%0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e67438 %T Physical Activity Measurement Reactivity Among Midlife Adults With Elevated Risk for Cardiovascular Disease: Protocol for Coordinated Analyses Across Six Studies %A Baga,Kiri %A Salvatore,Gabrielle M %A Bercovitz,Iris %A Folk,Amanda L %A Singh,Ria %A König,Laura M %A Butryn,Meghan L %A Mogle,Jacqueline A %A Arigo,Danielle %+ Department of Psychology, Rowan University, 201 Mullica Hill Road, Robinson 116G, Glassboro, NJ, 08028, United States, 1 8562564500, arigo@rowan.edu %K physical activity %K measurement reactivity %K gender difference %K cardiovascular risk %K intensive assessment %K midlife %K research participation effects %D 2025 %7 23.4.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Cardiovascular disease (CVD) remains the leading cause of death in the United States, and adults aged 40-60 years with specific health conditions are at particularly elevated risk for developing CVD. Physical activity (PA) is a key cardioprotective behavior and many interventions exist to promote PA in this group. Effective promotion requires accurate assessment of PA behavior; as PA is often estimated by averaging across multiple days, a threat to accurate assessment is measurement reactivity, or an atypical increase in PA behavior at the start of measurement periods that may bias conclusions. Evidence for PA measurement reactivity is equivocal, though concern has resulted in recommendations to add or drop PA measurement days from inclusion, which may introduce undue burden on participants. At present, the extent of PA measurement reactivity and the behaviors most likely to be affected (eg, steps vs minutes of exercise) among those at risk for CVD are unclear, as are participant characteristics such as gender and study expectations (eg, intervention vs observation only) that may contribute to differences in these patterns. Objective: The goal of this study is to improve on the current understanding of the extent of PA measurement reactivity and potential moderators among US adults aged 40-60 years with CVD risk factors. Methods: To achieve this goal, we will conduct coordinated multilevel analyses across 6 studies. Data are from nationally representative, publicly available datasets (observation only: 2 studies) and baseline weeks of observation from behavioral weight loss clinical trials (4 studies), all collected in the United States. The publicly available datasets National Health and Nutrition Examination Survey (NHANES; 2013-2014) and the Midlife in the United States (MIDUS) Study (2004-2009; total n=1385) were used, which are available from the Inter-university Consortium for Political and Social Research website. Behavioral weight loss trials were conducted by the Drexel University Weight Eating and Lifestyle (WELL) Center (2011-2023; total n=444), in person or remotely via Zoom. Relevant data from each study were extracted for adults aged 40-60 years who have ≥1 risk factor for CVD (total n=1832; 11,707 total days of PA measurement with 6-7 days per person). Changes in PA behavior across the measurement period will be examined at the day level, using 2-level multilevel models (days nested within persons) and cross-level interactions (for moderation effects). Results: This project was funded in August 2022 and received supplementary funding in September 2023. Dataset acquisition and data cleaning were completed in October 2024. Analyses are expected to be completed in April 2025, and findings are anticipated to be shared in July 2025. Conclusions: Results from this coordinated analysis project will provide the first large-scale estimation of the extent of PA measurement reactivity in an at-risk group. Findings will inform best practices for mitigating potential measurement reactivity in multiday assessments of PA behavior. International Registered Report Identifier (IRRID): DERR1-10.2196/67438 %M 40267469 %R 10.2196/67438 %U https://www.researchprotocols.org/2025/1/e67438 %U https://doi.org/10.2196/67438 %U http://www.ncbi.nlm.nih.gov/pubmed/40267469 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e59477 %T A Mobile Health App Informed by the Multi-Process Action Control Framework to Promote Physical Activity Among Inactive Adults: Iterative Usability Study %A Hollman,Heather %A Sui,Wuyou %A Zhang,Haowei %A Rhodes,Ryan E %+ Behavioural Medicine Lab, School of Exercise Science, Physical and Health Education, University of Victoria, 3800 Finnerty Rd, Room 173 McKinnon Building, Victoria, BC, V8P 5C2, Canada, 1 250 472 5288, heatherh@uvic.ca %K physical activity %K mobile apps %K mobile health %K mHealth %K usability study %K inactive adults %K smartphone %D 2025 %7 23.4.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Mobile health apps have high potential to address the widespread deficit in physical activity (PA); however, they have demonstrated greater impact on short-term PA compared to long-term PA. The multi-process action control (M-PAC) framework promotes sustained PA behavior by combining reflective (eg, attitudes) and regulatory (eg, planning and emotion regulation) constructs with reflexive (eg, habits and identity) constructs. Usability testing is important to determine the integrity of a mobile health app’s intrinsic properties and suggestions for improvement before feasibility and efficacy testing. Objective: This study aimed to gather usability feedback from end users on a first and a second version of an M-PAC app prototype. Methods: First, 3 workshops and focus groups, with 5 adult participants per group, were conducted to obtain first impressions of the M-PAC app interface and the first 3 lessons. The findings informed several modifications to the app program (eg, added cards with reduced content) and its interface (eg, created a link placeholder image and added a forgot password feature). Subsequently, a single-group pilot usability study was conducted with 14 adults who were not meeting 150 minutes per week of moderate-to-vigorous PA. They used the updated M-PAC app for 2 weeks, participated in semistructured interviews, and completed the Mobile App Usability Questionnaire (MAUQ) to provide usability and acceptability feedback. The focus groups and interviews were recorded, transcribed, and analyzed with content analysis informed by usability heuristics. The MAUQ scores were analyzed descriptively. Results: Participants from the workshops and focus groups (mean age 30.40, SD9.49 years) expressed overall satisfaction with the app layout and content. The language was deemed appropriate; however, some terms (eg, self-efficacy) and acronyms (eg, frequency, intensity, time, and type) needed definitions. Participants provided several recommendations for the visual design (eg, more cards with less text). They experienced challenges in accessing and using the help module and viewing some images, and were unsure how to create or reset the password. Findings from the usability pilot study (mean age 41.38, SD12.92 years; mean moderate-to-vigorous PA 66.07, SD57.92 min/week) revealed overall satisfaction with the app layout (13/13, 100%), content (10/13, 77%), and language (7/11, 64%). Suggestions included more enticing titles and additional and variable forms of content (eg, visual aids and videos). The app was easy to navigate (9/13, 69%); however, some errors were identified, such as PA monitoring connection problems, broken links, and difficulties entering and modifying data. The mean MAUQ total and subscale scores were as follows: total=5.06 (SD1.20), usefulness=4.17 (SD1.31), ease of use=5.36 (SD1.27), and interface and satisfaction=5.52 (SD1.42). Conclusions: Overall, the M-PAC app was deemed usable and acceptable. The findings will inform the development of the minimum viable product, which will undergo subsequent feasibility testing. %M 40267477 %R 10.2196/59477 %U https://formative.jmir.org/2025/1/e59477 %U https://doi.org/10.2196/59477 %U http://www.ncbi.nlm.nih.gov/pubmed/40267477 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e65968 %T Calibration and Validation of Machine Learning Models for Physical Behavior Characterization: Protocol and Methods for the Free-Living Physical Activity in Youth (FLPAY) Study %A LaMunion,Samuel Robert %A Hibbing,Paul Robert %A Crouter,Scott Edward %+ Department of Kinesiology, Recreation, and Sports Studies, The University of Tennessee Knoxville, 1914 Andy Holt Avenue, HPER 343, Knoxville, TN, 37996, United States, 1 865 974 1272, scrouter@utk.edu %K physical behavior assessment %K youth activity %K wearable devices %K activity monitoring %K digital health %K physical behavior characterization %K criterion dataset %D 2025 %7 16.4.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Wearable activity monitors are increasingly used to characterize physical behavior. The development and validation of these characterization methods require criterion-labeled data typically collected in a laboratory or simulated free-living environment, which does not generally translate well to free-living due to limited behavior engagement in development that is not representative of free living. Objective: The Free-Living Physical Activity in Youth (FLPAY) study was designed in 2 parts to establish a criterion dataset for novel method development for identifying periods of transition between activities in youth. Methods: The FLPAY study used criterion measures of behavior (direct observation) and energy expenditure (indirect calorimetry) to label data from research-grade accelerometer-based devices for the purpose of developing and cross-validating models to identify transitions, classify activity type, and estimate energy expenditure in youth aged 6-18 years. The first part of this study was a simulated free-living protocol in the laboratory, comprising short (roughly 60-90 s) and long (roughly 4-5 min) bouts of 16 activities that were completed in various orders over the span of 2 visits. The second part of this study involved an independent sample of participants who agreed to be measured twice (2 hours each time) in free-living environments such as the home and community. Results: The FLPAY study was funded from 2016 to 2020. A no-cost extension was granted for 2021. A few secondary outcomes have been published, but extensive analysis of primary data is ongoing. Conclusions: The 2-part design of the FLPAY study emphasized the collection of naturalistic behaviors and periods of transition between activities in both structured and unstructured environments. This filled an important gap, considering the traditional focus on scripted activity routines in structured laboratory environments. This protocol paper details the FLPAY procedures and participants, along with details about criterion datasets, which will be useful in future studies analyzing the wealth of device-based data in diverse ways. International Registered Report Identifier (IRRID): RR1-10.2196/65968 %M 40239195 %R 10.2196/65968 %U https://www.researchprotocols.org/2025/1/e65968 %U https://doi.org/10.2196/65968 %U http://www.ncbi.nlm.nih.gov/pubmed/40239195 %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 %@ 2561-7605 %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 %@ 1438-8871 %I JMIR Publications %V 27 %N %P e68199 %T Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis %A Wyatt,Bastien %A Forstmann,Nicolas %A Badier,Nolwenn %A Hamy,Anne-Sophie %A De Larochelambert,Quentin %A Antero,Juliana %A Danino,Arthur %A Vercamer,Vincent %A De Villele,Paul %A Vittrant,Benjamin %A Lanz,Thomas %A Reyal,Fabien %A Toussaint,Jean-François %A Delrieu,Lidia %+ , Institute for Research in bioMedicine and Epidemiology of Sport, Université Paris Cité, 11 Avenue du Tremblay, Paris, 75012, France, 33 141744307, lidia.delrieu@insep.fr %K Covid-19 %K pandemic %K physical activity %K step %K activity tracker %K public health %K Withings %K heart rate %K wearable sensors %K sleep duration %K sleep quality %K pre-pandemic %K public health %K sedentary behavior %D 2025 %7 4.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic disrupted behavior within populations, affecting physical activity (PA), heart rate (HR), and sleep characteristics in particular. Activity trackers provide unique insights into these changes, enabling large-scale, real-time monitoring. Objective: This study aims to analyze the associations between the features of the COVID-19 pandemic worldwide and PA, HR, and sleep parameters, using data collected from activity trackers over a 3-year period. Methods: We performed a retrospective analysis using anonymized data collected from the 208,818 users of Withings Steel HR activity trackers, spanning 34 countries, over a 3-year period from January 2019 to March 2022. Key metrics analyzed included daily step counts, average heart rate, and sleep duration. The statistical methods used included descriptive analyses, time-trend analysis, and mixed models to evaluate the impact of restriction measures, controlling for potential confounders such as sex, age, and seasonal variations. Results: We detected a significant decrease in PA, with a 12.3% reduction in daily step count (from 5802 to 5082 steps/d) over the 3 years. The proportion of sedentary individuals increased from 38% (n=14,177) in 2019 to 52% (n=19,510) in 2020 and remained elevated at 51% (n=18,972) in 2022, while the proportion of active individuals dropped from 8% (n=2857) to 6% (n=2352) in 2020 before returning to 8% (n=2877) in 2022. In 2022, the global population had not returned to prepandemic PA levels, with a noticeable persistence of inactivity. During lockdowns, HR decreased by 1.5%, which was associated with lower activity levels. Sleep duration increased during restrictions, particularly in the countries with the most severe lockdowns (eg, an increase of 15 min in countries with stringent measures compared to 5 min in less restricted regions). Conclusions: The sustained decrease in PA and its physiological consequences highlight the need for public health strategies to mitigate the long-term effects of the measures taken during the pandemic. Despite the gradual lifting of restrictions, PA levels have not fully recovered, with lasting implications for global health. If similar circumstances arise in the future, priority should be given to measures for effectively increasing PA to counter the increase in sedentary behavior, mitigate health risks, and prevent the rise of chronic diseases. %M 40184182 %R 10.2196/68199 %U https://www.jmir.org/2025/1/e68199 %U https://doi.org/10.2196/68199 %U http://www.ncbi.nlm.nih.gov/pubmed/40184182 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e52694 %T Analysis of Metabolic and Quality-of-Life Factors in Patients With Cancer for a New Approach to Classifying Walking Habits: Secondary Analysis of a Randomized Controlled Trial %A Tak,Yae Won %A Kim,Junetae %A Chung,Haekwon %A Lee,Sae Byul %A Park,In Ja %A Lee,Sei Won %A Jo,Min-Woo %A Lee,Jong Won %A Baek,Seunghee %A Lee,Yura %+ Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Pungnap2-dong, Songpa gu, Seoul, 05505, Republic of Korea, 82 1027216792, haepary@amc.seoul.kr %K telemedicine %K mobile phone %K physical activity %K mobile apps %K mobile health intervention %K cancer %K step count %D 2025 %7 1.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: As the number of people diagnosed with cancer continues to increase, self-management has become crucial for patients recovering from cancer surgery or undergoing chemotherapy. Technology has emerged as a key tool in supporting self-management, particularly through interventions that promote physical activity, which is important for improving health outcomes and quality of life for patients with cancer. Despite the growing availability of digital tools that facilitate physical activity tracking, high-level evidence of their long-term effectiveness remains limited. Objective: This study aimed to investigate the effect of long-term physical activity on patients with cancer by categorizing them into active and inactive groups based on step count time-series data using the mobile health intervention, the Walkon app (Swallaby Co, Ltd.). Methods: Patients with cancer who had previously used the Walkon app in a previous randomized controlled trial were chosen for this study. Walking step count data were acquired from the app users. Biometric measurements, including BMI, waist circumference, blood sugar levels, and body composition, along with quality of life (QOL) questionnaire responses (European Quality of Life 5 Dimensions 5 Level version and Health-related Quality of Life Instrument with 8 Items), were collected during both the baseline and 6-month follow-up at an outpatient clinic. To analyze step count patterns over time, the concept of sample entropy was used for patient clustering, distinguishing between the active walking group (AWG) and the inactive walking group (IWG). Statistical analysis was performed using the Shapiro-Wilk test for normality, with paired t tests for parametric data, Wilcoxon signed-rank tests for nonparametric data, and chi-square tests for categorical variables. Results: The proposed method effectively categorized the AWG (n=137) and IWG (n=75) based on step count trends, revealing significant differences in daily (4223 vs 5355), weekly (13,887 vs 40,247), and monthly (60,178 vs 174,405) step counts. Higher physical activity levels were observed in patients with breast cancer and younger individuals. In terms of biometric measurements, only waist circumference (P=.01) and visceral fat (P=.002) demonstrated a significant improvement exclusively within the AWG. Regarding QOL measurements, aspects such as energy (P=.01), work (P<.003), depression (P=.02), memory (P=.01), and happiness (P=.05) displayed significant improvements solely in the AWG. Conclusions: This study introduces a novel methodology for categorizing patients with cancer based on physical activity using step count data. Although significant improvements were noted in the AWG, particularly in QOL and specific physical metrics, differences in 6-month change between the AWG and IWG were statistically insignificant. These findings highlight the potential of digital interventions in improving outcomes for patients with cancer, contributing valuable insights into cancer care and self-management. Trial Registration: Clinical Research Information Service by Korea Centers for Diseases Control and Prevention, Republic of Korea KCT0005447; https://tinyurl.com/3zc7zvzz %M 40168661 %R 10.2196/52694 %U https://www.jmir.org/2025/1/e52694 %U https://doi.org/10.2196/52694 %U http://www.ncbi.nlm.nih.gov/pubmed/40168661 %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 e64384 %T User Experience With a Personalized mHealth Service for Physical Activity Promotion in University Students: Mixed Methods Study %A Wittmar,Silke %A Frankenstein,Tom %A Timm,Vincent %A Frei,Peter %A Kurpiers,Nicolas %A Wölwer,Stefan %A Schäfer,Axel Georg Meender %+ Faculty of Social Work and Health, HAWK University of Applied Sciences and Arts Hildesheim/Holzminden/Göttingen, Goschentor 1, Hildesheim, 31134, Germany, 49 5121881369, silke.wittmar@hawk.de %K usability testing %K health promotion %K exercise %K smartphone app %K mHealth %K physical activity %K user experience %K user %K university student %K undergraduate %K college %K student %K mixed methods %K physical fitness %K digital intervention %K mobile health %K promote %K engagement %K mobile phone %D 2025 %7 28.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Regular physical activity (PA) is known to offer substantial health benefits, including improved physical fitness, reduced risk of disease, enhanced psychological well-being, and better cognitive performance. Despite these benefits, many university students fail to meet recommended PA levels, risking long-term health consequences. Objective: This study evaluated the user experience (UX) of futur.move, a digital intervention aimed at promoting PA among university students. The service delivers personalized, evidence-based content to foster sustained engagement in PA. Methods: A mixed methods approach was used to evaluate the prototype of futur.move. UX assessments included on-site and online user tests, standardized questionnaires, and online focus groups. A total of 142 university students participated, with 23 joining additional focus groups. Each participant tested the service for 30 minutes. Quantitative data were collected using the User Experience Questionnaire and analyzed descriptively, followed by correlation analysis with variables such as PA level, age, gender, and experience with PA apps. Qualitative insights were gathered from transcribed focus group discussions and analyzed using content-structuring, qualitative content analysis. Quantitative findings were cross-validated with qualitative data. Results: The UX received positive ratings across 4 User Experience Questionnaire scales (range –3 to +3; higher numbers indicate positive UX): attractiveness (median 1.67, IQR 1.04-2.17), perspicuity (median 1.5, IQR 0.5-2), stimulation (median 1.5, IQR 1-2), and novelty (median 1.25, IQR 0.5-2). Weak correlations were found between adherence to World Health Organization guidelines for PA and the perspicuity subscale (η=0.232, P=.04), and between age and the perspicuity (Kendall τb=0.132, P=.03) and stimulation subscales (Kendall τb=0.144, P=.02), and a moderate correlation was found between gender and the novelty subscale (η=0.363, P=.004). Critical feedback from focus group discussions highlighted issues with manual data entry. Qualitative findings aligned with the quantitative results, emphasizing students’ appreciation for the personalized, diverse content and social networking features of futur.move. Conclusions: futur.move demonstrates favorable UX and aligns with student needs, particularly through its personalized content and social features. Improvements should focus on reducing manual data entry and enhancing feature clarity, particularly for the features “your condition” and “goal setting.” While correlations between UX ratings and demographic variables were weak to moderate, they warrant further investigation to better address the diverse target audience. The feedback from the students serves as a basis for further adapting the service to their needs and expectations. Future work will involve coding an advanced prototype and conducting a longitudinal study to assess its impact on PA behavior and sustained engagement. %M 40153787 %R 10.2196/64384 %U https://formative.jmir.org/2025/1/e64384 %U https://doi.org/10.2196/64384 %U http://www.ncbi.nlm.nih.gov/pubmed/40153787 %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 %@ 2561-1011 %I JMIR Publications %V 9 %N %P e67110 %T Wrist-Worn and Arm-Worn Wearables for Monitoring Heart Rate During Sedentary and Light-to-Vigorous Physical Activities: Device Validation Study %A Schweizer,Theresa %A Gilgen-Ammann,Rahel %K validity %K reliability %K accuracy %K wearable devices %K wearing position %K photoplethysmography %K heart rate %D 2025 %7 21.3.2025 %9 %J JMIR Cardio %G English %X Background: Heart rate (HR) is a vital physiological parameter, serving as an indicator of homeostasis and a key metric for monitoring cardiovascular health and physiological responses. Wearable devices using photoplethysmography (PPG) technology provide noninvasive HR monitoring in real-life settings, but their performance may vary due to factors such as wearing position, blood flow, motion, and device updates. Therefore, ongoing validation of their accuracy and reliability across different activities is essential. Objectives: This study aimed to assess the accuracy and reliability of the HR measurement from the PPG-based Polar Verity Sense and the Polar Vantage V2 devices across a range of physical activities and intensities as well as wearing positions (ie, upper arm, forearm, and both wrists). Methods: Sixteen healthy participants were recruited to participate in this study protocol, which involved 9 activities of varying intensities, ranging from lying down to high-intensity interval training, each repeated twice. The HR measurements from the Verity Sense and Vantage V2 were compared with the criterion measure Polar H10 electrocardiogram (ECG) chest strap. The data were processed to eliminate artifacts and outliers. Accuracy and reliability were assessed using multiple statistical methods, including systematic bias (mean of differences), mean absolute error (MAE) and mean absolute percentage error (MAPE), Pearson product moment correlation coefficient (r), Lin concordance correlation coefficient (CCC), and within-subject coefficient of variation (WSCV). Results: All 16 participants (female=7; male=9; mean 27.4, SD 5.8 years) completed the study. The Verity Sense, worn on the upper arm, demonstrated excellent accuracy across most activities, with a systematic bias of −0.05 bpm, MAE of 1.43 bpm, MAPE of 1.35%, r=1.00, and CCC=1.00. It also demonstrated high reliability across all activities with a WSCV of 2.57% and no significant differences between the 2 sessions. The wrist-worn Vantage V2 demonstrated moderate accuracy with a slight overestimation compared with the ECG and considerable variation in accuracy depending on the activity. For the nondominant wrist, it demonstrated a systematic bias of 2.56 bpm, MAE of 6.41 bpm, MAPE 6.82%, r=0.93, and CCC=0.92. Reliability varied considerably, ranging from a WSCV of 3.64% during postexercise sitting to 23.03% during lying down. Conclusions: The Verity Sense was found to be highly accurate and reliable, outperforming many other wearable HR devices and establishing itself as a strong alternative to ECG-based chest straps, especially when worn on the upper arm. The Vantage V2 was found to have moderate accuracy, with performance highly dependent on activity type and intensity. While it exhibited greater variability and limitations at lower HR, it performed better at higher intensities and outperformed several wrist-worn devices from previous research, particularly during vigorous activities. These findings highlight the importance of device selection and wearing position to ensure the highest possible accuracy in the intended context. %R 10.2196/67110 %U https://cardio.jmir.org/2025/1/e67110 %U https://doi.org/10.2196/67110 %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 %@ 1929-0748 %I JMIR Publications %V 14 %N %P e67972 %T Acceptability and Preliminary Efficacy of a Novel Web-Based Physical Activity for the Heart (PATH) Intervention Designed to Promote Physical Activity in Adults With Obesity: Protocol for a Pilot Randomized Controlled Trial %A Kariuki,Jacob %A Burke,Lora %A Erickson,Kirk %A Sereika,Susan %A Paul,Sudeshna %A Cheng,Jessica %A Biza,Heran %A Abdirahman,Amjad %A Wilbraham,Katherine %A Milton,Heather %A Brown,Cornelius %A Sells,Matthew %A Osei Baah,Foster %A Wells,Jessica %A Chandler,Rasheeta %A Barone Gibbs,Bethany %+ Emory University, 1520 Clifton Rd, Atlanta, GA, 30322, United States, 1 4047272353, jacob.kariuki@emory.edu %K obesity %K physical activity %K cardiometabolic risk, body positivity, cardiovascular fitness, self-efficacy %D 2025 %7 18.