance. Errors were recorded, and usability problems were defined. Problems were addressed in each subsequent iteration. Perceived usability in the home-based setting was assessed by use of the System Usability Scale, the User Experience Questionnaire, and semi-structured interviews. Results: In the first iteration, 7 usability problems were identified; 42 (42/189, 22.0%) and 127 (127/189, 67.2%) participants were able to correctly perform the Self-TUG and Self-Tandem, respectively. In the second iteration, errors caused by the problems identified in the first iteration were drastically reduced, and 108 (108/134, 83.1%) and 106 (106/134, 79.1%) of the participants correctly performed the Self-TUG and Self-Tandem, respectively. The first version of the Self-STS was also tested in this iteration, and 40 (40/134, 30.1%) of the participants performed it correctly. For the third usability test, the 7 usability problems initially identified were further improved. Testing the apps in a home setting gave rise to some new usability problems, and for Self-TUG and Self-STS, the rates of correctly performed trials were slightly reduced from the second version, while for Self-Tandem, the rate increased. The mean System Usability Scale score was 77.63 points (SD 16.1 points), and 80-95% of the participants reported the highest or second highest positive rating on all items in the User Experience Questionnaire. Conclusions: The study results suggest that the apps have the potential to be used to self-test physical function in seniors in a nonsupervised home-based setting. The participants reported a high degree of ease of use. Evaluating the usability in a home setting allowed us to identify new usability problems that could affect the validity of the tests. These usability problems are not easily found in the lab setting, indicating that, if possible, app usability should be evaluated in both settings. Before being made available to end users, the apps require further improvements and validation. %M 32338616 %R 10.2196/16507 %U http://mhealth.jmir.org/2020/4/e16507/ %U https://doi.org/10.2196/16507 %U http://www.ncbi.nlm.nih.gov/pubmed/32338616 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e15561 %T Developing Mental or Behavioral Health Mobile Apps for Pilot Studies by Leveraging Survey Platforms: A Do-it-Yourself Process %A Chow,Philip I %+ Center for Behavioral Health and Technology, Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, 560 Ray C. Hunt Dr., Charlottesville, VA, 22908, United States, 1 434 924 5401, philip.i.chow@gmail.com %K app %K mental health %K mHealth %D 2020 %7 20.4.2020 %9 Tutorial %J JMIR Mhealth Uhealth %G English %X Background: Behavioral health researchers are increasingly recognizing the potential of mobile phone apps to deliver empirically supported treatments. However, current options for developing apps typically require large amounts of expertise or money. Objective: This paper aims to describe a pragmatic do-it-yourself approach for researchers to create and pilot an Android mobile phone app using existing survey software (eg, Qualtrics survey platform). Methods: This study was conducted at an academic research center in the United States focused on developing and evaluating behavioral health technologies. The process outlined in this paper was derived and condensed from the steps to building an existing app intervention, iCanThrive, which was developed to enhance mental well-being in women cancer survivors. Results: This paper describes an inexpensive, practical process that uses a widely available survey software, such as Qualtrics, to create and pilot a mobile phone intervention that is presented to participants as a Web viewer app that is downloaded from the Google Play store. Health researchers who are interested in using this process to pilot apps are encouraged to inquire about the survey platforms available to them, the level of security those survey platforms provide, and the regulatory guidelines set forth by their institution. Conclusions: As app interventions continue to gain interest among researchers and consumers alike, it is important to find new ways to efficiently develop and pilot app interventions before committing a large amount of resources. Mobile phone app interventions are an important component to discovering new ways to reach and support individuals with behavioral or mental health disorders. %M 32310143 %R 10.2196/15561 %U https://mhealth.jmir.org/2020/4/e15561 %U https://doi.org/10.2196/15561 %U http://www.ncbi.nlm.nih.gov/pubmed/32310143 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e12677 %T Cyberphysical Human Sexual Behavior Acquisition System (SeBA): Development and Implementation Study in China %A Zhou,Xiaoping %A Zhao,Jichao %A Liang,Xun %+ School of Information Science, Qufu Normal University, #57 Jingxuan West Road, Qufu, 273165, China, 86 10 68322509, xunliangruc@163.com %K cyberphysical system %K sexual behavior %K smart sex toys %K mobile social network %D 2020 %7 3.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sexual health is one of the principal components of human well-being. Traditional methods for observing human sexual behavior typically adopt manual intervention approaches (eg, interviews). However, the data obtained by such traditional approaches suffer from intrinsic bias and limited sample sizes. Sexual behavioral data that are more reflective of the actual situation can be collected by equipping sex toys with sensors. Objective: To address the limitations of traditional human sexual behavior data observation methods, a novel cyberphysical system is proposed to capture natural human sexual behavior data in China at the nationwide level. Methods: A cyberphysical human sexual behavior acquisition system (SeBA) was designed and implemented. SeBA jointly utilizes state of the art information and communication technologies such as smart sex toys, smartphones, and mobile social networks. Smart sex toys enable objective collection of data on human sexual behavior, while the mobile social network provides the possibility of partnered sex in a cyberphysical manner. The objectives and function settings are discussed, and the overall framework of the system architecture is presented. Results: Operation and privacy policies are proposed and the technical solution of SeBA is described. The effectiveness of SeBA was verified based on analysis of users’ human sexual behavior data collected from January 2016 to June 2017. A total of 103,424 solo sexual behaviors were recorded involving 13,047 users, and 61,007 partnered sexual behaviors from 7,140 users were observed. The proportions of males and females in the solo and partnered sex groups were fairly consistent with recent statistics on unmarried individuals in China. We also found that only a small portion of individuals provided information on at least one other attribute besides the required input of gender, such as age, height, location, job, sex preferences, purposes, and interests. Conclusions: To the best of our knowledge, this is the first study to analyze objective human sexual behavior data at the nationwide level. Although the data are restricted to China, this study can provide insight for further research on human sexual behavior based on the huge amount of data available from wireless smart sex toys worldwide. It is anticipated that findings from such objective big data analyses can help deepen our understanding of sexual behavior, as well as improve sexual health and sexual wellness. %M 32271153 %R 10.2196/12677 %U https://mhealth.jmir.org/2020/4/e12677 %U https://doi.org/10.2196/12677 %U http://www.ncbi.nlm.nih.gov/pubmed/32271153 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e16142 %T Evaluating the Feasibility of Frequent Cognitive Assessment Using the Mezurio Smartphone App: Observational and Interview Study in Adults With Elevated Dementia Risk %A Lancaster,Claire %A Koychev,Ivan %A Blane,Jasmine %A Chinner,Amy %A Wolters,Leona %A Hinds,Chris %+ Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, United Kingdom, 44 1865 743893, claire.lancaster@bdi.ox.ac.uk %K technology assessment %K cognition %K smartphone %K mhealth %K mobile phone %K Alzheimer disease %K early diagnosis %K feasibility study %K ecological momentary assessment %D 2020 %7 2.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: By enabling frequent, sensitive, and economic remote assessment, smartphones will facilitate the detection of early cognitive decline at scale. Previous studies have sustained participant engagement with remote cognitive assessment over a week; extending this to a period of 1 month clearly provides a greater opportunity for measurement. However, as study durations are increased, the need to understand how participant burden and scientific value might be optimally balanced also increases. Objective: This study explored the little but often approach to assessment employed by the Mezurio app when prompting participants to interact every day for over a month. Specifically, this study aimed to understand whether this extended duration of remote study is feasible, and which factors promote sustained participant engagement over such periods. Methods: A total of 35 adults (aged 40-59 years) with no diagnosis of cognitive impairment were prompted to interact with the Mezurio smartphone app platform for up to 36 days, completing short, daily episodic memory tasks in addition to optional executive function and language tests. A subset (n=20) of participants completed semistructured interviews focused on their experience of using the app. Results: Participants complied with 80% of the daily learning tasks scheduled for subsequent tests of episodic memory, with 88% of participants still actively engaged by the final task. A thematic analysis of the participants’ experiences highlighted schedule flexibility, a clear user interface, and performance feedback as important considerations for engagement with remote digital assessment. Conclusions: Despite the extended study duration, participants demonstrated high compliance with the schedule of daily learning tasks and were extremely positive about their experiences. Long durations of remote digital interaction are therefore definitely feasible but only when careful attention is paid to the design of the users’ experience. %M 32238339 %R 10.2196/16142 %U https://mhealth.jmir.org/2020/4/e16142 %U https://doi.org/10.2196/16142 %U http://www.ncbi.nlm.nih.gov/pubmed/32238339 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e15152 %T Importance of Photography Education to Improve Image Quality for Accurate Remote Diagnoses in Dental Trauma Patients: Observational Study %A Jeong,Jin-Sun %A Pang,Nan-Sim %A Choi,Yiseul %A Park,Kyeong-Mee %A Kim,Taekbin %A Xu,Xin %A Park,Wonse %+ Department of Advanced General Dentistry, College of Dentistry, Yonsei University, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 2228 8980, wonse@yuhs.ac %K telemedicine %K remote consultation %K emergencies %K tooth injuries %K cell phone %D 2020 %7 26.3.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: High-quality photos are critical for the remote diagnosis of dental trauma and thus are beneficial to the prognosis. The quality of the images obtained using a cell phone depends on the level of dental and photography knowledge of the person who is taking the photos. Objective: This study aimed to determine the efficacy of photography education in improving images used for the remote diagnosis of dental trauma. Methods: The subjects comprised 30 laypeople and 30 dentists who were randomly assigned to 15 subgroups with 2 subjects in each. Each subject was asked to take photos of their own anterior teeth and those of their partner on the assumption that an accident occurred using both an iPhone 4s and iPhone 6. Education about how to take an appropriate photo of the anterior teeth for teleconsultation purposes was then provided, after which photos were taken again. Photos were assessed by a dentist for their usefulness in diagnosis. Results: This study analyzed 965 photos: 441 taken by laypeople and 524 taken by dentists. Photos taken after providing education had significantly higher scores for all assessment items than those taken before education (P<.05). The scores were also significantly higher for photos taken using the rear camera than those taken using the front camera (P<.02). The iPhone 6 did not have overwhelming advantages. The photos taken by dentists had significantly higher scores than those taken by laypeople for most of the evaluated items. Conclusions: Both laypeople and dentists might find photography education useful for when they are taking photos to be used in teleconsultations. The type of cell phone does not significantly affect the usefulness of such photos. %M 32213475 %R 10.2196/15152 %U http://mhealth.jmir.org/2020/3/e15152/ %U https://doi.org/10.2196/15152 %U http://www.ncbi.nlm.nih.gov/pubmed/32213475 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e15294 %T Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study %A Herzig,David %A Nakas,Christos T %A Stalder,Janine %A Kosinski,Christophe %A Laesser,Céline %A Dehais,Joachim %A Jaeggi,Raphael %A Leichtle,Alexander Benedikt %A Dahlweid,Fried-Michael %A Stettler,Christoph %A Bally,Lia %+ Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital, University of Bern, Freiburgstrasse 15, Bern, 3010, Switzerland, 41 31 632 36 77, lia.bally@insel.ch %K depth camera %K computer vision %K dietary assessment %K smartphone %D 2020 %7 25.3.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Quantification of dietary intake is key to the prevention and management of numerous metabolic disorders. Conventional approaches are challenging, laborious, and lack accuracy. The recent advent of depth-sensing smartphones in conjunction with computer vision could facilitate reliable quantification of food intake. Objective: The objective of this study was to evaluate the accuracy of a novel smartphone app combining depth-sensing hardware with computer vision to quantify meal macronutrient content using volumetry. Methods: The app ran on a smartphone with a built-in depth sensor applying structured light (iPhone X). The app estimated weight, macronutrient (carbohydrate, protein, fat), and energy content of 48 randomly chosen meals (breakfasts, cooked meals, snacks) encompassing 128 food items. The reference weight was generated by weighing individual food items using a precision scale. The study endpoints were (1) error of estimated meal weight, (2) error of estimated meal macronutrient content and energy content, (3) segmentation performance, and (4) processing time. Results: In both absolute and relative terms, the mean (SD) absolute errors of the app’s estimates were 35.1 g (42.8 g; relative absolute error: 14.0% [12.2%]) for weight; 5.5 g (5.1 g; relative absolute error: 14.8% [10.9%]) for carbohydrate content; 1.3 g (1.7 g; relative absolute error: 12.3% [12.8%]) for fat content; 2.4 g (5.6 g; relative absolute error: 13.0% [13.8%]) for protein content; and 41.2 kcal (42.5 kcal; relative absolute error: 12.7% [10.8%]) for energy content. Although estimation accuracy was not affected by the viewing angle, the type of meal mattered, with slightly worse performance for cooked meals than for breakfasts and snacks. Segmentation adjustment was required for 7 of the 128 items. Mean (SD) processing time across all meals was 22.9 seconds (8.6 seconds). Conclusions: This study evaluated the accuracy of a novel smartphone app with an integrated depth-sensing camera and found highly accurate volume estimation across a broad range of food items. In addition, the system demonstrated high segmentation performance and low processing time, highlighting its usability. %M 32209531 %R 10.2196/15294 %U http://mhealth.jmir.org/2020/3/e15294/ %U https://doi.org/10.2196/15294 %U http://www.ncbi.nlm.nih.gov/pubmed/32209531 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e15281 %T The Utility of SMS to Report Male Partner HIV Self-testing Outcomes Among Women Seeking Reproductive Health Services in Kenya: Cohort Study %A Drake,Alison L %A Begnel,Emily %A Pintye,Jillian %A Kinuthia,John %A Wagner,Anjuli D %A Rothschild,Claire W %A Otieno,Felix %A Kemunto,Valarie %A Baeten,Jared M %A John-Stewart,Grace %+ Department of Global Health, University of Washington, 325 9th Ave, Seattle, WA, United States, 1 206 543 5847, adrake2@uw.edu %K SMS %K HIV self-testing %K survey coverage %K HIV pre-exposure prophylaxis %D 2020 %7 25.3.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Use of SMS for data collection is expanding, but coverage, bias, and logistical constraints are poorly described. Objective: The aim of this study is to assess the use of SMS to capture clinical outcomes that occur at home and identify potential biases in reporting compared to in-person ascertainment. Methods: In the PrEP Implementation in Young Women and Adolescents program, which integrated pre-exposure prophylaxis (PrEP) into antenatal care, postnatal care, and family planning facilities in Kisumu County, Kenya, HIV-negative women 14 years of age or older were offered oral HIV self-tests (HIVSTs) to take home to male partners. Women that brought a phone with a Safaricom SIM to the clinic were offered registration in an automated SMS system (mSurvey) to collect information on HIVST outcomes. Women were asked if they offered the test to their male partners, and asked about the test process and results. HIVST outcomes were collected via SMS (sent 2.5 weeks later), in-person (if women returned for a follow-up scheduled 1 month later), or using both methods (if women initiated PrEP, they also had scheduled follow-up visits). The SMS prompted women to reply at no charge. HIVST outcomes were compared between women with scheduled follow-up visits and those without (follow-up visits were only scheduled for women who initiated PrEP). HIVST outcomes were also compared between women reporting via SMS and in-person. Results: Among 2123 women offered HIVSTs and mSurvey registration, 486 (23.89%) accepted HIVSTs, of whom 359 (73.87%) were eligible for mSurvey. Additionally, 76/170 (44.7%) women with scheduled follow-up visits and 146/189 (77.3%) without scheduled follow-up visits registered in mSurvey. Among the 76 women with scheduled follow-ups, 62 (82%) had HIVST outcomes collected: 19 (31%) in-person, 20 (32%) by SMS, and 23 (37%) using both methods. Among the 146 women without scheduled visits, 87 (59.6%) had HIVST outcomes collected: 3 (3%) in-person, 82 (94%) by SMS, and 2 (2%) using both methods. SMS increased the collection of HIVST outcomes substantially for women with scheduled follow-up visits (1.48-fold), and captured 82 additional reports from women without scheduled follow-up visits. Among 222 women with reported HIVST outcomes, frequencies of offering partners the HIVST (85/95, 89% in-person vs 96/102, 94% SMS; P=.31), partners using the HIVST (83/85, 98% vs 92/96, 96%; P=.50), women using HIVST with partners (82/83, 99% vs 91/92, 99%; P=.94), and seeing partner’s HIVST results (82/83, 99% vs 89/92, 97%; P=.56) were similar between women reporting in-person only versus by SMS only. However, frequency of reports of experiencing harm or negative reactions from partners was more commonly reported in the SMS group (17/102, 16.7% vs 2/85, 2%; P=.003). Barriers to the SMS system registration included not having a Safaricom SIM or a functioning phone. Conclusions: Our results suggest that the use of SMS substantially improves completeness of outcome data, does not bias reporting of nonsensitive information, and may increase reporting of sensitive information.   %M 32209530 %R 10.2196/15281 %U http://mhealth.jmir.org/2020/3/e15281/ %U https://doi.org/10.2196/15281 %U http://www.ncbi.nlm.nih.gov/pubmed/32209530 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 3 %P e15494 %T Mutual-Aid Mobile App for Emergency Care: Feasibility Study %A Chien,Shuo-Chen %A Islam,Md Mohaimenul %A Yeh,Chen-An %A Chien,Po-Han %A Chen,Chun You %A Chin,Yen-Po %A Lin,Ming-Chin %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 15F, No 172-1, Sec 2, Keelung Rd, Da’an District, Taipei, Taiwan, 886 2 6638 2736 ext 3351, arbiter@tmu.edu.tw %K technology acceptance model %K cardiopulmonary resuscitation %K mobile app %K emergency care %D 2020 %7 19.3.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Improving the quality of patient care through the use of mobile devices is one of the hot topics in the health care field. In unwanted situations like an accident, ambulances and rescuers often require a certain amount of time to arrive at the scene. Providing immediate cardiopulmonary resuscitation (CPR) to patients might improve survival. Objective: The primary objective of this study was to evaluate the feasibility of an emergency and mutual-aid app model in Taiwan and to provide a reference for government policy. Methods: A structured questionnaire was developed as a research tool. All questionnaires were designed according to the technology acceptance model, and a Likert scale was used to measure the degree of agreement or disagreement. Moreover, in-depth interviews were conducted with six experts from medical, legal, and mobile app departments. Each expert was interviewed once to discuss feasible countermeasures and suggestions. Statistical Package for the Social Sciences (SPSS version 19; IBM Corp, Armonk, New York) was used to perform all statistical analyses, including descriptive statistics, independent sample t-tests, variance analysis, and Pearson correlation analysis. Results: We conducted this study between October 20, 2017, and November 10, 2017, at the Taipei Medical University Hospital. Questionnaires were distributed to medical personnel, visiting guests, family members, and volunteers. A total of 113 valid questionnaires were finally obtained after the exclusion of incomplete questionnaires. Cronbach α values for self-efficacy (perceived ease of use), use attitude (perceived usefulness), and use willingness and frequency were above .85, meeting the criterion of greater than .70. We observed that the reliability of each subquestion was acceptable and the values for use attitude (perceive usefulness) and use willingness and frequency were more than .90. Conclusions: The findings suggest that perceived ease of use and perceived usefulness of the app model affect use willingness. However, perceived usefulness had an intermediary influence on use willingness. Experts in law, medical, and technology fields consider that an emergency and mutual-aid model can be implemented in Taiwan. Along with the development of an emergency and mutual-aid app model, we recommend an increase in the number of automated external defibrillators per region and promotion of correct knowledge about CPR in order to decrease morbidity and mortality. %M 32191212 %R 10.2196/15494 %U https://formative.jmir.org/2020/3/e15494 %U https://doi.org/10.2196/15494 %U http://www.ncbi.nlm.nih.gov/pubmed/32191212 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e16240 %T Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study %A Chung,Tammy %A Bae,Sang Won %A Mun,Eun-Young %A Suffoletto,Brian %A Nishiyama,Yuuki %A Jang,Serim %A Dey,Anind K %+ Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA, 15213, United States, 1 4123524651, tammychung111@gmail.com %K marijuana %K cannabis %K cell phone %K memory, short-term %K cognition %D 2020 %7 10.3.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile assessment of the effects of acute marijuana on cognitive functioning in the natural environment would provide an ecologically valid measure of the impacts of marijuana use on daily functioning. Objective: This study aimed to examine the association of reported acute subjective marijuana high (rated 0-10) with performance on 3 mobile cognitive tasks measuring visuospatial working memory (Flowers task), attentional bias to marijuana-related cues (marijuana Stroop), and information processing and psychomotor speed (digit symbol substitution task [DSST]). The effect of distraction as a moderator of the association between the rating of subjective marijuana high and task performance (ie, reaction time and number of correct responses) was explored. Methods: Young adults (aged 18-25 years; 37/60, 62% female) who reported marijuana use at least twice per week were recruited through advertisements and a participant registry in Pittsburgh, Pennsylvania. Phone surveys and mobile cognitive tasks were delivered 3 times per day and were self-initiated when starting marijuana use. Completion of phone surveys triggered the delivery of cognitive tasks. Participants completed up to 30 days of daily data collection. Multilevel models examined associations between ratings of subjective marijuana high (rated 0-10) and performance on each cognitive task (reaction time and number of correct responses) and tested the number of distractions (rated 0-4) during the mobile task session as a moderator of the association between ratings of subjective marijuana high and task performance. Results: Participants provided 2703 data points, representing 451 reports (451/2703, 16.7%) of marijuana use. Consistent with slight impairing effects of acute marijuana use, an increase in the average rating of subjective marijuana high was associated with slower average reaction time on all 3 tasks—Flowers (B=2.29; SE 0.86; P=.008), marijuana Stroop (B=2.74; SE 1.09; P=.01), and DSST (B=3.08; SE 1.41; P=.03)—and with fewer correct responses for Flowers (B=−0.03; SE 0.01; P=.01) and DSST (B=−0.18; SE 0.07; P=.01), but not marijuana Stroop (P=.45). Results for distraction as a moderator were statistically significant only for certain cognitive tasks and outcomes. Specifically, as hypothesized, a person’s average number of reported distractions moderated the association of the average rating of subjective marijuana high (over and above a session’s rating) with the reaction time for marijuana Stroop (B=−52.93; SE 19.38; P=.006) and DSST (B=−109.72; SE 42.50; P=.01) and the number of correct responses for marijuana Stroop (B=−0.22; SE 0.10; P=.02) and DSST (B=4.62; SE 1.81; P=.01). Conclusions: Young adults’ performance on mobile cognitive tasks in the natural environment was associated with ratings of acute subjective marijuana high, consistent with slight decreases in cognitive functioning. Monitoring cognitive functioning in real time in the natural environment holds promise for providing immediate feedback to guide personal decision making. %M 32154789 %R 10.2196/16240 %U http://mhealth.jmir.org/2020/3/e16240/ %U https://doi.org/10.2196/16240 %U http://www.ncbi.nlm.nih.gov/pubmed/32154789 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 2 %P e12452 %T Circadian Rhythms in the Telephone Calls of Older Adults: Observational Descriptive Study %A Aubourg,Timothée %A Demongeot,Jacques %A Provost,Hervé %A Vuillerme,Nicolas %+ Orange Labs, Chemin du Vieux Chêne, Meylan, France, 33 0438429192, timothee.aubourg@gmail.com %K outgoing telephone call %K circadian rhythm %K older adults %K call-detail records %K digital phenotyping %K digital biomarkers %K digital health %K mhealth %D 2020 %7 25.2.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Recent studies have thoughtfully and convincingly demonstrated the possibility of estimating the circadian rhythms of young adults’ social activity by analyzing their telephone call-detail records (CDRs). In the field of health monitoring, this development may offer new opportunities for supervising a patient’s health status by collecting objective, unobtrusive data about their daily social interactions. However, before considering this future perspective, whether and how similar results could be observed in other populations, including older ones, should be established. Objective: This study was designed specifically to address the circadian rhythms in the telephone calls of older adults. Methods: A longitudinal, 12-month dataset combining CDRs and questionnaire data from 26 volunteers aged 65 years or older was used to examine individual differences in the daily rhythms of telephone call activity. The study used outgoing CDRs only and worked with three specific telecommunication parameters: (1) call recipient (alter), (2) time of day, and (3) call duration. As did the studies involving young adults, we analyzed three issues: (1) the existence of circadian rhythms in the telephone call activity of older adults, (2) their persistence over time, and (3) the alter-specificity of calls by calculating relative entropy. Results: We discovered that older adults had their own specific circadian rhythms of outgoing telephone call activity whose salient features and preferences varied across individuals, from morning until night. We demonstrated that rhythms were consistent, as reflected by their persistence over time. Finally, results suggested that the circadian rhythms of outgoing telephone call activity were partly structured by how older adults allocated their communication time across their social network. Conclusions: Overall, these results are the first to have demonstrated the existence, persistence, and alter-specificity of the circadian rhythms of the outgoing telephone call activity of older adults. These findings suggest an opportunity to consider modern telephone technologies as potential sensors of daily activity. From a health care perspective, these sensors could be harnessed for unobtrusive monitoring purposes. %M 32130156 %R 10.2196/12452 %U http://mhealth.jmir.org/2020/2/e12452/ %U https://doi.org/10.2196/12452 %U http://www.ncbi.nlm.nih.gov/pubmed/32130156 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 3 %N 1 %P e16131 %T Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study %A Rabe,Sophie %A Azhand,Arash %A Pommer,Wolfgang %A Müller,Swantje %A Steinert,Anika %+ Lindera GmbH, Kottbusser Damm 79, Berlin, 10967, Germany, 49 030 12085471, sophie.rabe@lindera.de %K falls %K seniors %K fall risk assessment %K app %K mHealth %K retrospective cohort study %K discriminative ability %D 2020 %7 14.2.2020 %9 Original Paper %J JMIR Aging %G English %X Background: Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants. Objective: This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app’s discriminative ability based on real-world data. Methods: Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score's ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model. Results: Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4%) reported no previous falls (nonfaller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88%, specificity=62%, and accuracy=73%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88%, specificity=67%, and accuracy=76%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88%, specificity=57%, and accuracy=70%. Conclusions: Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93%, and an average specificity of 58%. Average overall accuracy was 73%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score’s prospective accuracy will be further validated in a prospective trial. %M 32130111 %R 10.2196/16131 %U http://aging.jmir.org/2020/1/e16131/ %U https://doi.org/10.2196/16131 %U http://www.ncbi.nlm.nih.gov/pubmed/32130111 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 2 %P e13468 %T Validation of an Electronic Visual Analog Scale mHealth Tool for Acute Pain Assessment: Prospective Cross-Sectional Study %A Escalona-Marfil,Carles %A Coda,Andrea %A Ruiz-Moreno,Jorge %A Riu-Gispert,Lluís Miquel %A Gironès,Xavier %+ Facultat de Ciències de la Salut de Manresa, Universitat de Vic–Universitat Central de Catalunya, Av Universitària, 4-6, Manresa, Spain, 34 938 77 41 79 ext 234, carlesescalona@gmail.com %K pain %K visual analog pain scale %K pain measurement %K mobile phone %K mHealth %K validation %K tablet %D 2020 %7 12.2.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Accurate measurement of pain is required to improve its management and in research. The visual analog scale (VAS) on paper format has been shown to be an accurate, valid, reliable, and reproducible way to measure pain intensity. However, some limitations should be considered, some of which can be implemented with the introduction of an electronic VAS version, suitable to be used both in a tablet and a smartphone. Objective: This study aimed to validate a new method of recording pain level by comparing the traditional paper VAS with the pain level module on the newly designed Interactive Clinics app. Methods: A prospective observational cross-sectional study was designed. The sample consisted of 102 participants aged 18 to 65 years. A Force Dial FDK 20 algometer (Wagner Instruments) was employed to induce mild pressure symptoms on the participants’ thumbs. Pain was measured using a paper VAS (10 cm line) and the app. Results: Intermethod reliability estimated by ICC(3,1) was 0.86 with a 95% confidence interval of 0.81 to 0.90, indicating good reliability. Intramethod reliability estimated by ICCa(3,1) was 0.86 with a 95% confidence interval of 0.81 to 0.90, also indicating good reliability. Bland-Altman analysis showed a difference of 0.175 (0.49), and limits of agreement ranged from –0.79 to 1.14. Conclusions: The pain level module on the app is highly reliable and interchangeable with the paper VAS version. This tool could potentially help clinicians and researchers precisely assess pain in a simple, economic way with the use of a ubiquitous technology. %M 32049063 %R 10.2196/13468 %U http://www.jmir.org/2020/2/e13468/ %U https://doi.org/10.2196/13468 %U http://www.ncbi.nlm.nih.gov/pubmed/32049063 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 1 %P e14503 %T “Asking Too Much?”: Randomized N-of-1 Trial Exploring Patient Preferences and Measurement Reactivity to Frequent Use of Remote Multidimensional Pain Assessments in Children and Young People With Juvenile Idiopathic Arthritis %A Lee,Rebecca Rachael %A Shoop-Worrall,Stephanie %A Rashid,Amir %A Thomson,Wendy %A Cordingley,Lis %+ National Institute for Health Research, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Stopford Building, Oxford Road, Manchester, United Kingdom, 44 161275 ext 7757, rebecca.lee-4@manchester.ac.uk %K mHealth %K pain %K pain assessment %K juvenile idiopathic arthritis %K patient reported outcomes %K pediatrics %D 2020 %7 30.1.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Remote monitoring of pain using multidimensional mobile health (mHealth) assessment tools is increasingly being adopted in research and care. This assessment method is valuable because it is challenging to capture pain histories, particularly in children and young people in diseases where pain patterns can be complex, such as juvenile idiopathic arthritis (JIA). With the growth of mHealth measures and more frequent assessment, it is important to explore patient preferences for the timing and frequency of administration of such tools and consider whether certain administrative patterns can directly impact on children’s pain experiences. Objective: This study aimed to explore the feasibility and influence (in terms of objective and subjective measurement reactivity) of several time sampling strategies in remote multidimensional pain reporting. Methods: An N-of-1 trial was conducted in a subset of children and young people with JIA and their parents recruited to a UK cohort study. Children were allocated to 1 of 4 groups. Each group followed a different schedule of completion of MPT for 8 consecutive weeks. Each schedule included 2 blocks, each comprising 4 different randomized time sampling strategies, with each strategy occurring once within each 4-week block. Children completed MPT according to time sampling strategies: once-a-day, twice-a-day, once-a-week, and as-and-when pain was experienced. Adherence to each strategy was calculated. Participants completed the Patient-Reported Outcomes Measurement Information System Pain Interference Scale at the end of each week to explore objective reactivity. Differences in pain interference scores between time sampling strategies were assessed graphically and using Friedman tests. Children and young people and their parents took part in a semistructured interview about their preferences for different time sampling strategies and to explore subjective reactivity. Results: A total of 14 children and young people (aged 7-16 years) and their parents participated. Adherence to pain reporting was higher in less intense time sampling strategies (once-a-week=63% [15/24]) compared with more intense time sampling strategies (twice-a-day=37.8% [127/336]). There were no statistically significant differences in pain interference scores between sampling strategies. Qualitative findings from interviews suggested that children preferred once-a-day (6/14, 43%) and as-and-when pain reporting (6/14, 43%). Creating routine was one of the most important factors for successful reporting, while still ensuring that comprehensive information about recent pain was captured. Conclusions: Once-a-day pain reporting provides rich contextual information. Although patients were less adherent to this preferred sampling strategy, once-a-day reporting still provides more frequent assessment opportunities compared with other less intense or overburdensome schedules. Important issues for the design of studies and care incorporating momentary assessment techniques were identified. We demonstrate that patient reporting preferences are key to accommodate and are important where data capture quality is key. Our findings support frequent administration of such tools, using daily reporting methods where possible. %M 32012051 %R 10.2196/14503 %U http://www.jmir.org/2020/1/e14503/ %U https://doi.org/10.2196/14503 %U http://www.ncbi.nlm.nih.gov/pubmed/32012051 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e14368 %T Engagement and Participant Experiences With Consumer Smartwatches for Health Research: Longitudinal, Observational Feasibility Study %A Beukenhorst,Anna L %A Howells,Kelly %A Cook,Louise %A McBeth,John %A O'Neill,Terence W %A Parkes,Matthew J %A Sanders,Caroline %A Sergeant,Jamie C %A Weihrich,Katy S %A Dixon,William G %+ Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Stopford Building, Oxford Road, Manchester, M139PL, United Kingdom, 44 161 275 5788, anna.beuk@manchester.ac.uk %K medical informatics computing %K mHealth %K patient-reported outcomes %K musculoskeletal diseases %K mobile phone %K smartwatch/wearable %K self-tracking %D 2020 %7 29.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated. Objective: This study aimed to investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3-month period in people living with knee osteoarthritis (OA). Methods: For the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study, a novel cellular smartwatch app for health data collection was developed. Participants (age ≥50 years; self-diagnosed knee OA) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis. Results: A total of 26 people participated in the study. Good use and engagement were observed over 3 months: most participants wore the watch on 75% (68/90) of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems, and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than 4 or 5 times per day. Conclusions: This study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3-month period. International Registered Report Identifier (IRRID): RR2-10.2196/10238 %M 32012078 %R 10.2196/14368 %U https://mhealth.jmir.org/2020/1/e14368 %U https://doi.org/10.2196/14368 %U http://www.ncbi.nlm.nih.gov/pubmed/32012078 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 1 %P e14111 %T Challenges and Lessons Learned From a Mobile Health, Web-Based Human Papillomavirus Intervention for Female Korean American College Students: Feasibility Experimental Study %A Kim,Minjin %A Lee,Haeok %A Allison,Jeroan %+ University of Massachusetts Medical School, Department of Population and Quantitative Health Sciences, 368 Plantation Street, Worcester, MA, 01605, United States, 1 7202095559, minjin.kim2@umassmed.edu %K mHealth %K Web-based intervention %K fraud %K experimental design %D 2020 %7 29.1.2020 %9 Short Paper %J JMIR Form Res %G English %X Background: Mobile health (mHealth) and Web-based research methods are becoming more commonplace for researchers. However, there is a lack of mHealth and Web-based human papillomavirus (HPV) prevention experimental studies that discuss potential issues that may arise. Objective: This study aimed to assess the feasibility of research procedures and discuss the challenges and lessons learned from an mHealth and Web-based HPV prevention experimental study targeting female Korean American college students in the United States. Methods: A pilot randomized controlled trial (RCT) was conducted in an mHealth and Web-based platform with 104 female Korean American college students aged 18-26 years between September 2016 and December 2016. Participants were randomized to either the experimental group (a storytelling video intervention) or the comparison group (a nonnarrative, information-based intervention). Outcomes included the feasibility of research procedures (recruitment, eligibility, randomization, and retention). Results: From September 2016 to October 2016, we recorded 225 entries in our initial eligibility survey. The eligibility rate was 54.2% (122/225). This study demonstrated a high recruitment rate (95.6%, 111/122) and retention rate (83.7%, 87/104) at the 2-month follow-up. Conclusions: Findings from this study demonstrated sufficient feasibility in terms of research procedures to justify a full-scale RCT. Given the increased possibility of invalid or misrepresentative entries in mHealth and Web-based studies, strategies for detection and prevention are critical. Trial Registration: ISRCTN Registry ISRCTN12175285; http://www.isrctn.com/ISRCTN12175285 %M 32012036 %R 10.2196/14111 %U http://formative.jmir.org/2020/1/e14111/ %U https://doi.org/10.2196/14111 %U http://www.ncbi.nlm.nih.gov/pubmed/32012036 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 1 %P e12917 %T Development of a Mobile Phone App to Promote Safe Sex Practice Among Youth in Stockholm, Sweden: Qualitative Study %A Nielsen,Anna %A Bågenholm,Aspasia %A De Costa,Ayesha %+ Karolinska Institutet, Department of Women's and Children's Health, Karolinska University Hospital, Stockholm, 17176, Sweden, 46 704078496, anna.nielsen.1@ki.se %K mHealth %K youth %K sexual health %K condoms %K Sweden %D 2020 %7 28.1.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Mobile health (mHealth) has been shown to be effective in increasing knowledge of sexual health among youth. To date, evaluations mostly refer to interventions delivered via computer, email, and text messages. The possibility of downloading apps on mobile devices has opened up opportunities to develop engaging interventions on safe sexual health promotion. To attract young users and have them engage with a sexual health app, it is important to involve youth in intervention development. Objective: This study aimed to obtain input from youth on the content of a mobile phone app intended to promote safe sex and increase condom use among youth in Stockholm. Methods: This study was conducted at the Youth Health Clinics (YHC) in Stockholm County, Sweden. A total of 15 individual in-depth interviews and 2 focus group discussions (with youth aged 18-23 years) were conducted at the YHC in Stockholm. Areas explored were: (1) youth perceptions of condom use (advantages and obstacles), (2) perceptions of mHealth to promote safe sexual practices, and (3) content development for a mobile phone app to promote safe sex. Results: The smartphone app was developed based on the categories that emerged from the data. With regard to content, youth requested sex education, including information on sexually transmitted infections. In addition, condom-specific information, including practical usage technique, advice on how to have the condom talk, and how to decrease shame related to condom use, was requested. Youth suggested different modes to deliver the content, including text messages, movie clips, and push notifications. It was suggested that the tone of the messages delivered should be fun, entertaining, and supportive. The inputs from youth influenced the development of the following sections of the app: Condom Obstacles and Solutions; Quiz; Games; Self-Refection; Challenges; Stories by Peers (stories from peers and information from a doctor); Condom Tips, Pep Talk, and Boosting; and Random Facts. Conclusions: It is important to use input from youth when developing a smartphone intervention since the success of the intervention largely depends on the level of engagement and usage by youth. Furthermore, if proven efficient in increasing condom use, it is important that the development, including content and mode, is thoroughly described so that the intervention can be replicated. Likewise, if proven inefficient, it is important to learn from mistakes to improve and adjust the intervention. The effect of this smartphone app on safe sexual practices among youth is being evaluated in a pragmatic randomized controlled trial in Stockholm (ISRCTN13212899) and will be reported separately. %M 32012038 %R 10.2196/12917 %U https://formative.jmir.org/2020/1/e12917 %U https://doi.org/10.2196/12917 %U http://www.ncbi.nlm.nih.gov/pubmed/32012038 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 1 %P e15283 %T A Smartphone App Combining Global Positioning System Data and Ecological Momentary Assessment to Track Individual Food Environment Exposure, Food Purchases, and Food Consumption: Protocol for the Observational FoodTrack Study %A Poelman,Maartje P %A van Lenthe,Frank J %A Scheider,Simon %A Kamphuis,Carlijn BM %+ Chair Group Consumption and Healthy Lifestyles, Wageningen University and Research, PO Box 8130, Wageningen, 6700 EW, Netherlands, 31 317 483401, maartje.poelman@wur.nl %K ecological momentary assessment %K eating behavior %K environmental exposure %K mobile apps %K smartphone %K geographic information systems %K food preferences %K diet records %D 2020 %7 28.1.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: Our understanding of how food choices are affected by exposure to the food environment is limited, and there are important gaps in the literature. Recently developed smartphone-based technologies, including global positioning systems and ecological momentary assessment, enable these gaps to be filled. Objective: We present the FoodTrack study design and methods, as well as participants’ compliance with the study protocol and their experiences with the app. We propose future analyses of the data to examine individual food environmental exposure taking into account the accessible food environment and individual time constraints; to assess people’s food choices in relation to food environmental exposure; and to examine the moderating role of individual and contextual determinants of food purchases and consumption. Methods: We conducted a 7-day observational study among adults (25-45 years of age) living in urban areas in the Netherlands. Participants completed a baseline questionnaire, used an app (incorporating global positioning system tracking and ecological momentary assessment) for 7 days, and then completed a closing survey. The app automatically collected global positioning system tracking data, and participants uploaded information on all food purchases over the 7-day period into the app. Participants also answered questions on contextual or individual purchase-related determinants directly after each purchase. During the final 3 days of the study, the participants also uploaded data on fruit, vegetable, and snack consumption and answered similar ecological momentary assessment questions after each intake. Results: In total, 140 participants completed the study. More than half of the participants said they liked the app (81/140, 57.9%) and found it easy to use (75/140, 53.6%). Of the 140 participants, 126 (90.0%) said that they had collected data on all or almost all purchases and intakes during the 7-day period. Most found the additional ecological momentary assessment questions “easy to answer” (113/140, 80.7%) with “no effort” (99/140, 70.7%). Of 106 participants who explored their trips in the app, 20 (18.8%) had trouble with their smartphone’s global positioning system tracking function. Therefore, we will not be able to include all participants in some of the proposed analyses, as we lack these data. We are analyzing data from the first study aim and we expect to publish the results in the spring of 2020. Conclusions: Participants perceived the FoodTrack app as a user-friendly tool. The app is particularly useful for observational studies that aim to gain insight into daily food environment exposure and food choices. Further analyses of the FoodTrack study data will provide novel insights into individual food environmental exposure, evidence on the individual food environment-diet interaction, and insights into the underlying individual and contextual mechanisms of food purchases and consumption. International Registered Report Identifier (IRRID): DERR1-10.2196/15283 %M 32012100 %R 10.2196/15283 %U http://www.researchprotocols.org/2020/1/e15283/ %U https://doi.org/10.2196/15283 %U http://www.ncbi.nlm.nih.gov/pubmed/32012100 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e14800 %T Spatiotemporal Analysis of Men Who Have Sex With Men in Mainland China: Social App Capture-Recapture Method %A Hu,Maogui %A Xu,Chengdong %A Wang,Jinfeng %+ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, A11, Datun road, Beijing, China, 86 10 64888965, wangjf@lreis.ac.cn %K HIV risk %K men who have sex with men %K MSM distribution %K migration %D 2020 %7 24.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In China, the cases of newly diagnosed HIV/AIDS in men who have sex with men (MSM) have increased more than tenfold since 2006. However, the MSM population size, geographical distribution, and migration patterns are largely unknown. Objective: Our aim is to estimate the number, spatial distribution, and migration of MSM populations in mainland China using big data from social networking. Methods: We collected 85 days of data on online users of a social networking MSM app in mainland China. Daily online MSM users and their migration across the country were investigated during a holiday period and a nonholiday period. Using the capture-mark-recapture model, we designed an experiment consisting of two independent samples to estimate the total provincial MSM population. Results: The estimate of MSM in mainland China was 8,288,536 (95% CI 8,274,931-8,302,141), accounting for 1.732% (95% CI 1.729%-1.734%) of adult men aged 18 to 64 years. The average daily number of MSM social networking online across mainland China was 1,198,682 during the nonholiday period. The five provinces (including municipalities) with the highest average number of daily online MSM numbers were Guangdong (n=141,712), Jiangsu (n=90,710), Zhejiang (n=72,212), Shandong (n=68,065), and Beijing (n=66,057). The proportion of daily online MSM among adult men in different cities varied from 0.04% to 0.96%, with a mean of 0.20% (SD 0.14%). Three migrating centers—Guangdong, Beijing, and the Yangtze River Delta (Shanghai-Zhejiang-Jiangsu)—accounted for 57.23% of MSM migrants in the county. Conclusions: The percentage of MSM among adult men in mainland China is at the middle level compared with other Asia and Pacific countries. However, the number of MSM is very large, and the distribution is uneven. Both MSM distribution and migration are highly affected by socioeconomic status. %M 32012086 %R 10.2196/14800 %U https://mhealth.jmir.org/2020/1/e14800 %U https://doi.org/10.2196/14800 %U http://www.ncbi.nlm.nih.gov/pubmed/32012086 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e15329 %T Quality Assurance of Health Wearables Data: Participatory Workshop on Barriers, Solutions, and Expectations %A Abdolkhani,Robab %A Gray,Kathleen %A Borda,Ann %A DeSouza,Ruth %+ Health and Biomedical Informatics Centre, The University of Melbourne, Level 13, 305 Grattan St, Melbourne, Victoria, 3000, Australia, 61 390358703, rabdolkhani@student.unimelb.edu.au %K remote sensing technology %K data quality assurance %K patient-generated health data %K wearable devices %K participatory research %D 2020 %7 22.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The ubiquity of health wearables and the consequent production of patient-generated health data (PGHD) are rapidly escalating. However, the utilization of PGHD in routine clinical practices is still low because of data quality issues. There is no agreed approach to PGHD quality assurance; therefore, realizing the promise of PGHD requires in-depth discussion among diverse stakeholders to identify the data quality assurance challenges they face and understand their needs for PGHD quality assurance. Objective: This paper reports findings from a workshop aimed to explore stakeholders’ data quality challenges, identify their needs and expectations, and offer practical solutions. Methods: A qualitative multi-stakeholder workshop was conducted as a half-day event on the campus of an Australian University located in a major health care precinct, namely the Melbourne Parkville Precinct. The 18 participants had experience of PGHD use in clinical care, including people who identified as health care consumers, clinical care providers, wearables suppliers, and health information specialists. Data collection was done by facilitators capturing written notes of the proceedings as attendees engaged in participatory design activities in written and oral formats, using a range of whole-group and small-group interactive methods. The collected data were analyzed thematically, using deductive and inductive coding. Results: The participants’ discussions revealed a range of technical, behavioral, operational, and organizational challenges surrounding PGHD, from the time when data are collected by patients to the time data are used by health care providers for clinical decision making. PGHD stakeholders found consensus on training and engagement needs, continuous collaboration among stakeholders, and development of technical and policy standards to assure PGHD quality. Conclusions: Assuring PGHD quality is a complex process that requires the contribution of all PGHD stakeholders. The variety and depth of inputs in our workshop highlighted the importance of co-designing guidance for PGHD quality guidance. %M 32012090 %R 10.2196/15329 %U https://mhealth.jmir.org/2020/1/e15329 %U https://doi.org/10.2196/15329 %U http://www.ncbi.nlm.nih.gov/pubmed/32012090 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e13191 %T Why We Eat What We Eat: Assessing Dispositional and In-the-Moment Eating Motives by Using Ecological Momentary Assessment %A Wahl,Deborah Ronja %A Villinger,Karoline %A Blumenschein,Michael %A König,Laura Maria %A Ziesemer,Katrin %A Sproesser,Gudrun %A Schupp,Harald Thomas %A Renner,Britta %+ Psychological Assessment and Health Psychology, Department of Psychology, University of Konstanz, PO Box 47, Konstanz, 78457, Germany, 49 7531 88 3977, deborah.wahl@uni-konstanz.de %K mHealth %K eating %K motivation %K mobile app %K EMA %K in-the-moment %K disposition %K trait %K state %D 2020 %7 7.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Why do we eat? Our motives for eating are diverse, ranging from hunger and liking to social norms and affect regulation. Although eating motives can vary from eating event to eating event, which implies substantial moment-to-moment differences, current ways of measuring eating motives rely on single timepoint questionnaires that assess eating motives as situation-stable dispositions (traits). However, mobile technologies including smartphones allow eating events and motives to be captured in real time and real life, thus capturing experienced eating motives in-the-moment (states). Objective: This study aimed to examine differences between why people think they eat (trait motives) and why they eat in the moment of consumption (state motives) by comparing a dispositional (trait) and an in-the-moment (state) assessment of eating motives. Methods: A total of 15 basic eating motives included in The Eating Motivation Survey (ie, liking, habit, need and hunger, health, convenience, pleasure, traditional eating, natural concerns, sociability, price, visual appeal, weight control, affect regulation, social norms, and social image) were assessed in 35 participants using 2 methodological approaches: (1) a single timepoint dispositional assessment and (2) a smartphone-based ecological momentary assessment (EMA) across 8 days (N=888 meals) capturing eating motives in the moment of eating. Similarities between dispositional and in-the-moment eating motive profiles were assessed according to 4 different indices of profile similarity, that is, overall fit, shape, scatter, and elevation. Moreover, a visualized person × motive data matrix was created to visualize and analyze between- and within-person differences in trait and state eating motives. Results: Similarity analyses yielded a good overall fit between the trait and state eating motive profiles across participants, indicated by a double-entry intraclass correlation of 0.52 (P<.001). However, although trait and state motives revealed a comparable rank order (r=0.65; P<.001), trait motives overestimated 12 of 15 state motives (P<.001; d=1.97). Specifically, the participants assumed that 6 motives (need and hunger, price, habit, sociability, traditional eating, and natural concerns) are more essential for eating than they actually were in the moment (d>0.8). Furthermore, the visualized person × motive data matrix revealed substantial interindividual differences in intraindividual motive profiles. Conclusions: For a comprehensive understanding of why we eat what we eat, dispositional assessments need to be extended by in-the-moment assessments of eating motives. Smartphone-based EMAs reveal considerable intra- and interindividual differences in eating motives, which are not captured by single timepoint dispositional assessments. Targeting these differences between why people think they eat what they eat and why they actually eat in the moment may hold great promise for tailored mobile health interventions facilitating behavior changes. %M 31909719 %R 10.2196/13191 %U https://mhealth.jmir.org/2020/1/e13191 %U https://doi.org/10.2196/13191 %U http://www.ncbi.nlm.nih.gov/pubmed/31909719 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e13756 %T The Mobile-Based 6-Minute Walk Test: Usability Study and Algorithm Development and Validation %A Salvi,Dario %A Poffley,Emma %A Orchard,Elizabeth %A Tarassenko,Lionel %+ Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, United Kingdom, 44 1865617679, dario.salvi@eng.ox.ac.uk %K cardiology %K exercise test %K pulmonary hypertension %K mobile apps %K digital signal processing %K global positioning system %D 2020 %7 3.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The 6-min walk test (6MWT) is a convenient method for assessing functional capacity in patients with cardiopulmonary conditions. It is usually performed in the context of a hospital clinic and thus requires the involvement of hospital staff and facilities, with their associated costs. Objective: This study aimed to develop a mobile phone–based system that allows patients to perform the 6MWT in the community. Methods: We developed 2 algorithms to compute the distance walked during a 6MWT using sensors embedded in a mobile phone. One algorithm makes use of the global positioning system to track the location of the phone when outdoors and hence computes the distance travelled. The other algorithm is meant to be used indoors and exploits the inertial sensors built into the phone to detect U-turns when patients walk back and forth along a corridor of fixed length. We included these algorithms in a mobile phone app, integrated with wireless pulse oximeters and a back-end server. We performed Bland-Altman analysis of the difference between the distances estimated by the phone and by a reference trundle wheel on 49 indoor tests and 30 outdoor tests, with 11 different mobile phones (both Apple iOS and Google Android operating systems). We also assessed usability aspects related to the app in a discussion group with patients and clinicians using a technology acceptance model to guide discussion. Results: The mean difference between the mobile phone-estimated distances and the reference values was −2.013 m (SD 7.84 m) for the indoor algorithm and −0.80 m (SD 18.56 m) for the outdoor algorithm. The absolute maximum difference was, in both cases, below the clinically significant threshold. A total of 2 pulmonary hypertension patients, 1 cardiologist, 2 physiologists, and 1 nurse took part in the discussion group, where issues arising from the use of the 6MWT in hospital were identified. The app was demonstrated to be usable, and the 2 patients were keen to use it in the long term. Conclusions: The system described in this paper allows patients to perform the 6MWT at a place of their convenience. In addition, the use of pulse oximetry allows more information to be generated about the patient’s health status and, possibly, be more relevant to the real-life impact of their condition. Preliminary assessment has shown that the developed 6MWT app is highly accurate and well accepted by its users. Further tests are needed to assess its clinical value. %M 31899457 %R 10.2196/13756 %U https://mhealth.jmir.org/2020/1/e13756 %U https://doi.org/10.2196/13756 %U http://www.ncbi.nlm.nih.gov/pubmed/31899457 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 12 %P e13433 %T Objectively Monitoring Amyotrophic Lateral Sclerosis Patient Symptoms During Clinical Trials With Sensors: Observational Study %A Garcia-Gancedo,Luis %A Kelly,Madeline L %A Lavrov,Arseniy %A Parr,Jim %A Hart,Rob %A Marsden,Rachael %A Turner,Martin R %A Talbot,Kevin %A Chiwera,Theresa %A Shaw,Christopher E %A Al-Chalabi,Ammar %+ Advanced Biostatistics & Data Analytics Centre of Excellence, R&D Projects Clinical Platforms & Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, SG1 2NY, United Kingdom, 44 1438 762129, luis.x.garcia-gancedo@gsk.com %K amyotrophic lateral sclerosis %K objective symptom monitoring %K clinical trial %K physical activity %K digital phenotyping %K digital biomarker %K heart rate %K speech %K accelerometer %K wearable %D 2019 %7 20.12.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Objective symptom monitoring of patients with Amyotrophic Lateral Sclerosis (ALS) has the potential to provide an important source of information to evaluate the impact of the disease on aspects of real-world functional capacity and activities of daily living in the home setting, providing useful objective outcome measures for clinical trials. Objective: This study aimed to investigate the feasibility of a novel digital platform for remote data collection of multiple symptoms—physical activity, heart rate variability (HRV), and digital speech characteristics—in 25 patients with ALS in an observational clinical trial setting to explore the impact of the devices on patients’ everyday life and to record tolerability related to the devices and study procedures over 48 weeks. Methods: In this exploratory, noncontrolled, nondrug study, patients attended a clinical site visit every 3 months to perform activity reference tasks while wearing a sensor, to conduct digital speech tests and for conventional ALS monitoring. In addition, patients wore the sensor in their daily life for approximately 3 days every month for the duration of the study. Results: The amount and quality of digital speech data captured at the clinical sites were as intended, and there were no significant issues. All the home monitoring sensor data available were propagated through the system and were received as expected. However, the amount and quality of physical activity home monitoring data were lower than anticipated. A total of 3 or more days (or partial days) of data were recorded for 65% of protocol time points, with no data collected for 24% of time points. At baseline, 24 of 25 patients provided data, reduced to 13 of 18 patients at Week 48. Lower-than-expected quality HRV data were obtained, likely because of poor contact between the sensor and the skin. In total, 6 of 25 patients had mild or moderate adverse events (AEs) in the skin and subcutaneous tissue disorders category because of skin irritation caused by the electrode patch. There were no reports of serious AEs or deaths. Most patients found the sensor comfortable, with no or minimal impact on daily activities. Conclusions: The platform can measure physical activity in patients with ALS in their home environment; patients used the equipment successfully, and it was generally well tolerated. The quantity of home monitoring physical activity data was lower than expected, although it was sufficient to allow investigation of novel physical activity end points. Good-quality in-clinic speech data were successfully captured for analysis. Future studies using objective patient monitoring approaches, combined with the most current technological advances, may be useful to elucidate novel digital biomarkers of disease progression. %M 31859676 %R 10.2196/13433 %U https://mhealth.jmir.org/2019/12/e13433 %U https://doi.org/10.2196/13433 %U http://www.ncbi.nlm.nih.gov/pubmed/31859676 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 12 %P e13305 %T Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization %A L'Hommedieu,Michelle %A L'Hommedieu,Justin %A Begay,Cynthia %A Schenone,Alison %A Dimitropoulou,Lida %A Margolin,Gayla %A Falk,Tiago %A Ferrara,Emilio %A Lerman,Kristina %A Narayanan,Shrikanth %+ Information Sciences Institute, University of Southern California, 3740 McClintock Ave, EEB 413, Los Angeles, CA, 90089, United States, 1 2137402318, mhasan@isi.edu %K research %K research techniques %K Ecological Momentary Assessment %K wearable electronic devices %D 2019 %7 10.12.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way. %M 31821155 %R 10.2196/13305 %U https://mhealth.jmir.org/2019/12/e13305 %U https://doi.org/10.2196/13305 %U http://www.ncbi.nlm.nih.gov/pubmed/31821155 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 12 %P e11643 %T How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review %A Ferreri,Florian %A Bourla,Alexis %A Peretti,Charles-Siegfried %A Segawa,Tomoyuki %A Jaafari,Nemat %A Mouchabac,Stéphane %+ Sorbonne Université, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, 184 rue du Faubourg Saint-Antoine, Paris, 75012, France, 33 149282635, alexis.bourla@aphp.fr %K obsessive-compulsive disorder %K ecological momentary assessment %K biofeedback %K digital biomarkers %K digital phenotyping %K mobile health %K virtual reality %K machine learning %D 2019 %7 10.12.2019 %9 Review %J JMIR Ment Health %G English %X Background: New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example. Objective: The impact of new technologies on health professionals’ practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD. Methods: We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video]. Results: We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention. Conclusions: The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles. %M 31821153 %R 10.2196/11643 %U https://mental.jmir.org/2019/12/e11643 %U https://doi.org/10.2196/11643 %U http://www.ncbi.nlm.nih.gov/pubmed/31821153 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 12 %P e14473 %T Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study %A Park,Sungkyu %A Lee,Sang Won %A Han,Sungwon %A Cha,Meeyoung %+ Data Science Group, Center for Mathematical and Computational Sciences, Institute for Basic Science, 55 EXPO-ro, Doryong-dong, Yuseong-gu, Daejeon, Republic of Korea, 82 42 878 9300, meeyoung.cha@gmail.com %K insomnia %K precision psychiatry %K cluster analysis %K time-series data %K unsupervised learning %K convolutional autoencoder %D 2019 %7 5.12.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As societies become more complex, larger populations suffer from insomnia. In 2014, the US Centers for Disease Control and Prevention declared that sleep disorders should be dealt with as a public health epidemic. However, it is hard to provide adequate treatment for each insomnia sufferer, since various behavioral characteristics influence symptoms of insomnia collectively. Objective: We aim to develop a neural-net based unsupervised user clustering method towards insomnia sufferers in order to clarify the unique traits for each derived groups. Unlike the current diagnosis of insomnia that requires qualitative analysis from interview results, the classification of individuals with insomnia by using various information modalities from smart bands and neural-nets can provide better insight into insomnia treatments. Methods: This study, as part of the precision psychiatry initiative, is based on a smart band experiment conducted over 6 weeks on individuals with insomnia. During the experiment period, a total of 42 participants (19 male; average age 22.00 [SD 2.79]) from a large university wore smart bands 24/7, and 3 modalities were collected and examined: sleep patterns, daily activities, and personal demographics. We considered the consecutive daily information as a form of images, learned the latent variables of the images via a convolutional autoencoder (CAE), and clustered and labeled the input images based on the derived features. We then converted consecutive daily information into a sequence of the labels for each subject and finally clustered the people with insomnia based on their predominant labels. Results: Our method identified 5 new insomnia-activity clusters of participants that conventional methods have not recognized, and significant differences in sleep and behavioral characteristics were shown among groups (analysis of variance on rank: F4,37=2.36, P=.07 for the sleep_min feature; F4,37=9.05, P<.001 for sleep_efficiency; F4,37=8.16, P<.001 for active_calorie; F4,37=6.53, P<.001 for walks; and F4,37=3.51, P=.02 for stairs). Analyzing the consecutive data through a CAE and clustering could reveal intricate connections between insomnia and various everyday activity markers. Conclusions: Our research suggests that unsupervised learning allows health practitioners to devise precise and tailored interventions at the level of data-guided user clusters (ie, precision psychiatry), which could be a novel solution to treating insomnia and other mental disorders. %M 31804187 %R 10.2196/14473 %U https://mhealth.jmir.org/2019/12/e14473 %U https://doi.org/10.2196/14473 %U http://www.ncbi.nlm.nih.gov/pubmed/31804187 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e13978 %T Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study %A Pryss,Rüdiger %A John,Dennis %A Schlee,Winfried %A Schlotz,Wolff %A Schobel,Johannes %A Kraft,Robin %A Spiliopoulou,Myra %A Langguth,Berthold %A Reichert,Manfred %A O'Rourke,Teresa %A Peters,Henning %A Pieh,Christoph %A Lahmann,Claas %A Probst,Thomas %+ Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, Ulm, 89081, Germany, 49 731 502 4136, ruediger.pryss@uni-ulm.de %K mHealth %K psychological stress %K crowdsensing %K ecological momentary assessment %K pilot study %D 2019 %7 30.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels. Objective: In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals’ perceived stress reactivity moderates stress level changes while using TYS. Methods: Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity. Results: Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04). Conclusions: TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure. %M 31670692 %R 10.2196/13978 %U http://mhealth.jmir.org/2019/10/e13978/ %U https://doi.org/10.2196/13978 %U http://www.ncbi.nlm.nih.gov/pubmed/31670692 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 10 %P e14115 %T A Novel Mobile Tool (Somatomap) to Assess Body Image Perception Pilot Tested With Fashion Models and Nonmodels: Cross-Sectional Study %A Ralph-Nearman,Christina %A Arevian,Armen C %A Puhl,Maria %A Kumar,Rajay %A Villaroman,Diane %A Suthana,Nanthia %A Feusner,Jamie D %A Khalsa,Sahib S %+ Laureate Institute for Brain Research, 6655 S Yale Avenue, Tulsa, OK, 74133, United States, 1 918 502 5100, ChristinaRalphNearman@gmail.com %K body image %K body perception %K body representation %K body image disorder %K eating disorder %K mobile health %K mental health %K mobile app %K digital health %D 2019 %7 29.10.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: Distorted perception of one’s body and appearance, in general, is a core feature of several psychiatric disorders including anorexia nervosa and body dysmorphic disorder and is operative to varying degrees in nonclinical populations. Yet, body image perception is challenging to assess, given its subjective nature and variety of manifestations. The currently available methods have several limitations including restricted ability to assess perceptions of specific body areas. To address these limitations, we created Somatomap, a mobile tool that enables individuals to visually represent their perception of body-part sizes and shapes as well as areas of body concerns and record the emotional valence of concerns. Objective: This study aimed to develop and pilot test the feasibility of a novel mobile tool for assessing 2D and 3D body image perception. Methods: We developed a mobile 2D tool consisting of a manikin figure on which participants outline areas of body concern and indicate the nature, intensity, and emotional valence of the concern. We also developed a mobile 3D tool consisting of an avatar on which participants select individual body parts and use sliders to manipulate their size and shape. The tool was pilot tested on 103 women: 65 professional fashion models, a group disproportionately exposed to their own visual appearance, and 38 nonmodels from the general population. Acceptability was assessed via a usability rating scale. To identify areas of body concern in 2D, topographical body maps were created by combining assessments across individuals. Statistical body maps of group differences in body concern were subsequently calculated using the formula for proportional z-score. To identify areas of body concern in 3D, participants’ subjective estimates from the 3D avatar were compared to corresponding measurements of their actual body parts. Discrepancy scores were calculated based on the difference between the perceived and actual body parts and evaluated using multivariate analysis of covariance. Results: Statistical body maps revealed different areas of body concern between models (more frequently about thighs and buttocks) and nonmodels (more frequently about abdomen/waist). Models were more accurate at estimating their overall body size, whereas nonmodels tended to underestimate the size of individual body parts, showing greater discrepancy scores for bust, biceps, waist, hips, and calves but not shoulders and thighs. Models and nonmodels reported high ease-of-use scores (8.4/10 and 8.5/10, respectively), and the resulting 3D avatar closely resembled their actual body (72.7% and 75.2%, respectively). Conclusions: These pilot results suggest that Somatomap is feasible to use and offers new opportunities for assessment of body image perception in mobile settings. Although further testing is needed to determine the applicability of this approach to other populations, Somatomap provides unique insight into how humans perceive and represent the visual characteristics of their body. %M 31469647 %R 10.2196/14115 %U http://mental.jmir.org/2019/10/e14115/ %U https://doi.org/10.2196/14115 %U http://www.ncbi.nlm.nih.gov/pubmed/31469647 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e12586 %T A Vendor-Independent Mobile Health Monitoring Platform for Digital Health Studies: Development and Usability Study %A Vandenberk,Thijs %A Storms,Valerie %A Lanssens,Dorien %A De Cannière,Hélène %A Smeets,Christophe JP %A Thijs,Inge M %A Batool,Tooba %A Vanrompay,Yves %A Vandervoort,Pieter M %A Grieten,Lars %+ Faculty of Medicine and Life Sciences, Hasselt University, Agorlaan, Diepenbeek, 3590, Belgium, 32 473900054, thijsvandenberk@hotmail.com %K information science %K patient care management %K mobile health %K telemonitoring %K monitoring, ambulatory %D 2019 %7 29.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Medical smartphone apps and mobile health devices are rapidly entering mainstream use because of the rising number of smartphone users. Consequently, a large amount of consumer-generated data is being collected. Technological advances in innovative sensory systems have enabled data connectivity and aggregation to become cornerstones in developing workable solutions for remote monitoring systems in clinical practice. However, few systems are currently available to handle such data, especially for clinical use. Objective: The aim of this study was to develop and implement the digital health research platform for mobile health (DHARMA) that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies. Methods: DHARMA comprises a smartphone app, a Web-based platform, and custom middleware and has been developed to collect, store, process, and visualize data from different vendor-specific sensors. The middleware is a component-based system with independent building blocks for user authentication, study and patient administration, data handling, questionnaire management, patient files, and reporting. Results: A prototype version of the research platform has been tested and deployed in multiple clinical studies. In this study, we used the platform for the follow-up of pregnant women at risk of developing pre-eclampsia. The patients’ blood pressure, weight, and activity were semi-automatically captured at home using different devices. DHARMA automatically collected and stored data from each source and enabled data processing for the end users in terms of study-specific parameters, thresholds, and visualization. Conclusions: The increasing use of mobile health apps and connected medical devices is leading to a large amount of data for collection. There has been limited investment in handling and aggregating data from different sources for use in academic and clinical research focusing on remote monitoring studies. In this study, we created a modular mobile health research platform to collect and integrate data from a variety of third-party devices in several patient populations. The functionality of the platform was demonstrated in a real-life setting among women with high-risk pregnancies. %M 31663862 %R 10.2196/12586 %U https://mhealth.jmir.org/2019/10/e12586 %U https://doi.org/10.2196/12586 %U http://www.ncbi.nlm.nih.gov/pubmed/31663862 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14769 %T Usability and Feasibility of a Smartphone App to Assess Human Behavioral Factors Associated with Tick Exposure (The Tick App): Quantitative and Qualitative Study %A Fernandez,Maria P %A Bron,Gebbiena M. %A Kache,Pallavi A %A Larson,Scott R %A Maus,Adam %A Gustafson Jr,David %A Tsao,Jean I %A Bartholomay,Lyric C %A Paskewitz,Susan M %A Diuk-Wasser,Maria A %+ Department of Ecology, Evolution and Environmental Biology, Columbia University, Schemerhorn Ext Building, 11th Floor, Room 1013, New York, NY, United States, 1 212 854 3355, mad2256@columbia.edu %K Lyme disease %K ticks %K ecological momentary assessment %K citizen science %D 2019 %7 24.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited. Objective: This study aimed to assess the usability and feasibility of The Tick App, the first tick research–focused app in the United States. Methods: The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users’ feedback. Results: Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; P<.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; P<.001 and 64.58%, 941/1457; P<.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; P<.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; P=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; P<.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged >55 years; IRR 3.4, 95% CI 1.5-7.6; P<.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; P<.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; P<.001). Conclusions: This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users’ characteristics. %M 31651409 %R 10.2196/14769 %U http://mhealth.jmir.org/2019/10/e14769/ %U https://doi.org/10.2196/14769 %U http://www.ncbi.nlm.nih.gov/pubmed/31651409 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 10 %P e14791 %T Participant-Centered Online Active Surveillance for Adverse Events Following Vaccination in a Large Clinical Trial: Feasibility and Usability Study %A Munnoch,Sally-Anne %A Cashman,Patrick %A Peel,Roseanne %A Attia,John %A Hure,Alexis %A Durrheim,David N %+ Hunter New England Local Health District, Locked Mail Bag 10, Wallsend, NSW, 2287, Australia, 61 429231908, sally.munnoch@health.nsw.gov.au %K clinical trials %K active surveillance %K adverse events following immunization %K technology %K vaccination %D 2019 %7 23.10.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Active participant monitoring of adverse events following immunization (AEFI) is a recent development to improve the speed and transparency of vaccine safety postmarketing. Vaxtracker, an online tool used to monitor vaccine safety, has successfully demonstrated its usefulness in postmarketing surveillance of newly introduced childhood vaccines. However, its use in older participants, or for monitoring patients participating in large clinical trials, has not been evaluated. Objective: The objective of this study was to monitor AEFIs in older participants enrolled in the Australian Study for the Prevention through the Immunisation of Cardiovascular Events (AUSPICE) trial, and to evaluate the usefulness and effectiveness of Vaxtracker in this research setting. Methods: AUSPICE is a multicenter, randomized, placebo-controlled, double-blinded trial in which participants aged 55 to 61 years were given either the pneumococcal polysaccharide vaccine (23vPPV) or 0.9% saline placebo. Vaxtracker was used to monitor AEFIs in participants in either treatment arm through the administration of two online questionnaires. A link to each questionnaire was sent to participants via email or short message service (SMS) text message 7 and 28 days following vaccination. Data were collated and analyzed in near-real time to identify any possible safety signals indicating problems with the vaccine or placebo. Results: All 4725 AUSPICE participants were enrolled in Vaxtracker. Participant response rates for the first and final survey were 96.47% (n=4558) and 96.65% (n=4525), respectively. The online survey was completed by 90.23% (4083/4525) of Vaxtracker participants within 3 days of receiving the link. AEFIs were reported by 34.40% (805/2340) of 23vPPV recipients and 10.29% (240/2332) of placebo recipients in the 7 days following vaccination. Dominant symptoms for vaccine and placebo recipients were pain at the injection site (587/2340, 25.09%) and fatigue (103/2332, 4.42%), respectively. Females were more likely to report symptoms following vaccination with 23vPPV compared with males (433/1138, 38.05% versus 372/1202, 30.95%; P<.001). Conclusions: Vaxtracker is an effective tool for monitoring AEFIs in the 55 to 61 years age group. Participant response rates were high for both surveys, in both treatment arms and for each method of sending the survey. This study indicates that administration of 23vPPV was well-tolerated in this cohort. Vaxtracker has successfully demonstrated its application in the monitoring of adverse events in near-real time following vaccination in people participating in a national clinical trial. Trial Registration: Australian New Zealand Trial Registry Number (ACTRN) 12615000536561; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368506 %M 31647470 %R 10.2196/14791 %U https://www.jmir.org/2019/10/e14791 %U https://doi.org/10.2196/14791 %U http://www.ncbi.nlm.nih.gov/pubmed/31647470 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e13757 %T Associations Between Heart Rate Variability Measured With a Wrist-Worn Sensor and Older Adults’ Physical Function: Observational Study %A Graham,Sarah Anne %A Jeste,Dilip V %A Lee,Ellen E %A Wu,Tsung-Chin %A Tu,Xin %A Kim,Ho-Cheol %A Depp,Colin A %+ Sam and Rose Stein Institute for Research on Aging, University of California San Diego, 9500 Gilman Drive, #0664, La Jolla, CA, 92093-0664, United States, 1 858 534 5433, sagraham@ucsd.edu %K wearable technology %K aging %K electrocardiogram %K geriatric assessment %D 2019 %7 23.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Heart rate variability (HRV), or variation in beat-to-beat intervals of the heart, is a quantitative measure of autonomic regulation of the cardiovascular system. Low HRV derived from electrocardiogram (ECG) recordings is reported to be related to physical frailty in older adults. Recent advances in wearable technology offer opportunities to more easily integrate monitoring of HRV into regular clinical geriatric health assessments. However, signals obtained from ECG versus wearable photoplethysmography (PPG) devices are different, and a critical first step preceding their widespread use is to determine whether HRV metrics derived from PPG devices also relate to older adults’ physical function. Objective: This study aimed to investigate associations between HRV measured with a wrist-worn PPG device, the Empatica E4 sensor, and validated clinical measures of both objective and self-reported physical function in a cohort of older adults living independently within a continuing care senior housing community. Our primary hypothesis was that lower HRV would be associated with lower physical function. In addition, we expected that HRV would explain a significant proportion of variance in measures of physical health status. Methods: We evaluated 77 participants from an ongoing study of older adults aged between 65 and 95 years. The assessments encompassed a thorough examination of domains typically included in a geriatric health evaluation. We collected HRV data with the Empatica E4 device and examined bivariate correlations between HRV quantified with the triangular index (HRV TI) and 3 widely used and validated measures of physical functioning—the Short Physical Performance Battery (SPPB), Timed Up and Go (TUG), and Medical Outcomes Study Short Form 36 (SF-36) physical composite scores. We further investigated the additional predictive power of HRV TI on physical health status, as characterized by SF-36 physical composite scores and Cumulative Illness Rating Scale for Geriatrics (CIRS-G) scores, using generalized estimating equation regression analyses with backward elimination. Results: We observed significant associations of HRV TI with SPPB (n=52; Spearman ρ=0.41; P=.003), TUG (n=51; ρ=−0.40; P=.004), SF-36 physical composite scores (n=49; ρ=0.37; P=.009), and CIRS-G scores (n=52, ρ=−0.43; P=.001). In addition, the HRV TI explained a significant proportion of variance in SF-36 physical composite scores (R2=0.28 vs 0.11 without HRV) and CIRS-G scores (R2=0.33 vs 0.17 without HRV). Conclusions: The HRV TI measured with a relatively novel wrist-worn PPG device was related to both objective (SPPB and TUG) and self-reported (SF-36 physical composite) measures of physical function. In addition, the HRV TI explained additional variance in self-reported physical function and cumulative illness severity beyond traditionally measured aspects of physical health. Future steps include longitudinal tracking of changes in both HRV and physical function, which will add important insights regarding the predictive value of HRV as a biomarker of physical health in older adults. %M 31647469 %R 10.2196/13757 %U http://mhealth.jmir.org/2019/10/e13757/ %U https://doi.org/10.2196/13757 %U http://www.ncbi.nlm.nih.gov/pubmed/31647469 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14276 %T The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study %A Kim,Myeongchan %A Yune,Sehyo %A Chang,Seyun %A Jung,Yuseob %A Sa,Soon Ok %A Han,Hyun Wook %+ Department of Biomedical Informatics, Graduate School of Medicine, CHA University, Pangyo-ro 335, Bundang-gu, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea, 82 31 881 7109, stepano7@gmail.com %K data collection %K detecting epidemics %K mobile app %K health care app %K influenza epidemics %K influenza in children %D 2019 %7 17.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease. Objective: This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities. Methods: Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC). Results: During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC. Conclusions: The Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network. %M 31625946 %R 10.2196/14276 %U https://mhealth.jmir.org/2019/10/e14276 %U https://doi.org/10.2196/14276 %U http://www.ncbi.nlm.nih.gov/pubmed/31625946 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 9 %P e13238 %T Comparison of On-Site Versus Remote Mobile Device Support in the Framingham Heart Study Using the Health eHeart Study for Digital Follow-up: Randomized Pilot Study Set Within an Observational Study Design %A Spartano,Nicole L %A Lin,Honghuang %A Sun,Fangui %A Lunetta,Kathryn L %A Trinquart,Ludovic %A Valentino,Maureen %A Manders,Emily S %A Pletcher,Mark J %A Marcus,Gregory M %A McManus,David D %A Benjamin,Emelia J %A Fox,Caroline S %A Olgin,Jeffrey E %A Murabito,Joanne M %+ Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University School of Medicine, 720 Harrison Ave, Suite 8100, Boston, MA, United States, 1 3154152040, spartano@bu.edu %K wearable electronic devices %K cell phone %K fitness trackers %K electrocardiography %K epidemiology %D 2019 %7 30.9.