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Even in the absence of weight loss, any level of physical activity (PA) can reduce the risk of cardiovascular disease among individuals with obesity. However, these individuals face multifaceted barriers that reduce their motivation and engagement in PA. They prefer programs that are convenient, fun to engage in, and feature people who they can relate to. Yet, there is a paucity of PA interventions that are designed to incorporate these preferences. We designed the web-based PA for The Heart (PATH) intervention to address this gap. Objective: This study aimed to describe the protocol of a study that aims to examine the acceptability and preliminary efficacy of PATH intervention among insufficiently active adults with obesity aged at least 18 years. Methods: This is a 6-month pilot randomized controlled trial (RCT), using a parallel design with 1:1 allocation to intervention or control group. The PATH intervention group is given access to the PATH platform, but the resources each participant can access are tailored according to their baseline fitness level. Control group receives a self-help PA handout. Both groups self-monitor their PA using Fitbit (Google) and have Zoom (Zoom Video Communications) meetings twice a month with either the health coach (intervention) or study coordinator (control). The outcomes at 6-months include acceptability, changes in PA, and cardiometabolic risk from baseline to 6-months. Results: We screened 763 individuals for eligibility and 89 participants were enrolled and randomized to the intervention (45/504, 50.6%) and control arms (44/504, 49.4%). The average age was 48.7 (SD 12.17) years, and most participants were female (81/504, 90.1%), Black (45/504, 50.6%), and non-Hispanic (83/504, 93.3%). No systematic differences in baseline characteristics were observed between the study arms. The 6-month intervention is currently underway, and the completion of follow-up data collection is expected in February 2025, with results to be published soon after. Conclusions: The PATH intervention offers a promising, evidence-based approach to overcoming the barriers that have hindered previous PA programs for adults with obesity. It can support new and existing programs to foster long-term maintenance of health-enhancing PA. Trial Registration: ClinicalTrials.gov NCT05803304; https://clinicaltrials.gov/study/NCT05803304 International Registered Report Identifier (IRRID): DERR1-10.2196/67972 %M 40101744 %R 10.2196/67972 %U https://www.researchprotocols.org/2025/1/e67972 %U https://doi.org/10.2196/67972 %U http://www.ncbi.nlm.nih.gov/pubmed/40101744 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63153 %T Reliability of Average Daily Steps Measured Through a Consumer Smartwatch in Parkinson Disease Phenotypes, Stages, and Severities: Cross-Sectional Study %A Bianchini,Edoardo %A Rinaldi,Domiziana %A De Carolis,Lanfranco %A Galli,Silvia %A Alborghetti,Marika %A Hansen,Clint %A Suppa,Antonio %A Salvetti,Marco %A Pontieri,Francesco Ernesto %A Vuillerme,Nicolas %K gait %K Parkinson disease %K phenotype %K wearable sensors %K smartwatch %K step count %K reliability %K activity monitor %K digital health technology %K digital outcome measures %K wearable %K mHealth %K motor %K quality of life %K fall %K posture %K mobile health %D 2025 %7 18.3.2025 %9 %J JMIR Form Res %G English %X Background: Average daily steps (avDS) could be a valuable indicator of real-world ambulation in people with Parkinson disease (PD), and previous studies have reported the validity and reliability of this measure. Nonetheless, no study has considered disease phenotype, stage, and severity when assessing the reliability of consumer wrist-worn devices to estimate daily step count in unsupervised, free-living conditions in PD. Objective: This study aims to assess and compare the reliability of a consumer wrist-worn smartwatch (Garmin Vivosmart 4) in counting avDS in people with PD in unsupervised, free-living conditions among disease phenotypes, stages, and severity groups. Methods: A total of 104 people with PD were monitored through Garmin Vivosmart 4 for 5 consecutive days. Total daily steps were recorded and avDS were calculated. Participants were dichotomized into tremor dominant (TD; n=39) or postural instability and gait disorder (PIGD; n=65), presence (n=57) or absence (n=47) of tremor, and mild (n=65) or moderate (n=39) disease severity. Based on the modified Hoehn and Yahr scale (mHY), participants were further dichotomized into earlier (mHY 1‐2; n=68) or intermediate (mHY 2.5‐3; n=36) disease stages. Intraclass correlation coefficient (ICC; 3,k), standard error of measurement (SEM), and minimal detectable change (MDC) were used to evaluate the reliability of avDS for each subgroup. The threshold for acceptability was set at an ICC ≥0.8 with a lower bound of 95% CI ≥0.75. The 2-tailed Student t tests for independent groups and analysis of 83.4% CI overlap were used to compare ICC between each group pair. Results: Reliability of avDS measured through Garmin Vivosmart 4 for 5 consecutive days in unsupervised, free-living conditions was acceptable in the overall population with an ICC of 0.89 (95% CI 0.85‐0.92), SEM below 10%, and an MDC of 1580 steps per day (27% of criterion). In all investigated subgroups, the reliability of avDS was also acceptable (ICC range 0.84‐0.94). However, ICCs were significantly lower in participants with tremor (P=.03), with mild severity (P=.04), and earlier stage (P=.003). Moreover, SEM was below 10% in participants with PIGD phenotype, without tremor, moderate disease severity, and intermediate disease stage, with an MDC ranging from 1148 to 1687 steps per day (18%‐25% of criterion). Conversely, in participants with TD phenotype, tremor, mild disease severity, and earlier disease stage, SEM was >10% of the criterion and MDC values ranged from 1401 to 2263 steps per day (30%‐33% of the criterion). Conclusions: In mild-to-moderate PD, avDS measured through a consumer smartwatch in unsupervised, free-living conditions for 5 consecutive days are reliable irrespective of disease phenotype, stage, and severity. However, in individuals with TD phenotype, tremor, mild disease severity, and earlier disease stages, reliability could be lower. These findings could facilitate a broader and informed implementation of avDS as an index of ambulatory activity in PD. %R 10.2196/63153 %U https://formative.jmir.org/2025/1/e63153 %U https://doi.org/10.2196/63153 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 12 %N %P e58715 %T Preadolescent Children Using Real-Time Heart Rate During Moderate to Vigorous Physical Activity: A Feasibility Study %A Lu,Lincoln %A Jake-Schoffman,Danielle E %A Lavoie,Hannah A %A Agharazidermani,Maedeh %A Boyer,Kristy Elizabeth %+ LearnDialogue Lab, Computer and Information Science and Engineering, University of Florida, 1889 Museum Road, Gainesville, FL, 32611, United States, 1 352 392 1133, lincolnlu@ufl.edu %K smartphone app %K physical activity %K heart rate %K wearable sensors %K youth %K commercial wearable device %K Garmin %K mobile phone %D 2025 %7 6.3.2025 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Given the global burden of insufficient physical activity (PA) in children, effective behavioral interventions are needed to increase PA levels. Novel technologies can help expand the reach and accessibility of these programs. Despite the potential to use heart rate (HR) to target moderate- to vigorous-intensity PA (MVPA), most HR research to date has focused on the accuracy of HR devices or used HR for PA surveillance rather than as an intervention tool. Furthermore, most commercial HR sensors are designed for adults, and their suitability for children is unknown. Further research about the feasibility and usability of commercial HR devices is required to understand how children may use HR during PA. Objective: This study aimed to explore the use of a chest-worn HR sensor paired with a real-time HR display as an intervention tool among preadolescent children and the usability of a custom-designed app (Connexx) for viewing real-time HR. Methods: We developed Connexx, an HR information display app with an HR analytics portal to view HR tracking. Children were recruited via flyers distributed at local public schools, word of mouth, and social media posts. Eligible participants were children aged 9 to 12 years who did not have any medical contraindications to MVPA. Participants took part in a single in-person study session where they monitored their own HR using a commercial HR sensor, learned about HR, and engaged in a series of PAs while using the Connexx app to view their real-time HR. We took field note observations about participant interactions with the HR devices. Participants engaged in a semistructured interview about their experience using Connexx and HR during PA and completed the System Usability Scale (SUS) about the Connexx app. Study sessions were audio and video recorded and transcribed verbatim. Results: A total of 11 participants (n=6, 55% male; n=9, 82%, non-Hispanic White) with an average age of 10.4 (SD 1.0) years were recruited for the study. Data from observations, interviews, and SUS indicated that preadolescent children can use real-time HR information during MVPA. Observational and interview data indicated that the participants were able to understand their HR after a basic lesson and demonstrated the ability to make use of their HR information during PA. Interview and SUS responses demonstrated that the Connexx app was highly usable, despite some accessibility challenges (eg, small display font). Feedback about usability issues has been incorporated into a redesign of the Connexx app, including larger, color-coded fonts for HR information. Conclusions: The results of this study indicate that preadolescent children understood their HR data and were able to use it in real time during PA. The findings suggest that future interventions targeting MVPA in this population should test strategies to use HR and HR monitoring as direct program targets. %M 40053729 %R 10.2196/58715 %U https://humanfactors.jmir.org/2025/1/e58715 %U https://doi.org/10.2196/58715 %U http://www.ncbi.nlm.nih.gov/pubmed/40053729 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 13 %N %P e64527 %T Comparative Effectiveness of Wearable Devices and Built-In Step Counters in Reducing Metabolic Syndrome Risk in South Korea: Population-Based Cohort Study %A Joung,Kyung-In %A An,Sook Hee %A Bang,Joon Seok %A Kim,Kwang Joon %K wearable devices %K built-in step counters %K mobile health %K public health intervention %K physical activity %K health behavior %K metabolic syndrome %K population-based %K cohort study %K South Korea %K mobile health technologies %K effectiveness %K activity tracker %K mobile app %K retrospective %K logistic regression %K mHealth %K digital health %K mobile phone %D 2025 %7 25.2.2025 %9 %J JMIR Mhealth Uhealth %G English %X Background: Mobile health technologies show promise in addressing metabolic syndrome, but their comparative effectiveness in large-scale public health interventions remains unclear. Objective: This study aims to compare the effectiveness of wearable devices (wearable activity trackers) and mobile app–based activity trackers (built-in step counters) in promoting walking practice, improving health behaviors, and reducing metabolic syndrome risk within a national mobile health care program operated by the Korea Health Promotion Institute. Methods: This retrospective cohort study analyzed data from 46,579 participants in South Korea’s national mobile health care program (2020‐2022). Participants used wearable devices for 12 weeks, after which some switched to built-in step counters. The study collected data on demographics, health behaviors, and metabolic syndrome risk factors at baseline, 12 weeks, and 24 weeks. Outcomes included changes in walking practice, health behaviors, and metabolic syndrome risk factors. Metabolic syndrome risk was assessed based on 5 factors: blood pressure, fasting glucose, waist circumference, triglycerides, and high-density lipoprotein cholesterol. Health behaviors included low-sodium diet preference, nutrition label reading, regular breakfast consumption, aerobic physical activity, and regular walking. To address potential selection bias, propensity score matching was performed, balancing the 2 groups on baseline characteristics including age, gender, education level, occupation, insurance type, smoking status, and alcohol consumption. Results: Both wearable activity tracker and built-in step counter groups exhibited significant improvements across all evaluated outcomes. The improvement rates for regular walking practice, health behavior changes, and metabolic syndrome risk reduction were high in both groups, with percentages ranging from 45.2% to 60.8%. After propensity score matching, both device types showed substantial improvements across all indicators. The built-in step counter group demonstrated greater reductions in metabolic syndrome risk compared to the wearable device group (odds ratio [OR] 1.20, 95% CI 1.05‐1.36). No significant differences were found in overall health behavior improvements (OR 0.95, 95% CI 0.83‐1.09) or walking practice (OR 0.84, 95% CI 0.70‐1.01) between the 2 groups. Age-specific subgroup analyses revealed that the association between built-in step counters and metabolic syndrome risk reduction was more pronounced in young adults aged 19‐39 years (OR 1.35, 95% CI 1.09‐1.68). Among Android use subgroups, built-in step counters were associated with a higher reduction in health risk factors (OR 1.20, 95% CI 1.03‐1.39). Conclusions: Both wearable devices and built-in step counters effectively reduced metabolic syndrome risk in a large-scale public health intervention, with built-in step counters showing a slight advantage. The findings suggest that personalized device recommendations based on individual characteristics, such as age and specific health risk factors, may enhance the effectiveness of mobile health interventions. Future research should explore the mechanisms behind these differences and their long-term impacts on health outcomes. %R 10.2196/64527 %U https://mhealth.jmir.org/2025/1/e64527 %U https://doi.org/10.2196/64527 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 11 %N %P e60034 %T Usability and Implementation Considerations of Fitbit and App Intervention for Diverse Cancer Survivors: Mixed Methods Study %A Dabbagh,Zakery %A Najjar,Reem %A Kamberi,Ariana %A Gerber,Ben S %A Singh,Aditi %A Soni,Apurv %A Cutrona,Sarah L %A McManus,David D %A Faro,Jamie M %K physical activity %K cancer survivor %K wearable device %K smartphone app %K diverse %K Fitbit %K wearable %K feasibility %K usability %K digital health %K digital health method %K breast cancer %K Hispanic %K women %K mobile health %K activity tracker %K mHealth %D 2025 %7 24.2.2025 %9 %J JMIR Cancer %G English %X Background: Despite the known benefits of physical activity, cancer survivors remain insufficiently active. Prior trials have adopted digital health methods, although several have been pedometer-based and enrolled mainly female, non-Hispanic White, and more highly educated survivors of breast cancer. Objective: The objective of this study was to test a previously developed mobile health system consisting of a Fitbit activity tracker and the MyDataHelps smartphone app for feasibility in a diverse group of cancer survivors, with the goal of refining the program and setting the stage for a larger future trial. Methods: Participants were identified from one academic medical center’s electronic health records, referred by a clinician, or self-referred to participate in the study. Participants were screened for eligibility, enrolled, provided a Fitbit activity tracker, and instructed to download the Fitbit: Health & Wellness and MyDataHelps apps. They completed usability surveys at 1 and 3 months. Interviews were conducted at the end of the 3-month intervention with participants and cancer care clinicians to assess the acceptability of the intervention and the implementation of the intervention into clinical practice, respectively. Descriptive statistics were calculated for demographics, usability surveys, and Fitbit adherence and step counts. Rapid qualitative analysis was used to identify key findings from interview transcriptions. Results: Of the 100 patients screened for eligibility, 31 were enrolled in the trial (mean age 64.8, SD 11.1 years; female patients=17/31, 55%; Hispanic or Latino=7/31, 23%; non-White=11/31, 35%; less than a bachelor’s degree=14/31, 45%; and household income 600 minutes/day. The mean (SD) number of steps was 8883 (3455) steps/day and the mean (SD) awake sedentary time was 564 (138) minutes/day. Male participants were more often engaged in very active (27 minutes/day) and moderately active physical activity (29 minutes/day) compared with female participants (15 and 17 minutes/day, respectively). Over 87% (588/677) of participants had sleep data available for 5 or more days, among whom the average nightly sleep duration was 7.9 (SD 0.9) hours. Conclusions: This study demonstrated the feasibility of using consumer wearable devices to measure physical activity and sleep in a cohort of US adolescents. The high compliance rates provide valuable insights into adolescent behavior patterns and their influence on chronic disease development and mental health outcomes. %R 10.2196/59159 %U https://pediatrics.jmir.org/2025/1/e59159 %U https://doi.org/10.2196/59159 %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 %@ 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 e62944 %T Exploring the Use of Smartwatches and Activity Trackers for Health-Related Purposes for Children Aged 5 to 11 years: Systematic Review %A Thompson,Lauren %A Charitos,Sydney %A Bird,Jon %A Marshall,Paul %A Brigden,Amberly %+ University of Bristol, 1 Cathedral Square, Trinity Street, Bristol, BS1 5DD, United Kingdom, 44 01173746653, lauren.thompson@bristol.ac.uk %K children %K systematic review %K wearable activity trackers %K smartwatches %K feasibility %K mobile phone %D 2025 %7 27.1.2025 %9 Review %J J Med Internet Res %G English %X Background:  Digital health interventions targeting behavior change are promising in adults and adolescents; however, less attention has been given to younger children. The proliferation of wearables, such as smartwatches and activity trackers, that support the collection of and reflection on personal health data highlights an opportunity to consider novel approaches to supporting health in young children (aged 5-11 y). Objective:  This review aims to investigate how smartwatches and activity trackers have been used across child health interventions (for children aged 5-11 y) for different health areas, specifically to identify the population characteristics of those being targeted, describe the characteristics of the devices being used, and report the feasibility and acceptability of these devices for health-related applications with children. Methods: We searched 10 databases (CINAHL, Embase, ACM Digital Library, IEEE Xplore, Cochrane Library, PsycINFO, Web of Science, PubMed, Scopus, and MEDLINE) to identify relevant literature in March 2023. The inclusion criteria for studies were as follows: (1) peer-reviewed, empirical studies; (2) published in English; (3) involved a child aged 5 to 11 years using a smartwatch for health-related purposes. Two researchers independently screened articles to assess eligibility. One researcher extracted data relating to the 3 aims and synthesized the results using narrative and thematic synthesis. Results:  The database searches identified 3312 articles, of which 15 (0.45%) were included in this review. Three (20%) articles referred to the same intervention. In 77% (10/13) of the studies, the devices were used to target improvements in physical activity. Other applications included using smartwatches to deliver interventions for emotional regulation and asthma management. In total, 9 commercial devices were identified, many of which delivered minimal data feedback on the smartwatch or activity tracker, instead relying on a partner app running on a linked parental smartphone with greater functionality. Of the 13 studies, 8 (62%) used devices designed for adults rather than children. User feedback was positive overall, demonstrating the acceptability and feasibility of using these devices with children. However, the studies often lacked a child-focused approach, with 3 (23%) studies gathering user feedback only from parents. Conclusions:  Interventions involving smartwatches and activity trackers for children aged 5 to 11 years remain limited, primarily focusing on enhancing physical activity, with few studies investigating other health applications. These devices often provide limited data feedback and functionality to support children’s independent engagement with the data, relying on paired smartphone apps managed by caregivers, who control access and facilitate children’s interaction with the data. Future research should adopt child-centered methods in the design and evaluation of these technologies, integrating children’s perspectives alongside their caregivers, to ensure that they are not only feasible and acceptable but also meaningful and effective for young children. Trial Registration: PROSPERO CRD42022373813, https://tinyurl.com/4kxu8zss %M 39870369 %R 10.2196/62944 %U https://www.jmir.org/2025/1/e62944 %U https://doi.org/10.2196/62944 %U http://www.ncbi.nlm.nih.gov/pubmed/39870369 %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 %@ 2561-7605 %I JMIR Publications %V 8 %N %P e63348 %T Exploring the Feasibility of a 5-Week mHealth Intervention to Enhance Physical Activity and an Active, Healthy Lifestyle in Community-Dwelling Older Adults: Mixed Methods Study %A Daniels,Kim %A Vonck,Sharona %A Robijns,Jolien %A Quadflieg,Kirsten %A Bergs,Jochen %A Spooren,Annemie %A Hansen,Dominique %A Bonnechère,Bruno %+ , Centre of Expertise in Care Innovation, Department of PXL – Healthcare, PXL University of Applied Sciences and Arts, Guffenslaan 39, Hasselt, 3500, Belgium, 32 485763451, kim.daniels@pxl.be %K mobile health %K mHealth %K feasibility %K physical activity %K older adults %K health promotion %K usability %K mobile phone %D 2025 %7 27.1.2025 %9 Original Paper %J JMIR Aging %G English %X Background: Advancements in mobile technology have paved the way for innovative interventions aimed at promoting physical activity (PA). Objective: The main objective of this feasibility study was to assess the feasibility, usability, and acceptability of the More In Action (MIA) app, designed to promote PA among older adults. MIA offers 7 features: personalized tips, PA literacy, guided peer workouts, a community calendar, a personal activity diary, a progression monitor, and a chatbot. Methods: Our study used a mixed methods approach to evaluate the MIA app’s acceptability, feasibility, and usability. First, a think-aloud method was used to provide immediate feedback during initial app use. Participants then integrated the app into their daily activities for 5 weeks. Behavioral patterns such as user session duration, feature use frequency, and navigation paths were analyzed, focusing on engagement metrics and user interactions. User satisfaction was assessed using the System Usability Scale, Net Promoter Score, and Customer Satisfaction Score. Qualitative data from focus groups conducted after the 5-week intervention helped gather insights into user experiences. Participants were recruited using a combination of web-based and offline strategies, including social media outreach, newspaper advertisements, and presentations at older adult organizations and local community services. Our target group consisted of native Dutch-speaking older adults aged >65 years who were not affected by severe illnesses. Initial assessments and focus groups were conducted in person, whereas the intervention itself was web based. Results: The study involved 30 participants with an average age of 70.3 (SD 4.8) years, of whom 57% (17/30) were female. The app received positive ratings, with a System Usability Scale score of 77.4 and a Customer Satisfaction Score of 86.6%. Analysis showed general satisfaction with the app’s workout videos, which were used in 585 sessions with a median duration of 14 (IQR 0-34) minutes per day. The Net Promoter Score was 33.34, indicating a good level of customer loyalty. Qualitative feedback highlighted the need for improvements in navigation, content relevance, and social engagement features, with suggestions for better calendar visibility, workout customization, and enhanced social features. Overall, the app demonstrated high usability and satisfaction, with near-daily engagement from participants. Conclusions: The MIA app shows significant potential for promoting PA among older adults, evidenced by its high usability and satisfaction scores. Participants engaged with the app nearly daily, particularly appreciating the workout videos and educational content. Future enhancements should focus on better calendar visibility, workout customization, and integrating social networking features to foster community and support. In addition, incorporating wearable device integration and predictive analytics could provide real-time health data, optimizing activity recommendations and health monitoring. These enhancements will ensure that the app remains user-friendly, relevant, and sustainable, promoting sustained PA and healthy behaviors among older adults. Trial Registration: ClinicalTrials.gov NCT05650515; https://clinicaltrials.gov/study/NCT05650515 %M 39869906 %R 10.2196/63348 %U https://aging.jmir.org/2025/1/e63348 %U https://doi.org/10.2196/63348 %U http://www.ncbi.nlm.nih.gov/pubmed/39869906 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 11 %N %P e58093 %T Changes in Physical Activity Across Cancer Diagnosis and Treatment Based on Smartphone Step Count Data Linked to a Japanese Claims Database: Retrospective Cohort Study %A Inayama,Yoshihide %A Yamaguchi,Ken %A Mizuno,Kayoko %A Tanaka-Mizuno,Sachiko %A Koike,Ayami %A Higashiyama,Nozomi %A Taki,Mana %A Yamanoi,Koji %A Murakami,Ryusuke %A Hamanishi,Junzo %A Yoshida,Satomi %A Mandai,Masaki %A Kawakami,Koji %+ Department of Gynecology and Obstetrics, Graduate School of Medicine and Faculty of Medicine, Kyoto University, 54 Shogoin Kawahara cho, Sakyo ku, Kyoto, 606-8507, Japan, 81 75 751 3269, soulken@kuhp.kyoto-u.ac.jp %K cancer %K lifelog data %K physical activity %K quality of life %K step count %K Japanese %K database %K smartphone %K mobile app %K exercise %K mobile phone %D 2025 %7 20.1.2025 %9 Original Paper %J JMIR Cancer %G English %X Background: Although physical activity (PA) is recommended for patients with cancer, changes in PA across cancer diagnosis and treatment have not been objectively evaluated. Objective: This study aimed to assess the impact of cancer diagnosis and treatment on PA levels. Methods: This was a retrospective cohort study using a Japanese claims database provided by DeSC Healthcare Inc, in which daily step count data, derived from smartphone pedometers, are linked to the claims data. In this study, we included patients newly diagnosed with cancer, along with those newly diagnosed with diabetes mellitus for reference. We collected data between April 2014 and September 2021 and analyzed them. The observation period spanned from 6 months before diagnosis to 12 months after diagnosis. We applied a generalized additive mixed model with a cubic spline to describe changes in step counts before and after diagnosis. Results: We analyzed the step count data of 326 patients with malignant solid tumors and 1388 patients with diabetes. Patients with cancer exhibited a 9.6% (95% CI 7.1%-12.1%; P<.001) reduction in step counts from baseline at the start of the diagnosis month, which further deepened to 12.4% (95% CI 9.5%-15.2%; P<.001) at 3 months and persisted at 7.1% (95% CI 4.2%-10.0%; P<.001) at 12 months, all relative to baseline. Conversely, in patients with diabetes, step counts remained relatively stable after diagnosis, with a slight upward trend, resulting in a change of +0.6% (95% CI –0.6% to 1.9%; P=.31) from baseline at 3 months after diagnosis. At 12 months after diagnosis, step counts remained decreased in the nonendoscopic subdiaphragmatic surgery group, with an 18.0% (95% CI 9.1%-26.2%; P<.001) reduction, whereas step counts returned to baseline in the laparoscopic surgery group (+0.3%, 95% CI –6.3% to 7.5%; P=.93). Conclusions: The analysis of objective pre- and postdiagnostic step count data provided fundamental information crucial for understanding changes in PA among patients with cancer. While cancer diagnosis and treatment reduced PA, the decline may have already started before diagnosis. The study findings may help tailor exercise recommendations based on lifelog data for patients with cancer in the future. %M 39726139 %R 10.2196/58093 %U https://cancer.jmir.org/2025/1/e58093 %U https://doi.org/10.2196/58093 %U http://www.ncbi.nlm.nih.gov/pubmed/39726139 %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 %@ 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 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 %@ 2561-326X %I JMIR Publications %V 9 %N %P e62910 %T Testing the Recruitment Frequency, Implementation Fidelity, and Feasibility of Outcomes of the Heart Failure Activity Coach Study (HEALTHY): Pilot Randomized Controlled Trial %A Blomqvist,Andreas %A Bäck,Maria %A Klompstra,Leonie %A Strömberg,Anna %A Jaarsma,Tiny %+ Department of Health, Medicine and Caring Sciences, Linköping University, Sandbäcksgatan 7, Linköping, SE-581 83, Sweden, 46 739617729, andreas.blomqvist@liu.se %K heart failure %K disease management %K physical activity %K sedentary %K older adults %K aging %K mobile health %K mHealth %K feasibility %K quality of life %K digital health %K smartphone %D 2025 %7 8.1.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Heart failure (HF) is a common and deadly disease, precipitated by physical inactivity and sedentary behavior. Although the 1-year survival rate after the first diagnosis is high, physical inactivity and sedentary behavior are associated with increased mortality and negatively impact the health-related quality of life (HR-QoL). Objective: We tested the recruitment frequency, implementation fidelity, and feasibility of outcomes of the Activity Coach app that was developed using an existing mobile health (mHealth) tool, Optilogg, to support older adults with HF to be more physically active and less sedentary. Methods: In this pilot clinical randomized controlled trial (RCT), patients with HF who were already using Optilogg to enhance self-care behavior were recruited from 5 primary care health centers in Sweden. Participants were randomized to either have their mHealth tool updated with the Activity Coach app (intervention group) or a sham version (control group). The intervention duration was 12 weeks, and in weeks 1 and 12, the participants wore an accelerometer daily to objectively measure their physical activity. The HR-QoL was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ), and subjective goal attainment was assessed using goal attainment scaling. Baseline data were collected from the participants’ electronic health records (EHRs). Results: We found 67 eligible people using the mHealth tool, of which 30 (45%) initially agreed to participate, with 20 (30%) successfully enrolled and randomized to the control and intervention groups in a ratio of 1:1. The participants’ daily adherence to registering physical activity in the Activity Coach app was 69% (range 24%-97%), and their weekly adherence was 88% (range 58%-100%). The mean goal attainment score was –1.0 (SD 1.1) for the control group versus 0.6 (SD 0.6) for the intervention group (P=.001). The mean change in the overall HR-QoL summary score was –9 (SD 10) for the control group versus 3 (SD 13) in the intervention group (P=.027). There was a significant difference in the physical limitation scores between the control (mean 45, SD 27) and intervention (mean 71, SD 20) groups (P=.04). The average length of sedentary bouts increased by 27 minutes to 458 (SD 84) in the control group minutes and decreased by 0.70 minutes to 391 (SD 117) in the intervention group (P=.22). There was a nonsignificant increase in the mean light physical activity (LPA): 146 (SD 46) versus 207 (SD 80) minutes in the control and intervention groups, respectively (P=.07). Conclusions: The recruitment rate was lower than anticipated. An active recruitment process is advised if a future efficacy study is to be conducted. Adherence to the Activity Coach app was high, and it may be able to support older adults with HF in being physically active. Trial Registration: ClinicalTrials.gov NCT05235763; https://clinicaltrials.gov/study/NCT05235763 %M 39778202 %R 10.2196/62910 %U https://formative.jmir.org/2025/1/e62910 %U https://doi.org/10.2196/62910 %U http://www.ncbi.nlm.nih.gov/pubmed/39778202 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63529 %T Validation of Sleep Measurements of an Actigraphy Watch: Instrument Validation Study %A Waki,Mari %A Nakada,Ryohei %A Waki,Kayo %A Ban,Yuki %A Suzuki,Ryo %A Yamauchi,Toshimasa %A Nangaku,Masaomi %A Ohe,Kazuhiko %+ Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, 7-chōme-3-1 Hongō, Bunkyo City, Tokyo, 113-8654, Japan, 81 03 5800 9129, kwaki-tky@m.u-tokyo.ac.jp %K actigraphy %K sleep %K Motion Watch 8 %K iAide2 %K total sleep time %D 2025 %7 6.1.2025 %9 Short Paper %J JMIR Form Res %G English %X Background: The iAide2 (Tokai) physical activity monitoring system includes diverse measurements and wireless features useful to researchers. The iAide2’s sleep measurement capabilities have not been compared to validated sleep measurement standards in any published work. Objective: We aimed to assess the iAide2’s sleep duration and total sleep time (TST) measurement performance and perform calibration if needed. Methods: We performed free-living sleep monitoring in 6 convenience-sampled participants without known sleep disorders recruited from within the Waki DTx Laboratory at the Graduate School of Medicine, University of Tokyo. To assess free-living sleep, we validated the iAide2 against a second actigraph that was previously validated against polysomnography, the MotionWatch 8 (MW8; CamNtech Ltd). The participants wore both devices on the nondominant arm, with the MW8 closest to the hand, all day except when bathing. The MW8 and iAide2 assessments both used the MW8 EVENT-marker button to record bedtime and risetime. For the MW8, MotionWare Software (version 1.4.20; CamNtech Ltd) provided TST, and we calculated sleep duration from the sleep onset and sleep offset provided by the software. We used a similar process with the iAide2, using iAide2 software (version 7.0). We analyzed 64 nights and evaluated the agreement between the iAide2 and the MW8 for sleep duration and TST based on intraclass correlation coefficients (ICCs). Results: The absolute ICCs (2-way mixed effects, absolute agreement, single measurement) for sleep duration (0.69, 95% CI –0.07 to 0.91) and TST (0.56, 95% CI –0.07 to 0.82) were moderate. The consistency ICC (2-way mixed effects, consistency, single measurement) was excellent for sleep duration (0.91, 95% CI 0.86-0.95) and moderate for TST (0.78, 95% CI 0.67-0.86). We determined a simple calibration approach. After calibration, the ICCs improved to 0.96 (95% CI 0.94-0.98) for sleep duration and 0.82 (95% CI 0.71-0.88) for TST. The results were not sensitive to the specific participants included, with an ICC range of 0.96-0.97 for sleep duration and 0.79-0.87 for TST when applying our calibration equation to data removing one participant at a time and 0.96-0.97 for sleep duration and 0.79-0.86 for TST when recalibrating while removing one participant at a time. Conclusions: The measurement errors of the uncalibrated iAide2 for both sleep duration and TST seem too large for them to be useful as absolute measurements, though they could be useful as relative measurements. The measurement errors after calibration are low, and the calibration approach is general and robust, validating the use of iAide2’s sleep measurement functions alongside its other features in physical activity research. %M 39761102 %R 10.2196/63529 %U https://formative.jmir.org/2025/1/e63529 %U https://doi.org/10.2196/63529 %U http://www.ncbi.nlm.nih.gov/pubmed/39761102 %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 %@ 2561-326X %I JMIR Publications %V 8 %N %P e59521 %T Accuracy of the Huawei GT2 Smartwatch for Measuring Physical Activity and Sleep Among Adults During Daily Life: Instrument Validation Study %A Mei,Longfei %A He,Ziwei %A Hu,Liang %K smartwatch %K accelerometry %K free-living %K physical activity %K sleep %K validity %D 2024 %7 20.12.2024 %9 %J JMIR Form Res %G English %X Background: Smartwatches are increasingly popular for physical activity and health promotion. However, ongoing validation studies on commercial smartwatches are still needed to ensure their accuracy in assessing daily activity levels, which is important for both promoting activity-related health behaviors and serving research purposes. Objective: This study aimed to evaluate the accuracy of a popular smartwatch, the Huawei Watch GT2, in measuring step count (SC), total daily activity energy expenditure (TDAEE), and total sleep time (TST) during daily activities among Chinese adults, and test whether there are population differences. Methods: A total of 102 individuals were recruited and divided into 2 age groups: young adults (YAs) and middle-aged and older (MAAO) adults. Participants’ daily activity data were collected for 1 week by wearing the Huawei Watch GT2 on their nondominant wrist and the Actigraph GT3X+ (ActiGraph) on their right hip as the reference measure. The accuracy of the GT2 was examined using the intraclass correlation coefficient (ICC), Pearson product-moment correlation coefficient (PPMCC), Bland-Altman analysis, mean percentage error, and mean absolute percentage error (MAPE). Results: The GT2 demonstrated reasonable agreement with the Actigraph, as evidenced by a consistency test ICC of 0.88 (P<.001) and an MAPE of 25.77% for step measurement, an ICC of 0.75 (P<.001) and an MAPE of 33.79% for activity energy expenditure estimation, and an ICC of 0.25 (P<.001) and an MAPE of 23.29% for sleep time assessment. Bland-Altman analysis revealed that the GT2 overestimated SC and underestimated TDAEE and TST. The GT2 was better at measuring SC and TDAEE among YAs than among MAAO adults, and there was no significant difference between these 2 groups in measuring TST (P=.12). Conclusions: The Huawei Watch GT2 demonstrates good accuracy in step counting. However, its accuracy in assessing activity energy expenditure and sleep time measurement needs further examination. The GT2 demonstrated higher accuracy in measuring SC and TDAEE in the YA group than in the MAAO group. However, the measurement errors for TST did not differ significantly between the 2 age groups. Therefore, the watch may be suitable for monitoring several key parameters (eg, SC) of daily activity, yet caution is advised for its use in research studies that require high accuracy. %R 10.2196/59521 %U https://formative.jmir.org/2024/1/e59521 %U https://doi.org/10.2196/59521 %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 %@ 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 e59708 %T Functions of Smartphone Apps and Wearable Devices Promoting Physical Activity: Six-Month Longitudinal Study on Japanese-Speaking Adults %A Konishi,Naoki %A Oba,Takeyuki %A Takano,Keisuke %A Katahira,Kentaro %A Kimura,Kenta %+ Human Informatics and Interaction Research Institute, The National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki, 305-8566, Japan, 81 50 3522 4500, keisuke.takano@aist.go.jp %K mHealth %K mobile health %K smartphone app %K physical activity %K wearable activity tracker %K longitudinal design %K wearable %K Japan %K health promotion %D 2024 %7 10.12.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphone apps and wearable activity trackers are increasingly recognized for their potential to promote physical activity (PA). While studies suggest that the use of commercial mobile health tools is associated with higher PA levels, most existing evidence is cross-sectional, leaving a gap in longitudinal data. Objective: This study aims to identify app-use patterns that are prospectively associated with increases in and maintenance of PA. The primary objective was to test whether continued app use is linked to adherence to the recommended PA levels (ie, 23 metabolic equivalent task [MET] hours per week for adults or 10 MET hours/week for individuals aged >65 years) during a follow-up assessment. The secondary objective was to explore which functions and features of PA apps predict changes in PA levels. Methods: A 2-wave longitudinal survey was conducted, with baseline and follow-up assessments separated by 6 months. A total of 20,573 Japanese-speaking online respondents participated in the baseline survey, and 16,286 (8289 women; mean age 54.7 years, SD 16.8 years) completed the follow-up. At both time points, participants reported their current PA levels and whether they were using any PA apps or wearables. Each participant was classified into 1 of the following 4 categories: continued users (those using apps at both the baseline and follow-up; n=2150, 13.20%), new users (those who started using apps before the follow-up; n=1462, 8.98%), discontinued users (those who had used apps at baseline but not at follow-up; n=1899, 11.66%), and continued nonusers (those who had never used apps; n=10,775, 66.16%). Results: The majority of continued users (1538/2150, 71.53%) either improved or maintained their PA at the recommended levels over 6 months. By contrast, discontinued users experienced the largest reduction in PA (−7.95 MET hours/week on average), with more than half failing to meet the recommended levels at the follow-up (n=968, 50.97%). Analyses of individual app functions revealed that both energy analysis (eg, app calculation of daily energy expenditure) and journaling (eg, users manually entering notes and maintaining an exercise diary) were significantly associated with increases in PA. Specifically, energy analysis was associated with an odds ratio (OR) of 1.67 (95% CI 1.05-2.64, P=.03), and journaling had an OR of 1.76 (95% CI 1.12-2.76, P=.01). By contrast, individuals who maintained the recommended PA levels at the follow-up were more likely to use the goal setting (OR 1.73, 95% CI 1.21-2.48, P=.003), sleep information (OR 1.66, 95% CI 1.03-2.68, P=.04), and blood pressure recording (OR 2.05, 95% CI 1.10-3.83, P=.02) functions. Conclusions: The results highlight the importance of continued app use in both increasing and maintaining PA levels. Different app functions may contribute to these outcomes, with features such as goal setting and journaling playing a key role in increasing PA, while functions related to overall health, such as sleep tracking and blood pressure monitoring, are more associated with maintaining high PA levels. %M 39658011 %R 10.2196/59708 %U https://mhealth.jmir.org/2024/1/e59708 %U https://doi.org/10.2196/59708 %U http://www.ncbi.nlm.nih.gov/pubmed/39658011 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e55925 %T Wearable Devices for Supporting Chronic Disease Self-Management: Scoping Review %A Gagnon,Marie-Pierre %A Ouellet,Steven %A Attisso,Eugène %A Supper,Wilfried %A Amil,Samira %A Rhéaume,Caroline %A Paquette,Jean-Sébastien %A Chabot,Christian %A Laferrière,Marie-Claude %A Sasseville,Maxime %+ Faculty of Nursing Sciences, Université Laval, 1050 Av. de la Médecine, Québec, QC, G1V 0A6, Canada, 1 418 656 2131 ext 407576, marie-pierre.gagnon@fsi.ulaval.ca %K chronic diseases %K self-care %K self-management %K empowerment %K mobile health %K mHealth %K wearable %K devices %K scoping %K review %K mobile phone %K PRISMA %D 2024 %7 9.12.2024 %9 Review %J Interact J Med Res %G English %X Background: People with chronic diseases can benefit from wearable devices in managing their health and encouraging healthy lifestyle habits. Wearables such as activity trackers or blood glucose monitoring devices can lead to positive health impacts, including improved physical activity adherence or better management of type 2 diabetes. Few literature reviews have focused on the intersection of various chronic diseases, the wearable devices used, and the outcomes evaluated in intervention studies, particularly in the context of primary health care. Objective: This study aims to identify and describe (1) the chronic diseases represented in intervention studies, (2) the types or combinations of wearables used, and (3) the health or health care outcomes assessed and measured. Methods: We conducted a scoping review following the Joanna Briggs Institute guidelines, searching the MEDLINE and Web of Science databases for studies published between 2012 and 2022. Pairs of reviewers independently screened titles and abstracts, applied the selection criteria, and performed full-text screening. We included interventions using wearables that automatically collected and transmitted data to adult populations with at least one chronic disease. We excluded studies with participants with only a predisposition to develop a chronic disease, hospitalized patients, patients with acute diseases, patients with active cancer, and cancer survivors. We included randomized controlled trials and cohort, pretest-posttest, observational, mixed methods, and qualitative studies. Results: After the removal of 1987 duplicates, we screened 4540 titles and abstracts. Of the remaining 304 articles after exclusions, we excluded 215 (70.7%) full texts and included 89 (29.3%). Of these 89 texts, 10 (11%) were related to the same interventions as those in the included studies, resulting in 79 studies being included. We structured the results according to chronic disease clusters: (1) diabetes, (2) heart failure, (3) other cardiovascular conditions, (4) hypertension, (5) multimorbidity and other combinations of chronic conditions, (6) chronic obstructive pulmonary disease, (7) chronic pain, (8) musculoskeletal conditions, and (9) asthma. Diabetes was the most frequent health condition (18/79, 23% of the studies), and wearable activity trackers were the most used (42/79, 53% of the studies). In the 79 included studies, 74 clinical, 73 behavioral, 36 patient technology experience, 28 health care system, and 25 holistic or biopsychosocial outcomes were reported. Conclusions: This scoping review provides an overview of the wearable devices used in chronic disease self-management intervention studies, revealing disparities in both the range of chronic diseases studied and the variety of wearable devices used. These findings offer researchers valuable insights to further explore health care outcomes, validate the impact of concomitant device use, and expand their use to other chronic diseases. Trial Registration: Open Science Framework Registries (OSF) s4wfm; https://osf.io/s4wfm %M 39652850 %R 10.2196/55925 %U https://www.i-jmr.org/2024/1/e55925 %U https://doi.org/10.2196/55925 %U http://www.ncbi.nlm.nih.gov/pubmed/39652850 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e53304 %T Common Physical Performance Tests for Evaluating Health in Older Adults: Cross-Sectional Study %A Banarjee,Chitra %A Choudhury,Renoa %A Park,Joon-Hyuk %A Xie,Rui %A Fukuda,David %A Stout,Jeffrey %A Thiamwong,Ladda %+ Department of Mechanical Engineering, University of Central Flordia, 12760 Pegasus Drive, Orlando, FL, 32816, United States, 1 4078232416, joonpark@ucf.edu %K functional capacity %K physical activity %K fear of falling %K physical performance tests %K Short Physical Performance Battery %K 6-minute walk test %K Incremental Shuttle Walk Test %K geriatrics %K aging %D 2024 %7 29.11.2024 %9 Original Paper %J Interact J Med Res %G English %X Background: Interdisciplinary evaluation of older adults’ health care is a priority in the prevention of chronic health conditions and maintenance of daily functioning. While many studies evaluate different physical performance tests (PPTs) from a retrospective view in predicting mortality or cardiopulmonary health, it remains unclear which of the commonly used PPTs is the most effective at evaluating the current health of older adults. Additionally, the time and participant burden for each PPT must be considered when planning and implementing them for clinical or research purposes. Objective: This cross-sectional study aimed to determine how elements of overall physical capacity, performance, and other nongait factors in older adults affect the results of 3 commonly used tests: the Short Physical Performance Battery (SPPB), 6-minute walk test (6MWT), and Incremental Shuttle Walk Test (ISWT). Methods: A total of 53 community-dwelling older adults met the inclusion and exclusion criteria (mean age 77.47, SD 7.25 years; n=41, 77% female; and n=21, 40% Hispanic). This study evaluated older adults using 3 different PPTs including the SPPB, 6MWT, and ISWT, as well as constructed multiple linear regression models with measures of physical activity, static balance, and fear of falling (FoF). The nongait measures included 7 days of physical activity monitoring using the ActiGraph GT9X Link instrument, objective measurement of static balance using the BTrackS Balance System, and FoF using the short Fall Efficacy Scale-International. Results: The models revealed that the complete SPPB provided the most comprehensive value, as indicated by a greater R2 value (0.523), and that performance on the SPPB was predicted by both moderate to vigorous physical activity (P=.01) and FoF (P<.001). The ISWT was predicted by moderate to vigorous physical activity (P=.02), BMI (P=.02), and FoF (P=.006) and had a similar R2 value (0.517), whereas the gait component of the SPPB (P=.001) and 6MWT (P<.001) was predicted by only FoF and had lower R2 values (0.375 and 0.228, respectively). Conclusions: The results indicated the value of a multicomponent, comprehensive test, such as the SPPB, in evaluating the health of older adults. Additionally, a comparison of the 2 field walking tests (ISWT and 6MWT) further distinguished the ISWT as more responsive to overall health in older adults. In comparing these commonly used PPTs, clinicians and researchers in the field can determine and select the most optimal test to evaluate older adults in communities and research settings. %M 39612490 %R 10.2196/53304 %U https://www.i-jmr.org/2024/1/e53304 %U https://doi.org/10.2196/53304 %U http://www.ncbi.nlm.nih.gov/pubmed/39612490 %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 %@ 2291-5222 %I JMIR Publications %V 12 %N %P e49443 %T An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial %A Doherty,Cailbhe %A Lambe,Rory %A O’Grady,Ben %A O’Reilly-Morgan,Diarmuid %A Smyth,Barry %A Lawlor,Aonghus %A Hurley,Neil %A Tragos,Elias %K reinforcement learning %K exercise therapy %K personal satisfaction %K satisfaction %K physiotherapy %K physical therapy %K exercise intensity %K mobile apps %K randomized controlled trial %K crossover trial %K apps %K exercise %K physical activity %K mobile phone %D 2024 %7 26.11.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: The increasing prevalence of sedentary lifestyles has prompted the development of innovative public health interventions, such as smartphone apps that deliver personalized exercise programs. The widespread availability of mobile technologies (eg, smartphone apps and wearable activity trackers) provides a cost-effective, scalable way to remotely deliver personalized exercise programs to users. Using machine learning (ML), specifically reinforcement learning (RL), may enhance user engagement and effectiveness of these programs by tailoring them to individual preferences and needs. Objective: The primary aim was to investigate the impact of the Samsung-developed i80 BPM app, implementing ML for exercise prescription, on user satisfaction and exercise intensity among the general population. The secondary objective was to assess the effectiveness of ML-generated exercise programs for remote prescription of exercise to members of the public. Methods: Participants were randomized to complete 3 exercise sessions per week for 12 weeks using the i80 BPM mobile app, crossing over weekly between intervention and control conditions. The intervention condition involved individualizing exercise sessions using RL, based on user preferences such as exercise difficulty, selection, and intensity, whereas under the control condition, exercise sessions were not individualized. Exercise intensity (measured by the 10-item Borg scale) and user satisfaction (measured by the 8-item version of the Physical Activity Enjoyment Scale) were recorded after the session. Results: In total, 62 participants (27 male and 42 female participants; mean age 43, SD 13 years) completed 559 exercise sessions over 12 weeks (9 sessions per participant). Generalized estimating equations showed that participants were more likely to exercise at a higher intensity (intervention: mean intensity 5.82, 95% CI 5.59‐6.05 and control: mean intensity 5.19, 95% CI 4.97‐5.41) and report higher satisfaction (RL: mean satisfaction 4, 95% CI 3.9-4.1 and baseline: mean satisfaction 3.73, 95% CI 3.6-3.8) in the RL model condition. Conclusions: The findings suggest that RL can effectively increase both the intensity with which people exercise and their enjoyment of the sessions, highlighting the potential of ML to enhance remote exercise interventions. This study underscores the benefits of personalized exercise prescriptions in increasing adherence and satisfaction, which are crucial for the long-term effectiveness of fitness programs. Further research is warranted to explore the long-term impacts and potential scalability of RL-enhanced exercise apps in diverse populations. This study contributes to the understanding of digital health interventions in exercise science, suggesting that personalized, app-based exercise prescriptions may be more effective than traditional, nonpersonalized methods. The integration of RL into exercise apps could significantly impact public health, particularly in enhancing engagement and reducing the global burden of physical inactivity. Trial Registration: ClinicalTrials.gov NCT06653049; https://clinicaltrials.gov/study/NCT06653049 %R 10.2196/49443 %U https://mhealth.jmir.org/2024/1/e49443 %U https://doi.org/10.2196/49443 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 12 %N %P e57352 %T The Effect of Young People–Assisted, Individualized, Motion-Based Video Games on Physical, Cognitive, and Social Frailty Among Community-Dwelling Older Adults With Frailty: Randomized Controlled Trial %A Wong,Arkers Kwan Ching %A Zhang,Melissa Qian %A Bayuo,Jonathan %A Chow,Karen Kit Sum %A Wong,Siu Man %A Wong,Bonnie Po %A Liu,Bob Chung Man %A Lau,David Chi Ho %A Kowatsch,Tobias %K frailty %K gaming intervention %K motion-based %K video games %K older adults %K gerontology %K geriatrics %K randomized controlled trial %K RCT %K physical fitness %K adolescents %K young people–assisted %K eHealth literacy %K well-being %K therapists %K youth volunteers %K social support %K exergames %K gamification %K active games %K physical activity %D 2024 %7 20.11.2024 %9 %J JMIR Serious Games %G English %X Background: The aging population highlights the need to maintain both physical and psychological well-being. Frailty, a multidimensional syndrome, increases vulnerability to adverse outcomes. Although physical exercise is effective, adherence among older adults with frailty is often low due to barriers. Motion-based video games (MBVGs) may enhance motivation and engagement. Objective: This study aims to evaluate the effect of individualized exercise programs that combine MBVGs, intergenerational support, and therapeutic frameworks on physical, cognitive, and social frailty outcomes in community-dwelling older adults. Methods: This randomized controlled trial was conducted from March 2022 to October 2023 across 6 community centers in Hong Kong. Participants aged 60 years and above with mild neurocognitive disorder were recruited, screened, and randomly assigned to either an intervention (n=101) or control group (n=101). The intervention included an 18-week program with 12 supervised exercise sessions utilizing motion-based technology, led by occupational therapists and assisted by youth volunteers. Data were collected at baseline (T1) and postintervention (T2), focusing on physical, cognitive, and social frailty outcomes, as well as client-related metrics. Statistical analyses were performed using SPSS, with significance set at P<.05. Results: A total of 202 participants were recruited, with a mean age of 78.8 years (SD 7.8). Both groups showed improvements in balance from T1 to T2, with a significant time effect (β=−0.63, P=.03). The intervention group demonstrated enhancements in hand strength and BMI, but no statistically significant between-group differences were observed. The intervention group also exhibited significant improvements in cognitive function (β=2.43, P<.001), while the control group’s scores declined. Short-term memory improved for both groups, with no significant differences noted. Both groups experienced a reduction in depression levels, with a significant within-group effect at T2 (β=−1.16, P=.001). Improvements in social connectedness and eHealth literacy were observed in both groups, with the latter showing a significant within-group effect at T2 (β=3.56, P=.002). No significant effects were found for social isolation, physical activities, or quality of life. Conclusions: The growing aging population necessitates innovative strategies to support aging in place. Results indicated statistically significant improvements only in BMI and cognition, while other outcomes such as loneliness, balance, and eHealth literacy showed positive trends but lacked significance. Despite the limitations observed, particularly regarding the role of volunteer support and the diverse needs of community-dwelling older adults, the findings contribute to the foundation for future research aimed at enhancing biopsychosocial outcomes. Future studies should explore tailored interventions that consider individual preferences and abilities, as well as evaluate specific components of motion-based video games to optimize their effectiveness. Trial Registration: ClinicalTrials.gov NCT05267444; https://clinicaltrials.gov/study/NCT05267444 %R 10.2196/57352 %U https://games.jmir.org/2024/1/e57352 %U https://doi.org/10.2196/57352 %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 %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60183 %T Efficacy of a Wearable Activity Tracker With Step-by-Step Goal-Setting on Older Adults’ Physical Activity and Sarcopenia Indicators: Clustered Trial %A Ho,Mu-Hsing %A Peng,Chi-Yuan %A Liao,Yung %A Yen,Hsin-Yen %+ School of Gerontology and Long-term Care, College of Nursing, Taipei Medical University, 250 Wuxing St, Taipei City, 11031, Taiwan, 886 2 2736 1661 ext 6326, yenken520@gmail.com %K behavioral change technique %K chronic disease prevention %K health promotion %K mHealth %K sedentary behavior %K smartwatch %D 2024 %7 1.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Smart wearable technology has potential benefits for promoting physical activity and preventing sarcopenia. Objective: The purpose of this study was to explore the efficacy of a wearable activity tracker with 2-stage goal-setting for daily steps on older adults’ physical activity and sarcopenia indicators. Methods: The study used a clustered trial design and was conducted in March to June 2022. Participants were community-dwelling adults older than 60 years who were recruited from 4 community centers in Taipei City. The intervention was designed with 2-stage goals set to 5000 steps/day in the first 4 weeks and 7500 steps/day in the final 4 weeks while wearing a commercial wearable activity tracker. Data were collected by self-reported questionnaires, a body composition analyzer, a handle grip tester, and 5 sit-to-stand tests. Results: All 27 participants in the experimental group and 31 participants in the control group completed the 8-week intervention. Total and light-intensity physical activities, skeletal muscle index, and muscle strength increased, while sedentary time, BMI, and the waist circumference of participants decreased in the experimental group, with significant group-by-time interactions compared to the control group. Conclusions: A wearable activity tracker with gradual goal-setting is an efficient approach to improve older adults’ physical activity and sarcopenia indicators. Smart wearable products with behavioral change techniques are recommended to prevent sarcopenia in older adult populations. %M 39486024 %R 10.2196/60183 %U https://www.jmir.org/2024/1/e60183 %U https://doi.org/10.2196/60183 %U http://www.ncbi.nlm.nih.gov/pubmed/39486024 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e60209 %T Assessment of Wearable Device Adherence for Monitoring Physical Activity in Older Adults: Pilot Cohort Study %A Ding,Huitong %A Ho,Kristi %A Searls,Edward %A Low,Spencer %A Li,Zexu %A Rahman,Salman %A Madan,Sanskruti %A Igwe,Akwaugo %A Popp,Zachary %A Burk,Alexa %A Wu,Huanmei %A Ding,Ying %A Hwang,Phillip H %A Anda-Duran,Ileana De %A Kolachalama,Vijaya B %A Gifford,Katherine A %A Shih,Ludy C %A Au,Rhoda %A Lin,Honghuang %K physical activity %K remote monitoring %K wearable device %K adherence %K older adults %D 2024 %7 25.10.2024 %9 %J JMIR Aging %G English %X Background: Physical activity has emerged as a modifiable behavioral factor to improve cognitive function. However, research on adherence to remote monitoring of physical activity in older adults is limited. Objective: This study aimed to assess adherence to remote monitoring of physical activity in older adults within a pilot cohort from objective user data, providing insights for the scalability of such monitoring approaches in larger, more comprehensive future studies. Methods: This study included 22 participants from the Boston University Alzheimer’s Disease Research Center Clinical Core. These participants opted into wearing the Verisense watch as part of their everyday routine during 14-day intervals every 3 months. Eighteen continuous physical activity measures were assessed. Adherence was quantified daily and cumulatively across the follow-up period. The coefficient of variation was used as a key metric to assess data consistency across participants over multiple days. Day-to-day variability was estimated by calculating intraclass correlation coefficients using a 2-way random-effects model for the baseline, second, and third days. Results: Adherence to the study on a daily basis outperformed cumulative adherence levels. The median proportion of adherence days (wearing time surpassed 90% of the day) stood at 92.1%, with an IQR spanning from 86.9% to 98.4%. However, at the cumulative level, 32% (7/22) of participants in this study exhibited lower adherence, with the device worn on fewer than 4 days within the requested initial 14-day period. Five physical activity measures have high variability for some participants. Consistent activity data for 4 physical activity measures might be attainable with just a 3-day period of device use. Conclusions: This study revealed that while older adults generally showed high daily adherence to the wearable device, consistent usage across consecutive days proved difficult. These findings underline the effectiveness of wearables in monitoring physical activity in older populations and emphasize the ongoing necessity to simplify usage protocols and enhance user engagement to guarantee the collection of precise and comprehensive data. %R 10.2196/60209 %U https://aging.jmir.org/2024/1/e60209 %U https://doi.org/10.2196/60209 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e56376 %T Cadence-Based Pedometer App With Financial Incentives to Enhance Moderate-to-Vigorous Physical Activity: Development and Single-Arm Feasibility Study %A Hayashi,Kosuke %A Imai,Hiromitsu %A Oikawa,Ichiro %A Ishihara,Yugo %A Wakuda,Hirokazu %A Miura,Iori %A Uenohara,Shingo %A Kuwae,Asuka %A Kai,Megumi %A Furuya,Ken'ichi %A Uemura,Naoto %+ Department of Clinical Pharmacology and Therapeutics, Oita University, 1-1 Idaigaoka, Hasama-Machi, Yufu, 8795593, Japan, 81 975865952, khayashi@oita-u.ac.jp %K physical activity %K behavioral economics %K pedometer %K arm %K cadence %K app %K public health %K walk %K Google Fit %K heart points %K exercise %K mobile phone %D 2024 %7 24.10.