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: New electronic cohort (e-Cohort) study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-Cohort study without direct, in-person participant contact can achieve successful participation rates. Objective: The objective of this study was to compare 2 distinct enrollment methods for setting up mobile health (mHealth) devices and to assess the ongoing adherence to device use in an e-Cohort pilot study. Methods: We coenrolled participants from the Framingham Heart Study (FHS) into the FHS–Health eHeart (HeH) pilot study, a digital cohort with infrastructure for collecting mHealth data. FHS participants who had an email address and smartphone were randomized to our FHS-HeH pilot study into 1 of 2 study arms: remote versus on-site support. We oversampled older adults (age ≥65 years), with a target of enrolling 20% of our sample as older adults. In the remote arm, participants received an email containing a link to enrollment website and, upon enrollment, were sent 4 smartphone-connectable sensor devices. Participants in the on-site arm were invited to visit an in-person FHS facility and were provided in-person support for enrollment and connecting the devices. Device data were tracked for at least 5 months. Results: Compared with the individuals who declined, individuals who consented to our pilot study (on-site, n=101; remote, n=93) were more likely to be women, highly educated, and younger. In the on-site arm, the connection and initial use of devices was ≥20% higher than the remote arm (mean percent difference was 25% [95% CI 17-35] for activity monitor, 22% [95% CI 12-32] for blood pressure cuff, 20% [95% CI 10-30] for scale, and 43% [95% CI 30-55] for electrocardiogram), with device connection rates in the on-site arm of 99%, 95%, 95%, and 84%. Once connected, continued device use over the 5-month study period was similar between the study arms. Conclusions: Our pilot study demonstrated that the deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared with offering only remote support. Once connected, the device use was similar in both groups. %M 31573928 %R 10.2196/13238 %U https://mhealth.jmir.org/2019/9/e13238 %U https://doi.org/10.2196/13238 %U http://www.ncbi.nlm.nih.gov/pubmed/31573928 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 3 %P e11617 %T A Mobile Patient-Reported Outcome Measure App With Talking Touchscreen: Usability Assessment %A Welbie,Marlies %A Wittink,Harriet %A Westerman,Marjan J %A Topper,Ilse %A Snoei,Josca %A Devillé,Walter L J M %+ Research Group Lifestyle and Health, Research Center Healthy and Sustainable Living, Utrecht University of Applied Sciences, Postbus 12011, Utrecht, 3501 AA, Netherlands, 31 638192100, marlies.welbie@hu.nl %K mHealth %K eHealth %K surveys and questionnaires %K physical therapy specialty %K qualitative research %D 2019 %7 27.9.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: In the past years, a mobile health (mHealth) app called the Dutch Talking Touch Screen Questionnaire (DTTSQ) was developed in The Netherlands. The aim of development was to enable Dutch physical therapy patients to autonomously complete a health-related questionnaire regardless of their level of literacy and digital skills. Objective: The aim of this study was to evaluate the usability (defined as the effectiveness, efficiency, and satisfaction) of the prototype of the DTTSQ for Dutch physical therapy patients with diverse levels of experience in using mobile technology. Methods: The qualitative Three-Step Test-Interview method, including both think-aloud and retrospective probing techniques, was used to gain insight into the usability of the DTTSQ. A total of 24 physical therapy patients were included. The interview data were analyzed using a thematic content analysis approach aimed at analyzing the accuracy and completeness with which participants completed the questionnaire (effectiveness), the time it took the participants to complete the questionnaire (efficiency), and the extent to which the participants were satisfied with the ease of use of the questionnaire (satisfaction). The problems encountered by the participants in this study were given a severity rating that was used to provide a rough estimate of the need for additional usability efforts. Results: All participants within this study were very satisfied with the ease of use of the DTTSQ. Overall, 9 participants stated that the usability of the app exceeded their expectations. The group of 4 average-/high-experienced participants encountered only 1 problem in total, whereas the 11 little-experienced participants encountered an average of 2 problems per person and the 9 inexperienced participants an average of 3 problems per person. A total of 13 different kind of problems were found during this study. Of these problems, 4 need to be addressed before the DTTSQ will be released because they have the potential to negatively influence future usage of the tool. The other 9 problems were less likely to influence future usage of the tool substantially. Conclusions: The usability of the DTTSQ needs to be improved before it can be released. No problems were found with satisfaction or efficiency during the usability test. The effectiveness needs to be improved by (1) making it easier to navigate through screens without the possibility of accidentally skipping one, (2) enabling the possibility to insert an answer by tapping on the text underneath a photograph instead of just touching the photograph itself, and (3) making it easier to correct wrong answers. This study shows the importance of including less skilled participants in a usability study when striving for inclusive design and the importance of measuring not just satisfaction but also efficiency and effectiveness during such studies. %M 31573909 %R 10.2196/11617 %U https://formative.jmir.org/2019/3/e11617 %U https://doi.org/10.2196/11617 %U http://www.ncbi.nlm.nih.gov/pubmed/31573909 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 9 %P e14657 %T Response Time as an Implicit Self-Schema Indicator for Depression Among Undergraduate Students: Preliminary Findings From a Mobile App–Based Depression Assessment %A Chung,Kyungmi %A Park,Jin Young %A Joung,DaYoung %A Jhung,Kyungun %+ Department of Psychiatry, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Simgokro 100gil 25 Seo-gu, Incheon, 22711, Republic of Korea, 82 32 290 3878, kyungun12@gmail.com %K depressive symptoms %K response time %K self-concept %K mobile phone %K mobile apps %K diagnostic screening programs %K self-assessment %K treatment adherence %K compliance %D 2019 %7 13.09.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Response times to depressive symptom items in a mobile-based depression screening instrument has potential as an implicit self-schema indicator for depression but has yet to be determined; the instrument was designed to readily record depressive symptoms experienced on a daily basis. In this study, the well-validated Korean version of the Center for Epidemiologic Studies Depression Scale-Revised (K-CESD-R) was adopted. Objective: The purpose of this study was to investigate the relationship between depression severity (ie, explicit measure: total K-CESD-R Mobile scores) and the latent trait of interest in schematic self-referent processing of depressive symptom items (ie, implicit measure: response times to items in the K-CESD-R Mobile scale). The purpose was to investigate this relationship among undergraduate students who had never been diagnosed with, but were at risk for, major depressive disorder (MDD) or comorbid MDD with other neurological or psychiatric disorders. Methods: A total of 70 participants—36 males (51%) and 34 females (49%)—aged 19-29 years (mean 22.66, SD 2.11), were asked to complete both mobile and standard K-CESD-R assessments via their own mobile phones. The mobile K-CESD-R sessions (binary scale: yes or no) were administered on a daily basis for 2 weeks. The standard K-CESD-R assessment (5-point scale) was administered on the final day of the 2-week study period; the assessment was delivered via text message, including a link to the survey, directly to participants’ mobile phones. Results: A total of 5 participants were excluded from data analysis. The result of polynomial regression analysis showed that the relationship between total K-CESD-R Mobile scores and the reaction times to the depressive symptom items was better explained by a quadratic trend—F (2, 62)=21.16, P<.001, R2=.41—than by a linear trend—F (1, 63)=25.43, P<.001, R2=.29. It was further revealed that the K-CESD-R Mobile app had excellent internal consistency (Cronbach alpha=.94); at least moderate concurrent validity with other depression scales, such as the Korean version of the Quick Inventory for Depressive Symptomatology-Self Report (ρ=.38, P=.002) and the Patient Health Questionnaire-9 (ρ=.48, P<.001); a high adherence rate for all participants (65/70, 93%); and a high follow-up rate for 10 participants whose mobile or standard K-CESD-R score was 13 or greater (8/10, 80%). Conclusions: As hypothesized, based on a self-schema model for depression that represented both item and person characteristics, the inverted U-shaped relationship between the explicit and implicit self-schema measures for depression showed the potential of an organizational breakdown; this also showed the potential for a subsequent return to efficient processing of schema-consistent information along a continuum, ranging from nondepression through mild depression to severe depression. Further, it is expected that the updated K-CESD-R Mobile app can play an important role in encouraging people at risk for depression to seek professional follow-up for mental health care. %M 31586362 %R 10.2196/14657 %U https://mhealth.jmir.org/2019/9/e14657/ %U https://doi.org/10.2196/14657 %U http://www.ncbi.nlm.nih.gov/pubmed/31586362 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 9 %P e11229 %T Home Videos as a Cost-Effective Tool for the Diagnosis of Paroxysmal Events in Infants: Prospective Study %A Huang,Lu-Lu %A Wang,Yang-Yang %A Liu,Li-Ying %A Tang,Hong-Ping %A Zhang,Meng-Na %A Ma,Shu-Fang %A Zou,Li-Ping %+ Chinese People's Liberation Army General Hospital, No 28 Fuxing Road, Haidian District, Beijing, 100853, China, 86 13911880469, zouliping21@hotmail.com %K paroxysmal events %K infant %K home videos %K online consultation %D 2019 %7 12.09.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The diagnosis of paroxysmal events in infants is often challenging. Reasons include the child’s inability to express discomfort and the inability to record video electroencephalography at home. The prevalence of mobile phones, which can record videos, may be beneficial to these patients. In China, this advantage may be even more significant given the vast population and the uneven distribution of medical resources. Objective: The aim of this study is to investigate the value of mobile phone videos in increasing the diagnostic accuracy and cost savings of paroxysmal events in infants. Methods: Clinical data, including descriptions and home videos of episodes, from 12 patients with paroxysmal events were collected. The investigation was conducted in six centers during pediatric academic conferences. All 452 practitioners present were asked to make their diagnoses by just the descriptions of the events, and then remake their diagnoses after watching the corresponding home videos of the episodes. The doctor’s information, including educational background, profession, working years, and working hospital level, was also recorded. The cost savings from accurate diagnoses were measured on the basis of using online consultation, which can also be done easily by mobile phone. All data were recorded in the form of questionnaires designed for this study. Results: We collected 452 questionnaires, 301 of which met the criteria (66.6%) and were analyzed. The mean correct diagnoses with and without videos was 8.4 (SD 1.7) of 12 and 7.5 (SD 1.7) of 12, respectively. For epileptic seizures, mobile phone videos increased the mean accurate diagnoses by 3.9%; for nonepileptic events, it was 11.5% and both were statistically different (P=.006 for epileptic events; P<.001 for nonepileptic events). Pediatric neurologists with longer working years had higher diagnostic accuracy; whereas, their working hospital level and educational background made no difference. For patients with paroxysmal events, at least US $673.90 per capita and US $128 million nationwide could be saved annually, which is 12.02% of the total cost for correct diagnosis. Conclusions: Home videos made on mobile phones are a cost-effective tool for the diagnosis of paroxysmal events in infants. They can facilitate the diagnosis of paroxysmal events in infants and thereby save costs. The best choice for infants with paroxysmal events on their initial visit is to record their events first and then show the video to a neurologist with longer working years through online consultation. %M 31516128 %R 10.2196/11229 %U https://mhealth.jmir.org/2019/9/e11229/ %U https://doi.org/10.2196/11229 %U http://www.ncbi.nlm.nih.gov/pubmed/31516128 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 9 %P e12590 %T Biofeedback-Assisted Resilience Training for Traumatic and Operational Stress: Preliminary Analysis of a Self-Delivered Digital Health Methodology %A Kizakevich,Paul N %A Eckhoff,Randall P %A Lewis,Gregory F %A Davila,Maria I %A Hourani,Laurel L %A Watkins,Rebecca %A Weimer,Belinda %A Wills,Tracy %A Morgan,Jessica K %A Morgan,Tim %A Meleth,Sreelatha %A Lewis,Amanda %A Krzyzanowski,Michelle C %A Ramirez,Derek %A Boyce,Matthew %A Litavecz,Stephen D %A Lane,Marian E %A Strange,Laura B %+ Bioinformatice Program, Research Computing Division, RTI International, 3040 Cornwallis Road, Research Triangle Park, NC, 27709, United States, 1 919 541 6639, kiz@rti.org %K resilience, psychological %K heart rate variability %K Personal Health Informatics and Intervention Toolkit %K PHIT %K respiratory sinus arrhythmia %K stress, psychological %K relaxation therapy %K biofeedback, psychology %K well-being %K mindfulness %K digital health %K mhealth %D 2019 %7 06.09.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Psychological resilience is critical to minimize the health effects of traumatic events. Trauma may induce a chronic state of hyperarousal, resulting in problems such as anxiety, insomnia, or posttraumatic stress disorder. Mind-body practices, such as relaxation breathing and mindfulness meditation, help to reduce arousal and may reduce the likelihood of such psychological distress. To better understand resilience-building practices, we are conducting the Biofeedback-Assisted Resilience Training (BART) study to evaluate whether the practice of slow, paced breathing with or without heart rate variability biofeedback can be effectively learned via a smartphone app to enhance psychological resilience. Objective: Our objective was to conduct a limited, interim review of user interactions and study data on use of the BART resilience training app and demonstrate analyses of real-time sensor-streaming data. Methods: We developed the BART app to provide paced breathing resilience training, with or without heart rate variability biofeedback, via a self-managed 6-week protocol. The app receives streaming data from a Bluetooth-linked heart rate sensor and displays heart rate variability biofeedback to indicate movement between calmer and stressful states. To evaluate the app, a population of military personnel, veterans, and civilian first responders used the app for 6 weeks of resilience training. We analyzed app usage and heart rate variability measures during rest, cognitive stress, and paced breathing. Currently released for the BART research study, the BART app is being used to collect self-reported survey and heart rate sensor data for comparative evaluation of paced breathing relaxation training with and without heart rate variability biofeedback. Results: To date, we have analyzed the results of 328 participants who began using the BART app for 6 weeks of stress relaxation training via a self-managed protocol. Of these, 207 (63.1%) followed the app-directed procedures and completed the training regimen. Our review of adherence to protocol and app-calculated heart rate variability measures indicated that the BART app acquired high-quality data for evaluating self-managed stress relaxation training programs. Conclusions: The BART app acquired high-quality data for studying changes in psychophysiological stress according to mind-body activity states, including conditions of rest, cognitive stress, and slow, paced breathing. %M 31493325 %R 10.2196/12590 %U https://mhealth.jmir.org/2019/9/e12590/ %U https://doi.org/10.2196/12590 %U http://www.ncbi.nlm.nih.gov/pubmed/31493325 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 3 %P e14329 %T Psychiatry Outpatients’ Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study %A Rieger,Agnes %A Gaines,Averi %A Barnett,Ian %A Baldassano,Claudia Frances %A Connolly Gibbons,Mary Beth %A Crits-Christoph,Paul %+ University of Pennsylvania, Suite 650, 3535 Market Street, Philadelphia, PA, 19104, United States, 1 215 662 7993, crits@pennmedicine.upenn.edu %K social media %K smartphone %K outpatients %K psychiatry %K psychotherapy %K digital health %K mhealth %K digital phenotyping %K privacy %K user preferences %D 2019 %7 29.8.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Psychiatry research has begun to leverage data collected from patients’ social media and smartphone use. However, information regarding the feasibility of utilizing such data in an outpatient setting and the acceptability of such data in research and practice is limited. Objective: This study aimed at understanding the outpatients’ willingness to have information from their social media posts and their smartphones used for clinical or research purposes. Methods: In this survey study, we surveyed patients (N=238) in an outpatient clinic waiting room. Willingness to share social media and passive smartphone data was summarized for the sample as a whole and broken down by sex, age, and race. Results: Most patients who had a social media account and who were receiving talk therapy treatment (74.4%, 99/133) indicated that they would be willing to share their social media posts with their therapists. The percentage of patients willing to share passive smartphone data with researchers varied from 40.8% (82/201) to 60.7% (122/201) depending on the parameter, with sleep duration being the parameter with the highest percentage of patients willing to share. A total of 30.4% of patients indicated that media stories of social media privacy breaches made them more hesitant about sharing passive smartphone data with researchers. Sex and race were associated with willingness to share smartphone data, with men and whites being the most willing to share. Conclusions: Our results indicate that most patients in a psychiatric outpatient setting would share social media and passive smartphone data and that further research elucidating patterns of willingness to share passive data is needed. %M 31493326 %R 10.2196/14329 %U http://formative.jmir.org/2019/3/e14329/ %U https://doi.org/10.2196/14329 %U http://www.ncbi.nlm.nih.gov/pubmed/31493326 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 9 %P e14569 %T From Paper to Digital Applications of the Pain Drawing: Systematic Review of Methodological Milestones %A Shaballout,Nour %A Neubert,Till-Ansgar %A Boudreau,Shellie %A Beissner,Florian %+ Somatosensory and Autonomic Therapy Research, Institute for Neuroradiology, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, 30625, Germany, 49 5115350 ext 8413, beissner.florian@mh-hannover.de %K pain drawing %K digital pain drawing %K pain chart %K pain map %K pain body map %K pain diagram %K ehealth %K medical app %D 2019 %7 05.09.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: In a pain drawing (PD), the patient shades or marks painful areas on an illustration of the human body. This simple yet powerful tool captures essential aspects of the subjective pain experience, such as localization, intensity, and distribution of pain, and enables the extraction of meaningful information, such as pain area, widespreadness, and segmental pattern. Starting as a simple pen-on-paper tool, PDs are now sophisticated digital health applications paving the way for many new and exciting basic translational and clinical applications. Objective: Grasping the full potential of digital PDs and laying the groundwork for future medical PD apps requires an understanding of the methodological developments that have shaped our current understanding of uses and design. This review presents methodological milestones in the development of both pen-on-paper and digital PDs, thereby offering insight into future possibilities created by the transition from paper to digital. Methods: We conducted a systematic literature search covering PD acquisition, conception of PDs, PD analysis, and PD visualization. Results: The literature search yielded 435 potentially relevant papers, from which 53 methodological milestones were identified. These milestones include, for example, the grid method to quantify pain area, the pain-frequency maps, and the use of artificial neural networks to facilitate diagnosis. Conclusions: Digital technologies have had a significant influence on the evolution of PDs, whereas their versatility is leading to ever new applications in the field of medical apps and beyond. In this process, however, there is a clear need for better standardization and a re-evaluation of methodological and technical limitations that no longer apply today. %M 31489841 %R 10.2196/14569 %U https://mhealth.jmir.org/2019/9/e14569/ %U https://doi.org/10.2196/14569 %U http://www.ncbi.nlm.nih.gov/pubmed/31489841 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 9 %P e14474 %T User Experience of 7 Mobile Electroencephalography Devices: Comparative Study %A Radüntz,Thea %A Meffert,Beate %+ Mental Health and Cognitive Capacity, Federal Institute for Occupational Safety and Health, Nöldnerstr 40-42, Berlin, 10317, Germany, 49 30 51548 4418, raduentz.thea@baua.bund.de %K wearable devices %K user experience %K electroencephalography %K mobile applications %K electrodes %K dry electrodes %D 2019 %7 03.09.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Registration of brain activity has become increasingly popular and offers a way to identify the mental state of the user, prevent inappropriate workload, and control other devices by means of brain-computer interfaces. However, electroencephalography (EEG) is often related to user acceptance issues regarding the measuring technique. Meanwhile, emerging mobile EEG technology offers the possibility of gel-free signal acquisition and wireless signal transmission. Nonetheless, user experience research about the new devices is lacking. Objective: This study aimed to evaluate user experience aspects of emerging mobile EEG devices and, in particular, to investigate wearing comfort and issues related to emotional design. Methods: We considered 7 mobile EEG devices and compared them for their wearing comfort, type of electrodes, visual appearance, and subjects’ preference for daily use. A total of 24 subjects participated in our study and tested every device independently of the others. The devices were selected in a randomized order and worn on consecutive day sessions of 60-min duration. At the end of each session, subjects rated the devices by means of questionnaires. Results: Results indicated a highly significant change in maximal possible wearing duration among the EEG devices (χ26=40.2, n=24; P<.001). Regarding the visual perception of devices’ headset design, results indicated a significant change in the subjects’ ratings (χ26=78.7, n=24; P<.001). Results of the subjects’ ratings regarding the practicability of the devices indicated highly significant differences among the EEG devices (χ26=83.2, n=24; P<.001). Ranking order and posthoc tests offered more insight and indicated that pin electrodes had the lowest wearing comfort, in particular, when coupled with a rigid, heavy headset. Finally, multiple linear regression for each device separately revealed that users were not willing to accept less comfort for a more attractive headset design. Conclusions: The study offers a differentiated look at emerging mobile and gel-free EEG technology and the relation between user experience aspects and device preference. Our research could be seen as a precondition for the development of usable applications with wearables and contributes to consumer health informatics and health-enabling technologies. Furthermore, our results provided guidance for the technological development direction of new EEG devices related to the aspects of emotional design. %M 31482852 %R 10.2196/14474 %U https://mhealth.jmir.org/2019/9/e14474/ %U https://doi.org/10.2196/14474 %U http://www.ncbi.nlm.nih.gov/pubmed/31482852 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 8 %P e14863 %T Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study %A Midaglia,Luciana %A Mulero,Patricia %A Montalban,Xavier %A Graves,Jennifer %A Hauser,Stephen L %A Julian,Laura %A Baker,Michael %A Schadrack,Jan %A Gossens,Christian %A Scotland,Alf %A Lipsmeier,Florian %A van Beek,Johan %A Bernasconi,Corrado %A Belachew,Shibeshih %A Lindemann,Michael %+ F Hoffmann–La Roche Ltd, 124 Grenzacherstrasse, Basel,, Switzerland, 41 61 687 5113, christian.gossens@roche.com %K multiple sclerosis %K patient adherence %K patient satisfaction %K smartphone %K wearable electronic devices %K mobile phone %D 2019 %7 30.08.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits. Objective: The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery. Methods: People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed. Results: People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis. Conclusions: People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings. Trial Registration: ClinicalTrials.gov: NCT02952911; https://clinicaltrials.gov/ct2/show/NCT02952911 %M 31471961 %R 10.2196/14863 %U http://www.jmir.org/2019/8/e14863/ %U https://doi.org/10.2196/14863 %U http://www.ncbi.nlm.nih.gov/pubmed/31471961 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 8 %P e12434 %T Intraindividual Variability Measurement of Fine Manual Motor Skills in Children Using an Electronic Pegboard: Cohort Study %A Rivera,Diego %A García,Antonio %A Ortega,Jose Eugenio %A Alarcos,Bernardo %A van der Meulen,Kevin %A Velasco,Juan R %A del Barrio,Cristina %+ Departamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Ctra Madrid-Barcelona, Km 33,600, Alcalá de Henares, 28871, Spain, 34 918856644, diego.rivera@uah.es %K child development %K psychology, developmental %K play and playthings %K motor skills %K smartphone %D 2019 %7 28.08.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Pegboard tests are a powerful technique used by health and education professionals to evaluate manual dexterity and fine motor speed, both in children and adults. Using traditional pegboards in tests, the total time that, for example, a 4-year-old child needs for inserting pegs in a pegboard, with the left or right hand, can be measured. However, these measurements only allow for studying the variability among individuals, whereas no data can be obtained on the intraindividual variability in inserting and removing these pegs with one and the other hand. Objective: The aim of this research was to study the intraindividual variabilities in fine manual motor skills of 2- to 3-year-old children during playing activities, using a custom designed electronic pegboard. Methods: We have carried out a pilot study with 39 children, aged between 25 and 41 months. The children were observed while performing a task involving removing 10 pegs from 10 holes on one side and inserting them in 10 holes on the other side of a custom-designed sensor-based electronic pegboard, which has been built to be able to measure the times between peg insertions and removals. Results: A sensor-based electronic pegboard was successfully developed, enabling the collection of single movement time data. In the piloting, a lower intraindividual variability was found in children with lower placement and removal times, confirming Adolph et al’s hypothesis. Conclusions: The developed pegboard allows for studying intraindividual variability using automated wirelessly transmitted data provided by its sensors. This novel technique has been useful in studying and validating the hypothesis that children with lower movement times present lower intraindividual variability. New research is necessary to confirm these findings. Research with larger sample sizes and age ranges that include additional testing of children’s motor development level is currently in preparation. %M 31464193 %R 10.2196/12434 %U http://mhealth.jmir.org/2019/8/e12434/ %U https://doi.org/10.2196/12434 %U http://www.ncbi.nlm.nih.gov/pubmed/31464193 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 8 %P e13418 %T The Validity of Daily Self-Assessed Perceived Stress Measured Using Smartphones in Healthy Individuals: Cohort Study %A Þórarinsdóttir,Helga %A Faurholt-Jepsen,Maria %A Ullum,Henrik %A Frost,Mads %A Bardram,Jakob E %A Kessing,Lars Vedel %+ The Copenhagen Affective Disorder Research Centre, Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, 2100, Denmark, 45 91104943, helgath90@gmail.com %K emotional stress %K smartphone %K ecological momentary assessment %K mobile phone %K self-report %K healthy individuals %D 2019 %7 19.08.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphones may offer a new and easy tool to assess stress, but the validity has never been investigated. Objective: This study aimed to investigate (1) the validity of smartphone-based self-assessed stress compared with Cohen Perceived Stress Scale (PSS) and (2) whether smartphone-based self-assessed stress correlates with neuroticism (Eysenck Personality Questionnaire-Neuroticism, EPQ-N), psychosocial functioning (Functioning Assessment Short Test, FAST), and prior stressful life events (Kendler Questionnaire for Stressful Life Events, SLE). Methods: A cohort of 40 healthy blood donors with no history of personal or first-generation family history of psychiatric illness and who used an Android smartphone were instructed to self-assess their stress level daily (on a scale from 0 to 2; beta values reflect this scale) for 4 months. At baseline, participants were assessed with the FAST rater-blinded and filled out the EPQ, the PSS, and the SLE. The PSS assessment was repeated after 4 months. Results: In linear mixed-effect regression and linear regression models, there were statistically significant positive correlations between self-assessed stress and the PSS (beta=.0167; 95% CI 0.0070-0.0026; P=.001), the EPQ-N (beta=.0174; 95% CI 0.0023-0.0325; P=.02), and the FAST (beta=.0329; 95% CI 0.0036-0.0622; P=.03). No correlation was found between smartphone-based self-assessed stress and the SLE. Conclusions: Daily smartphone-based self-assessed stress seems to be a valid measure of perceived stress. Our study contains a modest sample of 40 healthy participants and adds knowledge to a new but growing field of research. Smartphone-based self-assessed stress is a promising tool for measuring stress in real time in future studies of stress and stress-related behavior. %M 31429413 %R 10.2196/13418 %U http://mhealth.jmir.org/2019/8/e13418/ %U https://doi.org/10.2196/13418 %U http://www.ncbi.nlm.nih.gov/pubmed/31429413 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 8 %P e13516 %T Analysis of Digital Documentation Speed and Sequence Using Digital Paper and Pen Technology During the Refugee Crisis in Europe: Content Analysis %A Kehe,Kai %A Girgensohn,Roland %A Swoboda,Walter %A Bieler,Dan %A Franke,Axel %A Helm,Matthias %A Kulla,Martin %A Luepke,Kerstin %A Morwinsky,Thomas %A Blätzinger,Markus %A Rossmann,Katalyn %+ Department F, Bundeswehr Medical Academy, Ingolstädter Str 240, Munich, 80939, Germany, 49 31687422, kai.kehe@lrz.uni-muenchen.de %K digital documentation %K digital pen %K digital paper %K refugee camp %K refugee crisis %K Europe %K Germany %K epidemiology %D 2019 %7 19.08.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The Syria crisis has forced more than 4 million people to leave their homeland. As a result, in 2016, an overwhelming number of refugees reached Germany. In response to this, it was of utmost importance to set up refugee camps and to provide humanitarian aid, but a health surveillance system was also implemented in order to obtain rapid information about emerging diseases. Objective: The present study describes the effects of using digital paper and pen (DPP) technology on the speed, sequence, and behavior of epidemiological documentation in a refugee camp. Methods: DPP technology was used to examine documentation speed, sequence, and behavior. The data log of the digital pens used to fill in the documentation was analyzed, and each pen stroke in a field was recorded using a timestamp. Documentation time was the difference between first and last stroke on the paper, which includes clinical examination and translation. Results: For three months, 495 data sets were recorded. After corrections had been made, 421 data sets were considered valid and subjected to further analysis. The median documentation time was 41:41 min (interquartile range 29:54 min; mean 45:02 min; SD 22:28 min). The documentation of vital signs ended up having the strongest effect on the overall time of documentation. Furthermore, filling in the free-text field clinical findings or therapy or measures required the most time (mean 16:49 min; SD 20:32 min). Analysis of the documentation sequence revealed that the final step of coding the diagnosis was a time-consuming step that took place once the form had been completed. Conclusions: We concluded that medical documentation using DPP technology leads to both an increase in documentation speed and data quality through the compliance of the data recorders who regard the tool to be convenient in everyday routine. Further analysis of more data sets will allow optimization of the documentation form used. Thus, DPP technology is an effective tool for the medical documentation process in refugee camps. %M 31429420 %R 10.2196/13516 %U http://mhealth.jmir.org/2019/8/e13516/ %U https://doi.org/10.2196/13516 %U http://www.ncbi.nlm.nih.gov/pubmed/31429420 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 8 %P e11734 %T RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices %A Ranjan,Yatharth %A Rashid,Zulqarnain %A Stewart,Callum %A Conde,Pauline %A Begale,Mark %A Verbeeck,Denny %A Boettcher,Sebastian %A , %A Dobson,Richard %A Folarin,Amos %A , %+ The Institute of Psychiatry, Psychology & Neuroscience (IoPPN), Department of Biostatistics & Health Informatics, King's College London, SGDP Centre, IoPPN, PO Box 80 De Crespigny Park, Denmark Hill, London, SE5 8AF, United Kingdom, 44 02078480924, amos.folarin@kcl.ac.uk %K remote sensing technology %K mobile applications %K telemedicine %K mental health %D 2019 %7 01.08.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective: Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods: RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results: General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions: RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale. %M 31373275 %R 10.2196/11734 %U https://mhealth.jmir.org/2019/8/e11734/ %U https://doi.org/10.2196/11734 %U http://www.ncbi.nlm.nih.gov/pubmed/31373275 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 7 %P e12345 %T Electronic Patient-Reported Outcome Measures in Radiation Oncology: Initial Experience After Workflow Implementation %A Hauth,Franziska %A Bizu,Verena %A App,Rehan %A Lautenbacher,Heinrich %A Tenev,Alina %A Bitzer,Michael %A Malek,Nisar Peter %A Zips,Daniel %A Gani,Cihan %+ University Hospital Tübingen, Department of Radiation Oncology, Hoppe-Seyler-Str 3, Tübingen, 72076, Germany, 49 70712986142, cihan.gani@med.uni-tuebingen.de %K mHealth %K eHealth %K radiation oncology %K patient reported outcome measures %D 2019 %7 24.07.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) technologies are increasingly used in various medical fields. However, the potential of mHealth to improve patient care in radiotherapy by acquiring electronic patient reported outcome measures (ePROMs) during treatment has been poorly studied so far. Objective: The aim of this study was to develop and implement a novel Web app (PROMetheus) for patients undergoing radiotherapy. Herein, we have reported our experience with a focus on feasibility, patient acceptance, and a correlation of ePROMs with the clinical course of the patients. Methods: In the period between January and June 2018, 21 patients used PROMetheus to score side effects, symptoms, and quality of life–related parameters during and after their treatment. Items of the Patient Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) were chosen based on the primary site of disease, 27 items for head and neck tumors, 21 items for thoracic tumors, and 24 items for pelvic tumors. Results: In total, 17 out of the 21 patients (81%) regularly submitted ePROMs and more than 2500 data points were acquired. An average of 5.2, 3.5, and 3.3 min was required to complete the head and neck, thorax, and pelvis questionnaires, respectively. ePROMS were able to detect the occurrence of both expected and unexpected side effects during the treatment. In addition, a gradual increase in the severity of side effects over the course the treatment and their remission afterward could be observed with ePROMs. In total, 9 out of the 17 patients (53%), mostly those with head and neck and thoracic cancers, reported PRO-CTCAE grade III or IV fatigue with severe impairments of activities of daily life. Conclusions: This study shows the successful implementation of an ePROM system and a high patient acceptance. ePROMs have a great potential to improve patient care in radiotherapy by providing a comprehensive documentation of symptoms and side effects, especially of ones that are otherwise underreported. %M 31342906 %R 10.2196/12345 %U http://mhealth.jmir.org/2019/7/e12345/ %U https://doi.org/10.2196/12345 %U http://www.ncbi.nlm.nih.gov/pubmed/31342906 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 7 %P e13946 %T Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach %A Wshah,Safwan %A Skalka,Christian %A Price,Matthew %+ University of Vermont, 33 Colchester Ave, Burlington, VT, 05405, United States, 1 8026568086, safwan.wshah@uvm.edu %K PTSD %K machine learning %K predictive algorithms %D 2019 %7 22.07.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD). Objective: Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions. Methods: We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones. Results: We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma. Conclusions: These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention. %M 31333201 %R 10.2196/13946 %U http://mental.jmir.org/2019/7/e13946/ %U https://doi.org/10.2196/13946 %U http://www.ncbi.nlm.nih.gov/pubmed/31333201 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 7 %P e12952 %T “Seeing Pain Differently”: A Qualitative Investigation Into the Differences and Similarities of Pain and Rheumatology Specialists’ Interpretation of Multidimensional Mobile Health Pain Data From Children and Young People With Juvenile Idiopathic Arthritis %A Lee,Rebecca Rachael %A Rashid,Amir %A Ghio,Daniela %A Thomson,Wendy %A Cordingley,Lis %+ NIHR Manchester Musculoskeletal Biomedical Research Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Room 2.908, Stopford Building, Oxford Road, Manchester, M13 9PT, United Kingdom, 44 1612757757, rebecca.lee-4@manchester.ac.uk %K mHeath %K pain assessment %K juvenile idiopathic arthritis %K focus group %K qualitative research %D 2019 %7 02.07.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In contrast to the use of traditional unidimensional paper-based scales, a mobile health (mHealth) assessment of pain in children and young people (CYP) with juvenile idiopathic arthritis (JIA) enables comprehensive and complex multidimensional pain data to be captured remotely by individuals. However, how professionals use multidimensional pain data to interpret and synthesize pain reports gathered using mHealth tools is not yet known. Objective: The aim of this study was to explore the salience and prioritization of different mHealth pain features as interpreted by key stakeholders involved in research and management of pain in CYP with JIA. Methods: Pain and rheumatology specialists were purposively recruited via professional organizations. Face-to-face focus groups were conducted for each specialist group. Participants were asked to rank order 9 static vignette scenarios created from real patient mHealth multidimensional pain data. These data had been collected by a researcher in a separate study using My Pain Tracker, a valid and acceptable mHealth iPad pain communication tool that collects information about intensity, severity, location, emotion, and pictorial pain qualities. In the focus groups, specialists discussed their decision-making processes behind each rank order in the focus groups. The total group rank ordering of vignette scenarios was calculated. Qualitative data from discussions were analyzed using latent thematic analysis. Results: A total of 9 pain specialists took part in 1 focus group and 10 rheumatology specialists in another. In pain specialists, the consensus for the highest pain experience (44%) was poorer than their ranking of the lowest pain experiences (55%). Conversely, in rheumatology specialists, the consensus for the highest pain experience (70%) was stronger than their ranking of the lowest pain experience (50%). Pain intensity was a high priority for pain specialists, but rheumatology specialists gave high priority to intensity and severity taken together. Pain spread was highly prioritized, with the number of pain locations (particular areas or joints) being a high priority for both groups; radiating pain was a high priority for pain specialists only. Pain emotion was challenging for both groups and was only perceived to be a high priority when specialists had additional confirmatory evidence (such as information about pain interference or clinical observations) to validate the pain emotion report. Pain qualities such as particular word descriptors, use of the color red, and fire symbols were seen to be high priority by both groups in interpretation of CYP pain reports. Conclusions: Pain interpretation is complex. Findings from this study of specialists’ decision-making processes indicate which aspects of pain are prioritized and weighted more heavily than others by those interpreting mHealth data. Findings are useful for developing electronic graphical summaries which assist specialists in interpreting patient-reported mHealth pain data more efficiently in clinical and research settings. %M 31267979 %R 10.2196/12952 %U https://mhealth.jmir.org/2019/7/e12952/ %U https://doi.org/10.2196/12952 %U http://www.ncbi.nlm.nih.gov/pubmed/31267979 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 2 %P e12657 %T Feasibility of Using Short Message Service and In-Depth Interviews to Collect Data on Contraceptive Use Among Young, Unmarried, Sexually Active Men in Moshi, Tanzania, and Addis Ababa, Ethiopia: Mixed Methods Study With a Longitudinal Follow-Up %A Pima,Francis Maganga %A Oshosen,Martha %A Ngowi,Kennedy Michael %A Habte,Bruck Messele %A Maro,Eusebious %A Teffera,Belete Eshete %A Kisigo,Godfrey %A Swai,Iraseni Ufoo %A Msangi,Salim Semvua %A Ermias,Amha %A Mmbaga,Blandina T %A Both,Rosalijn %A Sumari-de Boer,Marion %+ Department of Clinical Trials, Kilimanjaro Clinical Research Institute, PO Box 2236, Moshi,, United Republic of Tanzania, 255 754331948, m.sumari@kcri.ac.tz %K SMS %K contraceptives %K sexual behavior %K feasibility %K young unmarried men %D 2019 %7 26.06.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Data on contraceptive needs and use among young unmarried men are limited. Conventional ways of data collection may lead to limited and unreliable information on contraceptive use due to sensitivity of the topic, as many young men feel ashamed to discuss their behavior of using contraceptives. As short message service (SMS) is anonymous and a commonly used means of communication, we believe that if deployed, it will create a promising user-friendly method of data collection. Objective: The objective was to investigate the feasibility of using SMS to collect data on sexually active, young, unmarried men’s sexual behavior and contraceptive preferences, practices, and needs in Addis Ababa, Ethiopia, and Moshi, Tanzania. Methods: We enrolled men aged 18-30 years who were students (in Ethiopia and Tanzania), taxi or local bus drivers/assistants (Ethiopia and Tanzania), Kilimanjaro porters (Tanzania), or construction workers (Ethiopia). Young men were interviewed using a topic list on contraceptive use. They were followed up for 6 months by sending fortnightly SMS texts with questions about contraceptive use. If the young men indicated that they needed contraceptives during the reporting period or were not satisfied with the method they used, they were invited for a follow-up interview. At the end of the study, we conducted exit interviews telephonically using a semistructured questionnaire to explore the feasibility, acceptability, and accuracy of using SMS to validate the study findings in both countries. Results: We enrolled 71 young unmarried men—35 in Tanzania and 36 in Ethiopia. In Moshi, 1908 messages were delivered to participants and 1119 SMS responses were obtained. In Ethiopia, however, only 525 messages were sent to participants and 248 replies were received. The question on dating a girl in the past weeks was asked 438 times in Tanzania and received 252 (58%) replies, of which 148 (59%) were “YES.” In Ethiopia, this question was asked 314 times and received 64 (20%) replies, of which 52 (81%) were “YES” (P=.02 for difference in replies between Tanzania and Ethiopia). In Tanzania, the question on contraceptive use was sent successfully 112 times and received 108 (96%) replies, of which 105 (94%) were “YES.” In Ethiopia, the question on contraceptive use was asked 17 times and received only 2 (11%) replies. Exit interviews in Tanzania showed that SMS was accepted as a means of data collection by 22 (88%) of the 25 interviewed participants. Conclusions: Despite network and individual challenges, the SMS system was found to be feasible in Moshi, but not in Addis Ababa. We recommend more research to scale up the method in different groups and regions. %M 31244476 %R 10.2196/12657 %U http://formative.jmir.org/2019/2/e12657/ %U https://doi.org/10.2196/12657 %U http://www.ncbi.nlm.nih.gov/pubmed/31244476 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 6 %P e13569 %T Understanding Pregnancy and Postpartum Health Using Ecological Momentary Assessment and Mobile Technology: Protocol for the Postpartum Mothers Mobile Study %A Mendez,Dara D %A Sanders,Sarah A %A Karimi,Hassan A %A Gharani,Pedram %A Rathbun,Stephen L %A Gary-Webb,Tiffany L %A Wallace,Meredith L %A Gianakas,John J %A Burke,Lora E %A Davis,Esa M %+ Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 De Soto Street, 5130 Public Health, Pittsburgh, PA, 15261, United States, 1 412 648 5664, ddm11@pitt.edu %K ecological momentary assessment (EMA) %K wireless technology %K remote sensing technology %K maternal health %K pregnancy %K postpartum %K body weight %K health status disparities %K health equity %D 2019 %7 26.06.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: There are significant racial disparities in pregnancy and postpartum health outcomes, including postpartum weight retention and cardiometabolic risk. These racial disparities are a result of a complex interplay between contextual, environmental, behavioral, and psychosocial factors. Objective: This protocol provides a description of the development and infrastructure for the Postpartum Mothers Mobile Study (PMOMS), designed to better capture women’s daily experiences and exposures from late pregnancy through 1 year postpartum. The primary aims of PMOMS are to understand the contextual, psychosocial, and behavioral factors contributing to racial disparities in postpartum weight and cardiometabolic health, with a focus on the daily experiences of stress and racism, as well as contextual forms of stress (eg, neighborhood stress and structural racism). Methods: PMOMS is a longitudinal observation study that is ancillary to an existing randomized control trial, GDM2 (Comparison of Two Screening Strategies for Gestational Diabetes). PMOMS uses an efficient and cost-effective approach for recruitment by leveraging the infrastructure of GDM2, facilitating enrollment of participants while consolidating staff support from both studies. The primary data collection method is ecological momentary assessment (EMA) and through smart technology (ie, smartphones and scales). The development of the study includes: (1) the pilot phase and development of the smartphone app; (2) feedback and further development of the app including selection of key measures; and (3) implementation, recruitment, and retention. Results: PMOMS aims to recruit 350 participants during pregnancy, to be followed through the first year after delivery. Recruitment and data collection started in December 2017 and are expected to continue through September 2020. Initial results are expected in December 2020. As of early May 2019, PMOMS recruited a total of 305 participants. Key strengths and features of PMOMS have included data collection via smartphone technology to reduce the burden of multiple on-site visits, low attrition rate because of participation in an ongoing trial in which women are already motivated and enrolled, high EMA survey completion and the use of EMA as a unique data collection method to understand daily experiences, and shorter than expected timeframe for enrollment because of the infrastructure of the GDM2 trial. Conclusions: This protocol outlines the development of the PMOMS, one of the first published studies to use an ongoing EMA and mobile technology protocol during pregnancy and throughout 1 year postpartum to understand the health of childbearing populations and enduring racial disparities in postpartum weight and cardiometabolic health. Our findings will contribute to the improvement of data collection methods, particularly the role of EMA in capturing multiple exposures and knowledge in real time. Furthermore, the results of the study will inform future studies investigating weight and cardiometabolic health during pregnancy and the postpartum period, including how social determinants produce population disparities in these outcomes. International Registered Report Identifier (IRRID): DERR1-10.2196/13569 %M 31244478 %R 10.2196/13569 %U http://www.researchprotocols.org/2019/6/e13569/ %U https://doi.org/10.2196/13569 %U http://www.ncbi.nlm.nih.gov/pubmed/31244478 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e12190 %T Consumer Wearable Deployments in Actigraphy Research: Evaluation of an Observational Study %A Duignan,Ciara %A Slevin,Patrick %A Sett,Niladri %A Caulfield,Brian %+ Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin,, Ireland, 353 17166500, ciara.duignan@insight-centre.org %K wearable electronic device %K digital divide %K activity trackers %K technology %K wearable challenges %D 2019 %7 24.06.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Consumer wearables can provide a practical and accessible method of data collection in actigraphy research. However, as this area continues to grow, it is becoming increasingly important for researchers to be aware of the many challenges facing the capture of quality data using consumer wearables. Objective: This study aimed to (1) present the challenges encountered by a research team in actigraphy data collection using a consumer wearable and (2) present considerations for researchers to apply in the pursuit of robust data using this approach. Methods: The Nokia Go was deployed to 33 elite Gaelic footballers from a single team for a planned period of 14 weeks. A bring-your-own-device model was employed for this study where the Health Mate app was downloaded on participants’ personal mobile phones and connected to the Nokia Go via Bluetooth. Retrospective evaluation of the researcher and participant experience was conducted through transactional data such as study logs and email correspondence. The participant experience of the data collection process was further explored through the design of a 34-question survey utilizing aspects of the Technology Acceptance Model. Results: Researcher challenges included device disconnection, logistics and monitoring, and rectifying of technical issues. Participant challenges included device syncing, loss of the device, and wear issues, particularly during contact sport. Following disconnection issues, the data collection period was defined as 87 days for which there were 18 remaining participants. Average wear time was 79 out of 87 days (90%) and 20.8 hours per day. The participant survey found mainly positive results regarding device comfort, perceived ease of use, and perceived usefulness. Conclusions: Although this study did not encounter some of the common published barriers to wearable data collection, our experience was impacted by technical issues such as disconnection and syncing challenges, practical considerations such as loss of the device, issues with personal mobile phones in the bring-your-own-device model, and the logistics and resources required to ensure a smooth data collection with an active cohort. Recommendations for achieving high-quality data are made for readers to consider in the deployment of consumer wearables in research. %M 31237237 %R 10.2196/12190 %U http://mhealth.jmir.org/2019/6/e12190/ %U https://doi.org/10.2196/12190 %U http://www.ncbi.nlm.nih.gov/pubmed/31237237 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 6 %P e12367 %T Creating Consumer-Generated Health Data: Interviews and a Pilot Trial Exploring How and Why Patients Engage %A Burns,Kara %A McBride,Craig A %A Patel,Bhaveshkumar %A FitzGerald,Gerard %A Mathews,Shane %A Drennan,Judy %+ QUT Business School, Queensland University of Technology, George St, Brisbane, 4000, Australia, 61 414294967, drkaraburns@gmail.com %K patient generated health data %K patient engagement %K patient participation %K mHealth %K photography %D 2019 %7 13.6.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Consumer-generated health data (CGHD) are any clinically relevant data collected by patients or their carers (consumers) that may improve health care outcomes. Like patient experience measures, these data reflect the consumer perspective and is part of a patient-centric agenda. The use of CGHD is believed to enhance diagnosis, patient engagement, and thus foster an improved therapeutic partnership with health care providers. Objective: The aim of this study was to further identify how these data were used by consumers and how it influences engagement via a validated framework. In addition, carer data has not been explored for the purpose of engagement. Methods: Study 1 used interviews with CGHD-experienced patients, carers, and doctors to understand attitudes about data collection and use, developing an ontological framework. Study 2 was a pilot trial with carers (parents) of children undergoing laparoscopic appendectomy. For 10 days carers generated and emailed surgical site photographs to a tertiary children’s hospital. Subsequently, carers were interviewed about the engagement framework. In total, 60 interviews were analyzed using theme and content analysis. Results: This study validates a framework anchored in engagement literature, which categorizes CGHD engagement outcomes into 4 domains: physiological, cognitive, emotional, and behavioral. CGHD use is complex, interconnected, and can be organized into 10 themes within these 4 domains. Conclusions: CGHD can instigate an ecosystem of engagement and provide clinicians with an enhanced therapeutic relationship through an extended view into the patient’s world. In addition to clinical diagnosis and efficient use of health care resources, data offer another tool to manage consumers service experience, especially the emotions associated with the health care journey. Collection and use of data increases consumers sense of reassurance, improves communication with providers, and promotes greater personal responsibility, indicating an empowering consumer process. Finally, it can also improve confidence and satisfaction in the service. %M 31199312 %R 10.2196/12367 %U http://www.jmir.org/2019/6/e12367/ %U https://doi.org/10.2196/12367 %U http://www.ncbi.nlm.nih.gov/pubmed/31199312 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 6 %P e13482 %T Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices %A Zhang,Zhongxing %A Cajochen,Christian %A Khatami,Ramin %+ Center for Sleep Medicine, Sleep Research and Epileptology, Clinic Barmelweid AG, , Barmelweid,, Switzerland, 41 62 857 22 38, zhongxing.zhang@barmelweid.ch %K chronotypes %K social jetlag %K wearable devices %K nap %K cardiopulmonary coupling %K sleep %K big data %D 2019 %7 11.5.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China. Objective: We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices. Methods: We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc. Results: The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13,266/49,573), 58.59% (29,045/49,573), and 14.64% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53%) of Chinese have SJL<0. The results showed that 53.72% (7127/13,266), 25.46% (7396/29,045), and 12.71% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively). Conclusions: Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL. %M 31199292 %R 10.2196/13482 %U https://www.jmir.org/2019/6/e13482/ %U https://doi.org/10.2196/13482 %U http://www.ncbi.nlm.nih.gov/pubmed/31199292 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 5 %P e13421 %T Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint %A Lin,Yu-Hsuan %A Wong,Bo-Yu %A Pan,Yuan-Chien %A Chiu,Yu-Chuan %A Lee,Yang-Han %+ Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli, 35053, Taiwan, 886 37 246166 ext 36383, yuhsuanlin@nhri.org.tw %K circadian rhythm %K sleep %K smartphone %K mobile applications %D 2019 %7 16.05.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. Objective: This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag. Methods: The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected. Results: No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87. Conclusions: The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm. %M 31099340 %R 10.2196/13421 %U https://mhealth.jmir.org/2019/5/e13421/ %U https://doi.org/10.2196/13421 %U http://www.ncbi.nlm.nih.gov/pubmed/31099340 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 2 %P e13882 %T Feasibility and Acceptability of Using a Mobile Phone App for Characterizing Auditory Verbal Hallucinations in Adolescents With Early-Onset Psychosis: Exploratory Study %A Smelror,Runar Elle %A Bless,Josef Johann %A Hugdahl,Kenneth %A Agartz,Ingrid %+ Department of Psychiatric Research, Diakonhjemmet Hospital, PO Box 85 Vinderen, Oslo, 0319, Norway, 47 95744029, runar.smelror@medisin.uio.no %K experience sampling method %K ecological momentary assessment %K schizophrenia %K mHealth %K health care technology %D 2019 %7 14.05.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Auditory verbal hallucinations (AVH) are the most frequent symptom in early-onset psychosis (EOP) and a risk factor for increased suicide attempts in adolescents. Increased knowledge of AVH characteristics can lead to better prediction of risk and precision of diagnosis and help identify individuals with AVH who need care. As 98% of Norwegian adolescents aged 12 to 16 years own a mobile phone, the use of mobile phone apps in symptom assessment and patient communication is a promising new tool. However, when introducing new technology to patients, their subjective experiences are crucial in identifying risks, further development, and potential integration into clinical care. Objective: The objective was to explore the feasibility and acceptability of a newly developed mobile phone app in adolescents with EOP by examining compliance with the app and user experiences. Indication of validity was explored by examining associations between AVH dimensions, which were correlated and analyzed. Methods: Three adolescents with EOP and active AVH were enrolled. Real-time AVH were logged on an iPod touch using the experience sampling method (ESM), for seven or more consecutive days. The app included five dimensions of AVH characteristics and was programmed with five daily notifications. Feasibility and acceptability were examined using the mean response rate of data sampling and by interviewing the participants. Validity was assessed by examining associations between the AVH dimensions using nonparametric correlation analysis and by visual inspection of temporal fluctuations of the AVH dimensions. Results: One participant was excluded from the statistical analyses but completed the interview and was included in the examination of acceptability. The sampling period of the two participants was mean 12 (SD 6) days with overall completed sampling rate of 74% (SD 30%), indicating adequate to high compliance with the procedure. The user experiences from the interviews clustered into four categories: (1) increased awareness, (2) personal privacy, (3) design and procedure, and (4) usefulness and clinical care. One participant experienced more commenting voices during the sampling period, and all three participants had concerns regarding personal privacy when using electronic devices in symptom assessment. The AVH dimensions of content, control, and influence showed moderate to strong significant correlations with all dimensions (P<.001). Days of data sampling showed weak to moderate correlations with localization (P<.001) and influence (P=.03). Visual inspection indicated that the app was able to capture fluctuations within and across days for all AVH dimensions. Conclusions: This study demonstrates the value of including patients’ experiences in the development and pilot-testing of new technology. Based on the small sample size, the use of mobile phones with ESM seems feasible for patients with EOP, but the acceptability of using apps should be considered. Further investigation with larger samples is warranted before definitive conclusions are made. %M 31094321 %R 10.2196/13882 %U http://formative.jmir.org/2019/2/e13882/ %U https://doi.org/10.2196/13882 %U http://www.ncbi.nlm.nih.gov/pubmed/31094321 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 4 %P e12251 %T Wearable Proximity Sensors for Monitoring a Mass Casualty Incident Exercise: Feasibility Study %A Ozella,Laura %A Gauvin,Laetitia %A Carenzo,Luca %A Quaggiotto,Marco %A Ingrassia,Pier Luigi %A Tizzoni,Michele %A Panisson,André %A Colombo,Davide %A Sapienza,Anna %A Kalimeri,Kyriaki %A Della Corte,Francesco %A Cattuto,Ciro %+ Data Science Laboratory, Institute for Scientific Interchange Foundation, Via Chisola 5, Torino, 10131, Italy, 39 3491973277, laura.ozella@gmail.com %K contact patterns %K contact networks %K wearable proximity sensors %K mass casualty incident %K simulation %K medical staff – patient interaction %K patients’ flow %D 2019 %7 26.04.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Over the past several decades, naturally occurring and man-made mass casualty incidents (MCIs) have increased in frequency and number worldwide. To test the impact of such events on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardized method to collect and analyze data from mass casualty exercises is needed to assess preparedness and performance of the health care staff involved. Objective: In this study, we aimed to assess the feasibility of using wearable proximity sensors to measure proximity events during an MCI simulation. In the first instance, our objective was to demonstrate how proximity sensors can collect spatial and temporal information about the interactions between medical staff and patients during an MCI exercise in a quasi-autonomous way. In addition, we assessed how the deployment of this technology could help improve future simulations by analyzing the flow of patients in the hospital. Methods: Data were obtained and collected through the deployment of wearable proximity sensors during an MCI functional exercise. The scenario included 2 areas: the accident site and the Advanced Medical Post, and the exercise lasted 3 hours. A total of 238 participants were involved in the exercise and classified in categories according to their role: 14 medical doctors, 16 nurses, 134 victims, 47 Emergency Medical Services staff members, and 27 health care assistants and other hospital support staff. Each victim was assigned a score related to the severity of his/her injury. Each participant wore a proximity sensor, and in addition, 30 fixed devices were placed in the field hospital. Results: The contact networks show a heterogeneous distribution of the cumulative time spent in proximity by the participants. We obtained contact matrices based on the cumulative time spent in proximity between the victims and rescuers. Our results showed that the time spent in proximity by the health care teams with the victims is related to the severity of the patient’s injury. The analysis of patients’ flow showed that the presence of patients in the rooms of the hospital is consistent with the triage code and diagnosis, and no obvious bottlenecks were found. Conclusions: Our study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during an MCI simulation. It represents, to our knowledge, the first example of unsupervised data collection—ie, without the need for the involvement of observers, which could compromise the realism of the exercise—of face-to-face contacts during an MCI exercise. Moreover, by permitting detailed data collection about the simulation, such as data related to the flow of patients in the hospital, such deployment provides highly relevant input for the improvement of MCI resource allocation and management. %M 31025944 %R 10.2196/12251 %U http://www.jmir.org/2019/4/e12251/ %U https://doi.org/10.2196/12251 %U http://www.ncbi.nlm.nih.gov/pubmed/31025944 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 4 %P e10111 %T Clinicians’ Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App %A Lemey,Christophe %A Larsen,Mark Erik %A Devylder,Jordan %A Courtet,Philippe %A Billot,Romain %A Lenca,Philippe %A Walter,Michel %A Baca-García,Enrique %A Berrouiguet,Sofian %+ URCI Mental Health Department, Brest Medical University Hospital, Route de Ploudalmézeau, Brest,, France, 33 619211032, christophe.lemey@chu-brest.fr %K acceptability %K feasibility studies %K mobile applications %K ecological momentary assessment %K decision support systems, clinical %K internet %K outpatients %K young adult %K prodromal symptoms %K mental health %D 2019 %7 25.04.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients’ environments using ecological momentary assessment techniques to support preventive therapeutic decision making. Objective: The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features. Methods: We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient’s emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies. Results: Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this. Conclusions: We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups. %M 31021327 %R 10.2196/10111 %U https://www.jmir.org/2019/4/e10111/ %U https://doi.org/10.2196/10111 %U http://www.ncbi.nlm.nih.gov/pubmed/31021327 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e12223 %T Postvaccination Fever Response Rates in Children Derived Using the Fever Coach Mobile App: A Retrospective Observational Study %A Ahn,Sang Hyun %A Zhiang,Jooho %A Kim,Hyery %A Chang,Seyun %A Shin,Jaewon %A Kim,Myeongchan %A Lee,Yura %A Lee,Jae-Ho %A Park,Yu Rang %+ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 2228 2493, yurangpark@yuhs.ac %K patient-generated health data %K vaccination %K postvaccination fever %K digital health care %K mobile app %D 2019 %7 22.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Postvaccination fever is a mild adverse event that naturally improves without complications, but is highly prevalent and can be accompanied by febrile convulsions in some cases. These adverse effects may cause parents to delay or avoid vaccinating their children. Objective: This study aimed to identify postvaccination fever patterns and the ability of antipyretics to affect changes in these patterns from data collected from a mobile app named Fever Coach. Methods: Data provided by parents of feverish children derived from a mobile app, Fever Coach, were used to identify postvaccination fever patterns according to vaccinations and the use of antipyretic drugs. We selected single vaccination records that contained five or more body temperature readings performed within 48 hours of vaccination, and we analyzed postvaccination fever onset, offset, duration, and maximum body temperature. Through observing the postvaccination fever response to vaccination, we identified the effects of antipyretic drugs on postvaccination fever onset, offset, and duration times; the extent of fever; and the rate of decline. We also performed logistic regression analysis to determine demographic variables (age, weight, and sex) involved in relatively high fevers (body temperature ≥39°C). Results: The total number of Fever Coach users was 25,037, with 3834 users having entered single vaccination records, including 4448 vaccinations and 55,783 body temperature records. Most records were obtained from children receiving the following vaccinations: pneumococcus (n=2069); Japanese encephalitis (n=911); influenza (n=669); diphtheria, tetanus, and pertussis (n=403); and hepatitis A (n=252). According to the 4448 vaccination records, 3427 (77.05%) children had taken antipyretic drugs, and 3238 (89.15%) children took antibiotics at body temperatures above 38°C. The number of children taking antipyretics at a body temperature of 38°C was more than four times that of those taking antipyretics at 37.9°C (307 vs 67 cases). The number of instances in which this temperature threshold was reached was more than four times greater than the number when the temperature was 37.9°C. A comparative analysis of antipyretic and nonantipyretic cases showed there was no difference in onset time; however, offset and duration times were significantly shorter in nonantipyretic cases than in antipyretic cases (P<.001). In nonantipyretic cases, offset times and duration times were 9.9 and 10.1 hours shorter, respectively, than in antipyretic cases. Body temperatures also decreased faster in nonantipyretic cases. Influenza vaccine-associated fevers lasted relatively longer, whereas pneumococcus vaccine-associated fevers were relatively short-lived. Conclusions: These findings suggest that postvaccination fever has its own fever pattern, which is dependent on vaccine type and the presence of antipyretic drugs, and that postvaccination temperature monitoring may ease fever phobia and reduce the unnecessary use of antipyretics in medical care. %M 31008712 %R 10.2196/12223 %U http://mhealth.jmir.org/2019/4/e12223/ %U https://doi.org/10.2196/12223 %U http://www.ncbi.nlm.nih.gov/pubmed/31008712 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e12451 %T Estimation of the Population Size of Men Who Have Sex With Men in Vietnam: Social App Multiplier Method %A Son,Vo Hai %A Safarnejad,Ali %A Nga,Nguyen Thien %A Linh,Vu Manh %A Tu,Le Thi Cam %A Manh,Pham Duc %A Long,Nguyen Hoang %A Abdul-Quader,Abu %+ The Joint United Nations Programme on HIV/AIDS, Green One United Nations House, , Hanoi, Vietnam, 84 +84243 850 1887, ali.safarnejad@gmail.com %K HIV %K AIDS %K population size estimation %K men who have sex with men %K respondent-driven sampling %K Vietnam %D 2019 %7 17.04.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Although the prevalence of HIV among men who have sex with men (MSM) in Vietnam has been increasing in recent years, there are no estimates of the population size of MSM based on tested empirical methods. Objective: This study aimed to estimate the size of the MSM population in 12 provinces in Vietnam and extrapolate from those areas to generate a national population estimate of MSM. A secondary aim of this study was to compare the feasibility of obtaining the number of users of a mobile social (chat and dating) app for MSM using 3 different approaches. Methods: This study used the social app multiplier method to estimate the size of MSM populations in 12 provinces using the count of users on a social app popular with MSM in Vietnam as the first data source and a questionnaire propagated through the MSM community using respondent-driven sampling as the second data source. A national estimation of the MSM population is extrapolated from the results in the study provinces, and the percentage of MSM reachable through online social networks is clarified. Results: The highest MSM population size among the 12 provinces is estimated in Hanoi and the lowest is estimated in Binh Dinh. On average, 37% of MSM in the provinces surveyed had used the social app Jack’d in the last 30 days (95% CI 27-48). Extrapolation of the results from the study provinces with reliable estimations results in an estimated national population of 178,000 MSM (95% CI 122,000-512,000) aged 15 to 49 years in Vietnam. The percentage of MSM among adult males aged 15 to 49 years in Vietnam is 0.68% (95% CI 0.46-1.95). Conclusions: This study is the first attempt to empirically estimate the population of MSM in Vietnam and highlights the feasibility of reaching a large proportion of MSM through a social app. The estimation reported in this study is within the bounds suggested by the Joint United Nations Programme on HIV/AIDS. This study provides valuable information on MSM population sizes in provinces where reliable estimates were obtained, which they can begin to work with in program planning and resource allocation. %M 30994469 %R 10.2196/12451 %U http://publichealth.jmir.org/2019/2/e12451/ %U https://doi.org/10.2196/12451 %U http://www.ncbi.nlm.nih.gov/pubmed/30994469 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e11531 %T Relative Validity of a Method Based on a Smartphone App (Electronic 12-Hour Dietary Recall) to Estimate Habitual Dietary Intake in Adults %A Béjar,Luis María %A García-Perea,María Dolores %A Reyes,Óscar Adrián %A Vázquez-Limón,Esther %+ Department of Preventive Medicine and Public Health, School of Medicine, University of Seville, Institute of Anatomy, 3rd Floor, Sánchez-Pizjuán Avenue, Seville, 41009, Spain, 34 954551771, lmbprado@us.es %K epidemiologic methods %K diet records %K mobile apps %K nutrition assessment %D 2019 %7 11.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Accurate dietary assessment is key to understanding nutrition-related outcomes and for estimating the dietary change in nutrition-based interventions. When researching the habitual consumption of selected food groups, it is essential to be aware of factors that could possibly affect reporting accuracy. Objective: This study aimed to evaluate the relative validity of the current-day dietary recall, a method based on a smartphone app called electronic 12-hour dietary recall (e-12HR), to categorize individuals according to habitual intake, in the whole sample of adults and in different strata thereof. Methods: University students and employees over 18 years recorded the consumption of 10 selected groups of food using e-12HR during 28 consecutive days. During this period, they also completed 4 dietary records. Once the period was finished, the subjects then completed a food frequency questionnaire (FFQ) and a usability-rating questionnaire for e-12HR. The food group intakes estimated by the e-12HR app, the dietary records, and the FFQ were categorized into sextiles: less than once a week, once or twice a week, 3-4 times a week, 5-6 times a week, once or twice a day, and 3 or more times a day. The 10 selected groups with e-12HR were compared with 4 dietary records and an FFQ reference method, in the whole sample and in different strata thereof: age (years): <25 and ≥25; gender: females and males; occupation: students and employees; smoking: no and yes; physical activity (minutes/week): ≥150 and <150; and body mass index (kg/m2): <25 and ≥25. The association between the different methods was assessed using Spearman correlation coefficient (SCC). Cross-classification and kappa statistic were used as a measure of agreement between the different methods. Results: In total, 203 participants completed the study (56.7% [115/203] women, and 43.3% [88/203] men). For all food groups and all participants, the mean SCC for e-12HR versus FFQ was 0.67 (≥0.62 for all strata). On average, 50.7% of participants were classified into the same category (≥47.0% for all strata) and 90.2% within the nearest category (≥88.6% for all strata). Mean weighted kappa was 0.49 (≥0.44 for all strata). For e-12HR versus RDs, mean SCC was 0.65 (≥0.57 for all strata). On average, 50.0% of participants were classified into the same category (≥47.0% for all strata) and 88.2% within the nearest category (≥86.1% for all strata). Mean weighted kappa was 0.50 (≥0.44 for all strata). Conclusions: The results indicate that e-12HR generated categories of dietary intake highly comparable with the 2 reference methods in the whole sample and in different strata thereof. The inclusion of photographs to facilitate estimation of the servings consumed generated correlation/agreement data between e-12HR and the FFQ that were similar to a previous study using an older version of the app, which did not include photographs. %M 30973343 %R 10.2196/11531 %U https://mhealth.jmir.org/2019/4/e11531/ %U https://doi.org/10.2196/11531 %U http://www.ncbi.nlm.nih.gov/pubmed/30973343 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e10894 %T Context-Sensitive Ecological Momentary Assessment: Application of User-Centered Design for Improving User Satisfaction and Engagement During Self-Report %A Srinivas,Preethi %A Bodke,Kunal %A Ofner,Susan %A Keith,NiCole R %A Tu,Wanzhu %A Clark,Daniel O %+ Indiana University, Center for Aging Research, 1101 West 10th Street, Indianapolis, IN, 46202, United States, 1 3172749000, daniclar@iu.edu %K mhealth %K health status %K obesity %K ecological momentary assessment %D 2019 %7 03.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Ecological momentary assessment (EMA) can be a useful tool for collecting real-time behavioral data in studies of health and health behavior. However, EMA administered through mobile technology can be burdensome, and it tends to suffer from suboptimal user engagement, particularly in low health-literacy populations. Objective: This study aimed to report a case study involving the design and evaluation of a mobile EMA tool that supports context-sensitive EMA-reporting of location and social situations accompanying eating and sedentary behavior. Methods: An iterative, user-centered design process with obese, middle-aged women seeking care in a safety-net health system was used to identify the preferred format of self-report measures and the look, feel, and interaction of the mobile EMA tool. A single-arm feasibility field trial with 21 participants receiving 12 prompts each day for momentary self-reports over a 4-week period (336 total prompts per participant) was used to determine user satisfaction with interface quality and user engagement, operationalized as response rate. A second trial among 38 different participants randomized to receive or not to receive a feature designed to improve engagement was conducted. Results: The feasibility trial results showed high interface satisfaction and engagement, with an average response rate of 50% over 4 weeks. Qualitative feedback pointed to the need for auditory alerts. We settled on 3 alerts at 10-min intervals to accompany each EMA-reporting prompt. The second trial testing this feature showed a statistically significant increase in the response rate between participants randomized to receive repeat auditory alerts versus those who were not (60% vs 40%). Conclusions: This paper reviews the design research and a set of design constraints that may be considered in the creation of mobile EMA interfaces personalized to users’ preferences. Novel aspects of the study include the involvement of low health-literacy adults in design research, the capture of data on time, place, and social context of eating and sedentary behavior, and reporting prompts tailored to an individual’s location and schedule. Trial Registration: ClinicalTrials.gov NCT03083964; https://clinicaltrials.gov/ct2/show/NCT03083964 %M 30942698 %R 10.2196/10894 %U http://mhealth.jmir.org/2019/4/e10894/ %U https://doi.org/10.2196/10894 %U http://www.ncbi.nlm.nih.gov/pubmed/30942698 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 4 %P e11671 %T Utilization of Patient-Generated Data Collected Through Mobile Devices: Insights From a Survey on Attitudes Toward Mobile Self-Monitoring and Self-Management Apps for Depression %A Hartmann,Ralf %A Sander,Christian %A Lorenz,Noah %A Böttger,Daniel %A Hegerl,Ulrich %+ Research Center of the German Depression Foundation, Semmelweisstraße 10, Leipzig, 04103, Germany, 49 3419724506, ralf.hartmann@medizin.uni-leipzig.de %K mHealth %K depression %K adherence %K mobile applications, self-management %D 2019 %7 03.04.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: Depression is a severe psychiatric disease with high prevalence and an elevated risk for recurrence and chronicity. A substantial proportion of individuals with a diagnosis of unipolar depressive disorder do not receive treatment as advised by national guidelines. Consequently, self-monitoring and self-management become increasingly important. New mobile technologies create unique opportunities to obtain and utilize patient-generated data. As common adherence rates to mobile technologies are scarce, a profound knowledge of user behavior and attitudes and preferences is important throughout any developmental process of mobile technologies and apps. Objective: The aim of this survey was to provide descriptive data upon usage and anticipated usage of self-monitoring and self-management of depression and preferences of potential users in terms of documented parameters and data-sharing options. Methods: A Web-based survey comprising 55 questions was conducted to obtain data on the usage of mobile devices, app usage, and participant’s attitudes and preferences toward mobile health apps for the self-monitoring and self-management of depression. Results: A total of 825 participants provided information. Moreover, two-thirds of the sample self-reported to be affected by depressive symptoms, but only 12.1% (81/668) of those affected by depression have ever used any mobile self-monitoring or self-management app. Analysis showed that people want personally relevant information and feedback but also focus on handling sensitive data. Conclusions: New mobile technologies and smartphone apps, especially in combination with mobile sensor systems, offer unique opportunities to overcome challenges in the treatment of depression by utilizing the potential of patient-generated data. Focus on patient-relevant information, security and safe handling of sensitive personal data, as well as options to share data with self-selected third parties should be considered mandatory throughout any development process. %M 30942693 %R 10.2196/11671 %U https://mental.jmir.org/2019/4/e11671/ %U https://doi.org/10.2196/11671 %U http://www.ncbi.nlm.nih.gov/pubmed/30942693 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 5 %N 2 %P e11666 %T An Automated Text-Messaging Platform for Enhanced Retention and Data Collection in a Longitudinal Birth Cohort: Cohort Management Platform Analysis %A Barry,Caroline M %A Sabhlok,Aditi %A Saba,Victoria C %A Majors,Alesha D %A Schechter,Julia C %A Levine,Erica L %A Streicher,Martin %A Bennett,Gary G %A Kollins,Scott H %A Fuemmeler,Bernard F %+ Cancer Prevention and Control, Department of Health Behavior and Policy, Virginia Commonwealth University, PO Box 980149, 830 E Main St, Richmond, VA, 23219, United States, 1 8048288892, bernard.fuemmeler@vcuhealth.org %K data collection %K longitudinal studies %K mobile health %K text messaging %D 2019 %7 02.04.2019 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Traditional methods for recruiting and maintaining contact with participants in cohort studies include print-based correspondence, which can be unidirectional, labor intensive, and slow. Leveraging technology can substantially enhance communication, maintain engagement of study participants in cohort studies, and facilitate data collection on a range of outcomes. Objective: This paper provides an overview of the development process and design of a cohort management platform (CMP) used in the Newborn Epigenetic STudy (NEST), a large longitudinal birth cohort study. Methods: The platform uses short message service (SMS) text messaging to facilitate interactive communication with participants; it also semiautomatically performs many recruitment and retention procedures typically completed by research assistants over the course of multiple study follow-up visits. Results: Since February 2016, 302 participants have consented to enrollment in the platform and 162 have enrolled with active engagement in the system. Daily reminders are being used to help improve adherence to the study’s accelerometer wear protocol. At the time of this report, 213 participants in our follow-up study who were also registered to use the CMP were eligible for the accelerometer protocol. Preliminary data show that texters (138/213, 64.8%), when compared to nontexters (75/213, 35.2%), had significantly longer average accelerometer-wearing hours (165.6 hours, SD 56.5, vs 145.3 hours, SD 58.5, P=.01) when instructed to wear the devices for 1 full week. Conclusions: This platform can serve as a model for enhancing communication and engagement with longitudinal study cohorts, especially those involved in studies assessing environmental exposures. %M 30938689 %R 10.2196/11666 %U https://publichealth.jmir.org/2019/2/e11666/ %U https://doi.org/10.2196/11666 %U http://www.ncbi.nlm.nih.gov/pubmed/30938689 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11157 %T A Smartphone App to Assess Alcohol Consumption Behavior: Development, Compliance, and Reactivity %A Poulton,Antoinette %A Pan,Jason %A Bruns Jr,Loren Richard %A Sinnott,Richard O %A Hester,Robert %+ Melbourne School of Psychological Sciences, University of Melbourne, Redmond Barry Building, Parkville, 3010, Australia, 61 83446377, antoinette.poulton@unimelb.edu.au %K alcohol drinking %K smartphone apps %K smartphone %K mobile phone %K research app development %K compliance %K reactivity %D 2019 %7 25.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There are disadvantages—largely related to cost, participant burden, and missing data—associated with traditional electronic methods of assessing drinking behavior in real time. This potentially diminishes some of the advantages—namely, enhanced sample size and diversity—typically attributed to these methods. Download of smartphone apps to participants’ own phones might preserve these advantages. However, to date, few researchers have detailed the process involved in developing custom-built apps for use in the experimental arena or explored methodological concerns regarding compliance and reactivity. Objective: The aim of this study was to describe the process used to guide the development of a custom-built smartphone app designed to capture alcohol intake behavior in the healthy population. Methodological issues related to compliance with and reactivity to app study protocols were examined. Specifically, we sought to investigate whether hazard and nonhazard drinkers would be equally compliant. We also explored whether reactivity in the form of a decrease in drinking or reduced responding (“yes”) to drinking behavior would emerge as a function of hazard or nonhazard group status. Methods: An iterative development process that included elements typical of agile software design guided the creation of the CNLab-A app. Healthy individuals used the app to record alcohol consumption behavior each day for 21 days. Submissions were either event- or notification-contingent. We considered the size and diversity of the sample, and assessed the data for evidence of app protocol compliance and reactivity as a function of hazard and nonhazard drinker status. Results: CNLab-A yielded a large and diverse sample (N=671, mean age 23.12). On average, participants submitted data on 20.27 (SD 1.88) out of 21 days (96.5%, 20.27/21). Both hazard and nonhazard drinkers were highly compliant with app protocols. There were no differences between groups in terms of number of days of app use (P=.49) or average number of app responses (P=.54). Linear growth analyses revealed hazardous drinkers decreased their alcohol intake by 0.80 standard drinks over the 21-day experimental period. There was no change to the drinking of nonhazard individuals. Both hazard and nonhazard drinkers showed a slight decrease in responding (“yes”) to drinking behavior over the same period. Conclusions: Smartphone apps participants download to their own phones are effective and methodologically sound means of obtaining alcohol consumption information for research purposes. Although further investigation is required, such apps might, in future, allow for a more thorough examination of the antecedents and consequences of drinking behavior. %M 30907738 %R 10.2196/11157 %U https://mhealth.jmir.org/2019/3/e11157/ %U https://doi.org/10.2196/11157 %U http://www.ncbi.nlm.nih.gov/pubmed/30907738 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 6 %N 1 %P e11852 %T High-Fidelity Prototyping for Mobile Electronic Data Collection Forms Through Design and User Evaluation %A Mugisha,Alice %A Babic,Ankica %A Wakholi,Peter %A Tylleskär,Thorkild %+ Center for International Health, Department of Global Public Health and Primary Care, University of Bergen, Årstadveien 21 Overlege Danielssens building, Bergen, 5020, Norway, 47 99884851, mugishaalice@gmail.com %K high-fidelity prototype %K group user testing %K mobile electronic data collection forms %K usability evaluation %D 2019 %7 22.03.2019 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Mobile data collection systems are often difficult to use for nontechnical or novice users. This can be attributed to the fact that developers of such tools do not adequately involve end users in the design and development of product features and functions, which often creates interaction challenges. Objective: The main objective of this study was to assess the guidelines for form design using high-fidelity prototypes developed based on end-user preferences. We also sought to investigate the association between the results from the System Usability Scale (SUS) and those from the Study Tailored Evaluation Questionnaire (STEQ) after the evaluation. In addition, we sought to recommend some practical guidelines for the implementation of the group testing approach particularly in low-resource settings during mobile form design. Methods: We developed a Web-based high-fidelity prototype using Axure RP 8. A total of 30 research assistants (RAs) evaluated this prototype in March 2018 by completing the given tasks during 1 common session. An STEQ comprising 13 affirmative statements and the commonly used and validated SUS were administered to evaluate the usability and user experience after interaction with the prototype. The STEQ evaluation was summarized using frequencies in an Excel sheet while the SUS scores were calculated based on whether the statement was positive (user selection minus 1) or negative (5 minus user selection). These were summed up and the score contributions multiplied by 2.5 to give the overall form usability from each participant. Results: Of the RAs, 80% (24/30) appreciated the form progress indication, found the form navigation easy, and were satisfied with the error messages. The results gave a SUS average score of 70.4 (SD 11.7), which is above the recommended average SUS score of 68, meaning that the usability of the prototype was above average. The scores from the STEQ, on the other hand, indicated a 70% (21/30) level of agreement with the affirmative evaluation statements. The results from the 2 instruments indicated a fair level of user satisfaction and a strong positive association as shown by the Pearson correlation value of .623 (P<.01). Conclusions: A high-fidelity prototype was used to give the users experience with a product they would likely use in their work. Group testing was done because of scarcity of resources such as costs and time involved especially in low-income countries. If embraced, this approach could help assess user needs of the diverse user groups. With proper preparation and the right infrastructure at an affordable cost, usability testing could lead to the development of highly usable forms. The study thus makes recommendations on the practical guidelines for the implementation of the group testing approach particularly in low-resource settings during mobile form design. %M 30900995 %R 10.2196/11852 %U http://humanfactors.jmir.org/2019/1/e11852/ %U https://doi.org/10.2196/11852 %U http://www.ncbi.nlm.nih.gov/pubmed/30900995 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11642 %T Mobile Health Systems for Community-Based Primary Care: Identifying Controls and Mitigating Privacy Threats %A Iwaya,Leonardo Horn %A Fischer-Hübner,Simone %A Åhlfeldt,Rose-Mharie %A Martucci,Leonardo A %+ Privacy and Security (PriSec), Department of Mathematics and Computer Science, Karlstad University, Universitetsgatan 2, Karlstad, 651 88, Sweden, 46 709225016, leonardo.horn.iwaya@hotmail.com %K mobile health %K mHealth %K data security %K privacy %K data protection %K privacy impact assessment %K public health %D 2019 %7 20.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Community-based primary care focuses on health promotion, awareness raising, and illnesses treatment and prevention in individuals, groups, and communities. Community Health Workers (CHWs) are the leading actors in such programs, helping to bridge the gap between the population and the health system. Many mobile health (mHealth) initiatives have been undertaken to empower CHWs and improve the data collection process in the primary care, replacing archaic paper-based approaches. A special category of mHealth apps, known as mHealth Data Collection Systems (MDCSs), is often used for such tasks. These systems process highly sensitive personal health data of entire communities so that a careful consideration about privacy is paramount for any successful deployment. However, the mHealth literature still lacks methodologically rigorous analyses for privacy and data protection. Objective: In this paper, a Privacy Impact Assessment (PIA) for MDCSs is presented, providing a systematic identification and evaluation of potential privacy risks, particularly emphasizing controls and mitigation strategies to handle negative privacy impacts. Methods: The privacy analysis follows a systematic methodology for PIAs. As a case study, we adopt the GeoHealth system, a large-scale MDCS used by CHWs in the Family Health Strategy, the Brazilian program for delivering community-based primary care. All the PIA steps were taken on the basis of discussions among the researchers (privacy and security experts). The identification of threats and controls was decided particularly on the basis of literature reviews and working group meetings among the group. Moreover, we also received feedback from specialists in primary care and software developers of other similar MDCSs in Brazil. Results: The GeoHealth PIA is based on 8 Privacy Principles and 26 Privacy Targets derived from the European General Data Protection Regulation. Associated with that, 22 threat groups with a total of 97 subthreats and 41 recommended controls were identified. Among the main findings, we observed that privacy principles can be enhanced on existing MDCSs with controls for managing consent, transparency, intervenability, and data minimization. Conclusions: Although there has been significant research that deals with data security issues, attention to privacy in its multiple dimensions is still lacking for MDCSs in general. New systems have the opportunity to incorporate privacy and data protection by design. Existing systems will have to address their privacy issues to comply with new and upcoming data protection regulations. However, further research is still needed to identify feasible and cost-effective solutions. %M 30892275 %R 10.2196/11642 %U http://mhealth.jmir.org/2019/3/e11642/ %U https://doi.org/10.2196/11642 %U http://www.ncbi.nlm.nih.gov/pubmed/30892275 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e13137 %T Validation in the General Population of the iHealth Track Blood Pressure Monitor for Self-Measurement According to the European Society of Hypertension International Protocol Revision 2010: Descriptive Investigation %A Mazoteras-Pardo,Victoria %A Becerro-De-Bengoa-Vallejo,Ricardo %A Losa-Iglesias,Marta Elena %A López-López,Daniel %A Palomo-López,Patricia %A Rodríguez-Sanz,David %A Calvo-Lobo,César %+ Research, Health and Podiatry Unit, Department of Health Sciences, Faculty of Nursing and Podiatry, Universidade da Coruña, Campus Universitario de Esteiro s/n, Ferrol, 15403, Spain, 34 981337400 ext 3546, daniel.lopez.lopez@udc.es %K blood pressure determination %K heart rate determination %K validation studies %K telemedicine %D 2019 %7 19.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: High blood pressure is one of the most common reasons why patients seek assistance in daily clinical practice. Screening for hypertension is fundamental and, because hypertension is identified only when blood pressure is measured, accurate measurements are key to the diagnosis and management of this disease. The European Society of Hypertension International Protocol revision 2010 (ESH-IP2) was developed to assess the validity of automatic blood pressure measuring devices that are increasingly being used to replace mercury sphygmomanometers. Objective: We sought to determine whether the iHealth Track blood pressure monitor meets ESH-IP2 requirements for self-measurement of blood pressure and heart rate at the brachial level and is appropriate for use in the general population. Methods: This study was a descriptive investigation. ESH-IP2 requires a total number of 33 participants. For each measure, the difference between observer and device blood pressure and heart rate values is calculated. In all, 99 pairs of blood pressure differences are classified into 3 categories (≤5, ≤10, and ≤15 mm Hg), and 99 pairs of heart rate differences are classified into 3 categories (≤3, ≤5, and ≤8 beats/min). We followed these protocol procedures in a convenience sample of 33 participants. Results: iHealth Track fulfilled ESH-IP2 requirements and passed the validation process successfully. We observed an absolute difference within 5 mm Hg in 75 of 99 comparisons for systolic blood pressure, 78 of 99 comparisons for diastolic blood pressure, and 89 of 99 comparisons for heart rate. The mean differences between the test and standard readings were 4.19 (SD 4.48) mm Hg for systolic blood pressure, 3.74 (SD 4.55) mm Hg for diastolic blood pressure, and 1.95 (SD 3.27) beats/min for heart rate. With regard to part 2 of ESH-IP2, we observed a minimum of 2 of 3 measurements within a 5-mm Hg difference in 29 of 33 participants for systolic blood pressure and 26 of 33 for diastolic blood pressure, and a minimum of 2 of 3 measurements within a 3-beat/min difference in 30 of 33 participants for heart rate. Conclusions: iHealth Track readings differed from the standard by less than 5, 10, and 15 mm Hg, fulfilling ESH-IP2 requirements. Consequently, this device is suitable for use in the general population. %M 30888331 %R 10.2196/13137 %U http://mhealth.jmir.org/2019/3/e13137/ %U https://doi.org/10.2196/13137 %U http://www.ncbi.nlm.nih.gov/pubmed/30888331 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e12084 %T Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data %A DaSilva,Alex W %A Huckins,Jeremy F %A Wang,Rui %A Wang,Weichen %A Wagner,Dylan D %A Campbell,Andrew T %+ Department of Psychological and Brain Sciences, Dartmouth College, 6207 Moore Hall, Hanover, NH, 03755, United States, 1 712 730 1404, Alexander.W.Dasilva.GR@dartmouth.edu %K psychology %K stress %K mobile sensing %K college campuses %D 2019 %7 19.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Stress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all-time high. Traditionally, the dominant way to assess stress levels has been through pen-and-paper surveys. Objective: The aim of this study is to use passive sensing data collected via mobile phones to obtain a rich and potentially less-biased source of data that can be used to help better understand stressors in the college experience. Methods: We used a mobile sensing app, StudentLife, in tandem with a pictorial mobile phone–based measure of stress, the Mobile Photographic Stress Meter, to investigate the situations and contexts that are more likely to precipitate stress. Results: Using recently developed methods for handling high-dimensional longitudinal data, penalized generalized estimating equations, we identified a set of mobile sensing features (absolute values of beta >0.001 and robust z>1.96) across the domains of social activity, movement, location, and ambient noise that were predictive of student stress levels. Conclusions: By combining recent statistical methods and mobile phone sensing, we have been able to study stressors in the college experience in a way that is more objective, detailed, and less intrusive than past research. Future work can leverage information gained from passive sensing and use that to develop real-time, targeted interventions for students experiencing a stressful time. %M 30888327 %R 10.2196/12084 %U http://mhealth.jmir.org/2019/3/e12084/ %U https://doi.org/10.2196/12084 %U http://www.ncbi.nlm.nih.gov/pubmed/30888327 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 2 %N 1 %P e10019 %T Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities %A Cosco,Theodore D %A Firth,Joseph %A Vahia,Ipsit %A Sixsmith,Andrew %A Torous,John %+ Gerontology Research Center, Simon Fraser University, #2800-515 West Hasting Street, Vancouver, BC, V6B 5K3, Canada, 1 7787825915, tcosco@sfu.ca %K mHealth %K older adults %K data collection %K digital divide %D 2019 %7 19.03.2019 %9 Viewpoint %J JMIR Aging %G English %X Worldwide, there is an unprecedented and ongoing expansion of both the proportion of older adults in society and innovations in digital technology. This rapidly increasing number of older adults is placing unprecedented demands on health care systems, warranting the development of new solutions. Although advancements in smart devices and wearables present novel methods for monitoring and improving the health of aging populations, older adults are currently the least likely age group to engage with such technologies. In this commentary, we critically examine the potential for technology-driven data collection and analysis mechanisms to improve our capacity to research, understand, and address the implications of an aging population. Alongside unprecedented opportunities to harness these technologies, there are equally unprecedented challenges. Notably, older adults may experience the first-level digital divide, that is, lack of access to technologies, and/or the second-level digital divide, that is, lack of use/skill, alongside issues with data input and analysis. To harness the benefits of these innovative approaches, we must first engage older adults in a meaningful manner and adjust the framework of smart devices to accommodate the unique physiological and psychological characteristics of the aging populace. Through an informed approach to the development of technologies with older adults, the field can leverage innovation to increase the quality and quantity of life for the expanding population of older adults. %M 31518253 %R 10.2196/10019 %U http://aging.jmir.org/2019/1/e10019/ %U https://doi.org/10.2196/10019 %U http://www.ncbi.nlm.nih.gov/pubmed/31518253 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11437 %T Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry) %A Fan,Yong-Yan %A Li,Yan-Guang %A Li,Jian %A Cheng,Wen-Kun %A Shan,Zhao-Liang %A Wang,Yu-Tang %A Guo,Yu-Tao %+ Department of Cardiology, Chinese People's Liberation Army General Hospital, 28 Fuxing Rd, Beijing, 100853, China, 86 13810021492, guoyutao2010@126.com %K atrial fibrillation %K photoplethysmography %K detection %K accuracy %K mobile phone %K smart band %K algorithm %D 2019 %7 05.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG). Objective: We investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG. Methods: A total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. Results: In all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99). Conclusions: The algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population. Trial Registration: Chinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6) %M 30835243 %R 10.2196/11437 %U http://mhealth.jmir.org/2019/3/e11437/ %U https://doi.org/10.2196/11437 %U http://www.ncbi.nlm.nih.gov/pubmed/30835243 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 3 %P e12143 %T Design and Preliminary Findings From a New Electronic Cohort Embedded in the Framingham Heart Study %A McManus,David D %A Trinquart,Ludovic %A Benjamin,Emelia J %A Manders,Emily S %A Fusco,Kelsey %A Jung,Lindsey S %A Spartano,Nicole L %A Kheterpal,Vik %A Nowak,Christopher %A Sardana,Mayank %A Murabito,Joanne M %+ Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, 73 Mount Wayte Ave, Framingham, MA, 01701, United States, 1 508 935 3400, murabito@bu.edu %K smartphone %K tele-medicine %K blood pressure monitoring %K ambulatory %K cohort studies %D 2019 %7 01.03.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: New models of scalable population-based data collection that integrate digital and mobile health (mHealth) data are necessary. Objective: The aim of this study was to describe a cardiovascular digital and mHealth electronic cohort (e-cohort) embedded in a traditional longitudinal cohort study, the Framingham Heart Study (FHS). Methods: We invited eligible and consenting FHS Generation 3 and Omni participants to download the electronic Framingham Heart Study (eFHS) app onto their mobile phones and co-deployed a digital blood pressure (BP) cuff. Thereafter, participants were also offered a smartwatch (Apple Watch). Participants are invited to complete surveys through the eFHS app, to perform weekly BP measurements, and to wear the smartwatch daily. Results: Up to July 2017, we enrolled 790 eFHS participants, representing 76% (790/1044) of potentially eligible FHS participants. eFHS participants were, on average, 53±8 years of age and 57% were women. A total of 85% (675/790) of eFHS participants completed all of the baseline survey and 59% (470/790) completed the 3-month survey. A total of 42% (241/573) and 76% (306/405) of eFHS participants adhered to weekly digital BP and heart rate (HR) uploads, respectively, over 12 weeks. Conclusions: We have designed an e-cohort focused on identifying novel cardiovascular disease risk factors using a new smartphone app, a digital BP cuff, and a smartwatch. Despite minimal training and support, preliminary findings over a 3-month follow-up period show that uptake is high and adherence to periodic app-based surveys, weekly digital BP assessments, and smartwatch HR measures is acceptable. %M 30821691 %R 10.2196/12143 %U http://www.jmir.org/2019/3/e12143/ %U https://doi.org/10.2196/12143 %U http://www.ncbi.nlm.nih.gov/pubmed/30821691 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 2 %P e11711 %T How Do Adolescents Use Electronic Diaries? A Mixed-Methods Study Among Adolescents With Depressive Symptoms %A Metsäranta,Kiki %A Kurki,Marjo %A Valimaki,Maritta %A Anttila,Minna %+ Department of Nursing Science, University of Turku, Hoitotieteen laitos, Joukahaisenkatu 3-5, Turun yliopisto, 20014, Finland, 358 456716156, kianme@utu.fi %K adolescent %K depression %K electronic diary %K mental health %K mobile phone %K outpatient care %D 2019 %7 20.02.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Depression in adolescence is common. Less than half of the adolescents with depression receive mental health care; furthermore, treatment tends to be suspended, and its success rates are low. There is a need for these adolescents to have a safe place to share their thoughts. Studies have shown that writing may be a useful treatment method for people with mental health problems. Objective: This study aims to describe the use of an electronic diary (e-diary) among adolescents with depressive symptoms. Methods: This paper describes a substudy of a randomized controlled trial. We used a mixed-methods approach to understand the way in which e-diaries were used by participants in the intervention under the randomized controlled trial. Data were collected during 2008-2010 at 2 university hospitals in Finland. Study participants (N=89) were 15-17-year-old adolescents who had been referred to an adolescent outpatient psychiatric clinic due to depressive symptoms. Participants were instructed to use the e-diary at least once a week to describe their thoughts, feelings, and moods. The content of the e-diary data was analyzed using descriptive statistics and inductive content analysis. Results: Overall, 53% (47/89) of the adolescents used the e-diary. Most of them (39/47, 83%) logged into the program during the first week, and about one-third (19/47, 40%) logged into the e-diary weekly as suggested. The number of words used in the e-diary per each log ranged between 8 and 1442 words. The 3 topics most often written about in the e-diary were related to mental health problems (mental disorder), social interaction (relationship), and one’s own development (identity). Conclusions: An e-diary may be a usable tool to reflect experiences and thoughts, especially among adolescents who have signs of depression. The results of this study can be used to develop user-centered electronic health applications that allow users to express their own thoughts and experiences in ways other than systematic mood monitoring. %M 30785408 %R 10.2196/11711 %U http://www.jmir.org/2019/2/e11711/ %U https://doi.org/10.2196/11711 %U http://www.ncbi.nlm.nih.gov/pubmed/30785408 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e11606 %T The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review %A Li,Ka Hou Christien %A White,Francesca Anne %A Tipoe,Timothy %A Liu,Tong %A Wong,Martin CS %A Jesuthasan,Aaron %A Baranchuk,Adrian %A Tse,Gary %A Yan,Bryan P %+ Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, 9/F, Lui Che Woo Clinical Sciences Building, Prince of Wales Hospital, Shatin, Hong Kong,, China (Hong Kong), 852 35051750, bryan.yan@cuhk.edu.hk %K mobile phone apps %K atrial fibrillation %K heart rate %K arrhythmia %K photoplethysmography %K electrocardiography %K mobile health %D 2019 %7 15.02.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband. Objective: This review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research. Methods: We conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV). Results: The findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF. Conclusions: A number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening. %M 30767904 %R 10.2196/11606 %U http://mhealth.jmir.org/2019/2/e11606/ %U https://doi.org/10.2196/11606 %U http://www.ncbi.nlm.nih.gov/pubmed/30767904 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e10931 %T Evaluating Mobile Health Apps for Customized Dietary Recording for Young Adults and Seniors: Randomized Controlled Trial %A Liu,Ying-Chieh %A Chen,Chien-Hung %A Tsou,Ya-Chi %A Lin,Yu-Sheng %A Chen,Hsin-Yun %A Yeh,Jou-Yin %A Chiu,Sherry Yueh-Hsia %+ Department of Health Care Management and Healthy Aging Research Center, College of Management, Chang Gung University, 259, Wen-Hwa 1st Road, Kwei-Shan, Taoyuan, 333, Taiwan, 886 3 2118800 ext 5250, sherrychiu@mail.cgu.edu.tw %K customized dietary recording %K prototypes %K user-centered design %K utilization %K mobile health %K mHealth %K randomized trial %D 2019 %7 15.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The role of individual-tailored dietary recording in mobile phone health apps has become increasingly important in management of self-health care and population-based preventive service. The development of such mobile apps for user-centered designing is still challengeable and requires further scientific evidence. Objective: This study aims to conduct a randomized trial to assess the accuracy and time efficiency of two prototypes for dietary recoding utilization related to the input method of food intake. Methods: We first present an innovative combinatorial concept for dietary recording to account for dish variation. One prototype was a self-chosen tab app that featured choosing each food ingredient to synthesize an individual dish, whereas the other was an autonomous exhaustive list app that provided one selection from a comprehensive list of dish items. The concept included commercially available choices that allowed users to more accurately account for their individual food selection. The two mobile apps were compared in a head-to-head parallel randomized trial evaluation. Young adults (n=70, aged 18-29) and older adults (n=35, aged 55-73) were recruited and randomized into two groups for accuracy and response time evaluation based on 12 types of food items in use of the developed self-chosen tab and autonomous exhaustive list apps, respectively. Results: For the trials based on the self-chosen tab (53 participants) and autonomous exhaustive list groups (52 participants), the two prototypes were found to be highly accurate (>98%). The self-chosen tab app was found to be more efficient, requiring significantly less time for input of 11 of 12 items (P<.05). The self-chosen tab users occasionally neglected to select food attributes, an issue which did not occur in the autonomous exhaustive list group. Conclusions: Our study contributes through the scientific evaluation of the transformation step into prototype development to demonstrate that a self-chosen tab app has potentially better opportunity in effectiveness and efficiency. The combinatorial concept offers potential for dietary recording and planning which can account for high food item variability. Our findings on prototype development of diversified dietary recordings provide design consideration and user interaction for related further app development and improvement. Trial Registration: ISRCTN Registry ISRCTN86142301; http://www.isrctn.com/ ISRCTN86142301 (Archived by WebCite at http://www.webcitation.org/74YLEPYnS) %M 30767906 %R 10.2196/10931 %U http://mhealth.jmir.org/2019/2/e10931/ %U https://doi.org/10.2196/10931 %U http://www.ncbi.nlm.nih.gov/pubmed/30767906 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e12264 %T Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study %A Hswen,Yulin %A Gopaluni,Anuraag %A Brownstein,John S %A Hawkins,Jared B %+ Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA,, United States, 1 617 775 1889, yuh958@mail.harvard.edu %K autism %K digital data %K emotion %K mobile phone %K obsessive-compulsive disorder %K social media %K textual analysis %K tweets %K Twitter %K infodemiology %D 2019 %7 12.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: More than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test exists to diagnose this disorder. Digital phenotyping holds promise to guide in the clinical diagnoses and screening of ASD. Objective: This study aims to explore the feasibility of using the Web-based social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with ASD. Methods: Data from Twitter were retrieved from 152 self-identified users with ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. We conducted a between-group comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with ASD. In addition, common emotional characteristics of persons with ASD, such as fear, paranoia, and anxiety, were examined between groups through textual analysis. Furthermore, we compared the timing of tweets between users with ASD and control users to identify patterns in communication. Results: Users with ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared with control users (P<.001). The textual analysis of obsessive-compulsive behavioral characteristics, such as fixate, excessive, and concern, were significantly higher among users with ASD compared with the control group (P<.001). In addition, emotional terms related to fear, paranoia, and anxiety were tweeted at a significantly higher rate among users with ASD compared with control users (P<.001). Users with ASD posted a smaller proportion of tweets during time intervals of 00:00-05:59 (P<.001), 06:00-11:59 (P<.001), and 18:00-23.59 (P<.001), as well as a greater proportion of tweets from 12:00 to 17:59 (P<.001) compared with control users. Conclusions: Social media may be a valuable resource for observing unique psychological characteristics of self-identified persons with ASD. Collecting and analyzing data from these digital platforms may afford opportunities to identify the characteristics of ASD and assist in the diagnosis or verification of ASD. This study highlights the feasibility of leveraging digital data for gaining new insights into various health conditions. %M 30747718 %R 10.2196/12264 %U http://mhealth.jmir.org/2019/2/e12264/ %U https://doi.org/10.2196/12264 %U http://www.ncbi.nlm.nih.gov/pubmed/30747718 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e10995 %T Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation %A Zeleke,Atinkut Alamirrew %A Worku,Abebaw Gebeyehu %A Demissie,Adina %A Otto-Sobotka,Fabian %A Wilken,Marc %A Lipprandt,Myriam %A Tilahun,Binyam %A Röhrig,Rainer %+ Division of Medical Informatics, Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Building V04-1-133, Ammerländer Heerstraße 140, Oldenburg, 26129, Germany, 49 1732587251, atinkut.alamirrew.zeleke@uni-oldenburg.de %K public health %K maternal health %K surveillance %K survey %K data collection %K data quality %K tablet computer %K mHealth %K Ethiopia %D 2019 %7 11.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes. Objective: This study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. Methods: A randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit–based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. Results: From the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. Conclusions: EDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site. %M 30741642 %R 10.2196/10995 %U http://mhealth.jmir.org/2019/2/e10995/ %U https://doi.org/10.2196/10995 %U http://www.ncbi.nlm.nih.gov/pubmed/30741642 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e11463 %T EVIDENT Smartphone App, a New Method for the Dietary Record: Comparison With a Food Frequency Questionnaire %A Recio-Rodriguez,Jose I %A Rodriguez-Martin,Carmela %A Gonzalez-Sanchez,Jesus %A Rodriguez-Sanchez,Emiliano %A Martin-Borras,Carme %A Martínez-Vizcaino,Vicente %A Arietaleanizbeaskoa,Maria Soledad %A Magdalena-Gonzalez,Olga %A Fernandez-Alonso,Carmen %A Maderuelo-Fernandez,Jose A %A Gomez-Marcos,Manuel A %A Garcia-Ortiz,Luis %A , %+ Department of Nursing, University of Extremadura, Plasencia Campus, Avda Comuneros 27-31, Salamanca,, Spain, 34 923231859, jesusgonzsan@gmail.com %K technology assessment, biomedical %K telemedicine %K energy intake %K diet records %K surveys and questionnaires %D 2019 %7 08.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: More alternatives are needed for recording people’s normal diet in different populations, especially adults or the elderly, as part of the investigation into the effects of nutrition on health. Objective: The aim of this study was to compare the estimated values of energy intake, macro- and micronutrient, and alcohol consumption gathered using the EVIDENT II smartphone app against the data estimated with a food frequency questionnaire (FFQ) in an adult population aged 18 to 70 years. Methods: We included 362 individuals (mean age 52 years, SD 12; 214/362, 59.1% women) who were part of the EVIDENT II study. The participants registered their food intake using the EVIDENT app during a period of 3 months and through an FFQ. Both methods estimate the average nutritional composition, including energy intake, macro- and micronutrients, and alcohol. Through the app, the values of the first week of food recording, the first month, and the entire 3-month period were estimated. The FFQ gathers data regarding the food intake of the year before the moment of interview. Results: The intraclass correlation for the estimation of energy intake with the FFQ and the app shows significant results, with the highest values returned when analyzing the app’s data for the full 3-month period (.304, 95% CI 0.144-0.434; P<.001). For this period, the correlation coefficient for energy intake is .233 (P<.001). The highest value corresponds to alcohol consumption and the lowest to the intake of polyunsaturated fatty acids (r=.676 and r=.155; P<.001), respectively. The estimation of daily intake of energy, macronutrients, and alcohol presents higher values in the FFQ compared with the EVIDENT app data. Considering the values recorded during the 3-month period, the FFQ for energy intake estimation (Kcal) was higher than that of the app (a difference of 408.7, 95% CI 322.7-494.8; P<.001). The same is true for the other macronutrients, with the exception g/day of saturated fatty acids (.4, 95% CI −1.2 to 2.0; P=.62). Conclusions: The EVIDENT app is significantly correlated to FFQ in the estimation of energy intake, macro- and micronutrients, and alcohol consumption. This correlation increases with longer app recording periods. The EVIDENT app can be a good alternative for recording food intake in the context of longitudinal or intervention studies. Trial Registration: ClinicalTrials.gov NCT02016014; http://clinicaltrials.gov/ct2/show/NCT02016014 (Archived by WebCite at http://www.webcitation.org/760i8EL8Q) %M 30735141 %R 10.2196/11463 %U http://mhealth.jmir.org/2019/2/e11463/ %U https://doi.org/10.2196/11463 %U http://www.ncbi.nlm.nih.gov/pubmed/30735141 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 6 %N 2 %P e10186 %T Identifying Behaviors Predicting Early Morning Emotions by Observing Permanent Supportive Housing Residents: An Ecological Momentary Assessment %A Nandy,Rajesh R %A Nandy,Karabi %A Hébert,Emily T %A Businelle,Michael S %A Walters,Scott T %+ Department of Biostatistics and Epidemiology, School of Public Health, University of North Texas Health Science Center, 3500 Camp Bowie Boulevard, Fort Worth, TX, 76107, United States, 1 2063296441, Rajesh.Nandy@unthsc.edu %K permanent supportive housing %K circumplex model of affect %K ecological momentary assessment %K emotion %K valence %K arousal %K hierarchical mixed effects model %K mobile phone %D 2019 %7 07.02.2019 %9 Original Paper %J JMIR Ment Health %G English %X Background: Behavior and emotions are closely intertwined. The relationship between behavior and emotions might be particularly important in populations of underserved people, such as people with physical or mental health issues. We used ecological momentary assessment (EMA) to examine the relationship between emotional state and other characteristics among people with a history of chronic homelessness who were participating in a health coaching program. Objective: The goal of this study was to identify relationships between daily emotional states (valence and arousal) shortly after waking and behavioral variables such as physical activity, diet, social interaction, medication compliance, and tobacco usage the prior day, controlling for demographic characteristics. Methods: Participants in m.chat, a technology-assisted health coaching program, were recruited from housing agencies in Fort Worth, Texas, United States. All participants had a history of chronic homelessness and reported at least one mental health condition. We asked a subset of participants to complete daily EMAs of emotions and other behaviors. From the circumplex model of affect, the EMA included 9 questions related to the current emotional state of the participant (happy, frustrated, sad, worried, restless, excited, calm, bored, and sluggish). The responses were used to calculate two composite scores for valence and arousal. Results: Nonwhites reported higher scores for both valence and arousal, but not at a statistically significant level after correcting for multiple testing. Among momentary predictors, greater time spent in one-on-one interactions, greater time spent in physical activities, a greater number of servings of fruits and vegetables, greater time spent interacting in a one-on-one setting as well as adherence to prescribed medication the previous day were generally associated with higher scores for both valence and arousal, and statistical significance was achieved in most cases. Number of cigarettes smoked the previous day was generally associated with lower scores on both valence and arousal, although statistical significance was achieved for valence only when correcting for multiple testing. Conclusions: This study provides an important glimpse into factors that predict morning emotions among people with mental health issues and a history of chronic homelessness. Behaviors considered to be positive (eg, physical activity and consumption of fruits and vegetables) generally enhanced positive affect and restrained negative affect the following morning. The opposite was true for behaviors such as smoking, which are considered to be negative. %M 30730296 %R 10.2196/10186 %U http://mental.jmir.org/2019/2/e10186/ %U https://doi.org/10.2196/10186 %U http://www.ncbi.nlm.nih.gov/pubmed/30730296 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e11270 %T Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study %A Davoudi,Anis %A Wanigatunga,Amal Asiri %A Kheirkhahan,Matin %A Corbett,Duane Benjamin %A Mendoza,Tonatiuh %A Battula,Manoj %A Ranka,Sanjay %A Fillingim,Roger Benton %A Manini,Todd Matthew %A Rashidi,Parisa %+ Department of Biomedical Engineering, University of Florida, 1064 Center Drive, NEB 459, Gainesville, FL, 32611, United States, 1 352 392 5469, parisa.rashidi@ufl.edu %K actigraphy %K activity recognition %K machine learning %K metabolic equivalent %K physical activity %D 2019 %7 06.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable accelerometers have greatly improved measurement of physical activity, and the increasing popularity of smartwatches with inherent acceleration data collection suggest their potential use in the physical activity research domain; however, their use needs to be validated. Objective: This study aimed to assess the validity of accelerometer data collected from a Samsung Gear S smartwatch (SGS) compared with an ActiGraph GT3X+ (GT3X+) activity monitor. The study aims were to (1) assess SGS validity using a mechanical shaker; (2) assess SGS validity using a treadmill running test; and (3) compare individual activity recognition, location of major body movement detection, activity intensity detection, locomotion recognition, and metabolic equivalent scores (METs) estimation between the SGS and GT3X+. Methods: To validate and compare the SGS accelerometer data with GT3X+ data, we collected data simultaneously from both devices during highly controlled, mechanically simulated, and less-controlled natural wear conditions. First, SGS and GT3X+ data were simultaneously collected from a mechanical shaker and an individual ambulating on a treadmill. Pearson correlation was calculated for mechanical shaker and treadmill experiments. Finally, SGS and GT3X+ data were simultaneously collected during 15 common daily activities performed by 40 participants (n=12 males, mean age 55.15 [SD 17.8] years). A total of 15 frequency- and time-domain features were extracted from SGS and GT3X+ data. We used these features for training machine learning models on 6 tasks: (1) individual activity recognition, (2) activity intensity detection, (3) locomotion recognition, (4) sedentary activity detection, (5) major body movement location detection, and (6) METs estimation. The classification models included random forest, support vector machines, neural networks, and decision trees. The results were compared between devices. We evaluated the effect of different feature extraction window lengths on model accuracy as defined by the percentage of correct classifications. In addition to these classification tasks, we also used the extracted features for METs estimation. Results: The results were compared between devices. Accelerometer data from SGS were highly correlated with the accelerometer data from GT3X+ for all 3 axes, with a correlation ≥.89 for both the shaker test and treadmill test and ≥.70 for all daily activities, except for computer work. Our results for the classification of activity intensity levels, locomotion, sedentary, major body movement location, and individual activity recognition showed overall accuracies of 0.87, 1.00, 0.98, 0.85, and 0.64, respectively. The results were not significantly different between the SGS and GT3X+. Random forest model was the best model for METs estimation (root mean squared error of .71 and r-squared value of .50). Conclusions: Our results suggest that a commercial brand smartwatch can be used in lieu of validated research grade activity monitors for individual activity recognition, major body movement location detection, activity intensity detection, and locomotion detection tasks. %M 30724739 %R 10.2196/11270 %U http://mhealth.jmir.org/2019/2/e11270/ %U https://doi.org/10.2196/11270 %U http://www.ncbi.nlm.nih.gov/pubmed/30724739 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 2 %P e11398 %T What Affects the Completion of Ecological Momentary Assessments in Chronic Pain Research? An Individual Patient Data Meta-Analysis %A Ono,Masakatsu %A Schneider,Stefan %A Junghaenel,Doerte U %A Stone,Arthur A %+ Center for Self-Report Science, Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089, United States, 1 213 821 8862, masakatsu.ono@manchester.ac.uk %K chronic pain %K completion rate %K compliance rate %K ecological momentary assessment %K experience sampling %K IPD meta-analysis %D 2019 %7 05.02.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Ecological momentary assessment (EMA) involves repeated sampling of people’s current experiences in real time in their natural environments, which offers a granular perspective on patients’ experience of pain and other symptoms. However, EMA can be burdensome to patients, and its benefits depend upon patients’ engagement in the assessments. Objective: The goal of this study was to investigate factors affecting EMA-completion rates among patients with chronic pain. Methods: This individual patient data meta-analysis was based on 12 EMA datasets that examined patients with chronic noncancer-related pain (n=701). The EMA-completion rates were calculated on a daily basis for each patient. Multilevel models were used to test the following predictors of completion rates at different levels: within-patient factors (days into the study and daily pain level), between-patient factors (age, sex, pain diagnosis, and average pain level per person), and between-study EMA design factors (study duration, sampling density, and survey length). Results: Across datasets, an EMA-completion rate of 85% was observed. The strongest results were found for the between-patient factor age: Younger respondents reported lower completion rates than older respondents (P=.002). One within-patient factor, study day, was associated with completion rates (P<.001): over the course of the studies, the completion rates declined. The two abovementioned factors interacted with each other (P=.02) in that younger participants showed a more rapid decline in EMA completion over time. In addition, none of the other hypothesized factors including gender, chronic pain diagnoses, pain intensity levels, or measures of study burden showed any significant effects. Conclusion: Many factors thought to influence the EMA-completion rates in chronic pain studies were not confirmed. However, future EMA research in chronic pain should note that study length and young age can impact the quality of the momentary data and devise strategies to maximize completion rates across different age groups and study days. %M 30720437 %R 10.2196/11398 %U https://www.jmir.org/2019/2/e11398/ %U https://doi.org/10.2196/11398 %U http://www.ncbi.nlm.nih.gov/pubmed/30720437 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e11898 %T Physical Activity Surveillance Through Smartphone Apps and Wearable Trackers: Examining the UK Potential for Nationally Representative Sampling %A Strain,Tessa %A Wijndaele,Katrien %A Brage,Søren %+ MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Addenbrookes Biomedical Campus, Cambridge, CB2 0QQ, United Kingdom, 44 1223 330315, tessa.strain@mrc-epid.cam.ac.uk %K adult %K exercise %K fitness trackers %K health surveys %K smartphone %K surveys and questionnaires %K United Kingdom %K mobile phone %D 2019 %7 29.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphones and wearable activity trackers present opportunities for large-scale physical activity (PA) surveillance that overcome some limitations of questionnaires or researcher-administered devices. However, it remains unknown whether current users of such technologies are representative of the UK population. Objective: The objective of this study was to investigate potential sociodemographic biases in individuals using, or with the potential to use, smartphone apps or wearable activity trackers for PA surveillance in the United Kingdom. Methods: We used data of adults (aged ≥16 years) from two nationally representative surveys. Using the UK-wide 2018 Ofcom Technology Tracker (unweighted N=3688), we derived mutually adjusted odds ratios (ORs; 95% CI) of personal use or household ownership of a smartwatch or fitness tracker and personal use of a smartphone by age, sex, social grade, activity- or work-limiting disability, urban or rural, and home nation. Using the 2016 Health Survey for England (unweighted N=4539), we derived mutually adjusted ORs of the use of wearable trackers or websites or smartphone apps for weight management. The explanatory variables were age, sex, PA, deprivation, and body mass index (BMI). Furthermore, we stratified these analyses by BMI, as these questions were asked in the context of weight management. Results: Smartphone use was the most prevalent of all technology outcomes, with 79.01% (weighted 2085/2639) of the Technology Tracker sample responding affirmatively. All other outcomes were <30% prevalent. Age ≥65 years was the strongest inverse correlate of all outcomes (eg, OR 0.03, 95% CI 0.02-0.05 for smartphone use compared with those aged 16-44 years). In addition, lower social grade and activity- or work-limiting disability were inversely associated with all Technology Tracker outcomes. Physical inactivity and male sex were inversely associated with both outcomes assessed in the Health Survey for England; higher levels of deprivation were only inversely associated with websites or phone apps used for weight management. The conclusions did not differ meaningfully in the BMI-stratified analyses, except for deprivation that showed stronger inverse associations with website or phone app use in the obese. Conclusions: The sole use of PA data from wearable trackers or smartphone apps for UK national surveillance is premature, as those using these technologies are more active, younger, and more affluent than those who do not. %M 30694198 %R 10.2196/11898 %U http://mhealth.jmir.org/2019/1/e11898/ %U https://doi.org/10.2196/11898 %U http://www.ncbi.nlm.nih.gov/pubmed/30694198 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e11041 %T Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study %A Sarda,Archana %A Munuswamy,Suresh %A Sarda,Shubhankar %A Subramanian,Vinod %+ Touchkin eServices Private Limited, 1st Floor, Manjusha, No 532, 16th Cross, 2nd Main Road, 2nd Stage, Indira Nagar, Bangalore, 560038, India, 91 9762665119, shubhankar@touchkin.com %K depression %K diabetes %K mental health %K comorbidity %K passive sensing %K smartphone %K classification %K machine learning %K mHealth %K risk assessment %D 2019 %7 29.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Research studies are establishing the use of smartphone sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns, and its analysis helps reveal well-being changes. Depression in diabetes goes highly underdiagnosed and underreported. The comorbidity has been associated with increased mortality and worse clinical outcomes, including poor glycemic control and self-management. Clinical-only intervention has been found to have a very modest effect on diabetes management among people with depression. Smartphone technologies could play a significant role in complementing comorbid care. Objective: This study aimed to analyze the association between smartphone-sensing parameters and symptoms of depression and to explore an approach to risk-stratify people with diabetes. Methods: A cross-sectional observational study (Project SHADO—Analyzing Social and Health Attributes through Daily Digital Observation) was conducted on 47 participants with diabetes. The study’s smartphone-sensing app passively collected data regarding activity, mobility, sleep, and communication from each participant. Self-reported symptoms of depression using a validated Patient Health Questionnaire-9 (PHQ-9) were collected once every 2 weeks from all participants. A descriptive analysis was performed to understand the representation of the participants. A univariate analysis was performed on each derived sensing variable to compare behavioral changes between depression states—those with self-reported major depression (PHQ-9>9) and those with none (PHQ-9≤9). A classification predictive modeling, using supervised machine-learning methods, was explored using derived sensing variables as input to construct and compare classifiers that could risk-stratify people with diabetes based on symptoms of depression. Results: A noticeably high prevalence of self-reported depression (30 out of 47 participants, 63%) was found among the participants. Between depression states, a significant difference was found for average activity rates (daytime) between participant-day instances with symptoms of major depression (mean 16.06 [SD 14.90]) and those with none (mean 18.79 [SD 16.72]), P=.005. For average number of people called (calls made and received), a significant difference was found between participant-day instances with symptoms of major depression (mean 5.08 [SD 3.83]) and those with none (mean 8.59 [SD 7.05]), P<.001. These results suggest that participants with diabetes and symptoms of major depression exhibited lower activity through the day and maintained contact with fewer people. Using all the derived sensing variables, the extreme gradient boosting machine-learning classifier provided the best performance with an average cross-validation accuracy of 79.07% (95% CI 74%-84%) and test accuracy of 81.05% to classify symptoms of depression. Conclusions: Participants with diabetes and self-reported symptoms of major depression were observed to show lower levels of social contact and lower activity levels during the day. Although findings must be reproduced in a broader randomized controlled study, this study shows promise in the use of predictive modeling for early detection of symptoms of depression in people with diabetes using smartphone-sensing information. %M 30694197 %R 10.2196/11041 %U http://mhealth.jmir.org/2019/1/e11041/ %U https://doi.org/10.2196/11041 %U http://www.ncbi.nlm.nih.gov/pubmed/30694197 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 1 %P e10238 %T Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study %A Beukenhorst,Anna L %A Parkes,Matthew J %A Cook,Louise %A Barnard,Rebecca %A van der Veer,Sabine N %A Little,Max A %A Howells,Kelly %A Sanders,Caroline %A Sergeant,Jamie C %A O'Neill,Terence W %A McBeth,John %A Dixon,William G %+ Arthritis Research United Kingdom Centre for Epidemiology, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Oxford Rd, Manchester, M13 9PT, United Kingdom, 44 0 161 275 1642, Will.Dixon@manchester.ac.uk %K medical informatics computing %K mHealth %K patient-reported outcomes %K musculoskeletal diseases %K mobile phone %D 2019 %7 23.01.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research. Objective: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies. Methods: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance. Results: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019. Conclusions: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data–quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated. International Registered Report Identifier (IRRID): DERR1-10.2196/10238 %M 30672745 %R 10.2196/10238 %U http://www.researchprotocols.org/2019/1/e10238/ %U https://doi.org/10.2196/10238 %U http://www.ncbi.nlm.nih.gov/pubmed/30672745 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 1 %P e12112 %T Investigating Health Risk Environments in Housing Programs for Young Adults: Protocol for a Geographically Explicit Ecological Momentary Assessment Study %A Henwood,Benjamin F %A Redline,Brian %A Dzubur,Eldin %A Madden,Danielle R %A Rhoades,Harmony %A Dunton,Genevieve F %A Rice,Eric %A Semborski,Sara %A Tang,Qu %A Intille,Stephen S %+ Suzanne Dworak-Peck School of Social Work, University of Southern California, 1150 S Olive Street, Suite 1429, Los Angeles, CA, 90039, United States, 1 6107316872, bhenwood@usc.edu %K homelessness %K ecological momentary assessment %K experience sampling %K social environment %K qualitative research %K geography %D 2019 %7 10.01.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Young adults who experience homelessness are exposed to environments that contribute to risk behavior. However, few studies have examined how access to housing may affect the health risk behaviors of young adults experiencing homelessness. Objective: This paper describes the Log My Life study that uses an innovative, mixed-methods approach based on geographically explicit ecological momentary assessment (EMA) through cell phone technology to understand the risk environment of young adults who have either enrolled in housing programs or are currently homeless. Methods: For the quantitative arm, study participants age 18-27 respond to momentary surveys via a smartphone app that collects geospatial information repeatedly during a 1-week period. Both EMAs (up to 8 per day) and daily diaries are prompted to explore within-day and daily variations in emotional affect, context, and health risk behavior, while also capturing infrequent risk behaviors such as sex in exchange for goods or services. For the qualitative arm, a purposive subsample of participants who indicated engaging in risky behaviors are asked to complete an in-depth qualitative interview using an interactive, personalized geospatial map rendering of EMA responses. Results: Recruitment began in June of 2017. To date, 170 participants enrolled in the study. Compliance with EMA and daily diary surveys was generally high. In-depth qualitative follow-ups have been conducted with 15 participants. We expect to recruit 50 additional participants and complete analyses by September of 2019. Conclusions: Mixing the quantitative and qualitative arms in this study will provide a more complete understanding of differences in risk environments between homeless and housed young adults. Furthermore, this approach can improve recall bias and enhance ecological validity. International Registered Report Identifier (IRRID): DERR1-10.2196/12112 %M 30632969 %R 10.2196/12112 %U http://www.researchprotocols.org/2019/1/e12112/ %U https://doi.org/10.2196/12112 %U http://www.ncbi.nlm.nih.gov/pubmed/30632969 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e11412 %T Digital Pain Drawings Can Improve Doctors’ Understanding of Acute Pain Patients: Survey and Pain Drawing Analysis %A Shaballout,Nour %A Aloumar,Anas %A Neubert,Till-Ansgar %A Dusch,Martin %A Beissner,Florian %+ Somatosensory and Autonomic Therapy Research, Institute for Diagnostic and Interventional Neuroradiology, Hannover Medical School, Carl-Neuberg-Strasse 1, Hannover, 30625, Germany, 49 511 53508413, Beissner.Florian@mh-hannover.de %K pain drawing %K symptom drawing %K manikins %K tablet computers %K eHealth %K app %K acute pain %D 2019 %7 10.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Pain drawings (PDs) are an important tool to evaluate, communicate, and objectify pain. In the past few years, there has been a shift toward tablet-based acquisition of PDs, and several studies have been conducted to test the usefulness, reliability, and repeatability of electronic PDs. However, to our knowledge, no study has investigated the potential role of electronic PDs in the clinical assessment and treatment of inpatients in acute pain situations. Objective: The aim of this study was to evaluate whether knowledge of the patients’ electronic PD has the potential to improve the doctors’ understanding of their patients and to influence their clinical decision making. Furthermore, we sought to identify differences between electronic PDs of patients and their treating pain specialists in an acute pain situation and to find those specific characteristics derived from the PDs that had the largest impact on doctors’ understanding. Methods: We obtained electronic PDs from 47 inpatients in acute pain situations before their consultation with a pain specialist on a tablet personal computer with a stylus. Before looking at their patients’ drawings, these specialists drew their own conception of the patients’ pain after anamnesis and physical examination. Patients’ drawings were then revealed to the doctors, and they were asked to evaluate how much the additional information improved their understanding of the case and how much it influenced their clinical decision on an 11-point Likert scale (0=“not at all” and 10=“very much”). Similarities and differences of patients’ and doctors’ PDs were assessed by visual inspection and by calculating Jaccard index and intraclass correlation coefficient (ICC) of the pain area and the number of pain clusters. Exploratory analyses were conducted by means of correlation tables to identify specific factors that influenced doctors’ understanding. Results: Patients’ PDs significantly improved the doctors’ understanding (mean score 4.81, SD 2.60, P<.001) and to a lesser extent their clinical decision (mean 2.68, SD 1.18, P<.001). Electronic PDs of patients and doctors showed fair to good similarity for pain extent (r=.454, P=.001) and widespreadness (P=.447, r=.002) were important factors helping doctors to understand their patients. Conclusions: In a clinical setting, electronic PDs can improve doctors’ understanding of patients in acute pain situations. The ability of electronic PDs to visualize differences between doctors’ and patients’ conception of pain has the potential to improve doctor-patient communication. %M 30632970 %R 10.2196/11412 %U https://mhealth.jmir.org/2019/1/e11412/ %U https://doi.org/10.2196/11412 %U http://www.ncbi.nlm.nih.gov/pubmed/30632970 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e10418 %T How Well iPhones Measure Steps in Free-Living Conditions: Cross-Sectional Validation Study %A Amagasa,Shiho %A Kamada,Masamitsu %A Sasai,Hiroyuki %A Fukushima,Noritoshi %A Kikuchi,Hiroyuki %A Lee,I-Min %A Inoue,Shigeru %+ Department of Preventive Medicine and Public Health, Tokyo Medical University, 6-1-1 Shinjuku, Shinjuku-ku, 160-8402, Japan, 81 3 3351 6141 ext 237, inoue@tokyo-med.ac.jp %K mobile phone %K step count %K physical activity %K pedometer %K epidemiology %K population %K validation %K free-living conditions %D 2019 %7 09.01.2019 %9 Short Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphones have great potential for monitoring physical activity. Although a previous laboratory-based study reported that smartphone apps were accurate for tracking step counts, little evidence on their accuracy in free-living conditions currently exists. Objective: We aimed to investigate the accuracy of step counts measured using iPhone in the real world. Methods: We recruited a convenience sample of 54 adults (mean age 31 [SD 10] years) who owned an iPhone and analyzed data collected in 2016 and 2017. Step count was simultaneously measured using a validated pedometer (Kenz Lifecorder) and the iPhone. Participants were asked to carry and use their own iPhones as they typically would while wearing a pedometer on the waist for 7 consecutive days during waking hours. To assess the agreement between the two measurements, we calculated Spearman correlation coefficients and prepared a Bland-Altman plot. Results: The mean step count measured using the iPhone was 9253 (3787) steps per day, significantly lower by 12% (1277/10,530) than that measured using the pedometer, 10,530 (3490) steps per day (P<.001). The Spearman correlation coefficient between devices was 0.78 (P<.001). The largest underestimation of steps by the iPhone was observed among those who reported to have seldom carried their iPhones (seldom carry: mean −3036, SD 2990, steps/day; sometimes carry: mean −1424, SD 2619, steps/day; and almost always carry: mean −929, SD 1443, steps/day; P for linear trend=.08). Conclusions: Smartphones may be of practical use to individuals, clinicians, and researchers for monitoring physical activity. However, their data on step counts should be interpreted cautiously because of the possibility of underestimation due to noncarrying time. %M 30626569 %R 10.2196/10418 %U http://mhealth.jmir.org/2019/1/e10418/ %U https://doi.org/10.2196/10418 %U http://www.ncbi.nlm.nih.gov/pubmed/30626569 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 1 %P e11939 %T Technical Support by Smart Glasses During a Mass Casualty Incident: A Randomized Controlled Simulation Trial on Technically Assisted Triage and Telemedical App Use in Disaster Medicine %A Follmann,Andreas %A Ohligs,Marian %A Hochhausen,Nadine %A Beckers,Stefan K %A Rossaint,Rolf %A Czaplik,Michael %+ Medical Technology Section, Department of Anaesthesiology, University Hospital RWTH Aachen, Pauwelsstraße 30, Aachen, D-52074, Germany, 49 2418036219, afollmann@ukaachen.de %K augmented reality %K disaster medicine %K emergency medical service physician %K mass casualty incident %K Smart Glasses %K telemedicine %K triage %D 2019 %7 03.01.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: To treat many patients despite lacking personnel resources, triage is important in disaster medicine. Various triage algorithms help but often are used incorrectly or not at all. One potential problem-solving approach is to support triage with Smart Glasses. Objective: In this study, augmented reality was used to display a triage algorithm and telemedicine assistance was enabled to compare the duration and quality of triage with a conventional one. Methods: A specific Android app was designed for use with Smart Glasses, which added information in terms of augmented reality with two different methods—through the display of a triage algorithm in data glasses and a telemedical connection to a senior emergency physician realized by the integrated camera. A scenario was created (ie, randomized simulation study) in which 31 paramedics carried out a triage of 12 patients in 3 groups as follows: without technical support (control group), with a triage algorithm display, and with telemedical contact. Results: A total of 362 assessments were performed. The accuracy in the control group was only 58%, but the assessments were quicker (on average 16.6 seconds). In contrast, an accuracy of 92% (P=.04) was achieved when using technical support by displaying the triage algorithm. This triaging took an average of 37.0 seconds. The triage group wearing data glasses and being telemedically connected achieved 90% accuracy (P=.01) in 35.0 seconds. Conclusions: Triage with data glasses required markedly more time. While only a tally was recorded in the control group, Smart Glasses led to digital capture of the triage results, which have many tactical advantages. We expect a high potential in the application of Smart Glasses in disaster scenarios when using telemedicine and augmented reality features to improve the quality of triage. %M 30609988 %R 10.2196/11939 %U https://www.jmir.org/2019/1/e11939/ %U https://doi.org/10.2196/11939 %U http://www.ncbi.nlm.nih.gov/pubmed/30609988 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 1 %P e11683 %T Use of In-Game Rewards to Motivate Daily Self-Report Compliance: Randomized Controlled Trial %A Taylor,Sara %A Ferguson,Craig %A Peng,Fengjiao %A Schoeneich,Magdalena %A Picard,Rosalind W %+ Affective Computing Group, Department of Media Arts and Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E14-348V, Cambridge, MA, 02142, United States, 1 6128608622, sataylor@mit.edu %K self-reports %K protocol compliance %K recreational games %D 2019 %7 03.01.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Encouraging individuals to report daily information such as unpleasant disease symptoms, daily activities and behaviors, or aspects of their physical and emotional state is difficult but necessary for many studies and clinical trials that rely on patient-reported data as primary outcomes. Use of paper diaries is the traditional method of completing daily diaries, but digital surveys are becoming the new standard because of their increased compliance; however, they still fall short of desired compliance levels. Objective: Mobile games using in-game rewards offer the opportunity to increase compliance above the rates of digital diaries and paper diaries. We conducted a 5-week randomized control trial to compare the completion rates of a daily diary across 3 conditions: a paper-based participant-reported outcome diary (Paper PRO), an electronic-based participant-reported outcome diary (ePRO), and a novel ePRO diary with in-game rewards (Game-Motivated ePRO). Methods: We developed a novel mobile game that is a combination of the idle and pet collection genres to reward individuals who complete a daily diary with an in-game reward. Overall, 197 individuals aged 6 to 24 years (male: 100 and female: 97) were enrolled in a 5-week study after being randomized into 1 of the 3 methods of daily diary completion. Moreover, 157 participants (male: 84 and female: 69) completed at least one diary and were subsequently included in analysis of compliance rates. Results: We observed a significant difference (F2,124=6.341; P=.002) in compliance to filling out daily diaries, with the Game-Motivated ePRO group having the highest compliance (mean completion 86.4%, SD 19.6%), followed by the ePRO group (mean completion 77.7%, SD 24.1%), and finally, the Paper PRO group (mean completion 70.6%, SD 23.4%). The Game-Motivated ePRO (P=.002) significantly improved compliance rates above the Paper PRO. In addition, the Game-Motivated ePRO resulted in higher compliance rates than the rates of ePRO alone (P=.09). Equally important, even though we observed significant differences in completion of daily diaries between groups, we did not observe any statistically significant differences in association between the responses to a daily mood question and study group, the average diary completion time (P=.52), or the System Usability Scale score (P=.88). Conclusions: The Game-Motivated ePRO system encouraged individuals to complete the daily diaries above the compliance rates of the Paper PRO and ePRO without altering the participants’ responses. Trial Registration: ClinicalTrials.gov NCT03738254; http://clinicaltrials.gov/ct2/show/NCT03738254 (Archived by WebCite at http://www.webcitation.org/74T1p8u52) %M 30609986 %R 10.2196/11683 %U https://www.jmir.org/2019/1/e11683/ %U https://doi.org/10.2196/11683 %U http://www.ncbi.nlm.nih.gov/pubmed/30609986 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 12 %P e10777 %T Development of an Electronic Data Collection System to Support a Large-Scale HIV Behavioral Intervention Trial: Protocol for an Electronic Data Collection System %A Comulada,W Scott %A Tang,Wenze %A Swendeman,Dallas %A Cooper,Amy %A Wacksman,Jeremy %A , %+ Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 10920 Wilshire Boulevard, Los Angeles, CA, 90024, United States, 1 310 794 8278, wcomulada@mednet.ucla.edu %K electronic data capture %K ecological momentary intervention %K HIV prevention and treatment %K mHealth %K mobile phone %K text messaging %D 2018 %7 14.12.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Advancing technology has increased functionality and permitted more complex study designs for behavioral interventions. Investigators need to keep pace with these technological advances for electronic data capture (EDC) systems to be appropriately executed and utilized at full capacity in research settings. Mobile technology allows EDC systems to collect near real-time data from study participants, deliver intervention directly to participants’ mobile devices, monitor staff activity, and facilitate near real-time decision making during study implementation. Objective: This paper presents the infrastructure of an EDC system designed to support a multisite HIV biobehavioral intervention trial in Los Angeles and New Orleans: the Adolescent Medicine Trials Network “Comprehensive Adolescent Research & Engagement Studies” (ATN CARES). We provide an overview of how multiple EDC functions can be integrated into a single EDC system to support large-scale intervention trials. Methods: The CARES EDC system is designed to monitor and document multiple study functions, including, screening, recruitment, retention, intervention delivery, and outcome assessment. Text messaging (short message service, SMS) and nearly all data collection are supported by the EDC system. The system functions on mobile phones, tablets, and Web browsers. Results: ATN CARES is enrolling study participants and collecting baseline and follow-up data through the EDC system. Besides data collection, the EDC system is being used to generate multiple reports that inform recruitment planning, budgeting, intervention quality, and field staff supervision. The system is supporting both incoming and outgoing text messages (SMS) and offers high-level data security. Intervention design details are also influenced by EDC system platform capabilities and constraints. Challenges of using EDC systems are addressed through programming updates and training on how to improve data quality. Conclusions: There are three key considerations in the development of an EDC system for an intervention trial. First, it needs to be decided whether the flexibility provided by the development of a study-specific, in-house EDC system is needed relative to the utilization of an existing commercial platform that requires less in-house programming expertise. Second, a single EDC system may not provide all functionality. ATN CARES is using a main EDC system for data collection, text messaging (SMS) interventions, and case management and a separate Web-based platform to support an online peer support intervention. Decisions need to be made regarding the functionality that is crucial for the EDC system to handle and what functionality can be handled by other systems. Third, data security is a priority but needs to be balanced with the need for flexible intervention delivery. For example, ATN CARES is delivering text messages (SMS) to study participants’ mobile phones. EDC data security protocols should be developed under guidance from security experts and with formative consulting with the target study population as to their perceptions and needs. International Registered Report Identifier (IRRID): DERR1-10.2196/10777 %M 30552083 %R 10.2196/10777 %U http://www.researchprotocols.org/2018/12/e10777/ %U https://doi.org/10.2196/10777 %U http://www.ncbi.nlm.nih.gov/pubmed/30552083 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 4 %P e10007 %T A Mobile App for the Self-Report of Psychological Well-Being During Pregnancy (BrightSelf): Qualitative Design Study %A Doherty,Kevin %A Barry,Marguerite %A Marcano-Belisario,José %A Arnaud,Bérenger %A Morrison,Cecily %A Car,Josip %A Doherty,Gavin %+ School of Computer Science and Statistics, Trinity College Dublin, College Green, Dublin,, Ireland, 353 1 8963858, Gavin.Doherty@tcd.ie %K engagement %K mental health %K mHealth %K midwifery %K perinatal depression %K pregnancy %K self-report %K well-being %K mobile phone %D 2018 %7 27.11.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: Maternal mental health impacts both parental well-being and childhood development. In the United Kingdom, 15% of women are affected by depression during pregnancy or within 1 year of giving birth. Suicide is a leading cause of perinatal maternal mortality, and it is estimated that >50% of perinatal depression cases go undiagnosed. Mobile technologies are potentially valuable tools for the early recognition of depressive symptoms, but complex design challenges must be addressed to enable their use in public health screening. Objective: The aim of this study was to explore the issues and challenges surrounding the use of mobile phones for the self-report of psychological well-being during pregnancy. Methods: This paper presents design research carried out as part of the development of BrightSelf, a mobile app for the self-report of psychological well-being during pregnancy. Design sessions were carried out with 38 participants, including pregnant women, mothers, midwives, and other health professionals. Overall, 19 hours of audio were fully transcribed and used as the basis of thematic analysis. Results: The study highlighted anxieties concerning the pregnancy journey, challenges surrounding current approaches to the appraisal of well-being in perinatal care, and the midwife-patient relationship. Designers should consider the framing of perinatal mental health technologies, the experience of self-report, supporting self-awareness and disclosure, providing value to users through both self-report and supplementary features, and designing for longitudinal engagement. Conclusions: This study highlights the needs, motivations, and anxieties of women with respect to technology use in pregnancy and implications for the design of mobile health technologies. %M 30482742 %R 10.2196/10007 %U http://mental.jmir.org/2018/4/e10007/ %U https://doi.org/10.2196/10007 %U http://www.ncbi.nlm.nih.gov/pubmed/30482742 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 11 %P e11170 %T Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review %A Schembre,Susan M %A Liao,Yue %A O'Connor,Sydney G %A Hingle,Melanie D %A Shen,Shu-En %A Hamoy,Katarina G %A Huh,Jimi %A Dunton,Genevieve F %A Weiss,Rick %A Thomson,Cynthia A %A Boushey,Carol J %+ Department of Family and Community Medicine, College of Medicine-Tucson, University of Arizona, Abrams Building Room 3345E, 3950 South Country Club Road, Tucson, AZ, 85714, United States, 1 520 626 7735, sschembre@email.arizona.edu %K diet surveys %K diet records %K mobile phone %K mobile apps %K ecological momentary assessment %D 2018 %7 20.11.2018 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: New methods for assessing diet in research are being developed to address the limitations of traditional dietary assessment methods. Mobile device–assisted ecological momentary diet assessment (mEMDA) is a new dietary assessment method that has not yet been optimized and has the potential to minimize recall biases and participant burden while maximizing ecological validity. There have been limited efforts to characterize the use of mEMDA in behavioral research settings. Objective: The aims of this study were to summarize mEMDA protocols used in research to date, to characterize key aspects of these assessment approaches, and to discuss the advantages and disadvantages of mEMDA compared with the traditional dietary assessment methods as well as implications for future mEMDA research. Methods: Studies that used mobile devices and described mEMDA protocols to assess dietary intake were included. Data were extracted according to Preferred Reporting of Systematic Reviews and Meta-Analyses and Cochrane guidelines and then synthesized narratively. Results: The review included 20 studies with unique mEMDA protocols. Of these, 50% (10/20) used participant-initiated reports of intake at eating events (event-contingent mEMDA), and 50% (10/20) used researcher-initiated prompts requesting that participants report recent dietary intake (signal-contingent mEMDA). A majority of the study protocols (60%, 12/20) enabled participants to use mobile phones to report dietary data. Event-contingent mEMDA protocols most commonly assessed diet in real time, used dietary records for data collection (60%, 6/10), and provided estimates of energy and nutrient intake (60%, 6/10). All signal-contingent mEMDA protocols used a near real-time recall approach with unannounced (ie, random) abbreviated diet surveys. Most signal-contingent protocols (70%, 7/10) assessed the frequency with which (targeted) foods or food groups were consumed. Relatively few (30%, 6/20) studies compared mEMDA with the traditional dietary assessment methods. Conclusions: This review demonstrates that mEMDA has the potential to reduce participant burden and recall bias, thus advancing the field beyond current dietary assessment methods while maximizing ecological validity. %M 30459148 %R 10.2196/11170 %U http://mhealth.jmir.org/2018/11/e11170/ %U https://doi.org/10.2196/11170 %U http://www.ncbi.nlm.nih.gov/pubmed/30459148 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 11 %P e12136 %T A Tool to Measure Young Adults’ Food Intake: Design and Development of an Australian Database of Foods for the Eat and Track Smartphone App %A Wellard-Cole,Lyndal %A Potter,Melisa %A Jung,Jisu (Joseph) %A Chen,Juliana %A Kay,Judy %A Allman-Farinelli,Margaret %+ School of Life and Environmental Science, The University of Sydney, Level 4 East, Charles Perkins Centre D17, University of Sydney, 2006, Australia, 61 286274854, lwel3754@uni.sydney.edu.au %K diet surveys %K smartphone %K mobile phone %K young adult %D 2018 %7 07.11.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Dietary assessment is reliant on the collection of accurate food and beverage consumption data. Technology has been harnessed to standardize recording and provide automatic nutritional analysis to reduce cost and researcher burden. Objective: To better assess the diet of young adults, especially relating to the contribution of foods prepared outside the home, a database was needed to support a mobile phone data collection app. The app also required usability testing to assure ease of entry of foods and beverages. This paper describes the development of the Eat and Track app (EaT app) and the database underpinning it. Methods: The Australian Food and Nutrient Database 2011-13, consisting of 5740 food items was modified. Four steps were undertaken: (1) foods not consumed by young adults were removed, (2) nutritionally similar foods were merged, (3) foods available from the 30 largest ready-to-eat food chains in Australia were added, and (4) long generic food names were shortened and simplified. This database was used to underpin the EaT app. Qualitative, iterative usability testing of the EaT app was conducted in three phases using the “Think Aloud” method. Responses were sorted and coded using content analysis. The System Usability Scale (SUS) was administered to measure the EaT app’s perceived usability. Results: In total, 1694 (29.51%) foods were removed from the Australian Food and Nutrient Database, including 608 (35.89%) ingredients, 81 (4.78%) foods already captured in the fast food chain information, 52 (3.07%) indigenous foods, 25 (1.48%) nutrients/dietary supplements, and 16 (0.94%) child-specific foods. The remaining 912 (53.84%) foods removed were not consumed by young adults in previous surveys or were “not defined” in the Australian Food and Nutrient Database. Another 220 (3.83%) nutritionally similar foods were combined. The final database consisted of 6274 foods. Fifteen participants completed usability testing. Issues identified by participants fell under six themes: keywords for searching, history list of entered foods, amounts and units, the keypad, food names, and search function. Suggestions for improvement were collected, incorporated, and tested in each iteration of the app. The SUS of the final version of the EaT app was rated 69. Conclusions: A food and beverage database has been developed to underpin the EaT app, enabling data collection on the eating-out habits of 18- to 30-year-old Australians. The development process has resulted in a database with commonly used food names, extensive coverage of foods from ready-to-eat chains, and commonly eaten portion sizes. Feedback from app usability testing led to enhanced keyword searching and the addition of functions to enhance usability such as adding brief instructional screens. There is potential for the features of the EaT app to facilitate the collection of more accurate dietary intake data. The database and the app will be valuable dietary assessment resources for researchers. %M 30404768 %R 10.2196/12136 %U http://mhealth.jmir.org/2018/11/e12136/ %U https://doi.org/10.2196/12136 %U http://www.ncbi.nlm.nih.gov/pubmed/30404768 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 10 %P e188 %T Capturing Rest-Activity Profiles in Schizophrenia Using Wearable and Mobile Technologies: Development, Implementation, Feasibility, and Acceptability of a Remote Monitoring Platform %A Meyer,Nicholas %A Kerz,Maximilian %A Folarin,Amos %A Joyce,Dan W %A Jackson,Richard %A Karr,Chris %A Dobson,Richard %A MacCabe,James %+ Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, United Kingdom, 44 0 207 848 0728, nicholas.meyer@kcl.ac.uk %K sleep %K circadian rhythm %K mHealth %K smartphone %K relapse %K psychosis %D 2018 %7 30.10.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is growing interest in the potential for wearable and mobile devices to deliver clinically relevant information in real-world contexts. However, there is limited information on their acceptability and barriers to long-term use in people living with psychosis. Objective: This study aimed to describe the development, implementation, feasibility, acceptability, and user experiences of the Sleepsight platform, which harnesses consumer wearable devices and smartphones for the passive and unobtrusive capture of sleep and rest-activity profiles in people with schizophrenia living in their homes. Methods: A total of 15 outpatients with a diagnosis of schizophrenia used a consumer wrist-worn device and smartphone to continuously and remotely gather rest-activity profiles over 2 months. Once-daily sleep and self-rated symptom diaries were also collected via a smartphone app. Adherence with the devices and smartphone app, end-of-study user experiences, and agreement between subjective and objective sleep measures were analyzed. Thresholds for acceptability were set at a wear time or diary response rate of 70% or greater. Results: Overall, 14 out of 15 participants completed the study. In individuals with a mild to moderate symptom severity at baseline (mean total Positive and Negative Syndrome Scale score 58.4 [SD 14.4]), we demonstrated high rates of engagement with the wearable device (all participants meeting acceptability criteria), sleep diary, and symptom diary (93% and 86% meeting criteria, respectively), with negative symptoms being associated with lower diary completion rate. The end-of-study usability and acceptability questionnaire and qualitative analysis identified facilitators and barriers to long-term use, and paranoia with study devices was not a significant barrier to engagement. Comparison between sleep diary and wearable estimated sleep times showed good correspondence (ρ=0.50, P<.001). Conclusions: Extended use of wearable and mobile technologies are acceptable to people with schizophrenia living in a community setting. In the future, these technologies may allow predictive, objective markers of clinical status, including early markers of impending relapse. %M 30377146 %R 10.2196/mhealth.8292 %U http://mhealth.jmir.org/2018/10/e188/ %U https://doi.org/10.2196/mhealth.8292 %U http://www.ncbi.nlm.nih.gov/pubmed/30377146 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 10 %P e175 %T Using Mobile Technology (pMOTAR) to Assess Reactogenicity: Protocol for a Pilot Randomized Controlled Trial %A Mngadi,Kathryn Therese %A Maharaj,Bhavna %A Duki,Yajna %A Grove,Douglas %A Andriesen,Jessica %+ Clinical Research Department, The Aurum Institute, The Ridge, Aurum House, 29 Queens Road, Parktown, Johannesburg, 1693, South Africa, 27 0825744541, kmngadi@auruminstitute.org %K research protocol %K mobile health application %K HIV preventive vaccines %K telemedicine %K mobile applications %K AIDS vaccines %D 2018 %7 03.10.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Accurate safety monitoring in HIV vaccine trials is vital to eventual licensure and consequent uptake of products. Current practice in preventive vaccine trials, under the HIV Vaccine Trials Network (HVTN), is to capture related side effects in a hardcopy tool. The reconciliation of this tool, 2 weeks after vaccination at the safety visit, is time consuming, laborious, and fraught with error. Unstructured Supplementary Service Data (USSD), commonly used to purchase airtime, has been suggested for collection of safety data in vaccine trials. With saturated access to mobile phones in South Africa, this cheap, accessible tool may improve accuracy and completeness of collected data and prove feasible and acceptable over the hardcopy tool. Objective: The objective of our study is to develop and implement a USSD tool for real-time safety data collection that is feasible and acceptable to participants and staff, allowing for a comparison with the hardcopy tool in terms of completeness and accuracy. Methods: This feasibility study is being conducted at a single study site, the Centre for the AIDS Programme of Research in South Africa eThekwini Clinical Research site, in South Africa. The feasibility study is nested within a parent phase 1/2a preventive HIV vaccine trial (HVTN 108) as an open-label, randomized controlled trial, open to all consenting parent trial participants. Participants are randomly assigned in a 1:1 ratio to the hardcopy or USSD tool, with data collection targeted to the third and fourth injection time points in the parent trial. Online feasibility and acceptability surveys will be completed by staff and participants at the safety visit. We will itemize and compare error rates between the hardcopy tool and the USSD printout and associated source documentation. We hypothesize that the USSD tool will be shown to be feasible and acceptable to staff and participants and to have superior quality and completion rates to the hardcopy tool. Results: The study has received regulatory approval. We have designed and developed the USSD tool to include all the data fields required for reactogenicity reporting. Online feasibility and accessibility surveys in both English and isiZulu have been successfully installed on a tablet. Data collection is complete, but analysis is pending. Conclusions: Several HIV preventive vaccine trials are active in Southern Africa, making tools to improve efficiencies and minimize error necessary. Our results will help to determine whether the USSD tool can be used in future vaccine studies and can eventually be rolled out. Trial Registration: ClincalTrials.gov NCT02915016; https://clinicaltrials.gov/ct2/show/NCT02915016 (Archived by WebCite at http://www.webcitation.org/71h0cztDM) Registered Report Identifier: RR1-10.2196/9396 %R 10.2196/resprot.9396 %U https://www.researchprotocols.org/2018/10/e175/ %U https://doi.org/10.2196/resprot.9396 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 3 %N 1 %P e11347 %T Relationship Between the Applied Occlusal Load and the Size of Markings Produced Due to Occlusal Contact Using Dental Articulating Paper and T-Scan: Comparative Study %A Reddy,Shravya %A Kumar,Preeti S %A Grandhi,Vyoma V %+ Department of Prosthodontics, The Oxford Dental College, 10th Mile Stone, Hosur Road, Bommanahalli, Bangalore, 560065, India, 91 9845493230, drpreetisatheesh@gmail.com %K occlusal indicator %K occlusal load %K articulating paper %K T-Scan %D 2018 %7 02.10.2018 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: The proposed experimental design was devised to determine whether a relationship exists between the occlusal load applied and the size of the markings produced from tooth contact when dental articulating paper and T-Scan are interposed alternatively. Objective: The objective of our study was to compare the relationship between contact markings on an articulating paper and T-Scan for an applied occlusal load. Methods: In this in vitro study, dentulous maxillary and mandibular dies were mounted on a metal jig and articulating paper and T-Scan sensor were placed alternatively between the casts. Loads simulating occlusal loads began at 25 N and incrementally continued up to 450 N. The resultant markings (180 marks resulting from articulating paper and 138 from T-Scan) were photographed, and the marks were analyzed using MOTIC image analysis and sketching software. Descriptive statistical analyses were performed using one-way analysis of variance, Student t test, and Pearson correlation coefficient method. Results: Statistical interpretation of the data indicated that with articulating paper, the mark area increased nonlinearly with increasing load and there was a false-positive result. The characteristics of the paper mark appearance did not describe the amount of occlusal load present on a given tooth. The contact marking obtained using T-Scan for an applied occlusal load indicated that the mark area increased with increase in the load and provided more predictable results of actual load content within the occlusal contact. Conclusions: The size of an articulating paper mark may not be a reliable predictor of the actual load content within the occlusal contact, whereas a T-Scan provides more predictable results of the actual load content within the occlusal contact. %R 10.2196/11347 %U http://biomedeng.jmir.org/2018/1/e11347/ %U https://doi.org/10.2196/11347 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 9 %P e10460 %T The Remote Food Photography Method and SmartIntake App for the Assessment of Alcohol Use in Young Adults: Feasibility Study and Comparison to Standard Assessment Methodology %A Fazzino,Tera L %A Martin,Corby K %A Forbush,Kelsie %+ Department of Psychology, University of Kansas, Fraser Hall, 4th Floor, 1415 Jayhawk Boulevard, Lawrence, KS, 66045, United States, 1 7858640062, tfazzino@ku.edu %K alcohol consumption %K alcohol college students %K alcohol assessment %K dietary assessment %K self report %K mobile phone %K mobile health %K ehealth %K photography %K young adults %D 2018 %7 24.9.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Heavy drinking is prevalent among young adults and may contribute to obesity. However, measurement tools for assessing caloric intake from alcohol are limited and rely on self-report, which is prone to bias. Objective: The purpose of our study was to conduct feasibility testing of the Remote Food Photography Method and the SmartIntake app to assess alcohol use in young adults. Aims consisted of (1) quantifying the ability of SmartIntake to capture drinking behavior, (2) assessing app usability with the Computer System Usability Questionnaire (CSUQ), (3) conducting a qualitative interview, and (4) comparing preference, usage, and alcohol use estimates (calories, grams per drinking episode) between SmartIntake and online diet recalls that participants completed for a parent study. Methods: College students (N=15) who endorsed a pattern of heavy drinking were recruited from a parent study. Participants used SmartIntake to send photographs of all alcohol and food intake over a 3-day period and then completed a follow-up interview and the CSUQ. CSUQ items range from 1-7, with lower scores indicating greater usability. Total drinking occasions were determined by adding the number of drinking occasions captured by SmartIntake plus the number of drinking occasions participants reported that they missed capturing. Usage was defined by the number of days participants provided food/beverage photos through the app, or the number of diet recalls completed. Results: SmartIntake captured 87% (13/15) of total reported drinking occasions. Participants rated the app as highly usable in the CSUQ (mean 2.28, SD 1.23). Most participants (14/15, 93%) preferred using SmartIntake versus recalls, and usage was significantly higher with SmartIntake than recalls (42/45, 93% vs 35/45, 78%; P=.04). Triple the number of participants submitted alcohol reports with SmartIntake compared to the recalls (SmartIntake 9/15, 60% vs recalls 3/15, 20%; P=.06), and 60% (9/15) of participants reported drinking during the study. Conclusions: SmartIntake was acceptable to college students who drank heavily and captured most drinking occasions. Participants had higher usage of SmartIntake compared to recalls, suggesting SmartIntake may be well suited to measuring alcohol consumption in young adults. However, 40% (6/15) did not drink during the brief testing period and, although findings are promising, a longer trial is needed. %M 30249590 %R 10.2196/10460 %U http://mhealth.jmir.org/2018/9/e10460/ %U https://doi.org/10.2196/10460 %U http://www.ncbi.nlm.nih.gov/pubmed/30249590 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 9 %P e177 %T Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study %A Crouthamel,Michelle %A Quattrocchi,Emilia %A Watts,Sarah %A Wang,Sherry %A Berry,Pamela %A Garcia-Gancedo,Luis %A Hamy,Valentin %A Williams,Rachel E %+ GlaxoSmithKline, 1000 Black Rock Road, Collegeville, PA, 19426, United States, 1 215 756 5101, ming-chih.h.crouthamel@gsk.com %K rheumatoid arthritis %K smartphone %K mobile phone %K patient-reported outcome measures %K mobile applications %D 2018 %7 13.9.