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: High levels of physical activity are key to improving health outcomes, yet many people fail to take action. Using pedometers to target steps per day and providing financial incentives is a simple and scalable approach to promoting public health. However, conventional pedometers do not account for “intensity” and “duration,” making it challenging to efficiently increase people’s moderate-to-vigorous physical activity (MVPA), which is expected to improve health outcomes. Based on these rationales, we developed a smartphone app that sets step cadence as a goal (defined as a daily challenge of walking more than 1500 steps in 15 minutes twice a day, which is a heuristic threshold for moderate physical activity) and provides financial incentive when the challenge is met. Objective: This study aimed to evaluate the feasibility of our novel app and explore whether its use can increase users’ daily MVPA. Methods: A single-arm pre-post study evaluated the feasibility and efficacy of the app. A total of 15 participants used app 1 (an app without financial incentives) for the first period (4 weeks) and then switched to app 2 (an app with financial incentives) for the second period (4 weeks). The primary outcome was the difference between the first and second periods in the number of successful challenge attempts per week. Secondary outcomes were differences between the first and second periods in daily steps and distance walked. Exploratory outcomes included the difference between the first and second periods in daily “heart points” as measured by Google Fit, a publicly available app that measures users’ daily MVPA. Results: The number of successful challenge attempts per week increased significantly compared to the first period (5.6 times per week vs 0.7 times per week; P<.001). Although not statistically significant, there was a trend toward an increase in the mean steps per day and distance walked per day (6586 steps per day vs 5950 steps per day; P=.19; and 4.69 km per day vs 3.85 km per day; P=.09, respectively). An exploratory end point examining daily MVPA by “heart points” collected from Google Fit also showed a significant increase compared to the first period (22.7 points per day vs 12.8 points per day; P=.02). Conclusions: Our app using step cadence as a goal and providing financial incentives seemed feasible and could be an effective app to increase users’ daily MVPA. Based on the results of this study, we are motivated to conduct a confirmatory study with a broader and larger number of participants. Trial Registration: UMIN 000050518; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000057420 %M 39447165 %R 10.2196/56376 %U https://formative.jmir.org/2024/1/e56376 %U https://doi.org/10.2196/56376 %U http://www.ncbi.nlm.nih.gov/pubmed/39447165 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e60925 %T Intensive, Real-Time Data Collection of Psychological and Physiological Stress During a 96-Hour Field Training Exercise at a Senior Military College: Feasibility and Acceptability Cohort Study %A Pojednic,Rachele %A Welch,Amy %A Thornton,Margaret %A Garvey,Meghan %A Grogan,Tara %A Roberts,Walter %A Ash,Garrett %+ Stanford Lifestyle Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, 3180 Porter Drive, Palo Alto, CA, 94303, United States, 1 617 833 7372, rpojedni@stanford.edu %K biomarker %K biometric %K heart rate variability %K saliva %K feasibility %K warfighter %K field training %K acceptability %K wearable biosensors %K real-time %K data collection %K physiological stress %K training exercise %K pilot study %K sweat sensors %D 2024 %7 18.10.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Poor physical fitness, stress, and fatigue are factors impacting military readiness, national security, and economic burden for the United States Department of Defense. Improved accuracy of wearable biosensors and remote field biologic sample collection strategies could make critical contributions to understanding how physical readiness and occupational stressors result in on-the-job and environment-related injury, sleep impairments, diagnosis of mental health disorders, and reductions in performance in war-fighters. Objective: This study aimed to evaluate the feasibility and acceptability of intensive biomarker and biometric data collection to understand physiological and psychological stress in Army Reserved Officer Training Corps cadets before, during, and after a 96-hour field training exercise (FTX). Methods: A prospective pilot study evaluated the feasibility and acceptability of multimodal field data collection using passive drool saliva sampling, sweat sensors, accelerometry, actigraphy, and photoplethysmography. In addition, physical fitness (Army Combat Fitness Test), self-reported injury, and psychological resilience (Brief Resilience Scale) were measured. Results: A total of 22 cadets were included. Two were lost to follow-up due to injury during FTX, for a retention rate of 91%. Assessments of performance and psychological resilience were completed for all remaining participants, resulting in 100% testing adherence. All participants provided saliva samples before the FTX, with 98% adherence at the second time point and 91% at the third. For sweat, data collection was not possible. Average daily wear time for photoplethysmography devices was good to excellent, meeting a 70% threshold with data collected for ≥80% of person-days at all time points. Of the participants who completed the FTX and 12 completed a post-FTX acceptability survey for a response rate of 60%. Overall, participant acceptance was high (≥80%) for all metrics and devices. Conclusions: This study demonstrates that wearable biosensors and remote field biologic sample collection strategies during a military FTX have the potential to be used in higher stakes tactical environments in the future for some, but not all, of the strategies. Overall, real-time biometric and biomarker sampling is feasible and acceptable during field-based training and provides insights and strategies for future interventions on military cadet and active-duty readiness, environmental stress, and recovery. %M 39422988 %R 10.2196/60925 %U https://formative.jmir.org/2024/1/e60925 %U https://doi.org/10.2196/60925 %U http://www.ncbi.nlm.nih.gov/pubmed/39422988 %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 %@ 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 %@ 1438-8871 %I JMIR Publications %V 26 %N %P e59497 %T Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review %A Liang,Ya-Ting %A Wang,Charlotte %A Hsiao,Chuhsing Kate %+ Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, No.17, Xu-Zhou Rd, Taipei, 10055, Taiwan, 886 2 33668032, ckhsiao@ntu.edu.tw %K accelerometer %K association %K behavioral study %K classification %K digital biomarkers %K digital health %K physical activity %K prediction %K statistical method %K wearable %D 2024 %7 11.9.2024 %9 Review %J J Med Internet Res %G English %X Background: Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized. Objective: This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual’s free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks? Methods: This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers. Results: After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%). Conclusions: Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals’ overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings. %M 39259962 %R 10.2196/59497 %U https://www.jmir.org/2024/1/e59497 %U https://doi.org/10.2196/59497 %U http://www.ncbi.nlm.nih.gov/pubmed/39259962 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e55207 %T The Impact of Air Pollution Information on Individuals’ Exercise Behavior: Empirical Study Using Wearable and Mobile Devices Data %A Yang,Yang %A Goh,Khim-Yong %A Teo,Hock Hai %A Tan,Sharon Swee-Lin %+ School of Business and Management, Royal Holloway, University of London, Egham Hill, Egham, TW20 0EX, United Kingdom, 44 1784 434 455, y.yang@rhul.ac.uk %K air pollution %K information sources %K exercise activity %K wearable and mobile devices %K econometric analysis %D 2024 %7 10.9.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical exercise and exposure to air pollution have counteracting effects on individuals’ health outcomes. Knowledge on individuals’ real-time exercise behavior response to different pollution information sources remains inadequate. Objective: This study aims to examine the extent to which individuals avoid polluted air during exercise activities in response to different air pollution information sources. Methods: We used data on individuals’ exercise behaviors captured by wearable and mobile devices in 83 Chinese cities over a 2-year time span. In our data set, 35.99% (5896/16,379) of individuals were female and 64% (10,483/16,379) were male, and their ages predominantly ranged from 18 to 50 years. We further augmented the exercise behavior data with air pollution information that included city-hourly level measures of the Air Quality Index and particulate matter 2.5 concentration (in µg/m3), and weather data that include city-hourly level measures of air temperature (ºC), dew point (ºC), wind speed (m/s), and wind direction (degrees). We used a linear panel fixed effect model to estimate individuals’ exercise-aversion behaviors (ie, running exercise distance at individual-hour, city-hour, or city-day levels) and conducted robustness checks using the endogenous treatment effect model and regression discontinuity method. We examined if alternative air pollution information sources could moderate (ie, substitute or complement) the role of mainstream air pollution indicators. Results: Our results show that individuals exhibit a reduction of running exercise behaviors by about 0.50 km (or 7.5%; P<.001) during instances of moderate to severe air pollution, and there is no evidence of reduced distances in instances of light air pollution. Furthermore, individuals’ exercise-aversion behaviors in response to mainstream air pollution information are heightened by different alternative information sources, such as social connections and social media user-generated content about air pollution. Conclusions: Our results highlight the complementary role of different alternative information sources of air pollution in inducing individuals’ aversion behaviors and the importance of using different information channels to increase public awareness beyond official air pollution alerts. %M 39255029 %R 10.2196/55207 %U https://mhealth.jmir.org/2024/1/e55207 %U https://doi.org/10.2196/55207 %U http://www.ncbi.nlm.nih.gov/pubmed/39255029 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e59243 %T Smart Device Ownership and Use of Social Media, Wearable Trackers, and Health Apps Among Black Women With Hypertension in the United States: National Survey Study %A Kalinowski,Jolaade %A Bhusal,Sandesh %A Pagoto,Sherry L %A Newton Jr,Robert %A Waring,Molly E %+ Department of Human Development and Family Sciences, University of Connecticut, 348 Mansfield Rd Unit 1058, Storrs, CT, 06269, United States, 1 203 251 8421, jolaade.kalinowski@uconn.edu %K Black women %K Black %K women %K tracker %K trackers %K wearable %K wearables %K hypertension %K hypertensive %K cardiology %K cardiovascular %K blood pressure %K social media %K technology %K usage %K digital health %K eHealth %K tablet %K mHealth %K mobile health %K app %K apps %K applications %K survey %K surveys %K questionnaire %K questionnaires %K Health Information National Trends Survey %K HINTS %D 2024 %7 9.9.2024 %9 Research Letter %J JMIR Cardio %G English %X The majority of Black women with hypertension in the United States have smartphones or tablets and use social media, and many use wearable activity trackers and health or wellness apps, digital tools that can be used to support lifestyle changes and medication adherence. %M 39250778 %R 10.2196/59243 %U https://cardio.jmir.org/2024/1/e59243 %U https://doi.org/10.2196/59243 %U http://www.ncbi.nlm.nih.gov/pubmed/39250778 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e59659 %T Accelerometer-Based Physical Activity and Health-Related Quality of Life in Korean Adults: Observational Study Using the Korea National Health and Nutrition Examination Survey %A Han,Sujeong %A Oh,Bumjo %A Kim,Ho Jun %A Hwang,Seo Eun %A Kim,Jong Seung %+ Department of Family Medicine, SMG-SNU Boramae Medical Center, 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea, 82 2 870 2681, atenae68@nate.com %K Health-Related Quality of Life (HRQoL) %K physical activity %K Accelerometer %K Korea National Health and Nutrition Examination Survey (KNHANES) %K mobile phone %D 2024 %7 3.9.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Health-related quality of life (HRQoL) reflects an individual's perception of their physical and mental health over time. Despite numerous studies linking physical activity to improved HRQoL, most rely on self-reported data, limiting the accuracy and generalizability of findings. This study leverages objective accelerometer data to explore the association between physical activity and HRQoL in Korean adults. Objective: The objective of this study is to analyze the relationship between objectively measured physical activity using accelerometers and HRQoL among Korean adults, aiming to inform targeted interventions for enhancing HRQoL through physical activity. Methods: This observational study included 1298 participants aged 19-64 years from the Korea National Health and Nutrition Examination Survey (KNHANES) VI, who wore an accelerometer for 7 consecutive days. HRQoL was assessed using the EQ-5D questionnaire, and physical activity was quantified as moderate-to-vigorous physical activity accelerometer-total (MVPA-AT) and accelerometer-bout (MVPA-AB). Data were analyzed using logistic regression to determine the odds ratio (ORs) for low HRQoL, adjusting for socioeconomic variables and mental health factors. Results: Participants with higher HRQoL were younger, more likely to be male, single, highly educated, employed in white-collar jobs, and had higher household incomes. They also reported less stress and better subjective health status. The high HRQoL group had significantly more participants meeting MVPA-AB ≥600 metabolic equivalents (P<.01). Logistic regression showed that participants meeting MVPA-AB ≥600 metabolic equivalents had higher odds of high HRQoL (OR 1.55, 95% CI 1.11-2.17). Adjusted models showed consistent results, although the association weakened when adjusting for mental health factors (OR 1.45, 95% CI 1.01-2.09). Conclusions: The study demonstrates a significant association between HRQoL and moderate to vigorous physical activity sustained for at least 10 minutes, as measured by accelerometer. These findings support promoting physical activity, particularly sustained moderate to vigorous activity, to enhance HRQoL. Further interventional studies focusing on specific physical activity domains such as occupational, leisure-time, and commuting activities are warranted. %M 39226099 %R 10.2196/59659 %U https://humanfactors.jmir.org/2024/1/e59659 %U https://doi.org/10.2196/59659 %U http://www.ncbi.nlm.nih.gov/pubmed/39226099 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e56962 %T A Clinician and Electronic Health Record Wearable Device Intervention to Increase Physical Activity in Patients With Obesity: Formative Qualitative Study %A Ayyaswami,Varun %A Subramanian,Jeevarathna %A Nickerson,Jenna %A Erban,Stephen %A Rosano,Nina %A McManus,David D %A Gerber,Ben S %A Faro,Jamie M %+ Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA, 01655, United States, 1 508 856 3898 ext 63898, varun.ayyaswami@umassmed.edu %K remote patient monitoring %K physical activity %K electronic health record %K wearable device %K patient monitoring %K health monitoring %K health monitor %K patient monitor %K remote patient monitor %K exercise %K exercises %K electronic health records %K patient record %K health record %K health records %K wearable devices %D 2024 %7 2.9.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The number of individuals using digital health devices has grown in recent years. A higher rate of use in patients suggests that primary care providers (PCPs) may be able to leverage these tools to effectively guide and monitor physical activity (PA) for their patients. Despite evidence that remote patient monitoring (RPM) may enhance obesity interventions, few primary care practices have implemented programs that use commercial digital health tools to promote health or reduce complications of the disease. Objective: This formative study aimed to assess the perceptions, needs, and challenges of implementation of an electronic health record (EHR)–integrated RPM program using wearable devices to promote patient PA at a large urban primary care practice to prepare for future intervention. Methods: Our team identified existing workflows to upload wearable data to the EHR (Epic Systems), which included direct Fitbit (Google) integration that allowed for patient PA data to be uploaded to the EHR. We identified pictorial job aids describing the clinical workflow to PCPs. We then performed semistructured interviews with PCPs (n=10) and patients with obesity (n=8) at a large urban primary care clinic regarding their preferences and barriers to the program. We presented previously developed pictorial aids with instructions for (1) providers to complete an order set, set step-count goals, and receive feedback and (2) patients to set up their wearable devices and connect them to their patient portal account. We used rapid qualitative analysis during and after the interviews to code and develop key themes for both patients and providers that addressed our research objective. Results: In total, 3 themes were identified from provider interviews: (1) providers’ knowledge of PA prescription is focused on general guidelines with limited knowledge on how to tailor guidance to patients, (2) providers were open to receiving PA data but were worried about being overburdened by additional patient data, and (3) providers were concerned about patients being able to equitably access and participate in digital health interventions. In addition, 3 themes were also identified from patient interviews: (1) patients received limited or nonspecific guidance regarding PA from providers and other resources, (2) patients want to share exercise metrics with the health care team and receive tailored PA guidance at regular intervals, and (3) patients need written resources to support setting up an RPM program with access to live assistance on an as-needed basis. Conclusions: Implementation of an EHR-based RPM program and associated workflow is acceptable to PCPs and patients but will require attention to provider concerns of added burdensome patient data and patient concerns of receiving tailored PA guidance. Our ongoing work will pilot the RPM program and evaluate feasibility and acceptability within a primary care setting. %M 39221852 %R 10.2196/56962 %U https://formative.jmir.org/2024/1/e56962 %U https://doi.org/10.2196/56962 %U http://www.ncbi.nlm.nih.gov/pubmed/39221852 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e56972 %T Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis %A Singh,Ben %A Chastin,Sebastien %A Miatke,Aaron %A Curtis,Rachel %A Dumuid,Dorothea %A Brinsley,Jacinta %A Ferguson,Ty %A Szeto,Kimberley %A Simpson,Catherine %A Eglitis,Emily %A Willems,Iris %A Maher,Carol %+ Allied Health & Human Performance, University of South Australia, Corner of North Terrace and Frome Road, Adelaide, 5001, Australia, 61 1300301703, ben.singh@unisa.edu.au %K wearable activity trackers %K disease detection %K atrial fibrillation %K COVID-19 diagnosis %K meta-analysis %K wearables %K wearable tracker %K tracker %K detection %K monitoring %K physiological %K diagnostic tool %K tool %K tools %K Fitbit %K atrial %K COVID-19 %K wearable %D 2024 %7 30.8.2024 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain. Objective: This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events. Methods: Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. Results: A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%). Conclusions: Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability. Trial Registration: Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867 %M 39213525 %R 10.2196/56972 %U https://mhealth.jmir.org/2024/1/e56972 %U https://doi.org/10.2196/56972 %U http://www.ncbi.nlm.nih.gov/pubmed/39213525 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e55254 %T Establishing a Consensus-Based Framework for the Use of Wearable Activity Trackers in Health Care: Delphi Study %A Szeto,Kimberley %A Arnold,John %A Horsfall,Erin Marie %A Sarro,Madeline %A Hewitt,Anthony %A Maher,Carol %+ Alliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Perfomance, University of South Australia, GPO Box 2471, Adelaide, 5001, Australia, 61 (08) 8302 2283, kimberley.szeto@mymail.unisa.edu.au %K wearable activity tracker %K health care %K physical activity %K sedentary behavior %K wearable %K wearables %K wearable tracker %K tracker %K wearable technology %K support %K exercise %K prevention %K management %K monitor %K promote %K survey %K utility %D 2024 %7 23.8.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings. Objective: This study aimed to develop a consensus-based framework for the optimal use of WATs in health care. Methods: A 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants’ opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist’s clarity, utility, and appropriateness. Results: Participation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients’ data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4. Conclusions: This study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector. %M 39178034 %R 10.2196/55254 %U https://mhealth.jmir.org/2024/1/e55254 %U https://doi.org/10.2196/55254 %U http://www.ncbi.nlm.nih.gov/pubmed/39178034 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50075 %T Association of Wearable Device–Measured Step Volume and Variability With Blood Pressure in Older Chinese Adults: Mobile-Based Longitudinal Observational Study %A Xiao,Han %A Zhou,Zechen %A Ma,Yujia %A Li,Xiaoyi %A Ding,Kexin %A Dai,Xiaotong %A Chen,Dafang %+ Department of Epidemiology and Biostatistics, Peking University, 38 Xueyuan Road, Haidian, Beijing, 100191, China, 86 13701248872, hx624@ic.ac.uk %K older adults %K physical activity %K step volume %K step variability %K blood pressure %K wearable devices %K mHealth apps %K mobile health apps %K mobile phone %D 2024 %7 14.8.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The paucity of evidence on longitudinal and consecutive recordings of physical activity (PA) and blood pressure (BP) under real-life conditions and their relationships is a vital research gap that needs to be addressed. Objective: This study aims to (1) investigate the short-term relationship between device-measured step volume and BP; (2) explore the joint effects of step volume and variability on BP; and (3) examine whether the association patterns between PA and BP varied across sex, hypertension status, and chronic condition status. Methods: This study used PA data of a prospective cohort of 3070 community-dwelling older adults derived from a mobile health app. Daily step counts, as a proxy of step volume, were derived from wearable devices between 2018 and 2022 and categorized into tertiles (low, medium, and high). Step variability was assessed using the SD of daily step counts. Consecutive daily step count recordings within 0 to 6 days preceding each BP measurement were analyzed. Generalized estimation equation models were used to estimate the individual and joint associations of daily step volume and variability with BP. Stratified analyses by sex, the presence of hypertension, and the number of morbidities were further conducted. Results: A total of 3070 participants, with a median age of 72 (IQR 67-77) years and 71.37% (2191/3070) women, were included. Participants walked a median of 7580 (IQR 4972-10,653) steps and 5523 (IQR 3590-7820) meters per day for a total of 592,597 person-days of PA monitoring. Our results showed that higher levels of daily step volume were associated with lower BP (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure). Compared with participants with low step volume (daily step counts <6000/d) and irregular steps, participants with high step volume (≥9500/d) and regular steps showed the strongest decrease in systolic BP (–1.69 mm Hg, 95% CI –2.2 to –1.18), while participants with medium step volume (6000/d to <9500/d) and regular steps were associated with the lowest diastolic BP (–1.067 mm Hg, 95% CI –1.379 to –0.755). Subgroup analyses indicated generally greater effects on women, individuals with normal BP, and those with only 1 chronic disease, but the effect pattern was varied and heterogeneous between participants with different characteristics. Conclusions: Increased step volume demonstrated a substantial protective effect on BP among older adults with chronic conditions. Furthermore, the beneficial association between step volume and BP was enhanced by regular steps, suggesting potential synergistic protective effects of both increased step volume and step regularity. Targeting both step volume and variability through PA interventions may yield greater benefits in BP control, particularly among participants with hypertension and a higher chronic disease burden. %M 39141900 %R 10.2196/50075 %U https://www.jmir.org/2024/1/e50075 %U https://doi.org/10.2196/50075 %U http://www.ncbi.nlm.nih.gov/pubmed/39141900 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e52428 %T Feasibility and Acceptability of a Mobile Health Exercise Intervention for Inactive Adults: 3-Arm Randomized Controlled Pilot Trial %A Dawson,Jacqueline Kiwata %A Ede,Alison %A Phan,Madeleine %A Sequeira,Alec %A Teng,Hsiang-Ling %A Donlin,Ayla %+ Department of Physical Therapy, California State University, Long Beach, ET-130, 1250 Bellflower Boulevard, Long Beach, CA, 90840, United States, 1 5629857139, jacqueline.dawson@csulb.edu %K digital health %K physical activity %K user experience %K heart rate monitor %K group exercise %K mHealth %K wearable %K group exercise %K feasibility %K acceptability %K mobile health %K mobile health exercise %K exercise %K adults %K randomized controlled trial %K exercise program %K support %K wearables %K screening %K effectiveness %K videoconference %D 2024 %7 9.8.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Objective monitoring of self-directed physical activity (PA) is a common approach used in both fitness and health settings to promote exercise behavior, but adherence has been poor. Newer mobile health (mHealth) technologies could be a cost-effective approach to broadening accessibility and providing support for PA behavior change; yet, the optimal method of delivery of such interventions is still unclear. Objective: This study aimed to determine the feasibility and acceptability of an mHealth exercise intervention delivered in combination with objective monitoring in 3 ways: health education emails, asynchronous exercise videos, or synchronous videoconference exercise classes. Methods: Physically inactive (<30 min/wk) adults (cisgender women aged 31.5, SD 11.3 years, cisgender men aged 34.1, SD 28.9 years, and nonbinary individuals aged 22.0, SD 0 years) were randomized (1:1:1) to 8 weeks of increasing PA behavioral support: level 1 (health education+objective monitoring, n=26), level 2 (asynchronous contact, level 1+prerecorded exercise videos, n=30), or level 3 (synchronous contact, level 1+videoconference group exercise, n=28). Participants used a heart rate monitor during exercise and a mobile app for interaction. Primary outcomes were feasibility (accrual, retention, and adherence) and acceptability (user experience survey). Secondary outcomes assessed at baseline and 8 weeks included resting heart rate, self-reported PA, and quality of life. The exercise dose was evaluated throughout the intervention. Results: Between August 2020 and August 2021, 204 adults were screened for eligibility. Out of 135 eligible participants, 84 (62%) enrolled in the study. Retention was 50% (13/26) in level 1, 60% (18/30) in level 2 and 82% (23/28) in level 3, while adherence was 31% (8/26) in level 1, 40% (12/30) in level 2 and 75% (21/28) in level 3. A total of 83% (70/84) of the study sample completed the intervention, but low response rates (64%, 54/84) were observed postintervention at week-8 assessments. Program satisfaction was highest in participants receiving exercise videos (level 2, 80%, 8/10) or exercise classes (level 3, 80%, 12/15), while only 63% (5/8) of level 1 reported the program as enjoyable. Level 3 was most likely to recommend the program (87%, 13/15), compared to 80% (8/10) in level 2 and 46% (5/8) in level 1. Self-reported PA significantly increased from baseline to intervention in level 3 (P<.001) and level 2 (P=.003), with no change in level 1. Level 3 appeared to exercise at higher doses throughout the intervention. Conclusions: Only the videoconference exercise class intervention met feasibility criteria, although postintervention response rates were low across all groups. Both videoconference and prerecorded videos had good acceptability, while objective monitoring and health education alone were not feasible or acceptable. Future studies are needed to examine the effectiveness of videoconference exercise interventions on health-related outcomes during nonpandemic times and how asynchronous interventions might maximize adherence. Trial Registration: ClinicalTrials.gov NCT05192421; https://clinicaltrials.gov/study/NCT05192421 %R 10.2196/52428 %U https://formative.jmir.org/2024/1/e52428 %U https://doi.org/10.2196/52428 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 9 %N %P e59459 %T Assessing the Accuracy of Smartwatch-Based Estimation of Maximum Oxygen Uptake Using the Apple Watch Series 7: Validation Study %A Caserman,Polona %A Yum,Sungsoo %A Göbel,Stefan %A Reif,Andreas %A Matura,Silke %+ Serious Games Research Group, Technical University of Darmstadt, Rundeturmstraße 10, Darmstadt, 64289, Germany, polona.caserman@tu-darmstadt.de %K maximal oxygen uptake %K oxygen consumption %K cardiorespiratory fitness %K physical fitness %K physical activity %K fitness tracker %K wearables %K wearable %K exercise %K fitness %K tracker %K trackers %K cardiorespiratory %K wrist worn device %K devices %K validation study %K VO2max %K sport watch %K fitness level %K mobile phone %D 2024 %7 31.7.2024 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Determining maximum oxygen uptake (VO2max) is essential for evaluating cardiorespiratory fitness. While laboratory-based testing is considered the gold standard, sports watches or fitness trackers offer a convenient alternative. However, despite the high number of wrist-worn devices, there is a lack of scientific validation for VO2max estimation outside the laboratory setting. Objective: This study aims to compare the Apple Watch Series 7’s performance against the gold standard in VO2max estimation and Apple’s validation findings. Methods: A total of 19 participants (7 female and 12 male), aged 18 to 63 (mean 28.42, SD 11.43) years were included in the validation study. VO2max for all participants was determined in a controlled laboratory environment using a metabolic gas analyzer. Thereby, they completed a graded exercise test on a cycle ergometer until reaching subjective exhaustion. This value was then compared with the estimated VO2max value from the Apple Watch, which was calculated after wearing the watch for at least 2 consecutive days and measured directly after an outdoor running test. Results: The measured VO2max (mean 45.88, SD 9.42 mL/kg/minute) in the laboratory setting was significantly higher than the predicted VO2max (mean 41.37, SD 6.5 mL/kg/minute) from the Apple Watch (t18=2.51; P=.01) with a medium effect size (Hedges g=0.53). The Bland-Altman analysis revealed a good overall agreement between both measurements. However, the intraclass correlation coefficient ICC(2,1)=0.47 (95% CI 0.06-0.75) indicated poor reliability. The mean absolute percentage error between the predicted and the actual VO2max was 15.79%, while the root mean square error was 8.85 mL/kg/minute. The analysis further revealed higher accuracy when focusing on participants with good fitness levels (mean absolute percentage error=14.59%; root-mean-square error=7.22 ml/kg/minute; ICC(2,1)=0.60 95% CI 0.09-0.87). Conclusions: Similar to other smartwatches, the Apple Watch also overestimates or underestimates the VO2max in individuals with poor or excellent fitness levels, respectively. Assessing the accuracy and reliability of the Apple Watch’s VO2max estimation is crucial for determining its suitability as an alternative to laboratory testing. The findings of this study will apprise researchers, physical training professionals, and end users of wearable technology, thereby enhancing the knowledge base and practical application of such devices in assessing cardiorespiratory fitness parameters. %M 39083800 %R 10.2196/59459 %U https://biomedeng.jmir.org/2024/1/e59459 %U https://doi.org/10.2196/59459 %U http://www.ncbi.nlm.nih.gov/pubmed/39083800 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e51206 %T Physical Activity, Body Composition, and Fitness Variables in Adolescents After Periods of Mandatory, Promoted or Nonmandatory, Nonpromoted Use of Step Tracker Mobile Apps: Randomized Controlled Trial %A Mateo-Orcajada,Adrián %A Vaquero-Cristóbal,Raquel %A Mota,Jorge %A Abenza-Cano,Lucía %+ Research Group Movement Sciences and Sport (MS&SPORT), Department of Physical Activity and Sport, Faculty of Sport Sciences, University of Murcia, C. Argentina 19, San Javier, Murcia, 30720, Spain, 34 868 88 86 84, raquel.vaquero@um.es %K body composition %K detraining %K new technologies %K physical education subject %K physical fitness %K youth %D 2024 %7 30.7.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: It is not known whether an intervention made mandatory as a physical education (PE) class assignment and aimed at promoting physical activity (PA) in adolescents can create a healthy walking habit, which would allow further improvements to be achieved after the mandatory and promoted intervention has been completed. Objective: The aims of this study were to (1) investigate whether, after a period of using a step tracker mobile app made mandatory and promoted as a PE class assignment, adolescents continue to use it when its use is no longer mandatory and promoted; (2) determine whether there are changes in the PA level, body composition, and fitness of adolescents when the use of the app is mandatory and promoted and when it is neither mandatory nor promoted; and (3) analyze whether the covariates maturity status, gender, and specific app used can have an influence. Methods: A total of 357 students in compulsory secondary education (age: mean 13.92, SD 1.91 y) participated in the study. A randomized controlled trial was conducted consisting of 2 consecutive 10-week interventions. Participants’ PA level, body composition, and fitness were measured at baseline (T1), after 10 weeks of mandatory and promoted app use (T2), and after 10 weeks of nonmandatory and nonpromoted app use (T3). Each participant in the experimental group (EG) used 1 of 4 selected step tracker mobile apps after school hours. Results: The results showed that when the use of the apps was neither mandatory nor promoted as a PE class assignment, only a few adolescents (18/216, 8.3%) continued the walking practice. After the mandatory and promoted intervention period (T1 vs T2), a decrease in the sum of 3 skinfolds (mean difference [MD] 1.679; P=.02) as well as improvements in the PA level (MD –0.170; P<.001), maximal oxygen uptake (MD –1.006; P<.001), countermovement jump test (MD –1.337; P=.04), curl-up test (MD –3.791; P<.001), and push-up test (MD –1.920; P<.001) in the EG were recorded. However, the changes between T1 and T2 were significantly greater in the EG than in the control group only in the PA level and curl-up test. Thus, when comparing the measurements taken between T1 and T3, no significant changes in body composition (P=.07) or fitness (P=.84) were observed between the EG and the control group. The covariates maturity status, gender, and specific app used showed a significant effect in most of the analyses performed. Conclusions: A period of mandatory and promoted use of step tracker mobile apps benefited the variables of body composition and fitness in adolescents but did not create a healthy walking habit in this population; therefore, when the use of these apps ceased to be mandatory and promoted, the effects obtained disappeared. Trial Registration: ClinicalTrials.gov NCT06164041; https://clinicaltrials.gov/study/NCT06164041 %M 39079110 %R 10.2196/51206 %U https://mhealth.jmir.org/2024/1/e51206 %U https://doi.org/10.2196/51206 %U http://www.ncbi.nlm.nih.gov/pubmed/39079110 %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 %@ 2561-326X %I JMIR Publications %V 8 %N %P e55575 %T Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers %A Xu,Qidi %A Kim,Yejin %A Chung,Karen %A Schulz,Paul %A Gottlieb,Assaf %+ McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, United States, 1 7135003698, assaf.gottlieb@uth.tmc.edu %K mild cognitive impairment %K Fitbits %K fitness trackers %K sleep %K physical activity %D 2024 %7 18.7.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI). Objective: This study aimed to use data collected from fitness trackers to predict MCI status. Methods: In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status. Results: Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time. Conclusions: Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies. %M 39024003 %R 10.2196/55575 %U https://formative.jmir.org/2024/1/e55575 %U https://doi.org/10.2196/55575 %U http://www.ncbi.nlm.nih.gov/pubmed/39024003 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e53180 %T Do Measures of Real-World Physical Behavior Provide Insights Into the Well-Being and Physical Function of Cancer Survivors? Cross-Sectional Analysis %A Bachman,Shelby L %A Gomes,Emma %A Aryal,Suvekshya %A Cella,David %A Clay,Ieuan %A Lyden,Kate %A Leach,Heather J %+ VivoSense, Inc, 27 Dorian, Newport Coast, CA, 92657, United States, 1 8588768486, shelby.bachman@vivosense.com %K accelerometer %K cancer survivorship %K cancer survivors %K digital health technology %K health-related quality of life %K physical behavior %K physical function %D 2024 %7 15.7.2024 %9 Original Paper %J JMIR Cancer %G English %X Background: As the number of cancer survivors increases, maintaining health-related quality of life in cancer survivorship is a priority. This necessitates accurate and reliable methods to assess how cancer survivors are feeling and functioning. Real-world digital measures derived from wearable sensors offer potential for monitoring well-being and physical function in cancer survivorship, but questions surrounding the clinical utility of these measures remain to be answered. Objective: In this secondary analysis, we used 2 existing data sets to examine how measures of real-world physical behavior, captured with a wearable accelerometer, were related to aerobic fitness and self-reported well-being and physical function in a sample of individuals who had completed cancer treatment. Methods: Overall, 86 disease-free cancer survivors aged 21-85 years completed self-report assessments of well-being and physical function, as well as a submaximal exercise test that was used to estimate their aerobic fitness, quantified as predicted submaximal oxygen uptake (VO2). A thigh-worn accelerometer was used to monitor participants’ real-world physical behavior for 7 days. Accelerometry data were used to calculate average values of the following measures of physical behavior: sedentary time, step counts, time in light and moderate to vigorous physical activity, time and weighted median cadence in stepping bouts over 1 minute, and peak 30-second cadence. Results: Spearman correlation analyses indicated that 6 (86%) of the 7 accelerometry-derived measures of real-world physical behavior were not significantly correlated with Functional Assessment of Cancer Therapy-General total well-being or linked Patient-Reported Outcomes Measurement Information System-Physical Function scores (Ps≥.08). In contrast, all but one of the physical behavior measures were significantly correlated with submaximal VO2 (Ps≤.03). Comparing these associations using likelihood ratio tests, we found that step counts, time in stepping bouts over 1 minute, and time in moderate to vigorous activity were more strongly associated with submaximal VO2 than with self-reported well-being or physical function (Ps≤.03). In contrast, cadence in stepping bouts over 1 minute and peak 30-second cadence were not more associated with submaximal VO2 than with the self-reported measures (Ps≥.08). Conclusions: In a sample of disease-free cancer survivors, we found that several measures of real-world physical behavior were more associated with aerobic fitness than with self-reported well-being and physical function. These results highlight the possibility that in individuals who have completed cancer treatment, measures of real-world physical behavior may provide additional information compared with self-reported and performance measures. To advance the appropriate use of digital measures in oncology clinical research, further research evaluating the clinical utility of real-world physical behavior over time in large, representative samples of cancer survivors is warranted. Trial Registration: ClinicalTrials.gov NCT03781154; https://clinicaltrials.gov/ct2/show/NCT03781154 %M 39008350 %R 10.2196/53180 %U https://cancer.jmir.org/2024/1/e53180 %U https://doi.org/10.2196/53180 %U http://www.ncbi.nlm.nih.gov/pubmed/39008350 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e51216 %T Deconstructing Fitbit to Specify the Effective Features in Promoting Physical Activity Among Inactive Adults: Pilot Randomized Controlled Trial %A Takano,Keisuke %A Oba,Takeyuki %A Katahira,Kentaro %A Kimura,Kenta %+ Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki, 305-8566, Japan, 81 298491456, keisuke.takano@aist.go.jp %K wearable activity tracker %K mHealth %K mobile health %K motivation %K physical activity %K lifestyle %K smartwatch %K wearables %K Fitbit %K exercise %K fitness %K BCT %K behavior change technique %K behavior change %K motivation %K adherence %K engagement %D 2024 %7 12.7.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers have become key players in mobile health practice as they offer various behavior change techniques (BCTs) to help improve physical activity (PA). Typically, multiple BCTs are implemented simultaneously in a device, making it difficult to identify which BCTs specifically improve PA. Objective: We investigated the effects of BCTs implemented on a smartwatch, the Fitbit, to determine how each technique promoted PA. Methods: This study was a single-blind, pilot randomized controlled trial, in which 70 adults (n=44, 63% women; mean age 40.5, SD 12.56 years; closed user group) were allocated to 1 of 3 BCT conditions: self-monitoring (feedback on participants’ own steps), goal setting (providing daily step goals), and social comparison (displaying daily steps achieved by peers). Each intervention lasted for 4 weeks (fully automated), during which participants wore a Fitbit and responded to day-to-day questionnaires regarding motivation. At pre- and postintervention time points (in-person sessions), levels and readiness for PA as well as different aspects of motivation were assessed. Results: Participants showed excellent adherence (mean valid-wear time of Fitbit=26.43/28 days, 94%), and no dropout was recorded. No significant changes were found in self-reported total PA (dz<0.28, P=.40 for the self-monitoring group, P=.58 for the goal setting group, and P=.19 for the social comparison group). Fitbit-assessed step count during the intervention period was slightly higher in the goal setting and social comparison groups than in the self-monitoring group, although the effects did not reach statistical significance (P=.052 and P=.06). However, more than half (27/46, 59%) of the participants in the precontemplation stage reported progress to a higher stage across the 3 conditions. Additionally, significant increases were detected for several aspects of motivation (ie, integrated and external regulation), and significant group differences were identified for the day-to-day changes in external regulation; that is, the self-monitoring group showed a significantly larger increase in the sense of pressure and tension (as part of external regulation) than the goal setting group (P=.04). Conclusions: Fitbit-implemented BCTs promote readiness and motivation for PA, although their effects on PA levels are marginal. The BCT-specific effects were unclear, but preliminary evidence showed that self-monitoring alone may be perceived demanding. Combining self-monitoring with another BCT (or goal setting, at least) may be important for enhancing continuous engagement in PA. Trial Registration: Open Science Framework; https://osf.io/87qnb/?view_only=f7b72d48bb5044eca4b8ce729f6b403b %M 38996332 %R 10.2196/51216 %U https://mhealth.jmir.org/2024/1/e51216 %U https://doi.org/10.2196/51216 %U http://www.ncbi.nlm.nih.gov/pubmed/38996332 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e51210 %T Heart Rate Monitoring Among Breast Cancer Survivors: Quantitative Study of Device Agreement in a Community-Based Exercise Program %A Page,Lindsey L %A Fanning,Jason %A Phipps,Connor %A Berger,Ann %A Reed,Elizabeth %A Ehlers,Diane %+ Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic Arizona, 200 First Street SW, Rochester, AZ, 55905, United States, 1 408 574 2739, ehlers.diane@mayo.edu %K wearable devices %K exercise prescription %K validity %K photoplethysmography %K monitoring %K wearables %K devices %K exercise %K heart rate %K breast cancer %K cancer %K cancer survivor %K community %K chest monitor %K Fitbit %K recovery %K safety %D 2024 %7 20.6.2024 %9 Original Paper %J JMIR Cancer %G English %X Background: Exercise intensity (eg, target heart rate [HR]) is a fundamental component of exercise prescription to elicit health benefits in cancer survivors. Despite the validity of chest-worn monitors, their feasibility in community and unsupervised exercise settings may be challenging. As wearable technology continues to improve, consumer-based wearable sensors may represent an accessible alternative to traditional monitoring, offering additional advantages. Objective: The purpose of this study was to examine the agreement between the Polar H10 chest monitor and Fitbit Inspire HR for HR measurement in breast cancer survivors enrolled in the intervention arm of a randomized, pilot exercise trial. Methods: Participants included breast cancer survivors (N=14; aged 38-72 years) randomized to a 12-week aerobic exercise program. This program consisted of three 60-minute, moderate-intensity walking sessions per week, either in small groups or one-on-one, facilitated by a certified exercise physiologist and held at local community fitness centers. As originally designed, the exercise prescription included 36 supervised sessions at a fitness center. However, due to the COVID-19 pandemic, the number of supervised sessions varied depending on whether participants enrolled before or after March 2020. During each exercise session, HR (in beats per minute) was concurrently measured via a Polar H10 chest monitor and a wrist-worn Fitbit Inspire HR at 5 stages: pre-exercise rest; midpoint of warm-up; midpoint of exercise session; midpoint of cool-down; and postexercise recovery. The exercise physiologist recorded the participant’s HR from each device at the midpoint of each stage. HR agreement between the Polar H10 and Fitbit Inspire HR was assessed using Lin concordance correlation coefficient (rc) with a 95% CI. Lin rc ranges from 0 to 1.00, with 0 indicating no concordance and 1.00 indicating perfect concordance. Relative error rates were calculated to examine differences across exercise session stages. Results: Data were available for 200 supervised sessions across the sample (session per participant: mean 13.33, SD 13.7). By exercise session stage, agreement between the Polar H10 monitor and the Fitbit was highest during pre-exercise seated rest (rc=0.76, 95% CI 0.70-0.81) and postexercise seated recovery (rc=0.89, 95% CI 0.86-0.92), followed by the midpoint of exercise (rc=0.63, 95% CI 0.55-0.70) and cool-down (rc=0.68, 95% CI 0.60-0.74). The agreement was lowest during warm-up (rc=0.39, 95% CI 0.27-0.49). Relative error rates ranged from –3.91% to 3.09% and were greatest during warm-up (relative error rate: mean –3.91, SD 11.92%). Conclusions: The Fitbit overestimated HR during peak exercise intensity, posing risks for overexercising, which may not be safe for breast cancer survivors’ fitness levels. While the Fitbit Inspire HR may be used to estimate exercise HR, precautions are needed when considering participant safety and data interpretation. Trial Registration: Clinicaltrials.gov NCT03980626; https://clinicaltrials.gov/study/NCT03980626?term=NCT03980626&rank=1 %M 38900505 %R 10.2196/51210 %U https://cancer.jmir.org/2024/1/e51210 %U https://doi.org/10.2196/51210 %U http://www.ncbi.nlm.nih.gov/pubmed/38900505 %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 %@ 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 %@ 2561-7605 %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 %@ 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 %@ 2561-326X %I JMIR Publications %V 8 %N %P e52312 %T Accuracy of the Apple Watch Series 4 and Fitbit Versa for Assessing Energy Expenditure and Heart Rate of Wheelchair Users During Treadmill Wheelchair Propulsion: Cross-sectional Study %A Danielsson,Marius Lyng %A Vergeer,Melanie %A Plasqui,Guy %A Baumgart,Julia Kathrin %+ Centre for Elite Sports Research, Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Smistadvegen 11, Trondheim, 7026, Norway, +47 47863154, mlyngd@gmail.com %K agreement %K validity %K accuracy %K cross sectional %K physiology %K disability %K disabled %K upper-body exercise %K upper body %K exercise %K physical activity %K ergospirometer %K fitness %K vital %K vitals %K energy %K expenditure %K mHealth %K wearable %K wearables %K mobile health %K smartwatch %K smartwatches %K apple watch %K fitbit %K digital health %K energy expenditure %K heart rate %K wheelchair %K wheelchairs %K fitness trackers %K tracker %K trackers %D 2024 %7 7.5.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: The Apple Watch (AW) Series 1 provides energy expenditure (EE) for wheelchair users but was found to be inaccurate with an error of approximately 30%, and the corresponding error for heart rate (HR) provided by the Fitbit Charge 2 was approximately 10% to 20%. Improved accuracy of estimated EE and HR is expected with newer editions of these smart watches (SWs). Objective: This study aims to assess the accuracy of the AW Series 4 (wheelchair-specific setting) and the Fitbit Versa (treadmill running mode) for estimating EE and HR during wheelchair propulsion at different intensities. Methods: Data from 20 manual wheelchair users (male: n=11, female: n=9; body mass: mean 75, SD 19 kg) and 20 people without a disability (male: n=11, female: n=9; body mass: mean 75, SD 11 kg) were included. Three 4-minute wheelchair propulsion stages at increasing speed were performed on 3 separate test days (0.5%, 2.5%, or 5% incline), while EE and HR were collected by criterion devices and the AW or Fitbit. The mean absolute percentage error (MAPE) was used to indicate the absolute agreement between the criterion device and SWs for EE and HR. Additionally, linear mixed model analyses assessed the effect of exercise intensity, sex, and group on the SW error. Interclass correlation coefficients were used to assess relative agreement between criterion devices and SWs. Results: The AW underestimated EE with MAPEs of 29.2% (SD 22%) in wheelchair users and 30% (SD 12%) in people without a disability. The Fitbit overestimated EE with MAPEs of 73.9% (SD 7%) in wheelchair users and 44.7% (SD 38%) in people without a disability. Both SWs underestimated HR. The device error for EE and HR increased with intensity for both SWs (all comparisons: P<.001), and the only significant difference between groups was found for HR in the AW (–5.27 beats/min for wheelchair users; P=.02). There was a significant effect of sex on the estimation error in EE, with worse accuracy for the AW (–0.69 kcal/min; P<.001) and better accuracy for the Fitbit (–2.08 kcal/min; P<.001) in female participants. For HR, sex differences were found only for the AW, with a smaller error in female participants (5.23 beats/min; P=.02). Interclass correlation coefficients showed poor to moderate relative agreement for both SWs apart from 2 stage-incline combinations (AW: 0.12-0.57 for EE and 0.11-0.86 for HR; Fitbit: 0.06-0.85 for EE and 0.03-0.29 for HR). Conclusions: Neither the AW nor Fitbit were sufficiently accurate for estimating EE or HR during wheelchair propulsion. The AW underestimated EE and the Fitbit overestimated EE, and both SWs underestimated HR. Caution is hence required when using SWs as a tool for training intensity regulation and energy balance or imbalance in wheelchair users. %M 38713497 %R 10.2196/52312 %U https://formative.jmir.org/2024/1/e52312 %U https://doi.org/10.2196/52312 %U http://www.ncbi.nlm.nih.gov/pubmed/38713497 %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 %@ 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 %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55156 %T KIJANI App to Promote Physical Activity in Children and Adolescents: Protocol for a Mixed Method Evaluation %A Willinger,Laura %A Böhm,Birgit %A Schweizer,Florian %A Reimer,Lara Marie %A Jonas,Stephan %A Scheller,Daniel A %A Oberhoffer-Fritz,Renate %A Müller,Jan %+ Chair of Preventive Pediatrics, Technical University of Munich, Georg-Brauchle-Ring 60/62, 80992 München, Munich, Germany, 49 28924900, laura.willinger@tum.de %K physical activity %K health promotion %K digital health %K gamification %K childhood %K adolescence %K adolescents %K adolescent %K children %K augmented reality %K KIJANI intervention %K KIJANI %K intervention %K user experience %D 2024 %7 3.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The prevalence of physical inactivity among children and adolescents is alarmingly high despite the well-documented and comprehensive benefits of regular physical activity (PA). Therefore, PA promotion should start early in childhood and adolescence. Although reducing recreational screen time in children and adolescents is an urgent concern, digital approaches have the potential to make activity promotion attractive and age appropriate for the target group. KIJANI is a mobile app approach to promote PA in children and adolescents via gamification and augmented reality. Objective: This study protocol aims to describe the KIJANI intervention in detail, as well as the evaluation approach. Methods: KIJANI is based on the concept that virtual coins can be earned through PA, for example, in the form of a collected step count. With these coins, in turn, blocks can be bought, which can be used to create virtual buildings and integrate them into the player’s real-world environment via augmented reality. PA of users is detected via accelerometers integrated into the smartphones. KIJANI can be played at predefined play locations that were comprehensively identified as safe, child-friendly, and attractive for PA by the target group in a partner project. The evaluation process will be divided into 2 different stages. The phase-I evaluation will be a mixed methods approach with one-on-one semistructured interviews and questionnaires to evaluate the user experience and receive feedback from the target group. After the implementation of results and feedback from the target group, the phase-II evaluation will proceed in the form of a 2-arm randomized controlled trial, in which the effectiveness of KIJANI will be assessed via objectively measured PA as well as questionnaires. Results: The study received ethical approval from the ethical board of the Technical University of Munich. Participants for the phase-I evaluation are currently being recruited. Conclusions: The study will help to determine the efficacy, applicability, and user experience of a gamified activity promotion application in children and adolescents. Overall, digital health approaches provide easy and wide reachability at low cost and are age appropriate and attractive for the target group of adolescents. Strategies have to be developed to apply digital health approaches in the best possible way for activity promotion. International Registered Report Identifier (IRRID): DERR1-10.2196/55156 %M 38700911 %R 10.2196/55156 %U https://www.researchprotocols.org/2024/1/e55156 %U https://doi.org/10.2196/55156 %U http://www.ncbi.nlm.nih.gov/pubmed/38700911 %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 %@ 1929-0748 %I JMIR Publications %V 13 %N %P e52898 %T Gait Features in Different Environments Contributing to Participation in Outdoor Activities in Old Age (GaitAge): Protocol for an Observational Cross-Sectional Study %A Rantakokko,Merja %A Matikainen-Tervola,Emmi %A Aartolahti,Eeva %A Sihvonen,Sanna %A Chichaeva,Julija %A Finni,Taija %A Cronin,Neil %+ Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, PO BOX 35 (viv), Jyväskylä, 40014, Finland, 358 503016860, merja.h.rantakokko@jyu.fi %K walking %K aging %K environment %K biomechanics %K kinematics %K spatiotemporal %K gait %K GaitAge %K observational cross-sectional study %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 health disparities %K health disparity %K assessment %K assessments %K physical test %K physical tests %K interview %K interviews %K biomechanic %K activities %K outdoor %K activity %K movement analysis %K analysis of walk %K posture %K free living %D 2024 %7 29.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The ability to walk is a key issue for independent old age. Optimizing older peoples’ opportunities for an autonomous and active life and reducing health disparities requires a better understanding of how to support independent mobility in older people. With increasing age, changes in gait parameters such as step length and cadence are common and have been shown to increase the risk of mobility decline. However, gait assessments are typically based on laboratory measures, even though walking in a laboratory environment may be significantly different from walking in outdoor environments. Objective: This project will study alterations in biomechanical features of gait by comparing walking on a treadmill in a laboratory, level outdoor, and hilly outdoor environments. In addition, we will study the possible contribution of changes in gait between these environments to outdoor mobility among older people. Methods: Participants of the study were recruited through senior organizations of Central Finland and the University of the Third Age, Jyväskylä. Inclusion criteria were community-dwelling, aged 70 years and older, able to walk at least 1 km without assistive devices, able to communicate, and living in central Finland. Exclusion criteria were the use of mobility devices, severe sensory deficit (vision and hearing), memory impairment (Mini-Mental State Examination ≤23), and neurological conditions (eg, stroke, Parkinson disease, and multiple sclerosis). The study protocol included 2 research visits. First, indoor measurements were conducted, including interviews (participation, health, and demographics), physical performance tests (short physical performance battery and Timed Up and Go), and motion analysis on a treadmill in the laboratory (3D Vicon and next-generation inertial measurement units [NGIMUs]). Second, outdoor walking tests were conducted, including walking on level (sports track) and hilly (uphill and downhill) terrain, while movement was monitored via NGIMUs, pressure insoles, heart rate, and video data. Results: A total of 40 people (n=26, 65% women; mean age 76.3, SD 5.45 years) met the inclusion criteria and took part in the study. Data collection took place between May and September 2022. The first result is expected to be published in the spring of 2024. Conclusions: This multidisciplinary study will provide new scientific knowledge about how gait biomechanics are altered in varied environments, and how this influences opportunities to participate in outdoor activities for older people. International Registered Report Identifier (IRRID): RR1-10.2196/52898 %M 38684085 %R 10.2196/52898 %U https://www.researchprotocols.org/2024/1/e52898 %U https://doi.org/10.2196/52898 %U http://www.ncbi.nlm.nih.gov/pubmed/38684085 %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 %@ 2561-326X %I JMIR Publications %V 8 %N %P e48783 %T Comparison of Self-Tracking Health Practices, eHealth Literacy, and Subjective Well-Being Between College Students With and Without Disabilities: Cross-Sectional Survey %A Choi,Soyoung %+ Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, 272 Freer Hall, 906 S. Goodwin Ave, Urbana, IL, 61801, United States, 1 2173332573, soyoung@illinois.edu %K college students %K personal health data %K self-tracking %K eHealth literacy %K well-being %K tracking %K students %K disability %K cross-sectional survey %K pediatric care %K adult care %K smartphone health app %K application %K literacy %D 2024 %7 10.4.