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Using smartphones to enroll, obtain consent, and gather self-reported data from patients has the potential to enhance our understanding of disease burden and quantify physiological impact in the real world. It may also be possible to harness integral smartphone sensors to facilitate remote collection of clinically relevant data. Objective: We conducted the Patient Rheumatoid Arthritis Data From the Real World (PARADE) observational study using a customized ResearchKit app with a bring-your-own-device approach. Our objective was to assess the feasibility of using an entirely digital approach (social media and smartphone app) to conduct a real-world observational study of patients with rheumatoid arthritis. Methods: We conducted this observational study using a customized ResearchKit app with a bring-your-own-device approach. To recruit patients, the PARADE app, designed to guide patients through a series of tasks, was publicized via social media platforms and made available for patients in the United States to download from the Apple App Store. We collected patient-reported data, such as medical history, rheumatoid arthritis-related medications (past and present), and a range of patient-reported outcome measures. We included in the assessment a joint-pain map and a novel objective assessment of wrist range of movement, measured by the smartphone-embedded gyroscope and accelerometer. Results: Within 1 month of recruitment via social media campaigns, 399 participants self-enrolled, self-consented, and provided complete demographic data. Joint pain was the most frequently reported rheumatoid arthritis symptom to bother study participants (344/393, 87.5%). Severe patient-reported wrist pain appeared to be inversely linked with the range of wrist movement measured objectively by the app. At study entry, 292 of 399 participants (73.2%) indicated a preference for participating in a mobile app–based study. The number of participants in the study declined to 45 of 399 (11.3%) at week 12. Conclusions: Despite the declining number of participants over time, the combination of social media and smartphone app with sensor integration was a feasible and cost-effective approach for the collection of patient-reported data in rheumatoid arthritis. Integral sensors within smartphones can be harnessed to provide novel end points, and the novel wrist range of movement test warrants further clinical validation. %M 30213779 %R 10.2196/mhealth.9656 %U http://mhealth.jmir.org/2018/9/e177/ %U https://doi.org/10.2196/mhealth.9656 %U http://www.ncbi.nlm.nih.gov/pubmed/30213779 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 3 %P e10104 %T Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques %A Heraz,Alicia %A Clynes,Manfred %+ The Brain Mining Lab, 300 Rue Saint-Georges, Montréal, QC, J4P 3P9, Canada, 1 5145508902, alicia@bmlabca.com %K emotional artificial intelligence %K human-computer interaction %K smartphone %K force-sensitive screens %K mental health %K positive computing %K artificial intelligence %K emotions %K emotional intelligence %D 2018 %7 30.08.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: Emotions affect our mental health: they influence our perception, alter our physical strength, and interfere with our reason. Emotions modulate our face, voice, and movements. When emotions are expressed through the voice or face, they are difficult to measure because cameras and microphones are not often used in real life in the same laboratory conditions where emotion detection algorithms perform well. With the increasing use of smartphones, the fact that we touch our phones, on average, thousands of times a day, and that emotions modulate our movements, we have an opportunity to explore emotional patterns in passive expressive touches and detect emotions, enabling us to empower smartphone apps with emotional intelligence. Objective: In this study, we asked 2 questions. (1) As emotions modulate our finger movements, will humans be able to recognize emotions by only looking at passive expressive touches? (2) Can we teach machines how to accurately recognize emotions from passive expressive touches? Methods: We were interested in 8 emotions: anger, awe, desire, fear, hate, grief, laughter, love (and no emotion). We conducted 2 experiments with 2 groups of participants: good imagers and emotionally aware participants formed group A, with the remainder forming group B. In the first experiment, we video recorded, for a few seconds, the expressive touches of group A, and we asked group B to guess the emotion of every expressive touch. In the second experiment, we trained group A to express every emotion on a force-sensitive smartphone. We then collected hundreds of thousands of their touches, and applied feature selection and machine learning techniques to detect emotions from the coordinates of participant’ finger touches, amount of force, and skin area, all as functions of time. Results: We recruited 117 volunteers: 15 were good imagers and emotionally aware (group A); the other 102 participants formed group B. In the first experiment, group B was able to successfully recognize all emotions (and no emotion) with a high 83.8% (769/918) accuracy: 49.0% (50/102) of them were 100% (450/450) correct and 25.5% (26/102) were 77.8% (182/234) correct. In the second experiment, we achieved a high 91.11% (2110/2316) classification accuracy in detecting all emotions (and no emotion) from 9 spatiotemporal features of group A touches. Conclusions: Emotions modulate our touches on force-sensitive screens, and humans have a natural ability to recognize other people’s emotions by watching prerecorded videos of their expressive touches. Machines can learn the same emotion recognition ability and do better than humans if they are allowed to continue learning on new data. It is possible to enable force-sensitive screens to recognize users’ emotions and share this emotional insight with users, increasing users’ emotional awareness and allowing researchers to design better technologies for well-being. %M 30166276 %R 10.2196/10104 %U http://mental.jmir.org/2018/3/e10104/ %U https://doi.org/10.2196/10104 %U http://www.ncbi.nlm.nih.gov/pubmed/30166276 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 3 %P e56 %T Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire %A Di Matteo,Daniel %A Fine,Alexa %A Fotinos,Kathryn %A Rose,Jonathan %A Katzman,Martin %+ The Centre for Automation of Medicine, The Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, DL Pratt Building, 6 King's College Road, Toronto, ON,, Canada, 1 416 978 6992, dandm@ece.utoronto.ca %K passive sensing %K mobile phone sensing %K psychiatric assessment %K mood and anxiety disorders %K digital privacy %K mobile apps %K mobile phone %K consent %D 2018 %7 29.08.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: It has become possible to use data from a patient’s mobile phone as an adjunct or alternative to the traditional self-report and interview methods of symptom assessment in psychiatry. Mobile data–based assessment is possible because of the large amounts of diverse information available from a modern mobile phone, including geolocation, screen activity, physical motion, and communication activity. This data may offer much more fine-grained insight into mental state than traditional methods, and so we are motivated to pursue research in this direction. However, passive data retrieval could be an unwelcome invasion of privacy, and some may not consent to such observation. It is therefore important to measure patients’ willingness to consent to such observation if this approach is to be considered for general use. Objective: The aim of this study was to measure the ownership rates of mobile phones within the patient population, measure the patient population’s willingness to have their mobile phone used as an experimental assessment tool for their mental health disorder, and, finally, to determine how likely patients would be to provide consent for each individual source of mobile phone–collectible data across the variety of potential data sources. Methods: New patients referred to a tertiary care mood and anxiety disorder clinic from August 2016 to October 2017 completed a survey designed to measure their mobile phone ownership, use, and willingness to install a mental health monitoring app and provide relevant data through the app. Results: Of the 82 respondents, 70 (85%) reported owning an internet-connected mobile phone. When asked about installing a hypothetical mobile phone app to assess their mental health disorder, 41% (33/80) responded with complete willingness to install with another 43% (34/80) indicating potential willingness to install such an app. Willingness to give permissions for specific types of data varied by data source, with respondents least willing to consent to audio recording and analysis (19% [15/80] willing respondents, 31% [25/80] potentially willing) and most willing to consent to observation of the mobile phone screen being on or off (46% [36/79] willing respondents and 23% [18/79] potentially willing). Conclusions: The patients surveyed had a high incidence of ownership of internet-connected mobile phones, which suggests some plausibility for the general approach of mental health state inference through mobile phone data. Patients were also relatively willing to consent to data collection from sources that were less personal but expressed less willingness for the most personal communication and location data. %M 30158102 %R 10.2196/mental.9539 %U http://mental.jmir.org/2018/3/e56/ %U https://doi.org/10.2196/mental.9539 %U http://www.ncbi.nlm.nih.gov/pubmed/30158102 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 8 %P e168 %T Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study %A Goodspeed,Robert %A Yan,Xiang %A Hardy,Jean %A Vydiswaran,VG Vinod %A Berrocal,Veronica J %A Clarke,Philippa %A Romero,Daniel M %A Gomez-Lopez,Iris N %A Veinot,Tiffany %+ Urban and Regional Planning Program, Taubman College of Architecture and Urban Planning, University of Michigan, 2000 Bonisteel Blvd, Ann Arbor, MI, 48109, United States, 1 734 615 7254, rgoodspe@umich.edu %K urban population %K spatial behavior %K mobile phone %K environment and public health %K data accuracy %D 2018 %7 13.08.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or mobile phones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices. Objective: In this study, we compared the data quality of location information collected from two mobile devices that determine location in different ways—a global positioning system (GPS) watch and a mobile phone with Google’s Location History feature enabled. Methods: A total of 21 chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A mobile phone–based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at 4 random times throughout each day. Participants also took part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent mobile phone and watch trace data to compare the devices’ trace data with their memory of their activities on those days. Trace data from the devices were compared on the basis of (1) missing data days, (2) reasons for missing data, (3) distance between the route data collected for matching day and the associated EMA survey locations, and (4) activity space total area and density surfaces. Results: The watch resulted in a much higher proportion of missing data days (P<.001), with missing data explained by technical differences between the devices as well as participant behaviors. The mobile phone was significantly more accurate in detecting home locations (P=.004) and marginally more accurate (P=.07) for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation. Conclusions: The most suitable mobile device for location-based health research depends on the particular study objectives. Furthermore, data generated from mobile devices, such as GPS phones and smartwatches, require careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from the use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or those that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the greater precision demonstrated by the mobile phone in recording indoor activities. %M 30104185 %R 10.2196/mhealth.9771 %U http://mhealth.jmir.org/2018/8/e168/ %U https://doi.org/10.2196/mhealth.9771 %U http://www.ncbi.nlm.nih.gov/pubmed/30104185 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 8 %P e165 %T Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review %A Rohani,Darius A %A Faurholt-Jepsen,Maria %A Kessing,Lars Vedel %A Bardram,Jakob E %+ Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Bldg 324, 1st Floor, Room 160, Kongens Lyngby, 2800, Denmark, 45 61452393, daroh@dtu.dk %K mood disorder %K affective disorder %K depression %K depressive mood symptoms %K bipolar disorder %K objective features %K correlation %K behavior %K sensor data %K mobile phone %K wearable devices %K systematic review %D 2018 %7 13.08.2018 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published. Objective: The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies. Methods: We conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017. Results: A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity). Conclusions: Several studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible. %M 30104184 %R 10.2196/mhealth.9691 %U http://mhealth.jmir.org/2018/8/e165/ %U https://doi.org/10.2196/mhealth.9691 %U http://www.ncbi.nlm.nih.gov/pubmed/30104184 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 8 %P e10130 %T Using Mobile Apps to Assess and Treat Depression in Hispanic and Latino Populations: Fully Remote Randomized Clinical Trial %A Pratap,Abhishek %A Renn,Brenna N %A Volponi,Joshua %A Mooney,Sean D %A Gazzaley,Adam %A Arean,Patricia A %A Anguera,Joaquin A %+ Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, UW Medicine South Lake Union, Building C, Box 358047, 850 Republican Street, Seattle, WA, 98109, United States, 1 206 928 8263, apratap@uw.edu %K mobile apps %K smartphone %K depression %K Hispanics %K Latinos %K clinical trial %K cognition %K problem solving %K mHealth %K minority groups %D 2018 %7 09.08.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Most people with mental health disorders fail to receive timely access to adequate care. US Hispanic/Latino individuals are particularly underrepresented in mental health care and are historically a very difficult population to recruit into clinical trials; however, they have increasing access to mobile technology, with over 75% owning a smartphone. This technology has the potential to overcome known barriers to accessing and utilizing traditional assessment and treatment approaches. Objective: This study aimed to compare recruitment and engagement in a fully remote trial of individuals with depression who either self-identify as Hispanic/Latino or not. A secondary aim was to assess treatment outcomes in these individuals using three different self-guided mobile apps: iPST (based on evidence-based therapeutic principles from problem-solving therapy, PST), Project Evolution (EVO; a cognitive training app based on cognitive neuroscience principles), and health tips (a health information app that served as an information control). Methods: We recruited Spanish and English speaking participants through social media platforms, internet-based advertisements, and traditional fliers in select locations in each state across the United States. Assessment and self-guided treatment was conducted on each participant's smartphone or tablet. We enrolled 389 Hispanic/Latino and 637 non-Hispanic/Latino adults with mild to moderate depression as determined by Patient Health Questionnaire-9 (PHQ-9) score≥5 or related functional impairment. Participants were first asked about their preferences among the three apps and then randomized to their top two choices. Outcomes were depressive symptom severity (measured using PHQ-9) and functional impairment (assessed with Sheehan Disability Scale), collected over 3 months. Engagement in the study was assessed based on the number of times participants completed active surveys. Results: We screened 4502 participants and enrolled 1040 participants from throughout the United States over 6 months, yielding a sample of 348 active users. Long-term engagement surfaced as a key issue among Hispanic/Latino participants, who dropped from the study 2 weeks earlier than their non-Hispanic/Latino counterparts (P<.02). No significant differences were observed for treatment outcomes between those identifying as Hispanic/Latino or not. Although depressive symptoms improved (beta=–2.66, P=.006) over the treatment course, outcomes did not vary by treatment app. Conclusions: Fully remote mobile-based studies can attract a diverse participant pool including people from traditionally underserved communities in mental health care and research (here, Hispanic/Latino individuals). However, keeping participants engaged in this type of “low-touch” research study remains challenging. Hispanic/Latino populations may be less willing to use mobile apps for assessing and managing depression. Future research endeavors should use a user-centered design to determine the role of mobile apps in the assessment and treatment of depression for this population, app features they would be interested in using, and strategies for long-term engagement. Trial Registration: Clinicaltrials.gov NCT01808976; https://clinicaltrials.gov/ct2/show/NCT01808976 (Archived by WebCite at http://www.webcitation.org/70xI3ILkz) %M 30093372 %R 10.2196/10130 %U http://www.jmir.org/2018/8/e10130/ %U https://doi.org/10.2196/10130 %U http://www.ncbi.nlm.nih.gov/pubmed/30093372 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 3 %P e10153 %T Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study %A Quiroz,Juan Carlos %A Geangu,Elena %A Yong,Min Hooi %+ Department of Psychology, Sunway University, 5 Jalan Universiti, Bandar Sunway, 47500, Malaysia, 60 374918622, mhyong@sunway.edu.my %K emotion recognition %K accelerometer %K supervised learning %K psychology %D 2018 %7 08.08.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: Research in psychology has shown that the way a person walks reflects that person’s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data. Objective: The objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual’s emotional state. We present our findings of a user study with 50 participants. Methods: The experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual “movie clips” and audio “music clips”). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual’s emotion. Results: Overall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P<.006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness. Conclusions: Our findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition. %M 30089610 %R 10.2196/10153 %U http://mental.jmir.org/2018/3/e10153/ %U https://doi.org/10.2196/10153 %U http://www.ncbi.nlm.nih.gov/pubmed/30089610 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e61 %T Using Geosocial Networking Apps to Understand the Spatial Distribution of Gay and Bisexual Men: Pilot Study %A Card,Kiffer George %A Gibbs,Jeremy %A Lachowsky,Nathan John %A Hawkins,Blake W %A Compton,Miranda %A Edward,Joshua %A Salway,Travis %A Gislason,Maya K %A Hogg,Robert S %+ School of Public Health and Social Policy, Faculty of Human and Social Development, University of Victoria, 3800 Finnerty Road, Victoria, BC, V8P 5C2, Canada, 1 250 213 1743, kcard@sfu.ca %K service access %K geosocial networking apps %K gay and bisexual men %K spatial distribution %K gay neighborhoods %D 2018 %7 08.08.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: While services tailored for gay, bisexual, and other men who have sex with men (gbMSM) may provide support for this vulnerable population, planning access to these services can be difficult due to the unknown spatial distribution of gbMSM outside of gay-centered neighborhoods. This is particularly true since the emergence of geosocial networking apps, which have become a widely used venue for meeting sexual partners. Objective: The goal of our research was to estimate the spatial density of app users across Metro Vancouver and identify the independent and adjusted neighborhood-level factors that predict app user density. Methods: This pilot study used a popular geosocial networking app to estimate the spatial density of app users across rural and urban Metro Vancouver. Multiple Poisson regression models were then constructed to model the relationship between app user density and areal population-weighted neighbourhood-level factors from the 2016 Canadian Census and National Household Survey. Results: A total of 2021 app user profiles were counted within 1 mile of 263 sampling locations. In a multivariate model controlling for time of day, app user density was associated with several dissemination area–level characteristics, including population density (per 100; incidence rate ratio [IRR] 1.03, 95% CI 1.02-1.04), average household size (IRR 0.26, 95% CI 0.11-0.62), average age of males (IRR 0.93, 95% CI 0.88-0.98), median income of males (IRR 0.96, 95% CI 0.92-0.99), proportion of males who were not married (IRR 1.08, 95% CI 1.02-1.13), proportion of males with a postsecondary education (IRR 1.06, 95% CI 1.03-1.10), proportion of males who are immigrants (IRR 1.04, 95% CI 1.004-1.07), and proportion of males living below the low-income cutoff level (IRR 0.93, 95% CI 0.89-0.98). Conclusions: This pilot study demonstrates how the combination of geosocial networking apps and administrative datasets might help care providers, planners, and community leaders target online and offline interventions for gbMSM who use apps. %M 30089609 %R 10.2196/publichealth.8931 %U http://publichealth.jmir.org/2018/3/e61/ %U https://doi.org/10.2196/publichealth.8931 %U http://www.ncbi.nlm.nih.gov/pubmed/30089609 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 3 %P e11203 %T Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study %A Nsabimana,Alain Placide %A Uzabakiriho,Bernard %A Kagabo,Daniel M %A Nduwayo,Jerome %A Fu,Qinyouen %A Eng,Allison %A Hughes,Joshua %A Sia,Samuel K %+ Junco Labs, 423 W 127th Street, Ground Floor, New York, NY,, United States, 1 518 880 9667, samuelsia@juncolabs.com %K HIV surveillance %K smartphones %K mobile phones %K geospatial data %D 2018 %7 07.08.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Precise measurements of HIV incidences at community level can help mount a more effective public health response, but the most reliable methods currently require labor-intensive population surveys. Novel mobile phone technologies are being tested for adherence to medical appointments and antiretroviral therapy, but using them to track HIV test results with automatically generated geospatial coordinates has not been widely tested. Objective: We customized a portable reader for interpreting the results of HIV lateral flow tests and developed a mobile phone app to track HIV test results in urban and rural locations in Rwanda. The objective was to assess the feasibility of this technology to collect front line HIV test results in real time and with geospatial context to help measure HIV incidences and improve epidemiological surveillance. Methods: Twenty health care workers used the technology to track the test results of 2190 patients across 3 hospital sites (2 urban sites in Kigali and a rural site in the Western Province of Rwanda). Mobile phones for less than US $70 each were used. The mobile phone app to record HIV test results could take place without internet connectivity with uploading of results to the cloud taking place later with internet. Results: A total of 91.51% (2004/2190) of HIV test results could be tracked in real time on an online dashboard with geographical resolution down to street level. Out of the 20 health care workers, 14 (70%) would recommend the lateral flow reader, and 100% would recommend the mobile phone app. Conclusions: Smartphones have the potential to simplify the input of HIV test results with geospatial context and in real time to improve public health surveillance of HIV. %M 30087088 %R 10.2196/11203 %U http://publichealth.jmir.org/2018/3/e11203/ %U https://doi.org/10.2196/11203 %U http://www.ncbi.nlm.nih.gov/pubmed/30087088 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 7 %P e10131 %T Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions %A Boonstra,Tjeerd W %A Nicholas,Jennifer %A Wong,Quincy JJ %A Shaw,Frances %A Townsend,Samuel %A Christensen,Helen %+ Black Dog Institute, University of New South Wales, Hospital Road, Sydney,, Australia, 61 2 9382 9285, t.boonstra@unsw.edu.au %K passive sensing %K mental health %K ubiquitous computing %K ethics %K depression %K mobile health %K smartphone %K wearable sensors %D 2018 %7 30.07.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition. Objective: The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data. Methods: Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, location, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32). Results: On average, sensor data were obtained for 55% (Android) and 45% (iOS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability. Conclusions: Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed. %M 30061092 %R 10.2196/10131 %U http://www.jmir.org/2018/7/e10131/ %U https://doi.org/10.2196/10131 %U http://www.ncbi.nlm.nih.gov/pubmed/30061092 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 5 %N 2 %P e14 %T An mHealth Platform for Supporting Clinical Data Integration into Augmentative and Alternative Communication Service Delivery: User-Centered Design and Usability Evaluation %A Wang,Erh-Hsuan %A Zhou,Leming %A Chen,Szu-Han Kay %A Hill,Katya %A Parmanto,Bambang %+ Department of Health Information Management, University of Pittsburgh, 6021 Forbes Tower, Pittsburgh, PA, 15260, United States, 1 412 383 6653, lmzhou@gmail.com %K Web-based portal %K data integration %K Augmentative and Alternative Communication %K service delivery %D 2018 %7 24.07.2018 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: The recent trend of increasing health care costs in the United States is likely not sustainable. To make health care more economically sustainable, attention must be directed toward improving the quality while simultaneously reducing the cost of health care. One of the recommended approaches to provide better care at a lower cost is to develop high-quality data collection and reporting systems, which support health care professionals in making optimal clinical decisions based on solid, extensive evidence. Objective: The objective of this project was to develop an integrated mobile health Augmentative and Alternative Communication (AAC) platform consisting of an AAC mobile app and a Web-based clinician portal for supporting evidence-based clinical service delivery. Methods: A questionnaire and interviews were used to collect clinicians’ ideas regarding what constitutes their desired “clinically relevant” data. In response, a Web-based portal was designed by combining mobile and Web technologies with an AAC intervention to create an integrated platform for supporting data collection, integration, and reporting. Finally, a usability study was conducted with health care professionals. Results: A Web-based portal was created and integrated with a tablet-based AAC mobile app and data analysis procedures. In the usability study, all participants agreed that the integrated platform provided the ability to collect comprehensive clinical evidence, automatically analyze collected data in real time, and generate clinically relevant performance measures through an easily accessible Web-based portal. Conclusions: The integrated platform offers a better approach for clinical data reporting and analytics. Additionally, the platform streamlines the workflow of AAC clinical service delivery. %M 30042092 %R 10.2196/rehab.9009 %U http://rehab.jmir.org/2018/2/e14/ %U https://doi.org/10.2196/rehab.9009 %U http://www.ncbi.nlm.nih.gov/pubmed/30042092 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 7 %P e241 %T Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study %A Zulueta,John %A Piscitello,Andrea %A Rasic,Mladen %A Easter,Rebecca %A Babu,Pallavi %A Langenecker,Scott A %A McInnis,Melvin %A Ajilore,Olusola %A Nelson,Peter C %A Ryan,Kelly %A Leow,Alex %+ University of Illinois at Chicago, 1601 W Taylor St, Chicago, IL, 60612, United States, 1 312 996 7383, aleow@psych.uic.edu %K digital phenotype %K mHealth %K ecological momentary assessment %K keystroke dynamics %K bipolar disorder %K depression %K mania %K mobile phone %D 2018 %7 20.07.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania. Objective: The objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales. Methods: Using a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly average 104,076 (68,912). Results: A statistically significant mixed-effects regression model for the prediction of HDRS-17 item scores was created: conditional R2=.63, P=.01. A mixed-effects regression model for YMRS scores showed the variance accounted for by random effect was zero, and so an ordinary least squares linear regression model was created: R2=.34, P=.001. Multiple significant variables were demonstrated for each measure. Conclusions: Mood states in bipolar disorder appear to correlate with specific changes in mobile phone usage. The creation of these models provides evidence for the feasibility of using passively collected keyboard metadata to detect and monitor mood disturbances. %M 30030209 %R 10.2196/jmir.9775 %U http://www.jmir.org/2018/7/e241/ %U https://doi.org/10.2196/jmir.9775 %U http://www.ncbi.nlm.nih.gov/pubmed/30030209 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 7 %P e161 %T Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review %A Jones,Kerina Helen %A Daniels,Helen %A Heys,Sharon %A Ford,David Vincent %+ Data Science Building, School of Medicine, Swansea University, Singleton Park, Swansea, SA28PP, United Kingdom, 44 1792602764, k.h.jones@swansea.ac.uk %K call detail records %K mobile phone data %K health research %D 2018 %7 19.07.2018 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Call detail records (CDRs) are collected by mobile network operators in the course of providing their service. CDRs are increasingly being used in research along with other forms of big data and represent an emerging data type with potential for public good. Many jurisdictions have infrastructures for health data research that could benefit from the integration of CDRs with health data. Objective: The objective of this study was to review how CDRs have been used in health research and to identify challenges and potential opportunities for their wider use in conjunction with health data. Methods: A literature review was conducted using structured search terms making use of major search engines. Initially, 4066 items were identified. Following screening, 46 full text articles were included in the qualitative synthesis. Information extracted included research topic area, population of study, datasets used, information governance and ethical considerations, study findings, and data limitations. Results: The majority of published studies were focused on low-income and middle-income countries. Making use of the location element in CDRs, studies often modeled the transmission of infectious diseases or estimated population movement following natural disasters with a view to implementing interventions. CDRs were used in anonymized or aggregated form, and the process of gaining regulatory approvals varied with data provider and by jurisdiction. None included public views on the use of CDRs in health research. Conclusions: Despite various challenges and limitations, anonymized mobile phone CDRs have been used successfully in health research. The use of aggregated data is a safeguard but also a further limitation. Greater opportunities could be gained if validated anonymized CDRs were integrated with routine health records at an individual level, provided that permissions and safeguards could be put in place. Further work is needed, including gaining public views, to develop an ethically founded framework for the use of CDRs in health research. %M 30026176 %R 10.2196/mhealth.9974 %U http://mhealth.jmir.org/2018/7/e161/ %U https://doi.org/10.2196/mhealth.9974 %U http://www.ncbi.nlm.nih.gov/pubmed/30026176 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 7 %P e166 %T Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults %A Rabbi,Mashfiqui %A Philyaw Kotov,Meredith %A Cunningham,Rebecca %A Bonar,Erin E %A Nahum-Shani,Inbal %A Klasnja,Predrag %A Walton,Maureen %A Murphy,Susan %+ Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA, 02138, United States, 1 6036671797, mrabbi@fas.harvard.edu %K engagement %K microrandomized trial %K just-in-time adaptive intervention %D 2018 %7 18.07.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Substance use is an alarming public health issue associated with significant morbidity and mortality. Adolescents and emerging adults are at particularly high risk because substance use typically initiates and peaks during this developmental period. Mobile health apps are a promising data collection and intervention delivery tool for substance-using youth as most teens and young adults own a mobile phone. However, engagement with data collection for most mobile health applications is low, and often, large fractions of users stop providing data after a week of use. Objective: Substance Abuse Research Assistant (SARA) is a mobile application to increase or sustain engagement of substance data collection overtime. SARA provides a variety of engagement strategies to incentivize data collection: a virtual aquarium in the app grows with fish and aquatic resources; occasionally, funny or inspirational contents (eg, memes or text messages) are provided to generate positive emotions. We plan to assess the efficacy of SARA’s engagement strategies over time by conducting a micro-randomized trial, where the engagement strategies will be sequentially manipulated. Methods: We aim to recruit participants (aged 14-24 years), who report any binge drinking or marijuana use in the past month. Participants are instructed to use SARA for 1 month. During this period, participants are asked to complete one survey and two active tasks every day between 6 pm and midnight. Through the survey, we assess participants’ daily mood, stress levels, loneliness, and hopefulness, while through the active tasks, we measure reaction time and spatial memory. To incentivize and support the data collection, a variety of engagement strategies are used. First, predata collection strategies include the following: (1) at 4 pm, a push notification may be issued with an inspirational message from a contemporary celebrity; or (2) at 6 pm, a push notification may be issued reminding about data collection and incentives. Second, postdata collection strategies include various rewards such as points which can be used to grow a virtual aquarium with fishes and other treasures and modest monetary rewards (up to US $12; US $1 for each 3-day streak); also, participants may receive funny or inspirational content as memes or gifs or visualizations of prior data. During the study, the participants will be randomized every day to receive different engagement strategies. In the primary analysis, we will assess whether issuing 4 pm push-notifications or memes or gifs, respectively, increases self-reporting on the current or the following day. Results: The microrandomized trial started on August 21, 2017 and the trial ended on February 28, 2018. Seventy-three participants were recruited. Data analysis is currently underway. Conclusions: To the best of our knowledge, SARA is the first mobile phone app that systematically manipulates engagement strategies in order to identify the best sequence of strategies that keep participants engaged in data collection. Once the optimal strategies to collect data are identified, future versions of SARA will use this data to provide just-in-time adaptive interventions to reduce substance use among youth. Trial Registration: ClinicalTrials.gov NCT03255317; https://clinicaltrials.gov/show/NCT03255317 (Archived by WebCite at http://www.webcitation.org/70raGWV0e) Registered Report Identifier: RR1-10.2196/9850 %M 30021714 %R 10.2196/resprot.9850 %U http://www.researchprotocols.org/2018/7/e166/ %U https://doi.org/10.2196/resprot.9850 %U http://www.ncbi.nlm.nih.gov/pubmed/30021714 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 7 %P e159 %T Clinical Feasibility of Monitoring Resting Heart Rate Using a Wearable Activity Tracker in Patients With Thyrotoxicosis: Prospective Longitudinal Observational Study %A Lee,Jie-Eun %A Lee,Dong Hwa %A Oh,Tae Jung %A Kim,Kyoung Min %A Choi,Sung Hee %A Lim,Soo %A Park,Young Joo %A Park,Do Joon %A Jang,Hak Chul %A Moon,Jae Hoon %+ Department of Internal Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro, 173 Beon-gil, Sungnam-si, 13620, Republic Of Korea, 82 31 787 7068, jaemoon76@gmail.com %K activity tracker %K wearable device %K heart rate %K thyrotoxicosis %K hyperthyroidism %K Graves’ disease %D 2018 %7 13.07.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Symptoms and signs of thyrotoxicosis are nonspecific and assessing its clinical status is difficult with conventional physical examinations and history taking. Increased heart rate (HR) is one of the easiest signs to quantify this, and current wearable devices can monitor HR. Objective: We assessed the association between thyroid function and resting HR measured by a wearable activity tracker (WD-rHR) and evaluated the clinical feasibility of using this method in patients with thyrotoxicosis. Methods: Thirty patients with thyrotoxicosis and 10 controls were included in the study. Participants were instructed to use the wearable activity tracker during the study period so that activity and HR data could be collected. The primary study outcomes were verification of changes in WD-rHR during thyrotoxicosis treatment and associations between WD-rHR and thyroid function. Linear and logistic model generalized estimating equation analyses were performed and the results were compared to conventionally obtained resting HR during clinic visits (on-site resting HR) and the Hyperthyroidism Symptom Scale. Results: WD-rHR was higher in thyrotoxic patients than in the control groups and decreased in association with improvement of thyrotoxicosis. A one standard deviation–increase of WD-rHR of about 11 beats per minute (bpm) was associated with the increase of serum free T4 levels (beta=.492, 95% CI 0.367-0.616, P<.001) and thyrotoxicosis risk (odds ratio [OR] 3.840, 95% CI 2.113-6.978, P<.001). Although the Hyperthyroidism Symptom Scale showed similar results with WD-rHR, a 1 SD-increase of on-site rHR (about 16 beats per minute) showed a relatively lower beta and OR (beta=.396, 95% CI 0.204-0.588, P<.001; OR 2.114, 95% CI 1.365-3.273, P<.001) compared with WD-rHR. Conclusions: Heart rate data measured by a wearable device showed reasonable predictability of thyroid function. This simple, easy-to-measure parameter is clinically feasible and has the potential to manage thyroid dysfunction. Trial Registration: ClinicalTrials.gov NCT03009357; https://clinicaltrials.gov/ct2/show/NCT03009357 (Archived by WebCite at http://www.webcitation.org/70h55Llyg) %M 30006328 %R 10.2196/mhealth.9884 %U http://mhealth.jmir.org/2018/7/e159/ %U https://doi.org/10.2196/mhealth.9884 %U http://www.ncbi.nlm.nih.gov/pubmed/30006328 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e148 %T Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study %A Schobel,Johannes %A Pryss,Rüdiger %A Probst,Thomas %A Schlee,Winfried %A Schickler,Marc %A Reichert,Manfred %+ Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, Ulm, 89081, Germany, 49 731 50 24229, johannes.schobel@uni-ulm.de %K mHealth %K data collection %K mobile apps %D 2018 %7 29.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Many research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios. Objective: In order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants. Methods: Across 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework. Results: Statistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P<.001), and time (P<.001) from the first to the last use of the configurator. Experts showed significant learning effects for operations (P=.001) and time (P<.001), but not for errors as the experts’ errors were already very low at the first modeling of the data collection instrument. Moreover, regarding the time and operations needed, novices got significantly better at the third modeling task than experts were at the first one (t tests; P<.001 for time and P=.002 for operations). Regarding errors, novices did not get significantly better at working with any of the 10 data collection instruments than experts were at the first modeling task, but novices’ error rates for all 5 data collection instruments at Session 2 were not significantly different anymore from those of experts at the first modeling task. After 7 days of not using the configurator (from Session 1 to Session 2), the experts’ learning effect at the end of Session 1 remained stable at the beginning of Session 2, but the novices’ learning effect at the end of Session 1 showed a significant decay at the beginning of Session 2 regarding time and operations (t tests; P<.001 for time and P=.03 for operations). Conclusions: In conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own. %M 29959107 %R 10.2196/mhealth.9826 %U http://mhealth.jmir.org/2018/6/e148/ %U https://doi.org/10.2196/mhealth.9826 %U http://www.ncbi.nlm.nih.gov/pubmed/29959107 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e150 %T Association Between Self-Reported and Objective Activity Levels by Demographic Factors: Ecological Momentary Assessment Study in Children %A Zink,Jennifer %A Belcher,Britni R %A Dzubur,Eldin %A Ke,Wangjing %A O'Connor,Sydney %A Huh,Jimi %A Lopez,Nanette %A Maher,Jaclyn P %A Dunton,Genevieve F %+ Department of Preventive Medicine, University of Southern California, 2001 North Soto Street, Third Floor, Los Angeles, CA, 90032, United States, 1 323 442 8224, dunton@usc.edu %K sedentary behavior %K physical activity %K measurement %K mobile devices %K children %D 2018 %7 28.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: To address the limitations of the retrospective self-reports of activity, such as its susceptibility to recall bias, researchers have shifted toward collecting real-time activity data on mobile devices via ecological momentary assessment (EMA). Although EMA is becoming increasingly common, it is not known how EMA self-reports of physical activity and sedentary behaviors relate to the objective measures of activity or whether there are factors that may influence the strength of association between these two measures. Understanding the relationship between EMA and accelerometry can optimize future instrument selection in studies assessing activity and health outcomes. Objective: The aim of this study was to examine the associations between EMA-reported sports or exercise using the accelerometer-measured moderate-to-vigorous physical activity (MVPA) and EMA-reported TV, videos, or video games with the accelerometer-measured sedentary time (ST) in children during matched 2-h windows and test potential moderators. Methods: Children (N=192; mean age 9.6 years; 94/192, 49.0% male; 104/192, 54.2% Hispanic; and 73/192, 38.0% overweight or obese) wore an accelerometer and completed up to 7 EMA prompts per day for 8 days during nonschool time, reporting on past 2-h sports or exercise and TV, videos, or video games. Multilevel models were used to assess the relationship between the accelerometer-measured ST and EMA-reported TV, videos, or video games. Given the zero-inflated distribution of MVPA, 2-part models were used assess the relationship between the accelerometer-measured MVPA and EMA-reported sports or exercise. Results: EMA-reported TV, videos, or video games were associated with a greater accelerometer-measured ST (beta=7.3, 95% CI 5.5 to 9.0, P<.001). This relationship was stronger in boys (beta=9.9, 95% CI 7.2 to 12.6, P<.001) than that in girls (beta=4.9, 95% CI 2.6 to 7.2, P≤.001). EMA-reported sports or exercise was associated with a greater accelerometer-measured MVPA (zero portion P<.001; positive portion P<.001). This relationship was stronger on weekends, in older children, and in non-Hispanic children (zero portion all P values<.001; positive portion all P values<.001). Conclusions: EMA reports highly relate to accelerometer measures. However, the differences in the strength of association depending on various demographic characteristics suggest that future research should use both EMA and accelerometers to measure activity to collect complementary activity data. %M 29954723 %R 10.2196/mhealth.9592 %U http://mhealth.jmir.org/2018/6/e150/ %U https://doi.org/10.2196/mhealth.9592 %U http://www.ncbi.nlm.nih.gov/pubmed/29954723 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e10409 %T Electronic 12-Hour Dietary Recall (e-12HR): Comparison of a Mobile Phone App for Dietary Intake Assessment With a Food Frequency Questionnaire and Four Dietary Records %A Béjar,Luis María %A Reyes,Óscar Adrián %A García-Perea,María Dolores %+ Department of Preventive Medicine and Public Health, School of Medicine, University of Seville, Institute of Anatomy, 3rd Floor, Sánchez-Pizjuán Avenue, Seville, 41009, Spain, 34 954551771, lmbprado@us.es %K dietary assessment %K food frequency questionnaire %K 24-hour dietary recalls %K dietary record %K mobile phone app %D 2018 %7 15.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: One of the greatest challenges in nutritional epidemiology is improving upon traditional self-reporting methods for the assessment of habitual dietary intake. Objective: The aim of this study was to evaluate the relative validity of a new method known as the current-day dietary recall (or current-day recall), based on a smartphone app called 12-hour dietary recall, for determining the habitual intake of a series of key food and drink groups using a food frequency questionnaire (FFQ) and four dietary records as reference methods. Methods: University students over the age of 18 years recorded their consumption of certain groups of food and drink using 12-hour dietary recall for 28 consecutive days. During this 28-day period, they also completed four dietary records on randomly selected days. Once the monitoring period was over, subjects then completed an FFQ. The two methods were compared using the Spearman correlation coefficient (SCC), a cross-classification analysis, and weighted kappa. Results: A total of 87 participants completed the study (64% women, 56/87; 36% men, 31/87). For e-12HR versus FFQ, for all food and drink groups, the average SCC was 0.70. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 51.5%; exact agreement + adjacent was 91.8%, and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.51. For e-12HR versus the four dietary records, for all food and drink groups, the average SCC was 0.63. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 47.1%; exact agreement + adjacent was 89.2%; and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.47. Conclusions: Current-day recall, based on the 12-hour dietary recall app, was found to be in good agreement with the two reference methods (FFQ & four dietary records), demonstrating its potential usefulness for categorizing individuals according to their habitual dietary intake of certain food and drink groups. %M 29907555 %R 10.2196/10409 %U http://mhealth.jmir.org/2018/6/e10409/ %U https://doi.org/10.2196/10409 %U http://www.ncbi.nlm.nih.gov/pubmed/29907555 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e142 %T Tobacco-Smoking, Alcohol-Drinking, and Betel-Quid-Chewing Behaviors: Development and Use of a Web-Based Survey System %A Hsu,Kuo-Yao %A Tsai,Yun-Fang %A Huang,Chu-Ching %A Yeh,Wen-Ling %A Chang,Kai-Ping %A Lin,Chen-Chun %A Chen,Ching-Yen %A Lee,Hsiu-Lan %+ School of Nursing, College of Medicine, Chang Gung University, 259, Wen-Hwa 1st Road, Tao-Yuan, 333, Taiwan, 886 32118800 ext 3204, yftsai@mail.cgu.edu.tw %K tobacco smoking %K alcohol drinking %K betel-quid chewing %K Web-based survey system %D 2018 %7 11.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smoking tobacco, drinking alcohol, and chewing betel quid are health-risk behaviors for several diseases, such as cancer, cardiovascular disease, and diabetes, with severe impacts on health. However, health care providers often have limited time to assess clients’ behaviors regarding smoking tobacco, drinking alcohol, and chewing betel quid and intervene, if needed. Objective: The objective of this study was to develop a Web-based survey system; determine the rates of tobacco-smoking, alcohol-drinking, and betel-quid-chewing behaviors; and estimate the efficiency of the system (time to complete the survey). Methods: Patients and their family members or friends were recruited from gastrointestinal medical–surgical, otolaryngology, orthopedics, and rehabilitation clinics or wards at a medical center in northern Taiwan. Data for this descriptive, cross-sectional study were extracted from a large series of research studies. A Web-based survey system was developed using a Linux, Apache, MySQL, PHP stack solution. The Web survey was set up to include four questionnaires: the Chinese-version Fagerstrom Tolerance Questionnaire, the Chinese-version Alcohol Use Disorders Identification Test, the Betel Nut Dependency Scale, and a sociodemographic form with several chronic diseases. After the participants completed the survey, the system automatically calculated their score, categorized their risk level for each behavior, and immediately presented and explained their results. The system also recorded the time each participant took to complete the survey. Results: Of 782 patient participants, 29.6% were addicted to nicotine, 13.3% were hazardous, harmful, or dependent alcohol drinkers, and 1.5% were dependent on chewing betel quid. Of 425 family or friend participants, 19.8% were addicted to nicotine, 5.6% were hazardous, harmful, or dependent alcohol drinkers, and 0.9% were dependent on chewing betel quid. Regarding the mean time to complete the survey, patients took 7.9 minutes (SD 3.0; range 3-20) and family members or friends took 7.7 minutes (SD 2.8; range 3-18). Most of the participants completed the survey within 5-10 minutes. Conclusions: The Web-based survey was easy to self-administer. Health care providers can use this Web-based survey system to save time in assessing these risk behaviors in clinical settings. All smokers had mild-to-severe nicotine addiction, and 5.6%-12.3% of patients and their family members or friends were at risk of alcohol dependence. Considering that these three behaviors, particularly in combination, dramatically increase the risk of esophageal cancer, appropriate and convenient interventions are necessary for preserving public health in Taiwan. %M 29891467 %R 10.2196/mhealth.9783 %U http://mhealth.jmir.org/2018/6/e142/ %U https://doi.org/10.2196/mhealth.9783 %U http://www.ncbi.nlm.nih.gov/pubmed/29891467 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 6 %P e210 %T Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study %A Sano,Akane %A Taylor,Sara %A McHill,Andrew W %A Phillips,Andrew JK %A Barger,Laura K %A Klerman,Elizabeth %A Picard,Rosalind %+ Affective Computing Group, Media Lab, Massachusetts Institute of Technology, 75 Amherst Street, Cambridge, MA, 02139, United States, 1 6178999468, akanes@media.mit.edu %K mobile health %K mood %K machine learning %K wearable electronic devices %K smartphone %K mobile phone %K mental health %K psychological stress %D 2018 %7 08.06.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. Objective: We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. Methods: We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures. Results: We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification. Conclusions: New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping. %M 29884610 %R 10.2196/jmir.9410 %U http://www.jmir.org/2018/6/e210/ %U https://doi.org/10.2196/jmir.9410 %U http://www.ncbi.nlm.nih.gov/pubmed/29884610 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e136 %T A New Influenza-Tracking Smartphone App (Flu-Report) Based on a Self-Administered Questionnaire: Cross-Sectional Study %A Fujibayashi,Kazutoshi %A Takahashi,Hiromizu %A Tanei,Mika %A Uehara,Yuki %A Yokokawa,Hirohide %A Naito,Toshio %+ Department of General Medicine, School of Medicine, Juntendo University, 3-1-3, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3 5802 1190, kfujiba@juntendo.ac.jp %K influenza %K epidemiology %K pandemics %K internet %K participatory surveillance %K participatory epidemiology %D 2018 %7 06.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Influenza infections can spread rapidly, and influenza outbreaks are a major public health concern worldwide. Early detection of signs of an influenza pandemic is important to prevent global outbreaks. Development of information and communications technologies for influenza surveillance, including participatory surveillance systems involving lay users, has recently increased. Many of these systems can estimate influenza activity faster than the conventional influenza surveillance systems. Unfortunately, few of these influenza-tracking systems are available in Japan. Objective: This study aimed to evaluate the flu-tracking ability of Flu-Report, a new influenza-tracking mobile phone app that uses a self-administered questionnaire for the early detection of influenza activity. Methods: Flu-Report was used to collect influenza-related information (ie, dates on which influenza infections were diagnosed) from November 2016 to March 2017. Participants were adult volunteers from throughout Japan, who also provided information about their cohabiting family members. The utility of Flu-Report was evaluated by comparison with the conventional influenza surveillance information and basic information from an existing large-scale influenza-tracking system (an automatic surveillance system based on electronic records of prescription drug purchases). Results: Information was obtained through Flu-Report for approximately 10,094 volunteers. In total, 2134 participants were aged <20 years, 6958 were aged 20-59 years, and 1002 were aged ≥60 years. Between November 2016 and March 2017, 347 participants reported they had influenza or an influenza-like illness in the 2016 season. Flu-Report-derived influenza infection time series data displayed a good correlation with basic information obtained from the existing influenza surveillance system (rho, ρ=.65, P=.001). However, the influenza morbidity ratio for our participants was approximately 25% of the mean influenza morbidity ratio for the Japanese population. The Flu-Report influenza morbidity ratio was 5.06% (108/2134) among those aged <20 years, 3.16% (220/6958) among those aged 20-59 years, and 0.59% (6/1002) among those aged ≥60 years. In contrast, influenza morbidity ratios for Japanese individuals aged <20 years, 20-59 years, and ≥60 years were recently estimated at 31.97% to 37.90%, 8.16% to 9.07%, and 2.71% to 4.39%, respectively. Conclusions: Flu-Report supports easy access to near real-time information about influenza activity via the accumulation of self-administered questionnaires. However, Flu-Report users may be influenced by selection bias, which is a common issue associated with surveillance using information and communications technologies. Despite this, Flu-Report has the potential to provide basic data that could help detect influenza outbreaks. %R 10.2196/mhealth.9834 %U http://mhealth.jmir.org/2018/6/e136/ %U https://doi.org/10.2196/mhealth.9834 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e10422 %T Implementing Systematically Collected User Feedback to Increase User Retention in a Mobile App for Self-Management of Low Back Pain: Retrospective Cohort Study %A Clement,Innocent %A Lorenz,Andreas %A Ulm,Bernhard %A Plidschun,Anne %A Huber,Stephan %+ Kaia Health Software, Infanteriestr 11a, Munich, D-80797, Germany, 49 89 20207057, stephan@kaia-health.com %K low back pain %K app %K mHealth %K retrospective cohort study %K self-management %K user feedback %K quality management %K usability %D 2018 %7 06.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Promising first results for Kaia, a mobile app digitalizing multidisciplinary rehabilitation for low back pain, were recently published. It remains unclear whether the implementation of user feedback in an updated version of this app leads to desired effects in terms of increased app usage and clinical outcomes. Objective: The aim is to elucidate the effect on user retention and clinical outcomes of an updated version of the Kaia app where user feedback was included during development. Methods: User feedback of the initial app versions (0.x) was collected in a quality management system and systematically analyzed to define requirements of a new version. For this study, the anonymized data of Kaia users was analyzed retrospectively and users were grouped depending on the available version at the time of the sign-up (0.x vs 1.x). The effect on the duration of activity of users in the app, the number of completed exercises of each type, and user-reported pain levels were compared. Results: Overall, data of 1251 users fulfilled the inclusion criteria, of which 196 users signed up using version 0.x and 1055 users signed up with version 1.x. There were significant differences in the demographic parameters for both groups. A log-rank test showed no significant differences for the duration of activity in the app between groups (P=.31). Users signing up during availability of the 1.x version completed significantly more exercises of each type in the app (physical exercises: 0.x mean 1.99, SD 1.61 units/week vs 1.x mean 3.15, SD1.72 units/week; P<.001; mindfulness exercises: 0.x mean 1.36, SD 1.43 units/week vs 1.x mean 2.42, SD 1.82 units/week; P<.001; educational content: 0.x mean 1.51, SD 1.42 units/week vs 1.x mean 2.71, SD 1.89 units/week; P<.001). This translated into a stronger decrease in user-reported pain levels in versions 1.x (F1,1233=7.084, P=.008). Conclusions: Despite the limitations of retrospective cohort studies, this study indicates that the implementation of systematically collected user feedback during development of updated versions can contribute to improvements in terms of frequency of use and potentially even clinical endpoints such as pain level. The clinical efficiency of the Kaia app needs to be validated in prospective controlled trials to exclude bias. %R 10.2196/10422 %U http://mhealth.jmir.org/2018/6/e10422/ %U https://doi.org/10.2196/10422 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e131 %T Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes %A Weatherall,James %A Paprocki,Yurek %A Meyer,Theresa M %A Kudel,Ian %A Witt,Edward A %+ Kantar Health, 700 Dresher Road, Suite 200, Horsham, PA, 19044, United States, 1 484 442 1415, Theresa.Meyer@kantarhealth.com %K Fitbit charge HR %K type 2 diabetes mellitus %K sleep %K health outcomes %K health behaviors %D 2018 %7 05.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist. Objective: The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM). Methods: This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating. Results: The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02). Conclusions: Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes. %R 10.2196/mhealth.8122 %U http://mhealth.jmir.org/2018/6/e131/ %U https://doi.org/10.2196/mhealth.8122 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 5 %P e127 %T Designing a Tablet-Based Software App for Mapping Bodily Symptoms: Usability Evaluation and Reproducibility Analysis %A Neubert,Till-Ansgar %A Dusch,Martin %A Karst,Matthias %A Beissner,Florian %+ Somatosensory and Autonomic Therapy Research, Institute for Diagnostic and Interventional Neuroradiology, Hannover Medical School, Carl-Neuberg-Strasse 1, Hannover, 30625, Germany, 49 511 53508413, beissner.florian@mh-hannover.de %K pain drawing %K symptom drawing %K body outline %K usability testing %K reproducibility %K tablet computers %K eHealth %K app %K chronic pain %D 2018 %7 30.05.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Symptom drawings are widely used as a qualitative and quantitative method of assessing pain symptoms for both clinical and research purposes. As electronic drawings offer many advantages over classical pen-and-paper drawings, the last years have seen a shift toward tablet-based acquisition of symptom drawings. However, software that is used in clinical care requires special attention to usability aspects and design to provide easy access for physically impaired or elderly patients. Objective: The aims of this project were to develop a new tablet-based software app specifically designed to collect patients’ and doctors’ drawings of pain and related bodily symptoms and test it for usability in 2 samples of chronic pain patients (Aim 1) and their treating doctors (Aim 2) as well as for test-retest reliability (Aim 3). Methods: In 2 separate studies, symptom drawings from 103 chronic pain patients and their treating doctors were collected using 2 different versions of the app. Both patients and doctors evaluated usability aspects of the app through questionnaires. Results from study 1 were used to improve certain features of the app, which were then evaluated in study 2. Furthermore, a subgroup of 25 patients in study 2 created 2 consecutive symptom drawings for test-retest reproducibility analysis. Usability of both app versions was compared, and reproducibility was calculated for symptom extent, number of symptom clusters, and the whole symptom pattern. Results: The changes we made to the app and the body outline led to significant improvements in patients’ usability evaluation regarding the identification with the body outline (P=.007) and the evaluation of symptom depth (P=.02), and the overall difficultness of the drawing process (P=.003) improved significantly. Doctors’ usability evaluation of the final app showed good usability with 75.63 (SD 19.51) points on the System Usability Scale, Attrakdiff 2 scores from 0.93 to 1.41, and ISONORM 9241/10 scores from −0.05 to 1.80. Test-retest analysis showed excellent reproducibility for pain extent (intraclass correlation coefficient, ICC=0.92) and good results for the number of symptom clusters (ICC=0.70) and a mean overlap of 0.47 (Jaccard index). Conclusions: We developed a tablet-based symptom drawing app and improved it based on usability assessment in a sample of chronic pain patients and their treating doctors. Increases in usability of the improved app comprised identification with the body outline, symptom depth evaluation, and difficultness of the drawing process. Test-retest reliability of symptom drawings by chronic pain patients showed fair to excellent reproducibility. Patients’ usability evaluation is an important factor that should not be neglected when designing apps for mobile or eHealth apps. %R 10.2196/mhealth.8409 %U http://mhealth.jmir.org/2018/5/e127/ %U https://doi.org/10.2196/mhealth.8409 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 2 %P e38 %T Temporal Associations Between Social Activity and Mood, Fatigue, and Pain in Older Adults With HIV: An Ecological Momentary Assessment Study %A Paolillo,Emily W %A Tang,Bin %A Depp,Colin A %A Rooney,Alexandra S %A Vaida,Florin %A Kaufmann,Christopher N %A Mausbach,Brent T %A Moore,David J %A Moore,Raeanne C %+ Department of Psychiatry, University of California, San Diego, 220 Dickinson Street, Suite B, San Diego, CA, 92103, United States, 1 619 543 5378, r6moore@ucsd.edu %K AIDS %K ecological momentary assessment %K social isolation %K happiness %K quality of life %D 2018 %7 14.05.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: Social isolation is associated with an increased risk for mental and physical health problems, especially among older persons living with HIV (PLWH). Thus, there is a need to better understand real-time temporal associations between social activity and mood- and health-related factors in this population to inform possible future interventions. Objective: This study aims to examine real-time relationships between social activity and mood, fatigue, and pain in a sample of older PLWH. Methods: A total of 20 older PLWH, recruited from the University of California, San Diego HIV Neurobehavioral Research Program in 2016, completed smartphone-based ecological momentary assessment (EMA) surveys 5 times per day for 1 week. Participants reported their current social activity (alone vs not alone and number of social interactions) and levels of mood (sadness, happiness, and stress), fatigue, and pain. Mixed-effects regression models were used to analyze concurrent and lagged associations among social activity, mood, fatigue, and pain. Results: Participants (mean age 58.8, SD 4.3 years) reported being alone 63% of the time, on average, (SD 31.5%) during waking hours. Being alone was related to lower concurrent happiness (beta=−.300; 95% CI −.525 to −.079; P=.008). In lagged analyses, social activity predicted higher levels of fatigue later in the day (beta=−1.089; 95% CI −1.780 to −0.396; P=.002), and higher pain levels predicted being alone in the morning with a reduced likelihood of being alone as the day progressed (odds ratio 0.945, 95% CI 0.901-0.992; P=.02). Conclusions: The use of EMA elucidated a high rate of time spent alone among older PLWH. Promoting social activity despite the presence of pain or fatigue may improve happiness and psychological well-being in this population. %M 29759960 %R 10.2196/mental.9802 %U http://mental.jmir.org/2018/2/e38/ %U https://doi.org/10.2196/mental.9802 %U http://www.ncbi.nlm.nih.gov/pubmed/29759960 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 5 %P e115 %T Development and Validation of a Multidisciplinary Mobile Care System for Patients With Advanced Gastrointestinal Cancer: Interventional Observation Study %A Soh,Ji Yeong %A Cha,Won Chul %A Chang,Dong Kyung %A Hwang,Ji Hye %A Kim,Kihyung %A Rha,Miyong %A Kwon,Hee %+ Samsung Advanced Institute for Health Sciences & Technology, Department of Digital Health, Sungkyunkwan University, Gangnam-gu, 81, Irwon-ro, Seoul,, Republic Of Korea, 82 10 5386 6597, wc.cha@samsung.com %K mobile health %K health apps %K mobile phone %K mobile care system %D 2018 %7 07.05.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health apps have emerged as supportive tools in the management of advanced cancers. However, only a few apps have self-monitoring features, and they are not standardized and validated. Objective: This study aimed to develop and validate a multidisciplinary mobile care system with self-monitoring features that can be useful for patients with advanced gastrointestinal cancer. Methods: The development of the multidisciplinary mobile health management system was divided into 3 steps. First, the service scope was set up, and the measurement tools were standardized. Second, the service flow of the mobile care system was organized. Third, the mobile app (Life Manager) was developed. The app was developed to achieve 3 major clinical goals: support for quality of life, nutrition, and rehabilitation. Three main functional themes were developed to achieve clinical goals: a to-do list, health education, and in-app chat. Thirteen clinically oriented measures were included: the modified Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events questionnaire, Scored Patient-Generated Subjective Global Assessment (PG-SGA), distress, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, International Physical Activity Questionnaire–Short Form, Low anterior resection syndrome score, satisfaction rate, etc. To validate the system, a prospective observational study was conducted. Patients with gastric cancer or colon cancer undergoing chemotherapy were recruited. We followed the subjects for 12 weeks, and selected clinical measures were taken online and offline. Results: After the development process, a multidisciplinary app, the Life Manager, was launched. For evaluation, 203 patients were recruited for the study, of whom 101 (49.8%) had gastric cancer, and 102 (50.2%) were receiving palliative care. Most patients were in their fifties (35.5%), and 128 (63.1%) were male. Overall, 176 subjects (86.7%) completed the study. Among subjects who dropped out, the most common reason was the change of patient’s clinical condition (51.9%). During the study period, subjects received multiple health education sessions. For the gastric cancer group, the “general gastric cancer education” was most frequently viewed (322 times), and for the colon cancer group, the “warming-up exercise” was most viewed (340 times). Of 13 measurements taken from subjects, 9 were taken offline (response rate: 52.0% to 90.1%), and 3 were taken online (response rate: 17.6% to 57.4%). The overall satisfaction rate among subjects was favorable and ranged from 3.93 (SD 0.88) to 4.01 (SD 0.87) on the 5-point Likert scale. Conclusions: A multidisciplinary mobile care system for patients with advanced gastrointestinal cancer was developed with clinically oriented measures. A prospective study was performed for its evaluation, which showed favorable satisfaction. %M 29735478 %R 10.2196/mhealth.9363 %U http://mhealth.jmir.org/2018/5/e115/ %U https://doi.org/10.2196/mhealth.9363 %U http://www.ncbi.nlm.nih.gov/pubmed/29735478 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 4 %P e102 %T Capturing Ultraviolet Radiation Exposure and Physical Activity: Feasibility Study and Comparison Between Self-Reports, Mobile Apps, Dosimeters, and Accelerometers %A Hacker,Elke %A Horsham,Caitlin %A Allen,Martin %A Nathan,Andrea %A Lowe,John %A Janda,Monika %+ Institute of Health and Biomedical Innovation, School of Public Health and Social Work, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, Brisbane, 4059, Australia, 61 7 3138 9674, elke.hacker@qut.edu.au %K sun-protection %K sunburn %K health behaviour %K health promotion %K formative research %D 2018 %7 17.04.2018 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Skin cancer is the most prevalent cancer in Australia. Skin cancer prevention programs aim to reduce sun exposure and increase sun protection behaviors. Effectiveness is usually assessed through self-report. Objective: It was the aim of this study to test the acceptance and validity of a newly developed ultraviolet radiation (UVR) exposure app, designed to reduce the data collection burden to research participants. Physical activity data was collected because a strong focus on sun avoidance may result in unhealthy reductions in physical activity. This paper provides lessons learned from collecting data from participants using paper diaries, a mobile app, dosimeters, and accelerometers for measuring end-points of UVR exposure and physical activity. Methods: Two participant groups were recruited through social and traditional media campaigns 1) Group A—UVR Diaries and 2) Group B—Physical Activity. In Group A, nineteen participants wore an UVR dosimeter wristwatch (University of Canterbury, New Zealand) when outside for 7 days. They also recorded their sun exposure and physical activity levels using both 1) the UVR diary app and 2) a paper UVR diary. In Group B, 55 participants wore an accelerometer (Actigraph, Pensacola, FL, USA) for 14 days and completed the UVR diary app. Data from the UVR diary app were compared with UVR dosimeter wristwatch, accelerometer, and paper UVR diary data. Cohen kappa coefficient score was used to determine if there was agreement between categorical variables for different UVR data collection methods and Spearman rank correlation coefficient was used to determine agreement between continuous accelerometer data and app-collected self-report physical activity. Results: The mean age of participants in Groups A (n=19) and B (n=55) was 29.3 and 25.4 years, and 63% (12/19) and 75% (41/55) were females, respectively. Self-reported sun exposure data in the UVR app correlated highly with UVR dosimetry (κ=0.83, 95% CI 0.64-1.00, P<.001). Correlation between self-reported UVR app and accelerometer-collected moderate to vigorous physical activity data was low (ρ=0.23, P=.10), while agreement for low-intensity physical activity was significantly different (ρ=-0.49, P<.001). Seventy-nine percent of participants preferred the app over the paper diary for daily self-report of UVR exposure and physical activity. Conclusions: This feasibility study highlights self-report using an UVR app can reliably collect personal UVR exposure, but further improvements are required before the app can also be used to collect physical activity data. %M 29666044 %R 10.2196/resprot.9695 %U http://www.researchprotocols.org/2018/4/e102/ %U https://doi.org/10.2196/resprot.9695 %U http://www.ncbi.nlm.nih.gov/pubmed/29666044 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e83 %T Relationship Between Weekly Patterns of Caloric Intake and Reported Weight Loss Outcomes: Retrospective Cohort Study %A Hill,Christine %A Weir,Brian W %A Fuentes,Laura W %A Garcia-Alvarez,Alicia %A Anouti,Danya P %A Cheskin,Lawrence J %+ Johns Hopkins Bloomberg School of Public Health, Lerner Center for Public Health Promotion, Johns Hopkins University, 624 N Broadway, Room 904B, Baltimore, MD, 21205, United States, 1 410 502 1811, laura.w.fuentes@jhu.edu %K mobile apps %K weight reduction %K caloric restriction %K diet habits %D 2018 %7 16.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Although millions of overweight and obese adults use mobile phone apps for weight loss, little is known about the predictors of success. Objective: The objective of this study was to understand the relationship between weight loss outcomes and weekly patterns of caloric intake among overweight and obese adults using a mobile phone app for weight loss. Methods: We examined the relationship between weekly patterns of caloric intake and weight loss outcomes among adults who began using a weight loss app in January 2016 and continued consistent use for at least 5 months (N=7007). Unadjusted and adjusted linear regression analyses were used to evaluate the predictors of percentage of bodyweight lost for women and men separately, including age, body mass index category, weight loss plan, and difference in daily calories consumed on weekend days (Saturday and Sunday) versus Monday. Results: In adjusted linear regression, percentage of bodyweight lost was significantly associated with age (for women), body mass index (for men), weight loss plan, and differences in daily caloric intake on Mondays versus weekend days. Compared with women consuming at least 500 calories more on weekend days than on Mondays, those who consumed 50 to 250 calories more on weekend days or those with balanced consumption (±50 calories) lost 1.64% more and 1.82% more bodyweight, respectively. Women consuming 250 to 500 calories or more than 500 calories more on Mondays than on weekend days lost 1.35% more and 3.58% more bodyweight, respectively. Compared with men consuming at least 500 calories more on weekend days than on Mondays, those consuming 250 to 500 calories or more than 500 calories more on Mondays than on weekend days lost 2.27% and 3.42% less bodyweight, respectively. Conclusions: Consistent caloric intake on weekend days and Mondays or consuming slightly fewer calories per day on Mondays versus weekend days was associated with more successful weight loss. Trial Registration: ClinicalTrials.gov NCT03136692; https://clinicaltrials.gov/ct2/show/NCT03136692 (Archived by WebCite at http://www.webcitation.org/6y9JvHya4) %M 29661750 %R 10.2196/mhealth.8320 %U http://mhealth.jmir.org/2018/4/e83/ %U https://doi.org/10.2196/mhealth.8320 %U http://www.ncbi.nlm.nih.gov/pubmed/29661750 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 4 %P e84 %T A Novel Mobile App and Population Management System to Manage Rheumatoid Arthritis Flares: Protocol for a Randomized Controlled Trial %A Wang,Penny %A Luo,Dee %A Lu,Fengxin %A Elias,Josephine S %A Landman,Adam B %A Michaud,Kaleb D %A Lee,Yvonne C %+ Division of Rheumatology, Department of Medicine, Northwestern University Feinberg School of Medicine, Suite M-300, 240 East Huron Street, Chicago, IL, 60611, United States, 1 312 503 1960, yvonne.lee@northwestern.edu %K arthritis, rheumatoid %K symptom flare up %K telemedicine %K mobile applications %D 2018 %7 11.04.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Rheumatoid arthritis flares have a profound effect on patients, causing pain and disability. However, flares often occur between regularly scheduled health care provider visits and are, therefore, difficult to monitor and manage. We sought to develop a mobile phone app combined with a population management system to help track RA flares between visits. Objective: The objective of this study is to implement the mobile app plus the population management system to monitor rheumatoid arthritis disease activity between scheduled health care provider visits over a period of 6 months. Methods: This is a randomized controlled trial that lasts for 6 months for each participant. We aim to recruit 190 patients, randomized 50:50 to the intervention group versus the control group. The intervention group will be assigned the mobile app and be prompted to answer daily questionnaires sent to their mobile devices. Both groups will be assigned a population manager, who will communicate with the participants via telephone at 6 weeks and 18 weeks. The population manager will also communicate with the participants in the intervention group if their responses indicate a sustained increase in rheumatoid arthritis disease activity. To assess patient satisfaction, the primary outcomes will be scores on the Treatment Satisfaction Questionnaire for Medication as well as the Perceived Efficacy in Patient-Physician Interactions questionnaire at 6 months. To determine the effect of the mobile app on rheumatoid arthritis disease activity, the primary outcome will be the Clinical Disease Activity Index at 6 months. Results: The trial started in November 2016, and an estimated 2.5 years will be necessary to complete the study. Study results are expected to be published by the end of 2019. Conclusions: The completion of this study will provide important data regarding the following: (1) the assessment of validated outcome measures to assess rheumatoid arthritis disease activity with a mobile app between routinely scheduled health care provider visits, (2) patient engagement in monitoring their condition, and (3) communication between patients and health care providers through the population management system. Trial Registration: ClinicalTrials.gov NCT02822521, http://clinicaltrials.gov/ct2/show/NCT02822521 (Archived by WebCite at http://www.webcitation.org/6xed3kGPd) %M 29643053 %R 10.2196/resprot.8771 %U http://www.researchprotocols.org/2018/4/e84/ %U https://doi.org/10.2196/resprot.8771 %U http://www.ncbi.nlm.nih.gov/pubmed/29643053 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 2 %P e39 %T Predicting Sexual Behaviors Among Homeless Young Adults: Ecological Momentary Assessment Study %A Santa Maria,Diane %A Padhye,Nikhil %A Yang,Yijiong %A Gallardo,Kathryn %A Businelle,Michael %+ School of Nursing, University of Texas Health Science Center at Houston, 6901 Bertner Ave, Houston, TX, 77030, United States, 1 713 500 2187, diane.m.santamaria@uth.tmc.edu %K homeless youth %K sexual behaviors %K ecological momentary assessment %D 2018 %7 10.04.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Homeless youth continue to be disproportionately affected by HIV compared with their housed peers, with prevalence rates as high as 13%. Yet, HIV prevention in this high-risk population has been only marginally effective. Objective: The aim of this study was to use ecological momentary assessments to examine real-time factors to determine the predictors of sexual activity among homeless youth. Methods: Youth experiencing homelessness aged between 18 and 24 years were recruited from a drop-in center in Houston, Texas, between August 2015 and May 2016. All the participants received a study-issued mobile phone that prompted brief ecological momentary assessments (EMAs) 5 times a day for 21 days. EMA items assessed near real-time sexual behaviors, cognitions, stress, affect, environmental factors, and environmental circumstances. Results: Participants (N=66) were predominantly male (41/66, 64%) and black (43/66, 66%) with a median age of 20 years. The mean number of EMAs completed by each participant was 45 out of 105 possible observations. During the study, 70% (46/66) of participants were sexually active and reported condomless sex in 102 of the 137 cases of sexual intercourse (74.5%). In total, 82% (38/46) of the youth who reported having sex during the 3 weeks of data collection also reported engaging in high-risk sexual activities, including having condomless sex (24/46, 53%), having multiple sexual partners on the same day (12/46, 26%), trading sex (7/46, 16%), and sharing needles while injecting drugs (1/46, 3%). Of those, 71% (27/38) were engaged in multiple sexual risk behaviors. The predictive model was based on observations from 66 subjects who reported 137 cases of sexual intercourse over 811 days; sexual orientation, race, mental health, drug use, and sexual urge were included as predictors in the parsimonious generalized linear mixed model selected on the basis of the Akaike information criterion. The estimated odds ratios (ORs) were notable for same-day drug use (OR 8.80, 95% CI 4.48-17.31; P<.001) and sexual urge (OR 4.23, 95% CI 1.60-11.28; P=.004). The performance of the risk estimator was satisfactory, as indicated by the value of 0.834 for the area under the receiver operating characteristic curve. Conclusions: Real-time EMA data can be used to predict sexual intercourse among a sample of high-risk, predominately unsheltered homeless youth. Sexual urge and drug use accounts for increased odds of engaging in sexual activity on any given day. Interventions targeting sexual urge and drug use may help predict sexual activity among a population at high risk of HIV. %M 29636318 %R 10.2196/publichealth.9020 %U http://publichealth.jmir.org/2018/2/e39/ %U https://doi.org/10.2196/publichealth.9020 %U http://www.ncbi.nlm.nih.gov/pubmed/29636318 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e89 %T Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data %A Park,Yu Rang %A Lee,Yura %A Kim,Ji Young %A Kim,Jeonghoon %A Kim,Hae Reong %A Kim,Young-Hak %A Kim,Woo Sung %A Lee,Jae-Ho %+ Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic Of Korea, 82 23010 3350, rufiji@gmail.com %K personal health record %K mobile health %K patient engagement %K patient-generated health data %K health records, personal %K telemedicine %K patient participation %D 2018 %7 09.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Personal health records (PHRs) and mHealth apps are considered essential tools for patient engagement. Mobile PHRs (mPHRs) can be a platform to integrate patient-generated health data (PGHD) and patients’ medical information. However, in previous studies, actual usage data and PGHD from mPHRs have not been able to adequately represent patient engagement. Objective: By analyzing 5 years’ PGHD from an mPHR system developed by a tertiary hospital in South Korea, we aimed to evaluate how PGHD were managed and identify issues in PGHD management based on actual usage data. Additionally, we analyzed how to improve patient engagement with mPHRs by analyzing the actively used services and long-term usage patterns. Methods: We gathered 5 years (December 2010 to December 2015) of log data from both hospital patients and general users of the app. We gathered data from users who entered PGHD on body weight, blood pressure (BP), blood glucose levels, 10-year cardiovascular disease (CVD) risk, metabolic syndrome risk, medication schedule, insulin, and allergy. We classified users according to whether they were patients or general users based on factors related to continuous use (≥28 days for weight, BP, and blood glucose, and ≥180 days for CVD and metabolic syndrome), and analyzed the patients’ characteristics. We compared PGHD entry counts and the proportion of continuous users for each PGHD by user type. Results: The total number of mPHR users was 18,265 (patients: n=16,729, 91.59%) with 3620 users having entered weight, followed by BP (n=1625), blood glucose (n=1374), CVD (n=764), metabolic syndrome (n=685), medication (n=252), insulin (n=72), and allergy (n=61). Of those 18,256 users, 3812 users had at least one PGHD measurement, of whom 175 used the PGHD functions continuously (patients: n=142, 81.14%); less than 1% of the users had used it for more than 4 years. Except for weight, BP, blood glucose, CVD, and metabolic syndrome, the number of PGHD records declined. General users’ continuous use of PGHD was significantly higher than that of patients in the blood glucose (P<.001) and BP (P=.03) functions. Continuous use of PGHD in health management (BP, blood glucose, and weight) was significantly greater among older users (P<.001) and men (P<.001). In health management (BP, weight, and blood glucose), overall chronic disease and continuous use of PGHD were not statistically related (P=.08), but diabetes (P<.001) and cerebrovascular diseases (P=.03) were significant. Conclusions: Although a small portion of users managed PGHD continuously, PGHD has the potential to be useful in monitoring patient health. To realize the potential, specific groups of continuous users must be identified, and the PGHD service must target them. Further evaluations for the clinical application of PGHD, feedback regarding user interfaces, and connections with wearable devices are needed. %M 29631989 %R 10.2196/mhealth.9620 %U http://mhealth.jmir.org/2018/4/e89/ %U https://doi.org/10.2196/mhealth.9620 %U http://www.ncbi.nlm.nih.gov/pubmed/29631989 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e80 %T Perceptions of Adolescents With Cancer Related to a Pain Management App and Its Evaluation: Qualitative Study Nested Within a Multicenter Pilot Feasibility Study %A Jibb,Lindsay A %A Stevens,Bonnie J %A Nathan,Paul C %A Seto,Emily %A Cafazzo,Joseph A %A Johnston,Donna L %A Hum,Vanessa %A Stinson,Jennifer N %+ School of Nursing, Faculty of Health Sciences, University of Ottawa, Roger Guindon Hall, 451 Smyth Rd, Ottawa, ON, K1H8M, Canada, 1 613 562 5800 ext 4253, ljibb@uottawa.ca %K pain %K adolescent %K cancer %K supportive care %K mHealth %K qualitative %D 2018 %7 06.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Pain in adolescents with cancer is common and negatively impacts health-related quality of life. The Pain Squad+ smartphone app, capable of providing adolescents with real-time pain management support, was developed to enhance pain management using a phased approach (ie, systematic review, consensus conference and vetting, iterative usability testing cycles). A 28-day Pain Squad+ pilot was conducted with 40 adolescents with cancer to evaluate the feasibility of implementing the app in a future clinical trial and to obtain estimates of treatment effect. Objective: The objective of our nested qualitative study was to elucidate the perceptions of adolescents with cancer to determine the acceptability and perceived helpfulness of Pain Squad+, suggestions for app improvement, and satisfaction with the pilot study protocol. Methods: Post pilot study participation, telephone-based, semistructured, and audio-recorded exit interviews were conducted with 20 adolescents with cancer (12-18 years). All interviews were transcribed and independently coded by 2 study team members. Content analysis was conducted to identify data categories and overarching themes. Results: Five major themes comprising multiple categories and codes emerged. These themes focused on the acceptability of the intervention, acceptability of the study, the perceived active ingredients of the intervention, the suitability of the intervention to adolescents’ lives, and recommendations for intervention improvement. Conclusions: Overall, Pain Squad+ and the pilot study protocol were acceptable to adolescents with cancer. Suggestions for intervention and study improvements will be incorporated into the design of a future randomized clinical trial (RCT) aimed at assessing the effectiveness of Pain Squad+ on adolescents with cancer health outcomes. %M 29625951 %R 10.2196/mhealth.9319 %U http://mhealth.jmir.org/2018/4/e80/ %U https://doi.org/10.2196/mhealth.9319 %U http://www.ncbi.nlm.nih.gov/pubmed/29625951 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e79 %T Management of the General Process of Parenteral Nutrition Using mHealth Technologies: Evaluation and Validation Study %A Cervera Peris,Mercedes %A Alonso Rorís,Víctor Manuel %A Santos Gago,Juan Manuel %A Álvarez Sabucedo,Luis %A Wanden-Berghe,Carmina %A Sanz-Valero,Javier %+ Department of Public Health and History of Science, School of Medicine, Miguel Hernandez University, Campus Sant Joan d'Alacant, Alicante,, Spain, 34 666 840 787, jsanz@umh.es %K parenteral nutrition %K mobile apps %K quality control %K validation software %D 2018 %7 03.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Any system applied to the control of parenteral nutrition (PN) ought to prove that the process meets the established requirements and include a repository of records to allow evaluation of the information about PN processes at any time. Objective: The goal of the research was to evaluate the mobile health (mHealth) app and validate its effectiveness in monitoring the management of the PN process. Methods: We studied the evaluation and validation of the general process of PN using an mHealth app. The units of analysis were the PN bags prepared and administered at the Son Espases University Hospital, Palma, Spain, from June 1 to September 6, 2016. For the evaluation of the app, we used the Poststudy System Usability Questionnaire and subsequent analysis with the Cronbach alpha coefficient. Validation was performed by checking the compliance of control for all operations on each of the stages (validation and transcription of the prescription, preparation, conservation, and administration) and by monitoring the operative control points and critical control points. Results: The results obtained from 387 bags were analyzed, with 30 interruptions of administration. The fulfillment of stages was 100%, including noncritical nonconformities in the storage control. The average deviation in the weight of the bags was less than 5%, and the infusion time did not present deviations greater than 1 hour. Conclusions: The developed app successfully passed the evaluation and validation tests and was implemented to perform the monitoring procedures for the overall PN process. A new mobile solution to manage the quality and traceability of sensitive medicines such as blood-derivative drugs and hazardous drugs derived from this project is currently being deployed. %M 29615389 %R 10.2196/mhealth.9896 %U http://mhealth.jmir.org/2018/4/e79/ %U https://doi.org/10.2196/mhealth.9896 %U http://www.ncbi.nlm.nih.gov/pubmed/29615389 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 1 %N 1 %P e2 %T Sex Similarities in Postoperative Recovery and Health Care Contacts Within 14 Days With mHealth Follow-Up: Secondary Analysis of a Randomized Controlled Trial %A Jaensson,Maria %A Dahlberg,Karuna %A Nilsson,Ulrica %+ School of Health Sciences, Faculty of Medicine and Health, Örebro University, Fakultetsgatan 1, Örebro, 701 82, Sweden, 46 19303000, maria.jaensson@oru.se %K sex %K mHealth %K telemedicine %K mobile phone %K cell phone %K patient outcome assessment %K postoperative complications %K postoperative period %D 2018 %7 26.03.2018 %9 Original Paper %J JMIR Perioper Med %G English %X Background: Previous studies have shown that women tend to have a poorer postanesthesia recovery than men. Our research group has developed a mobile phone app called Recovery Assessment by Phone Points (RAPP) that includes the Swedish Web version of the Quality of Recovery (SwQoR) questionnaire to monitor and assess postoperative recovery. Objective: The aim of this study was to investigate sex differences in postoperative recovery and the number of health care contacts within 14 postoperative days in a cohort of day-surgery patients using RAPP. Methods: This study was a secondary analysis from a single-blind randomized controlled trial. Therefore, we did not calculate an a priori sample size regarding sex differences. We conducted the study at 4 day-surgery settings in Sweden from October 2015 to July 2016. Included were 494 patients (220 male and 274 female participants) undergoing day surgery. The patients self-assessed their postoperative recovery for 14 postoperative days using the RAPP. Results: There were no significant sex differences in postoperative recovery or the number of health care contacts. Subgroup analysis showed that women younger than 45 years reported significantly higher global scores in the SwQoR questionnaire (hence a poorer recovery) on postoperative days 1 to 10 than did women who were 45 years of age or older (P=.001 to P=.008). Men younger than 45 years reported significantly higher global scores on postoperative days 2 to 6 than did men 45 years of age or older (P=.001 to P=.006). Sex differences in postoperative recovery were not significant between the age groups. Conclusions: This study found sex similarities in postoperative recovery and the number of health care contacts. However, subgroup analysis showed that age might be an independent factor for poorer recovery in both women and men. This knowledge can be used when informing patients what to expect after discharge. Trial Registration: ClinicalTrials.gov NCT02492191; https://clinicaltrials.gov/ct2/show/NCT02492191 (Archived by WebCite at http://www.webcitation.org/6y2UtMbvz) %M 33401367 %R 10.2196/periop.9874 %U http://periop.jmir.org/2018/1/e2/ %U https://doi.org/10.2196/periop.9874 %U http://www.ncbi.nlm.nih.gov/pubmed/33401367 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 3 %P e110 %T Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables %A Henriksen,André %A Haugen Mikalsen,Martin %A Woldaregay,Ashenafi Zebene %A Muzny,Miroslav %A Hartvigsen,Gunnar %A Hopstock,Laila Arnesdatter %A Grimsgaard,Sameline %+ Department of Community Medicine, University of Tromsø – The Arctic University of Norway, Postboks 6050 Langnes, Tromsø, 9037, Norway, 47 77644000, andre.henriksen@uit.no %K motor activity %K physical activity %K fitness trackers %K heart rate %K photoplethysmography %D 2018 %7 22.03.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. Objective: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. Methods: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. Results: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. Conclusions: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated. %M 29567635 %R 10.2196/jmir.9157 %U http://www.jmir.org/2018/3/e110/ %U https://doi.org/10.2196/jmir.9157 %U http://www.ncbi.nlm.nih.gov/pubmed/29567635 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 3 %P e50 %T Reliability of Self-Reported Mobile Phone Ownership in Rural North-Central Nigeria: Cross-Sectional Study %A Menson,William Nii Ayitey %A Olawepo,John Olajide %A Bruno,Tamara %A Gbadamosi,Semiu Olatunde %A Nalda,Nannim Fazing %A Anyebe,Victor %A Ogidi,Amaka %A Onoka,Chima %A Oko,John Okpanachi %A Ezeanolue,Echezona Edozie %+ Global Health Initiative, School of Community Health Sciences, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, Las Vegas, NV, 89154, United States, 1 443 682 5034, william.menson@unlv.edu %K reliability %K phone ownership %K resource-limited setting %K cell phone use %K rural population %K developing countries %K self report %K Nigeria %K telemedicine %D 2018 %7 01.03.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: mHealth practitioners seek to leverage the ubiquity of the mobile phone to increase the impact and robustness of their interventions, particularly in resource-limited settings. However, data on the reliability of self-reported mobile phone access is minimal. Objective: We sought to ascertain the reliability of self-reported ownership of and access to mobile phones among a population of rural dwellers in north-central Nigeria. Methods: We contacted participants in a community-based HIV testing program by phone to determine actual as opposed to self-reported mobile phone access. A phone script was designed to conduct these calls and descriptive analyses conducted on the findings. Results: We dialed 349 numbers: 110 (31.5%) were answered by participants who self-reported ownership of the mobile phone; 123 (35.2%) of the phone numbers did not ring at all; 28 (8.0%) rang but were not answered; and 88 (25.2%) were answered by someone other than the participant. We reached a higher proportion of male participants (68/133, 51.1%) than female participants (42/216, 19.4%; P<.001). Conclusions: Self-reported access to mobile phones in rural and low-income areas in north-central Nigeria is higher than actual access. This has implications for mHealth programming, particularly for women’s health. mHealth program implementers and researchers need to be cognizant of the low reliability of self-reported mobile phone access. These observations should therefore affect sample-size calculations and, where possible, alternative means of reaching research participants and program beneficiaries should be established. %M 29496656 %R 10.2196/mhealth.8760 %U https://mhealth.jmir.org/2018/3/e50/ %U https://doi.org/10.2196/mhealth.8760 %U http://www.ncbi.nlm.nih.gov/pubmed/29496656 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 2 %P e49 %T Cardiac Auscultation Using Smartphones: Pilot Study %A Kang,Si-Hyuck %A Joe,Byunggill %A Yoon,Yeonyee %A Cho,Goo-Yeong %A Shin,Insik %A Suh,Jung-Won %+ Division of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, 13620, Republic Of Korea, 82 31 787 7016, suhjw1@gmail.com %K cardiac auscultation %K physical examination %K smartphone %K mobile health care %K telemedicine %D 2018 %7 28.02.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits. Objective: The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage. Methods: We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values. Results: A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices. Conclusions: Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge. Trial Registration: ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu) %M 29490899 %R 10.2196/mhealth.8946 %U http://mhealth.jmir.org/2018/2/e49/ %U https://doi.org/10.2196/mhealth.8946 %U http://www.ncbi.nlm.nih.gov/pubmed/29490899 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 1 %P e15 %T A Mobile Health Platform for Clinical Monitoring in Early Psychosis: Implementation in Community-Based Outpatient Early Psychosis Care %A Kumar,Divya %A Tully,Laura M %A Iosif,Ana-Maria %A Zakskorn,Lauren N %A Nye,Kathleen E %A Zia,Aqsa %A Niendam,Tara Ann %+ Department of Psychiatry and Behavioral Sciences, University of California, Davis, 4701 X Street, Sacramento, CA, 95817, United States, 1 916 734 7927, lmtully@ucdavis.edu %K mHealth %K schizophrenia %K smartphone %K ecological momentary assessment %K experience sampling %D 2018 %7 27.02.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: A growing body of literature indicates that smartphone technology is a feasible add-on tool in the treatment of individuals with early psychosis (EP) . However, most studies to date have been conducted independent of outpatient care or in a research clinic setting, often with financial incentives to maintain user adherence to the technology. Feasibility of dissemination and implementation of smartphone technology into community mental health centers (CMHCs) has yet to be tested, and whether young adults with EP will use this technology for long periods of time without incentive is unknown. Furthermore, although EP individuals willingly adopt smartphone technology as part of their treatment, it remains unclear whether providers are amenable to integrating smartphone technology into treatment protocols. Objective: This study aimed to establish the feasibility of implementing a smartphone app and affiliated Web-based dashboard in 4 community outpatient EP clinics in Northern California. Methods: EP individuals in 4 clinics downloaded an app on their smartphone and responded to daily surveys regarding mood and symptoms for up to 5 months. Treatment providers at the affiliated clinics viewed survey responses on a secure Web-based dashboard in sessions with their clients and between appointments. EP clients and treatment providers filled out satisfaction surveys at study end regarding usability of the app. Results: Sixty-one EP clients and 20 treatment providers enrolled in the study for up to 5 months. Forty-one EP clients completed the study, and all treatment providers remained in the study for their duration in the clinic. Survey completion for all 61 EP clients was moderate: 40% and 39% for daily and weekly surveys, respectively. Completion rates were slightly higher in the participants who completed the study: 44% and 41% for daily and weekly surveys, respectively. Twenty-seven of 41 (66%) EP clients who completed the study and 11 of 13 (85%) treatment providers who responded to satisfaction surveys reported they would continue to use the app as part of treatment services. Six (15%; 6/41) clients and 3 providers (23%; 3/13) stated that technological glitches impeded their engagement with the platform. Conclusions: EP clients and treatment providers in community-based outpatient clinics are responsive to integrating smartphone technology into treatment services. There were logistical and technical challenges associated with enrolling individuals in CMHCs. To be most effective, implementing smartphone technology in CMHC EP care necessitates adequate technical staff and support for utilization of the platform. %M 29487044 %R 10.2196/mental.8551 %U http://mental.jmir.org/2018/1/e15/ %U https://doi.org/10.2196/mental.8551 %U http://www.ncbi.nlm.nih.gov/pubmed/29487044 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 3 %N 1 %P e1 %T Wireless Surface Electromyography and Skin Temperature Sensors for Biofeedback Treatment of Headache: Validation Study with Stationary Control Equipment %A Stubberud,Anker %A Omland,Petter Moe %A Tronvik,Erling %A Olsen,Alexander %A Sand,Trond %A Linde,Mattias %+ Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Nevro Øst, Edvard Griegs Gate 8, Trondheim, 7030, Norway, 47 73 59 20 20, ankers@stud.ntnu.no %K biofeedback %K mobile phone %K app %K migraine %K pediatric %D 2018 %7 23.02.2018 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: The use of wearables and mobile phone apps in medicine is gaining attention. Biofeedback has the potential to exploit the recent advances in mobile health (mHealth) for the treatment of headaches. Objectives: The aim of this study was to assess the validity of selected wireless wearable health monitoring sensors (WHMS) for measuring surface electromyography (SEMG) and peripheral skin temperature in combination with a mobile phone app. This proof of concept will form the basis for developing innovative mHealth delivery of biofeedback treatment among young persons with primary headache. Methods: Sensors fulfilling the following predefined criteria were identified: wireless, small size, low weight, low cost, and simple to use. These sensors were connected to an app and used by 20 healthy volunteers. Validity was assessed through the agreement with simultaneous control measurements made with stationary neurophysiological equipment. The main variables were (1) trapezius muscle tension during different degrees of voluntary contraction and (2) voluntary increase in finger temperature. Data were statistically analyzed using Bland-Altman plots, intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Results: The app was programmed to receive data from the wireless sensors, process them, and feed them back to the user through a simple interface. Excellent agreement was found for the temperature sensor regarding increase in temperature (CCC .90; 95% CI 0.83-0.97). Excellent to fair agreement was found for the SEMG sensor. The ICC for the average of 3 repetitions during 4 different target levels ranged from .58 to .81. The wireless sensor showed consistency in muscle tension change during moderate muscle activity. Electrocardiography artifacts were avoided through right-sided use of the SEMG sensors. Participants evaluated the setup as usable and tolerable. Conclusions: This study confirmed the validity of wireless WHMS connected to a mobile phone for monitoring neurophysiological parameters of relevance for biofeedback therapy. %R 10.2196/biomedeng.9062 %U http://biomedeng.jmir.org/2018/1/e1/ %U https://doi.org/10.2196/biomedeng.9062 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 2 %P e45 %T Mobile App Delivery of the EORTC QLQ-C30 Questionnaire to Assess Health-Related Quality of Life in Oncological Patients: Usability Study %A Kessel,Kerstin A %A Vogel,Marco ME %A Alles,Anna %A Dobiasch,Sophie %A Fischer,Hanna %A Combs,Stephanie E %+ Department of Radiation Oncology, Technical University of Munich, Ismaninger Straße 22, Munich, 81675, Germany, 49 0894140 ext 4502, kerstin.kessel@tum.de %K radiation oncology %K healthcare surveys %K mobile applications %K mobile apps %K telemedicine %K health-related quality of life %K questionnaires %K oncology  %D 2018 %7 20.02.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile apps are evolving in the medical field. However, ongoing discussions have questioned whether such apps are really valuable and whether patients will accept their use in day-to-day clinical life. Therefore, we initiated a usability study in our department. Objective: We present our results of the first app prototype and patient testing of health-related quality of life (HRQoL) assessment in oncological patients. Methods: We developed an app prototype for the iOS operating system within eight months in three phases: conception, initial development, and pilot testing. For the HRQoL assessment, we chose to implement only the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30; German version 3). Usability testing was conducted for three months. Participation was voluntary and pseudonymized. After completion of the QLQ-C30 questionnaire using iPads provided by our department, we performed a short survey with 10 questions. This survey inquired about patients’ opinions regarding general aspects, including technical advances in medicine, mobile and app assistance during cancer treatment, and the app-specific functions (eg, interface and navigation). Results: After logging into the app, the user can choose between starting a questionnaire, reviewing answers (administrators only), and logging out. The questionnaire is displayed with the same information, questions, and answers as on the original QLQ-C30 sheet. No alterations in wording were made. Usability was tested with 81 patients; median age was 55 years. The median time for completing the HRQoL questionnaire on the iPad was 4.0 minutes. Of all participants, 84% (68/81) owned a mobile device. Similarly, 84% (68/81) of participants would prefer a mobile version of the HRQoL questionnaire instead of a paper-based version. Using the app in daily life during and after cancer treatment would be supported by 83% (67/81) of participants. In the prototype version of the app, data were stored on the device; in the future, 79% (64/81) of the patients would agree to transfer data via the Internet. Conclusions: Our usability test showed good results regarding attractiveness, operability, and understandability. Moreover, our results demonstrate a high overall acceptance of mobile apps and telemedicine in oncology. The HRQoL assessment via the app was accepted thoroughly by patients, and individuals are keen to use it in clinical routines, while data privacy and security must be ensured. %M 29463489 %R 10.2196/mhealth.9486 %U http://mhealth.jmir.org/2018/2/e45/ %U https://doi.org/10.2196/mhealth.9486 %U http://www.ncbi.nlm.nih.gov/pubmed/29463489 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 2 %P e21 %T Mobile Phone Ownership Is Not a Serious Barrier to Participation in Studies: Descriptive Study %A Harvey,Emily J %A Rubin,Leslie F %A Smiley,Sabrina L %A Zhou,Yitong %A Elmasry,Hoda %A Pearson,Jennifer L %+ Truth Initiative, Schroeder Institute for Tobacco Research and Policy Studies, 900 G St NW Fourth Floor, Washington, DC, 20001, United States, 1 2024545768, eharvey@truthinitiative.org %K smoking %K smartphone ownership %K online survey screener %K ecological momentary assessment %K tobacco products/utilization %K electronic cigarettes %K observational study %K United States %D 2018 %7 19.02.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Rather than providing participants with study-specific data collection devices, their personal mobile phones are increasingly being used as a means for collecting geolocation and ecological momentary assessment (EMA) data in public health research. Objective: The purpose of this study was to (1) describe the sociodemographic characteristics of respondents to an online survey screener assessing eligibility to participate in a mixed methods study collecting geolocation and EMA data via the participants’ personal mobile phones, and (2) examine how eligibility criteria requiring mobile phone ownership and an unlimited text messaging plan affected participant inclusion. Methods: Adult (≥18 years) daily smokers were recruited via public advertisements, free weekly newspapers, printed flyers, and word of mouth. An online survey screener was used as the initial method of determining eligibility for study participation. The survey screened for twenty-eight inclusion criteria grouped into three categories, which included (1) cell phone use, (2) tobacco use, and (3) additional criteria Results: A total of 1003 individuals completed the online screener. Respondents were predominantly African American (605/1003, 60.3%) (60.4%), male (514/1003, 51.3%), and had a median age of 35 years (IQR 26-50). Nearly 50% (496/1003, 49.5%) were unemployed. Most smoked menthol cigarettes (699/1003, 69.7%), and had a median smoking history of 11 years (IQR 5-21). The majority owned a mobile phone (739/1003, 73.7%), could install apps (86.8%), used their mobile phone daily (89.5%), and had an unlimited text messaging plan (871/1003, 86.8%). Of those who completed the online screener, 302 were eligible to participate in the study; 163 were eligible after rescreening, and 117 were enrolled in the study. Compared to employed individuals, a significantly greater proportion of those who were unemployed were ineligible for the study based on mobile phone inclusion criteria (P<.001); yet, 46.4% (333/717) of the individuals who were unemployed met all mobile phone inclusion criteria. Conclusions: Inclusion criteria requiring participants to use their personal mobile phones for data collection was not a major barrier to study participation for most respondents who completed the online screener, including those who were unemployed. Trial Registration: ClinicalTrials.gov NCT02261363; https://clinicaltrials.gov/ct2/show/NCT02261363 (Archived by WebCite at http://www.webcitation.org/6wOmDluSt) %M 29459355 %R 10.2196/mhealth.8123 %U http://mhealth.jmir.org/2018/2/e21/ %U https://doi.org/10.2196/mhealth.8123 %U http://www.ncbi.nlm.nih.gov/pubmed/29459355 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e10 %T Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis %A Fukuoka,Yoshimi %A Zhou,Mo %A Vittinghoff,Eric %A Haskell,William %A Goldberg,Ken %A Aswani,Anil %+ Department of Physiological Nursing/Institute for Health & Aging, University of California, San Francisco, 2 Koret Way, Box 0610, San Francisco, CA, 94116, United States, 1 415 476 8419, Yoshimi.Fukuoka@ucsf.edu %K accelerometer %K physical activity %K cluster analysis %K women %K randomized controlled trial %K machine learning %K body mass index %K metabolism %K primary prevention %K mHealth %D 2018 %7 01.02.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Determining patterns of physical activity throughout the day could assist in developing more personalized interventions or physical activity guidelines in general and, in particular, for women who are less likely to be physically active than men. Objective: The aims of this report are to identify clusters of women based on accelerometer-measured baseline raw metabolic equivalent of task (MET) values and a normalized version of the METs ≥3 data, and to compare sociodemographic and cardiometabolic risks among these identified clusters. Methods: A total of 215 women who were enrolled in the Mobile Phone Based Physical Activity Education (mPED) trial and wore an accelerometer for at least 8 hours per day for the 7 days prior to the randomization visit were analyzed. The k-means clustering method and the Lloyd algorithm were used on the data. We used the elbow method to choose the number of clusters, looking at the percentage of variance explained as a function of the number of clusters. Results: The results of the k-means cluster analyses of raw METs revealed three different clusters. The unengaged group (n=102) had the highest depressive symptoms score compared with the afternoon engaged (n=65) and morning engaged (n=48) groups (overall P<.001). Based on a normalized version of the METs ≥3 data, the moderate-to-vigorous physical activity (MVPA) evening peak group (n=108) had a higher body mass index (P=.03), waist circumference (P=.02), and hip circumference (P=.03) than the MVPA noon peak group (n=61). Conclusions: Categorizing physically inactive individuals into more specific activity patterns could aid in creating timing, frequency, duration, and intensity of physical activity interventions for women. Further research is needed to confirm these cluster groups using a large national dataset. Trial Registration: ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/6vVyLzwft) %M 29391341 %R 10.2196/publichealth.9138 %U http://publichealth.jmir.org/2018/1/e10/ %U https://doi.org/10.2196/publichealth.9138 %U http://www.ncbi.nlm.nih.gov/pubmed/29391341 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 1 %P e36 %T Smartphone App–Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability %A Manor,Brad %A Yu,Wanting %A Zhu,Hao %A Harrison,Rachel %A Lo,On-Yee %A Lipsitz,Lewis %A Travison,Thomas %A Pascual-Leone,Alvaro %A Zhou,Junhong %+ Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, 1200 Centre St, Roslindale, MA,, United States, 1 617 971 5332, bradmanor@hsl.harvard.edu %K smartphone %K gait assessment %K pocket %K dual task %K validity %K reliability %K mobile applications %D 2018 %7 30.01.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Walking is a complex cognitive motor task that is commonly completed while performing another task such as talking or making decisions. Gait assessments performed under normal and “dual-task” walking conditions thus provide important insights into health. Such assessments, however, are limited primarily to laboratory-based settings. Objective: The objective of our study was to create and test a smartphone-based assessment of normal and dual-task walking for use in nonlaboratory settings. Methods: We created an iPhone app that used the phone’s motion sensors to record movements during walking under normal conditions and while performing a serial-subtraction dual task, with the phone placed in the user’s pants pocket. The app provided the user with multimedia instructions before and during the assessment. Acquired data were automatically uploaded to a cloud-based server for offline analyses. A total of 14 healthy adults completed 2 laboratory visits separated by 1 week. On each visit, they used the app to complete three 45-second trials each of normal and dual-task walking. Kinematic data were collected with the app and a gold-standard–instrumented GAITRite mat. Participants also used the app to complete normal and dual-task walking trials within their homes on 3 separate days. Within laboratory-based trials, GAITRite-derived heel strikes and toe-offs of the phone-side leg aligned with smartphone acceleration extrema, following filtering and rotation to the earth coordinate system. We derived stride times—a clinically meaningful metric of locomotor control—from GAITRite and app data, for all strides occurring over the GAITRite mat. We calculated stride times and the dual-task cost to the average stride time (ie, percentage change from normal to dual-task conditions) from both measurement devices. We calculated similar metrics from home-based app data. For these trials, periods of potential turning were identified via custom-developed algorithms and omitted from stride-time analyses. Results: Across all detected strides in the laboratory, stride times derived from the app and GAITRite mat were highly correlated (P<.001, r2=.98). These correlations were independent of walking condition and pocket tightness. App- and GAITRite-derived stride-time dual-task costs were also highly correlated (P<.001, r2=.95). The error of app-derived stride times (mean 16.9, SD 9.0 ms) was unaffected by the magnitude of stride time, walking condition, or pocket tightness. For both normal and dual-task trials, average stride times derived from app walking trials demonstrated excellent test-retest reliability within and between both laboratory and home-based assessments (intraclass correlation coefficient range .82-.94). Conclusions: The iPhone app we created enabled valid and reliable assessment of stride timing—with the smartphone in the pocket—during both normal and dual-task walking and within both laboratory and nonlaboratory environments. Additional work is warranted to expand the functionality of this tool to older adults and other patient populations. %M 29382625 %R 10.2196/mhealth.8815 %U http://mhealth.jmir.org/2018/1/e36/ %U https://doi.org/10.2196/mhealth.8815 %U http://www.ncbi.nlm.nih.gov/pubmed/29382625 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 1 %P e24 %T Examining the Frequency and Contribution of Foods Eaten Away From Home in the Diets of 18- to 30-Year-Old Australians Using Smartphone Dietary Assessment (MYMeals): Protocol for a Cross-Sectional Study %A Wellard-Cole,Lyndal %A Jung,Jisu %A Kay,Judy %A Rangan,Anna %A Chapman,Kathy %A Watson,Wendy L %A Hughes,Clare %A Ni Mhurchu,Cliona %A Bauman,Adrian %A Gemming,Luke %A Yacef,Kalina %A Koprinska,Irena %A Allman-Farinelli,Margaret %+ Nutrition and Dietetics Group, School of Life and Environmental Sciences, The University of Sydney, Level 4 East, Charles Perkins Centre, Sydney, 2006, Australia, 61 286274854, lwel3754@uni.sydney.edu.au %K diet %K fast foods %K young adult %K feeding behavior %K nutritional status %K cell phone %D 2018 %7 26.01.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Young Australians aged between 18 and 30 years have experienced the largest increase in the body mass index and spend the largest proportion of their food budget on fast food and eating out. Frequent consumption of foods purchased and eaten away from home has been linked to poorer diet quality and weight gain. There has been no Australian research regarding quantities, type, or the frequency of consumption of food prepared outside the home by young adults and its impact on their energy and nutrient intakes. Objectives: The objective of this study was to determine the relative contributions of different food outlets (eg, fast food chain, independent takeaway food store, coffee shop, etc) to the overall food and beverage intake of young adults; to assess the extent to which food and beverages consumed away from home contribute to young adults’ total energy and deleterious nutrient intakes; and to study social and physical environmental interactions with consumption patterns of young adults. Methods: A cross-sectional study of 1008 young adults will be conducted. Individuals are eligible to participate if they: (1) are aged between 18 and 30 years; (2) reside in New South Wales, Australia; (3) own or have access to a smartphone; (4) are English-literate; and (5) consume at least one meal, snack, or drink purchased outside the home per week. An even spread of gender, age groups (18 to 24 years and 25 to 30 years), metropolitan or regional geographical areas, and high and low socioeconomic status areas will be included. Participants will record all food and drink consumed over 3 consecutive days, together with location purchased and consumed in our customized smartphone app named Eat and Track (EaT). Participants will then complete an extensive demographics questionnaire. Mean intakes of energy, nutrients, and food groups will be calculated along with the relative contribution of foods purchased and eaten away from home. A subsample of 19.84% (200/1008) of the participants will complete three 24-hour recall interviews to compare with the data collected using EaT. Data mining techniques such as clustering, decision trees, neural networks, and support vector machines will be used to build predictive models and identify important patterns. Results: Recruitment is underway, and results will be available in 2018. Conclusions: The contribution of foods prepared away from home, in terms of energy, nutrients, deleterious nutrients, and food groups to young people’s diets will be determined, as will the impact on meeting national recommendations. Foods and consumption behaviors that should be targeted in future health promotion efforts for young adults will be identified. %M 29374002 %R 10.2196/resprot.9038 %U http://www.researchprotocols.org/2018/1/e24/ %U https://doi.org/10.2196/resprot.9038 %U http://www.ncbi.nlm.nih.gov/pubmed/29374002 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 1 %P e15 %T Concussion Assessment With Smartglasses: Validation Study of Balance Measurement Toward a Lightweight, Multimodal, Field-Ready Platform %A Salisbury,Joseph P %A Keshav,Neha U %A Sossong,Anthony D %A Sahin,Ned T %+ Empowerment Lab, Brain Power, LLC, 1 Broadway 14th Fl, Cambridge, MA, 02142, United States, 1 617 758 4100, sahin@post.harvard.edu %K postural balance %K wearable technology %K accelerometry %K mild traumatic brain injury %D 2018 %7 23.01.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Lightweight and portable devices that objectively measure concussion-related impairments could improve injury detection and critical decision-making in contact sports and the military, where brain injuries commonly occur but remain underreported. Current standard assessments often rely heavily on subjective methods such as symptom self-reporting. Head-mounted wearables, such as smartglasses, provide an emerging platform for consideration that could deliver the range of assessments necessary to develop a rapid and objective screen for brain injury. Standing balance assessment, one parameter that may inform a concussion diagnosis, could theoretically be performed quantitatively using current off-the-shelf smartglasses with an internal accelerometer. However, the validity of balance measurement using smartglasses has not been investigated. Objective: This study aimed to perform preliminary validation of a smartglasses-based balance accelerometer measure (BAM) compared with the well-described and characterized waist-based BAM. Methods: Forty-two healthy individuals (26 male, 16 female; mean age 23.8 [SD 5.2] years) participated in the study. Following the BAM protocol, each subject performed 2 trials of 6 balance stances while accelerometer and gyroscope data were recorded from smartglasses (Glass Explorer Edition). Test-retest reliability and correlation were determined relative to waist-based BAM as used in the National Institutes of Health’s Standing Balance Toolbox. Results: Balance measurements obtained using a head-mounted wearable were highly correlated with those obtained through a waist-mounted accelerometer (Spearman rho, ρ=.85). Test-retest reliability was high (intraclass correlation coefficient, ICC2,1=0.85, 95% CI 0.81-0.88) and in good agreement with waist balance measurements (ICC2,1=0.84, 95% CI 0.80-0.88). Considering the normalized path length magnitude across all 3 axes improved interdevice correlation (ρ=.90) while maintaining test-retest reliability (ICC2,1=0.87, 95% CI 0.83-0.90). All subjects successfully completed the study, demonstrating the feasibility of using a head-mounted wearable to assess balance in a healthy population. Conclusions: Balance measurements derived from the smartglasses-based accelerometer were consistent with those obtained using a waist-mounted accelerometer. Additional research is necessary to determine to what extent smartglasses-based accelerometry measures can detect balance dysfunction associated with concussion. However, given the potential for smartglasses to perform additional concussion-related assessments in an integrated, wearable platform, continued development and validation of a smartglasses-based balance assessment is warranted. This approach could lead to a wearable platform for real-time assessment of concussion-related impairments that could be further augmented with telemedicine capabilities to integrate professional clinical guidance. Smartglasses may be superior to fully immersive virtual reality headsets for this application, given their lighter weight and reduced likelihood of potential safety concerns. %M 29362210 %R 10.2196/mhealth.8478 %U http://mhealth.jmir.org/2018/1/e15/ %U https://doi.org/10.2196/mhealth.8478 %U http://www.ncbi.nlm.nih.gov/pubmed/29362210 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 1 %P e10 %T Hearing Tests Based on Biologically Calibrated Mobile Devices: Comparison With Pure-Tone Audiometry %A Masalski,Marcin %A Grysiński,Tomasz %A Kręcicki,Tomasz %+ Department and Clinic of Otolaryngology, Head and Neck Surgery, Faculty of Postgraduate Medical Training, Wroclaw Medical University, Borowska 213, Wrocław, 50-556, Poland, 48 515086252, marcin.masalski@pwr.edu.pl %K hearing test %K mobile health %K mobile apps %K pure-tone audiometry %D 2018 %7 10.01.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Hearing screening tests based on pure-tone audiometry may be conducted on mobile devices, provided that the devices are specially calibrated for the purpose. Calibration consists of determining the reference sound level and can be performed in relation to the hearing threshold of normal-hearing persons. In the case of devices provided by the manufacturer, together with bundled headphones, the reference sound level can be calculated once for all devices of the same model. Objective: This study aimed to compare the hearing threshold measured by a mobile device that was calibrated using a model-specific, biologically determined reference sound level with the hearing threshold obtained in pure-tone audiometry. Methods: Trial participants were recruited offline using face-to-face prompting from among Otolaryngology Clinic patients, who own Android-based mobile devices with bundled headphones. The hearing threshold was obtained on a mobile device by means of an open access app, Hearing Test, with incorporated model-specific reference sound levels. These reference sound levels were previously determined in uncontrolled conditions in relation to the hearing threshold of normal-hearing persons. An audiologist-assisted self-measurement was conducted by the participants in a sound booth, and it involved determining the lowest audible sound generated by the device within the frequency range of 250 Hz to 8 kHz. The results were compared with pure-tone audiometry. Results: A total of 70 subjects, 34 men and 36 women, aged 18-71 years (mean 36, standard deviation [SD] 11) participated in the trial. The hearing threshold obtained on mobile devices was significantly different from the one determined by pure-tone audiometry with a mean difference of 2.6 dB (95% CI 2.0-3.1) and SD of 8.3 dB (95% CI 7.9-8.7). The number of differences not greater than 10 dB reached 89% (95% CI 88-91), whereas the mean absolute difference was obtained at 6.5 dB (95% CI 6.2-6.9). Sensitivity and specificity for a mobile-based screening method were calculated at 98% (95% CI 93-100.0) and 79% (95% CI 71-87), respectively. Conclusions: The method of hearing self-test carried out on mobile devices with bundled headphones demonstrates high compatibility with pure-tone audiometry, which confirms its potential application in hearing monitoring, screening tests, or epidemiological examinations on a large scale. %M 29321124 %R 10.2196/mhealth.7800 %U https://mhealth.jmir.org/2018/1/e10/ %U https://doi.org/10.2196/mhealth.7800 %U http://www.ncbi.nlm.nih.gov/pubmed/29321124 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 1 %P e1 %T Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study %A Jacquemin,Charlotte %A Servy,Hervé %A Molto,Anna %A Sellam,Jérémie %A Foltz,Violaine %A Gandjbakhch,Frédérique %A Hudry,Christophe %A Mitrovic,Stéphane %A Fautrel,Bruno %A Gossec,Laure %+ Rheumatology Department, Pitié Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, 47-83 bd de l’hôpital, Paris, 75013, France, 33 688624123, jacquemin.charlotte@gmail.com %K fitness tracker %K exercise %K rheumatoid arthritis %K axial spondylarthritis %D 2018 %7 02.01.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA). Objective: The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population. Methods: This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire. Results: A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10. Conclusions: Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases. %M 29295810 %R 10.2196/mhealth.7948 %U http://mhealth.jmir.org/2018/1/e1/ %U https://doi.org/10.2196/mhealth.7948 %U http://www.ncbi.nlm.nih.gov/pubmed/29295810 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 12 %P e189 %T Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study %A Cole,Casey A %A Anshari,Dien %A Lambert,Victoria %A Thrasher,James F %A Valafar,Homayoun %+ Computational Biology Research Group, Department of Computer Science, University of South Carolina, 315 Main St., Columbia, SC, 29208, United States, 1 8036298785, homayoun@cse.sc.edu %K machine learning %K neural networks %K automated pattern recognition %K smoking cessation %K ecological momentary assessment %K digital signal processing %K data mining %D 2017 %7 13.12.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. Objective: This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. Methods: A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. Results: In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. Conclusions: Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events. %M 29237580 %R 10.2196/mhealth.9035 %U http://mhealth.jmir.org/2017/12/e189/ %U https://doi.org/10.2196/mhealth.9035 %U http://www.ncbi.nlm.nih.gov/pubmed/29237580 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 12 %P e188 %T The Swedish Web Version of the Quality of Recovery Scale Adapted for Use in a Mobile App: Prospective Psychometric Evaluation Study %A Nilsson,Ulrica %A Dahlberg,Karuna %A Jaensson,Maria %+ School of Health Sciences, Faculty of Medicine and Health, Örebro University, Fakultetsgatan, Örebro, 70182, Sweden, 46 762132685, ulrica.nilsson@oru.se %K psychometric evaluation %K postoperative recovery %K Web version %K evaluation studies %K mobile application %K Quality of Recovery scale %D 2017 %7 3.12.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The 40-item Quality of Recovery (QoR-40) questionnaire is well validated for measuring self-assessed postoperative recovery. The Swedish version of the 40-item Quality of Recovery (QoR-40) has been developed into a Web-based questionnaire, the Swedish Web version of the Quality of Recovery (SwQoR) questionnaire, adapted for use in a mobile app, Recovery Assessment by Phone Points, or RAPP. Objective: The aim of this study was to test the validity, reliability, responsiveness, and clinical acceptability and feasibility of SwQoR. Methods: We conducted a prospective psychometric evaluation study including 494 patients aged ≥18 years undergoing day surgery at 4 different day-surgery departments in Sweden. SwQoR was completed daily on postoperative days 1 to 14. Results: All a priori hypotheses were confirmed, supporting convergent validity. There was excellent internal consistency (Cronbach alpha range .91-.93), split-half reliability (coefficient range .87-.93), and stability (ri=.99, 95% CI .96-.99; P<.001). Cohen d effect size was 1.00, with a standardized response mean of 1.2 and a percentage change from baseline of 59.1%. An exploratory factor analysis found 5 components explaining 57.8% of the total variance. We noted a floor effect only on postoperative day 14; we found no ceiling effect. Conclusions: SwQoR is valid, has excellent reliability and high responsiveness, and is clinically feasible for the systematic follow-up of patients’ postoperative recovery. %M 29229590 %R 10.2196/mhealth.9061 %U http://mhealth.jmir.org/2017/12/e188/ %U https://doi.org/10.2196/mhealth.9061 %U http://www.ncbi.nlm.nih.gov/pubmed/29229590 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 11 %P e171 %T Using Android and Open Data Kit Technology in Data Management for Research in Resource-Limited Settings in the Niger Delta Region of Nigeria: Cross-Sectional Household Survey %A Maduka,Omosivie %A Akpan,Godwin %A Maleghemi,Sylvester %+ Department of Preventive and Social Medicine, College of Health Sciences, University of Port Harcourt, Alakahia, Choba, Port Harcourt, 50001, Nigeria, 234 8033298096, omosivie.maduka@uniport.edu.ng %K mobile phones %K technology %K Africa %D 2017 %7 30.11.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Data collection in Sub-Saharan Africa has traditionally been paper-based. However, the popularization of Android mobile devices and data capture software has brought paperless data management within reach. We used Open Data Kit (ODK) technology on Android mobile devices during a household survey in the Niger Delta region of Nigeria. Objective: The aim of this study was to describe the pros and cons of deploying ODK for data management. Methods: A descriptive cross-sectional household survey was carried out by 6 data collectors between April and May 2016. Data were obtained from 1706 persons in 601 households across 6 communities in 3 states in the Niger Delta. The use of Android mobile devices and ODK technology involved form building, testing, collection, aggregation, and download for data analysis. The median duration for data collection per household and per individual was 25.7 and 9.3 min, respectively. Results: Data entries per device ranged from 33 (33/1706, 1.93%) to 482 (482/1706, 28.25%) individuals between 9 (9/601, 1.5%) and 122 (122/601, 20.3%) households. The most entries (470) were made by data collector 5. Only 2 respondents had data entry errors (2/1706, 0.12%). However, 73 (73/601, 12.1%) households had inaccurate date and time entries for when data collection started and ended. The cost of deploying ODK was estimated at US $206.7 in comparison with the estimated cost of US $466.7 for paper-based data management. Conclusions: We found the use of mobile data capture technology to be efficient and cost-effective. As Internet services improve in Africa, we advocate their use as effective tools for health information management. %M 29191798 %R 10.2196/mhealth.7827 %U http://mhealth.jmir.org/2017/11/e171/ %U https://doi.org/10.2196/mhealth.7827 %U http://www.ncbi.nlm.nih.gov/pubmed/29191798 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 11 %P e181 %T Detecting Acute Otitis Media Symptom Episodes Using a Mobile App: Cohort Study %A Prins-van Ginkel,Annemarijn C %A de Hoog,Marieke LA %A Uiterwaal,C %A Smit,Henriette A %A Bruijning-Verhagen,Patricia CJ %+ Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Huispost nr STR 6.131, PO Box 85500, Utrecht, 3508 GA, Netherlands, 31 887568044, A.C.Prins@umcutrecht.nl %K smartphone %K mobile app %K infectious diseases, cohort studies, acute otitis media, underreporting, patient compliance %K mobile applications %K communicable diseases %K otitis media %D 2017 %7 28.11.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Population cohort studies are useful to study infectious diseases episodes not attended by health care services, but conventional paper diaries and questionnaires to capture cases are prone to noncompliance and recall bias. Use of smart technology in this setting may improve case finding. Objective: The objective of our study was to validate an interactive mobile app for monitoring occurrence of acute infectious diseases episodes in individuals, independent of health care seeking, using acute otitis media (AOM) symptom episodes in infants as a case study. We were interested in determining participant compliance and app performance in detecting and ascertaining (parent-reported) AOM symptom episodes with this novel tool compared with traditional methods used for monitoring study participants. Methods: We tested the InfectieApp research app to detect AOM symptom episodes. In 2013, we followed 155 children aged 0 to 3 years for 4 months. Parents recorded the presence of AOM symptoms in a paper diary for 4 consecutive months and completed additional disease questionnaires when AOM symptoms were present. In 2015 in a similar cohort of 69 children, parents used an AOM diary and questionnaire app instead. Results: During conventional and app-based recording, 93.13% (17,244/18,516) and 94.56% (7438/7866) of symptom diaries were returned, respectively, and at least one symptom was recorded for 32.50% (n=5606) and 43.99% (n=3272) of diary days (P<.01). The incidence of AOM symptom episodes was 605 and 835 per 1000 child-years, respectively. Disease questionnaires were completed for 59% (17/29) of episodes when participants were using conventional recording, compared with 100% (18/18) for app-based recording. Conclusions: The use of the study’s smart diary app improved AOM case finding and disease questionnaire completeness. For common infectious diseases that often remain undetected by health care services, use of this technology can substantially improve the accurateness of disease burden estimates. %M 29183869 %R 10.2196/mhealth.7505 %U http://mhealth.jmir.org/2017/11/e181/ %U https://doi.org/10.2196/mhealth.7505 %U http://www.ncbi.nlm.nih.gov/pubmed/29183869 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 11 %P e391 %T Relationship Between the Menstrual Cycle and Timing of Ovulation Revealed by New Protocols: Analysis of Data from a Self-Tracking Health App %A Sohda,Satoshi %A Suzuki,Kenta %A Igari,Ichiro %+ Biodiversity Conservation Planning Section, Center for Environmental Biology and Ecosystem Studies, National Institute for Environmental Studies, EEF, 3rd Floor, 16-2 Onogawa, Tsukuba, 305-8506, Japan, 81 029 850 2747, kenta11514201@gmail.com %K self-tracking %K person generated health data %K calendar calculation %K fertility %K menstrual cycle %D 2017 %7 27.11.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: There are many mobile phone apps aimed at helping women map their ovulation and menstrual cycles and facilitating successful conception (or avoiding pregnancy). These apps usually ask users to input various biological features and have accumulated the menstrual cycle data of a vast number of women. Objective: The purpose of our study was to clarify how the data obtained from a self-tracking health app for female mobile phone users can be used to improve the accuracy of prediction of the date of next ovulation. Methods: Using the data of 7043 women who had reliable menstrual and ovulation records out of 8,000,000 users of a mobile phone app of a health care service, we analyzed the relationship between the menstrual cycle length, follicular phase length, and luteal phase length. Then we fitted a linear function to the relationship between the length of the menstrual cycle and timing of ovulation and compared it with the existing calendar-based methods. Results: The correlation between the length of the menstrual cycle and the length of the follicular phase was stronger than the correlation between the length of the menstrual cycle and the length of the luteal phase, and there was a positive correlation between the lengths of past and future menstrual cycles. A strong positive correlation was also found between the mean length of past cycles and the length of the follicular phase. The correlation between the mean cycle length and the luteal phase length was also statistically significant. In most of the subjects, our method (ie, the calendar-based method based on the optimized function) outperformed the Ogino method of predicting the next ovulation date. Our method also outperformed the ovulation date prediction method that assumes the middle day of a mean menstrual cycle as the date of the next ovulation. Conclusions: The large number of subjects allowed us to capture the relationships between the lengths of the menstrual cycle, follicular phase, and luteal phase in more detail than previous studies. We then demonstrated how the present calendar methods could be improved by the better grouping of women. This study suggested that even without integrating various biological metrics, the dataset collected by a self-tracking app can be used to develop formulas that predict the ovulation day when the data are aggregated. Because the method that we developed requires data only on the first day of menstruation, it would be the best option for couples during the early stages of their attempt to have a baby or for those who want to avoid the cost associated with other methods. Moreover, the result will be the baseline for more advanced methods that integrate other biological metrics. %M 29180346 %R 10.2196/jmir.7468 %U http://www.jmir.org/2017/11/e391/ %U https://doi.org/10.2196/jmir.7468 %U http://www.ncbi.nlm.nih.gov/pubmed/29180346 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e87 %T Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala %A Prieto,José Tomás %A Jara,Jorge H %A Alvis,Juan Pablo %A Furlan,Luis R %A Murray,Christian Travis %A Garcia,Judith %A Benghozi,Pierre-Jean %A Kaydos-Daniels,Susan Cornelia %+ Center for Health Studies, Universidad del Valle de Guatemala, 18 Av. 11-95, Zona 15, Vista Hermosa III, Guatemala City, 01015, Guatemala, +1 4044216455, josetomasprieto@gmail.com %K crowdsourcing %K human flu %K influenza %K grippe %K mHealth %K texting %K mobile apps %K short message service %K text message %K developing countries %D 2017 %7 14.11.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system. Objective: The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI. Methods: We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system. Results: Nearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017). Conclusions: Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people’s fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions. %M 29138128 %R 10.2196/publichealth.8610 %U http://publichealth.jmir.org/2017/4/e87/ %U https://doi.org/10.2196/publichealth.8610 %U http://www.ncbi.nlm.nih.gov/pubmed/29138128 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 11 %P e368 %T Performance of a Mobile Phone App-Based Participatory Syndromic Surveillance System for Acute Febrile Illness and Acute Gastroenteritis in Rural Guatemala %A Olson,Daniel %A Lamb,Molly %A Lopez,Maria Renee %A Colborn,Kathryn %A Paniagua-Avila,Alejandra %A Zacarias,Alma %A Zambrano-Perilla,Ricardo %A Rodríguez-Castro,Sergio Ricardo %A Cordon-Rosales,Celia %A Asturias,Edwin Jose %+ University of Colorado School of Medicine, Section of Pediatric Infectious Diseases, 13123 East 16th Avenue, Box 055, Aurora, CO, 80045, United States, 1 7207772838, daniel.olson@ucdenver.edu %K mobile phone %K app %K participatory %K syndromic surveillance %K norovirus %K dengue %K acute febrile illness %K diarrhea %K Guatemala %D 2017 %7 09.11.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: With their increasing availability in resource-limited settings, mobile phones may provide an important tool for participatory syndromic surveillance, in which users provide symptom data directly into a centralized database. Objective: We studied the performance of a mobile phone app-based participatory syndromic surveillance system for collecting syndromic data (acute febrile illness and acute gastroenteritis) to detect dengue virus and norovirus on a cohort of children living in a low-resource and rural area of Guatemala. Methods: Randomized households were provided with a mobile phone and asked to submit weekly reports using a symptom diary app (Vigilant-e). Participants reporting acute febrile illness or acute gastroenteritis answered additional questions using a decision-tree algorithm and were subsequently visited at home by a study nurse who performed a second interview and collected samples for dengue virus if confirmed acute febrile illness and norovirus if acute gastroenteritis. We analyzed risk factors associated with decreased self-reporting of syndromic data using the Vigilant-e app and evaluated strategies to improve self-reporting. We also assessed agreement between self-report and nurse-collected data obtained during home visits. Results: From April 2015 to June 2016, 469 children in 207 households provided 471 person-years of observation. Mean weekly symptom reporting rate was 78% (range 58%-89%). Households with a poor (<70%) weekly reporting rate using the Vigilant-e app during the first 25 weeks of observation (n=57) had a greater number of children (mean 2.8, SD 1.5 vs mean 2.5, SD 1.3; risk ratio [RR] 1.2, 95% CI 1.1-1.4), were less likely to have used mobile phones for text messaging at study enrollment (61%, 35/57 vs 76.7%, 115/150; RR 0.6, 95% CI 0.4-0.9), and were less likely to access care at the local public clinic (35%, 20/57 vs 67.3%, 101/150; RR 0.4, 95% CI 0.2-0.6). Parents of female enrolled participants were more likely to have low response rate (57.1%, 84/147 vs 43.8%, 141/322; RR 1.4, 95% CI 1.1-1.9). Several external factors (cellular tower collapse, contentious elections) were associated with periods of decreased reporting. Poor response rate (<70%) was associated with lower case reporting of acute gastroenteritis, norovirus-associated acute gastroenteritis, acute febrile illness, and dengue virus-associated acute febrile illness (P<.001). Parent-reported syndromic data on the Vigilant-e app demonstrated agreement with nurse-collected data for fever (kappa=.57, P<.001), vomiting (kappa=.63, P<.001), and diarrhea (kappa=.61, P<.001), with decreased agreement as the time interval between parental report and nurse home visit increased (<1 day: kappa=.65-.70; ≥2 days: kappa=.08-.29). Conclusions: In a resource-limited area of rural Guatemala, a mobile phone app-based participatory syndromic surveillance system demonstrated a high reporting rate and good agreement between parental reported data and nurse-reported data during home visits. Several household-level and external factors were associated with decreased syndromic reporting. Poor reporting rate was associated with decreased syndromic and pathogen-specific case ascertainment. %M 29122738 %R 10.2196/jmir.8041 %U http://www.jmir.org/2017/11/e368/ %U https://doi.org/10.2196/jmir.8041 %U http://www.ncbi.nlm.nih.gov/pubmed/29122738 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 10 %P e332 %T Exploring the Potential of a Wearable Camera to Examine the Early Obesogenic Home Environment: Comparison of SenseCam Images to the Home Environment Interview %A Schrempft,Stephanie %A van Jaarsveld,Cornelia HM %A Fisher,Abigail %+ Department of Behavioural Science and Health, University College London, 1-19 Torrington Place, London,, United Kingdom, 44 02076791722, abigail.fisher@ucl.ac.uk %K environment and public health %K obesity %K parents %D 2017 %7 12.10.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The obesogenic home environment is usually examined via self-report, and objective measures are required. Objective: This study explored whether the wearable camera SenseCam can be used to examine the early obesogenic home environment and whether it is useful for validation of self-report measures. Methods: A total of 15 primary caregivers of young children (mean age of child 4 years) completed the Home Environment Interview (HEI). Around 12 days after the HEI, participants wore the SenseCam at home for 4 days. A semistructured interview assessed participants’ experience of wearing the SenseCam. Intraclass correlation coefficients (ICCs), percent agreement, and kappa statistics were used as validity estimates for 54 home environment features. Results: Wearing the SenseCam was generally acceptable to those who participated. The SenseCam captured all 54 HEI features but with varying detail; 36 features (67%) had satisfactory validity (ICC or kappa ≥0.40; percent agreement ≥80 where kappa could not be calculated). Validity was good or excellent (ICC or kappa ≥0.60) for fresh fruit and vegetable availability, fresh vegetable variety, display of food and drink (except sweet snacks), family meals, child eating lunch or dinner while watching TV, garden and play equipment, the number of TVs and DVD players, and media equipment in the child’s bedroom. Validity was poor (ICC or kappa <0.40) for tinned and frozen vegetable availability and variety, and sweet snack availability. Conclusions: The SenseCam has the potential to objectively examine and validate multiple aspects of the obesogenic home environment. Further research should aim to replicate the findings in a larger, representative sample. %M 29025695 %R 10.2196/jmir.7748 %U http://www.jmir.org/2017/10/e332/ %U https://doi.org/10.2196/jmir.7748 %U http://www.ncbi.nlm.nih.gov/pubmed/29025695 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e151 %T Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications %A Shawen,Nicholas %A Lonini,Luca %A Mummidisetty,Chaithanya Krishna %A Shparii,Ilona %A Albert,Mark V %A Kording,Konrad %A Jayaraman,Arun %+ Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, 355 E Erie St, Suite #11-1101, Chicago, IL, 60611, United States, 1 312 238 1619, llonini@ricres.org %K fall detection %K lower limb amputation %K mobile phones %K machine learning %D 2017 %7 11.10.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Automatically detecting falls with mobile phones provides an opportunity for rapid response to injuries and better knowledge of what precipitated the fall and its consequences. This is beneficial for populations that are prone to falling, such as people with lower limb amputations. Prior studies have focused on fall detection in able-bodied individuals using data from a laboratory setting. Such approaches may provide a limited ability to detect falls in amputees and in real-world scenarios. Objective: The aim was to develop a classifier that uses data from able-bodied individuals to detect falls in individuals with a lower limb amputation, while they freely carry the mobile phone in different locations and during free-living. Methods: We obtained 861 simulated indoor and outdoor falls from 10 young control (non-amputee) individuals and 6 individuals with a lower limb amputation. In addition, we recorded a broad database of activities of daily living, including data from three participants’ free-living routines. Sensor readings (accelerometer and gyroscope) from a mobile phone were recorded as participants freely carried it in three common locations—on the waist, in a pocket, and in the hand. A set of 40 features were computed from the sensors data and four classifiers were trained and combined through stacking to detect falls. We compared the performance of two population-specific models, trained and tested on either able-bodied or amputee participants, with that of a model trained on able-bodied participants and tested on amputees. A simple threshold-based classifier was used to benchmark our machine-learning classifier. Results: The accuracy of fall detection in amputees for a model trained on control individuals (sensitivity: mean 0.989, 1.96*standard error of the mean [SEM] 0.017; specificity: mean 0.968, SEM 0.025) was not statistically different (P=.69) from that of a model trained on the amputee population (sensitivity: mean 0.984, SEM 0.016; specificity: mean 0.965, SEM 0.022). Detection of falls in control individuals yielded similar results (sensitivity: mean 0.979, SEM 0.022; specificity: mean 0.991, SEM 0.012). A mean 2.2 (SD 1.7) false alarms per day were obtained when evaluating the model (vs mean 122.1, SD 166.1 based on thresholds) on data recorded as participants carried the phone during their daily routine for two or more days. Machine-learning classifiers outperformed the threshold-based one (P<.001). Conclusions: A mobile phone-based fall detection model can use data from non-amputee individuals to detect falls in individuals walking with a prosthesis. We successfully detected falls when the mobile phone was carried across multiple locations and without a predetermined orientation. Furthermore, the number of false alarms yielded by the model over a longer period of time was reasonably low. This moves the application of mobile phone-based fall detection systems closer to a real-world use case scenario. %M 29021127 %R 10.2196/mhealth.8201 %U http://mhealth.jmir.org/2017/10/e151/ %U https://doi.org/10.2196/mhealth.8201 %U http://www.ncbi.nlm.nih.gov/pubmed/29021127 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e104 %T Mobile Phone-Based Measures of Activity, Step Count, and Gait Speed: Results From a Study of Older Ambulatory Adults in a Naturalistic Setting %A Rye Hanton,Cassia %A Kwon,Yong-Jun %A Aung,Thawda %A Whittington,Jackie %A High,Robin R %A Goulding,Evan H %A Schenk,A Katrin %A Bonasera,Stephen J %+ Department of Internal Medicine, Division of Geriatrics, University of Nebraska Medical Center, 986155 Nebraska Medical Center, Omaha, NE, 68198-6155, United States, 1 402 559 8409, sbonasera@unmc.edu %K mobile phone %K functional status %K mobility %K gait speed %K mobility measures %K LLFDI %K SAFFE %K PROMIS short %K PROMIS Global %K step count %K behavioral classification %K frailty phenotype %K normal aging %D 2017 %7 03.10.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cellular mobile telephone technology shows much promise for delivering and evaluating healthcare interventions in cost-effective manners with minimal barriers to access. There is little data demonstrating that these devices can accurately measure clinically important aspects of individual functional status in naturalistic environments outside of the laboratory. Objective: The objective of this study was to demonstrate that data derived from ubiquitous mobile phone technology, using algorithms developed and previously validated by our lab in a controlled setting, can be employed to continuously and noninvasively measure aspects of participant (subject) health status including step counts, gait speed, and activity level, in a naturalistic community setting. A second objective was to compare our mobile phone-based data against current standard survey-based gait instruments and clinical physical performance measures in order to determine whether they measured similar or independent constructs. Methods: A total of 43 ambulatory, independently dwelling older adults were recruited from Nebraska Medicine, including 25 (58%, 25/43) healthy control individuals from our Engage Wellness Center and 18 (42%, 18/43) functionally impaired, cognitively intact individuals (who met at least 3 of 5 criteria for frailty) from our ambulatory Geriatrics Clinic. The following previously-validated surveys were obtained on study day 1: (1) Late Life Function and Disability Instrument (LLFDI); (2) Survey of Activities and Fear of Falling in the Elderly (SAFFE); (3) Patient Reported Outcomes Measurement Information System (PROMIS), short form version 1.0 Physical Function 10a (PROMIS-PF); and (4) PROMIS Global Health, short form version 1.1 (PROMIS-GH). In addition, clinical physical performance measurements of frailty (10 foot Get up and Go, 4 Meter walk, and Figure-of-8 Walk [F8W]) were also obtained. These metrics were compared to our mobile phone-based metrics collected from the participants in the community over a 24-hour period occurring within 1 week of the initial assessment. Results: We identified statistically significant differences between functionally intact and frail participants in mobile phone-derived measures of percent activity (P=.002, t test), active versus inactive status (P=.02, t test), average step counts (P<.001, repeated measures analysis of variance [ANOVA]) and gait speed (P<.001, t test). In functionally intact individuals, the above mobile phone metrics assessed aspects of functional status independent (Bland-Altman and correlation analysis) of both survey- and/or performance battery-based functional measures. In contrast, in frail individuals, the above mobile phone metrics correlated with submeasures of both SAFFE and PROMIS-GH. Conclusions: Continuous mobile phone-based measures of participant community activity and mobility strongly differentiate between persons with intact functional status and persons with a frailty phenotype. These measures assess dimensions of functional status independent of those measured using current validated questionnaires and physical performance assessments to identify functional compromise. Mobile phone-based gait measures may provide a more readily accessible and less-time consuming measure of gait, while further providing clinicians with longitudinal gait measures that are currently difficult to obtain. %M 28974482 %R 10.2196/mhealth.5090 %U http://mhealth.jmir.org/2017/10/e104/ %U https://doi.org/10.2196/mhealth.5090 %U http://www.ncbi.nlm.nih.gov/pubmed/28974482 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 4 %P e65 %T Lessons From the Implementation of Mo-Buzz, a Mobile Pandemic Surveillance System for Dengue %A Lwin,May Oo %A Jayasundar,Karthikayen %A Sheldenkar,Anita %A Wijayamuni,Ruwan %A Wimalaratne,Prasad %A Ernst,Kacey C %A Foo,Schubert %+ Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore, 637718, Singapore, 65 67906669, tmaylwin@ntu.edu.sg %K pandemics %K dengue %K health communication %K telemedicine %K epidemiology %K participatory surveillance %K participatory epidemiology %D 2017 %7 02.10.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Approximately 128 countries and 3.9 billion people are at risk of dengue infection. Incidence of dengue has increased over the past decades, becoming a growing public health concern for countries with populations that are increasingly susceptible to this vector-borne disease, such as Sri Lanka. Almost 55,150 dengue cases were reported in Sri Lanka in 2016, with more than 30.40% of cases (n=16,767) originating from Colombo, which struggles with an outdated manual paper-based dengue outbreak management system. Community education and outreach about dengue are also executed using paper-based media channels such as pamphlets and brochures. Yet, Sri Lanka is one of the countries with the most affordable rates of mobile services in the world, with penetration rates higher than most developing countries. Objectives: To combat the issues of an exhausted dengue management system and to make use of new technology, in 2015, a mobile participatory system for dengue surveillance called Mo-Buzz was developed and launched in Colombo, Sri Lanka. This paper describes the system’s components and uptake, along with other similar disease surveillance systems. Methods: We developed Mo-Buzz and tested its feasibility for dengue. Two versions of the app were developed. The first was for use by public health inspectors (PHIs) to digitize form filling and recording of site visit information, and track dengue outbreaks on a real-time dengue hotspot map using the global positioning system technology. The system also provides updated dengue infographics and educational materials for the PHIs to educate the general public. The second version of Mo-Buzz was created for use by the general public. This system uses dynamic mapping to help educate and inform the general public about potential outbreak regions and allow them to report dengue symptoms and post pictures of potential dengue mosquito–breeding sites, which are automatically sent to the health authorities. Targeted alerts can be sent to users depending on their geographical location. Results: We assessed the usage and the usability of the app and its impact on overall dengue transmission in Colombo. Initial uptake of Mo-Buzz for PHIs was low; however, after more training and incentivizing of usage, the uptake of the app in PHIs increased from less than 10% (n=3) to 76% (n=38). The general public user evaluation feedback was fruitful in providing improvements to the app, and at present, a number of solutions are being reviewed as viable options to boost user uptake. Conclusions: From our Mo-Buzz study, we have learned that initial acceptance of such systems can be slow but eventually positive. Mobile and social media interventions, such as Mo-Buzz, are poised to play a greater role in shaping risk perceptions and managing seasonal and sporadic outbreaks of infectious diseases in Asia and around the world. %M 28970191 %R 10.2196/publichealth.7376 %U https://publichealth.jmir.org/2017/4/e65/ %U https://doi.org/10.2196/publichealth.7376 %U http://www.ncbi.nlm.nih.gov/pubmed/28970191 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 9 %P e132 %T Development of a Whole Slide Imaging System on Smartphones and Evaluation With Frozen Section Samples %A Yu,Hong %A Gao,Feng %A Jiang,Liren %A Ma,Shuoxin %+ TerryDr Info Technology Co., Ltd, Room A3-701, #180 Ruanjiandadao, Yuhuatai District, Nanjing, Jiangsu, 210000, China, 86 13813998278, sxma@terrydr.com %K mobile health %K image processing %K cloud computing for health care %K whole slide imaging %D 2017 %7 15.09.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The aim was to develop scalable Whole Slide Imaging (sWSI), a WSI system based on mainstream smartphones coupled with regular optical microscopes. This ultra-low-cost solution should offer diagnostic-ready imaging quality on par with standalone scanners, supporting both oil and dry objective lenses of different magnifications, and reasonably high throughput. These performance metrics should be evaluated by expert pathologists and match those of high-end scanners. Objective: The aim was to develop scalable Whole Slide Imaging (sWSI), a whole slide imaging system based on smartphones coupled with optical microscopes. This ultra-low-cost solution should offer diagnostic-ready imaging quality on par with standalone scanners, supporting both oil and dry object lens of different magnification. All performance metrics should be evaluated by expert pathologists and match those of high-end scanners. Methods: In the sWSI design, the digitization process is split asynchronously between light-weight clients on smartphones and powerful cloud servers. The client apps automatically capture FoVs at up to 12-megapixel resolution and process them in real-time to track the operation of users, then give instant feedback of guidance. The servers first restitch each pair of FoVs, then automatically correct the unknown nonlinear distortion introduced by the lens of the smartphone on the fly, based on pair-wise stitching, before finally combining all FoVs into one gigapixel VS for each scan. These VSs can be viewed using Internet browsers anywhere. In the evaluation experiment, 100 frozen section slides from patients randomly selected among in-patients of the participating hospital were scanned by both a high-end Leica scanner and sWSI. All VSs were examined by senior pathologists whose diagnoses were compared against those made using optical microscopy as ground truth to evaluate the image quality. Results: The sWSI system is developed for both Android and iPhone smartphones and is currently being offered to the public. The image quality is reliable and throughput is approximately 1 FoV per second, yielding a 15-by-15 mm slide under 20X object lens in approximately 30-35 minutes, with little training required for the operator. The expected cost for setup is approximately US $100 and scanning each slide costs between US $1 and $10, making sWSI highly cost-effective for infrequent or low-throughput usage. In the clinical evaluation of sample-wise diagnostic reliability, average accuracy scores achieved by sWSI-scan-based diagnoses were as follows: 0.78 for breast, 0.88 for uterine corpus, 0.68 for thyroid, and 0.50 for lung samples. The respective low-sensitivity rates were 0.05, 0.05, 0.13, and 0.25 while the respective low-specificity rates were 0.18, 0.08, 0.20, and 0.25. The participating pathologists agreed that the overall quality of sWSI was generally on par with that produced by high-end scanners, and did not affect diagnosis in most cases. Pathologists confirmed that sWSI is reliable enough for standard diagnoses of most tissue categories, while it can be used for quick screening of difficult cases. Conclusions: As an ultra-low-cost alternative to whole slide scanners, diagnosis-ready VS quality and robustness for commercial usage is achieved in the sWSI solution. Operated on main-stream smartphones installed on normal optical microscopes, sWSI readily offers affordable and reliable WSI to resource-limited or infrequent clinical users. %M 28916508 %R 10.2196/mhealth.8242 %U http://mhealth.jmir.org/2017/9/e132/ %U https://doi.org/10.2196/mhealth.8242 %U http://www.ncbi.nlm.nih.gov/pubmed/28916508 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 8 %P e297 %T Validation Relaxation: A Quality Assurance Strategy for Electronic Data Collection %A Kenny,Avi %A Gordon,Nicholas %A Griffiths,Thomas %A Kraemer,John D %A Siedner,Mark J %+ Last Mile Health, 205 Portland St #200, Boston, MA, 02114, United States, 1 9143163681, akenny@lastmilehealth.org %K data accuracy %K data collection %K surveys %K survey methodology %K research methodology %K questionnaire design %K mHealth %K eHealth %D 2017 %7 18.08.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of mobile devices for data collection in developing world settings is becoming increasingly common and may offer advantages in data collection quality and efficiency relative to paper-based methods. However, mobile data collection systems can hamper many standard quality assurance techniques due to the lack of a hardcopy backup of data. Consequently, mobile health data collection platforms have the potential to generate datasets that appear valid, but are susceptible to unidentified database design flaws, areas of miscomprehension by enumerators, and data recording errors. Objective: We describe the design and evaluation of a strategy for estimating data error rates and assessing enumerator performance during electronic data collection, which we term “validation relaxation.” Validation relaxation involves the intentional omission of data validation features for select questions to allow for data recording errors to be committed, detected, and monitored. Methods: We analyzed data collected during a cluster sample population survey in rural Liberia using an electronic data collection system (Open Data Kit). We first developed a classification scheme for types of detectable errors and validation alterations required to detect them. We then implemented the following validation relaxation techniques to enable data error conduct and detection: intentional redundancy, removal of “required” constraint, and illogical response combinations. This allowed for up to 11 identifiable errors to be made per survey. The error rate was defined as the total number of errors committed divided by the number of potential errors. We summarized crude error rates and estimated changes in error rates over time for both individuals and the entire program using logistic regression. Results: The aggregate error rate was 1.60% (125/7817). Error rates did not differ significantly between enumerators (P=.51), but decreased for the cohort with increasing days of application use, from 2.3% at survey start (95% CI 1.8%-2.8%) to 0.6% at day 45 (95% CI 0.3%-0.9%; OR=0.969; P<.001). The highest error rate (84/618, 13.6%) occurred for an intentional redundancy question for a birthdate field, which was repeated in separate sections of the survey. We found low error rates (0.0% to 3.1%) for all other possible errors. Conclusions: A strategy of removing validation rules on electronic data capture platforms can be used to create a set of detectable data errors, which can subsequently be used to assess group and individual enumerator error rates, their trends over time, and categories of data collection that require further training or additional quality control measures. This strategy may be particularly useful for identifying individual enumerators or systematic data errors that are responsive to enumerator training and is best applied to questions for which errors cannot be prevented through training or software design alone. Validation relaxation should be considered as a component of a holistic data quality assurance strategy. %M 28821474 %R 10.2196/jmir.7813 %U http://www.jmir.org/2017/8/e297/ %U https://doi.org/10.2196/jmir.7813 %U http://www.ncbi.nlm.nih.gov/pubmed/28821474 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 8 %P e125 %T Validation of a Smartphone-Based Approach to In Situ Cognitive Fatigue Assessment %A Price,Edward %A Moore,George %A Galway,Leo %A Linden,Mark %+ Computer Science Research Institute, School of Computing, Ulster University, Jordanstown Campus, Shore Road, Newtownabbey, BT37 0QB, United Kingdom, 44 02890366584, g.moore@ulster.ac.uk %K mental fatigue %K fatigue %K acquired brain injury %K cognitive tests %K assistive technology %K smartphone %D 2017 %7 17.08.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Acquired Brain Injuries (ABIs) can result in multiple detrimental cognitive effects, such as reduced memory capability, concentration, and planning. These effects can lead to cognitive fatigue, which can exacerbate the symptoms of ABIs and hinder management and recovery. Assessing cognitive fatigue is difficult due to the largely subjective nature of the condition and existing assessment approaches. Traditional methods of assessment use self-assessment questionnaires delivered in a medical setting, but recent work has attempted to employ more objective cognitive tests as a way of evaluating cognitive fatigue. However, these tests are still predominantly delivered within a medical environment, limiting their utility and efficacy. Objective: The aim of this research was to investigate how cognitive fatigue can be accurately assessed in situ, during the quotidian activities of life. It was hypothesized that this assessment could be achieved through the use of mobile assistive technology to assess working memory, sustained attention, information processing speed, reaction time, and cognitive throughput. Methods: The study used a bespoke smartphone app to track daily cognitive performance, in order to assess potential levels of cognitive fatigue. Twenty-one participants with no prior reported brain injuries took place in a two-week study, resulting in 81 individual testing instances being collected. The smartphone app delivered three cognitive tests on a daily basis: (1) Spatial Span to measure visuospatial working memory; (2) Psychomotor Vigilance Task (PVT) to measure sustained attention, information processing speed, and reaction time; and (3) a Mental Arithmetic Test to measure cognitive throughput. A smartphone-optimized version of the Mental Fatigue Scale (MFS) self-assessment questionnaire was used as a baseline to assess the validity of the three cognitive tests, as the questionnaire has already been validated in multiple peer-reviewed studies. Results: The most highly correlated results were from the PVT, which showed a positive correlation with those from the prevalidated MFS, measuring 0.342 (P<.008). Scores from the cognitive tests were entered into a regression model and showed that only reaction time in the PVT was a significant predictor of fatigue (P=.016, F=2.682, 95% CI 9.0-84.2). Higher scores on the MFS were related to increases in reaction time during our mobile variant of the PVT. Conclusions: The results show that the PVT mobile cognitive test developed for this study could be used as a valid and reliable method for measuring cognitive fatigue in situ. This test would remove the subjectivity associated with established self-assessment approaches and the need for assessments to be performed in a medical setting. Based on our findings, future work could explore delivering a small set of tests with increased duration to further improve measurement reliability. Moreover, as the smartphone assessment tool can be used as part of everyday life, additional sources of data relating to physiological, psychological, and environmental context could be included within the analysis to improve the nature and precision of the assessment process. %M 28818818 %R 10.2196/mhealth.6333 %U http://mhealth.jmir.org/2017/8/e125/ %U https://doi.org/10.2196/mhealth.6333 %U http://www.ncbi.nlm.nih.gov/pubmed/28818818 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 8 %P e112 %T Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety %A Saeb,Sohrab %A Lattie,Emily G %A Kording,Konrad P %A Mohr,David C %+ Center for Behavioral Intervention Technologies (CBITs), Department of Preventive Medicine, Northwestern University, 10th Fl., 750 N Lake Shore Dr., Chicago, IL, 60611, United States, 1 3125034626, s-saeb@northwestern.edu %K semantic location %K geographic positioning systems %K mobile phone %K classification %K decision tree ensembles %K extreme gradient boosting %K depression %K anxiety %D 2017 %7 10.08.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Is someone at home, at their friend’s place, at a restaurant, or enjoying the outdoors? Knowing the semantic location of an individual matters for delivering medical interventions, recommendations, and other context-aware services. This knowledge is particularly useful in mental health care for monitoring relevant behavioral indicators to improve treatment delivery. Local search-and-discovery services such as Foursquare can be used to detect semantic locations based on the global positioning system (GPS) coordinates, but GPS alone is often inaccurate. Mobile phones can also sense other signals (such as movement, light, and sound), and the use of these signals promises to lead to a better estimation of an individual’s semantic location. Objective: We aimed to examine the ability of mobile phone sensors to estimate semantic locations, and to evaluate the relationship between semantic location visit patterns and depression and anxiety. Methods: A total of 208 participants across the United States were asked to log the type of locations they visited daily, using their mobile phones for a period of 6 weeks, while their phone sensor data was recorded. Using the sensor data and Foursquare queries based on GPS coordinates, we trained models to predict these logged locations, and evaluated their prediction accuracy on participants that models had not seen during training. We also evaluated the relationship between the amount of time spent in each semantic location and depression and anxiety assessed at baseline, in the middle, and at the end of the study. Results: While Foursquare queries detected true semantic locations with an average area under the curve (AUC) of 0.62, using phone sensor data alone increased the AUC to 0.84. When we used Foursquare and sensor data together, the AUC further increased to 0.88. We found some significant relationships between the time spent in certain locations and depression and anxiety, although these relationships were not consistent. Conclusions: The accuracy of location services such as Foursquare can significantly benefit from using phone sensor data. However, our results suggest that the nature of the places people visit explains only a small part of the variation in their anxiety and depression symptoms. %M 28798010 %R 10.2196/mhealth.7297 %U http://mhealth.jmir.org/2017/8/e112/ %U https://doi.org/10.2196/mhealth.7297 %U http://www.ncbi.nlm.nih.gov/pubmed/28798010 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 8 %P e119 %T Validation of a Smartphone App for the Assessment of Sedentary and Active Behaviors %A Toledo,Meynard John %A Hekler,Eric %A Hollingshead,Kevin %A Epstein,Dana %A Buman,Matthew %+ Arizona State University, School of Nutrition and Health Promotion, Arizona Biomedical Collaborative, 550 N 3rd St, Phoenix, AZ, 85004, United States, 1 6028272289, mbuman@asu.edu %K Sedentary and physical activity measurement %K smartphone daily-log app %K self-monitoring app %K BeWell24 %D 2017 %7 09.08.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus, self-reports still remain an important method of measurement for physical activity and sedentary behaviors. Objective: This study evaluated the reliability, validity, and sensitivity to change of a smartphone app in assessing sitting, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA). Methods: Adults (N=28; 49.0 years old, standard deviation [SD] 8.9; 85% men; 73% Caucasian; body mass index=35.0, SD 8.3 kg/m2) reported their sitting, LPA, and MVPA over an 11-week behavioral intervention. During three separate 7-day periods, participants wore the activPAL3c accelerometer/inclinometer as a criterion measure. Intraclass correlation (ICC; 95% CI) and bias estimates (mean difference [δ] and root of mean square error [RMSE]) were used to compare app-based reported behaviors to measured sitting time (lying/seated position), LPA (standing or stepping at <100 steps/minute), and MVPA (stepping at >100 steps/minute). Results: Test-retest results suggested moderate agreement with the criterion for sedentary time, LPA, and MVPA (ICC=0.65 [0.43-0.82], 0.67 [0.44-0.83] and 0.69 [0.48-0.84], respectively). The agreement between the two measures was poor (ICC=0.05-0.40). The app underestimated sedentary time (δ=-45.9 [-67.6, -24.2] minutes/day, RMSE=201.6) and overestimated LPA and MVPA (δ=18.8 [-1.30 to 38.9] minutes/day, RMSE=183; and δ=29.3 [25.3 to 33.2] minutes/day, RMSE=71.6, respectively). The app underestimated change in time spent during LPA and MVPA but overestimated change in sedentary time. Both measures showed similar directions in changed scores on sedentary time and LPA. Conclusions: Despite its inaccuracy, the app may be useful as a self-monitoring tool in the context of a behavioral intervention. Future research may help to clarify reasons for under- or over-reporting of behaviors. %M 28793982 %R 10.2196/mhealth.6974 %U http://mhealth.jmir.org/2017/8/e119/ %U https://doi.org/10.2196/mhealth.6974 %U http://www.ncbi.nlm.nih.gov/pubmed/28793982 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 7 %P e262 %T Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review %A Dogan,Ezgi %A Sander,Christian %A Wagner,Xenija %A Hegerl,Ulrich %A Kohls,Elisabeth %+ Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Semmelweisstrasse 10, Haus 13, Leipzig, 04103, Germany, 49 341 9724558, Christian.Sander@medizin.uni-leipzig.de %K review %K mood disorders %K smartphone %K ecological momentary assessment %D 2017 %7 24.07.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones. Objective: We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes. Methods: We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols. Results: A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage. Conclusions: The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field. %M 28739561 %R 10.2196/jmir.7006 %U http://www.jmir.org/2017/7/e262/ %U https://doi.org/10.2196/jmir.7006 %U http://www.ncbi.nlm.nih.gov/pubmed/28739561 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 7 %P e253 %T Ecological Momentary Assessment of Physical Activity: Validation Study %A Knell,Gregory %A Gabriel,Kelley Pettee %A Businelle,Michael S %A Shuval,Kerem %A Wetter,David W %A Kendzor,Darla E %+ Michael and Susan Dell Center for Healthy Living, Department of Health Promotion and Behavioral Sciences, University of Texas Health Science Center (UTHealth) at Houston, 7000 Fannin, #2528, Houston, TX, 77030, United States, 1 713 500 9678, gregory.knell@uth.tmc.edu %K accelerometry %K behavioral risk factor surveillance system %K ecological momentary assessment %K self-report %K data accuracy %D 2017 %7 18.07.