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: College students with disabilities need to transition from pediatric-centered care to adult care. However, they may become overwhelmed by multiple responsibilities, such as academic activities, peer relationships, career preparation, job seeking, independent living, as well as managing their health and promoting healthy behaviors. Objective: As the use of smartphones and wearable devices for collecting personal health data becomes popular, this study aimed to compare the characteristics of self-tracking health practices between college students with disabilities and their counterparts. In addition, this study examined the relationships between disability status, self-tracking health practices, eHealth literacy, and subjective well-being among college students. Methods: The web-based questionnaire was designed using Qualtrics for the cross-sectional online survey. The survey data were collected from February 2023 to April 2023 and included responses from 702 participants. Results: More than 80% (563/702, 80.2%) of the respondents participated voluntarily in self-tracking health practices. College students with disabilities (n=83) showed significantly lower levels of eHealth literacy and subjective well-being compared with college students without disabilities (n=619). The group with disabilities reported significantly lower satisfaction (t411=–5.97, P<.001) and perceived efficacy (t411=–4.85, P<.001) when using smartphone health apps and wearable devices. Finally, the study identified a significant correlation between subjective well-being in college students and disability status (β=3.81, P<.001), self-tracking health practices (β=2.22, P=.03), and eHealth literacy (β=24.29, P<.001). Conclusions: Given the significant relationships among disability status, self-tracking health practices, eHealth literacy, and subjective well-being in college students, it is recommended to examine their ability to leverage digital technology for self-care. Offering learning opportunities to enhance eHealth literacy and self-tracking health strategies within campus environments could be a strategic approach to improve the quality of life and well-being of college students. %M 38598285 %R 10.2196/48783 %U https://formative.jmir.org/2024/1/e48783 %U https://doi.org/10.2196/48783 %U http://www.ncbi.nlm.nih.gov/pubmed/38598285 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 11 %N %P e52733 %T Exploring the Major Barriers to Physical Activity in Persons With Multiple Sclerosis: Observational Longitudinal Study %A Sieber,Chloé %A Haag,Christina %A Polhemus,Ashley %A Haile,Sarah R %A Sylvester,Ramona %A Kool,Jan %A Gonzenbach,Roman %A von Wyl,Viktor %+ Institute for Implementation Science in Health Care, University of Zurich, Universitätstrasse 84, Zurich, 8006, Switzerland, 41 44 63 46380, viktor.vonwyl@uzh.ch %K physical activity %K barriers to physical activity %K Barriers to Health Promoting Activities for Disabled Persons scale %K BHADP scale %K multiple sclerosis %K Fitbit %K wearable %D 2024 %7 18.3.2024 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Physical activity (PA) represents a low-cost and readily available means of mitigating multiple sclerosis (MS) symptoms and alleviating the disease course. Nevertheless, persons with MS engage in lower levels of PA than the general population. Objective: This study aims to enhance the understanding of the barriers to PA engagement in persons with MS and to evaluate the applicability of the Barriers to Health Promoting Activities for Disabled Persons (BHADP) scale for assessing barriers to PA in persons with MS, by comparing the BHADP score with self-reported outcomes of fatigue, depression, self-efficacy, and health-related quality of life, as well as sensor-measured PA. Methods: Study participants (n=45; median age 46, IQR 40-51 years; median Expanded Disability Status Scale score 4.5, IQR 3.5-6) were recruited among persons with MS attending inpatient neurorehabilitation. They wore a Fitbit Inspire HR (Fitbit Inc) throughout their stay at the rehabilitation clinic (phase 1; 2-4 wk) and for the 4 following weeks at home (phase 2; 4 wk). Sensor-based step counts and cumulative minutes in moderate to vigorous PA were computed for the last 7 days at the clinic and at home. On the basis of PA during the last 7 end-of-study days, we grouped the study participants as active (≥10,000 steps/d) and less active (<10,000 steps/d) to explore PA barriers compared with PA level. PA barriers were repeatedly assessed through the BHADP scale. We described the relevance of the 18 barriers of the BHADP scale assessed at the end of the study and quantified their correlations with the Spearman correlation test. We evaluated the associations of the BHADP score with end-of-study reported outcomes of fatigue, depression, self-efficacy, and health-related quality of life with multivariable regression models. We performed separate regression analyses to examine the association of the BHADP score with different sensor-measured outcomes of PA. Results: The less active group reported higher scores for the BHADP items Feeling what I do doesn’t help, No one to help me, and Lack of support from family/friends. The BHADP items Not interested in PA and Impairment were positively correlated. The BHADP score was positively associated with measures of fatigue and depression and negatively associated with self-efficacy and health-related quality of life. The BHADP score showed an inverse relationship with the level of PA measured but not when dichotomized according to the recommended PA level thresholds. Conclusions: The BHADP scale is a valid and well-adapted tool for persons with MS because it reflects common MS symptoms such as fatigue and depression, as well as self-efficacy and health-related quality of life. Moreover, decreases in PA levels are often related to increases in specific barriers in the lives of persons with MS and should hence be addressed jointly in health care management. %M 38498024 %R 10.2196/52733 %U https://rehab.jmir.org/2024/1/e52733 %U https://doi.org/10.2196/52733 %U http://www.ncbi.nlm.nih.gov/pubmed/38498024 %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 %@ 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 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 %@ 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 %@ 2561-7605 %I JMIR Publications %V 6 %N %P e41549 %T Acceptance of the Apple Watch Series 6 for Telemonitoring of Older Adults With Chronic Obstructive Pulmonary Disease: Qualitative Descriptive Study Part 1 %A Arnaert,Antonia %A Sumbly,Pia %A da Costa,Daniel %A Liu,Yuxin %A Debe,Zoumanan %A Charbonneau,Sylvain %+ Ingram School of Nursing, McGill University, 680 Sherbrooke West, Montreal, QC, H3A 2M7, Canada, 1 514 398 5624, antonia.arnaert@mcgill.ca %K Apple Watch %K chronic obstructive pulmonary disease %K digital health %K older adults %K qualitative descriptive %K technology acceptance %K telemonitoring %D 2023 %7 26.12.2023 %9 Original Paper %J JMIR Aging %G English %X Background: The Apple Watch is not a medical device per se; it is a smart wearable device that is increasingly being used for health monitoring. Evidence exists that the Apple Watch Series 6 can reliably measure blood oxygen saturation (SpO2) in patients with chronic obstructive pulmonary disease under controlled circumstances. Objective: This study aimed to better understand older adults’ acceptance of the Watch as a part of telemonitoring, even with these advancements. Methods: This study conducted content analysis on data collected from 10 older adults with chronic obstructive pulmonary disease who consented to wear the Watch. Results: Using the Extended Unified Theory of Acceptance and Use of Technology model, results showed that participants experienced potential health benefits; however, the inability of the Watch to reliably measure SpO2 when in respiratory distress was concerning. Participants’ level of tech savviness varied, which caused some fear and frustration at the start, yet all felt supported by family and would have explored more features if they owned the Watch. All agreed that the Watch is mainly a medical tool and not a gadget. Conclusions: To conclude, although the Watch may enhance their physical health and well-being, results indicated that participants are more likely to accept the Watch if it ultimately proves to be useful when experiencing respiratory distress. %M 38147371 %R 10.2196/41549 %U https://aging.jmir.org/2023/1/e41549 %U https://doi.org/10.2196/41549 %U http://www.ncbi.nlm.nih.gov/pubmed/38147371 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e52519 %T Assessment of Heart Rate Monitoring During Exercise With Smart Wristbands and a Heart Rhythm Patch: Validation and Comparison Study %A Wang,Tse-Lun %A Wu,Hao-Yi %A Wang,Wei-Yun %A Chen,Chao-Wen %A Chien,Wu-Chien %A Chu,Chi-Ming %A Wu,Yi-Syuan %+ Division of Trauma and Surgical Critical Care, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, PO Box 48, Kaohsiung City, 807, Taiwan, 886 73121101 ext 6020, pu1254@gmail.com %K running %K wearable device %K photoplethysmography %K heart rhythm patch, smart wristband %D 2023 %7 14.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: The integration of wearable devices into fitness routines, particularly in military settings, necessitates a rigorous assessment of their accuracy. This study evaluates the precision of heart rate measurements by locally manufactured wristbands, increasingly used in military academies, to inform future device selection for military training activities. Objective: This research aims to assess the reliability of heart rate monitoring in chest straps versus wearable wristbands. Methods: Data on heart rate and acceleration were collected using the Q-Band Q-69 smart wristband (Mobile Action Technology Inc) and compared against the Zephyr Bioharness standard measuring device. The Lin concordance correlation coefficient, Pearson product moment correlation coefficient, and intraclass correlation coefficient were used for reliability analysis. Results: Participants from a Northern Taiwanese medical school were enrolled (January 1-June 31, 2021). The Q-Band Q-69 demonstrated that the mean absolute percentage error (MAPE) of women was observed to be 13.35 (SD 13.47). Comparatively, men exhibited a lower MAPE of 8.54 (SD 10.49). The walking state MAPE was 7.79 for women and 10.65 for men. The wristband’s accuracy generally remained below 10% MAPE in other activities. Pearson product moment correlation coefficient analysis indicated gender-based performance differences, with overall coefficients of 0.625 for women and 0.808 for men, varying across walking, running, and cooldown phases. Conclusions: This study highlights significant gender and activity-dependent variations in the accuracy of the MobileAction Q-Band Q-69 smart wristband. Reduced accuracy was notably observed during running. Occasional extreme errors point to the necessity of caution in relying on such devices for exercise monitoring. The findings emphasize the limitations and potential inaccuracies of wearable technology, especially in high-intensity physical activities. %M 38096010 %R 10.2196/52519 %U https://formative.jmir.org/2023/1/e52519 %U https://doi.org/10.2196/52519 %U http://www.ncbi.nlm.nih.gov/pubmed/38096010 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e48704 %T Wearable Activity Trackers That Motivate Women to Increase Physical Activity: Mixed Methods Study %A Peterson,Neil E %A Bate,Danielle A %A Macintosh,Janelle LB %A Trujillo Tanner,Corinna %+ College of Nursing, Brigham Young University, 500 Spencer W. Kimball Tower, Provo, UT, 84602, United States, 1 8014224893, neil-peterson@byu.edu %K physical activity %K women %K motivation %K wearable activity trackers %K mobile health %K mHealth %K self-determination %K mobile phone %D 2023 %7 14.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical inactivity is a significant public health concern, particularly among women in the United States. Wearable activity trackers (WATs) have been proposed as a potential solution to increase awareness of and engagement in physical activity (PA). However, to be effective, WATs must include features and designs that encourage daily use. Objective: This study aims to explore the features and designs of WATs that appeal to women and determine whether devices with these attributes are effective motivators for women to be physically active. Methods: A mixed methods study guided by the self-determination theory was conducted among 15 women. Participants trialed 3 WATs with influence in their respective accessory domains: Apple Watch for the wrist; Oura Ring for the finger; and Bellabeat Leaf Urban for multiple sites (it can be worn as a bracelet, necklace, or clip). Participants documented their daily PA levels and rated their satisfaction with each device’s comfort, features, and motivational effect. Focus groups were also conducted to gather additional feedback and experiences within the a priori areas of comfort, features, and motivation. Results: Behavioral Regulation in Exercise Questionnaire–2 scores indicated that most participants (14/15, 93%) were motivated at baseline (amotivation score: mean 0.13, SD 0.45), but on average, participants did not meet the national minimum PA guidelines according to the self-reported Physical Activity Vital Sign questionnaire (moderate to vigorous PA score: mean 144, SD 97.5 min/wk). Mean WAT wear time was 16.9 (SD 4.4) hours, 19.4 (SD 5.3) hours, and 20.4 (SD 4.7) hours for Apple Watch, Bellabeat Leaf Urban, and Oura Ring, respectively. During focus groups, participants reinforced their quantitative ratings and rankings of the WATs based on personal experiences. Participants shared a variety of both activity-related and non–activity-related features that they look for in a motivating device. When considering what the ideal WAT would be for a woman, participants suggested features of (1) comfort, (2) extended battery life, (3) durability, (4) immediate PA feedback, (5) intuitive PA sensing, and (6) programmability. Conclusions: This study is the first to specifically address women’s experiences with and preferences for different types of WATs. Those who work with women should realize how they view WATs and the role they play in motivation to be active. %M 38096000 %R 10.2196/48704 %U https://formative.jmir.org/2023/1/e48704 %U https://doi.org/10.2196/48704 %U http://www.ncbi.nlm.nih.gov/pubmed/38096000 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e50729 %T Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals %A Lu,Ziyuan %A Signer,Tabea %A Sylvester,Ramona %A Gonzenbach,Roman %A von Wyl,Viktor %A Haag,Christina %+ Institute for Implementation Science in Health Care, University of Zurich, Universitätstrasse 84, Zurich, 8006, Switzerland, 41 446346380, viktor.vonwyl@uzh.ch %K physical activity %K activity sensor %K normalization process theory %K rehabilitation %K chronic disease %K chronic %K aftercare %K sensor %K sensors %K exercise %K neurology %K neuroscience %K neurorehabilitation %K adherence %K need %K needs %K experience %K experiences %K questionnaire %K questionnaires %K mobile phone %D 2023 %7 8.12.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity is central to maintaining the quality of life for patients with complex chronic conditions and is thus at the core of neurorehabilitation. However, maintaining activity improvements in daily life is challenging. The novel Stay With It program aims to promote physical activity after neurorehabilitation by cultivating self-monitoring skills and habits. Objective: We examined the implementation of the Stay With It program at the Valens Rehabilitation Centre in Switzerland using the normalization process theory framework, focusing on 3 research aims. We aimed to examine the challenges and facilitators of program implementation from the perspectives of patients and health care professionals. We aimed to evaluate the potential of activity sensors to support program implementation and patient acceptance. Finally, we aimed to evaluate patients’ engagement in physical activity after rehabilitation, patients’ self-reported achievement of home activity goals, and factors influencing physical activity. Methods: Patients were enrolled if they had a disease that was either chronic or at risk for chronicity and participated in the Stay With It program. Patients were assessed at baseline, the end of rehabilitation, and a 3-month follow-up. The health care professionals designated to deliver the program were surveyed before and after program implementation. We used a mixed methods approach combining standardized questionnaires, activity-sensing data (patients only), and free-text questions. Results: This study included 23 patients and 13 health care professionals. The diverse needs of patients and organizational hurdles were major challenges to program implementation. Patients’ intrinsic motivation and health care professionals’ commitment to refining the program emerged as key facilitators. Both groups recognized the value of activity sensors in supporting program implementation and sustainability. Although patients appreciated the sensor’s ability to monitor, motivate, and quantify activity, health care professionals saw the sensor as a motivational tool but expressed concerns about technical difficulties and potential inaccuracies. Physical activity levels after patients returned home varied considerably, both within and between individuals. The self-reported achievement of activity goals at home also varied, in part because of vague definitions. Common barriers to maintaining activity at home were declining health and fatigue often resulting from heat and pain. At the 3-month follow-up, 35% (8/23) of the patients withdrew from the study, with most citing deteriorating physical health as the reason and that monitoring and discussing their low activity would negatively affect their mental health. Conclusions: Integrating aftercare programs like Stay With It into routine care is vital for maintaining physical activity postrehabilitation. Although activity trackers show promise in promoting motivation through monitoring, they may lead to frustration during health declines. Their acceptability may also be influenced by an individual’s health status, habits, and technical skills. Our study highlights the importance of considering health care professionals’ perspectives when integrating new interventions into routine care. %M 38064263 %R 10.2196/50729 %U https://mhealth.jmir.org/2023/1/e50729 %U https://doi.org/10.2196/50729 %U http://www.ncbi.nlm.nih.gov/pubmed/38064263 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 6 %N %P e44425 %T Within-Person Associations of Accelerometer-Assessed Physical Activity With Time-Varying Determinants in Older Adults: Time-Based Ecological Momentary Assessment Study %A Maes,Iris %A Mertens,Lieze %A Poppe,Louise %A Vetrovsky,Tomas %A Crombez,Geert %A De Backere,Femke %A Brondeel,Ruben %A Van Dyck,Delfien %+ Department of Movement and Sports Sciences, Ghent University, Watersportlaan 2, Ghent, 9000, Belgium, 32 9 264 63 63, iris.maes@ugent.be %K ecological momentary assessment %K EMA %K associations %K emotions %K physical concerns %K intention %K self-efficacy %K older adult %K mobile phone %D 2023 %7 23.11.2023 %9 Original Paper %J JMIR Aging %G English %X Background: Despite the availability of physical activity (PA) interventions, many older adults are still not active enough. This might be partially explained by the often-limited effects of PA interventions. In general, health behavior change interventions often do not focus on contextual and time-varying determinants, which may limit their effectiveness. However, before the dynamic tailoring of interventions can be developed, one should know which time-dependent determinants are associated with PA and how strong these associations are. Objective: The aim of this study was to examine within-person associations between multiple determinants of the capability, opportunity, motivation, and behavior framework assessed using Ecological Momentary Assessment (EMA) and accelerometer-assessed light PA, moderate to vigorous PA, and total PA performed at 15, 30, 60, and 120 minutes after the EMA trigger. Methods: Observational data were collected from 64 healthy older adults (36/64, 56% men; mean age 72.1, SD 5.6 y). Participants were asked to answer a time-based EMA questionnaire 6 times per day that assessed emotions (ie, relaxation, satisfaction, irritation, and feeling down), the physical complaint fatigue, intention, intention, and self-efficacy. An Axivity AX3 was wrist worn to capture the participants’ PA. Multilevel regression analyses in R were performed to examine these within-person associations. Results: Irritation, feeling down, intention, and self-efficacy were positively associated with subsequent light PA or moderate to vigorous PA at 15, 30, 60, or 120 minutes after the trigger, whereas relaxation, satisfaction, and fatigue were negatively associated. Conclusions: Multiple associations were observed in this study. This knowledge in combination with the time dependency of the determinants is valuable information for future interventions so that suggestions to be active can be provided when the older adult is most receptive. %M 37995131 %R 10.2196/44425 %U https://aging.jmir.org/2023/1/e44425 %U https://doi.org/10.2196/44425 %U http://www.ncbi.nlm.nih.gov/pubmed/37995131 %0 Journal Article %@ 2291-5222 %I %V 11 %N %P e47891 %T Technical Assistance Received by Older Adults to Use Commercially Available Physical Activity Monitors (Ready Steady 3.0 Trial): Ad-Hoc Descriptive Longitudinal Study %A Choma,Elizabeth A %A Hayes,Shannon %A Lewis,Beth A %A Rothman,Alexander J %A Wyman,Jean F %A Guan,Weihua %A McMahon,Siobhan K %K wearable device %K digital health %K physical activity monitor %K PAM %K older adult %K intervention %K physical activity %K usability %K technical assistance %K supportive structures %K monitoring %K promote %K community %K support %D 2023 %7 22.11.2023 %9 %J JMIR Mhealth Uhealth %G English %X Background: Despite evidence that regular physical activity (PA) among older adults confers numerous health and functional benefits, PA participation rates are low. Using commercially available wearable PA monitors (PAMs) is one way to augment PA promotion efforts. However, while expert recommendations exist for the specific information needed at the beginning of PAM ownership and the general ongoing need for structures that support as-needed technical troubleshooting, information is lacking about the type, frequency, and modes of assistance needed during initial and long-term ownership. Objective: This paper describes problems reported and technical assistance received by older adults who used PAMs during the 18 months they participated in a community-based PA trial: Ready Steady 3.0 (RS3). Methods: This was an ad-hoc longitudinal analysis of process variables representing technical problems reported and assistance received by 113 RS3 study participants in the 18 months after their orientation to PAMs. Variables included date of contact, problem(s) reported, mode of technical assistance, and whether the equipment was replaced. The descriptive analysis included frequencies and incidence rates of distinct contacts, types of problems, and technical assistance modes. Results: On average, participants were aged 77 (SD 5.2) years. Most identified as female (n=87, 77%), reported experience using smartphones (n=92, 81.4%), and used the PAM between 2 and 18 months. Eighty-two participants (72.6%) reported between 1 to 9 problems with using PAMs, resulting in a total of 150 technical assistance contacts with a mean of 1.3 (SD 1.3) contacts. The incidence rate of new, distinct contacts for technical assistance was 99 per 100 persons per year from 2018 to 2021. The most common problems were wearing the PAM (n=43, 28.7%), reading its display (n=23, 15.3%), logging into its app (n=20, 13.3%), charging it (n=18, 12%), and synchronizing it to the app (n=16, 10.7%). The modalities of technical assistance were in person (n=53, 35.3%), by telephone (n=51, 34%), by email (n=25, 16.7%), and by postal mail (n=21, 14%). Conclusions: In general, the results of this study show that after receiving orientation to PAMs, problems such as uncomfortable wristbands, difficulty using the PAM or its related app, and obtaining or interpreting relevant personal data were occasionally reported by participants in RS3. Trained staff helped participants troubleshoot and solve these technical problems primarily in person or by phone. Results also underscore the importance of involving older adults in the design, usability testing, and supportive material development processes to prevent technical problems for the initial and ongoing use of PAMs. Clinicians and researchers should further assess technical assistance needed by older adults, accounting for variations in PAM models and wear time, while investigating additional assistance strategies, such as proactive support, short GIF videos, and video calls. %R 10.2196/47891 %U https://mhealth.jmir.org/2023/1/e47891 %U https://doi.org/10.2196/47891 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e49144 %T Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data %A Jang,Hyunchul %A Lee,Siwoo %A Son,Yunhee %A Seo,Sumin %A Baek,Younghwa %A Mun,Sujeong %A Kim,Hoseok %A Kim,Icktae %A Kim,Junho %+ KM Data Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon, 34054, Republic of Korea, 82 42 868 9555, bfree@kiom.re.kr %K sleep %K sleep time %K chat %K self-report %K sleep log %K sleep diary %K wearables %K Fitbit %K patient-generated health data %K PGHD %D 2023 %7 21.11.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient’s subjective response is still important information that cannot be replaced by wearable devices. Objective: To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals’ time perception in comparison with wearable data. Methods: Sleep data were acquired for 2 weeks using a Fitbit. Participants’ sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared. Results: In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants’ actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant’s AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively. Conclusions: The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants’ sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices. %M 37988148 %R 10.2196/49144 %U https://mhealth.jmir.org/2023/1/e49144 %U https://doi.org/10.2196/49144 %U http://www.ncbi.nlm.nih.gov/pubmed/37988148 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 6 %N %P e41539 %T Experiences of Patients With Chronic Obstructive Pulmonary Disease Using the Apple Watch Series 6 Versus the Traditional Finger Pulse Oximeter for Home SpO2 Self-Monitoring: Qualitative Study Part 2 %A Liu,Yuxin %A Arnaert,Antonia %A da Costa,Daniel %A Sumbly,Pia %A Debe,Zoumanan %A Charbonneau,Sylvain %+ Ingram School of Nursing, McGill University, 680 Sherbrooke West, Montreal, QC, H3A 2M7, Canada, 1 514 726 0235, antonia.arnaert@mcgill.ca %K Apple Watch %K chronic obstructive pulmonary disease %K pulse oximeter %K qualitative descriptive %K self-monitoring %K smartwatch %D 2023 %7 2.11.2023 %9 Original Paper %J JMIR Aging %G English %X Background: Amid the rise in mobile health, the Apple Watch now has the capability to measure peripheral blood oxygen saturation (SpO2). Although the company indicated that the Watch is not a medical device, evidence suggests that SpO2 measurements among patients with chronic obstructive pulmonary disease (COPD) are accurate in controlled settings. Yet, to our knowledge, the SpO2 function has not been validated for patients with COPD in naturalistic settings. Objective: This qualitative study explored the experiences of patients with COPD using the Apple Watch Series 6 versus a traditional finger pulse oximeter for home SpO2 self-monitoring. Methods: We conducted individual semistructured interviews with 8 female and 2 male participants with moderate to severe COPD, and transcripts were qualitatively analyzed. All received a watch to monitor their SpO2 for 5 months. Results: Due to respiratory distress, the watch was unable to collect reliable SpO2 measurements, as it requires the patient to remain in a stable position. However, despite the physical limitations and lack of reliable SpO2 values, participants expressed a preference toward the watch. Moreover, participants’ health needs and their unique accessibility experiences influenced which device was more appropriate for self-monitoring purposes. Overall, all shared the perceived importance of prioritizing their physical COPD symptoms over device selection to manage their disease. Conclusions: Differing results between participant preferences and smartwatch limitations warrant further investigation into the reliability and accuracy of the SpO2 function of the watch and the balance among self-management, medical judgment, and dependence on self-monitoring technology. %M 37917147 %R 10.2196/41539 %U https://aging.jmir.org/2023/1/e41539 %U https://doi.org/10.2196/41539 %U http://www.ncbi.nlm.nih.gov/pubmed/37917147 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e48270 %T Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study %A Kowahl,Nathan %A Shin,Sooyoon %A Barman,Poulami %A Rainaldi,Erin %A Popham,Sara %A Kapur,Ritu %+ Verily Life Sciences, 269 E Grand Ave, South San Francisco, CA, 94080, United States, 1 415 504 2681, natekowahl@verily.com %K digital measurements %K wearable technology %K mobility measurements %K walking patterns %K wearable %K wearables %K sensor %K sensors %K mobility %K measurement %K measurements %K walk %K walking %K gait %K step %K wrist-worn %K reliability %K accuracy %D 2023 %7 3.8.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Mobility is a meaningful aspect of an individual’s health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring. Objective: Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies. Methods: We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure. Results: In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device–based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P<.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of >0.75 by 16 days of wear time. Conclusions: We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions. %M 37535417 %R 10.2196/48270 %U https://humanfactors.jmir.org/2023/1/e48270 %U https://doi.org/10.2196/48270 %U http://www.ncbi.nlm.nih.gov/pubmed/37535417 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e44753 %T Parents’ Perceptions of Children’s and Adolescents’ Use of Electronic Devices to Promote Physical Activity: Systematic Review of Qualitative Evidence %A Visier-Alfonso,María Eugenia %A Sánchez-López,Mairena %A Rodríguez-Martín,Beatriz %A Ruiz-Hermosa,Abel %A Bartolomé-Gutiérrez,Raquel %A Sequí-Domínguez,Irene %A Martínez-Vizcaíno,Vicente %+ Faculty of Nursing, University of Castilla-La Mancha, Camino de Nohales 4, Cuenca, 16071, Spain, 34 630872012, mariaeugenia.visier@uclm.es %K physical activity %K electronic devices %K eHealth %K parents’ perceptions %K children %K adolescents %K systematic review %K qualitative %D 2023 %7 20.7.2023 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The use of physical activity (PA) electronic devices offers a unique opportunity to engage children and adolescents in PA. For this age group (2-17 years), parents play a key role in promoting healthy lifestyles and regulating the use of electronic devices. Therefore, parents’ perceptions of the use of electronic devices for PA in children and adolescents are critical for efficient intervention. Objective: The aim of this qualitative systematic review was to improve the understanding of parents’ perceptions of the use of electronic devices for PA in children and adolescents. Methods: A systematic search of electronic databases (Medline/PubMed, SPORTDiscus, Web of Science, Scopus, OpenGrey, and Deep Blue) was conducted. Studies from inception (2010) to May 2022 were identified. Qualitative studies on the perceptions of healthy children’s and adolescents’ (aged 2-17 years) parents regarding PA interventions performed on electronic devices were included according to the Cochrane Qualitative and Implementation Methods Group Guidance Series and the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement. The Joanna Briggs Institute Qualitative Assessment and Review Instrument was used for methodological validity. Results: In total, 18 studies with 410 parents, mostly mothers, were included. Parents’ perceptions were grouped into 4 categories: usefulness, advantages, general perceptions (electronic devices for health promotion, preferences for real-life PA, and concerns), and acceptability (barriers and facilitators) of electronic devices for PA. Parents perceived electronic devices as useful for increasing PA, learning new skills, and increasing motivation for PA and valued those devices that promoted socialization and family and peer bonding. In terms of general perceptions, parents had positive attitudes toward PA electronic devices; however, they preferred outdoor and real-life PA, especially for preschoolers and children. Concerns, such as physical and psychological harm, addiction, conflicts, and compliance difficulties, were found. Facilitators were identified as ease of use, appropriate feedback, promotion of socialization, and motivational strategies, such as rewards, challenges, and attractiveness. Barriers, such as discomfort, price, and difficulties in using or understanding electronic devices, were also identified. For older children and adolescents, parents were more concerned about high levels of screen time and setting limits on electronic devices and therefore preferred PA electronic devices rather than traditional ones. Conclusions: Overall, the participants had positive attitudes toward electronic devices for PA and perceived them as an effective way to promote PA in children and adolescents. They also perceived several benefits of using electronic devices, such as health promotion, increased awareness and motivation, and socialization, as well as barriers, facilitators, and age differences. The results of this study could provide researchers with insights into designing more effective, age-appropriate PA electronic devices for children and adolescents and improving adherence to their use. Trial Registration: PROSPERO CRD42021292340; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=292340 %M 37471127 %R 10.2196/44753 %U https://mhealth.jmir.org/2023/1/e44753 %U https://doi.org/10.2196/44753 %U http://www.ncbi.nlm.nih.gov/pubmed/37471127 %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 %@ 2291-5222 %I JMIR Publications %V 11 %N %P e44442 %T Exploring the Feasibility and Usability of Smartphones for Monitoring Physical Activity in Orthopedic Patients: Prospective Observational Study %A Ghaffari,Arash %A Kildahl Lauritsen,Rikke Emilie %A Christensen,Michael %A Rolighed Thomsen,Trine %A Mahapatra,Harshit %A Heck,Robert %A Kold,Søren %A Rahbek,Ole %+ Interdisciplinary Orthopaedics, Aalborg University Hospital, Hobrovej 18 - 22, Aalborg, 9000, Denmark, 45 91483966, a.ghaffari@rn.dk %K remote monitoring %K physical activity %K step count %K smartphone application %K wearable sensors %K mixed effects modeling %K step count prediction %K mobile phone %D 2023 %7 4.7.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smartphones are often equipped with inertial sensors that measure individuals’ physical activity (PA). However, their role in remote monitoring of the patients’ PAs in telemedicine needs to be adequately explored. Objective: This study aimed to explore the correlation between a participant’s actual daily step counts and the daily step counts reported by their smartphone. In addition, we inquired about the usability of smartphones for collecting PA data. Methods: This prospective observational study was conducted among patients undergoing lower limb orthopedic surgery and a group of nonpatients as control. The data from the patients were collected from 2 weeks before surgery until 4 weeks after the surgery, whereas the data collection period for the nonpatients was 2 weeks. The participant’s daily step count was recorded by PA trackers worn 24/7. In addition, a smartphone app collected the number of daily steps registered by the participants’ smartphones. We compared the cross-correlation between the daily steps time series obtained from the smartphones and PA trackers in different groups of participants. We also used mixed modeling to estimate the total number of steps, using smartphone step counts and the characteristics of the patients as independent variables. The System Usability Scale was used to evaluate the participants’ experience with the smartphone app and the PA tracker. Results: Overall, 1067 days of data were collected from 21 patients (n=11, 52% female patients) and 10 nonpatients (n=6, 60% female patients). The median cross-correlation coefficient on the same day was 0.70 (IQR 0.53-0.83). The correlation in the nonpatient group was slightly higher than that in the patient group (median 0.74, IQR 0.60-0.90 and median 0.69, IQR 0.52-0.81, respectively). The likelihood ratio tests on the models fitted by mixed effects methods demonstrated that the smartphone step count was positively correlated with the PA tracker’s total number of steps (χ21=34.7, P<.001). In addition, the median usability score for the smartphone app was 78 (IQR 73-88) compared with median 73 (IQR 68-80) for the PA tracker. Conclusions: Considering the ubiquity, convenience, and practicality of smartphones, the high correlation between the smartphones and the total daily step count time series highlights the potential usefulness of smartphones in detecting changes in the number of steps in remote monitoring of a patient’s PA. %M 37283228 %R 10.2196/44442 %U https://mhealth.jmir.org/2023/1/e44442 %U https://doi.org/10.2196/44442 %U http://www.ncbi.nlm.nih.gov/pubmed/37283228 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e43418 %T Testing Behavior Change Techniques to Increase Physical Activity in Middle-Aged and Older Adults: Protocol for a Randomized Personalized Trial Series %A Friel,Ciaran P %A Robles,Patrick L %A Butler,Mark %A Pahlevan-Ibrekic,Challace %A Duer-Hefele,Joan %A Vicari,Frank %A Chandereng,Thevaa %A Cheung,Ken %A Suls,Jerry %A Davidson,Karina W %+ Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, 140 East 59th Street, New York, NY, 10022, United States, 1 9172027729, cfriel@northwell.edu %K behavior change techniques %K N-of-1 %K personalized trials %K physical activity %K behavior change %K aging %K quality of life %K feasibility %K acceptability %K effectiveness %K web-based %K intervention %K text %K email %K survey %D 2023 %7 14.6.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Being physically active is critical to successful aging, but most middle-aged and older adults do not move enough. Research has shown that even small increases in activity can have a significant impact on risk reduction and improve quality of life. Some behavior change techniques (BCTs) can increase activity, but prior studies on their effectiveness have primarily tested them in between-subjects trials and in aggregate. These design approaches, while robust, fail to identify those BCTs most influential for a given individual. In contrast, a personalized, or N-of-1, trial design can assess a person’s response to each specific intervention. Objective: This study is designed to test the feasibility, acceptability, and preliminary effectiveness of a remotely delivered personalized behavioral intervention to increase low-intensity physical activity (ie, walking) in adults aged 45 to 75 years. Methods: The intervention will be administered over 10 weeks, starting with a 2-week baseline period followed by 4 BCTs (goal-setting, self-monitoring, feedback, and action planning) delivered one at a time, each for 2 weeks. In total, 60 participants will be randomized post baseline to 1 of 24 intervention sequences. Physical activity will be continuously measured by a wearable activity tracker, and intervention components and outcome measures will be delivered and collected by email, SMS text messages, and surveys. The effect of the overall intervention on step counts relative to baseline will be examined using generalized linear mixed models with an autoregressive model that accounts for possible autocorrelation and linear trends for daily steps across time. Participant satisfaction with the study components and attitudes and opinions toward personalized trials will be measured at the intervention's conclusion. Results: Pooled change in daily step count will be reported between baseline and individual BCTs and baseline versus overall intervention. Self-efficacy scores will be compared between baseline and individual BCTs and between baseline and the overall intervention. Mean and SD will be reported for survey measures (participant satisfaction with study components and attitudes and opinions toward personalized trials). Conclusions: Assessing the feasibility and acceptability of delivering a personalized, remote physical activity intervention for middle-aged and older adults will inform what steps will be needed to scale up to a fully powered and within-subjects experimental design remotely. Examining the effect of each BCT in isolation will allow for their unique impact to be assessed and support design of future behavioral interventions. In using a personalized trial design, the heterogeneity of individual responses for each BCT can be quantified and inform later National Institutes of Health stages of intervention development trials. Trial Registration: clinicaltrials.gov NCT04967313; https://clinicaltrials.gov/ct2/show/NCT04967313 International Registered Report Identifier (IRRID): RR1-10.2196/43418 %M 37314839 %R 10.2196/43418 %U https://www.researchprotocols.org/2023/1/e43418 %U https://doi.org/10.2196/43418 %U http://www.ncbi.nlm.nih.gov/pubmed/37314839 %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 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 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 e43162 %T Assessing the Pragmatic Nature of Mobile Health Interventions Promoting Physical Activity: Systematic Review and Meta-analysis %A Stecher,Chad %A Pfisterer,Bjorn %A Harden,Samantha M %A Epstein,Dana %A Hirschmann,Jakob M %A Wunsch,Kathrin %A Buman,Matthew P %+ College of Health Solutions, Arizona State University, 500 N 3rd Street, Room 438, Phoenix, AZ, 85004, United States, 1 6024960957, chad.stecher@asu.edu %K physical activity %K mobile health %K mHealth %K Reach, Effectiveness, Adoption, Implementation, Maintenance %K RE-AIM %K Pragmatic-Explanatory Continuum Indicator Summary-2 %K PRECIS-2 %K systematic review %K meta-analysis %K digital health %K mobile phone %D 2023 %7 4.5.2023 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) apps can promote physical activity; however, the pragmatic nature (ie, how well research translates into real-world settings) of these studies is unknown. The impact of study design choices, for example, intervention duration, on intervention effect sizes is also understudied. Objective: This review and meta-analysis aims to describe the pragmatic nature of recent mHealth interventions for promoting physical activity and examine the associations between study effect size and pragmatic study design choices. Methods: The PubMed, Scopus, Web of Science, and PsycINFO databases were searched until April 2020. Studies were eligible if they incorporated apps as the primary intervention, were conducted in health promotion or preventive care settings, included a device-based physical activity outcome, and used randomized study designs. Studies were assessed using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) and Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) frameworks. Study effect sizes were summarized using random effect models, and meta-regression was used to examine treatment effect heterogeneity by study characteristics. Results: Overall, 3555 participants were included across 22 interventions, with sample sizes ranging from 27 to 833 (mean 161.6, SD 193.9, median 93) participants. The study populations’ mean age ranged from 10.6 to 61.5 (mean 39.6, SD 6.5) years, and the proportion of males included across all studies was 42.8% (1521/3555). Additionally, intervention lengths varied from 2 weeks to 6 months (mean 60.9, SD 34.9 days). The primary app- or device-based physical activity outcome differed among interventions: most interventions (17/22, 77%) used activity monitors or fitness trackers, whereas the rest (5/22, 23%) used app-based accelerometry measures. Data reporting across the RE-AIM framework was low (5.64/31, 18%) and varied within specific dimensions (Reach=44%; Effectiveness=52%; Adoption=3%; Implementation=10%; Maintenance=12.4%). PRECIS-2 results indicated that most study designs (14/22, 63%) were equally explanatory and pragmatic, with an overall PRECIS-2 score across all interventions of 2.93/5 (SD 0.54). The most pragmatic dimension was flexibility (adherence), with an average score of 3.73 (SD 0.92), whereas follow-up, organization, and flexibility (delivery) appeared more explanatory with means of 2.18 (SD 0.75), 2.36 (SD 1.07), and 2.41 (SD 0.72), respectively. An overall positive treatment effect was observed (Cohen d=0.29, 95% CI 0.13-0.46). Meta-regression analyses revealed that more pragmatic studies (−0.81, 95% CI −1.36 to −0.25) were associated with smaller increases in physical activity. Treatment effect sizes were homogenous across study duration, participants’ age and gender, and RE-AIM scores. Conclusions: App-based mHealth physical activity studies continue to underreport several key study characteristics and have limited pragmatic use and generalizability. In addition, more pragmatic interventions observe smaller treatment effects, whereas study duration appears to be unrelated to the effect size. Future app-based studies should more comprehensively report real-world applicability, and more pragmatic approaches are needed for maximal population health impacts. Trial Registration: PROSPERO CRD42020169102; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=169102 %M 37140972 %R 10.2196/43162 %U https://mhealth.jmir.org/2023/1/e43162 %U https://doi.org/10.2196/43162 %U http://www.ncbi.nlm.nih.gov/pubmed/37140972 %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 %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42815 %T Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study %A Angelucci,Alessandra %A Greco,Massimiliano %A Canali,Stefano %A Marelli,Giovanni %A Avidano,Gaia %A Goretti,Giulia %A Cecconi,Maurizio %A Aliverti,Andrea %+ Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy, 39 0223991, alessandra.angelucci@polimi.it %K wearable devices %K smartwatch data %K preoperative risk assessment %K ethics in wearables %K mobile phone %D 2023 %7 13.4.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Preoperative assessment is crucial to prevent the risk of complications of surgical operations and is usually focused on functional capacity. The increasing availability of wearable devices (smartwatches, trackers, rings, etc) can provide less intrusive assessment methods, reduce costs, and improve accuracy. Objective: The aim of this study was to present and evaluate the possibility of using commercial smartwatch data, such as those retrieved from the Fitbit Inspire 2 device, to assess functional capacity before elective surgery and correlate such data with the current gold standard measure, the 6-Minute Walk Test (6MWT) distance. Methods: During the hospital visit, patients were evaluated in terms of functional capacity using the 6MWT. Patients were asked to wear the Fitbit Inspire 2 for 7 days (with flexibility of –2 to +2 days) after the hospital visit, before their surgical operation. Resting heart rate and daily steps data were retrieved directly from the smartwatch. Feature engineering techniques allowed the extraction of heart rate over steps (HROS) and a modified version of Non-Exercise Testing Cardiorespiratory Fitness. All measures were correlated with 6MWT. Results: In total, 31 patients were enrolled in the study (n=22, 71% men; n=9, 29% women; mean age 76.06, SD 4.75 years). Data were collected between June 2021 and May 2022. The parameter that correlated best with the 6MWT was the Non-Exercise Testing Cardiorespiratory Fitness index (r=0.68; P<.001). The average resting heart rate over the whole acquisition period for each participant had r=−0.39 (P=.03), even if some patients did not wear the device at night. The correlation of the 6MWT distance with the HROS evaluated at 1% quantile was significant, with Pearson coefficient of −0.39 (P=.04). Fitbit step count had a fair correlation of 0.59 with 6MWT (P<.001). Conclusions: Our study is a promising starting point for the adoption of wearable technology in the evaluation of functional capacity of patients, which was strongly correlated with the gold standard. The study also identified limitations in the availability of metrics, variability of devices, accuracy and quality of data, and accessibility as crucial areas of focus for future studies. %M 37052980 %R 10.2196/42815 %U https://www.jmir.org/2023/1/e42815 %U https://doi.org/10.2196/42815 %U http://www.ncbi.nlm.nih.gov/pubmed/37052980 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42455 %T The Added Value of Remote Technology in Cardiac Rehabilitation on Physical Function, Anthropometrics, and Quality of Life: Cluster Randomized Controlled Trial %A Lahtio,Heli %A Heinonen,Ari %A Paajanen,Teemu %A Sjögren,Tuulikki %+ Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35, Jyväskylä, 40014, Finland, 358 14260 1211, heli.lahtio@gmail.com %K weight loss %K cardiac rehabilitation %K remote technology %K physical function %K 6-minute walk test %K overweight %K obesity %K body mass %K BMI %K waist circumference %K quality of life %K QoL %K mobile phone %D 2023 %7 12.4.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Cardiovascular diseases (CVDs) cause most deaths globally and can reduce quality of life (QoL) of rehabilitees with cardiac disease. The risk factors of CVDs are physical inactivity and increased BMI. With physical activity, it is possible to prevent CVDs, improve QoL, and help maintain a healthy body mass. Current literature shows the possibilities of digitalization and advanced technology in supporting independent self-rehabilitation. However, the interpretation of the results is complicated owing to the studies’ high heterogeneity. In addition, the added value of this technology has not been studied well, especially in cardiac rehabilitation. Objective: We aimed to examine the effectiveness of added remote technology in cardiac rehabilitation on physical function, anthropometrics, and QoL in rehabilitees with CVD compared with conventional rehabilitation. Methods: Rehabilitees were cluster randomized into 3 remote technology intervention groups (n=29) and 3 reference groups (n=30). The reference group received conventional cardiac rehabilitation, and the remote technology intervention group received conventional cardiac rehabilitation with added remote technology, namely, the Movendos mCoach app and Fitbit charge accelerometer. The 12 months of rehabilitation consisted of three 5-day in-rehabilitation periods in the rehabilitation center. Between these periods were two 6-month self-rehabilitation periods. Outcome measurements included the 6-minute walk test, body mass, BMI, waist circumference, and World Health Organization QoL-BREF questionnaire at baseline and at 6 and 12 months. Between-group differences were assessed using 2-tailed t tests and Mann-Whitney U test. Within-group differences were analyzed using a paired samples t test or Wilcoxon signed-rank test. Results: Overall, 59 rehabilitees aged 41 to 66 years (mean age 60, SD 6 years; n=48, 81% men) were included in the study. Decrement in waist circumference (6 months: 1.6 cm; P=.04; 12 months: 3 cm; P<.001) and increment in self-assessed QoL were greater (environmental factors: 0.5; P=.02) in the remote technology intervention group than the reference group. Both groups achieved statistically significant improvements in the 6-minute walk test in both time frames (P=.01-.03). Additionally, the remote technology intervention group achieved statistically significant changes in the environmental domain at 0-6 months (P=.03) and waist circumference at both time frames (P=.01), and reference group achieve statistically significant changes in waist circumference at 0-6 months (P=.02). Conclusions: Remote cardiac rehabilitation added value to conventional cardiac rehabilitation in terms of waist circumference and QoL. The results were clinically small, but the findings suggest that adding remote technology to cardiac rehabilitation may increase beneficial health outcomes. There was some level of systematic error during rehabilitation intervention, and the sample size was relatively small. Therefore, care must be taken when generalizing the study results beyond the target population. To confirm assumptions of the added value of remote technology in rehabilitation interventions, more studies involving different rehabilitees with cardiac disease are required. Trial Registration: ISRCTN Registry ISRCTN61225589; https://www.isrctn.com/ISRCTN61225589 %M 37043264 %R 10.2196/42455 %U https://www.jmir.org/2023/1/e42455 %U https://doi.org/10.2196/42455 %U http://www.ncbi.nlm.nih.gov/pubmed/37043264 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e43236 %T The Effect of a mHealth App (KENPO-app) for Specific Health Guidance on Weight Changes in Adults With Obesity and Hypertension: Pilot Randomized Controlled Trial %A Sakane,Naoki %A Suganuma,Akiko %A Domichi,Masayuki %A Sukino,Shin %A Abe,Keiko %A Fujisaki,Akiyoshi %A Kanazawa,Ai %A Sugimoto,Mamiko %+ Division of Preventive Medicine, Clinical Research Institute, National Hospital Organization Kyoto Medical Center, 1-1 Mukaihata-cho, Fukakusa, Fushimi-ku, Kyoto, 612-8555, Japan, 81 75 641 9161, nsakane@gf6.so-net.ne.jp %K obesity %K hypertension %K mobile health care app %K specific health guidance %K obese %K weight %K mHealth %K mobile health %K mobile app %K health app %D 2023 %7 12.4.2023 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Commercial smartphone apps that promote self-monitoring of weight loss are widely available. The development of disease-specific apps has begun, but there is no app for specific health guidance (SHG) to prevent metabolic syndrome, type 2 diabetes, and cardiovascular diseases in middle-aged adults in Japan. Objective: This study aimed to determine the efficacy of an SHG mobile health app in facilitating weight loss in Japanese adults with obesity and hypertension. Methods: In a 12-week, statistician-blinded, randomized parallel controlled trial, 78 overweight and obese men aged 40-69 years were assigned in a 1:1 ratio to either the usual support plus KENPO-app group (intervention group) or the active control group. KENPO-app (release April 10, 2019; OMRON Healthcare Co., Ltd.) was developed by the study team and focus groups and uses behavior change techniques (ie, self-monitoring and goal-setting theory). This app was developed for SHG based on the four specific health checkups and guidance system in Japan: (1) focusing primarily on achieving the target (weight loss of ≥2 kg); (2) assessing healthy eating, exercise habits, smoking habits, relaxation, and self-weighing; (3) providing information on the results of specific health checkups; and (4) starting an intervention period of 6 months with the interim assessment at 3 months. The initial assessment explored the following: personality traits (4 types), health checkup data concerns (10 items), symptom concerns (10 items), and the aim of the intervention (weight loss, improving fitness, symptoms, laboratory data). Chatbot-supported health information on health and health behavior was selected from 392 quizzes based on app data and was provided to participants. The KENPO-app had chatbot-supported feedback and information provision combined with a self-monitoring tool (weight, steps, and blood pressure). Data on active exercise, healthy eating, and healthy lifestyle habits were obtained using a web-based self-administered questionnaire at baseline and 12 weeks. Results: The trial’s retention rate was 95% (74/78). The adherence to daily self-weighing, wearing the pedometer, and blood pressure monitoring in the KENPO-app group was significantly higher than those in the active control group. Compared with the active control group, the median body weight and BMI of the intervention group significantly decreased at 3 months (–0.4, IQR –2.0 to 0.6 kg vs –1.1, IQR –2.7 to –0.5 kg; P=.03; –0.1, IQR –0.6 to 0.3 kg vs –0.4, IQR –0.8 to –0.2 kg; P=.02, respectively). The intervention increased the percentage of participants who self-reported taking ≥8000 steps, eating vegetables before rice, eating slowly, and relaxing. Personality traits were associated with the degree of weight loss in the intervention group. Conclusions: The SHG-specific KENPO-app was feasible and induced modest but significant weight loss in adults with obesity. Trial Registration: University Hospital Medical Information Network Center UMIN000046263; https://tinyurl.com/bderys3b %M 37043287 %R 10.2196/43236 %U https://mhealth.jmir.org/2023/1/e43236 %U https://doi.org/10.2196/43236 %U http://www.ncbi.nlm.nih.gov/pubmed/37043287 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e43619 %T Evaluation of the Effectiveness of a Whole-System Intervention to Increase the Physical Activity of Children Aged 5 to 11 Years (Join Us: Move Play, JU:MP): Protocol for a Quasiexperimental Trial %A Bingham,Daniel D %A Daly-Smith,Andy %A Seims,Amanda %A Hall,Jennifer %A Eddy,Lucy %A Helme,Zoe %A Barber,Sally E %+ Faculty of Health Studies, University of Bradford, Richmond Road, Bradford, BD7 1DP, United Kingdom, 44 1274 232323, d.bingham@bradford.ac.uk %K physical activity %K accelerometry %K complex intervention %K whole system %K children %K quasiexperimental %D 2023 %7 31.3.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Daily physical activity is vital for the health and development of children. However, many children are inactive. Previous attempts to achieve sustained increases in daily physical activity in children have been ineffective. Join Us: Move Play (JU:MP) is a whole-system, complex, community-based intervention aiming to increase the physical activity levels of children aged 7 to 11 years who live in areas of Bradford, England, which are multicultural and have high levels of deprivation. Objective: The purpose of this quasiexperimental controlled trial is to assess whether the JU:MP program increases primary school children’s physical activity. Methods: The study has a 2-arm, quasiexperimental, nonblinded, nonequivalent group design and will be conducted with primary school children aged 5 to 11 years at 3 timepoints, including baseline (before intervention), 24 months (during intervention), and 36 months (after intervention). Children attending primary schools within the intervention area will be invited to participate. Children attending similar schools within similar neighborhoods based on school and community census demographics (deprivation, free school meals, and ethnicity) outside of the JU:MP geographical area will be invited to participate in the control condition. At each timepoint, consenting participants will wear an accelerometer for 7 consecutive days (24 hours a day) to measure the primary outcome (average daily moderate-to-vigorous physical activity). Multivariable mixed effects linear regression will be applied to estimate differences in the primary outcome between the 2 arms at 24 months and 36 months on an intention-to-treat basis. The secondary outcome analysis will explore changes in socioemotional well-being (teacher reported), quality of life (parental/carer reported), and other contextual factors (parents/carer reported), as well as segments of the day activity, sleep, sedentary screen time, frequency of places to be active, parent practices (nondirective support and autonomy support), social cohesion, and neighborhood walking/exercise environment. Results: Recruitment occurred from July 2021 to March 2022, and baseline data were collected from September 2021 to March 2022. As of March 2022 (end of baseline data collection), a total of 1454 children from 37 schools (17 intervention schools and 20 control schools) have been recruited. The first follow-up data collection will occur from September 2023 to March 2024, and the second and final follow-up data collection will occur from September 2024 to March 2025. Data analysis has not begun, and the final results will be published in December 2025. Conclusions: This article describes the protocol for a quasiexperimental controlled trial examining a novel whole-system intervention. Trial Registration: ISRCTN ISRCTN14332797; https://www.isrctn.com/ISRCTN14332797 International Registered Report Identifier (IRRID): DERR1-10.2196/43619 %M 37000512 %R 10.2196/43619 %U https://www.researchprotocols.org/2023/1/e43619 %U https://doi.org/10.2196/43619 %U http://www.ncbi.nlm.nih.gov/pubmed/37000512 %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 %@ 2561-3278 %I JMIR Publications %V 8 %N %P e43726 %T An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study %A Popham,Sara %A Burq,Maximilien %A Rainaldi,Erin E %A Shin,Sooyoon %A Dunn,Jessilyn %A Kapur,Ritu %+ Verily Life Sciences, 269 E Grand Ave, South San Francisco, CA, 94080, United States, 1 650 253 0000, spopham@verily.com %K digital measurement %K wearable sensor %K machine learning %K ambulatory status %K Project Baseline Health Study %K physical activity %D 2023 %7 7.3.2023 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status. Objective: This study’s aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology. Methods: Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups. Results: The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups. Conclusions: Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users’ walking activity and help researchers gain insights on users’ health status. %M 38875664 %R 10.2196/43726 %U https://biomedeng.jmir.org/2023/1/e43726 %U https://doi.org/10.2196/43726 %U http://www.ncbi.nlm.nih.gov/pubmed/38875664 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43123 %T Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis %A Shapira-Daniels,Ayelet %A Kornej,Jelena %A Spartano,Nicole L %A Wang,Xuzhi %A Zhang,Yuankai %A Pathiravasan,Chathurangi H %A Liu,Chunyu %A Trinquart,Ludovic %A Borrelli,Belinda %A McManus,David D %A Murabito,Joanne M %A Benjamin,Emelia J %A Lin,Honghuang %+ Department of Medicine, University of Massachusetts Chan Medical School, 55 North Lake Avenue, Worcester, MA, 01655, United States, 1 774 455 4881, Honghuang.Lin@umassmed.edu %K atrial fibrillation %K physical activity %K fitness tracker %K cardiovascular epidemiology %K fitness %K exercise %K tracker %K cardiology %K heart %K walk %K step count %K smartwatch %K wearable %K risk %K cross-sectional analysis %D 2023 %7 6.3.