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Ecological momentary assessment (EMA) may elicit physical activity (PA) estimates that are less prone to bias than traditional self-report measures while providing context. Objectives: The objective of this study was to examine the convergent validity of EMA-assessed PA compared with accelerometry. Methods: The participants self-reported their PA using International Physical Activity Questionnaire (IPAQ) and Behavioral Risk Factor Surveillance System (BRFSS) and wore an accelerometer while completing daily EMAs (delivered through the mobile phone) for 7 days. Weekly summary estimates included sedentary time and moderate-, vigorous-, and moderate-to vigorous-intensity physical activity (MVPA). Spearman coefficients and Lin’s concordance correlation coefficients (LCC) examined the linear association and agreement for EMA and the questionnaires as compared with accelerometry. Results: Participants were aged 43.3 (SD 13.1) years, 51.7% (123/238) were African American, 74.8% (178/238) were overweight or obese, and 63.0% (150/238) were low income. The linear associations of EMA and traditional self-reports with accelerometer estimates were statistically significant (P<.05) for sedentary time (EMA: ρ=.16), moderate-intensity PA (EMA: ρ=.29; BRFSS: ρ=.17; IPAQ: ρ=.24), and MVPA (EMA: ρ=.31; BRFSS: ρ=.17; IPAQ: ρ=.20). Only EMA estimates of PA were statistically significant compared with accelerometer for agreement. Conclusions: The mobile EMA showed better correlation and agreement to accelerometer estimates than traditional self-report methods. These findings suggest that mobile EMA may be a practical alternative to accelerometers to assess PA in free-living settings. %M 28720556 %R 10.2196/jmir.7602 %U http://www.jmir.org/2017/7/e253/ %U https://doi.org/10.2196/jmir.7602 %U http://www.ncbi.nlm.nih.gov/pubmed/28720556 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 3 %P e42 %T A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports %A Quade,Patrick %A Nsoesie,Elaine Okanyene %+ Iwaspoisoned.com, 322 W 52nd St #633, New York, NY, 10101, United States, 1 9179034815, patrick@dinesafe.org %K foodborne illness surveillance %K crowdsourced surveillance %K foodborne diseases %K infectious diseases %K outbreaks %K food poisoning %K Internet %K mobile %K participatory surveillance %K participatory epidemiology %D 2017 %7 05.07.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Underreporting of foodborne illness makes foodborne disease burden estimation, timely outbreak detection, and evaluation of policies toward improving food safety challenging. Objective: The objective of this study was to present and evaluate Iwaspoisoned.com, an openly accessible Internet-based crowdsourcing platform that was launched in 2009 for the surveillance of foodborne illness. The goal of this system is to collect data that can be used to augment traditional approaches to foodborne disease surveillance. Methods: Individuals affected by a foodborne illness can use this system to report their symptoms and the suspected location (eg, restaurant, hotel, hospital) of infection. We present descriptive statistics of users and businesses and highlight three instances where reports of foodborne illness were submitted before the outbreaks were officially confirmed by the local departments of health. Results: More than 49,000 reports of suspected foodborne illness have been submitted on Iwaspoisoned.com since its inception by individuals from 89 countries and every state in the United States. Approximately 95.51% (42,139/44,119) of complaints implicated restaurants as the source of illness. Furthermore, an estimated 67.55% (3118/4616) of users who responded to a demographic survey were between the ages of 18 and 34, and 60.14% (2776/4616) of the respondents were female. The platform is also currently used by health departments in 90% (45/50) of states in the US to supplement existing programs on foodborne illness reporting. Conclusions: Crowdsourced disease surveillance through systems such as Iwaspoisoned.com uses the influence and familiarity of social media to create an infrastructure for easy reporting and surveillance of suspected foodborne illness events. If combined with traditional surveillance approaches, these systems have the potential to lessen the problem of foodborne illness underreporting and aid in early detection and monitoring of foodborne disease outbreaks. %M 28679492 %R 10.2196/publichealth.7076 %U http://publichealth.jmir.org/2017/3/e42/ %U https://doi.org/10.2196/publichealth.7076 %U http://www.ncbi.nlm.nih.gov/pubmed/28679492 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 6 %P e87 %T Acceptance of Mobile Health in Communities Underrepresented in Biomedical Research: Barriers and Ethical Considerations for Scientists %A Nebeker,Camille %A Murray,Kate %A Holub,Christina %A Haughton,Jessica %A Arredondo,Elva M %+ Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, 9500 Gilman Drive, MC 0725, La Jolla, CA, 92093, United States, 1 858 534 7786, nebeker@eng.ucsd.edu %K telemedicine %K cultural diversity %K ethics, research %K ethics committees %K research %K privacy %K informed consent %D 2017 %7 28.06.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Background: The rapid expansion of direct-to-consumer wearable fitness products (eg, Flex 2, Fitbit) and research-grade sensors (eg, SenseCam, Microsoft Research; activPAL, PAL Technologies) coincides with new opportunities for biomedical and behavioral researchers. Underserved communities report among the highest rates of chronic disease and could benefit from mobile technologies designed to facilitate awareness of health behaviors. However, new and nuanced ethical issues are introduced with new technologies, which are challenging both institutional review boards (IRBs) and researchers alike. Given the potential benefits of such technologies, ethical and regulatory concerns must be carefully considered. Objective: Our aim was to understand potential barriers to using wearable sensors among members of Latino, Somali and Native Hawaiian Pacific Islander (NHPI) communities. These ethnic groups report high rates of disparate health conditions and could benefit from wearable technologies that translate the connection between physical activity and desired health outcomes. Moreover, these groups are traditionally under-represented in biomedical research. Methods: We independently conducted formative research with individuals from southern California, who identified as Latino, Somali, or Native Hawaiian Pacific Islander (NHPI). Data collection methods included survey (NHPI), interview (Latino), and focus group (Somali) with analysis focusing on cross-cutting themes. Results: The results pointed to gaps in informed consent, challenges to data management (ie, participant privacy, data confidentiality, and data sharing conventions), social implications (ie, unwanted attention), and legal risks (ie, potential deportation). Conclusions: Results shed light on concerns that may escalate the digital divide. Recommendations include suggestions for researchers and IRBs to collaborate with a goal of developing meaningful and ethical practices that are responsive to diverse research participants who can benefit from technology-enabled research methods. Trial Registration: ClinicalTrials.gov NCT02505165; https://clinicaltrials.gov/ct2/show/NCT02505165 (Archived by WebCite at http://www.Webcitation.org/6r9ZSUgoT) %M 28659258 %R 10.2196/mhealth.6494 %U http://mhealth.jmir.org/2017/6/e87/ %U https://doi.org/10.2196/mhealth.6494 %U http://www.ncbi.nlm.nih.gov/pubmed/28659258 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 6 %P e114 %T Phone-Based Interventions in Adolescent Psychiatry: A Perspective and Proof of Concept Pilot Study With a Focus on Depression and Autism %A Chen,Robert Yuzen %A Feltes,Jordan Robert %A Tzeng,William Shun %A Lu,Zoe Yunzhu %A Pan,Michael %A Zhao,Nan %A Talkin,Rebecca %A Javaherian,Kavon %A Glowinski,Anne %A Ross,Will %+ Washington University School of Medicine, Farrell Learning and Teaching Center, 660 S Euclid Avenue, St. Louis, MO, 63110, United States, 1 425 753 4101, robert.chen@wustl.edu %K telemedicine %K depression %K autistic disorder %K mobile applications %K text messaging %K child %K mental health %D 2017 %7 16.06.2017 %9 Viewpoint %J JMIR Res Protoc %G English %X Background: Telemedicine has emerged as an innovative platform to diagnose and treat psychiatric disorders in a cost-effective fashion. Previous studies have laid the functional framework for monitoring and treating child psychiatric disorders electronically using videoconferencing, mobile phones (smartphones), and Web-based apps. However, phone call and text message (short message service, SMS) interventions in adolescent psychiatry are less studied than other electronic platforms. Further investigations on the development of these interventions are needed. Objective: The aim of this paper was to explore the utility of text message interventions in adolescent psychiatry and describe a user feedback-driven iterative design process for text message systems. Methods: We developed automated text message interventions using a platform for both depression (EpxDepression) and autism spectrum disorder (ASD; EpxAutism) and conducted 2 pilot studies for each intervention (N=3 and N=6, respectively). The interventions were prescribed by and accessible to the patients’ healthcare providers. EpxDepression and EpxAutism utilized an automated system to triage patients into 1 of 3 risk categories based on their text responses and alerted providers directly via phone and an online interface when patients met provider-specified risk criteria. Rapid text-based feedback from participants and interviews with providers allowed for quick iterative cycles to improve interventions. Results: Patients using EpxDepression had high weekly response rates (100% over 2 to 4 months), but exhibited message fatigue with daily prompts with mean (SD) overall response rates of 66.3% (21.6%) and 64.7% (8.2%) for mood and sleep questionnaires, respectively. In contrast, parents using EpxAutism displayed both high weekly and overall response rates (100% and 85%, respectively, over 1 to 4 months) that did not decay significantly with time. Monthly participant feedback surveys for EpxDepression (7 surveys) and EpxAutism (18 surveys) preliminarily indicated that for both interventions, daily messages constituted the “perfect amount” of contact and that EpxAutism, but not EpxDepression, improved patient communication with providers. Notably, EpxDepression detected thoughts of self-harm in patients before their case managers or caregivers were aware of such ideation. Conclusions: Text-message interventions in adolescent psychiatry can provide a cost-effective and engaging method to track symptoms, behavior, and ideation over time. Following the collection of pilot data and feedback from providers and patients, larger studies are already underway to validate the clinical utility of EpxDepression and EpxAutism. Trial Registration: Clinicaltrials.gov NCT03002311; https://clinicaltrials.gov/ct2/show/NCT03002311 (Archived by WebCite at http://www.webcitation.org/6qQtlCIS0) %M 28623183 %R 10.2196/resprot.7245 %U http://www.researchprotocols.org/2017/6/e114/ %U https://doi.org/10.2196/resprot.7245 %U http://www.ncbi.nlm.nih.gov/pubmed/28623183 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 5 %P e84 %T Comparison of Ecological Momentary Assessment Versus Direct Measurement of E-Cigarette Use With a Bluetooth-Enabled E-Cigarette: A Pilot Study %A Pearson,Jennifer L %A Elmasry,Hoda %A Das,Babita %A Smiley,Sabrina L %A Rubin,Leslie F %A DeAtley,Teresa %A Harvey,Emily %A Zhou,Yitong %A Niaura,Raymond %A Abrams,David B %+ Truth Initiative, Schroeder Institute for Tobacco Research and Policy Studies, 900 G St NW, Fourth Floor, Washington, DC,, United States, 1 202 454 5768, jpearson@truthinitiative.org %K smoking %K humans %K tobacco products/utilization %K electronic cigarettes %K observational study %K United States %D 2017 %7 29.05.2017 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Assessing the frequency and intensity of e-cigarette use presents special challenges beyond those posed by cigarette use. Accurate measurement of e-cigarette consumption, puff duration, and the stability of these measures over time will be informative for estimating the behavioral and health effects of e-cigarette use. Objective: The purpose of this pilot study was to compare the accuracy of self-reported e-cigarette puff counts collected via ecological momentary assessment (EMA) to objective puff count data collected by a Bluetooth-enabled e-cigarette device and to examine the feasibility and acceptability of using a second-generation e-cigarette among adult smokers. Methods: A total of 5 adult smokers were enrolled in a longitudinal parent study assessing how e-cigarette use affects cigarette use among e-cigarette–naïve smokers. Using a text message–based EMA system, participants reported e-cigarette puffs for 2 weeks. Participants were also given a Bluetooth-enabled e-cigarette (Smokio) that passively collected puff counts and puff duration. Comparisons between mean reports of Smokio (device-report) and EMA (self-report) use were evaluated using paired t tests. Correlation and agreement between device- and self-reports were evaluated using Pearson correlation and the concordance correlation coefficient (CCC), respectively. A linear mixed effect model was used to determine the fixed effect of timing and Smokio-reported daily puffs on report accuracy. We examined the relationship between time of day and reporting accuracy using Tukey's test for multiple pairwise comparisons. Results: A total of 5 African American participants, 4 men and 1 woman, who ranged in age from 24 to 59 years completed the study, resulting in 5180 observations (device-report) of e-cigarette use. At baseline, participants reported smoking for 5 to 25 years and consumed a mean of 7 to 13 cigarettes per day (CPD); 4 smoked within 30 minutes of waking. At the 30-day follow-up, CPD range decreased to 1 to 3 cigarettes; 4 participants reported past 7-day e-cigarette use, and 1 participant reported no cigarette smoking in the past 7 days. Over 2 weeks of e-cigarette use, participants took an average of 1074 e-cigarette (SD 779.0) puffs per person as captured by the device reports. Each participant took a mean of 75.0 (SD 58.8) puffs per day, with each puff lasting an average of 3.6 (SD 2.4) seconds. Device reports captured an average of 33.3 (SD 47.8) more puffs per person per day than the self-reported e-cigarette puffs. In 87% of days, participants underestimated the number of puffs they had taken on the Smokio. There was significant moderate correlation (r=.47, P<.001) but poor agreement (pc=0.31, 95% CI 0.15-0.46) between the device- and self-reported data. Reporting accuracy was affected by amount and timing of e-cigarette use. Conclusions: Compared to self-reported e-cigarette use, the Bluetooth-enabled device captured significantly more e-cigarette use and allowed for examination of puff duration in addition to puff counts. A Bluetooth-enabled e-cigarette is a powerful and feasible tool for objective collection of e-cigarette use behavior in the real world. %M 28554877 %R 10.2196/resprot.6501 %U http://www.researchprotocols.org/2017/5/e84/ %U https://doi.org/10.2196/resprot.6501 %U http://www.ncbi.nlm.nih.gov/pubmed/28554877 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 5 %P e68 %T Cognitive Testing in People at Increased Risk of Dementia Using a Smartphone App: The iVitality Proof-of-Principle Study %A Jongstra,Susan %A Wijsman,Liselotte Willemijn %A Cachucho,Ricardo %A Hoevenaar-Blom,Marieke Peternella %A Mooijaart,Simon Pieter %A Richard,Edo %+ Academic Medical Center, Department of Neurology, University of Amsterdam, Meiberdreef 9, Amsterdam, 1105 AZ, Netherlands, 31 205663446, s.jongstra@amc.uva.nl %K telemedicine %K cognition %K neuropsychological tests %D 2017 %7 25.05.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone-assisted technologies potentially provide the opportunity for large-scale, long-term, repeated monitoring of cognitive functioning at home. Objective: The aim of this proof-of-principle study was to evaluate the feasibility and validity of performing cognitive tests in people at increased risk of dementia using smartphone-based technology during a 6 months follow-up period. Methods: We used the smartphone-based app iVitality to evaluate five cognitive tests based on conventional neuropsychological tests (Memory-Word, Trail Making, Stroop, Reaction Time, and Letter-N-Back) in healthy adults. Feasibility was tested by studying adherence of all participants to perform smartphone-based cognitive tests. Validity was studied by assessing the correlation between conventional neuropsychological tests and smartphone-based cognitive tests and by studying the effect of repeated testing. Results: We included 151 participants (mean age in years=57.3, standard deviation=5.3). Mean adherence to assigned smartphone tests during 6 months was 60% (SD 24.7). There was moderate correlation between the firstly made smartphone-based test and the conventional test for the Stroop test and the Trail Making test with Spearman ρ=.3-.5 (P<.001). Correlation increased for both tests when comparing the conventional test with the mean score of all attempts a participant had made, with the highest correlation for Stroop panel 3 (ρ=.62, P<.001). Performance on the Stroop and the Trail Making tests improved over time suggesting a learning effect, but the scores on the Letter-N-back, the Memory-Word, and the Reaction Time tests remained stable. Conclusions: Repeated smartphone-assisted cognitive testing is feasible with reasonable adherence and moderate relative validity for the Stroop and the Trail Making tests compared with conventional neuropsychological tests. Smartphone-based cognitive testing seems promising for large-scale data-collection in population studies. %M 28546139 %R 10.2196/mhealth.6939 %U http://mhealth.jmir.org/2017/5/e68/ %U https://doi.org/10.2196/mhealth.6939 %U http://www.ncbi.nlm.nih.gov/pubmed/28546139 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 4 %N 2 %P e15 %T Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment %A Tsanas,Athanasios %A Saunders,Kate %A Bilderbeck,Amy %A Palmius,Niclas %A Goodwin,Guy %A De Vos,Maarten %+ Usher Institute of Population Health Sciences and Informatics, Medical School, University of Edinburgh, Nine Edinburgh Bioquarter, 9 Little France road, Edinburgh, EH16 4UX, United Kingdom, 44 131 651 7884, Athanasios.Tsanas@ed.ac.uk %K bipolar disorder %K borderline personality disorder %K depression %K mania %K latent variable structure %K mood monitoring %K patient reported outcome measures %K mhealth %K mobile app %D 2017 %7 25.05.2017 %9 Original Paper %J JMIR Ment Health %G English %X Background: We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. Objective: The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. Methods: We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. Results: We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. Conclusions: This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice. %M 28546141 %R 10.2196/mental.6917 %U http://mental.jmir.org/2017/2/e15/ %U https://doi.org/10.2196/mental.6917 %U http://www.ncbi.nlm.nih.gov/pubmed/28546141 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 5 %P e99 %T Development of a Modular Research Platform to Create Medical Observational Studies for Mobile Devices %A Zens,Martin %A Grotejohann,Birgit %A Tassoni,Adrian %A Duttenhoefer,Fabian %A Südkamp,Norbert P %A Niemeyer,Philipp %+ Medical Center - University of Freiburg, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, Freiburg, 79106, Germany, 49 1633374461, martin.zens@me.com %K mHealth %K telemedicine %K mobile health %K app-based study %K research platform %D 2017 %7 23.05.2017 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Observational studies have proven to be a valuable resource in medical research, especially when performed on a large scale. Recently, mobile device-based observational studies have been discovered by an increasing number of researchers as a promising new source of information. However, the development and deployment of app-based studies is not trivial and requires profound programming skills. Objective: The aim of this project was to develop a modular online research platform that allows researchers to create medical studies for mobile devices without extensive programming skills. Methods: The platform approach for a modular research platform consists of three major components. A Web-based platform forms the researchers’ main workplace. This platform communicates via a shared database with a platform independent mobile app. Furthermore, a separate Web-based login platform for physicians and other health care professionals is outlined and completes the concept. Results: A prototype of the research platform has been developed and is currently in beta testing. Simple questionnaire studies can be created within minutes and published for testing purposes. Screenshots of an example study are provided, and the general working principle is displayed. Conclusions: In this project, we have created a basis for a novel research platform. The necessity and implications of a modular approach were displayed and an outline for future development given. International researchers are invited and encouraged to participate in this ongoing project %M 28536095 %R 10.2196/resprot.7705 %U http://www.researchprotocols.org/2017/5/e99/ %U https://doi.org/10.2196/resprot.7705 %U http://www.ncbi.nlm.nih.gov/pubmed/28536095 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e140 %T Building the Evidence Base for Remote Data Collection in Low- and Middle-Income Countries: Comparing Reliability and Accuracy Across Survey Modalities %A Greenleaf,Abigail R %A Gibson,Dustin G %A Khattar,Christelle %A Labrique,Alain B %A Pariyo,George W %+ Johns Hopkins Bloomberg School of Public Health, Department of Population, Family and Reproductive Health, 615 N Wolfe Street, Baltimore, MD, 21205, United States, 1 410 955 3543, agreenleaf@jhu.edu %K mHealth %K developing countries %K Africa South of the Sahara %K cell phones %K health surveys %K reproducibility of results %K surveys and questionnaires %K text messaging %K interviews as topic %K humans %K research design %K data collection methods %D 2017 %7 05.05.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Given the growing interest in mobile data collection due to the proliferation of mobile phone ownership and network coverage in low- and middle-income countries (LMICs), we synthesized the evidence comparing estimates of health outcomes from multiple modes of data collection. In particular, we reviewed studies that compared a mode of remote data collection with at least one other mode of data collection to identify mode effects and areas for further research. Objective: The study systematically reviewed and summarized the findings from articles and reports that compare a mode of remote data collection to at least one other mode. The aim of this synthesis was to assess the reliability and accuracy of results. Methods: Seven online databases were systematically searched for primary and grey literature pertaining to remote data collection in LMICs. Remote data collection included interactive voice response (IVR), computer-assisted telephone interviews (CATI), short message service (SMS), self-administered questionnaires (SAQ), and Web surveys. Two authors of this study reviewed the abstracts to identify articles which met the primary inclusion criteria. These criteria required that the survey collected the data from the respondent via mobile phone or landline. Articles that met the primary screening criteria were read in full and were screened using secondary inclusion criteria. The four secondary inclusion criteria were that two or more modes of data collection were compared, at least one mode of data collection in the study was a mobile phone survey, the study had to be conducted in a LMIC, and finally, the study should include a health component. Results: Of the 11,568 articles screened, 10 articles were included in this study. Seven distinct modes of remote data collection were identified: CATI, SMS (singular sitting and modular design), IVR, SAQ, and Web surveys (mobile phone and personal computer). CATI was the most frequent remote mode (n=5 articles). Of the three in-person modes (face-to-face [FTF], in-person SAQ, and in-person IVR), FTF was the most common (n=11) mode. The 10 articles made 25 mode comparisons, of which 12 comparisons were from a single article. Six of the 10 articles included sensitive questions. Conclusions: This literature review summarizes the existing research about remote data collection in LMICs. Due to both heterogeneity of outcomes and the limited number of comparisons, this literature review is best positioned to present the current evidence and knowledge gaps rather than attempt to draw conclusions. In order to advance the field of remote data collection, studies that employ standardized sampling methodologies and study designs are necessary to evaluate the potential for differences by survey modality. %M 28476728 %R 10.2196/jmir.7331 %U http://www.jmir.org/2017/5/e140/ %U https://doi.org/10.2196/jmir.7331 %U http://www.ncbi.nlm.nih.gov/pubmed/28476728 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e137 %T Leveraging Mobile Phones for Monitoring Risks for Noncommunicable Diseases in the Future %A Ellis,Jennifer A %+ Bloomberg Philanthropies, 25 E 78th St., New York, NY,, United States, 1 212 205 0129, Jennifer@bloomberg.org %K mHealth %K low- and middle-income countries %K noncommunicable diseases %K health systems strengthening %D 2017 %7 05.05.2017 %9 Guest Editorial %J J Med Internet Res %G English %X %M 28476721 %R 10.2196/jmir.7622 %U http://www.jmir.org/2017/5/e137/ %U https://doi.org/10.2196/jmir.7622 %U http://www.ncbi.nlm.nih.gov/pubmed/28476721 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e129 %T Using mHealth to Predict Noncommunicable Diseases: A Public Health Opportunity for Low- and Middle-Income Countries %A Rosskam,Ellen %A Hyder,Adnan A %+ ER Global Consult, 34D Route du Prieur, Landecy, La Croix-de-Rozon, 1257, Switzerland, 41 223476846, ellenrosskam@gmail.com %K mHealth %K low- and middle-income countries %K noncommunicable diseases %K research agenda %K population health surveys %D 2017 %7 05.05.2017 %9 Editorial %J J Med Internet Res %G English %X %M 28476727 %R 10.2196/jmir.7593 %U http://www.jmir.org/2017/5/e129/ %U https://doi.org/10.2196/jmir.7593 %U http://www.ncbi.nlm.nih.gov/pubmed/28476727 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e139 %T Mobile Phone Surveys for Collecting Population-Level Estimates in Low- and Middle-Income Countries: A Literature Review %A Gibson,Dustin G %A Pereira,Amanda %A Farrenkopf,Brooke A %A Labrique,Alain B %A Pariyo,George W %A Hyder,Adnan A %+ Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 443 287 8763, dgibso28@jhu.edu %K survey methodology %K cellular phone %K interactive voice response %K short messages service %K computer-assisted telephone interview %K mobile phone surveys %D 2017 %7 05.05.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: National and subnational level surveys are important for monitoring disease burden, prioritizing resource allocation, and evaluating public health policies. As mobile phone access and ownership become more common globally, mobile phone surveys (MPSs) offer an opportunity to supplement traditional public health household surveys. Objective: The objective of this study was to systematically review the current landscape of MPSs to collect population-level estimates in low- and middle-income countries (LMICs). Methods: Primary and gray literature from 7 online databases were systematically searched for studies that deployed MPSs to collect population-level estimates. Titles and abstracts were screened on primary inclusion and exclusion criteria by two research assistants. Articles that met primary screening requirements were read in full and screened for secondary eligibility criteria. Articles included in review were grouped into the following three categories by their survey modality: (1) interactive voice response (IVR), (2) short message service (SMS), and (3) human operator or computer-assisted telephone interviews (CATI). Data were abstracted by two research assistants. The conduct and reporting of the review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Results: A total of 6625 articles were identified through the literature review. Overall, 11 articles were identified that contained 19 MPS (CATI, IVR, or SMS) surveys to collect population-level estimates across a range of topics. MPSs were used in Latin America (n=8), the Middle East (n=1), South Asia (n=2), and sub-Saharan Africa (n=8). Nine articles presented results for 10 CATI surveys (10/19, 53%). Two articles discussed the findings of 6 IVR surveys (6/19, 32%). Three SMS surveys were identified from 2 articles (3/19, 16%). Approximately 63% (12/19) of MPS were delivered to mobile phone numbers collected from previously administered household surveys. The majority of MPS (11/19, 58%) were panel surveys where a cohort of participants, who often were provided a mobile phone upon a face-to-face enrollment, were surveyed multiple times. Conclusions: Very few reports of population-level MPS were identified. Of the MPS that were identified, the majority of surveys were conducted using CATI. Due to the limited number of identified IVR and SMS surveys, the relative advantages and disadvantages among the three survey modalities cannot be adequately assessed. The majority of MPS were sent to mobile phone numbers that were collected from a previously administered household survey. There is limited evidence on whether a random digit dialing (RDD) approach or a simple random sample of mobile network provided list of numbers can produce a population representative survey. %M 28476725 %R 10.2196/jmir.7428 %U http://www.jmir.org/2017/5/e139/ %U https://doi.org/10.2196/jmir.7428 %U http://www.ncbi.nlm.nih.gov/pubmed/28476725 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e112 %T The Development of an Interactive Voice Response Survey for Noncommunicable Disease Risk Factor Estimation: Technical Assessment and Cognitive Testing %A Gibson,Dustin G %A Farrenkopf,Brooke A %A Pereira,Amanda %A Labrique,Alain B %A Pariyo,George William %+ Johns Hopkins Bloomberg School of Public Health, Department of International Health, 615 N Wolfe St, Baltimore, MD, 21231, United States, 1 4432878763, dgibso28@jhu.edu %K interactive voice response %K noncommunicable disease %K survey methodology %K public health surveillance %K cellular phone %K risk factors %D 2017 %7 05.05.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The rise in mobile phone ownership in low- and middle-income countries (LMICs) presents an opportunity to transform existing data collection and surveillance methods. Administering surveys via interactive voice response (IVR) technology—a mobile phone survey (MPS) method—has potential to expand the current surveillance coverage and data collection, but formative work to contextualize the survey for LMIC deployment is needed. Objective: The primary objectives of this study were to (1) cognitively test and identify challenging questions in a noncommunicable disease (NCD) risk factor questionnaire administered via an IVR platform and (2) assess the usability of the IVR platform. Methods: We conducted two rounds of pilot testing the IVR survey in Baltimore, MD. Participants were included in the study if they identified as being from an LMIC. The first round included individual interviews to cognitively test the participant’s understanding of the questions. In the second round, participants unique from those in round 1 were placed in focus groups and were asked to comment on the usability of the IVR platform. Results: A total of 12 participants from LMICs were cognitively tested in round 1 to assess their understanding and comprehension of questions in an IVR-administered survey. Overall, the participants found that the majority of the questions were easy to understand and did not have difficulty recording most answers. The most frequent recommendation was to use country-specific examples and units of measurement. In round 2, a separate set of 12 participants assessed the usability of the IVR platform. Overall, participants felt that the length of the survey was appropriate (average: 18 min and 31 s), but the majority reported fatigue in answering questions that had a similar question structure. Almost all participants commented that they thought an IVR survey would lead to more honest, accurate responses than face-to-face questionnaires, especially for sensitive topics. Conclusions: Overall, the participants indicated a clear comprehension of the IVR-administered questionnaire and that the IVR platform was user-friendly. Formative research and cognitive testing of the questionnaire is needed for further adaptation before deploying in an LMIC. %M 28476724 %R 10.2196/jmir.7340 %U http://www.jmir.org/2017/5/e112/ %U https://doi.org/10.2196/jmir.7340 %U http://www.ncbi.nlm.nih.gov/pubmed/28476724 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e121 %T Health Surveys Using Mobile Phones in Developing Countries: Automated Active Strata Monitoring and Other Statistical Considerations for Improving Precision and Reducing Biases %A Labrique,Alain %A Blynn,Emily %A Ahmed,Saifuddin %A Gibson,Dustin %A Pariyo,George %A Hyder,Adnan A %+ Johns Hopkins Bloomberg School of Public Health, Department of International Health, W5501, Johns Hopkins Bloomberg SPH, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 4102361568, alabriqu@jhsph.edu %K surveys and questionnaires %K sampling studies %K mobile health %K mobile phone %K research methodology %D 2017 %7 05.05.2017 %9 Policy Proposal %J J Med Internet Res %G English %X In low- and middle-income countries (LMICs), historically, household surveys have been carried out by face-to-face interviews to collect survey data related to risk factors for noncommunicable diseases. The proliferation of mobile phone ownership and the access it provides in these countries offers a new opportunity to remotely conduct surveys with increased efficiency and reduced cost. However, the near-ubiquitous ownership of phones, high population mobility, and low cost require a re-examination of statistical recommendations for mobile phone surveys (MPS), especially when surveys are automated. As with landline surveys, random digit dialing remains the most appropriate approach to develop an ideal survey-sampling frame. Once the survey is complete, poststratification weights are generally applied to reduce estimate bias and to adjust for selectivity due to mobile ownership. Since weights increase design effects and reduce sampling efficiency, we introduce the concept of automated active strata monitoring to improve representativeness of the sample distribution to that of the source population. Although some statistical challenges remain, MPS represent a promising emerging means for population-level data collection in LMICs. %M 28476726 %R 10.2196/jmir.7329 %U http://www.jmir.org/2017/5/e121/ %U https://doi.org/10.2196/jmir.7329 %U http://www.ncbi.nlm.nih.gov/pubmed/28476726 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 5 %P e81 %T Evaluation of Mechanisms to Improve Performance of Mobile Phone Surveys in Low- and Middle-Income Countries: Research Protocol %A Gibson,Dustin G %A Pariyo,George William %A Wosu,Adaeze C %A Greenleaf,Abigail R %A Ali,Joseph %A Ahmed,Saifuddin %A Labrique,Alain B %A Islam,Khaleda %A Masanja,Honorati %A Rutebemberwa,Elizeus %A Hyder,Adnan A %+ Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 443 287 8763, dgibso28@jhu.edu %K IVR %K CATI %K Bangladesh %K Tanzania %K Uganda %K mHealth %K mobile phone survey %K noncommunicable diseases %K survey methodology %D 2017 %7 05.05.2017 %9 Protocol %J JMIR Res Protoc %G English %X Background: Mobile phone ownership and access have increased rapidly across low- and middle-income countries (LMICs) within the last decade. Concomitantly, LMICs are experiencing demographic and epidemiologic transitions, where non-communicable diseases (NCDs) are increasingly becoming leading causes of morbidity and mortality. Mobile phone surveys could aid data collection for prevention and control of these NCDs but limited evidence of their feasibility exists. Objective: The objective of this paper is to describe a series of sub-studies aimed at optimizing the delivery of interactive voice response (IVR) and computer-assisted telephone interviews (CATI) for NCD risk factor data collection in LMICs. These sub-studies are designed to assess the effect of factors such as airtime incentive timing, amount, and structure, survey introduction characteristics, different sampling frames, and survey modality on key survey metrics, such as survey response, completion, and attrition rates. Methods: In a series of sub-studies, participants will be randomly assigned to receive different airtime incentive amounts (eg, 10 minutes of airtime versus 20 minutes of airtime), different incentive delivery timings (airtime delivered before survey begins versus delivery upon completion of survey), different survey introductions (informational versus motivational), different narrative voices (male versus female), and different sampling frames (random digit dialing versus mobile network operator-provided numbers) to examine which study arms will yield the highest response and completion rates. Furthermore, response and completion rates and the inter-modal reliability of the IVR and CATI delivery methods will be compared. Results: Research activities are expected to be completed in Bangladesh, Tanzania, and Uganda in 2017. Conclusions: This is one of the first studies to examine the feasibility of using IVR and CATI for systematic collection of NCD risk factor information in LMICs. Our findings will inform the future design and implementation of mobile phone surveys in LMICs. %M 28476729 %R 10.2196/resprot.7534 %U http://www.researchprotocols.org/2017/5/e81/ %U https://doi.org/10.2196/resprot.7534 %U http://www.ncbi.nlm.nih.gov/pubmed/28476729 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e110 %T Ethics Considerations in Global Mobile Phone-Based Surveys of Noncommunicable Diseases: A Conceptual Exploration %A Ali,Joseph %A Labrique,Alain B %A Gionfriddo,Kara %A Pariyo,George %A Gibson,Dustin G %A Pratt,Bridget %A Deutsch-Feldman,Molly %A Hyder,Adnan A %+ Berman Institute of Bioethics, Johns Hopkins University, Rm 208, 1809 Ashland Avenue, Baltimore, MD, 21205, United States, 1 410 614 5370, jali@jhu.edu %K ethics %K mobile phone survey %K mHealth %K noncommunicable diseases %K research ethics %K bioethics %D 2017 %7 05.05.2017 %9 Viewpoint %J J Med Internet Res %G English %X Mobile phone coverage has grown, particularly within low- and middle-income countries (LMICs), presenting an opportunity to augment routine health surveillance programs. Several LMICs and global health partners are seeking opportunities to launch basic mobile phone–based surveys of noncommunicable diseases (NCDs). The increasing use of such technology in LMICs brings forth a cluster of ethical challenges; however, much of the existing literature regarding the ethics of mobile or digital health focuses on the use of technologies in high-income countries and does not consider directly the specific ethical issues associated with the conduct of mobile phone surveys (MPS) for NCD risk factor surveillance in LMICs. In this paper, we explore conceptually several of the central ethics issues in this domain, which mainly track the three phases of the MPS process: predata collection, during data collection, and postdata collection. These include identifying the nature of the activity; stakeholder engagement; appropriate design; anticipating and managing potential harms and benefits; consent; reaching intended respondents; data ownership, access and use; and ensuring LMIC sustainability. We call for future work to develop an ethics framework and guidance for the use of mobile phones for disease surveillance globally. %M 28476723 %R 10.2196/jmir.7326 %U http://www.jmir.org/2017/5/e110/ %U https://doi.org/10.2196/jmir.7326 %U http://www.ncbi.nlm.nih.gov/pubmed/28476723 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e115 %T Moving the Agenda on Noncommunicable Diseases: Policy Implications of Mobile Phone Surveys in Low and Middle-Income Countries %A Pariyo,George W %A Wosu,Adaeze C %A Gibson,Dustin G %A Labrique,Alain B %A Ali,Joseph %A Hyder,Adnan A %+ Johns Hopkins Bloomberg School of Public Health, Department of International Health, 615 North Wolfe Street E 8648, Baltimore, MD, 21205, United States, 1 410 502 5790, gpariyo1@jhu.edu %K NCDs %K policy %K mHealth %K policy analysis %K surveys %D 2017 %7 05.05.2017 %9 Viewpoint %J J Med Internet Res %G English %X The growing burden of noncommunicable diseases (NCDs), for example, cardiovascular diseases and chronic respiratory diseases, in low- and middle-income countries (LMICs) presents special challenges for policy makers, due to resource constraints and lack of timely data for decision-making. Concurrently, the increasing ubiquity of mobile phones in LMICs presents possibilities for rapid collection of population-based data to inform the policy process. The objective of this paper is to highlight potential benefits of mobile phone surveys (MPS) for developing, implementing, and evaluating NCD prevention and control policies. To achieve this aim, we first provide a brief overview of major global commitments to NCD prevention and control, and subsequently explore how countries can translate these commitments into policy action at the national level. Using the policy cycle as our frame of reference, we highlight potential benefits of MPS which include (1) potential cost-effectiveness of using MPS to inform NCD policy actions compared with using traditional household surveys; (2) timeliness of assessments to feed into policy and planning cycles; (3) tracking progress of interventions, hence assessment of reach, coverage, and distribution; (4) better targeting of interventions, for example, to high-risk groups; (5) timely course correction for suboptimal or non-effective interventions; (6) assessing fairness in financial contribution and financial risk protection for those affected by NCDs in the spirit of universal health coverage (UHC); and (7) monitoring progress in reducing catastrophic medical expenditure due to chronic health conditions in general, and NCDs in particular. We conclude that MPS have potential to become a powerful data collection tool to inform policies that address public health challenges such as NCDs. Additional forthcoming assessments of MPS in LMICs will inform opportunities to maximize this technology. %M 28476720 %R 10.2196/jmir.7302 %U http://www.jmir.org/2017/5/e115/ %U https://doi.org/10.2196/jmir.7302 %U http://www.ncbi.nlm.nih.gov/pubmed/28476720 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e133 %T Noncommunicable Disease Risk Factors and Mobile Phones: A Proposed Research Agenda %A Hyder,Adnan A %A Wosu,Adaeze C %A Gibson,Dustin G %A Labrique,Alain B %A Ali,Joseph %A Pariyo,George W %+ Johns Hopkins Bloomberg School of Public Health, Department of International Health, 615 North Wolfe Street, Suite E8132, Baltimore, MD, 21205, United States, 1 410 502 8947, ahyder1@jhu.edu %K mHealth %K noncommunicable disease %K mobile phone %K research agenda %K survey %D 2017 %7 05.05.2017 %9 Viewpoint %J J Med Internet Res %G English %X Noncommunicable diseases (NCDs) account for two-thirds of all deaths globally, with 75% of these occurring in low- and middle-income countries (LMICs). Many LMICs seek cost-effective methods to obtain timely and quality NCD risk factor data that could inform resource allocation, policy development, and assist evaluation of NCD trends over time. Over the last decade, there has been a proliferation of mobile phone ownership and access in LMICs, which, if properly harnessed, has great potential to support risk factor data collection. As a supplement to traditional face-to-face surveys, the ubiquity of phone ownership has made large proportions of most populations reachable through cellular networks. However, critical gaps remain in understanding the ways by which mobile phone surveys (MPS) could aid in collection of NCD data in LMICs. Specifically, limited information exists on the optimization of these surveys with regard to incentives and structure, comparative effectiveness of different MPS modalities, and key ethical, legal, and societal issues (ELSI) in the development, conduct, and analysis of these surveys in LMIC settings. We propose a research agenda that could address important knowledge gaps in optimizing MPS for the collection of NCD risk factor data in LMICs and provide an example of a multicountry project where elements of that agenda aim to be integrated over the next two years. %M 28476722 %R 10.2196/jmir.7246 %U http://www.jmir.org/2017/5/e133/ %U https://doi.org/10.2196/jmir.7246 %U http://www.ncbi.nlm.nih.gov/pubmed/28476722 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 4 %P e66 %T Establishing Linkages Between Distributed Survey Responses and Consumer Wearable Device Datasets: A Pilot Protocol %A Brinton,Julia E %A Keating,Mike D %A Ortiz,Alexa M %A Evenson,Kelly R %A Furberg,Robert D %+ RTI International, 3040 E Cornwallis Dr, Durham, NC, 27709, United States, 1 919 485 5613, jbrinton@rti.org %K Fitbit %K Mturk %K mHealth %K clinical research protocol %K consumer wearable %K physical activity tracker %D 2017 %7 27.04.2017 %9 Protocol %J JMIR Res Protoc %G English %X Background: As technology increasingly becomes an integral part of everyday life, many individuals are choosing to use wearable technology such as activity trackers to monitor their daily physical activity and other health-related goals. Researchers would benefit from learning more about the health of these individuals remotely, without meeting face-to-face with participants and avoiding the high cost of providing consumer wearables to participants for the study duration. Objective: The present study seeks to develop the methods to collect data remotely and establish a linkage between self-reported survey responses and consumer wearable device biometric data, ultimately producing a de-identified and linked dataset. Establishing an effective protocol will allow for future studies of large-scale deployment and participant management. Methods: A total of 30 participants who use a Fitbit will be recruited on Mechanical Turk Prime and asked to complete a short online self-administered questionnaire. They will also be asked to connect their personal Fitbit activity tracker to an online third-party software system, called Fitabase, which will allow access to 1 month’s retrospective data and 1 month’s prospective data, both from the date of consent. Results: The protocol will be used to create and refine methods to establish linkages between remotely sourced and de-identified survey responses on health status and consumer wearable device data. Conclusions: The refinement of the protocol will inform collection and linkage of similar datasets at scale, enabling the integration of consumer wearable device data collection in cross-sectional and prospective cohort studies. %M 28450274 %R 10.2196/resprot.6513 %U http://www.researchprotocols.org/2017/4/e66/ %U https://doi.org/10.2196/resprot.6513 %U http://www.ncbi.nlm.nih.gov/pubmed/28450274 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 4 %P e132 %T Compliance With Mobile Ecological Momentary Assessment Protocols in Children and Adolescents: A Systematic Review and Meta-Analysis %A Wen,Cheng K Fred %A Schneider,Stefan %A Stone,Arthur A %A Spruijt-Metz,Donna %+ Department of Preventive Medicine, University of Southern California, Soto Bldg 1, 3rd Fl, 2001 N Soto St, Los Angeles, CA, 90033, United States, 1 5626822468, chengkuw@usc.edu %K ecological momentary assessment %K compliance %K youth %K mHealth %D 2017 %7 26.04.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Mobile device-based ecological momentary assessment (mobile-EMA) is increasingly used to collect participants' data in real-time and in context. Although EMA offers methodological advantages, these advantages can be diminished by participant noncompliance. However, evidence on how well participants comply with mobile-EMA protocols and how study design factors associated with participant compliance is limited, especially in the youth literature. Objective: To systematically and meta-analytically examine youth’s compliance to mobile-EMA protocols and moderators of participant compliance in clinical and nonclinical settings. Methods: Studies using mobile devices to collect EMA data among youth (age ≤18 years old) were identified. A systematic review was conducted to describe the characteristics of mobile-EMA protocols and author-reported factors associated with compliance. Random effects meta-analyses were conducted to estimate the overall compliance across studies and to explore factors associated with differences in youths’ compliance. Results: This review included 42 unique studies that assessed behaviors, subjective experiences, and contextual information. Mobile phones were used as the primary mode of EMA data collection in 48% (20/42) of the reviewed studies. In total, 12% (5/42) of the studies used wearable devices in addition to the EMA data collection platforms. About half of the studies (62%, 24/42) recruited youth from nonclinical settings. Most (98%, 41/42) studies used a time-based sampling protocol. Among these studies, most (95%, 39/41) prompted youth 2-9 times daily, for a study length ranging from 2-42 days. Sampling frequency and study length did not differ between studies with participants from clinical versus nonclinical settings. Most (88%, 36/41) studies with a time-based sampling protocol defined compliance as the proportion of prompts to which participants responded. In these studies, the weighted average compliance rate was 78.3%. The average compliance rates were not different between studies with clinical (76.9%) and nonclinical (79.2%; P=.29) and studies that used only a mobile-EMA platform (77.4%) and mobile platform plus additional wearable devices (73.0%, P=.36). Among clinical studies, the mean compliance rate was significantly lower in studies that prompted participants 2-3 times (73.5%) or 4-5 times (66.9%) compared with studies with a higher sampling frequency (6+ times: 89.3%). Among nonclinical studies, a higher average compliance rate was observed in studies that prompted participants 2-3 times daily (91.7%) compared with those that prompted participants more frequently (4-5 times: 77.4%; 6+ times: 75.0%). The reported compliance rates did not differ by duration of EMA period among studies from either clinical or nonclinical settings. Conclusions: The compliance rate among mobile-EMA studies in youth is moderate but suboptimal. Study design may affect protocol compliance differently between clinical and nonclinical participants; including additional wearable devices did not affect participant compliance. A more consistent compliance-related result reporting practices can facilitate understanding and improvement of participant compliance with EMA data collection among youth. %M 28446418 %R 10.2196/jmir.6641 %U http://www.jmir.org/2017/4/e132/ %U https://doi.org/10.2196/jmir.6641 %U http://www.ncbi.nlm.nih.gov/pubmed/28446418 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 4 %P e61 %T Using Mobile Phones to Collect Patient Data: Lessons Learned From the SIMPle Study %A Duane,Sinead %A Tandan,Meera %A Murphy,Andrew W %A Vellinga,Akke %+ Discipline of General Practice, National University of Ireland Galway, 1 Distillery Road, Galway,, Ireland, 353 91493855, sinead.duane@nuigalway.ie %K mobile phone apps %K mobile survey %K antimicrobial resistance %K primary care %K quantitative %K prescribing %K urinary tract infection %D 2017 %7 25.04.2017 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Mobile phones offer new opportunities to efficiently and interactively collect real-time data from patients with acute illnesses, such as urinary tract infections (UTIs). One of the main benefits of using mobile data collection methods is automated data upload, which can reduce the chance of data loss, an issue when using other data collection methods such as paper-based surveys. Objective: The aim was to explore differences in collecting data from patients with UTI using text messaging, a mobile phone app (UTI diary), and an online survey. This paper provides lessons learned from integrating mobile data collection into a randomized controlled trial. Methods: Participants included UTI patients consulting in general practices that were participating in the Supporting the Improvement and Management of UTI (SIMPle) study. SIMPle was designed to improve prescribing antimicrobial therapies for UTI in the community. Patients were invited to reply to questions regarding their UTI either via a prospective text message survey, a mobile phone app (UTI diary), or a retrospective online survey. Data were collected from 329 patients who opted in to the text message survey, 71 UTI patients through the mobile phone UTI symptom diary app, and 91 online survey participants. Results: The age profile of UTI diary app users was younger than that of the text message and online survey users. The largest dropout for both the text message survey respondents and UTI diary app users was after the initial opt-in message; once the participants completed question 1 of the text message survey or day 2 in the UTI diary app, they were more likely to respond to the remaining questions/days. Conclusions: This feasibility study highlights the potential of using mobile data collection methods to capture patient data. As well as improving the efficiency of data collection, these novel approaches highlight the advantage of collecting data in real time across multiple time points. There was little variation in the number of patients responding between text message survey, UTI diary, and online survey, but more patients participated in the text message survey than the UTI diary app. The choice between designing a text message survey or UTI diary app will depend on the age profile of patients and the type of information the researchers’ desire. Trial Registration: ClinicalTrials.gov NCT01913860; https://clinicaltrials.gov/ct2/show/NCT01913860 (Archived by WebCite at http://www.webcitation.org/6pfgCztgT). %M 28442451 %R 10.2196/resprot.6389 %U http://www.researchprotocols.org/2017/4/e61/ %U https://doi.org/10.2196/resprot.6389 %U http://www.ncbi.nlm.nih.gov/pubmed/28442451 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 3 %P e37 %T Cloudy with a Chance of Pain: Engagement and Subsequent Attrition of Daily Data Entry in a Smartphone Pilot Study Tracking Weather, Disease Severity, and Physical Activity in Patients With Rheumatoid Arthritis %A Reade,Samuel %A Spencer,Karen %A Sergeant,Jamie C %A Sperrin,Matthew %A Schultz,David M %A Ainsworth,John %A Lakshminarayana,Rashmi %A Hellman,Bruce %A James,Ben %A McBeth,John %A Sanders,Caroline %A Dixon,William G %+ Manchester Academic Health Science Centre, Arthritis Research UK Centre for Epidemiology, University of Manchester, Stopford Building,, Oxford Road, Manchester, M13 9PT., United Kingdom, 44 161 275 1642, will.dixon@manchester.ac.uk %K smartphone %K mHealth %K attrition %K weather %K arthritis %D 2017 %7 24.03.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The increasing ownership of smartphones provides major opportunities for epidemiological research through self-reported and passively collected data. Objective: This pilot study aimed to codesign a smartphone app to assess associations between weather and joint pain in patients with rheumatoid arthritis (RA) and to study the success of daily self-reported data entry over a 60-day period and the enablers of and barriers to data collection. Methods: A patient and public involvement group (n=5) and 2 focus groups of patients with RA (n=9) supported the codesign of the app collecting self-reported symptoms. A separate “capture app” was designed to collect global positioning system (GPS) and continuous raw accelerometer data, with the GPS-linking providing local weather data. A total of 20 patients with RA were then recruited to collect daily data for 60 days, with entry and exit interviews. Of these, 17 were loaned an Android smartphone, whereas 3 used their own Android smartphones. Results: Of the 20 patients, 6 (30%) withdrew from the study: 4 because of technical challenges and 2 for health reasons. The mean completion of daily entries was 68% over 2 months. Patients entered data at least five times per week 65% of the time. Reasons for successful engagement included a simple graphical user interface, automated reminders, visualization of data, and eagerness to contribute to this easily understood research question. The main barrier to continuing engagement was impaired battery life due to the accelerometer data capture app. For some, successful engagement required ongoing support in using the smartphones. Conclusions: This successful pilot study has demonstrated that daily data collection using smartphones for health research is feasible and achievable with high levels of ongoing engagement over 2 months. This result opens important opportunities for large-scale longitudinal epidemiological research. %M 28341616 %R 10.2196/mhealth.6496 %U http://mhealth.jmir.org/2017/3/e37/ %U https://doi.org/10.2196/mhealth.6496 %U http://www.ncbi.nlm.nih.gov/pubmed/28341616 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 3 %P e75 %T Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders %A Place,Skyler %A Blanch-Hartigan,Danielle %A Rubin,Channah %A Gorrostieta,Cristina %A Mead,Caroline %A Kane,John %A Marx,Brian P %A Feast,Joshua %A Deckersbach,Thilo %A Pentland,Alex “Sandy” %A Nierenberg,Andrew %A Azarbayejani,Ali %+ Department of Natural and Applied Sciences, Bentley University, 175 Forest Street, 106 Jennison, Waltham, MA, 02452, United States, 1 7818912066, danielleblanch@gmail.com %K mHealth %K post-traumatic stress disorders %K depression %K behavioral symptoms %D 2017 %7 16.03.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. Objective: The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. Methods: A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants’ mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns. Results: Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36). Conclusions: Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed. %M 28302595 %R 10.2196/jmir.6678 %U http://www.jmir.org/2017/3/e75/ %U https://doi.org/10.2196/jmir.6678 %U http://www.ncbi.nlm.nih.gov/pubmed/28302595 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 3 %P e77 %T Ecological Momentary Assessment in Behavioral Research: Addressing Technological and Human Participant Challenges %A Burke,Lora E %A Shiffman,Saul %A Music,Edvin %A Styn,Mindi A %A Kriska,Andrea %A Smailagic,Asim %A Siewiorek,Daniel %A Ewing,Linda J %A Chasens,Eileen %A French,Brian %A Mancino,Juliet %A Mendez,Dara %A Strollo,Patrick %A Rathbun,Stephen L %+ Department of Health & Community Systems, University of Pittsburgh School of Nursing, 415 Victoria Building, 3500 Victoria Street, Pittsburgh, PA, 15261, United States, 1 412 624 2305, lbu100@pitt.edu %K ecological momentary assessment %K relapse %K obesity %K smartphone %K eating behavior %K adherence %D 2017 %7 15.03.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Ecological momentary assessment (EMA) assesses individuals’ current experiences, behaviors, and moods as they occur in real time and in their natural environment. EMA studies, particularly those of longer duration, are complex and require an infrastructure to support the data flow and monitoring of EMA completion. Objective: Our objective is to provide a practical guide to developing and implementing an EMA study, with a focus on the methods and logistics of conducting such a study. Methods: The EMPOWER study was a 12-month study that used EMA to examine the triggers of lapses and relapse following intentional weight loss. We report on several studies that informed the implementation of the EMPOWER study: (1) a series of pilot studies, (2) the EMPOWER study’s infrastructure, (3) training of study participants in use of smartphones and the EMA protocol and, (4) strategies used to enhance adherence to completing EMA surveys. Results: The study enrolled 151 adults and had 87.4% (132/151) retention rate at 12 months. Our learning experiences in the development of the infrastructure to support EMA assessments for the 12-month study spanned several topic areas. Included were the optimal frequency of EMA prompts to maximize data collection without overburdening participants; the timing and scheduling of EMA prompts; technological lessons to support a longitudinal study, such as proper communication between the Android smartphone, the Web server, and the database server; and use of a phone that provided access to the system’s functionality for EMA data collection to avoid loss of data and minimize the impact of loss of network connectivity. These were especially important in a 1-year study with participants who might travel. It also protected the data collection from any server-side failure. Regular monitoring of participants’ response to EMA prompts was critical, so we built in incentives to enhance completion of EMA surveys. During the first 6 months of the 12-month study interval, adherence to completing EMA surveys was high, with 88.3% (66,978/75,888) completion of random assessments and around 90% (23,411/25,929 and 23,343/26,010) completion of time-contingent assessments, despite the duration of EMA data collection and challenges with implementation. Conclusions: This work informed us of the necessary preliminary steps to plan and prepare a longitudinal study using smartphone technology and the critical elements to ensure participant engagement in the potentially burdensome protocol, which spanned 12 months. While this was a technology-supported and -programmed study, it required close oversight to ensure all elements were functioning correctly, particularly once human participants became involved. %M 28298264 %R 10.2196/jmir.7138 %U http://www.jmir.org/2017/3/e77/ %U https://doi.org/10.2196/jmir.7138 %U http://www.ncbi.nlm.nih.gov/pubmed/28298264 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 1 %N 1 %P e1 %T Bioimpedance-Based Heart Failure Deterioration Prediction Using a Prototype Fluid Accumulation Vest-Mobile Phone Dyad: An Observational Study %A Darling,Chad Eric %A Dovancescu,Silviu %A Saczynski,Jane S %A Riistama,Jarno %A Sert Kuniyoshi,Fatima %A Rock,Joseph %A Meyer,Theo E %A McManus,David D %+ UMass Memorial Health Care, Department of Emergency Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, United States, 1 508 421 1464, chad.darling@umassmed.edu %K telemedicine %K outpatient monitoring %K heart failure %K electric impedance %D 2017 %7 13.03.2017 %9 Original Paper %J JMIR Cardio %G English %X Background: Recurrent heart failure (HF) events are common in patients discharged after acute decompensated heart failure (ADHF). New patient-centered technologies are needed to aid in detecting HF decompensation. Transthoracic bioimpedance noninvasively measures pulmonary fluid retention. Objective: The objectives of our study were to (1) determine whether transthoracic bioimpedance can be measured daily with a novel, noninvasive, wearable fluid accumulation vest (FAV) and transmitted using a mobile phone and (2) establish whether an automated algorithm analyzing daily thoracic bioimpedance values would predict recurrent HF events. Methods: We prospectively enrolled patients admitted for ADHF. Participants were trained to use a FAV–mobile phone dyad and asked to transmit bioimpedance measurements for 45 consecutive days. We examined the performance of an algorithm analyzing changes in transthoracic bioimpedance as a predictor of HF events (HF readmission, diuretic uptitration) over a 75-day follow-up. Results: We observed 64 HF events (18 HF readmissions and 46 diuretic uptitrations) in the 106 participants (67 years; 63.2%, 67/106, male; 48.1%, 51/106, with prior HF) who completed follow-up. History of HF was the only clinical or laboratory factor related to recurrent HF events (P=.04). Among study participants with sufficient FAV data (n=57), an algorithm analyzing thoracic bioimpedance showed 87% sensitivity (95% CI 82-92), 70% specificity (95% CI 68-72), and 72% accuracy (95% CI 70-74) for identifying recurrent HF events. Conclusions: Patients discharged after ADHF can measure and transmit daily transthoracic bioimpedance using a FAV–mobile phone dyad. Algorithms analyzing thoracic bioimpedance may help identify patients at risk for recurrent HF events after hospital discharge. %M 31758769 %R 10.2196/cardio.6057 %U http://cardio.jmir.org/2017/1/e1/ %U https://doi.org/10.2196/cardio.6057 %U http://www.ncbi.nlm.nih.gov/pubmed/31758769 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 3 %P e33 %T Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study %A Yan,Bryan P %A Chan,Christy KY %A Li,Christien KH %A To,Olivia TL %A Lai,William HS %A Tse,Gary %A Poh,Yukkee C %A Poh,Ming-Zher %+ Division of Cardiology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong and Prince of Wales Hospital, 9/F, Division of Cardiology, Department of Medicine and Therapeutics, Clinical Sciences Building, Prince of Wales Hospital, Shatin, N.T., Hong Kong,, China (Hong Kong), 852 2632 3142, bryan.yan@cuhk.edu.hk %K heart rate %K mobile apps %K photoplethysmography %K smartphone %K mobile phone %D 2017 %7 13.03.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Modern smartphones allow measurement of heart rate (HR) by detecting pulsatile photoplethysmographic (PPG) signals with built-in cameras from the fingertips or the face, without physical contact, by extracting subtle beat-to-beat variations of skin color. Objective: The objective of our study was to evaluate the accuracy of HR measurements at rest and after exercise using a smartphone-based PPG detection app. Methods: A total of 40 healthy participants (20 men; mean age 24.7, SD 5.2 years; von Luschan skin color range 14-27) underwent treadmill exercise using the Bruce protocol. We recorded simultaneous PPG signals for each participant by having them (1) facing the front camera and (2) placing their index fingertip over an iPhone’s back camera. We analyzed the PPG signals from the Cardiio-Heart Rate Monitor + 7 Minute Workout (Cardiio) smartphone app for HR measurements compared with a continuous 12-lead electrocardiogram (ECG) as the reference. Recordings of 20 seconds’ duration each were acquired at rest, and immediately after moderate- (50%-70% maximum HR) and vigorous- (70%-85% maximum HR) intensity exercise, and repeated successively until return to resting HR. We used Bland-Altman plots to examine agreement between ECG and PPG-estimated HR. The accuracy criterion was root mean square error (RMSE) ≤5 beats/min or ≤10%, whichever was greater, according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation EC-13 standard. Results: We analyzed a total of 631 fingertip and 626 facial PPG measurements. Fingertip PPG-estimated HRs were strongly correlated with resting ECG HR (r=.997, RMSE=1.03 beats/min or 1.40%), postmoderate-intensity exercise (r=.994, RMSE=2.15 beats/min or 2.53%), and postvigorous-intensity exercise HR (r=.995, RMSE=2.01 beats/min or 1.93%). The correlation of facial PPG-estimated HR was stronger with resting ECG HR (r=.997, RMSE=1.02 beats/min or 1.44%) than with postmoderate-intensity exercise (r=.982, RMSE=3.68 beats/min or 4.11%) or with postvigorous-intensity exercise (r=.980, RMSE=3.84 beats/min or 3.73%). Bland-Altman plots showed better agreement between ECG and fingertip PPG-estimated HR than between ECG and facial PPG-estimated HR. Conclusions: We found that HR detection by the Cardiio smartphone app was accurate at rest and after moderate- and vigorous-intensity exercise in a healthy young adult sample. Contact-free facial PPG detection is more convenient but is less accurate than finger PPG due to body motion after exercise. %M 28288955 %R 10.2196/mhealth.7275 %U http://mhealth.jmir.org/2017/3/e33/ %U https://doi.org/10.2196/mhealth.7275 %U http://www.ncbi.nlm.nih.gov/pubmed/28288955 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 3 %P e66 %T Mobile Device–Based Electronic Data Capture System Used in a Clinical Randomized Controlled Trial: Advantages and Challenges %A Zhang,Jing %A Sun,Lei %A Liu,Yu %A Wang,Hongyi %A Sun,Ningling %A Zhang,Puhong %+ The George Institute for Global Health at Peking University Health Science Center, Level 18, Tower B, Horizon Tower, No. 6 Zhichun Rd Haidian District | Beijing, 100088 P.R. China, Beijing,, China, 86 1082800577 ext 512, zpuhong@georgeinstitute.org.cn %K mEDC %K electronic data capture %K mobile data capture %K mhealth %K randomized controlled trial %K clinical research %D 2017 %7 08.03.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic data capture (EDC) systems have been widely used in clinical research, but mobile device–based electronic data capture (mEDC) system has not been well evaluated. Objective: The aim of our study was to evaluate the feasibility, advantages, and challenges of mEDC in data collection, project management, and telemonitoring in a randomized controlled trial (RCT). Methods: We developed an mEDC to support an RCT called “Telmisartan and Hydrochlorothiazide Antihypertensive Treatment (THAT)” study, which was a multicenter, double-blinded, RCT, with the purpose of comparing the efficacy of telmisartan and hydrochlorothiazide (HCTZ) monotherapy in high-sodium-intake patients with mild to moderate hypertension during a 60 days follow-up. Semistructured interviews were conducted during and after the trial to evaluate the feasibility, advantage, and challenge of mEDC. Nvivo version 9.0 (QSR International) was used to analyze records of interviews, and a thematic framework method was used to obtain outcomes. Results: The mEDC was successfully used to support the data collection and project management in all the 14 study hospitals. A total of 1333 patients were recruited with support of mEDC, of whom 1037 successfully completed all 4 visits. Across all visits, the average time needed for 141 questions per patient was 53 min, which were acceptable to both doctors and patients. All the interviewees, including 24 doctors, 53 patients, 1 clinical research associate (CRA), 1 project manager (PM), and 1 data manager (DM), expressed their satisfaction to nearly all the functions of the innovative mEDC in randomization, data collection, project management, quality control, and remote monitoring in real time. The average satisfaction score was 9.2 (scale, 0-10). The biggest challenge came from the stability of the mobile or Wi-Fi signal although it was not a problem in THAT study. Conclusions: The innovative mEDC has many merits and is well acceptable in supporting data collection and project management in a timely manner in clinical trial. %M 28274907 %R 10.2196/jmir.6978 %U http://www.jmir.org/2017/3/e66/ %U https://doi.org/10.2196/jmir.6978 %U http://www.ncbi.nlm.nih.gov/pubmed/28274907 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 3 %P e62 %T Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students %A Chow,Philip I %A Fua,Karl %A Huang,Yu %A Bonelli,Wesley %A Xiong,Haoyi %A Barnes,Laura E %A Teachman,Bethany A %+ School of Engineering and Applied Science, University of Virginia, 151 Engineer's Way, Olsson Hall 101B, PO Box 400747, Charlottesville, VA, 22904-4747, United States, 1 434 924 1723, lbarnes@virginia.edu %K mental health %K depression %K social anxiety %K affect %K homestay %K mobile health %K mHealth %D 2017 %7 03.03.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms. Objective: We hypothesized that more time spent at home would be associated with more negative and less positive affect. Methods: We recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants’ personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation. Results: We tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day. Conclusions: Results demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression. %M 28258049 %R 10.2196/jmir.6820 %U http://www.jmir.org/2017/3/e62/ %U https://doi.org/10.2196/jmir.6820 %U http://www.ncbi.nlm.nih.gov/pubmed/28258049 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 2 %P e23 %T “Back on Track”: A Mobile App Observational Study Using Apple’s ResearchKit Framework %A Zens,Martin %A Woias,Peter %A Suedkamp,Norbert P %A Niemeyer,Philipp %+ Department of Orthopedic Surgery and Traumatology, University Medical Center Freiburg, University of Freiburg, Hugstetter Str 55, Freiburg, D-79106, Germany, 49 1633374461, martin.zens@me.com %K mHealth %K mobile health %K anterior cruciate ligament injury %D 2017 %7 28.02.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In March 2015, Apple Inc announced ResearchKit, a novel open-source framework intended to help medical researchers to easily create apps for medical studies. With the announcement of this framework, Apple presented 5 apps built in a beta phase based on this framework. Objective: The objective of this study was to better understand decision making in patients with acute anterior cruciate ligament (ACL) ruptures. Here, we describe the development of a ResearchKit app for this study. Methods: A multilanguage observatory study was conducted. At first a suitable research topic, target groups, participating territories, and programming method were carefully identified. The ResearchKit framework was used to program the app. A secure server connection was realized via Secure Sockets Layer. A data storage and security concept separating personal information and study data was proposed. Furthermore, an efficient method to allow multilanguage support and distribute the app in many territories was presented. Ethical implications were considered and taken into account regarding privacy policies. Results: An app study based on ResearchKit was developed without comprehensive iPhone Operating System (iOS) development experience. The Apple App Store is a major distribution channel causing significant download rates (>1.200/y) without active recruitment. Preliminary data analysis showed moderate dropout rates and a good quality of data. A total of 180 participants were currently enrolled with 107 actively participating and producing 424 completed surveys in 9 out of 24 months. Conclusions: ResearchKit is an easy-to-use framework and powerful tool to create medical studies. Advantages are the modular built, the extensive reach of iOS devices, and the convenient programming environment. %M 28246069 %R 10.2196/mhealth.6259 %U http://mhealth.jmir.org/2017/2/e23/ %U https://doi.org/10.2196/mhealth.6259 %U http://www.ncbi.nlm.nih.gov/pubmed/28246069 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 2 %P e14 %T Formative Evaluation of Participant Experience With Mobile eConsent in the App-Mediated Parkinson mPower Study: A Mixed Methods Study %A Doerr,Megan %A Maguire Truong,Amy %A Bot,Brian M %A Wilbanks,John %A Suver,Christine %A Mangravite,Lara M %+ Sage Bionetworks, 1100 Fairview Ave N, M1-C108, Seattle, WA, 98109, United States, 1 2066674265, megan.doerr@sagebase.org %K informed consent %K research ethics %K mobile applications %K smartphone %K Parkinson disease %D 2017 %7 16.02.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: To fully capitalize on the promise of mobile technology to enable scalable, participant-centered research, we must develop companion self-administered electronic informed consent (eConsent) processes. As we do so, we have an ethical obligation to ensure that core tenants of informed consent—informedness, comprehension, and voluntariness—are upheld. Furthermore, we should be wary of recapitulating the pitfalls of “traditional” informed consent processes. Objective: Our objective was to describe the essential qualities of participant experience, including delineation of common and novel themes relating to informed consent, with a self-administered, smartphone-based eConsent process. We sought to identify participant responses related to informedness, comprehension, and voluntariness as well as to capture any emergent themes relating to the informed consent process in an app-mediated research study. Methods: We performed qualitative thematic analysis of participant responses to a daily general prompt collected over a 6-month period within the Parkinson mPower app. We employed a combination of a priori and emergent codes for our analysis. A priori codes focused on the core concepts of informed consent; emergent codes were derived to capture additional themes relating to self-administered consent processes. We used self-reported demographic information from the study’s baseline survey to characterize study participants and respondents. Results: During the study period, 9846 people completed the eConsent process and enrolled in the Parkinson mPower study. In total, 2758 participants submitted 7483 comments; initial categorization identified a subset of 3875 germane responses submitted by 1678 distinct participants. Respondents were more likely to self-report a Parkinson disease diagnosis (30.21% vs 11.10%), be female (28.26% vs 20.18%), be older (42.89 years vs 34.47 years), and have completed more formal education (66.23% with a 4-year college degree or more education vs 55.77%) than all the mPower participants (P<.001 for all values). Within our qualitative analysis, 3 conceptual domains emerged. First, consistent with fully facilitated in-person informed consent settings, we observed a broad spectrum of comprehension of core research concepts following eConsent. Second, we identified new consent themes born out of the remote mobile research setting, for example the impact of the study design on the engagement of controls and the misconstruction of the open response field as a method for responsive communication with researchers, that bear consideration for inclusion within self-administered eConsent. Finally, our findings highlighted participants’ desire to be empowered as partners. Conclusions: Our study serves as a formative evaluation of participant experience with a self-administered informed consent process via a mobile app. Areas for future investigation include direct comparison of the efficacy of self-administered eConsent with facilitated informed consent processes, exploring the potential benefits and pitfalls of smartphone user behavioral habits on participant engagement in research, and developing best practices to increase informedness, comprehension, and voluntariness via participant coengagement in the research endeavor. %M 28209557 %R 10.2196/mhealth.6521 %U http://mhealth.jmir.org/2017/2/e14/ %U https://doi.org/10.2196/mhealth.6521 %U http://www.ncbi.nlm.nih.gov/pubmed/28209557 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 3 %N 2 %P e13 %T Validated Smartphone-Based Apps for Ear and Hearing Assessments: A Review %A Bright,Tess %A Pallawela,Danuk %+ London School of Hygiene & Tropical Medicine, Keppel St, London, WC1E 7HT, United Kingdom, 44 (0)20 7636 8636, tess.bright1@lshtm.ac.uk %K hearing %K testing %K mobile %K audiometry %K smartphone %K applications %K app %K hearing loss %K hearing impairment %K surveys %K prevalence %D 2016 %7 23.12.2016 %9 Review %J JMIR Rehabil Assist Technol %G English %X Background: An estimated 360 million people have a disabling hearing impairment globally, the vast majority of whom live in low- and middle-income countries (LMICs). Early identification through screening is important to negate the negative effects of untreated hearing impairment. Substantial barriers exist in screening for hearing impairment in LMICs, such as the requirement for skilled hearing health care professionals and prohibitively expensive specialist equipment to measure hearing. These challenges may be overcome through utilization of increasingly available smartphone app technologies for ear and hearing assessments that are easy to use by unskilled professionals. Objective: Our objective was to identify and compare available apps for ear and hearing assessments and consider the incorporation of such apps into hearing screening programs Methods: In July 2015, the commercial app stores Google Play and Apple App Store were searched to identify apps for ear and hearing assessments. Thereafter, six databases (EMBASE, MEDLINE, Global Health, Web of Science, CINAHL, and mHealth Evidence) were searched to assess which of the apps identified in the commercial review had been validated against gold standard measures. A comparison was made between validated apps. Results: App store search queries returned 30 apps that could be used for ear and hearing assessments, the majority of which are for performing audiometry. The literature search identified 11 eligible validity studies that examined 6 different apps. uHear, an app for self-administered audiometry, was validated in the highest number of peer reviewed studies against gold standard pure tone audiometry (n=5). However, the accuracy of uHear varied across these studies. Conclusions: Very few of the available apps have been validated in peer-reviewed studies. Of the apps that have been validated, further independent research is required to fully understand their accuracy at detecting ear and hearing conditions. %M 28582261 %R 10.2196/rehab.6074 %U http://rehab.jmir.org/2016/2/e13/ %U https://doi.org/10.2196/rehab.6074 %U http://www.ncbi.nlm.nih.gov/pubmed/28582261 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 2 %P e169 %T Effect of Performance Feedback on Community Health Workers’ Motivation and Performance in Madhya Pradesh, India: A Randomized Controlled Trial %A Kaphle,Sangya %A Matheke-Fischer,Michael %A Lesh,Neal %+ Dimagi Software Innovations, 585 Massachusetts Ave #3, Cambridge, MA, 02139, United States, 1 617 649 2214, sangyakaphle@gmail.com %K community health workers %K performance feedback %K motivation %K supportive supervision %K mHealth apps %D 2016 %7 07.12.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Small-scale community health worker (CHW) programs provide basic health services and strengthen health systems in resource-poor settings. This paper focuses on improving CHW performance by providing individual feedback to CHWs working with an mHealth program to address malnutrition in children younger than 5 years. Objective: The paper aims to evaluate the immediate and retention effects of providing performance feedback and supportive supervision on CHW motivation and performance for CHWs working with an mHealth platform to reduce malnutrition in five districts of Madhya Pradesh, India. We expected a positive impact on CHW performance for the indicator they received feedback on. Performance on indicators the CHW did not receive feedback on was not expected to change. Methods: In a randomized controlled trial, 60 CHWs were randomized into three treatment groups based on overall baseline performance ranks to achieve balanced treatment groups. Data for each treatment indicator were analyzed with the other two treatments acting as the control. In total, 10 CHWs were lost to follow-up. There were three performance indicators: case activity, form submissions, and duration of counseling. Each group received weekly calls to provide performance targets and discuss their performance on the specific indicator they were allocated to as well as any challenges or technical issues faced during the week for a 6-week period. Data were collected for a further 4 weeks to assess intertemporal sustained effects of the intervention. Results: We found positive and significant impacts on duration of counseling, whereas case activity and number of form submissions did not show significant improvements as a result of the intervention. We found a moderate to large effect (Glass’s delta=0.97, P=.004) of providing performance feedback on counseling times in the initial 6 weeks. These effects were sustained in the postintervention period (Glass’s delta=1.69, P<.001). The counseling times decreased slightly from the intervention to postintervention period by 2.14 minutes (P=.01). Case activity improved for all CHWs after the intervention. We also performed the analysis by replacing the CHWs lost to follow-up with those in their treatment groups with the closest ranks in baseline performance and found similar results. Conclusions: Calls providing performance feedback are effective in improving CHW motivation and performance. Providing feedback had a positive effect on performance in the case of duration of counseling. The results suggest that difficulty in achieving the performance target can affect results of performance feedback. Regardless of the performance information disclosed, calls can improve performance due to elements of supportive supervision included in the calls encouraging CHW motivation. %M 27927607 %R 10.2196/publichealth.3381 %U http://publichealth.jmir.org/2016/2/e169/ %U https://doi.org/10.2196/publichealth.3381 %U http://www.ncbi.nlm.nih.gov/pubmed/27927607 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 3 %N 4 %P e51 %T Ecological Momentary Assessment of Adolescent Problems, Coping Efficacy, and Mood States Using a Mobile Phone App: An Exploratory Study %A Kenny,Rachel %A Dooley,Barbara %A Fitzgerald,Amanda %+ Youth Mental Health Lab, School of Psychology, University College Dublin, Belfield, Dublin, Ireland, 353 1 7168147, rachel.kenny.3@ucdconnect.ie %K adolescent %K affect %K ecological momentary assessment %K mobile apps %D 2016 %7 29.11.2016 %9 Original Paper %J JMIR Ment Health %G English %X Background: Mobile technologies have the potential to be used as innovative tools for conducting research on the mental health and well-being of young people. In particular, they have utility for carrying out ecological momentary assessment (EMA) research by capturing data from participants in real time as they go about their daily lives. Objective: The aim of this study was to explore the utility of a mobile phone app as a means of collecting EMA data pertaining to mood, problems, and coping efficacy in a school-based sample of Irish young people. Methods: The study included a total of 208 participants who were aged 15-18 years, 64% female (113/208), recruited from second-level schools in Ireland, and who downloaded the CopeSmart mobile phone app as part of a randomized controlled trial. On the app, participants initially responded to 5 single-item measures of key protective factors in youth mental health (formal help-seeking, informal help-seeking, sleep, exercise, and sense of belonging). They were then encouraged to use the app daily to input data relating to mood states (happiness, sadness, anger, stress, and worry), daily problems, and coping self-efficacy. The app automatically collected data pertaining to user engagement over the course of the 28-day intervention period. Students also completed pen and paper questionnaires containing standardized measures of emotional distress (Depression, Anxiety, and Stress Scale; DASS-21), well-being (World Health Organization Well-Being Index; WHO-5), and coping (Coping Strategies Inventory; CSI). Results: On average the participants completed 18% (5/28) of daily ratings, and engagement levels did not differ across gender, age, school, socioeconomic status, ethnicity, or nationality. On a scale of 1 to 10, happiness was consistently the highest rated mood state (overall mean 6.56), and anger was consistently the lowest (overall mean 2.11). Pearson correlations revealed that average daily ratings of emotional states were associated with standardized measures of emotional distress (rhappiness=–.45, rsadness=.51, ranger=.32, rstress=.41, rworry=.48) and well-being (rhappiness=.39, rsadness =–.43, ranger=–.27, rstress=–.35, rworry=–.33). Inferential statistics indicated that single-item indicators of key protective factors were related to emotional distress, well-being, and average daily mood states, as measured by EMA ratings. Hierarchical regressions revealed that greater daily problems were associated with more negative daily mood ratings (all at the P<.001 level); however, when coping efficacy was taken into account, the relationship between problems and happiness, sadness, and anger became negligible. Conclusions: While engagement with the app was low, overall the EMA data collected in this exploratory study appeared valid and provided useful insights into the relationships between daily problems, coping efficacy, and mood states. Future research should explore ways to increase engagement with EMA mobile phone apps in adolescent populations to maximize the amount of data captured by these tools. Trial Registration: Clinicaltrials.gov NCT02265978; http://clinicaltrials.gov/ct2/show/NCT02265978 (Archived by WebCite at http://www.webcitation.org/6mMeYqseA). %M 27899340 %R 10.2196/mental.6361 %U http://mental.jmir.org/2016/4/e51/ %U https://doi.org/10.2196/mental.6361 %U http://www.ncbi.nlm.nih.gov/pubmed/27899340 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 2 %P e167 %T Qualitative Analysis of Cognitive Interviews With School Children: A Web-Based Food Intake Questionnaire %A Fernandes Davies,Vanessa %A Kupek,Emil %A Faria Di Pietro,Patricia %A Altenburg de Assis,Maria Alice %A GK Vieira,Francilene %A Perucchi,Clarice %A Mafra,Rafaella %A Thompson,Debbe %A Baranowski,Thomas %+ Public Health, Santa Catarina Federal University, R. Delfino Conti, S/N - Trindade, Florianopolis, 88036-020, Brazil, 55 (48) 3721 9394, va.davies@hotmail.co.uk %K dietary assessment %K children %K computer %K questionnaire %D 2016 %7 28.11.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The use of computers to administer dietary assessment questionnaires has shown potential, particularly due to the variety of interactive features that can attract and sustain children’s attention. Cognitive interviews can help researchers to gain insights into how children understand and elaborate their response processes in this type of questionnaire. Objective: To present the cognitive interview results of children who answered the WebCAAFE, a Web-based questionnaire, to obtain an in-depth understanding of children’s response processes. Methods: Cognitive interviews were conducted with children (using a pretested interview script). Analyses were carried out using thematic analysis within a grounded theory framework of inductive coding. Results: A total of 40 children participated in the study, and 4 themes were identified: (1) the meaning of words, (2) understanding instructions, (3) ways to resolve possible problems, and (4) suggestions for improving the questionnaire. Most children understood questions that assessed nutritional intake over the past 24 hours, although the structure of the questionnaire designed to facilitate recall of dietary intake was not always fully understood. Younger children (7 and 8 years old) had more difficulty relating the food images to mixed dishes and foods eaten with bread (eg, jam, cheese). Children were able to provide suggestions for improving future versions of the questionnaire. Conclusions: More attention should be paid to children aged 8 years or below, as they had the greatest difficulty completing the WebCAAFE. %M 27895005 %R 10.2196/publichealth.5024 %U http://publichealth.jmir.org/2016/2/e167/ %U https://doi.org/10.2196/publichealth.5024 %U http://www.ncbi.nlm.nih.gov/pubmed/27895005 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 11 %P e311 %T Survey Email Scheduling and Monitoring in eRCTs (SESAMe): A Digital Tool to Improve Data Collection in Randomized Controlled Clinical Trials %A Skonnord,Trygve %A Steen,Finn %A Skjeie,Holgeir %A Fetveit,Arne %A Brekke,Mette %A Klovning,Atle %+ Institute of Health and Society, Department of General Practice, University of Oslo, Postbox 1130 Blindern, Oslo, N-0318, Norway, 47 41323232, trygve.skonnord@medisin.uio.no %K randomized controlled trials %K data collection %K surveys and questionnaires %K quality improvement %K sample size %K Internet %K email %K text messaging %D 2016 %7 22.11.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic questionnaires can ease data collection in randomized controlled trials (RCTs) in clinical practice. We found no existing software that could automate the sending of emails to participants enrolled into an RCT at different study participant inclusion time points. Objective: Our aim was to develop suitable software to facilitate data collection in an ongoing multicenter RCT of low back pain (the Acuback study). For the Acuback study, we determined that we would need to send a total of 5130 emails to 270 patients recruited at different centers and at 19 different time points. Methods: The first version of the software was tested in a pilot study in November 2013 but was unable to deliver multiuser or Web-based access. We resolved these shortcomings in the next version, which we tested on the Web in February 2014. Our new version was able to schedule and send the required emails in the full-scale Acuback trial that started in March 2014. The system architecture evolved through an iterative, inductive process between the project study leader and the software programmer. The program was tested and updated when errors occurred. To evaluate the development of the software, we used a logbook, a research assistant dialogue, and Acuback trial participant queries. Results: We have developed a Web-based app, Survey Email Scheduling and Monitoring in eRCTs (SESAMe), that monitors responses in electronic surveys and sends reminders by emails or text messages (short message service, SMS) to participants. The overall response rate for the 19 surveys in the Acuback study increased from 76.4% (655/857) before we introduced reminders to 93.11% (1149/1234) after the new function (P<.001). Further development will aim at securing encryption and data storage. Conclusions: The SESAMe software facilitates consecutive patient data collection in RCTs and can be used to increase response rates and quality of research, both in general practice and in other clinical trial settings. %M 27876689 %R 10.2196/jmir.6560 %U http://www.jmir.org/2016/11/e311/ %U https://doi.org/10.2196/jmir.6560 %U http://www.ncbi.nlm.nih.gov/pubmed/27876689 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 4 %P e128 %T Formative Work to Develop a Tailored HIV Testing Smartphone App for Diverse, At-Risk, HIV-Negative Men Who Have Sex With Men: A Focus Group Study %A Mitchell,Jason W %A Torres,Maria Beatriz %A Joe,Jennifer %A Danh,Thu %A Gass,Bobbi %A Horvath,Keith J %+ Office of Public Health Studies, University of Hawai’i at Mānoa, 1960 East-West Road, Biomed D104AA, Honolulu, HI, 96822, United States, 1 808 956 3342, jasonmit@hawaii.edu %K smartphone apps %K mHealth %K HIV testing %K HIV-negative men who have sex with men %K men who have sex with men %K MSM %D 2016 %7 16.11.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Although gay, bisexual, and other men who have sex with men (MSM) are disproportionately affected by human immunodeficiency virus (HIV) infection, few test for HIV at regular intervals. Smartphone apps may be an ideal tool to increase regular testing among MSM. However, the success of apps to encourage regular testing among MSM will depend on how frequently the apps are downloaded, whether they continue to be used over months or years, and the degree to which such apps are tailored to the needs of this population. Objective: The primary objectives of this study were to answer the following questions. (1) What features and functions of smartphone apps do MSM believe are associated with downloading apps to their mobile phones? (2) What features and functions of smartphone apps are most likely to influence MSM’s sustained use of apps over time? (3) What features and functions do MSM prefer in an HIV testing smartphone app? Methods: We conducted focus groups (n=7, with a total of 34 participants) with a racially and ethnically diverse group of sexually active HIV-negative MSM (mean age 32 years; 11/34 men, 33%, tested for HIV ≥10 months ago) in the United States in Miami, Florida and Minneapolis, Minnesota. Focus groups were digitally recorded, transcribed verbatim, and deidentified for analysis. We used a constant comparison method (ie, grounded theory coding) to examine and reexamine the themes that emerged from the focus groups. Results: Men reported cost, security, and efficiency as their primary reasons influencing whether they download an app. Usefulness and perceived necessity, as well as peer and posted reviews, affected whether they downloaded and used the app over time. Factors that influenced whether they keep and continue to use an app over time included reliability, ease of use, and frequency of updates. Poor performance and functionality and lack of use were the primary reasons why men would delete an app from their phone. Participants also shared their preferences for an app to encourage regular HIV testing by providing feedback on test reminders, tailored testing interval recommendations, HIV test locator, and monitoring of personal sexual behaviors. Conclusions: Mobile apps for HIV prevention have proliferated, despite relatively little formative research to understand best practices for their development and implementation. The findings of this study suggest key design characteristics that should be used to guide development of an HIV testing app to promote regular HIV testing for MSM. The features and functions identified in this and prior research, as well as existing theories of behavior change, should be used to guide mobile app development in this critical area. %M 27852558 %R 10.2196/mhealth.6178 %U http://mhealth.jmir.org/2016/4/e128/ %U https://doi.org/10.2196/mhealth.6178 %U http://www.ncbi.nlm.nih.gov/pubmed/27852558 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 4 %P e219 %T Pixel or Paper? Validation of a Mobile Technology for Collecting Patient-Reported Outcomes in Rheumatoid Arthritis %A Epis,Oscar Massimiliano %A Casu,Cinzia %A Belloli,Laura %A Schito,Emanuela %A Filippini,Davide %A Muscarà,Marina %A Gentile,Maria Giovanna %A Perez Cagnone,Paula Carina %A Venerelli,Chiara %A Sonnati,Massimo %A Schiavetti,Irene %A Bruschi,Eleonora %+ Hippocrates Sintech Srl, via XX Settembre 30/4, Genova, 16121, Italy, 39 3491720506, p.perez@hippocrates-sintech.it %K validation %K rheumatoid arthritis %K PROs %K monitoring %K electronic device %K tablet %K questionnaire %K paper %D 2016 %7 16.11.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: In the management of chronic disease, new models for telemonitoring of patients combined with the choice of electronic patient-reported outcomes (ePRO) are being encouraged, with a clear improvement of both patients’ and parents’ quality of life. An Italian study demonstrated that ePRO were welcome in patients with rheumatoid arthritis (RA), with excellent matching data. Objective: The aim of this study is to evaluate the level of agreement between electronic and paper-and-pencil questionnaire responses. Methods: This is an observational prospective study. Patients were randomly assigned to first complete the questionnaire by paper and pencil and then by tablet or in the opposite order. The questionnaire consisted of 3 independent self-assessment visual rating scales (Visual Analog Scale, Global Health score, Patient Global Assessment of Disease Activity) commonly used in different adult patients, including those with rheumatic diseases. Results: A total of 185 consecutive RA patients were admitted to hospital and were enrolled and completed the questionnaire both on paper and on electronic versions. For all the evaluated items, the intrarater degree of agreement between 2 approaches was found to be excellent (intraclass correlation coefficient>0.75, P<.001). Conclusions: An electronic questionnaire is uploaded in a dedicated Web-based tool that could implement a telemonitoring system aimed at improving the follow-up of RA patients. High intrarater reliability between paper and electronic methods of data collection encourage the use of a new digital app with consequent benefit for the overall health care system. %M 27852561 %R 10.2196/resprot.5631 %U http://www.researchprotocols.org/2016/4/e219/ %U https://doi.org/10.2196/resprot.5631 %U http://www.ncbi.nlm.nih.gov/pubmed/27852561 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 4 %P e123 %T A Brief Tool to Assess Image-Based Dietary Records and Guide Nutrition Counselling Among Pregnant Women: An Evaluation %A Ashman,Amy M %A Collins,Clare E %A Brown,Leanne J %A Rae,Kym M %A Rollo,Megan E %+ Priority Research Centre in Physical Activity and Nutrition, Faculty of Health and Medicine, School of Health Sciences Office, University of Newcastle, Room HA12, Hunter Building, University Drive, Callaghan, 2308, Australia, 61 (02) 4921 5649, megan.rollo@newcastle.edu.au %K nutrition assessment %K pregnancy %K telehealth %K image-based dietary records %D 2016 %7 04.11.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Dietitians ideally should provide personally tailored nutrition advice to pregnant women. Provision is hampered by a lack of appropriate tools for nutrition assessment and counselling in practice settings. Smartphone technology, through the use of image-based dietary records, can address limitations of traditional methods of recording dietary intake. Feedback on these records can then be provided by the dietitian via smartphone. Efficacy and validity of these methods requires examination. Objective: The aims of the Australian Diet Bytes and Baby Bumps study, which used image-based dietary records and a purpose-built brief Selected Nutrient and Diet Quality (SNaQ) tool to provide tailored nutrition advice to pregnant women, were to assess relative validity of the SNaQ tool for analyzing dietary intake compared with nutrient analysis software, to describe the nutritional intake adequacy of pregnant participants, and to assess acceptability of dietary feedback via smartphone. Methods: Eligible women used a smartphone app to record everything they consumed over 3 nonconsecutive days. Records consisted of an image of the food or drink item placed next to a fiducial marker, with a voice or text description, or both, providing additional detail. We used the SNaQ tool to analyze participants’ intake of daily food group servings and selected key micronutrients for pregnancy relative to Australian guideline recommendations. A visual reference guide consisting of images of foods and drinks in standard serving sizes assisted the dietitian with quantification. Feedback on participants’ diets was provided via 2 methods: (1) a short video summary sent to participants’ smartphones, and (2) a follow-up telephone consultation with a dietitian. Agreement between dietary intake assessment using the SNaQ tool and nutrient analysis software was evaluated using Spearman rank correlation and Cohen kappa. Results: We enrolled 27 women (median age 28.8 years, 8 Indigenous Australians, 15 primiparas), of whom 25 completed the image-based dietary record. Median intakes of grains, vegetables, fruit, meat, and dairy were below recommendations. Median (interquartile range) intake of energy-dense, nutrient-poor foods was 3.5 (2.4-3.9) servings/day and exceeded recommendations (0-2.5 servings/day). Positive correlations between the SNaQ tool and nutrient analysis software were observed for energy (ρ=.898, P<.001) and all selected micronutrients (iron, calcium, zinc, folate, and iodine, ρ range .510-.955, all P<.05), both with and without vitamin and mineral supplements included in the analysis. Cohen kappa showed moderate to substantial agreement for selected micronutrients when supplements were included (kappa range .488-.803, all P ≤.001) and for calcium, iodine, and zinc when excluded (kappa range .554-.632, all P<.001). A total of 17 women reported changing their diet as a result of the personalized nutrition advice. Conclusions: The SNaQ tool demonstrated acceptable validity for assessing adequacy of key pregnancy nutrient intakes and preliminary evidence of utility to support dietitians in providing women with personalized advice to optimize nutrition during pregnancy. %M 27815234 %R 10.2196/mhealth.6469 %U http://mhealth.jmir.org/2016/4/e123/ %U https://doi.org/10.2196/mhealth.6469 %U http://www.ncbi.nlm.nih.gov/pubmed/27815234 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 4 %P e208 %T The e-EPIDEMIOLOGY Mobile Phone App for Dietary Intake Assessment: Comparison with a Food Frequency Questionnaire %A Bejar,Luis Maria %A Sharp,Brett Northrop %A García-Perea,María Dolores %+ Department of Preventive Medicine and Public Health, University of Seville, Institute of Anatomy, 3rd floor, Sánchez-Pizjuán Avenue, Seville, 41009, Spain, 34 954551771, lmbprado@us.es %K dietary assessment %K mobile phone application %K food frequency questionnaire %K epidemiological methods %D 2016 %7 02.11.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: There is a great necessity for new methods of evaluation of dietary intake that overcome the limitations of traditional self-reporting methods. Objective: The objective of this study was to develop a new method, based on an app for mobile phones called e-EPIDEMIOLOGY, which was designed to collect individual consumption data for a series of foods/drinks, and to compare this app with a previously validated paper food frequency questionnaire (FFQ). Methods: University students >18 years of age recorded the consumption of certain foods/drinks using e-EPIDEMIOLOGY during 28 consecutive days and then filled out a paper FFQ at the end of the study period. To evaluate the agreement between the categories of habitual consumption for each of the foods/drinks included in the study, cross-classification analysis and a weighted kappa statistic were used. Results: A total of 119 participants completed the study (71% female, 85/119; 29% male, 34/119). Cross-classification analysis showed that 79.8% of the participants were correctly classified into the same category and just 1.1% were misclassified into opposite categories. The average weighted kappa statistic was good (κ=.64). Conclusions: The results indicate that e-EPIDEMIOLOGY generated ranks of dietary intakes that were highly comparable with the previously validated paper FFQ. However, it was noted that further testing of e-EPIDEMIOLOGY is required to establish its wider utility. %M 27806922 %R 10.2196/resprot.5782 %U http://www.researchprotocols.org/2016/4/e208/ %U https://doi.org/10.2196/resprot.5782 %U http://www.ncbi.nlm.nih.gov/pubmed/27806922 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 10 %P e275 %T Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt %A Businelle,Michael S %A Ma,Ping %A Kendzor,Darla E %A Frank,Summer G %A Wetter,David W %A Vidrine,Damon J %+ Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, 655 Research Parkway, Suite 400, Oklahoma City, OK, 73104, United States, 1 4052718001 ext 50460, michael-businelle@ouhsc.edu %K smartphone %K mobile app %K mhealth %K ecological momentary assessment %K smoking cessation %K socioeconomic disadvantage, risk estimation %D 2016 %7 17.10.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Mobile phone‒based real-time ecological momentary assessments (EMAs) have been used to record health risk behaviors, and antecedents to those behaviors, as they occur in near real time. Objective: The objective of this study was to determine if intensive longitudinal data, collected via mobile phone, could be used to identify imminent risk for smoking lapse among socioeconomically disadvantaged smokers seeking smoking cessation treatment. Methods: Participants were recruited into a randomized controlled smoking cessation trial at an urban safety-net hospital tobacco cessation clinic. All participants completed in-person EMAs on mobile phones provided by the study. The presence of six commonly cited lapse risk variables (ie, urge to smoke, stress, recent alcohol consumption, interaction with someone smoking, cessation motivation, and cigarette availability) collected during 2152 prompted or self-initiated postcessation EMAs was examined to determine whether the number of lapse risk factors was greater when lapse was imminent (ie, within 4 hours) than when lapse was not imminent. Various strategies were used to weight variables in efforts to improve the predictive utility of the lapse risk estimator. Results: Participants (N=92) were mostly female (52/92, 57%), minority (65/92, 71%), 51.9 (SD 7.4) years old, and smoked 18.0 (SD 8.5) cigarettes per day. EMA data indicated significantly higher urges (P=.01), stress (P=.002), alcohol consumption (P<.001), interaction with someone smoking (P<.001), and lower cessation motivation (P=.03) within 4 hours of the first lapse compared with EMAs collected when lapse was not imminent. Further, the total number of lapse risk factors present within 4 hours of lapse (mean 2.43, SD 1.37) was significantly higher than the number of lapse risk factors present during periods when lapse was not imminent (mean 1.35, SD 1.04), P<.001. Overall, 62% (32/52) of all participants who lapsed completed at least one EMA wherein they reported ≥3 lapse risk factors within 4 hours of their first lapse. Differentially weighting lapse risk variables resulted in an improved risk estimator (weighted area=0.76 vs unweighted area=0.72, P<.004). Specifically, 80% (42/52) of all participants who lapsed had at least one EMA with a lapse risk score above the cut-off within 4 hours of their first lapse. Conclusions: Real-time estimation of smoking lapse risk is feasible and may pave the way for development of mobile phone‒based smoking cessation treatments that automatically tailor treatment content in real time based on presence of specific lapse triggers. Interventions that identify risk for lapse and automatically deliver tailored messages or other treatment components in real time could offer effective, low cost, and highly disseminable treatments to individuals who do not have access to other more standard cessation treatments. %M 27751985 %R 10.2196/jmir.6307 %U http://www.jmir.org/2016/10/e275/ %U https://doi.org/10.2196/jmir.6307 %U http://www.ncbi.nlm.nih.gov/pubmed/27751985 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 3 %P e192 %T Evaluation of the Swedish Web-Version of Quality of Recovery (SwQoR): Secondary Step in the Development of a Mobile Phone App to Measure Postoperative Recovery %A Dahlberg,Karuna %A Jaensson,Maria %A Eriksson,Mats %A Nilsson,Ulrica %+ School of Health Sciences, Örebro University, Örebro, 70182, Sweden, 46 19303000, karuna.dahlberg@oru.se %K mHealth %K ambulatory surgical procedures %K postoperative period %K mobile phones %D 2016 %7 28.09.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: The majority of all surgeries are performed on an outpatient basis (day surgery). The Recovery Assessment by Phone Points (RAPP) app is an app for the Swedish Web-version of Quality of Recovery (SwQoR), developed to assess and follow-up on postoperative recovery after day surgery. Objectives: The objectives of this study are (1) to estimate the extent to which the paper and app versions of the SwQoR provide equivalent values; (2) to contribute evidence as to the feasibility and acceptability of a mobile phone Web-based app for measuring postoperative recovery after day surgery and enabling contact with a nurse; and (3) to contribute evidence as to the content validity of the SwQoR. Methods: Equivalence between the paper and app versions of the SwQoR was measured using a randomized crossover design, in which participants used both the paper and app version. Feasibility and acceptability was evaluated by a questionnaire containing 16 questions regarding the value of the app for follow-up care after day surgery. Content validity evaluation was based on responses by day surgery patients and the staff of the day surgery department. Results: A total of 69 participants completed the evaluation of equivalence between the paper and app versions of the SwQoR. The intraclass correlation coefficient (ICC) for the SwQoR was .89 (95% CI 0.83-0.93) and .13 to .90 for the items. Of the participants, 63 continued testing the app after discharge and completed the follow-up questionnaire. The median score was 69 (inter-quartile range, IQR 66-73), indicating a positive attitude toward using an app for follow-up after day surgery. A total of 18 patients and 12 staff members participated in the content validity evaluation. The item-level content validity index (I-CVI) for the staff group was in the 0.64 to 1.0 range, with a scale-level content validity index (S-CVI) of 0.88. For the patient group, I-CVI was in the range 0.30 to 0.92 and S-CVI was 0.67. The content validity evaluation of the SwQoR, together with three new items, led to a reduction from 34 to 24 items. Conclusions: Day surgery patients had positive attitudes toward using the app for follow-up after surgery, and stated a preference for using the app again if they were admitted for a future day surgery procedure. Equivalence between the app and paper version of the SwQoR was found, but at the item level, the ICC was less than .7 for 9 items. In the content validity evaluation of the SwQoR, staff found more items relevant than the patients, and no items found relevant by either staff or patients were excluded when revising the SwQoR. %M 27679867 %R 10.2196/resprot.5881 %U http://www.researchprotocols.org/2016/3/e192/ %U https://doi.org/10.2196/resprot.5881 %U http://www.ncbi.nlm.nih.gov/pubmed/27679867 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 3 %P e111 %T Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild %A Wahle,Fabian %A Kowatsch,Tobias %A Fleisch,Elgar %A Rufer,Michael %A Weidt,Steffi %+ University of St Gallen, Institute of Technology Management, Dufourstrasse 40a, Büro 1-236, St Gallen, 9000, Switzerland, 41 712247244, tobias.kowatsch@unisg.ch %K depression %K mHealth %K  activities of daily living %K classification %K context awareness %K cognitive behavioral therapy %D 2016 %7 21.09.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. Objective: The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. Methods: A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject’s preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery. Results: A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. Conclusions: Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states. %M 27655245 %R 10.2196/mhealth.5960 %U http://mhealth.jmir.org/2016/3/e111/ %U https://doi.org/10.2196/mhealth.5960 %U http://www.ncbi.nlm.nih.gov/pubmed/27655245 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 3 %P e104 %T Physical Activity, Mind Wandering, Affect, and Sleep: An Ecological Momentary Assessment %A Fanning,Jason %A Mackenzie,Michael %A Roberts,Sarah %A Crato,Ines %A Ehlers,Diane %A McAuley,Edward %+ Exercise Psychology Lab, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Louise Freer Hall, 906 S. Goodwin Avenue, Urbana, IL, 61801, United States, 1 217 300 5306, fanning4@illinois.edu %K physical activity %K mHealth %K attention %K sleep %K affect %D 2016 %7 31.08.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A considerable portion of daily thought is spent in mind wandering. This behavior has been related to positive (eg, future planning, problem solving) and negative (eg, unhappiness, impaired cognitive performance) outcomes. Objective: Based on previous research suggesting future-oriented (ie, prospective) mind wandering may support autobiographical planning and self-regulation, this study examined associations between hourly mind wandering and moderate-to-vigorous physical activity (MVPA), and the impact of affect and daily sleep on these relations. Methods: College-aged adults (N=33) participated in a mobile phone-delivered ecological momentary assessment study for 1 week. Sixteen hourly prompts assessing mind wandering and affect were delivered daily via participants’ mobile phones. Perceived sleep quality and duration was assessed during the first prompt each day, and participants wore an ActiGraph accelerometer during waking hours throughout the study week. Results: Study findings suggest present-moment mind wandering was positively associated with future MVPA (P=.03), and this relationship was moderated by affective state (P=.04). Moreover, excessive sleep the previous evening was related to less MVPA across the following day (P=.007). Further, mind wandering was positively related to activity only among those who did not oversleep (P=.007). Conclusions: Together, these results have implications for multiple health behavior interventions targeting physical activity, affect, and sleep. Researchers may also build on this work by studying these relationships in the context of other important behaviors and psychosocial factors (eg, tobacco use, depression, loneliness). %M 27580673 %R 10.2196/mhealth.5855 %U http://mhealth.jmir.org/2016/3/e104/ %U https://doi.org/10.2196/mhealth.5855 %U http://www.ncbi.nlm.nih.gov/pubmed/27580673 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 3 %P e120 %T The Development and Piloting of a Mobile Data Collection Protocol to Assess Compliance With a National Tobacco Advertising, Promotion, and Product Display Ban at Retail Venues in the Russian Federation %A Grant,Ashley S %A Kennedy,Ryan D %A Spires,Mark H %A Cohen,Joanna E %+ Institute for Global Tobacco Control, Department of Health, Behavior & Society, Johns Hopkins Bloomberg School of Public Health, 4th Floor, 2213 McElderry Street, Baltimore, MA, 21205, United States, 1 955 3435, rdkennedy@jhu.edu %K tobacco %K tobacco marketing %K retail environments %K compliance assessment %K policy implementation %K point-of-sale %K Russia %K mobile data collection %K mobile devices %D 2016 %7 31.08.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Tobacco control policies that lead to a significant reduction in tobacco industry marketing can improve public health by reducing consumption of tobacco and preventing initiation of tobacco use. Laws that ban or restrict advertising and promotion in point-of-sale (POS) environments, in the moment when consumers decide whether or not to purchase a tobacco product, must be correctly implemented to achieve the desired public health benefits. POS policy compliance assessments can support implementation; however, there are challenges to conducting evaluations that are rigorous, cost-effective, and timely. Data collection must be discreet, accurate, and systematic, and ideally collected both before and after policies take effect. The use of mobile phones and other mobile technology provide opportunities to efficiently collect data and support effective tobacco control policies. The Russian Federation (Russia) passed a comprehensive national tobacco control law that included a ban on most forms of tobacco advertising and promotion, effective November 15, 2013. The legislation further prohibited the display of tobacco products at retail trade sites and eliminated kiosks as a legal trade site, effective June 1, 2014. Objective: The objective of the study was to develop and test a mobile data collection protocol including: (1) retailer sampling, (2) adaptation of survey instruments for mobile phones, and (3) data management protocols. Methods: Two waves of observations were conducted; wave 1 took place during April-May 2014, after the advertising and promotion bans were effective, and again in August-September 2014, after the product display ban and elimination of tobacco sales in kiosks came into effect. Sampling took place in 5 Russian cities: Moscow, St. Petersburg, Novosibirsk, Yekaterinburg, and Kazan. Lack of access to a comprehensive list of licensed tobacco retailers necessitated a sampling approach that included the development of a walking protocol to identify tobacco retailers to observe. Observation instruments were optimized for use on mobile devices and included the collection of images/photos and the geographic location of retailers. Data were uploaded in real-time to a remote (“cloud-based”) server accessible via Internet and verified with the use of a data management protocol that included submission of daily field notes from the research team for review by project managers. Results: The walking protocol was a practical means of identifying 780 relevant retail venues in Russia, in the absence of reliable sampling resources. Mobile phones were convenient tools for completing observation checklists discretely and accurately. Daily field notes and meticulous oversight of collected data were critical to ensuring data quality. Conclusions: Mobile technology can support timely and accurate data collection and also help monitor data quality through the use of real-time uploads. These protocols can be adapted to assess compliance with other types of public health policies. %M 27580800 %R 10.2196/resprot.5302 %U http://www.researchprotocols.org/2016/3/e120/ %U https://doi.org/10.2196/resprot.5302 %U http://www.ncbi.nlm.nih.gov/pubmed/27580800 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 3 %P e160 %T Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study %A Hung,Galen Chin-Lun %A Yang,Pei-Ching %A Chang,Chia-Chi %A Chiang,Jung-Hsien %A Chen,Ying-Yeh %+ Department of General Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital, No. 309,, Songde Rd., Xinyi Dst.,, Taipei, 106, Taiwan, 886 2 27263141 ext 1270, galenhung@tpech.gov.tw %K mobile phone usage %K depression %K emotion %K machine learning %K affective computing %D 2016 %7 10.08.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Prompt recognition and intervention of negative emotions is crucial for patients with depression. Mobile phones and mobile apps are suitable technologies that can be used to recognize negative emotions and intervene if necessary. Objective: Mobile phone usage patterns can be associated with concurrent emotional states. The objective of this study is to adapt machine-learning methods to analyze such patterns for the prediction of negative emotion. Methods: We developed an Android-based app to capture emotional states and mobile phone usage patterns, which included call logs (and use of apps). Visual analog scales (VASs) were used to report negative emotions in dimensions of depression, anxiety, and stress. In the system-training phase, participants were requested to tag their emotions for 14 consecutive days. Five feature-selection methods were used to determine individual usage patterns and four machine-learning methods were tested. Finally, rank product scoring was used to select the best combination to construct the prediction model. In the system evaluation phase, participants were then requested to verify the predicted negative emotions for at least 5 days. Results: Out of 40 enrolled healthy participants, we analyzed data from 28 participants, including 30% (9/28) women with a mean (SD) age of 29.2 (5.1) years with sufficient emotion tags. The combination of time slots of 2 hours, greedy forward selection, and Naïve Bayes method was chosen for the prediction model. We further validated the personalized models in 18 participants who performed at least 5 days of model evaluation. Overall, the predictive accuracy for negative emotions was 86.17%. Conclusion: We developed a system capable of predicting negative emotions based on mobile phone usage patterns. This system has potential for ecological momentary intervention (EMI) for depressive disorders by automatically recognizing negative emotions and providing people with preventive treatments before it escalates to clinical depression. %M 27511748 %R 10.2196/resprot.5551 %U http://www.researchprotocols.org/2016/3/e160/ %U https://doi.org/10.2196/resprot.5551 %U http://www.ncbi.nlm.nih.gov/pubmed/27511748 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 8 %P e216 %T Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients %A Kim,Junetae %A Lim,Sanghee %A Min,Yul Ha %A Shin,Yong-Wook %A Lee,Byungtae %A Sohn,Guiyun %A Jung,Kyung Hae %A Lee,Jae-Ho %A Son,Byung Ho %A Ahn,Sei Hyun %A Shin,Soo-Yong %A Lee,Jong Won %+ Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic Of Korea, 82 2 3010 5603, jongwonlee116@gmail.com %K depression %K smartphone applications %K mental health %K breast cancer (neoplasms) %D 2016 %7 04.08.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Mobile mental-health trackers are mobile phone apps that gather self-reported mental-health ratings from users. They have received great attention from clinicians as tools to screen for depression in individual patients. While several apps that ask simple questions using face emoticons have been developed, there has been no study examining the validity of their screening performance. Objective: In this study, we (1) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening. Methods: We analyzed 5792 sets of daily mental-health ratings collected from 78 breast cancer patients over a 48-week period. Using the Patient Health Questionnaire-9 (PHQ-9) as the measure of true depression status, we conducted a random-effect logistic panel regression and receiver operating characteristic (ROC) analysis to evaluate the screening performance of the mobile mental-health tracker. In addition, we classified patients into two subgroups based on their adherence level (higher adherence and lower adherence) using a k-means clustering algorithm and compared the screening accuracy between the two groups. Results: With the ratio approach, the area under the ROC curve (AUC) is 0.8012, indicating that the performance of depression screening using daily mental-health ratings gathered via mobile mental-health trackers is comparable to the results of PHQ-9 tests. Also, the AUC is significantly higher (P=.002) for the higher adherence group (AUC=0.8524) than for the lower adherence group (AUC=0.7234). This result shows that adherence to self-reporting is associated with a higher accuracy of depression screening. Conclusions: Our results support the potential of a mobile mental-health tracker as a tool for screening for depression in practice. Also, this study provides clinicians with a guideline for generating indicator variables from daily mental-health ratings. Furthermore, our results provide empirical evidence for the critical role of adherence to self-reporting, which represents crucial information for both doctors and patients. %M 27492880 %R 10.2196/jmir.5598 %U http://www.jmir.org/2016/8/e216/ %U https://doi.org/10.2196/jmir.5598 %U http://www.ncbi.nlm.nih.gov/pubmed/27492880 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 2 %P e139 %T Feasibility of an Electronic Survey on iPads with In-Person Data Collectors for Data Collection with Health Care Professionals and Health Care Consumers in General Emergency Departments %A Scott,Shannon D %A Albrecht,Lauren %A Given,Lisa M %A Arseneau,Danielle %A Klassen,Terry P %+ University of Alberta, Faculty of Nursing, 3rd floor, Edmonton Clinic Health Adacemy, 11405 87 Ave, Edmonton, AB, T6G 1C9, Canada, 1 7804921037, ss14@ualberta.ca %K survey development %K electronic survey %K survey implementation %K needs assessment %K pediatric emergency medicine %D 2016 %7 29.06.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Translating Emergency Knowledge for Kids was established to bridge the research-practice gap in pediatric emergency care by bringing the best evidence to Canadian general emergency departments (EDs). The first step in this process was to conduct a national needs assessment to determine the information needs and preferences of health professionals and parents in this clinical setting. Objective: To describe the development and implementation of two electronic surveys, and determine the feasibility of collecting electronic survey data on iPads with in-person data collectors in a busy clinical environment. Methods: Two descriptive surveys were conducted in 32 general EDs. Specific factors were addressed in four survey development and implementation stages: survey design, survey delivery, survey completion, and survey return. Feasibility of the data collection approach was determined by evaluating participation rates, completion rates, average survey time to completion, and usability of the platform. Usability was assessed with the in-person data collectors on five key variables: interactivity, portability, innovativeness, security, and proficiency. Results: Health professional participation rates (1561/2575, 60.62%) and completion rates (1471/1561, 94.23%) were strong. Parental participation rates (974/1099, 88.63%) and completion rates (897/974, 92.09%) were excellent. Mean time to survey completion was 28.08 minutes for health professionals and 43.23 minutes for parents. Data collectors rated the platform “positively” to “very positively” on all five usability variables. Conclusions: A number of design and implementation considerations were explored and integrated into this mixed-mode survey data collection approach. Feasibility was demonstrated by the robust survey participation and completion rates, reasonable survey completion times, and very positive usability evaluation results. %M 27358205 %R 10.2196/resprot.5170 %U http://www.researchprotocols.org/2016/2/e139/ %U https://doi.org/10.2196/resprot.5170 %U http://www.ncbi.nlm.nih.gov/pubmed/27358205 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 2 %P e66 %T Experiences With a Self-Reported Mobile Phone-Based System Among Patients With Colorectal Cancer: A Qualitative Study %A Drott,Jenny %A Vilhelmsson,Maria %A Kjellgren,Karin %A Berterö,Carina %+ Division of Nursing Science, Department of Medical and Health Sciences, Linköping University, IMH, Linköping, 581 83, Sweden, 46 013 286820, Jenny.Drott@liu.se %K cancer %K conventional content analysis %K informatics technology systems %K mHealth %K self-reported mobile phone-based system %K symptom monitoring %D 2016 %7 09.06.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In cancer care, mobile phone-based systems are becoming more widely used in the assessment, monitoring, and management of side effects. Objective: To explore the experiences of patients with colorectal cancer on using a mobile phone-based system for reporting neurotoxic side effects. Methods: Eleven patients were interviewed (ages 44-68 years). A semistructured interview guide was used to perform telephone interviews. The interviews were transcribed verbatim and analyzed with qualitative content analysis. Results: The patients' experiences of using a mobile phone-based system were identified and constructed as: “being involved,” “pacing oneself,” and “managing the questions.” “Being involved” refers to their individual feelings. Patients were participating in their own care by being observant of the side effects they were experiencing. They were aware that the answers they gave were monitored in real time and taken into account by health care professionals when planning further treatment. “Pacing oneself” describes how the patients can have an impact on the time and place they choose to answer the questions. Answering the questionnaire was easy, and despite the substantial number of questions, it was quickly completed. “Managing the questions” pointed out that the patients needed to be observant because of the construction of the questions. They could not routinely answer all the questions. Patients understood that side effects can vary during the cycles of treatment and need to be assessed repeatedly during treatment. Conclusions: This mobile phone-based system reinforced the patients’ feeling of involvement in their own care. The patients were comfortable with the technology and appreciated that the system was not time consuming. %M 27282257 %R 10.2196/mhealth.5426 %U http://mhealth.jmir.org/2016/2/e66/ %U https://doi.org/10.2196/mhealth.5426 %U http://www.ncbi.nlm.nih.gov/pubmed/27282257 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 2 %P e56 %T Walking as a Contributor to Physical Activity in Healthy Older Adults: 2 Week Longitudinal Study Using Accelerometry and the Doubly Labeled Water Method %A Valenti,Giulio %A Bonomi,Alberto G %A Westerterp,Klaas R %+ Department of Human Biology, Maastricht University, Universiteitssingel 50, Maastricht, 6200 MD, PO Box 616, Netherlands, 31 433882124, g.valenti@maastrichtuniversity.nl %K aging %K walking %K physical activity %K accelerometry %K monitoring, ambulatory/instrumentation %D 2016 %7 07.06.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Physical activity is recommended to promote healthy aging. Defining the importance of activities such as walking in achieving higher levels of physical activity might provide indications for interventions. Objective: To describe the importance of walking in achieving higher levels of physical activity in older adults. Methods: The study included 42 healthy subjects aged between 51 and 84 years (mean body mass index 25.6 kg/m2 [SD 2.6]). Physical activity, walking, and nonwalking activity were monitored with an accelerometer for 2 weeks. Physical activity was quantified by accelerometer-derived activity counts. An algorithm based on template matching and signal power was developed to classify activity counts into nonwalking counts, short walk counts, and long walk counts. Additionally, in a subgroup of 31 subjects energy expenditure was measured using doubly labeled water to derive physical activity level (PAL). Results: Subjects had a mean PAL of 1.84 (SD 0.19, range 1.43-2.36). About 20% of the activity time (21% [SD 8]) was spent walking, which accounted for about 40% of the total counts (43% [SD 11]). Short bouts composed 83% (SD 9) of walking time, providing 81% (SD 11) of walking counts. A stepwise regression model to predict PAL included nonwalking counts and short walk counts, explaining 58% of the variance of PAL (standard error of the estimate=0.12). Walking activities produced more counts per minute than nonwalking activities (P<.001). Long walks produced more counts per minute than short walks (P=.001). Nonwalking counts were independent of walking counts (r=−.05, P=.38). Conclusions: Walking activities are a major contributor to physical activity in older adults. Walking activities occur at higher intensities than nonwalking activities, which might prevent individuals from engaging in more walking activity. Finally, subjects who engage in more walking activities do not tend to compensate by limiting nonwalking activities. Trial Registration: ClinicalTrials.gov NCT01609764; https://clinicaltrials.gov/ct2/show/NCT01609764 (Archived by WebCite at http://www.webcitation.org/6grls0wAp) %M 27268471 %R 10.2196/mhealth.5445 %U http://mhealth.jmir.org/2016/2/e56/ %U https://doi.org/10.2196/mhealth.5445 %U http://www.ncbi.nlm.nih.gov/pubmed/27268471 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 2 %P e46 %T Survalytics: An Open-Source Cloud-Integrated Experience Sampling, Survey, and Analytics and Metadata Collection Module for Android Operating System Apps %A O'Reilly-Shah,Vikas %A Mackey,Sean %+ Assistant Professor of Anesthesiology, Emory University and Children's Healthcare of Atlanta, 3B South, Room B347, 1364 Clifton Road NE, Atlanta, GA, 30322, United States, 1 615 335 3808, voreill@emory.edu %K experiential sampling %K ecological momentary assessment %K quantified self %K analytics %K Android %K Amazon Web Services %K DynamoDB %K NoSQL %K surveys %K microsurveys %K mobile surveys %D 2016 %7 03.06.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: We describe here Survalytics, a software module designed to address two broad areas of need. The first area is in the domain of surveys and app analytics: developers of mobile apps in both academic and commercial environments require information about their users, as well as how the apps are being used, to understand who their users are and how to optimally approach app development. The second area of need is in the field of ecological momentary assessment, also referred to as experience sampling: researchers in a wide variety of fields, spanning from the social sciences to psychology to clinical medicine, would like to be able to capture daily or even more frequent data from research subjects while in their natural environment. Objective: Survalytics is an open-source solution for the collection of survey responses as well as arbitrary analytic metadata from users of Android operating system apps. Methods: Surveys may be administered in any combination of one-time questions and ongoing questions. The module may be deployed as a stand-alone app for experience sampling purposes or as an add-on to existing apps. The module takes advantage of free-tier NoSQL cloud database management offered by the Amazon Web Services DynamoDB platform to package a secure, flexible, extensible data collection module. DynamoDB is capable of Health Insurance Portability and Accountability Act compliant storage of personal health information. Results: The provided example app may be used without modification for a basic experience sampling project, and we provide example questions for daily collection of blood glucose data from study subjects. Conclusions: The module will help researchers in a wide variety of fields rapidly develop tailor-made Android apps for a variety of data collection purposes. %M 27261155 %R 10.2196/mhealth.5397 %U http://mhealth.jmir.org/2016/2/e46/ %U https://doi.org/10.2196/mhealth.5397 %U http://www.ncbi.nlm.nih.gov/pubmed/27261155 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e28 %T Development and Implementation of Culturally Tailored Offline Mobile Health Surveys %A McIntosh,Scott %A Pérez-Ramos,José %A Demment,Margaret M %A Vélez Vega,Carmen %A Avendaño,Esteban %A Ossip,Deborah J %A Dye,Timothy D %+ School of Medicine & Dentistry, Department of Public Health Sciences, University of Rochester, CU420644, 265 Crittenden Blvd, Rochester, NY, 14642, United States, 1 585 802 9944, scott_mcintosh@urmc.rochester.edu %K mobile health %K survey research %K ethical review %D 2016 %7 02.06.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In low and middle income countries (LMICs), and other areas with low resources and unreliable access to the Internet, understanding the emerging best practices for the implementation of new mobile health (mHealth) technologies is needed for efficient and secure data management and for informing public health researchers. Innovations in mHealth technology can improve on previous methods, and dissemination of project development details and lessons learned during implementation are needed to provide lessons learned to stakeholders in both the United States and LMIC settings. Objective: The aims of this paper are to share implementation strategies and lessons learned from the development and implementation stages of two survey research projects using offline mobile technology, and to inform and prepare public health researchers and practitioners to implement new mobile technologies in survey research projects in LMICs. Methods: In 2015, two survey research projects were developed and piloted in Puerto Rico and pre-tested in Costa Rica to collect face-to-face data, get formative evaluation feedback, and to test the feasibility of an offline mobile data collection process. Fieldwork in each setting involved survey development, back translation with cultural tailoring, ethical review and approvals, data collector training, and piloting survey implementation on mobile tablets. Results: Critical processes and workflows for survey research projects in low resource settings were identified and implemented. This included developing a secure mobile data platform tailored to each survey, establishing user accessibility, and training and eliciting feedback from data collectors and on-site LMIC project partners. Conclusions: Formative and process evaluation strategies are necessary and useful for the development and implementation of survey research projects using emerging mHealth technologies in LMICs and other low resource settings. Lessons learned include: (1) plan institutional review board (IRB) approvals in multiple countries carefully to allow for development, implementation, and feedback, (2) in addition to testing the content of survey instruments, allow time and consideration for testing the use of novel mHealth technology (hardware and software), (3) incorporate training for and feedback from project staff, LMIC partner staff, and research participants, and (4) change methods accordingly, including content, as mHealth technology usage influences and is influenced by the content and structure of the survey instrument. Lessons learned from early phases of LMIC research projects using emerging mHealth technologies are critical for informing subsequent research methods and study designs. %M 27256208 %R 10.2196/publichealth.5408 %U http://publichealth.jmir.org/2016/1/e28/ %U https://doi.org/10.2196/publichealth.5408 %U http://www.ncbi.nlm.nih.gov/pubmed/27256208 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 5 %P e130 %T Hearing Tests on Mobile Devices: Evaluation of the Reference Sound Level by Means of Biological Calibration %A Masalski,Marcin %A Kipiński,Lech %A Grysiński,Tomasz %A Kręcicki,Tomasz %+ Department and Clinic of Otolaryngology, Head and Neck Surgery, Wroclaw Medical University, Borowska 213, Wrocław, 50-556, Poland, 48 515086252, marcin.masalski@pwr.edu.pl %K hearing test, mobile device, calibration %D 2016 %7 30.05.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Hearing tests carried out in home setting by means of mobile devices require previous calibration of the reference sound level. Mobile devices with bundled headphones create a possibility of applying the predefined level for a particular model as an alternative to calibrating each device separately. Objective: The objective of this study was to determine the reference sound level for sets composed of a mobile device and bundled headphones. Methods: Reference sound levels for Android-based mobile devices were determined using an open access mobile phone app by means of biological calibration, that is, in relation to the normal-hearing threshold. The examinations were conducted in 2 groups: an uncontrolled and a controlled one. In the uncontrolled group, the fully automated self-measurements were carried out in home conditions by 18- to 35-year-old subjects, without prior hearing problems, recruited online. Calibration was conducted as a preliminary step in preparation for further examination. In the controlled group, audiologist-assisted examinations were performed in a sound booth, on normal-hearing subjects verified through pure-tone audiometry, recruited offline from among the workers and patients of the clinic. In both the groups, the reference sound levels were determined on a subject’s mobile device using the Bekesy audiometry. The reference sound levels were compared between the groups. Intramodel and intermodel analyses were carried out as well. Results: In the uncontrolled group, 8988 calibrations were conducted on 8620 different devices representing 2040 models. In the controlled group, 158 calibrations (test and retest) were conducted on 79 devices representing 50 models. Result analysis was performed for 10 most frequently used models in both the groups. The difference in reference sound levels between uncontrolled and controlled groups was 1.50 dB (SD 4.42). The mean SD of the reference sound level determined for devices within the same model was 4.03 dB (95% CI 3.93-4.11). Statistically significant differences were found across models. Conclusions: Reference sound levels determined in the uncontrolled group are comparable to the values obtained in the controlled group. This validates the use of biological calibration in the uncontrolled group for determining the predefined reference sound level for new devices. Moreover, due to a relatively small deviation of the reference sound level for devices of the same model, it is feasible to conduct hearing screening on devices calibrated with the predefined reference sound level. %M 27241793 %R 10.2196/jmir.4987 %U http://www.jmir.org/2016/5/e130/ %U https://doi.org/10.2196/jmir.4987 %U http://www.ncbi.nlm.nih.gov/pubmed/27241793 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 4 %N 1 %P e7 %T A Serious Game for Clinical Assessment of Cognitive Status: Validation Study %A Tong,Tiffany %A Chignell,Mark %A Tierney,Mary C. %A Lee,Jacques %+ Interactive Media Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON,, Canada, 1 416 978 7581, tiffany.tong@mail.utoronto.ca %K cognitive assessments %K cognitive screening tools %K computerized assessments %K games %K human computer interaction %K human factors %K neuropsychological tests %K screening %K serious games %K tablet computers %K technology assessment %K usability %K validation studies %K video games %D 2016 %7 27.05.2016 %9 Original Paper %J JMIR Serious Games %G English %X Background: We propose the use of serious games to screen for abnormal cognitive status in situations where it may be too costly or impractical to use standard cognitive assessments (eg, emergency departments). If validated, serious games in health care could enable broader availability of efficient and engaging cognitive screening. Objective: The objective of this work is to demonstrate the feasibility of a game-based cognitive assessment delivered on tablet technology to a clinical sample and to conduct preliminary validation against standard mental status tools commonly used in elderly populations. Methods: We carried out a feasibility study in a hospital emergency department to evaluate the use of a serious game by elderly adults (N=146; age: mean 80.59, SD 6.00, range 70-94 years). We correlated game performance against a number of standard assessments, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Confusion Assessment Method (CAM). Results: After a series of modifications, the game could be used by a wide range of elderly patients in the emergency department demonstrating its feasibility for use with these users. Of 146 patients, 141 (96.6%) consented to participate and played our serious game. Refusals to play the game were typically due to concerns of family members rather than unwillingness of the patient to play the game. Performance on the serious game correlated significantly with the MoCA (r=–.339, P <.001) and MMSE (r=–.558, P <.001), and correlated (point-biserial correlation) with the CAM (r=.565, P <.001) and with other cognitive assessments. Conclusions: This research demonstrates the feasibility of using serious games in a clinical setting. Further research is required to demonstrate the validity and reliability of game-based assessments for clinical decision making. %M 27234145 %R 10.2196/games.5006 %U http://games.jmir.org/2016/1/e7/ %U https://doi.org/10.2196/games.5006 %U http://www.ncbi.nlm.nih.gov/pubmed/27234145 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 2 %P e44 %T Concordance of Text Message Ecological Momentary Assessment and Retrospective Survey Data Among Substance-Using Men Who Have Sex With Men: A Secondary Analysis of a Randomized Controlled Trial %A Rowe,Christopher %A Hern,Jaclyn %A DeMartini,Anna %A Jennings,Danielle %A Sommers,Mathew %A Walker,John %A Santos,Glenn-Milo %+ San Francisco Department of Public Health, 25 Van Ness, Suite 500, San Francisco, CA, 94102, United States, 1 415 437 6283, chris.rowe@sfdph.org %K data collection %K cell phones %K drug users %K drinking behavior %K homosexuality, male %D 2016 %7 26.05.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Alcohol and illicit drug use is more prevalent among men who have sex with men (MSM) compared to the general population and has been linked to HIV transmission in this population. Research assessing individual patterns of substance use often utilizes questionnaires or interviews that rely on retrospective self-reported information, which can be subject to recall bias. Ecological momentary assessment (EMA) is a set of methods developed to mitigate recall bias by collecting data about subjects’ mental states and behaviors on a near real-time basis. EMA remains underutilized in substance use and HIV research. Objective: To assess the concordance between daily reports of substance use collected by EMA text messages (short message service, SMS) and retrospective questionnaires and identify predictors of daily concordance in a sample of MSM. Methods: We conducted a secondary analysis of EMA text responses (regarding behavior on the previous day) and audio computer-assisted self-interview (ACASI) survey data (14-day recall) from June 2013 to September 2014 as part of a randomized controlled trial assessing a pharmacologic intervention to reduce methamphetamine and alcohol use among nondependent MSM in San Francisco, California. Reports of daily methamphetamine use, alcohol use, and binge alcohol use (5 or more drinks on one occasion) were collected via EMA and ACASI and compared using McNemar’s tests. Demographic and behavioral correlates of daily concordance between EMA and ACASI were assessed for each substance, using separate multivariable logistic regression models, fit with generalized estimating equations. Results: Among 30 MSM, a total of 994 days were included in the analysis for methamphetamine use, 987 for alcohol use, and 981 for binge alcohol use. Methamphetamine (EMA 20%, ACASI 11%, P<.001) and alcohol use (EMA 40%, ACASI 35%, P=.001) were reported significantly more frequently via EMA versus ACASI. In multivariable analysis, text reporting of methamphetamine (adjusted odds ratio 0.06, 95% CI 0.04-0.10), alcohol (0.48, 0.33-0.69), and binge alcohol use (0.27, 0.17-0.42) was negatively associated with daily concordance in the reporting of each respective substance. Compared to white participants, African American participants were less likely to have daily concordance in methamphetamine (0.15, 0.05-0.43) and alcohol (0.2, 0.05-0.54) reporting, and other participants of color (ie, Asian, Hispanic, multi-racial) were less likely to have daily concordance in methamphetamine reporting (0.34, 0.12-1.00). College graduates were more likely to have daily concordance in methamphetamine reporting (6.79, 1.84-25.04) compared to those with no college experience. Conclusions: We found that methamphetamine and alcohol use were reported more frequently with daily EMA texts compared to retrospective ACASI, concordance varied among different racial/ethnic subgroups and education levels, and reported substance use by EMA text was associated with lower daily concordance with retrospective ACASI. These findings suggest that EMA methods may provide more complete reporting of frequent, discrete behaviors such as substance use. %M 27230545 %R 10.2196/mhealth.5368 %U http://mhealth.jmir.org/2016/2/e44/ %U https://doi.org/10.2196/mhealth.5368 %U http://www.ncbi.nlm.nih.gov/pubmed/27230545 %0 Journal Article %@ 2368-7959 %I JMIR Publications Inc. %V 3 %N 2 %P e16 %T New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research %A Torous,John %A Kiang,Mathew V %A Lorme,Jeanette %A Onnela,Jukka-Pekka %+ Department of Biostatistics, Harvard TH Chan School of Public Health, Harvard University, Building 2, Room 423, 655 Huntington Avenue, Boston, MA, MA, United States, 1 617 495 1000, onnela@hsph.harvard.edu %K mental health %K schizophrenia %K evaluation %K smartphone %K informatics %D 2016 %7 05.05.2016 %9 Original Paper %J JMIR Mental Health %G English %X Background: A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective: Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods: We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results: We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions: Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health. %M 27150677 %R 10.2196/mental.5165 %U http://mental.jmir.org/2016/2/e16/ %U https://doi.org/10.2196/mental.5165 %U http://www.ncbi.nlm.nih.gov/pubmed/27150677 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 1 %P e23 %T “Smart” RCTs: Development of a Smartphone App for Fully Automated Nutrition-Labeling Intervention Trials %A Volkova,Ekaterina %A Li,Nicole %A Dunford,Elizabeth %A Eyles,Helen %A Crino,Michelle %A Michie,Jo %A Ni Mhurchu,Cliona %+ National Institute for Health Innovation, School of Population Health, University of Auckland, Private Bag 92019, Auckland Mail Center 1142, Auckland, 1072, New Zealand, 64 (09) 9234742, k.volkova@auckland.ac.nz %K randomized controlled trial %K smartphone %K public health %K nutrition labeling %D 2016 %7 17.03.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: There is substantial interest in the effects of nutrition labels on consumer food-purchasing behavior. However, conducting randomized controlled trials on the impact of nutrition labels in the real world presents a significant challenge. Objective: The Food Label Trial (FLT) smartphone app was developed to enable conducting fully automated trials, delivering intervention remotely, and collecting individual-level data on food purchases for two nutrition-labeling randomized controlled trials (RCTs) in New Zealand and Australia. Methods: Two versions of the smartphone app were developed: one for a 5-arm trial (Australian) and the other for a 3-arm trial (New Zealand). The RCT protocols guided requirements for app functionality, that is, obtaining informed consent, two-stage eligibility check, questionnaire administration, randomization, intervention delivery, and outcome assessment. Intervention delivery (nutrition labels) and outcome data collection (individual shopping data) used the smartphone camera technology, where a barcode scanner was used to identify a packaged food and link it with its corresponding match in a food composition database. Scanned products were either recorded in an electronic list (data collection mode) or allocated a nutrition label on screen if matched successfully with an existing product in the database (intervention delivery mode). All recorded data were transmitted to the RCT database hosted on a server. Results: In total approximately 4000 users have downloaded the FLT app to date; 606 (Australia) and 1470 (New Zealand) users met the eligibility criteria and were randomized. Individual shopping data collected by participants currently comprise more than 96,000 (Australia) and 229,000 (New Zealand) packaged food and beverage products. Conclusions: The FLT app is one of the first smartphone apps to enable conducting fully automated RCTs. Preliminary app usage statistics demonstrate large potential of such technology, both for intervention delivery and data collection. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12614000964617. New Zealand trial: Australian New Zealand Clinical Trials Registry ACTRN12614000644662. %M 26988128 %R 10.2196/mhealth.5219 %U http://mhealth.jmir.org/2016/1/e23/ %U https://doi.org/10.2196/mhealth.5219 %U http://www.ncbi.nlm.nih.gov/pubmed/26988128 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 1 %P e27 %T Ecological Momentary Assessment of Illicit Drug Use Compared to Biological and Self-Reported Methods %A Linas,Beth S. %A Genz,Andrew %A Westergaard,Ryan P %A Chang,Larry W %A Bollinger,Robert C %A Latkin,Carl %A Kirk,Gregory D %+ Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, 615 N. Wolfe St., E6532, Baltimore, MD, 21205, United States, 1 9175367535, Blinas@jhu.edu %K mHealth %K ecological momentary assessment %K illicit drug use %K sweat patch %K ACASI %D 2016 %7 15.03.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: The use of mHealth methods for capturing illicit drug use and associated behaviors have become more widely used in research settings, yet there is little research as to how valid these methods are compared to known measures of capturing and quantifying drug use. Objective: We examined the concordance of ecological momentary assessment (EMA) of drug use to previously validated biological and audio-computer assisted self-interview (ACASI) methods. Methods: The Exposure Assessment in Current Time (EXACT) study utilized EMA methods to assess drug use in real-time in participants’ natural environments. Utilizing mobile devices, participants self-reported each time they used heroin or cocaine over a 4-week period. Each week, PharmChek sweat patch samples were collected for measurement of heroin and cocaine and participants answered an ACASI-based questionnaire to report behaviors and drug using events during the prior week. Reports of cocaine and heroin use captured through EMA were compared to weekly biological or self-report measures through percent agreement and concordance correlation coefficients to account for repeated measures. Correlates of discordance were obtained from logistic regression models. Results: A total of 109 participants were a median of 48.5 years old, 90% African American, and 52% male. During 436 person-weeks of observation, we recorded 212 (49%) cocaine and 103 (24%) heroin sweat patches, 192 (44%) cocaine and 161 (37%) heroin ACASI surveys, and 163 (37%) cocaine and 145 (33%) heroin EMA reports. The percent agreement between EMA and sweat patch methods was 70% for cocaine use and 72% for heroin use, while the percent agreement between EMA and ACASI methods was 77% for cocaine use and 79% for heroin use. Misreporting of drug use by EMA compared to sweat patch and ACASI methods were different by illicit drug type. Conclusions: Our work demonstrates moderate to good agreement of EMA to biological and standard self-report methods in capturing illicit drug use. Limitations occur with each method and accuracy may differ by type of illicit drugs used. %M 26980400 %R 10.2196/mhealth.4470 %U http://mhealth.jmir.org/2016/1/e27/ %U https://doi.org/10.2196/mhealth.4470 %U http://www.ncbi.nlm.nih.gov/pubmed/26980400 %0 Journal Article %@ 2368-7959 %I JMIR Publications Inc. %V 3 %N 1 %P e7 %T Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments %A Bakker,David %A Kazantzis,Nikolaos %A Rickwood,Debra %A Rickard,Nikki %+ School of Psychology and Monash Institute of Cognitive and Clinical Neurosciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, 18 Innovation Walk, Wellington Road, Clayton, 3800, Australia, 61 3 9905 4301, david.bakker@monash.edu %K mobile phones %K mental health %K smartphones %K apps %K mobile apps %K depression %K anxiety %K cognitive behavior therapy %K cognitive behavioral therapy %K clinical psychology %D 2016 %7 01.03.2016 %9 Review %J JMIR Mental Health %G English %X Background: The number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available. Objective: To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. Methods: A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design. Results: Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp. Conclusions: Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems. %M 26932350 %R 10.2196/mental.4984 %U http://mental.jmir.org/2016/1/e7/ %U https://doi.org/10.2196/mental.4984 %U http://www.ncbi.nlm.nih.gov/pubmed/26932350 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 18 %N 1 %P e22 %T Estimating Skin Cancer Risk: Evaluating Mobile Computer-Adaptive Testing %A Djaja,Ngadiman %A Janda,Monika %A Olsen,Catherine M %A Whiteman,David C %A Chien,Tsair-Wei %+ Research Department, Chi-Mei Medical Center, No. 901, Chung Hwa Road, Yung Kung Dist., Tainan 710, Taiwan, Tainan, 710, Taiwan, 886 937399106, smile@mail.chimei.org.tw %K computer adaptive testing %K skin cancer risk scale %K non adaptive test %K Rasch analysis %K partial credit model %D 2016 %7 22.01.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Response burden is a major detriment to questionnaire completion rates. Computer adaptive testing may offer advantages over non-adaptive testing, including reduction of numbers of items required for precise measurement. Objective: Our aim was to compare the efficiency of non-adaptive (NAT) and computer adaptive testing (CAT) facilitated by Partial Credit Model (PCM)-derived calibration to estimate skin cancer risk. Methods: We used a random sample from a population-based Australian cohort study of skin cancer risk (N=43,794). All 30 items of the skin cancer risk scale were calibrated with the Rasch PCM. A total of 1000 cases generated following a normal distribution (mean [SD] 0 [1]) were simulated using three Rasch models with three fixed-item (dichotomous, rating scale, and partial credit) scenarios, respectively. We calculated the comparative efficiency and precision of CAT and NAT (shortening of questionnaire length and the count difference number ratio less than 5% using independent t tests). Results: We found that use of CAT led to smaller person standard error of the estimated measure than NAT, with substantially higher efficiency but no loss of precision, reducing response burden by 48%, 66%, and 66% for dichotomous, Rating Scale Model, and PCM models, respectively. Conclusions: CAT-based administrations of the skin cancer risk scale could substantially reduce participant burden without compromising measurement precision. A mobile computer adaptive test was developed to help people efficiently assess their skin cancer risk. %M 26800642 %R 10.2196/jmir.4736 %U http://www.jmir.org/2016/1/e22/ %U https://doi.org/10.2196/jmir.4736 %U http://www.ncbi.nlm.nih.gov/pubmed/26800642 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 2 %N 1 %P e2 %T Mobile Technology for Empowering Health Workers in Underserved Communities: New Approaches to Facilitate the Elimination of Neglected Tropical Diseases %A Stanton,Michelle %A Molineux,Andrew %A Mackenzie,Charles %A Kelly-Hope,Louise %+ Liverpool School of Tropical Medicine, Department of Parasitology, Wolfson Building, Liverpool, L3 5QA, United Kingdom, 44 151 705 3336, Louise.Kelly-Hope@lstmed.ac.uk %K mhealth %K lymphatic filariasis %K LF %K elephantiasis %K neglected tropical diseases %K NTDs %K community engagement %K SMS %K smartphones %K apps %D 2016 %7 14.01.2016 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: As global mobile phone penetration increases, direct health information communication from hard-to-reach communities is becoming commonplace. Mobile health (mHealth) tools that enable disease control programs to benefit from this information, while simultaneously empowering community members to take control of their own health, are vital to the goal of universal health care. Objective: Our aim was to highlight the development of the Liverpool mHealth Suite (LMS), which has been designed to address this need and improve health services for neglected tropical diseases being targeted for global elimination, such as lymphatic filariasis. Methods: The LMS has two main communication approaches—short message service and mobile phone apps—to facilitate real-time mass drug administration (MDA) coverage, reporting patient numbers, managing stock levels of treatment supplies, and exchanging health information to improve the quality of care of those affected. Results: The LMS includes the MeasureSMS-MDA tool to improve drug supplies and MDA coverage rates in real-time (currently being trialed in urban Tanzania); the MeasureSMS-Morbidity tool to map morbidity, including lymphedema and hydrocele cases (initially piloted in rural Malawi and Ghana, then extended to Ethiopia, and scaled up to large urban areas in Bangladesh and Tanzania); the LyMSS-lymphedema management supply system app to improve distribution of treatments (trialed for 6 months in Malawi with positive impacts on health workers and patients); and the HealthFront app to improve education and training (in development with field trials planned). Conclusions: The current success and scale-up of the LMS by many community health workers in rural and urban settings across Africa and Asia highlights the value of this simple and practical suite of tools that empowers local health care workers to contribute to local, national, and global elimination of disease. %M 27227155 %R 10.2196/publichealth.5064 %U http://publichealth.jmir.org/2016/1/e2/ %U https://doi.org/10.2196/publichealth.5064 %U http://www.ncbi.nlm.nih.gov/pubmed/27227155 %0 Journal Article %@ 1929-073X %I JMIR Publications Inc. %V 5 %N 1 %P e3 %T Accuracy, Validity, and Reliability of an Electronic Visual Analog Scale for Pain on a Touch Screen Tablet in Healthy Older Adults: A Clinical Trial %A Bird,Marie-Louise %A Callisaya,Michele L %A Cannell,John %A Gibbons,Timothy %A Smith,Stuart T %A Ahuja,Kiran DK %+ School of Health Sciences, University of Tasmania, Locked Bag 1322, Launceston, , Australia, 61 363245478, Kiran.Ahuja@utas.edu.au %K pain %K VAS %K technology %K scale %D 2016 %7 14.01.2016 %9 Original Paper %J Interact J Med Res %G English %X Background: New technology for clinical data collection is rapidly evolving and may be useful for both researchers and clinicians; however, this new technology has not been tested for accuracy, reliability, or validity. Objective: This study aims to test the accuracy of visual analog scale (VAS) for pain on a newly designed application on the iPad (iPadVAS) and measure the reliability and validity of iPadVAS compared to a paper copy (paperVAS). Methods: Accuracy was determined by physically measuring an iPad scale on screen and comparing it to the results from the program, with a researcher collecting 101 data points. A total of 22 healthy community dwelling older adults were then recruited to test reliability and validity. Each participant completed 8 VAS (4 using each tool) in a randomized order. Reliability was measured using interclass correlation coefficient (ICC) and validity measured using Bland-Altman graphs and correlations. Results: Of the measurements for accuracy, 64 results were identical, 2 results were manually measured as being 1 mm higher than the program, and 35 as 1 mm lower. Reliability for the iPadVAS was excellent with individual ICC 0.90 (95% CI 0.82-0.95) and averaged ICC 0.97 (95% CI 0.95-1.0) observed. Linear regression demonstrated a strong relationship with a small negative bias towards the iPad (−2.6, SD 5.0) with limits of agreement from −12.4 to 7.1. Conclusions: The iPadVAS provides a convenient, user-friendly, and efficient way of collecting data from participants in measuring their current pain levels. It has potential use in documentation management and may encourage participatory healthcare. Trial Registration: Australia New Zealand Clinical Trials Registry (ANZCTR): 367297; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367297&isReview=true (Archived by Webcite at http://www.webcitation.org/6d9xYoUbD). %M 26769149 %R 10.2196/ijmr.4910 %U http://www.i-jmr.org/2016/1/e3/ %U https://doi.org/10.2196/ijmr.4910 %U http://www.ncbi.nlm.nih.gov/pubmed/26769149 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 1 %P e2 %T Developing an Internet- and Mobile-Based System to Measure Cigarette Use Among Pacific Islanders: An Ecological Momentary Assessment Study %A Pike,James Russell %A Xie,Bin %A Tan,Nasya %A Sabado-Liwag,Melanie Dee %A Orne,Annette %A Toilolo,Tupou %A Cen,Steven %A May,Vanessa %A Lee,Cevadne %A Pang,Victor Kaiwi %A Rainer,Michelle A %A Vaivao,Dorothy Etimani S %A Lepule,Jonathan Tana %A Tanjasiri,Sora Park %A Palmer,Paula Healani %+ School of Community and Global Health, Claremont Graduate University, 675 West Foothill Boulevard, Suite 310, Claremont, CA, 91711-3475, United States, 1 818 406 0286, James.Pike@cgu.edu %K Pacific Islander %K tobacco use %K cigarette use %K mobile phone %K text message %K ecological momentary assessment %D 2016 %7 07.01.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Recent prevalence data indicates that Pacific Islanders living in the United States have disproportionately high smoking rates when compared to the general populace. However, little is known about the factors contributing to tobacco use in this at-risk population. Moreover, few studies have attempted to determine these factors utilizing technology-based assessment techniques. Objective: The objective was to develop a customized Internet-based Ecological Momentary Assessment (EMA) system capable of measuring cigarette use among Pacific Islanders in Southern California. This system integrated the ubiquity of text messaging, the ease of use associated with mobile phone apps, the enhanced functionality offered by Internet-based Cell phone-optimized Assessment Techniques (ICAT), and the high survey completion rates exhibited by EMA studies that used electronic diaries. These features were tested in a feasibility study designed to assess whether Pacific Islanders would respond to this method of measurement and whether the data gathered would lead to novel insights regarding the intrapersonal, social, and ecological factors associated with cigarette use. Methods: 20 young adult smokers in Southern California who self-identified as Pacific Islanders were recruited by 5 community-based organizations to take part in a 7-day EMA study. Participants selected six consecutive two-hour time blocks per day during which they would be willing to receive a text message linking them to an online survey formatted for Web-enabled mobile phones. Both automated reminders and community coaches were used to facilitate survey completion. Results: 720 surveys were completed from 840 survey time blocks, representing a completion rate of 86%. After adjusting for gender, age, and nicotine dependence, feeling happy (P=<.001) or wanting a cigarette while drinking alcohol (P=<.001) were positively associated with cigarette use. Being at home (P=.02) or being around people who are not smoking (P=.01) were negatively associated with cigarette use. Conclusions: The results of the feasibility study indicate that customized systems can be used to conduct technology-based assessments of tobacco use among Pacific Islanders. Such systems can foster high levels of survey completion and may lead to novel insights for future research and interventions. %M 26743132 %R 10.2196/mhealth.4437 %U http://mhealth.jmir.org/2016/1/e2/ %U https://doi.org/10.2196/mhealth.4437 %U http://www.ncbi.nlm.nih.gov/pubmed/26743132 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 5 %N 1 %P e4 %T How to Conduct Multimethod Field Studies in the Operating Room: The iPad Combined With a Survey App as a Valid and Reliable Data Collection Tool %A Tscholl,David W %A Weiss,Mona %A Spahn,Donat R %A Noethiger,Christoph B %+ Institute for Anesthesiology, University and University Hospital Zurich, Raemistrasse 100, Zurich, , Switzerland, 41 044 255 2695, christoph.noethiger@usz.ch %K data collection %K empirical research %K observation %K computers %K informatics %K anesthesiology %D 2016 %7 05.01.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Tablet computers such as the Apple iPad are progressively replacing traditional paper-and-pencil-based data collection. We combined the iPad with the ready-to-use survey software, iSurvey (from Harvestyourdata), to create a straightforward tool for data collection during the Anesthesia Pre-Induction Checklist (APIC) study, a hospital-wide multimethod intervention study involving observation of team performance and team member surveys in the operating room (OR). Objective: We aimed to provide an analysis of the factors that led to the use of the iPad- and iSurvey-based tool for data collection, illustrate our experiences with the use of this data collection tool, and report the results of an expert survey about user experience with this tool. Methods: We used an iPad- and iSurvey-based tool to observe anesthesia inductions conducted by 205 teams (N=557 team members) in the OR. In Phase 1, expert raters used the iPad- and iSurvey-based tool to rate team performance during anesthesia inductions, and anesthesia team members were asked to indicate their perceptions after the inductions. In Phase 2, we surveyed the expert raters about their perceptions regarding the use of the iPad- and iSurvey-based tool to observe, rate, and survey teams in the ORs. Results: The results of Phase 1 showed that training data collectors on the iPad- and iSurvey-based data collection tool was effortless and there were no serious problems during data collection, upload, download, and export. Interrater agreement of the combined data collection tool was found to be very high for the team observations (median Fleiss’ kappa=0.88, 95% CI 0.78-1.00). The results of the follow-up expert rater survey (Phase 2) showed that the raters did not prefer a paper-and-pencil-based data collection method they had used during other earlier studies over the iPad- and iSurvey-based tool (median response 1, IQR 1-1; 1=do not agree, 2=somewhat disagree, 3=neutral, 4=somewhat agree, 5=fully agree). They found the iPad (median 5, IQR 4.5-5) and iSurvey (median 4, IQR 4-5) to be working flawlessly and easy to use (median 5, IQR 4-5). Expert ratings also showed that the anesthesia team members (ie, the surveyed doctors and nurses) who used the iPad- and iSurvey-based tool in the OR liked it (median 4, IQR 3-4.5). Conclusions: The combination of the iPad and iSurvey provides an efficient and unobtrusive method to observe teams in their natural environment in the OR and to survey team members immediately after completing their task (ie, anesthesia induction). The expert raters positively evaluated the use of the device and user perceptions. Considering these comprehensive results, we can recommend the use of the iPad- and iSurvey-based tool for studying team performance and team member perceptions in the OR. %M 26732090 %R 10.2196/resprot.4713 %U http://www.researchprotocols.org/2016/1/e4/ %U https://doi.org/10.2196/resprot.4713 %U http://www.ncbi.nlm.nih.gov/pubmed/26732090 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 4 %P e108 %T Let Visuals Tell the Story: Medication Adherence in Patients with Type II Diabetes Captured by a Novel Ingestion Sensor Platform %A Browne,Sara H %A Behzadi,Yashar %A Littlewort,Gwen %+ University of California, San Diego, School of Medicine, 9500 Gilman Drive, Mail Code 0640, La Jolla, CA, 92093-0640, United States, 1 858 822 6563, shbrowne@ucsd.edu %K ingestion sensor platform %K data visualization %K time domain methods %K medication adherence %K patient self-management %D 2015 %7 31.12.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Chronic diseases such as diabetes require high levels of medication adherence and patient self-management for optimal health outcomes. A novel sensing platform, Digital Health Feedback System (Proteus Digital Health, Redwood City, CA), can for the first time detect medication ingestion events and physiological measures simultaneously, using an edible sensor, personal monitor patch, and paired mobile device. The Digital Health Feedback System (DHFS) generates a large amount of data. Visual analytics of this rich dataset may provide insights into longitudinal patterns of medication adherence in the natural setting and potential relationships between medication adherence and physiological measures that were previously unknown. Objective: Our aim was to use modern methods of visual analytics to represent continuous and discrete data from the DHFS, plotting multiple different data types simultaneously to evaluate the potential of the DHFS to capture longitudinal patterns of medication-taking behavior and self-management in individual patients with type II diabetes. Methods: Visualizations were generated using time domain methods of oral metformin medication adherence and physiological data obtained by the DHFS use in 5 patients with type II diabetes over 37-42 days. The DHFS captured at-home metformin adherence, heart rate, activity, and sleep/rest. A mobile glucose monitor captured glucose testing and level (mg/dl). Algorithms were developed to analyze data over varying time periods: across the entire study, daily, and weekly. Following visualization analysis, correlations between sleep/rest and medication ingestion were calculated across all subjects. Results: A total of 197 subject days, encompassing 141,840 data events were analyzed. Individual continuous patch use varied between 87-98%. On average, the cohort took 78% (SD 12) of prescribed medication and took 77% (SD 26) within the prescribed ±2-hour time window. Average activity levels per subjects ranged from 4000-12,000 steps per day. The combination of activity level and heart rate indicated different levels of cardiovascular fitness between subjects. Visualizations over the entire study captured the longitudinal pattern of missed doses (the majority of which took place in the evening), the timing of ingestions in individual subjects, and the range of medication ingestion timing, which varied from 1.5-2.4 hours (Subject 3) to 11 hours (Subject 2). Individual morning self-management patterns over the study period were obtained by combining the times of waking, metformin ingestion, and glucose measurement. Visualizations combining multiple data streams over a 24-hour period captured patterns of broad daily events: when subjects rose in the morning, tested their blood glucose, took their medications, went to bed, hours of sleep/rest, and level of activity during the day. Visualizations identified highly consistent daily patterns in Subject 3, the most adherent participant. Erratic daily patterns including sleep/rest were demonstrated in Subject 2, the least adherent subject. Correlation between sleep /rest and medication ingestion in each individual subject was evaluated. Subjects 2 and 4 showed correlation between amount of sleep/rest over a 24-hour period and medication-taking the following day (Subject 2: r=.47, P<.02; Subject 4: r=.35, P<.05). With Subject 2, sleep/rest disruptions during the night were highly correlated (r=.47, P<.009) with missing doses the following day. Conclusions: Visualizations integrating medication ingestion and physiological data from the DHFS over varying time intervals captured detailed individual longitudinal patterns of medication adherence and self-management in the natural setting. Visualizing multiple data streams simultaneously, providing a data-rich representation, revealed information that would not have been shown by plotting data streams individually. Such analyses provided data far beyond traditional adherence summary statistics and may form the foundation of future personalized predictive interventions to drive longitudinal adherence and support optimal self-management in chronic diseases such as diabetes. %M 26721413 %R 10.2196/mhealth.4292 %U http://mhealth.jmir.org/2015/4/e108/ %U https://doi.org/10.2196/mhealth.4292 %U http://www.ncbi.nlm.nih.gov/pubmed/26721413 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 1 %N 2 %P e20 %T Implementation of a Multimodal Mobile System for Point-of-Sale Surveillance: Lessons Learned From Case Studies in Washington, DC, and New York City %A Cantrell,Jennifer %A Ganz,Ollie %A Ilakkuvan,Vinu %A Tacelosky,Michael %A Kreslake,Jennifer %A Moon-Howard,Joyce %A Aidala,Angela %A Vallone,Donna %A Anesetti-Rothermel,Andrew %A Kirchner,Thomas R %+ Truth Initiative, Evaluation Science and Research, 900 G Street, NW, Fourth Floor, Washington, DC, 20001, United States, 1 202 436 2118, jcantrell@truthinitiative.org %K mobile technology %K public health surveillance %K tobacco %K point-of-sale %K implementation %K tobacco industry advertising %K marketing %D 2015 %7 26.11.2015 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: In tobacco control and other fields, point-of-sale surveillance of the retail environment is critical for understanding industry marketing of products and informing public health practice. Innovations in mobile technology can improve existing, paper-based surveillance methods, yet few studies describe in detail how to operationalize the use of technology in public health surveillance. Objective: The aims of this paper are to share implementation strategies and lessons learned from 2 tobacco, point-of-sale surveillance projects to inform and prepare public health researchers and practitioners to implement new mobile technologies in retail point-of-sale surveillance systems. Methods: From 2011 to 2013, 2 point-of-sale surveillance pilot projects were conducted in Washington, DC, and New York, New York, to capture information about the tobacco retail environment and test the feasibility of a multimodal mobile data collection system, which included capabilities for audio or video recording data, electronic photographs, electronic location data, and a centralized back-end server and dashboard. We established a preimplementation field testing process for both projects, which involved a series of rapid and iterative tests to inform decisions and establish protocols around key components of the project. Results: Important components of field testing included choosing a mobile phone that met project criteria, establishing an efficient workflow and accessible user interfaces for each component of the system, training and providing technical support to fieldworkers, and developing processes to integrate data from multiple sources into back-end systems that can be utilized in real-time. Conclusions: A well-planned implementation process is critical for successful use and performance of multimodal mobile surveillance systems. Guidelines for implementation include (1) the need to establish and allow time for an iterative testing framework for resolving technical and logistical challenges; (2) developing a streamlined workflow and user-friendly interfaces for data collection; (3) allowing for ongoing communication, feedback, and technology-related skill-building among all staff; and (4) supporting infrastructure for back-end data systems. Although mobile technologies are evolving rapidly, lessons learned from these case studies are essential for ensuring that the many benefits of new mobile systems for rapid point-of-sale surveillance are fully realized. %M 27227138 %R 10.2196/publichealth.4191 %U http://publichealth.jmir.org/2015/2/e20/ %U https://doi.org/10.2196/publichealth.4191 %U http://www.ncbi.nlm.nih.gov/pubmed/27227138 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 4 %P e93 %T A Mobile Phone App for Dietary Intake Assessment in Adolescents: An Evaluation Study %A Svensson,Åsa %A Larsson,Christel %+ Department of Food and Nutrition, and Sport Science, University of Gothenburg, Box 300, Gothenburg, 40530, Sweden, 46 709116519, christel.larsson@gu.se %K adolescents %K dietary assessment %K mobile phone app %K energy %K SenseWear Armband %D 2015 %7 03.11.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: There is a great need for dietary assessment methods that suit the adolescent lifestyle and give valid intake data. Objective: To develop a mobile phone app and evaluate its ability to assess energy intake (EI) and total energy expenditure (TEE) compared with objectively measured TEE. Furthermore, to investigate the impact of factors on reporting accuracy of EI, and to compare dietary intake with a Web-based method. Methods: Participants 14 to 16 years of age were recruited from year nine in schools in Gothenburg, Sweden. In total, 81 adolescents used the mobile phone app over 1 to 6 days. TEE was measured with the SenseWear Armband (SWA) during the same or proximate days. Individual factors were assessed with a questionnaire. A total of 15 participants also recorded dietary intake using a Web-based method. Results: The mobile phone app underestimated EI by 29% on a group level (P<.001) compared to TEE measured with the SWA, and there was no significant correlation between EI and TEE. Accuracy of EI relative to TEE increased with a weekend day in the record (P=.007) and lower BMI z-score (P=.001). TEE assessed with the mobile phone app was 1.19 times the value of TEE measured by the SWA on a group level (P<.001), and the correlation between the methods was .75 (P<.001). Analysis of physical activity levels (PAL) from the mobile phone app stratified by gender showed that accuracy of the mobile phone app was higher among boys. EI, nutrients, and food groups assessed with the mobile phone app and Web-based method among 15 participants were not significantly different and several were significantly correlated, but strong conclusions cannot be drawn due to the low number of participants. Conclusions: By using a mobile phone dietary assessment app, on average 71% of adolescents’ EI was captured. The accuracy of reported dietary intake was higher with lower BMI z-score and if a weekend day was included in the record. The daily question in the mobile phone app about physical activity could accurately rank the participants’ TEE. %M 26534783 %R 10.2196/mhealth.4804 %U http://mhealth.jmir.org/2015/4/e93/ %U https://doi.org/10.2196/mhealth.4804 %U http://www.ncbi.nlm.nih.gov/pubmed/26534783 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 4 %P e98 %T Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-Hour Dietary Recalls %A Rangan,Anna M %A O'Connor,Sarah %A Giannelli,Valentina %A Yap,Megan LH %A Tang,Lie Ming %A Roy,Rajshri %A Louie,Jimmy Chun Yu %A Hebden,Lana %A Kay,Judy %A Allman-Farinelli,Margaret %+ School of Molecular Bioscience, Charles Perkins Centre, University of Sydney, Level 4 East, Charles Perkins Centre, University of Sydney, Camperdown, 2006, Australia, 61 2 93513816, anna.rangan@sydney.edu.au %K validity %K dietary assessment %K mobile phone app %K young adult %D 2015 %7 27.10.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: The electronic Dietary Intake Assessment (e-DIA), a digital entry food record mobile phone app, was developed to measure energy and nutrient intake prospectively. This can be used in monitoring population intakes or intervention studies in young adults. Objective: The objective was to assess the relative validity of e-DIA as a dietary assessment tool for energy and nutrient intakes using the 24-hour dietary recall as a reference method. Methods: University students aged 19 to 24 years recorded their food and drink intake on the e-DIA for five days consecutively and completed 24-hour dietary recalls on three random days during this 5-day study period. Mean differences in energy, macro-, and micronutrient intakes were evaluated between the methods using paired t tests or Wilcoxon signed-rank tests, and correlation coefficients were calculated on unadjusted, energy-adjusted, and deattenuated values. Bland-Altman plots and cross-classification into quartiles were used to assess agreement between the two methods. Results: Eighty participants completed the study (38% male). No significant differences were found between the two methods for mean intakes of energy or nutrients. Deattenuated correlation coefficients ranged from 0.55 to 0.79 (mean 0.68). Bland-Altman plots showed wide limits of agreement between the methods but without obvious bias. Cross-classification into same or adjacent quartiles ranged from 75% to 93% (mean 85%). Conclusions: The e-DIA shows potential as a dietary intake assessment tool at a group level with good ranking agreement for energy and all nutrients. %M 26508282 %R 10.2196/mhealth.4613 %U http://mhealth.jmir.org/2015/4/e98/ %U https://doi.org/10.2196/mhealth.4613 %U http://www.ncbi.nlm.nih.gov/pubmed/26508282 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 3 %P e86 %T The Development of the Recovery Assessments by Phone Points (RAPP): A Mobile Phone App for Postoperative Recovery Monitoring and Assessment %A Jaensson,Maria %A Dahlberg,Karuna %A Eriksson,Mats %A Grönlund,Åke %A Nilsson,Ulrica %+ Faculty of Medicine and Health, School of Health and Medicine Sciences, Örebro University, Örebro, 701 82, Sweden, 46 19303405, maria.jaensson@oru.se %K cellular phone %K postoperative recovery %K day care %D 2015 %7 11.09.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: In Sweden, day surgery is performed in almost 2 million patients per year. Patient satisfaction is closely related to potential adverse events during the recovery process. A way to empower patients and give them the opportunity to affect care delivery is to let them evaluate their recovery process. The most common evaluation method is a follow-up telephone call by a nurse one or two days after surgery. In recent years, mHealth apps have been used to evaluate the nurse-patient relationship for self-management in chronic diseases or to evaluate pain after surgery. To the best of our knowledge, no previous research has explored the recovery process after day surgery via mobile phone in a Swedish cohort. Objective: The objective of the study is to describe the process of developing a mobile phone app using a Swedish Web-based Quality of Recovery (SwQoR) questionnaire to evaluate postoperative recovery after day surgery. Methods: The development process included five steps: (1) setting up an interdisciplinary task force, (2) evaluating the potential needs of app users, (3) developing the Swedish Web version of a QoR questionnaire, (4) constructing a mobile phone app, and (5) evaluating the interface and design by staff working in a day-surgery department and patients undergoing day surgery. A task force including specialists in information and communication technology, eHealth, and nursing care worked closely together to develop a Web-based app. Modifications to the QoR questionnaire were inspired by instruments used in the field of recovery for both children and adults. The Web-based app, Recovery Assessment by Phone Points (RAPP) consists of two parts: (1) a mobile app installed on the patient’s private mobile phone, and (2) an administrator interface for the researchers. Results: The final version of the SwQoR questionnaire, which includes 31 items, was successfully installed in RAPP. The interface and the design were evaluated by asking for user opinions about the design and usefulness of the app with 10 day surgery patients. Some minor adjustments were made concerning text size and screen color. Conclusions: Taking advantage of joint expertise, a useable Web-based app adaptable to different technical platforms was constructed. In addition, the SwQoR was successfully transferred into digital format for use on mobile phones. %M 26362403 %R 10.2196/mhealth.4649 %U http://mhealth.jmir.org/2015/3/e86/ %U https://doi.org/10.2196/mhealth.4649 %U http://www.ncbi.nlm.nih.gov/pubmed/26362403 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 3 %P e75 %T Effectiveness of Using Mobile Phone Image Capture for Collecting Secondary Data: A Case Study on Immunization History Data Among Children in Remote Areas of Thailand %A Jandee,Kasemsak %A Kaewkungwal,Jaranit %A Khamsiriwatchara,Amnat %A Lawpoolsri,Saranath %A Wongwit,Waranya %A Wansatid,Peerawat %+ Center of Excellence for Biomedical and Public Health Informatics (BIOPHICS), Faculty of Tropical Medicine, Mahidol University, The 60 Anniversary of His Majesty the King's Accession to the Throne Building, 420/6, Ratchawithi Road, Ratchathewi, Bangkok, 10400, Thailand, 66 23549181, jaranitk@biophics.org %K health care information system %K DEPIC %K mobile technology %K maternal and child health %K mHealth %K vaccine record %K electronic data capture %D 2015 %7 20.07.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Entering data onto paper-based forms, then digitizing them, is a traditional data-management method that might result in poor data quality, especially when the secondary data are incomplete, illegible, or missing. Transcription errors from source documents to case report forms (CRFs) are common, and subsequently the errors pass from the CRFs to the electronic database. Objective: This study aimed to demonstrate the usefulness and to evaluate the effectiveness of mobile phone camera applications in capturing health-related data, aiming for data quality and completeness as compared to current routine practices exercised by government officials. Methods: In this study, the concept of “data entry via phone image capture” (DEPIC) was introduced and developed to capture data directly from source documents. This case study was based on immunization history data recorded in a mother and child health (MCH) logbook. The MCH logbooks (kept by parents) were updated whenever parents brought their children to health care facilities for immunization. Traditionally, health providers are supposed to key in duplicate information of the immunization history of each child; both on the MCH logbook, which is returned to the parents, and on the individual immunization history card, which is kept at the health care unit to be subsequently entered into the electronic health care information system (HCIS). In this study, DEPIC utilized the photographic functionality of mobile phones to capture images of all immunization-history records on logbook pages and to transcribe these records directly into the database using a data-entry screen corresponding to logbook data records. DEPIC data were then compared with HCIS data-points for quality, completeness, and consistency. Results: As a proof-of-concept, DEPIC captured immunization history records of 363 ethnic children living in remote areas from their MCH logbooks. Comparison of the 2 databases, DEPIC versus HCIS, revealed differences in the percentage of completeness and consistency of immunization history records. Comparing the records of each logbook in the DEPIC and HCIS databases, 17.3% (63/363) of children had complete immunization history records in the DEPIC database, but no complete records were reported in the HCIS database. Regarding the individual’s actual vaccination dates, comparison of records taken from MCH logbook and those in the HCIS found that 24.2% (88/363) of the children’s records were absolutely inconsistent. In addition, statistics derived from the DEPIC records showed a higher immunization coverage and much more compliance to immunization schedule by age group when compared to records derived from the HCIS database. Conclusions: DEPIC, or the concept of collecting data via image capture directly from their primary sources, has proven to be a useful data collection method in terms of completeness and consistency. In this study, DEPIC was implemented in data collection of a single survey. The DEPIC concept, however, can be easily applied in other types of survey research, for example, collecting data on changes or trends based on image evidence over time. With its image evidence and audit trail features, DEPIC has the potential for being used even in clinical studies since it could generate improved data integrity and more reliable statistics for use in both health care and research settings. %M 26194880 %R 10.2196/mhealth.4183 %U http://mhealth.jmir.org/2015/3/e75/ %U https://doi.org/10.2196/mhealth.4183 %U http://www.ncbi.nlm.nih.gov/pubmed/26194880 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 3 %P e86 %T A Validation Study of the Web-Based Physical Activity Questionnaire Active-Q Against the GENEA Accelerometer %A Bonn,Stephanie Erika %A Bergman,Patrick %A Trolle Lagerros,Ylva %A Sjölander,Arvid %A Bälter,Katarina %+ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12a, Stockholm, SE-171 77, Sweden, 46 852482298, stephanie.bonn@ki.se %K accelerometer %K activity assessment %K epidemiology %K Internet %K self report %K validity %D 2015 %7 16.07.2015 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Valid physical activity assessment in epidemiological studies is essential to study associations with various health outcomes. Objective: To validate the Web-based physical activity questionnaire Active-Q by comparing results of time spent at different physical activity levels with results from the GENEA accelerometer and to assess the reproducibility of Active-Q by comparing two admissions of the questionnaire. Methods: A total of 148 men (aged 33 to 86 years) responded to Active-Q twice and wore the accelerometer during seven consecutive days on two occasions. Time spent on six different physical activity levels including sedentary, light (LPA), moderate (MPA), and vigorous (VPA) as well as additional combined categories of sedentary-to-light and moderate-to-vigorous (MVPA) physical activity was assessed. Validity of Active-Q was determined using Spearman correlation coefficients with 95% confidence intervals (CI) and the Bland-Altman method. Reproducibility was assessed using intraclass correlation coefficients (ICCs) comparing two admissions of the questionnaire. Results: The validity correlation coefficients were statistically significant for time spent at all activity levels; sedentary (r=0.19, 95% CI: 0.04-0.34), LPA (r=0.15, 95% CI: 0.00-0.31), sedentary-to-light (r=0.35, 95% CI: 0.19-0.51), MPA (r=0.27, 95% CI: 0.12-0.42), VPA (r=0.54, 95% CI: 0.42-0.67), and MVPA (r=0.35, 95% CI: 0.21-0.48). The Bland-Altman plots showed a negative mean difference for time in LPA and positive mean differences for time spent in MPA, VPA and MVPA. The ICCs of test-retest reliability ranged between r=0.51-0.80 for the different activity levels in Active-Q. Conclusions: More moderate and vigorous activities and less light activities were reported in Active-Q compared to accelerometer measurements. Active-Q shows comparable validity and reproducibility to other physical activity questionnaires used today. %M 26183896 %R 10.2196/resprot.3896 %U http://www.researchprotocols.org/2015/3/e86/ %U https://doi.org/10.2196/resprot.3896 %U http://www.ncbi.nlm.nih.gov/pubmed/26183896 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 2 %P e76 %T Using Ecological Momentary Assessment to Study Tobacco Behavior in Urban India: There’s an App for That %A Soong,Andrea %A Chen,Julia Cen %A Borzekowski,Dina LG %+ Institute for Global Tobacco Control, Department of Health, Behavior & Society, Johns Hopkins Bloomberg School of Public Health, 2213 McElderry St, 4th Floor, Baltimore, MD, 21205, United States, 1 410 502 2482, asoong@jhu.edu %K ecological momentary assessment %K tobacco control %K cell phones %K mobile phones %K mHealth %K telemedicine %K smoking %D 2015 %7 24.06.2015 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Ecological momentary assessment (EMA) uses real-time data collection to assess participants’ behaviors and environments. This paper explores the strengths and limitations of using EMA to examine social and environmental exposure to tobacco in urban India among older adolescents and adults. Objective: Objectives of this study were (1) to describe the methods used in an EMA study of tobacco use in urban India using a mobile phone app for data collection, (2) to determine the feasibility of using EMA in the chosen setting by drawing on participant completion and compliance rates with the study protocol, and (3) to provide recommendations on implementing mobile phone EMA research in India and other low- and middle-income countries. Methods: Via mobile phones and the Internet, this study used two EMA surveys: (1) a momentary survey, sent multiple times per day at random to participants, which asked about their real-time tobacco use (smoked and smokeless) and exposure to pro- and antitobacco messaging in their location, and 2) an end-of-day survey sent at the end of each study day. Trained participants, from Hyderabad and Kolkata, India, reported on their social and environmental exposure to tobacco over 10 consecutive days. This feasibility study examined participant compliance, exploring factors related to the successful completion of surveys and the validity of EMA data. Results: The sample included 205 participants, the majority of whom were male (135/205, 65.9%). Almost half smoked less than daily (56/205, 27.3%) or daily (43/205, 21.0%), and 4.4% (9/205) used smokeless tobacco products. Participants completed and returned 46.87% and 73.02% of momentary and end-of-day surveys, respectively. Significant predictors of momentary survey completion included employment and completion of end-of-day surveys. End-of-day survey completion was only significantly predicted by momentary survey completion. Conclusions: This first study of EMA in India offers promising results, although more research is needed on how to increase compliance. End-of-day survey completion, which has a lower research burden, may be the more appropriate approach to understanding behaviors such as tobacco use within vulnerable populations in challenging locations. Compliance may also be improved by increasing the number of study visits, compliance checks, or opportunities for retraining participants before and during data collection. %M 26109369 %R 10.2196/resprot.4408 %U http://www.researchprotocols.org/2015/2/e76/ %U https://doi.org/10.2196/resprot.4408 %U http://www.ncbi.nlm.nih.gov/pubmed/26109369 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 2 %P e67 %T Feasibility and Acceptability of Smartphone-Based Ecological Momentary Assessment of Alcohol Use Among African American Men Who Have Sex With Men in Baltimore %A Yang,Cui %A Linas,Beth %A Kirk,Gregory %A Bollinger,Robert %A Chang,Larry %A Chander,Geetanjali %A Siconolfi,Daniel %A Braxton,Sharif %A Rudolph,Abby %A Latkin,Carl %+ Johns Hopkins School of Public Health, Department of Health, Behavior and Society, 2213 McElderry St. 2n FL, Baltimore, MD, 21205, United States, 1 4105025368, cyang29@jhu.edu %K ecological momentary assessment (EMA) %K alcohol use %K HIV %K African American %K men who have sex with men (MSM) %D 2015 %7 17.06.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Alcohol use is a risk factor for the acquisition of human immunodeficiency virus (HIV) among African American men who have sex with men (MSM). Mobile phone-based ecological momentary assessments (EMA) could minimize bias due to retrospective recall and thus provide a better understanding of the social and structural context of alcohol use and its relationship with HIV-related risk behaviors in this population as well as other highly stigmatized populations. Objective: We describe the study design and the implementation, feasibility, reactivity, and acceptability of an EMA study of alcohol use and HIV-related behaviors among African American MSM in Baltimore. Methods: Participants were recruited through flyers and word-of-mouth in Baltimore from September 2013 to November 2014. Each participant was loaned an Android smartphone and instructed to respond to multiple prompts from the mobile app for 4 weeks. Data were collected through (1) random prompts delivered three times daily assessing participants’ location, activity, mood, and social context, (2) daily prompts capturing drinking and sex events occurring in the past 24 hours, and (3) event-contingent responses collecting participants’ self-reported episodes of drinking. Results: A total of 16 participants enrolled in the study. The current analyses focused on 15 participants who completed at least 24 days of follow-up (mean follow-up time 29 days; range 24-35 days). Study participants (N=15) were a median 38 years of age (range 27-62 years) with low levels of income and educational attainment. Ten individuals self-reported living with HIV/AIDS, over half reported drinking alcohol at least 2-3 times a week, and a third reported binge drinking (ie, 6 or more drinks on one occasion) on a weekly basis. Based on the Alcohol Use Disorders Identification Test (AUDIT) score, nearly half were classified as hazardous drinkers (score 8-15) and a fifth were likely dependent (score ≥16). A total of 140 participant-initiated events were reported, and 75% of 1308 random prompts and 81% of 436 daily prompts delivered were answered. Of seven devices used during the study, five were reported lost by participants. We did not observe strong reactivity effects, and self-reported acceptability to study procedures was uniformly favorable. Conclusions: This study provides evidence to support the feasibility and acceptability of using EMA methods for collecting data on alcohol use among African American men who have sex with men living in urban settings. These data provide the basis for future studies of EMA-informed mHealth interventions to promote the reduction of substance use and HIV risk-taking behaviors among African American MSM living in urban settings. %M 26085078 %R 10.2196/mhealth.4344 %U http://mhealth.jmir.org/2015/2/e67/ %U https://doi.org/10.2196/mhealth.4344 %U http://www.ncbi.nlm.nih.gov/pubmed/26085078 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 2 %P e38 %T A New Mobile Phone-Based Tool for Assessing Energy and Certain Food Intakes in Young Children: A Validation Study %A Henriksson,Hanna %A Bonn,Stephanie E %A Bergström,Anna %A Bälter,Katarina %A Bälter,Olle %A Delisle,Christine %A Forsum,Elisabet %A Löf,Marie %+ Karolinska Institutet, Department of Biosciences and Nutrition, Novum, Huddinge, 14183, Sweden, 46 734426417, marie.lof@ki.se %K cell phone %K digital camera %K food intake %K energy intake %K child %K DLW %K FFQ %D 2015 %7 24.04.2015 %9 Short Paper %J JMIR mHealth uHealth %G English %X Background: Childhood obesity is an increasing health problem globally. Obesity may be established already at pre-school age. Further research in this area requires accurate and easy-to-use methods for assessing the intake of energy and foods. Traditional methods have limited accuracy, and place large demands on the study participants and researchers. Mobile phones offer possibilities for methodological advancements in this area since they are readily available, enable instant digitalization of collected data, and also contain a camera to photograph pre- and post-meal food items. We have recently developed a new tool for assessing energy and food intake in children using mobile phones called the Tool for Energy Balance in Children (TECH). Objective: The main aims of our study are to (1) compare energy intake by means of TECH with total energy expenditure (TEE) measured using a criterion method, the doubly labeled water (DLW) method, and (2) to compare intakes of fruits and berries, vegetables, juice, and sweetened beverages assessed by means of TECH with intakes obtained using a Web-based food frequency questionnaire (KidMeal-Q) in 3 year olds. Methods: In this study, 30 Swedish 3 year olds were included. Energy intake using TECH was compared to TEE measured using the DLW method. Intakes of vegetables, fruits and berries, juice, as well as sweetened beverages were assessed using TECH and compared to the corresponding intakes assessed using KidMeal-Q. Wilcoxon matched pairs test, Spearman rank order correlations, and the Bland-Altman procedure were applied. Results: The mean energy intake, assessed by TECH, was 5400 kJ/24h (SD 1500). This value was not significantly different (P=.23) from TEE (5070 kJ/24h, SD 600). However, the limits of agreement (2 standard deviations) in the Bland-Altman plot for energy intake estimated using TECH compared to TEE were wide (2990 kJ/24h), and TECH overestimated high and underestimated low energy intakes. The Bland-Altman plots for foods showed similar patterns. The mean intakes of vegetables, fruits and berries, juice, and sweetened beverages estimated using TECH were not significantly different from the corresponding intakes estimated using KidMeal-Q. Moderate but statistically significant correlations (ρ=.42-.46, P=.01-.02) between TECH and KidMeal-Q were observed for intakes of vegetables, fruits and berries, and juice, but not for sweetened beverages. Conclusion: We found that one day of recordings using TECH was not able to accurately estimate intakes of energy or certain foods in 3 year old children. %M 25910494 %R 10.2196/mhealth.3670 %U http://mhealth.jmir.org/2015/2/e38/ %U https://doi.org/10.2196/mhealth.3670 %U http://www.ncbi.nlm.nih.gov/pubmed/25910494 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 2 %P e36 %T Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies %A Hekler,Eric B %A Buman,Matthew P %A Grieco,Lauren %A Rosenberger,Mary %A Winter,Sandra J %A Haskell,William %A King,Abby C %+ Arizona State University, School of Nutrition and Health Promotion, 500 N. 3rd St., Phoenix, AZ, 85003, United States, 1 6028272271, ehekler@asu.edu %K telemedicine %K cell phones %K accelerometry %K motor activity %K validation studies %D 2015 %7 15.04.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: There is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach. Objective: The purpose of this work was to determine the validity and reliability of 3 Android smartphones for measuring physical activity among midlife and older adults. Methods: A laboratory (study 1) and a free-living (study 2) protocol were conducted. In study 1, individuals engaged in prescribed activities including sedentary (eg, sitting), light (sweeping), moderate (eg, walking 3 mph on a treadmill), and vigorous (eg, jogging 5 mph on a treadmill) activity over a 2-hour period wearing both an ActiGraph and 3 Android smartphones (ie, HTC MyTouch, Google Nexus One, and Motorola Cliq). In the free-living study, individuals engaged in usual daily activities over 7 days while wearing an Android smartphone (Google Nexus One) and an ActiGraph. Results: Study 1 included 15 participants (age: mean 55.5, SD 6.6 years; women: 56%, 8/15). Correlations between the ActiGraph and the 3 phones were strong to very strong (ρ=.77-.82). Further, after excluding bicycling and standing, cut-point derived classifications of activities yielded a high percentage of activities classified correctly according to intensity level (eg, 78%-91% by phone) that were similar to the ActiGraph’s percent correctly classified (ie, 91%). Study 2 included 23 participants (age: mean 57.0, SD 6.4 years; women: 74%, 17/23). Within the free-living context, results suggested a moderate correlation (ie, ρ=.59, P<.001) between the raw ActiGraph counts/minute and the phone’s raw counts/minute and a strong correlation on minutes of moderate-to-vigorous physical activity (MVPA; ie, ρ=.67, P<.001). Results from Bland-Altman plots suggested close mean absolute estimates of sedentary (mean difference=–26 min/day of sedentary behavior) and MVPA (mean difference=–1.3 min/day of MVPA) although there was large variation. Conclusions: Overall, results suggest that an Android smartphone can provide comparable estimates of physical activity to an ActiGraph in both a laboratory-based and free-living context for estimating sedentary and MVPA and that different Android smartphones may reliably confer similar estimates. %M 25881662 %R 10.2196/mhealth.3505 %U http://mhealth.jmir.org/2015/2/e36/ %U https://doi.org/10.2196/mhealth.3505 %U http://www.ncbi.nlm.nih.gov/pubmed/25881662 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 1 %P e15 %T Knowledge, Attitudes, and Practices Regarding Avian Influenza A (H7N9) Among Mobile Phone Users: A Survey in Zhejiang Province, China %A Gu,Hua %A Jiang,Zhenggang %A Chen,Bin %A Zhang,Jueman (Mandy) %A Wang,Zhengting %A Wang,Xinyi %A Cai,Jian %A Chen,Yongdi %A Zheng,Dawei %A Jiang,Jianmin %+ Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Binjiang District, Hangzhou, 310051, China, 86 57187115009, jmjiang@cdc.zj.cn %K influenza A virus, subtype H7N9 %K knowledge %K attitude %K surveillance %D 2015 %7 04.02.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Understanding people’s knowledge, attitudes, and practices (KAP) regarding a new infectious disease is crucial to the prevention and control of it. Human infection with avian influenza A (H7N9) was first identified on March 31, 2013 in China. Out of the total number of 134 cases confirmed from March to September 2013 in China, Zhejiang Province saw the greatest number (46 cases). Objective: This study employed a mobile Internet survey to assess KAP regarding H7N9 among mobile phone users in Zhejiang Province. This study intended to examine KAP by region and the association between sociodemographic variables and KAP. Methods: An anonymous questionnaire was designed by Zhejiang Provincial Center for Disease Control and Prevention (CDC). A cross-sectional survey was executed through a mobile Internet application platform of China Unicom in 5 regions in Zhejiang Province. Stratified and clustered sampling methods were applied and mobile phone users were invited to participate in the study voluntarily. Results: A total of 9582 eligible mobile phone users participated in the survey with a response rate of 1.92% (9582/5,000,000). A total of 9105 valid responses (95.02%) were included for statistical analysis. Generally, more than three-quarters of the participants had some basic knowledge of H7N9 and held the attitude recommended by the Zhejiang CDC toward eating cooked poultry (77.55%, 7061/9105) and visiting a hospital at the occurrence of symptoms (78.51%, 7148/9105). Approximately half of the participants worried about contracting H7N9, and took preventive practices recommended by the Zhejiang CDC. But only 14.29% (1301/9105) of participants kept eating cooked poultry as usual. Although worry about H7N9 infection did not differ by region, Hangzhou saw the largest proportion of participants with knowledge of H7N9, which was probably because Hangzhou had the greatest number of H7N9 cases. KAP varied by some sociodemographic variables. Female participants were more likely to know about symptoms of H7N9 (OR 1.32, 95% CI 1.08-1.61), to worry about contracting it (OR 1.15, 95% CI 1.04-1.27), and to report their lives being influenced by it (OR 1.27, 95% CI 1.15-1.41). They were also more likely to take the recommended precautions. Male participants and younger participants were less likely to comply with advocated protective practices. Conclusions: The results suggest that health education should be customized depending on sociodemographic variables to achieve more effective behavioral outcomes. %M 25653213 %R 10.2196/mhealth.3394 %U http://mhealth.jmir.org/2015/1/e15/ %U https://doi.org/10.2196/mhealth.3394 %U http://www.ncbi.nlm.nih.gov/pubmed/25653213 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 1 %P e9 %T Validation of a Portable Device for Mapping Motor and Gait Disturbances in Parkinson’s Disease %A Rodríguez-Molinero,Alejandro %A Samà,Albert %A Pérez-Martínez,David A %A Pérez López,Carlos %A Romagosa,Jaume %A Bayés,Àngels %A Sanz,Pilar %A Calopa,Matilde %A Gálvez-Barrón,César %A de Mingo,Eva %A Rodríguez Martín,Daniel %A Gonzalo,Natalia %A Formiga,Francesc %A Cabestany,Joan %A Catalá,Andreu %+ Fundación Sant Antoni Abat (Consorci Sanitiari del Garraf), C/ Sant Josep 21-23, Vilanova i la Geltru, , Spain, 34 938931616, rodriguez.molinero@gmail.com %K accelerometer %K kinematic sensor %K motor fluctuations %D 2015 %7 02.02.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Patients with severe idiopathic Parkinson’s disease experience motor fluctuations, which are often difficult to control. Accurate mapping of such motor fluctuations could help improve patients’ treatment. Objective: The objective of the study was to focus on developing and validating an automatic detector of motor fluctuations. The device is small, wearable, and detects the motor phase while the patients walk in their daily activities. Methods: Algorithms for detection of motor fluctuations were developed on the basis of experimental data from 20 patients who were asked to wear the detector while performing different daily life activities, both in controlled (laboratory) and noncontrolled environments. Patients with motor fluctuations completed the experimental protocol twice: (1) once in the ON, and (2) once in the OFF phase. The validity of the algorithms was tested on 15 different patients who were asked to wear the detector for several hours while performing daily activities in their habitual environments. In order to assess the validity of detector measurements, the results of the algorithms were compared with data collected by trained observers who were accompanying the patients all the time. Results: The motor fluctuation detector showed a mean sensitivity of 0.96 (median 1; interquartile range, IQR, 0.93-1) and specificity of 0.94 (median 0.96; IQR, 0.90-1). Conclusions: ON/OFF motor fluctuations in Parkinson's patients can be detected with a single sensor, which can be worn in everyday life. %M 25648406 %R 10.2196/mhealth.3321 %U http://mhealth.jmir.org/2015/1/e9/ %U https://doi.org/10.2196/mhealth.3321 %U http://www.ncbi.nlm.nih.gov/pubmed/25648406 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 4 %P e50 %T Development and Evaluation of an iPad App for Measuring the Cost of a Nutritious Diet %A Palermo,Claire %A Perera-Schulz,Dharani %A Kannan,Anitha %A Truby,Helen %A Shiell,Alan %A Emilda,Sindhu %A Quenette,Steve %+ Department of Nutrition and Dietetics, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill, 3168, Australia, 61 3 99024270, claire.palermo@monash.edu %K portable digital device %K iPad %K healthy food %K food cost %D 2014 %7 04.12.2014 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Monitoring food costs informs governments of the affordability of healthy diets. Many countries have adopted a standardized healthy food basket. The Victorian Healthy Food Basket contains 44 food items necessary to meet the nutritional requirements of four different Australian family types for a fortnight. Objective: The aim of this study was to describe the development of a new iPad app as core to the implementation of the Victorian Healthy Food Basket. The app significantly automates the data collection. We evaluate if the new technology enhanced the quality and efficacy of the research. Methods: Time taken for data collection and entry was recorded. Semi-structured evaluative interviews were conducted with five field workers during the pilot of the iPad app. Field workers were familiar with previous manual data collection methods. Qualitative process evaluation data was summarized against key evaluation questions. Results: Field workers reported that using the iPad for data collection resulted in increased data accuracy, time savings, and efficient data management, and was preferred over manual collection. Conclusions: Portable digital devices may be considered to improve and extend data collection in the field of food cost monitoring. %M 25486678 %R 10.2196/mhealth.3314 %U http://mhealth.jmir.org/2014/4/e50/ %U https://doi.org/10.2196/mhealth.3314 %U http://www.ncbi.nlm.nih.gov/pubmed/25486678 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 4 %P e39 %T Accuracy, Consistency, and Reproducibility of the Triaxial Accelerometer in the iPod Touch: A Pilot Study %A Khoo Chee Han,Christopher %A Shanmugam,Rukmanikanthan AL %A Choon Siew Kit,David %+ Department of Orthopaedic Surgery, Faculty of Medicine, University Malaya, Lembah Pantai, Kuala Lumpur, 59100, Malaysia, 60 379494444, christopforher@yahoo.co.uk %K accelerometry %K tri-axial accelerometer %K iPod Touch %D 2014 %7 24.11.2014 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: The use of a mobile consumer communicative device as a motion analysis tool for patients has been researched and documented previously, examining the triaxial accelerometer embedded in such devices. However, there have been few reports in the literature testing the sensitivity of an embedded triaxial accelerometer. Objective: Our goal in this study was to test the accuracy, consistency, and reproducibility of the triaxial accelerometer in the iPod Touch. Methods: In this pilot study, we subjected the triaxial accelerometer in the iPod Touch to a free fall from a height of 100 cm in order to test its accuracy, consistency, and reproducibility under dynamic conditions. Results: The resultant vectorial sum acceleration was mean 0.999 g (standard gravity; SD 1.51%; 95% CI 0.99-1.01), indicating very high accuracy and sensitivity under dynamic conditions. Conclusions: Our results highlighted the reproducibility of the capability of the triaxial accelerometer in the iPod Touch to capture data accurately and consistently. Thus, the device has huge potential as a motion analysis tool for measuring gait and studying balance and mobility in patients before and after surgery. %M 25486896 %R 10.2196/mhealth.3008 %U http://mhealth.jmir.org/2014/4/e39/ %U https://doi.org/10.2196/mhealth.3008 %U http://www.ncbi.nlm.nih.gov/pubmed/25486896 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 4 %P e40 %T The Medium and the Message: Fitting Sound Health Promotion Methodology Into 160 Characters %A Lim,Megan S C %A Wright,Cassandra %A Hellard,Margaret E %+ Burnet Institute, Centre for Population Health, 85 Commercial Rd, Melbourne, 3004, Australia, 61 385062403, lim@burnet.edu.au %K text messaging %K mobile phone %K health promotion %K program evaluation  %D 2014 %7 03.11.2014 %9 Editorial %J JMIR mHealth uHealth %G English %X Text messaging health promotion projects continue to proliferate due to their relative low-cost, simplicity, non-intrusiveness, and proven effectiveness in several randomized controlled trials. In these past trials, participants have typically been recruited through traditional means, received the text messaging intervention, and then completed evaluation. In this issue of the Journal of Medical Internet Research, Sheoran et al have demonstrated how use of text messaging alone can be a feasible method for all three stages: recruitment, intervention, and evaluation. Use of text messages without any other modes of communication could be a key to population-level dissemination and wider uptake of health promotion messages. However, in the rush to utilize new technologies and in the brevity of 160 characters, it should not be forgotten that quality, rigour, and careful development remain essential in any health promotion practice. %M 25367387 %R 10.2196/mhealth.3888 %U http://mhealth.jmir.org/2014/4/e40/ %U https://doi.org/10.2196/mhealth.3888 %U http://www.ncbi.nlm.nih.gov/pubmed/25367387 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 4 %P e51 %T The Hookup: Collaborative Evaluation of a Youth Sexual Health Program Using Text Messaging Technology %A Sheoran,Bhupendra %A Braun,Rebecca A %A Gaarde,Jenna Patrice %A Levine,Deborah K %+ Internet Sexuality Information Services, Inc (dba YTH), 409 13th Street, Oakland, CA, 94612, United States, 1 510 835 9400, sheoran@yth.org %K sexual health %K STDs %K HIV %K mobile phone %K youth %K SMS %K text messaging %K program evaluation %D 2014 %7 03.11.2014 %9 Short Paper %J JMIR mHealth uHealth %G English %X Background: The Hookup is a collaborative project reaching young people in California with valuable sexual and reproductive health information and linkage to local resources. Due to limited access to subscriber contact information, it has been a challenge to evaluate the program. Objective: The aims of this study were to determine the feasibility of using text messaging (short message service, SMS) as an evaluation tool for an educational text message-based program and to evaluate the program itself. Methods: All subscribers of The Hookup were sent four survey questions via SMS about age, gender, location, referral source and behavior change. An incentive was offered for completing the survey and an opt-out option was provided in the initial message. Results: All existing subscribers of The Hookup (N=2477) received a request to complete the survey using the SMS application on their mobile phones. A total of 832 (33.6%) subscribers responded to the initial question and 481 (20%) answered all four questions. Of the responses, 85% were received in the first two hours of the initial request. Respondents who answered the question about behavior change, 90% reported having made some positive change since subscribing to Hookup, including getting tested for STDs and HIV. Conclusions: The survey methodology initiated a high response rate from The Hookup subscribers. The survey was able to provide data about subscribers in a short time period at minimal cost. The results show potential for using mobile SMS applications to evaluate SMS campaigns. The findings also support using SMS to provide young people with sexual health prevention messaging and linkage to health services. %M 25367444 %R 10.2196/mhealth.3583 %U http://mhealth.jmir.org/2014/4/e51/ %U https://doi.org/10.2196/mhealth.3583 %U http://www.ncbi.nlm.nih.gov/pubmed/25367444 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 16 %N 10 %P e239 %T Tablet, Web-Based, or Paper Questionnaires for Measuring Anxiety in Patients Suspected of Breast Cancer: Patients' Preferences and Quality of Collected Data %A Barentsz,Maarten W %A Wessels,Hester %A van Diest,Paul J %A Pijnappel,Ruud M %A Haaring,Cees %A van der Pol,Carmen C %A Witkamp,Arjen J %A van den Bosch,Maurice A %A Verkooijen,Helena M %+ University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, Netherlands, 31 887556689, m.barentsz@umcutrecht.nl %K breast cancer %K electronic questionnaires %K paper questionnaires %K quality of collected data %D 2014 %7 31.10.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic applications are increasingly being used in hospitals for numerous purposes. Objective: Our aim was to assess differences in the characteristics of patients who choose paper versus electronic questionnaires and to evaluate the data quality of both approaches. Methods: Between October 2012 and June 2013, 136 patients participated in a study on diagnosis-induced stress and anxiety. Patients were asked to fill out questionnaires at six different moments during the diagnostic phase. They were given the opportunity to fill out the questionnaires on paper or electronically (a combination of tablet and Web-based questionnaires). Demographic characteristics and completeness of returned data were compared between groups. Results: Nearly two-thirds of patients (88/136, 64.7%) chose to fill out the questionnaires on paper, and just over a third (48/136, 35.3%) preferred the electronic option. Patients choosing electronic questionnaires were significantly younger (mean 47.3 years vs mean 53.5 in the paper group, P=.01) and higher educated (P=.004). There was significantly more missing information (ie, at least one question not answered) in the paper group during the diagnostic day compared to the electronic group (using a tablet) (28/88 vs 1/48, P<.001). However, in the week after the diagnostic day, missing information was significantly higher in the electronic group (Web-based questionnaires) compared to the paper group (41/48 vs 38/88, P<.001). Conclusions: Younger patients and patients with a higher level of education have a preference towards filling out questionnaires electronically. In the hospital, a tablet is an excellent medium for patients to fill out questionnaires with very little missing information. However, for filling out questionnaires at home, paper questionnaires resulted in a better response than Web-based questionnaires. %M 25364951 %R 10.2196/jmir.3578 %U http://www.jmir.org/2014/10/e239/ %U https://doi.org/10.2196/jmir.3578 %U http://www.ncbi.nlm.nih.gov/pubmed/25364951 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 4 %P e49 %T Consumers' Perspectives on National Health Insurance in South Africa: Using a Mobile Health Approach %A Weimann,Edda %A Stuttaford,Maria C %+ School of Public Health and Family Medicine, Health Sciences Faculty, University of Cape Town, PBag, Anzio Road, Observatory, Cape Town, 7925, South Africa, 27 794981377, prof.dr.weimann@gmail.com %K health systems reform %K public consultation %K South Africa %K National Health Insurance (NHI) %K health systems strengthening (HSS) %K WHO building blocks %K social media, GINI Index %D 2014 %7 28.10.2014 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Building an equitable health system is a cornerstone of the World Health Organization (WHO) health system building block framework. Public participation in any such reform process facilitates successful implementation. South Africa has embarked on a major reform in health policy that aims at redressing inequity and enabling all citizens to have equal access to efficient and quality health services. Objective: This research is based on a survey using Mxit as a mobile phone–based social media network. It was intended to encourage comments on the proposed National Health Insurance (NHI) and to raise awareness among South Africans about their rights to free and quality health care. Methods: Data were gathered by means of a public e-consultation, and following a qualitative approach, were then examined and grouped in a theme analysis. The WHO building blocks were used as the conceptual framework in analysis and discussion of the identified themes. Results: Major themes are the improvement of service delivery and patient-centered health care, enhanced accessibility of health care providers, and better health service surveillance. Furthermore, health care users demand stronger outcome-based rather than rule-based indicators of the health system’s governance. Intersectoral solidarity and collaboration between private and public health care providers are suggested. Respondents also propose a code of ethical values for health care professionals to address corruption in the health care system. It is noteworthy that measures for dealing with corruption or implementing ethical values are neither described in the WHO building blocks nor in the NHI. Conclusions: The policy makers of the new health system for South Africa should address the lack of trust in the health care system that this study has exposed. Furthermore, the study reveals discrepancies between the everyday lived reality of public health care consumers and the intended health policy reform. %M 25351980 %R 10.2196/mhealth.3533 %U http://mhealth.jmir.org/2014/4/e49/ %U https://doi.org/10.2196/mhealth.3533 %U http://www.ncbi.nlm.nih.gov/pubmed/25351980 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 3 %N 3 %P e38 %T A Comparison of Tablet Computer and Paper-Based Questionnaires in Healthy Aging Research %A Fanning,Jason %A McAuley,Edward %+ Exercise Psychology Laboratory, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Freer Hall, 906 S Goodwin Ave, Urbana, IL, 61801, United States, 1 217 300 5306, fanning4@illinois.edu %K healthy aging %K questionnaire %K tablet computer %K behavioral psychology %D 2014 %7 16.07.2014 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Digital questionnaire delivery offers many advantages to investigators and participants alike; however, evidence supporting digital questionnaire delivery via touchscreen device in the older adult population is lacking. Objective: The objective of this study was to compare the use of tablet computer-delivered and printed questionnaires as vehicles for the collection of psychosocial data from older adults to determine whether this digital platform would be readily adopted by the sample, and to identify whether tablet delivery influences the content of data received. Methods: The participants completed three questionnaires using both delivery methods, followed by a brief evaluation. Results: A nonparametric one-sample binomial test indicated a significantly greater proportion of individuals preferred the tablet-delivered questionnaires (z=4.96, SE 3.428, P<.001). Paired sample t tests and Wilcoxon sign-rank tests indicated that measures collected by each method were not significantly different (all P≥.273). Ease of use of the tablet interface and anxiety while completing the digital questionnaires were significantly correlated with preferences, (rs=.665, P<.001 and rs=.552, P<.001, respectively). Participants most frequently reported that the tablet delivery increased speed of use and improved data entry, although navigation was perceived as being more difficult. By comparison, participants felt that the paper packet was easier to read and navigate, but was slow and cumbersome, and they disliked the lack of dynamic features. Conclusions: This study provides preliminary evidence suggesting that questionnaires delivered to older adults using contemporary tablet computers may be acceptable and do not substantively influence the content of the collected data. %M 25048799 %R 10.2196/resprot.3291 %U http://www.researchprotocols.org/2014/3/e38/ %U https://doi.org/10.2196/resprot.3291 %U http://www.ncbi.nlm.nih.gov/pubmed/25048799 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 16 %N 5 %P e135 %T Daily Collection of Self-Reporting Sleep Disturbance Data via a Smartphone App in Breast Cancer Patients Receiving Chemotherapy: A Feasibility Study %A Min,Yul Ha %A Lee,Jong Won %A Shin,Yong-Wook %A Jo,Min-Woo %A Sohn,Guiyun %A Lee,Jae-Ho %A Lee,Guna %A Jung,Kyung Hae %A Sung,Joohon %A Ko,Beom Seok %A Yu,Jong-Han %A Kim,Hee Jeong %A Son,Byung Ho %A Ahn,Sei Hyun %+ Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea, 82 230105603, jongwonlee116@gmail.com %K mobile applications %K self report %K compliance %K breast cancer %D 2014 %7 23.05.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: Improvements in mobile telecommunication technologies have enabled clinicians to collect patient-reported outcome (PRO) data more frequently, but there is as yet limited evidence regarding the frequency with which PRO data can be collected via smartphone applications (apps) in breast cancer patients receiving chemotherapy. Objective: The primary objective of this study was to determine the feasibility of an app for sleep disturbance-related data collection from breast cancer patients receiving chemotherapy. A secondary objective was to identify the variables associated with better compliance in order to identify the optimal subgroups to include in future studies of smartphone-based interventions. Methods: Between March 2013 and July 2013, patients who planned to receive neoadjuvant chemotherapy for breast cancer at Asan Medical Center who had access to a smartphone app were enrolled just before the start of their chemotherapy and asked to self-report their sleep patterns, anxiety severity, and mood status via a smartphone app on a daily basis during the 90-day study period. Push notifications were sent to participants daily at 9 am and 7 pm. Data regarding the patients’ demographics, interval from enrollment to first self-report, baseline Beck’s Depression Inventory (BDI) score, and health-related quality of life score (as assessed using the EuroQol Five Dimensional [EQ5D-3L] questionnaire) were collected to ascertain the factors associated with compliance with the self-reporting process. Results: A total of 30 participants (mean age 45 years, SD 6; range 35-65 years) were analyzed in this study. In total, 2700 daily push notifications were sent to these 30 participants over the 90-day study period via their smartphones, resulting in the collection of 1215 self-reporting sleep-disturbance data items (overall compliance rate=45.0%, 1215/2700). The median value of individual patient-level reporting rates was 41.1% (range 6.7-95.6%). The longitudinal day-level compliance curve fell to 50.0% at day 34 and reached a nadir of 13.3% at day 90. The cumulative longitudinal compliance curve exhibited a steady decrease by about 50% at day 70 and continued to fall to 45% on day 90. Women without any form of employment exhibited the higher compliance rate. There was no association between any of the other patient characteristics (ie, demographics, and BDI and EQ5D-3L scores) and compliance. The mean individual patient-level reporting rate was higher for the subgroup with a 1-day lag time, defined as starting to self-report on the day immediately after enrollment, than for those with a lag of 2 or more days (51.6%, SD 24.0 and 29.6%, SD 25.3, respectively; P=.03). Conclusions: The 90-day longitudinal collection of daily self-reporting sleep-disturbance data via a smartphone app was found to be feasible. Further research should focus on how to sustain compliance with this self-reporting for a longer time and select subpopulations with higher rates of compliance for mobile health care. %M 24860070 %R 10.2196/jmir.3421 %U http://www.jmir.org/2014/5/e135/ %U https://doi.org/10.2196/jmir.3421 %U http://www.ncbi.nlm.nih.gov/pubmed/24860070 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 2 %P e22 %T Real-Time Monitoring of School Absenteeism to Enhance Disease Surveillance: A Pilot Study of a Mobile Electronic Reporting System %A Lawpoolsri,Saranath %A Khamsiriwatchara,Amnat %A Liulark,Wongwat %A Taweeseneepitch,Komchaluch %A Sangvichean,Aumnuyphan %A Thongprarong,Wiraporn %A Kaewkungwal,Jaranit %A Singhasivanon,Pratap %+ Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, 420/6 Ratchawithi Road, Bangkok, 10400, Thailand, 66 23069100 ext 1695, pratap.sin@mahidol.ac.th %K syndromic surveillance %K schools %K absenteeism %K tablets %K reporting system %D 2014 %7 12.05.2014 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: School absenteeism is a common source of data used in syndromic surveillance, which can eventually be used for early outbreak detection. However, the absenteeism reporting system in most schools, especially in developing countries, relies on a paper-based method that limits its use for disease surveillance or outbreak detection. Objective: The objective of this study was to develop an electronic real-time reporting system on school absenteeism for syndromic surveillance. Methods: An electronic (Web-based) school absenteeism reporting system was developed to embed it within the normal routine process of absenteeism reporting. This electronic system allowed teachers to update students' attendance status via mobile tablets. The data from all classes and schools were then automatically sent to a centralized database for further analysis and presentation, and for monitoring temporal and spatial patterns of absent students. In addition, the system also had a disease investigation module, which provided a link between absenteeism data from schools and local health centers, to investigate causes of fever among sick students. Results: The electronic school absenteeism reporting system was implemented in 7 primary schools in Bangkok, Thailand, with total participation of approximately 5000 students. During May-October 2012 (first semester), the percentage of absentees varied between 1% and 10%. The peak of school absenteeism (sick leave) was observed between July and September 2012, which coincided with the peak of dengue cases in children aged 6-12 years being reported to the disease surveillance system. Conclusions: The timeliness of a reporting system is a critical function in any surveillance system. Web-based application and mobile technology can potentially enhance the use of school absenteeism data for syndromic surveillance and outbreak detection. This study presents the factors that determine the implementation success of this reporting system. %M 25099501 %R 10.2196/mhealth.3114 %U http://mhealth.jmir.org/2014/2/e22/ %U https://doi.org/10.2196/mhealth.3114 %U http://www.ncbi.nlm.nih.gov/pubmed/25099501 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 1 %P e16 %T Usage of Social Media and Smartphone Application in Assessment of Physical and Psychological Well-Being of Individuals in Times of a Major Air Pollution Crisis %A Zhang,Melvyn WB %A Ho,Cyrus SH %A Fang,Pan %A Lu,Yanxia %A Ho,Roger CM %+ Southeast Asian Haze Research Consortium, Department of Medical Psychology, School of Medicine, Shandong University, Department of Medical Psychology, School of Medicine, Shandong University, China, 250102, China, 86 053188382039, melvynzhangweibin@gmail.com %K crisis %K haze %K Internet %K Web-based medium %K social networking %K smartphone application %D 2014 %7 25.03.2014 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Crisis situations bring about many challenges to researchers, public institutions, and governments in collecting data and conducting research in affected individuals. Recent developments in Web-based and smartphone technologies have offered government and nongovernment organizations a new system to disseminate and acquire information. However, research into this area is still lacking. The current study focuses largely on how new social networking websites and, in particular, smartphone technologies could have helped in the acquisition of crucial research data from the general population during the recent 2013 Southeast Asian Haze. This crisis lasted only for 1 week, and is unlike other crisis where there are large-scale consequential after-effects. Objective: To determine whether respondents will make use of Internet, social media, and smartphone technologies to provide feedback regarding their physical and psychological wellbeing during a crisis, and if so, will these new mechanisms be as effective as conventional, technological, Internet-based website technologies. Methods: A Web-based database and a smartphone application were developed. Participants were recruited by snowball sampling. The participants were recruited either via a self-sponsored Facebook post featuring a direct link to the questionnaire on physical and psychological wellbeing and also a smartphone Web-based application; or via dissemination of the questionnaire link by emails, directed to the same group of participants. Information pertaining to physical and psychological wellbeing was collated. Results: A total of 298 respondents took part in the survey. Most of them were between the ages of 20 to 29 years and had a university education. More individuals preferred the option of accessing and providing feedback to a survey on physical and psychological wellbeing via direct access to a Web-based questionnaire. Statistical analysis showed that demographic variables like age, gender, and educational levels did not influence the mechanism of access. In addition, the participants reported a mean number of 4.03 physical symptoms (SD 2.6). The total Impact of Event Scale–Revised (IES-R) score was 18.47 (SD 11.69), which indicated that the study population did experience psychological stress but not post-traumatic stress disorder. The perceived dangerous Pollutant Standards Index (PSI) level and the number of physical symptoms were associated with higher IES-R Score (P<.05). Conclusions: This is one of the first few studies demonstrating the use of Internet in data collection during an air-pollution crisis. Our results demonstrated that the newer technological modalities have the potential to acquire data, similar to that of conventional technologies. Demographic variables did not influence the mechanism of usage. In addition, our findings also suggested that there are acute physical and psychological impacts on the population from an air-pollution crisis. %M 25098255 %R 10.2196/mhealth.2827 %U http://mhealth.jmir.org/2014/1/e16/ %U https://doi.org/10.2196/mhealth.2827 %U http://www.ncbi.nlm.nih.gov/pubmed/25098255 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 1 %P e14 %T Using the iPod Touch for Patient Health Behavior Assessment and Health Promotion in Primary Care %A Forjuoh,Samuel N %A Ory,Marcia G %A Wang,Suojin %A des Bordes,Jude KA %A Hong,Yan %+ School of Public Health, Texas A&M University, 1266 TAMU, College Station, TX, 77843-1266, United States, 1 979 862 1700, yhong@srph.tamhsc.edu %K iPod touch %K behavior change %K health behavior assessment %K health promotion and disease prevention %K patient-physician communication %K mobile health technology %K mHealth %D 2014 %7 21.03.2014 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is a growing recognition of the importance of lifestyle behavior change for health promotion and disease prevention, as well as the concomitant influence of patient–physician communication on effective behavior change. Mobile technology is increasingly being recognized as an important and efficient tool to collect patients’ health behavior data and facilitate patient–physician communication. Objective: The aim of this study was to assess the feasibility of an iPod touch-based health behavior assessment (HBA) tool in enhancing patient–physician collaborative goal-setting for health promotion in primary care. Methods: A total of 109 patients from three primary care clinics in central Texas completed a brief HBA, which was programmed on an iPod touch device. An instant feedback report was generated for the patient and their physician simultaneously to facilitate collaborative goal-setting. Within approximately 7 days of the HBA, the patients were phoned for a follow-up survey for their feedback on the iPod touch–based HBA and resultant patient–physician communication. Results: Patients were able to complete an HBA on the iPod touch with ease. Among those who completed the follow-up survey (n=83), 30% (25/83) reported that their physicians discussed the HBA report with them, while 29% (24/83) established behavior change goals with them. More than 90% (75/83) of the patients reported positive experiences with the iPod touch–based HBA. Conclusions: It is feasible to use mobile tools for HBA in the primary care setting. The HBA also facilitated patient–physician communication on behavior change. However, more research is needed on the effectiveness of large scale dissemination of mobile-based HBA technology on health communication and behavior change for preventing or managing lifestyle-related chronic conditions, such as obesity, diabetes, cancer, or heart diseases. %M 25100308 %R 10.2196/mhealth.2927 %U http://mhealth.jmir.org/2014/1/e14/ %U https://doi.org/10.2196/mhealth.2927 %U http://www.ncbi.nlm.nih.gov/pubmed/25100308 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 1 %P e13 %T Measuring the Lifespace of People With Parkinson’s Disease Using Smartphones: Proof of Principle %A Liddle,Jacki %A Ireland,David %A McBride,Simon J %A Brauer,Sandra G %A Hall,Leanne M %A Ding,Hang %A Karunanithi,Mohan %A Hodges,Paul W %A Theodoros,Deborah %A Silburn,Peter A %A Chenery,Helen J %+ UQ Centre for Clinical Research, Asia-Pacific Centre for Neuromodulation, The University of Queensland, Building 71/918, Royal Brisbane and Women’s Hospital Campus,, The University of Queensland, Herston, QLD, 4029, Australia, 61 7 3346 5583, j.liddle@uq.edu.au %K Parkinson's disease %K community %K telemedicine %K mHealth %D 2014 %7 12.03.2014 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Lifespace is a multidimensional construct that describes the geographic area in which a person lives and conducts their activities, and reflects mobility, health, and well-being. Traditionally, it has been measured by asking older people to self-report the length and frequency of trips taken and assistance required. Global Positioning System (GPS) sensors on smartphones have been used to measure Lifespace of older people, but not with people with Parkinson’s disease (PD). Objective: The objective of this study was to investigate whether GPS data collected via smartphones could be used to indicate the Lifespace of people with PD. Methods: The dataset was supplied via the Michael J Fox Foundation Data Challenge and included 9 people with PD and 7 approximately matched controls. Participants carried smartphones with GPS sensors over two months. Data analysis compared the PD group and the control group. The impact of symptom severity on Lifespace was also investigated. Results: Visualization methods for comparing Lifespace were developed including scatterplots and heatmaps. Lifespace metrics for comparison included average daily distance, percentage of time spent at home, and number of trips into the community. There were no significant differences between the PD and the control groups on Lifespace metrics. Visual representations of Lifespace were organized based on the self-reported severity of symptoms, suggesting a trend of decreasing Lifespace with increasing PD symptoms. Conclusions: Lifespace measured by GPS-enabled smartphones may be a useful concept to measure the progression of PD and the impact of various therapies and rehabilitation programs. Directions for future use of GPS-based Lifespace are provided. %M 25100206 %R 10.2196/mhealth.2799 %U http://mhealth.jmir.org/2014/1/e13/ %U https://doi.org/10.2196/mhealth.2799 %U http://www.ncbi.nlm.nih.gov/pubmed/25100206 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 1 %P e10 %T The Schisto Track: A System for Gathering and Monitoring Epidemiological Surveys by Connecting Geographical Information Systems in Real Time %A Leal Neto,Onicio B %A Albuquerque,Cesar M %A Albuquerque,Jones O %A Barbosa,Constança S %+ Aggeu Magalhaes Research Center, Schistosomiasis Reference Service, Oswaldo Cruz Foundation, Professor Moraes Rego Avenue, Cidade Universitaria., Recife, 50670420, Brazil, 55 21012572, onicio@gmail.com %K epidemiological survey %K schistosomiasis %K public health %D 2014 %7 10.03.2014 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Using the Android platform as a notification instrument for diseases and disorders forms a new alternative for computerization of epidemiological studies. Objective: The objective of our study was to construct a tool for gathering epidemiological data on schistosomiasis using the Android platform. Methods: The developed application (app), named the Schisto Track, is a tool for data capture and analysis that was designed to meet the needs of a traditional epidemiological survey. An initial version of the app was finished and tested in both real situations and simulations for epidemiological surveys. Results: The app proved to be a tool capable of automation of activities, with data organization and standardization, easy data recovery (to enable interfacing with other systems), and totally modular architecture. Conclusions: The proposed Schisto Track is in line with worldwide trends toward use of smartphones with the Android platform for modeling epidemiological scenarios. %M 25099881 %R 10.2196/mhealth.2859 %U http://mhealth.jmir.org/2014/1/e10/ %U https://doi.org/10.2196/mhealth.2859 %U http://www.ncbi.nlm.nih.gov/pubmed/25099881 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 1 %P e7 %T Customized-Language Voice Survey on Mobile Devices for Text and Image Data Collection Among Ethnic Groups in Thailand: A Proof-of-Concept Study %A Jandee,Kasemsak %A Lawpoolsri,Saranath %A Taechaboonsermsak,Pimsurang %A Khamsiriwatchara,Amnat %A Wansatid,Peerawat %A Kaewkungwal,Jaranit %+ Center of Excellence for Biomedical and Public Health Informatics (BIOPHICS), Mahidol University, Faculty of Tropical Medicine, Mahidol University, 420/6 Ratchawithi Road, Ratchathewi, Bangkok, 10400, Thailand, 66 23549181 ext 412, jaranit.kae@mahidol.ac.th %K expanded program on immunization %K EPI %K ethnicity %K mobile technology %K smartphone questionnaire survey %K voiced question %D 2014 %7 06.03.2014 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Public health surveys are often conducted using paper-based questionnaires. However, many problems are associated with this method, especially when collecting data among ethnic groups who speak a different language from the survey interviewer. The process can be time-consuming and there is the risk of missing important data due to incomplete surveys. Objective: This study was conducted as a proof-of-concept to develop a new electronic tool for data collection, and compare it with standard paper-based questionnaire surveys using the research setting of assessing Knowledge Attitude and Practice (KAP) toward the Expanded Program on Immunization (EPI) among 6 ethnic groups in Chiang Rai Province, Thailand. The two data collection methods were compared on data quality in terms of data completeness and time consumed in collecting the information. In addition, the initiative assessed the participants’ satisfaction toward the use of a smartphone customized-language voice-based questionnaire in terms of perceived ease of use and perceived usefulness. Methods: Following a cross-over design, all study participants were interviewed using two data collection methods after a one-week washout period. Questions in the paper-based questionnaires in Thai language were translated to each ethnic language by the interviewer/translator when interviewing the study participant. The customized-language voice-based questionnaires were programmed to a smartphone tablet in six, selectable dialect languages and used by the trained interviewer when approaching participants. Results: The study revealed positive data quality outcomes when using the smartphone, voice-based questionnaire survey compared with the paper-based questionnaire survey, both in terms of data completeness and time consumed in data collection process. Since the smartphone questionnaire survey was programmed to ask questions in sequence, no data was missing and there were no entry errors. Participants had positive attitudes toward answering the smartphone questionnaire; 69% (48/70) reported they understood the questions easily, 71% (50/70) found it convenient, and 66% (46/70) reported a reduced time in data collection. The smartphone data collection method was acceptable by both the interviewers and by the study participants of different ethnicities. Conclusions: To our knowledge, this is the first study showing that the application of specific features of mobile devices like smartphone tablets (including dropdown choices, capturing pictures, and voiced questions) can be successfully used for data collection. The mobile device can be effectively used for capturing photos of secondary data and collecting primary data with customized-language and voiced questionnaire survey. Using smartphone questionnaires can minimize or eliminate missing data and reduce the time consumed during the data collection process. Smartphone customized-language, voice-based questionnaires for data collection can be an alternative and better approach than standard translated paper-based questionnaires for public health surveys, especially when collecting data among ethnic and hard-to-reach groups residing in multilanguage-speaking settings. %M 25098776 %R 10.2196/mhealth.3058 %U http://mhealth.jmir.org/2014/1/e7/ %U https://doi.org/10.2196/mhealth.3058 %U http://www.ncbi.nlm.nih.gov/pubmed/25098776 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 2 %N 1 %P e3 %T Teenagers and Texting: Use of a Youth Ecological Momentary Assessment System in Trajectory Health Research With Latina Adolescents %A Garcia,Carolyn %A Hardeman,Rachel R %A Kwon,Gyu %A Lando-King,Elizabeth %A Zhang,Lei %A Genis,Therese %A Brady,Sonya S %A Kinder,Elizabeth %+ University of Minnesota, School of Nursing, 5-140 Weaver Densford Hall, 308 Harvard Street SE, Minneapolis, MN, 55455, United States, 1 6126246179, garcia@umn.edu %K texting %K data collection %K intervention research %K longitudinal %K trajectory %D 2014 %7 24.01.2014 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Adolescent females send and receive more text messages than any others, with an average of 4050 texts a month. Despite this technological inroad among adolescents, few researchers are utilizing text messaging technology to collect real time, contextualized data. Temporal variables (ie, mood) collected regularly over a period of time could yield useful insights, particularly for evaluating health intervention outcomes. Use of text messaging technology has multiple benefits, including capacity of researchers to immediately act in response to texted information. Objective: The objective of our study was to custom build a short messaging service (SMS) or text messaging assessment delivery system for use with adolescents. The Youth Ecological Momentary Assessment System (YEMAS) was developed to collect automated texted reports of daily activities, behaviors, and attitudes among adolescents, and to examine the feasibility of YEMAS. This system was created to collect and transfer real time data about individual- and social-level factors that influence physical, mental, emotional, and social well-being. Methods: YEMAS is a custom designed system that interfaces with a cloud-based communication system to automate scheduled delivery of survey questions via text messaging; we designed this university-based system to meet data security and management standards. This was a two-phase study that included development of YEMAS and a feasibility pilot with Latino adolescent females. Relative homogeneity of participants was desired for the feasibility pilot study; adolescent Latina youth were sought because they represent the largest and fastest growing ethnic minority group in the United States. Females were targeted because they demonstrate the highest rate of text messaging and were expected to be interested in participating. Phase I involved development of YEMAS and Phase II involved piloting of the system with Latina adolescents. Girls were eligible to participate if they were attending one of the participating high schools and self-identified as Latina. We contacted 96 adolescents; of these, 24 returned written parental consent forms, completed assent processes, and enrolled in the study. Results: YEMAS was collaboratively developed and implemented. Feasibility was established with Latina adolescents (N=24), who responded to four surveys daily for two two-week periods (four weeks total). Each survey had between 12 and 17 questions, with responses including yes/no, Likert scale, and open-ended options. Retention and compliance rates were high, with nearly 18,000 texts provided by the girls over the course of the pilot period. Conclusions: Pilot results support the feasibility and value of YEMAS, an automated SMS-based text messaging data collection system positioned within a secure university environment. This approach capitalizes on immediate data transfer protocols and enables the documentation of participants’ thoughts, feelings, and behaviors in real time. Data are collected using mobile devices that are familiar to participants and nearly ubiquitous in developed countries. %M 25098355 %R 10.2196/mhealth.2576 %U http://www.mhealth.jmir.org/2014/1/e3/ %U https://doi.org/10.2196/mhealth.2576 %U http://www.ncbi.nlm.nih.gov/pubmed/25098355 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 15 %N 12 %P e269 %T Text Messaging Data Collection for Monitoring an Infant Feeding Intervention Program in Rural China: Feasibility Study %A Li,Ye %A Wang,Wei %A van Velthoven,Michelle Helena %A Chen,Li %A Car,Josip %A Rudan,Igor %A Zhang,Yanfeng %A Wu,Qiong %A Du,Xiaozhen %A Scherpbier,Robert W %+ Department of Integrated Early Childhood Development, Capital Institute of Pediatrics, No. 2 Yabao Road, Chaoyang District, Beijing, 100020, China, 86 1085695554, summyzh@126.com %K text messaging %K data collection %K program evaluation %K child nutrition sciences %D 2013 %7 04.12.2013 %9 Original Paper %J J Med Internet Res %G English %X Background: An effective data collection method is crucial for high quality monitoring of health interventions. The traditional face-to-face data collection method is labor intensive, expensive, and time consuming. With the rapid increase of mobile phone subscribers, text messaging has the potential to be used for evaluation of population health interventions in rural China. Objective: The objective of this study was to explore the feasibility of using text messaging as a data collection tool to monitor an infant feeding intervention program. Methods: Participants were caregivers of children aged 0 to 23 months in rural China who participated in an infant feeding health education program. We used the test-retest method. First, we collected data with a text messaging survey and then with a face-to-face survey for 2 periods of 3 days. We compared the response rate, data agreement, costs, and participants’ acceptability of the two methods. Also, we interviewed participants to explore their reasons for not responding to the text messages and the reasons for disagreement in the two methods. In addition, we evaluated the most appropriate time during the day for sending text messages. Results: We included 258 participants; 99 (38.4%) participated in the text messaging survey and 177 (68.6%) in the face-to-face survey. Compared with the face-to-face survey, the text messaging survey had much lower response rates to at least one question (38.4% vs 68.6%) and to all 7 questions (27.9% vs 67.4%) with moderate data agreement (most kappa values between .5 and .75, the intraclass correlation coefficients between .53 to .72). Participants who took part in both surveys gave the same acceptability rating for both methods (median 4.0 for both on a 5-point scale, 1=disliked very much and 5=liked very much). The costs per questionnaire for the text messaging method were much lower than the costs for the face-to-face method: ¥19.7 (US $3.13) versus ¥33.9 (US $5.39) for all questionnaires, and ¥27.1 (US $4.31) versus ¥34.4 (US $5.47) for completed questionnaires. The main reasons for not replying were that participants did not receive text messages, they were too busy to reply, or they did not see text messages in time. The main reasons for disagreement in responses were that participants forgot their answers in the text messaging survey and that they changed their minds. We found that participants were more likely to reply to text messages immediately during 2 time periods: 8 AM to 3 PM and 8 PM to 9 PM. Conclusions: The text messaging method had reasonable data agreement and low cost, but a low response rate. Further research is needed to evaluate effectiveness of measures that can increase the response rate, especially in collecting longitudinal data by text messaging. %M 24305514 %R 10.2196/jmir.2906 %U http://www.jmir.org/2013/12/e269/ %U https://doi.org/10.2196/jmir.2906 %U http://www.ncbi.nlm.nih.gov/pubmed/24305514 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 1 %N 2 %P e21 %T Differences in Trunk Accelerometry Between Frail and Nonfrail Elderly Persons in Sit-to-Stand and Stand-to-Sit Transitions Based on a Mobile Inertial Sensor %A Galán-Mercant,Alejandro %A Cuesta-Vargas,Antonio I %+ Faculty of Health Sciences, Department of Physiotherapy, University of Malaga, Av. Martiricos s/n 29009 Malaga, Malaga, , Spain, 34 0034667455544, acuesta@uma.es %K frail syndrome %K sit-to-stand %K stand-to-sit %K mobile phone %K inertial sensor %D 2013 %7 16.08.2013 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Clinical frailty syndrome is a common geriatric syndrome, which is characterized by physiological reserve decreases and increased vulnerability. The changes associated to ageing and frailties are associated to changes in gait characteristics and the basic functional capacities. Traditional clinical evaluation of Sit-to-Stand (Si-St) and Stand-to-Sit (St-Si) transition is based on visual observation of joint angle motion to describe alterations in coordination and movement pattern. The latest generation smartphones often include inertial sensors with subunits such as accelerometers and gyroscopes, which can detect acceleration. Objective: Firstly, to describe the variability of the accelerations, angular velocity, and displacement of the trunk during the Sit-to-Stand and Stand-to-Sit transitions in two groups of frail and physically active elderly persons, through instrumentation with the iPhone 4 smartphone. Secondly, we want to analyze the differences between the two study groups. Methods: A cross-sectional study that involved 30 subjects over 65 years, 14 frail and 16 fit subjects. The participants were classified with frail syndrome by the Fried criteria. Linear acceleration was measured along three orthogonal axes using the iPhone 4 accelerometer. Each subject performed up to three successive Si-St and St-Si postural transitions using a standard chair with armrest. Results: Significant differences were found between the two groups of frail and fit elderly persons in the accelerometry and angular displacement variables obtained in the kinematic readings of the trunk during both transitions. Conclusions: The inertial sensor fitted in the iPhone 4 is able to study and analyze the kinematics of the Si-St and St-Si transitions in frail and physically active elderly persons. The accelerometry values for the frail elderly are lower than for the physically active elderly, while variability in the readings for the frail elderly is also lower than for the control group. %M 25098977 %R 10.2196/mhealth.2710 %U http://mhealth.jmir.org/2013/2/e21/ %U https://doi.org/10.2196/mhealth.2710 %U http://www.ncbi.nlm.nih.gov/pubmed/25098977 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 15 %N 6 %P e116 %T Collecting Maternal Health Information From HIV-Positive Pregnant Women Using Mobile Phone-Assisted Face-to-Face Interviews in Southern Africa %A van Heerden,Alastair %A Norris,Shane %A Tollman,Stephen %A Richter,Linda %A Rotheram-Borus,Mary Jane %+ Human Sciences Research Council, PO Box 90, Msunduzi, Pietermaritzburg, 3201, South Africa, 27 333245015, avanheerden@hsrc.ac.za %K mobile phones %K human immunodeficiency virus %K mobile health %D 2013 %7 10.06.2013 %9 Original Paper %J J Med Internet Res %G English %X Background: Most of the world’s women living with human immunodeficiency virus (HIV) reside in sub-Saharan Africa. Although efforts to reduce mother-to-child transmission are underway, obtaining complete and accurate data from rural clinical sites to track progress presents a major challenge. Objective: To describe the acceptability and feasibility of mobile phones as a tool for clinic-based face-to-face data collection with pregnant women living with HIV in South Africa. Methods: As part of a larger clinic-based trial, 16 interviewers were trained to conduct mobile phone–assisted personal interviews (MPAPI). These interviewers (participant group 1) completed the same short questionnaire based on items from the Technology Acceptance Model at 3 different time points. Questions were asked before training, after training, and 3 months after deployment to clinic facilities. In addition, before the start of the primary intervention trial in which this substudy was undertaken, 12 mothers living with HIV (MLH) took part in a focus group discussion exploring the acceptability of MPAPI (participant group 2). Finally, a sample of MLH (n=512) enrolled in the primary trial were asked to assess their experience of being interviewed by MPAPI (participant group 3). Results: Acceptability of the method was found to be high among the 16 interviewers in group 1. Perceived usefulness was reported to be slightly higher than perceived ease of use across the 3 time points. After 3 months of field use, interviewer perceptions of both perceived ease of use and perceived usefulness were found to be higher than before training. The feasibility of conducting MPAPI interviews in this setting was found to be high. Network coverage was available in all clinics and hardware, software, cost, and secure transmission to the data center presented no significant challenges over the 21-month period. For the 12 MHL participants in group 2, anxiety about the multimedia capabilities of the phone was evident. Their concern centered on the possibility that their privacy may be invaded by interviewers using the mobile phone camera to photograph them. For participants in group 3, having the interviewer sit beside vs across from the interviewee during the MPAPI interview was received positively by 94.7% of MHL. Privacy (6.3%) and confidentiality (5.3%) concerns were low for group 3 MHL. Conclusions: Mobile phones were found both to be acceptable and feasible in the collection of maternal and child health data from women living with HIV in South Africa. Trial Registration: Clinicaltrials.gov NCT00972699; http://clinicaltrials.gov/ct2/show/NCT00972699 (Archived by WebCite at http://clinicaltrials.gov/ct2/show/NCT00972699) %M 23748182 %R 10.2196/jmir.2207 %U http://www.jmir.org/2013/6/e116/ %U https://doi.org/10.2196/jmir.2207 %U http://www.ncbi.nlm.nih.gov/pubmed/23748182 %0 Journal Article %@ 1438-8871 %I Gunther Eysenbach %V 15 %N 3 %P e51 %T Development and Testing of a Multidimensional iPhone Pain Assessment Application for Adolescents with Cancer %A Stinson,Jennifer N %A Jibb,Lindsay A %A Nguyen,Cynthia %A Nathan,Paul C %A Maloney,Anne Marie %A Dupuis,L Lee %A Gerstle,J Ted %A Alman,Benjamin %A Hopyan,Sevan %A Strahlendorf,Caron %A Portwine,Carol %A Johnston,Donna L %A Orr,Mike %+ The Hospital for Sick Children, 555 University Ave, Toronto, ON, , Canada, 1 416 813 8501 ext 4514, jennifer.stinson@sickkids.ca %K neoplasms %K pain %K child %K adolescent %K youth %K cellular phone %K game %D 2013 %7 08.03.2013 %9 Original Paper %J J Med Internet Res %G English %X Background: Pain is one of the most common and distressing symptoms reported by adolescents with cancer. Despite advancements in pain assessment and management research, pain due to cancer and/or its treatments continues to be poorly managed. Our research group has developed a native iPhone application (app) called Pain Squad to tackle the problem of poorly managed pain in the adolescent with cancer group. The app functions as an electronic pain diary and is unique in its ability to collect data on pain intensity, duration, location, and the impact pain has on an adolescent’s life (ie, relationships, school work, sleep, mood). It also evaluates medications and other physical and psychological pain management strategies used. Users are prompted twice daily at configurable times to complete 20 questions characterizing their pain and the app transmits results to a database for aggregate reporting through a Web interface. Each diary entry represents a pain case filed by an adolescent with cancer and a reward system (ie, moving up through law-enforcement team ranks, built-in videotaped acknowledgements from fictitious officers) encourages consistent use of the diary. Objective: Our objective was to design, develop, and test the usability, feasibility, compliance, and satisfaction of a game-based smartphone pain assessment tool for adolescents with cancer. Methods: We used both low- and high-fidelity qualitative usability testing with qualitative semi-structured, audio-taped interviews and iterative cycles to design and refine the iPhone based Pain Squad app. Qualitative thematic analysis of interviews using constant comparative methodology captured emergent themes related to app usability. Content validity was assessed using question importance-rating surveys completed by participants. Compliance and satisfaction data were collected following a 2-week feasibility trial where users were alarmed to record their pain twice daily on the app. Results: Thematic analysis of usability interviews showed the app to be appealing overall to adolescents. Analyses of both low- and high-fidelity testing resulted in minor revisions to the app to refine the theme and improve its usability. Adolescents resoundingly endorsed the game-based nature of the app and its virtual reward system. The importance of app pain diary questions was established by content validity analysis. Compliance with the app, assessed during feasibility testing, was high (mean 81%, SD 22%) and adolescents from this phase of the study found the app likeable, easy to use, and not bothersome to complete. Conclusions: A multifaceted usability approach demonstrated how the Pain Squad app could be made more appealing to children and adolescents with cancer. The game-based nature and built-in reward system of the app was appealing to adolescents and may have resulted in the high compliance rates and satisfaction ratings observed during clinical feasibility testing. %M 23475457 %R 10.2196/jmir.2350 %U http://www.jmir.org/2013/3/e51/ %U https://doi.org/10.2196/jmir.2350 %U http://www.ncbi.nlm.nih.gov/pubmed/23475457 %0 Journal Article %@ 1438-8871 %I Gunther Eysenbach %V 15 %N 3 %P e54 %T Using Text Messaging to Assess Adolescents' Health Information Needs: An Ecological Momentary Assessment %A Schnall,Rebecca %A Okoniewski,Anastasia %A Tiase,Victoria %A Low,Alexander %A Rodriguez,Martha %A Kaplan,Steven %+ Columbia University, School of Nursing, 617 W 168th Street, New York, NY, 10032, United States, 1 212 342 6886, rb897@columbia.edu %K text messaging %K ecological momentary assessment %K mobile health technology %D 2013 %7 06.03.2013 %9 Original Paper %J J Med Internet Res %G English %X Background: Use of mobile technology has made a huge impact on communication, access, and information/resource delivery to adolescents. Mobile technology is frequently used by adolescents. Objective: The purpose of this study was to understand the health information needs of adolescents in the context of their everyday lives and to assess how they meet their information needs. Methods: We gave 60 adolescents smartphones with unlimited text messaging and data for 30 days. Each smartphone had applications related to asthma, obesity, human immunodeficiency virus, and diet preinstalled on the phone. We sent text messages 3 times per week and asked the following questions: (1) What questions did you have about your health today? (2) Where did you look for an answer (mobile device, mobile application, online, friend, book, or parent)? (3) Was your question answered and how? (4) Anything else? Results: Our participants ranged from 13-18 years of age, 37 (62%) participants were male and 22 (37%) were female. Of the 60 participants, 71% (42/60) participants identified themselves as Hispanic and 77% (46/60) were frequent users of mobile devices. We had a 90% (1935/2150) response rate to our text messages. Participants sent a total of 1935 text messages in response to the ecological momentary assessment questions. Adolescents sent a total of 421 text messages related to a health information needs, and 516 text messages related to the source of information to the answers of their questions, which were related to parents, friends, online, mobile apps, teachers, or coaches. Conclusions: Text messaging technology is a useful tool for assessing adolescents’ health behavior in real-time. Adolescents are willing to use text messaging to report their health information. Findings from this study contribute to the evidence base on addressing the health information needs of adolescents. In particular, attention should be paid to issues related to diet and exercise. These findings may be the harbinger for future obesity prevention programs for adolescents. %M 23467200 %R 10.2196/jmir.2395 %U http://www.jmir.org/2013/3/e54/ %U https://doi.org/10.2196/jmir.2395 %U http://www.ncbi.nlm.nih.gov/pubmed/23467200