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk. Objective: The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF. Methods: Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals’ 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)–AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI≥30 kg/m2), as well as the relation between self-reported physical activity and predicted 5-year AF risk. Results: We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: n=563, 61%) who had a median daily step count of 7227 (IQR 5699-8970). Most participants (n=823, 89.2%) had a <2.5% CHARGE-AF risk. Every 1000 steps were associated with a 0.08% lower CHARGE-AF risk (P<.001). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk. Conclusions: Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation. %M 36877540 %R 10.2196/43123 %U https://www.jmir.org/2023/1/e43123 %U https://doi.org/10.2196/43123 %U http://www.ncbi.nlm.nih.gov/pubmed/36877540 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e41153 %T Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review %A Diaz,Claudio %A Caillaud,Corinne %A Yacef,Kalina %+ School of Computer Science, The University of Sydney, Building J12/1 Cleveland Street, Camperdown NSW, Sydney, 2006, Australia, 61 (02) 9351 2222, kalina.yacef@sydney.edu.au %K activity tracker %K wearable electronic devices %K fitness trackers %K data mining %K artificial intelligence %K health %K education %K behavior change %K physical activity %K wearable devices %K trackers %K health education %K sensor data %D 2023 %7 6.3.2023 %9 Review %J JMIR Med Inform %G English %X Background: Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants’ physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants’ physical activity evolves. Objective: The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? Methods: The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. Results: All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%). Conclusions: Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce. %M 36877559 %R 10.2196/41153 %U https://medinform.jmir.org/2023/1/e41153 %U https://doi.org/10.2196/41153 %U http://www.ncbi.nlm.nih.gov/pubmed/36877559 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 10 %N %P e42611 %T The Use of Sensors to Detect Anxiety for In-the-Moment Intervention: Scoping Review %A Dobson,Rosie %A Li,Linwei Lily %A Garner,Katie %A Tane,Taria %A McCool,Judith %A Whittaker,Robyn %+ National Institute for Health Innovation, University of Auckland, Building 507, Grafton Campus, 22-30 Park Avenue, Grafton, Auckland, 1023, New Zealand, 64 9 373 7599, r.dobson@auckland.ac.nz %K anxiety %K wearables %K sensors %K mental health %K digital mental health %K digital health intervention %K wearable device %D 2023 %7 2.2.2023 %9 Review %J JMIR Ment Health %G English %X Background: With anxiety a growing issue and barriers to accessing support services, there is a need for innovative solutions to provide early intervention. In-the-moment interventions support individuals to recognize early signs of distress and use coping mechanisms to prevent or manage this distress. There is potential for wearable sensors linked to an individual’s mobile phone to provide in-the-moment support tailored to individual needs and physiological responses. Objective: The aim of this scoping review is to examine the role of sensors in detecting the physiological signs of anxiety to initiate and direct interventions for its management. Methods: Relevant studies were identified through searches conducted in Embase, MEDLINE, APA PsycINFO, ProQuest, and Scopus. Studies were identified if they were conducted with people with stress or anxiety or at risk of anxiety and included a wearable sensor providing real-time data for in-the-moment management of anxiety. Results: Of the 1087 studies identified, 11 studies were included in the review, including 5 randomized controlled trials and 6 pilot or pretesting studies. The results showed that most studies successfully demonstrated improvements in their target variables. This included overall anxiety and stress levels, and the implementation of in-the-moment stress and anxiety management techniques such as diaphragmatic breathing. There was wide variation in the types of sensors used, physiological measures, and sensor-linked interventions. Conclusions: This review indicates that sensors are potentially a useful tool in detecting anxiety and facilitating the implementation of a known control mechanism to reduce anxiety and improve mood, but further work is needed to understand the acceptability and effectiveness of this type of intervention. %M 36729590 %R 10.2196/42611 %U https://mental.jmir.org/2023/1/e42611 %U https://doi.org/10.2196/42611 %U http://www.ncbi.nlm.nih.gov/pubmed/36729590 %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 %@ 2561-326X %I JMIR Publications %V 7 %N %P e38877 %T A Novel Approach to Assess Weekly Self-efficacy for Meeting Personalized Physical Activity Goals Via a Cellphone: 12-Week Longitudinal Study %A Oh,Yoo Jung %A Hoffmann,Thomas J %A Fukuoka,Yoshimi %+ Department of Communication, University of California, Davis, 469 Kerr Hall, Davis, CA, 95616, United States, 1 530 760 5509, yjeoh@ucdavis.edu %K self-efficacy %K physical activity %K exercise %K cellphone %K mobile phone %K application %K app %K Ecological Momentary Assessment %D 2023 %7 27.1.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Despite the health benefits of engaging in regular physical activity (PA), the majority of American adults do not meet the PA guidelines for aerobic and muscle-strengthening activities. Self-efficacy, the belief that one can execute specific actions, has been suggested to be a strong determinant of PA behaviors. With the increasing availability of digital technologies, collecting longitudinal real-time self-efficacy and PA data has become feasible. However, evidence in longitudinal real-time assessment of self-efficacy in relation to objectively measured PA is scarce. Objective: This study aimed to examine a novel approach to measure individuals' real-time weekly self-efficacy in response to their personalized PA goals and performance over the 12-week intervention period in community-dwelling women who were not meeting PA guidelines. Methods: In this secondary data analysis, 140 women who received a 12-week PA intervention were asked to report their real-time weekly self-efficacy via a study mobile app. PA (daily step counts) was measured by an accelerometer every day for 12 weeks. Participants rated their self-efficacy on meeting PA goals (ranging from “not confident” to “very confident”) at the end of each week via a mobile app. We used a logistic mixed model to examine the association between weekly self-efficacy and weekly step goal success, controlling for age, BMI, self-reported White race, having a college education or higher, being married, and being employed. Results: The mean age was 52.7 (SD 11.5, range 25-68) years. Descriptive analyses showed the dynamics of real-time weekly self-efficacy on meeting PA goals and weekly step goal success. The majority (74.4%) of participants reported being confident in the first week, whereas less than half of them (46.4%) reported confidence in the final week of the intervention. Participants who met weekly step goals were 4.41 times more likely to be confident about achieving the following week's step goals than those who did not meet weekly step goals (adjusted odds ratio 4.41; 95% CI 2.59-7.50; P<.001). Additional analysis revealed that participants who were confident about meeting the following week’s step goals were 2.07 times more likely to meet their weekly step goals in the following week (adjusted odds ratio 2.07; 95% CI 1.16-3.70; P=.01). The significant bidirectional association between real-time self-efficacy and weekly step goal success was confirmed in a series of sensitivity analyses. Conclusions: This study demonstrates the potential utility of a novel approach to examine self-efficacy in real time for analysis of self-efficacy in conjunction with objectively measured PA. Discovering the dynamic patterns and changes in weekly self-efficacy on meeting PA goals may aid in designing a personalized PA intervention. Evaluation of this novel approach in an RCT is warranted. %M 36705945 %R 10.2196/38877 %U https://formative.jmir.org/2023/1/e38877 %U https://doi.org/10.2196/38877 %U http://www.ncbi.nlm.nih.gov/pubmed/36705945 %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 %@ 1438-8871 %I JMIR Publications %V 24 %N 12 %P e42359 %T Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial %A Helmer,Philipp %A Hottenrott,Sebastian %A Rodemers,Philipp %A Leppich,Robert %A Helwich,Maja %A Pryss,Rüdiger %A Kranke,Peter %A Meybohm,Patrick %A Winkler,Bernd E %A Sammeth,Michael %+ Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, Würzburg, 97070, Germany, 49 93120130574, helmer_p@ukw.de %K health tracker %K smartwatch %K internet of things %K personalized medicine %K photoplethysmography %K wearable %K Garmin Fenix 6 Pro %K Apple Watch 7 %K Fitbit Sense %K Withings ScanWatch %D 2022 %7 30.12.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. Objective: However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. Methods: We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). Results: All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). Conclusions: Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate. Trial Registration: ClinicalTrials.gov NCT05418881; https://www.clinicaltrials.gov/ct2/show/NCT05418881 %M 36583938 %R 10.2196/42359 %U https://www.jmir.org/2022/12/e42359 %U https://doi.org/10.2196/42359 %U http://www.ncbi.nlm.nih.gov/pubmed/36583938 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 12 %P e40379 %T Feasibility, Usability and Acceptability of a mHealth Intervention to Reduce Cardiovascular Risk in Rural Hispanic Adults: Descriptive Study %A Rowland,Sheri %A Ramos,Athena K %A Trinidad,Natalia %A Quintero,Sophia %A Johnson Beller,Rebecca %A Struwe,Leeza %A Pozehl,Bunny %+ College of Nursing, University of Nebraska Medical Center, 550 N. 19th Street, Lincoln, NE, 68508-0620, United States, 1 402 472 5959, sheri.rowland@unmc.edu %K mHealth %K health behavior %K self-management %K Hispanic/Latino %K rural %K apps %K feasibility %K acceptability %K participation %K engagement %K wearable device %K tracking %K smartphone %D 2022 %7 23.12.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Mobile health (mHealth) technology using apps or devices to self-manage health behaviors is an effective strategy to improve lifestyle-related health problems such as hypertension, obesity, and diabetes. However, few studies have tested an mHealth intervention with Hispanic/Latino adults, and no studies were found testing mHealth with rural Hispanic/Latino adults, the fastest-growing population in rural areas. Objective: The purpose of this study was to evaluate the feasibility, usability, and acceptability of an mHealth cardiovascular risk self-management intervention with rural Hispanic/Latino adults. Methods: A descriptive study using quantitative and qualitative methods was used to evaluate the feasibility, usability, and acceptability of delivering a 12-week mHealth self-management intervention to reduce cardiovascular risk with rural Hispanic/Latino adults who were randomized to 1 of 2 groups. Both groups were asked to use MyFitnessPal to self-monitor daily steps, weight, and calories. The intervention group received support to download, initiate, and troubleshoot technology challenges with MyFitnessPal (Under Armour) and a smart scale, while the enhanced usual care group received only a general recommendation to use MyFitnessPal to support healthy behaviors. The usability of MyFitnessPal and the smart scale was measured using an adapted Health Information Technology Usability EvaluationScale (Health-ITUES). Adherence data in the intervention group (daily steps, weight, and calories) were downloaded from MyFitnessPal. Acceptability was evaluated using semistructured interviews in a subsample (n=5) of intervention group participants. Results: A sample of 70 eligible participants (enhanced usual care group n=34; intervention group n=36) were enrolled between May and December 2019. The overall attrition was 28% at 12 weeks and 54% at 24 weeks. mHealth usability in the intervention group increased at each time point (6, 12, and 24 weeks). Adherence to self-monitoring using mHealth in the intervention group after week 1 was 55% for steps, 39% for calories, and 35% for weights; at the end of the 12-week intervention, the adherence to self-monitoring was 31% for steps, 11% for weight, and 8% for calories. Spikes in adherence coincided with scheduled in-person study visits. Structured interviews identified common technology challenges including scale and steps not syncing with the app and the need for additional technology support for those with limited mHealth experience. Conclusions: Recruitment of rural Hispanic/Latino adults into the mHealth study was feasible using provider and participant referrals. The use of MyFitnessPal, the smart scale, and SMS text messages to self-monitor daily steps, weights, and calories was acceptable and feasible if technology support was provided. Future research should evaluate and support participants’ baseline technology skill level, provide training if needed, and use a phone call or SMS text message follow-ups as a strategy to minimize attrition. A wearable device, separate from the smartphone app, is recommended for activity tracking. %M 36563025 %R 10.2196/40379 %U https://formative.jmir.org/2022/12/e40379 %U https://doi.org/10.2196/40379 %U http://www.ncbi.nlm.nih.gov/pubmed/36563025 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 12 %P e37885 %T Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study %A Gagnon,Joel %A Khau,Michelle %A Lavoie-Hudon,Léandre %A Vachon,François %A Drapeau,Vicky %A Tremblay,Sébastien %+ School of Psychology, Faculty of Social Sciences, Laval University, Pavillon Félix-Antoine-Savard, 1144, 2325, rue des Bibliothèques, Québec, QC, G1V 0A6, Canada, 1 8193830645, joel.gagnon.2@ulaval.ca %K Fitbit device %K wearable %K heart rate %K measurement accuracy %K criterion validity %K interdevice agreement %K psychological stress %K stress %K physiological %K behavioral %K mental health %K well-being %D 2022 %7 21.12.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Wearable devices collect physiological and behavioral data that have the potential to identify individuals at risk of declining mental health and well-being. Past research has mainly focused on assessing the accuracy and the agreement of heart rate (HR) measurement of wearables under different physical exercise conditions. However, the capacity of wearables to sense physiological changes, assessed by increasing HR, caused by a stressful event has not been thoroughly studied. Objective: This study followed 3 objectives: (1) to test the ability of a wearable device (Fitbit Versa 2) to sense an increase in HR upon induction of psychological stress in the laboratory; (2) to assess the accuracy of the wearable device to capture short-term HR variations caused by psychological stress compared to a gold-standard electrocardiogram (ECG) measure (Biopac); and (3) to quantify the degree of agreement between the wearable device and the gold-standard ECG measure across different experimental conditions. Methods: Participants underwent the Trier Social Stress Test protocol, which consists of an oral phase, an arithmetic stress phase, an anticipation phase, and 2 relaxation phases (at the beginning and the end). During the stress protocol, the participants wore a Fitbit Versa 2 and were also connected to a Biopac. A mixed-effect modeling approach was used (1) to assess the effect of experimental conditions on HR, (2) to estimate several metrics of accuracy, and (3) to assess the agreement: the Bland-Altman limits of agreement (LoA), the concordance correlation coefficient, the coverage probability, the total deviation index, and the coefficient of an individual agreement. Mean absolute error and mean absolute percent error were calculated as accuracy indices. Results: A total of 34 university students were recruited for this study (64% of participants were female with a mean age of 26.8 years, SD 8.3). Overall, the results showed significant HR variations across experimental phases. Post hoc tests revealed significant pairwise differences for all phases. Accuracy analyses revealed acceptable accuracy according to the analyzed metrics of accuracy for the Fitbit Versa 2 to capture the short-term variations in psychological stress levels. However, poor indices of agreement between the Fitbit Versa 2 and the Biopac were found. Conclusions: Overall, the results support the use of the Fitbit Versa 2 to capture short-term stress variations. The Fitbit device showed acceptable levels of accuracy but poor agreement with an ECG gold standard. Greater inaccuracy and smaller agreement were found for stressful experimental conditions that induced a higher HR. Fitbit devices can be used in research to measure HR variations caused by stress, although they cannot replace an ECG instrument when precision is of utmost importance. %M 36542432 %R 10.2196/37885 %U https://formative.jmir.org/2022/12/e37885 %U https://doi.org/10.2196/37885 %U http://www.ncbi.nlm.nih.gov/pubmed/36542432 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 5 %N 1 %P e40352 %T Personal Devices to Monitor Physical Activity and Nutritional Intake After Colorectal Cancer Surgery: Feasibility Study %A van der Linden,Manouk J W %A Nahar van Venrooij,Lenny M W %A Verdaasdonk,Emiel G G %+ Department of Dietetics, Jeroen Bosch Hospital, Henri Dunantstraat 1, ’s-Hertogenbosch, 5223 GZ, Netherlands, 31 735532019, manouk.v.d.linden@jbz.nl %K eHealth %K fitness trackers %K diet records %K colorectal neoplasm %K colorectal cancer %K surgery %K self management %K patient care %K physical activity %K tracking %K activity tracking %K self-monitoring %K feasibility %K usability %D 2022 %7 13.12.2022 %9 Original Paper %J JMIR Perioper Med %G English %X Background: The use of self-monitoring devices is promising for improving perioperative physical activity and nutritional intake. Objective: This study aimed to assess the feasibility, usability, and acceptability of a physical activity tracker and digital food record in persons scheduled for colorectal cancer (CRC) surgery. Methods: This observational cohort study was conducted at a large training hospital between November 2019 and November 2020. The study population consisted of persons with CRC between 18- and 75 years of age who were able to use a smartphone or tablet and scheduled for elective surgery with curative intent. Excluded were persons not proficient in Dutch or following a protein-restricted diet. Participants used an activity tracker (Fitbit Charge 3) from 4 weeks before until 6 weeks after surgery. In the week before surgery (preoperative) and the fifth week after surgery (postoperative), participants also used a food record for 1 week. They shared their experience regarding usability (system usability scale, range 0-100) and acceptability (net promoter score, range –100 to +100). Results: In total, 28 persons were included (n=16, 57% male, mean age 61, SD 8 years), and 27 shared their experiences. Scores regarding the activity tracker were as follows: preoperative median system usability score, 85 (IQR 73-90); net promoter score, +65; postoperative median system usability score, 78 (IQR 68-85); net promotor score, +67. The net promoter scores regarding the food record were +37 (preoperative) and–7 (postoperative). Conclusions: The perioperative use of a physical activity tracker is considered feasible, usable, and acceptable by persons with CRC in this study. Preoperatively, the use of a digital food record was acceptable, and postoperatively, the acceptability decreased. %M 36512385 %R 10.2196/40352 %U https://periop.jmir.org/2022/1/e40352 %U https://doi.org/10.2196/40352 %U http://www.ncbi.nlm.nih.gov/pubmed/36512385 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 12 %P e40271 %T Assessing the Acceptability and Effectiveness of Mobile-Based Physical Activity Interventions for Midlife Women During Menopause: Systematic Review of the Literature %A AlSwayied,Ghada %A Guo,Haoyue %A Rookes,Tasmin %A Frost,Rachael %A Hamilton,Fiona L %+ UCL Research Department of Primary Care and Population Health, University College London, Upper 3rd Floor, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, United Kingdom, 44 02077940500 ext 31498, zczlgbi@ucl.ac.uk %K mobile app %K mobile health %K mHealth %K smartphone %K smartphone apps %K physical activity %K exercise %K midlife women %K menopause %K menopausal symptoms %K behavior change %K women’s health %K wearable %K activity tracker %K effectiveness %K acceptability %K review %K meta-analysis %K mobile phone %D 2022 %7 9.12.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Midlife women with menopausal symptoms are less likely to meet the recommended level of physical activity (PA). Promoting PA among women in midlife could reduce their risk of cardiovascular diseases and perhaps improve menopausal symptoms. Mobile PA interventions in the form of smartphone apps and wearable activity trackers can potentially encourage users to increase PA levels and address time and resource barriers to PA. However, evidence on the acceptability and effectiveness of these interventions among midlife women is unclear. Objective: This systematic review evaluated the effectiveness, acceptability, and active behavior change techniques (BCTs) of mobile PA technologies among midlife menopausal women. Methods: A mixed methods systematic review of qualitative and quantitative studies was conducted. MEDLINE (Ovid), Embase, Scopus, CINAHL, Web of Science, SPORTDiscus, CENTRAL, PsycINFO, and the ProQuest Sports Medicine and Education Index were systematically searched. Studies were selected and screened according to predetermined eligibility criteria. In total, 2 reviewers independently assessed the risk of bias using the Mixed Methods Appraisal Tool and completed BCT mapping of the included interventions using the BCT Taxonomy v1. Results: A total of 12 studies were included in this review. Overall risk of bias was “Moderate to high” in 58% (7/12) of the included studies and “low” in 42% (5/12) of the studies. Of the 12 studies, 7 (58%) assessed changes in PA levels. The pooled effect size of 2 randomized controlled trials resulted in a small to moderate increase in moderate to vigorous PA of approximately 61.36 weekly minutes among midlife women, at least in the short term (95% CI 17.70-105.01; P=.006). Although a meta-analysis was not feasible because of heterogeneity, positive improvements were also found in a range of menopause-related outcomes such as weight reduction, anxiety management, sleep quality, and menopause-related quality of life. Midlife women perceived mobile PA interventions to be acceptable and potentially helpful in increasing PA and daily steps. The average number of BCTs per mobile PA intervention was 8.8 (range 4-13) according to the BCT Taxonomy v1. “Self-monitoring of behaviour,” “Biofeedback,” and “Goal setting (behaviour)” were the most frequently described BCTs across the included interventions. Conclusions: This review demonstrated that mobile PA interventions in the form of smartphone apps and wearable trackers are potentially effective for small to moderate increases in moderate to vigorous PA among midlife women with menopausal symptoms. Although menopause is a natural condition affecting half the population worldwide, there is a substantial lack of evidence to support the acceptability and effectiveness of mobile PA interventions on menopause-related outcomes, which needs further investigation. Trial Registration: PROSPERO CRD42021273062; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=273062 %M 36485026 %R 10.2196/40271 %U https://mhealth.jmir.org/2022/12/e40271 %U https://doi.org/10.2196/40271 %U http://www.ncbi.nlm.nih.gov/pubmed/36485026 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 11 %P e33952 %T Fitbits for Monitoring Depressive Symptoms in Older Aged Persons: Qualitative Feasibility Study %A Mughal,Fiza %A Raffe,William %A Stubbs,Peter %A Kneebone,Ian %A Garcia,Jaime %+ Faculty of Engineering and IT, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, 2007, Australia, 61 4 5262 7824, fiza.mughal@uts.edu.au %K digital mental health %K Fitbit %K smartwatch %K smart wearable %K geriatric %K aging %K health informatics %K feasibility %K usability %K older aged %D 2022 %7 29.11.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: In 2022, an estimated 1.105 billion people used smart wearables and 31 million used Fitbit devices worldwide. Although there is growing evidence for the use of smart wearables to benefit physical health, more research is required on the feasibility of using these devices for mental health and well-being. In studies focusing on emotion recognition, emotions are often inferred and dependent on external cues, which may not be representative of true emotional states. Objective: The aim of this study was to evaluate the feasibility and acceptability of using consumer-grade activity trackers for apps in the remote mental health monitoring of older aged people. Methods: Older adults were recruited using criterion sampling. Participants were provided an activity tracker (Fitbit Alta HR) and completed weekly online questionnaires, including the Geriatric Depression Scale, for 4 weeks. Before and after the study period, semistructured qualitative interviews were conducted to provide insight into the acceptance and feasibility of performing the protocol over a 4-week period. Interview transcripts were analyzed using a hybrid inductive-deductive thematic analysis. Results: In total, 12 participants enrolled in the study, and 9 returned for interviews after the study period. Participants had positive attitudes toward being remotely monitored, with 78% (7/9) of participants experiencing no inconvenience throughout the study period. Moreover, 67% (6/9) were interested in trialing our prototype when it is implemented. Participants stated they would feel more comfortable if mental well-being was being monitored by carers remotely. Conclusions: Fitbit-like devices were an unobtrusive and convenient tool to collect physiological user data. Future research should integrate physiological user inputs to differentiate and predict depressive tendencies in users. %M 36268552 %R 10.2196/33952 %U https://formative.jmir.org/2022/11/e33952 %U https://doi.org/10.2196/33952 %U http://www.ncbi.nlm.nih.gov/pubmed/36268552 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 5 %N 1 %P e40815 %T Determining the Reliable Measurement Period for Preoperative Baseline Values With Telemonitoring Before Major Abdominal Surgery: Pilot Cohort Study %A Haveman,Marjolein E %A van Melzen,Rianne %A El Moumni,Mostafa %A Schuurmann,Richte C L %A Hermens,Hermie J %A Tabak,Monique %A de Vries,Jean-Paul P M %+ Department of Surgery, Division of Vascular Surgery, University Medical Center Groningen, University of Groningen, BA60, Hanzeplein 1, Groningen, 9713 GZ, Netherlands, 31 62564683, m.e.haveman@umcg.nl %K telemonitoring %K major abdominal surgery %K preoperative %K wearable sensor %K vital signs %K patient-reported outcome measure %K PROM %K surgery %K major surgery %K abdominal surgery %K observational study %K pain %K anxiety %K fatigue %K nausea %K heart rate %K step count %D 2022 %7 28.11.2022 %9 Original Paper %J JMIR Perioper Med %G English %X Background: Preoperative telemonitoring of vital signs, physical activity, and well-being might be able to optimize prehabilitation of the patient’s physical and mental condition prior to surgery, support setting alarms during in-hospital monitoring, and allow personalization of the postoperative recovery process. Objective: The primary aim of this study was to evaluate when and how long patients awaiting major abdominal surgery should be monitored to get reliable preoperative individual baseline values of heart rate (HR), daily step count, and patient-reported outcome measures (PROMs). The secondary aim was to describe the perioperative course of these measurements at home. Methods: In this observational single-center cohort study, patients used a wearable sensor during waking hours and reported PROMs (pain, anxiety, fatigue, nausea) on a tablet twice a day. Intraclass correlation coefficients (ICCs) were used to evaluate the reliability of mean values on 2 specific preoperative days (the first day of telemonitoring and the day before hospital admission) and randomly selected preoperative periods compared to individual reference values. Mean values of HR, step count, and PROMs per day were visualized in a boxplot from 14 days before hospital admission until 30 days after surgery. Results: A total of 16 patients were included in the data analyses. The ICCs of mean values on the first day of telemonitoring were 0.91 for HR, 0.71 for steps, and at least 0.86 for PROMs. The day before hospital admission showed reliability coefficients of 0.76 for HR, 0.71 for steps, and 0.92-0.99 for PROMs. ICC values of randomly selected measurement periods increased over the continuous period of time from 0.68 to 0.99 for HR and daily step counts. A lower bound of the 95% CI of at least 0.75 was determined after 3 days of measurements. The ICCs of randomly selected PROM measurements were 0.89-0.94. Visualization of mean values per day mainly showed variable preoperative daily step counts (median 2409, IQR 1735-4661 steps/day) and lower postoperative daily step counts (median 884, IQR 474-1605 steps/day). In addition, pain was visually reduced until 30 days after surgery at home. Conclusions: In this prospective pilot study, for patients awaiting major abdominal surgery, baseline values for HR and daily step count could be measured reliably by a wearable sensor worn for at least 3 consecutive days and PROMs during any preoperative day. No clear conclusions were drawn from the description of the perioperative course by showing mean values of HR, daily step count, and PROM values over time in the home situation. %M 36441586 %R 10.2196/40815 %U https://periop.jmir.org/2022/1/e40815 %U https://doi.org/10.2196/40815 %U http://www.ncbi.nlm.nih.gov/pubmed/36441586 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 11 %P e40391 %T Wearable Activity Tracker Use and Physical Activity Among Informal Caregivers in the United States: Quantitative Study %A Mahmood,Asos %A Kim,Hyunmin %A Kedia,Satish %A Dillon,Patrick %+ School of Health Professions, The University of Southern Mississippi, 118 College Drive #5122, Hattiesburg, MS, 39406, United States, 1 6015969087, hyunmin.kim@usm.edu %K informal caregivers %K caregiving %K health and activity trackers %K wearables %K physical activity %K health-promoting behavior %K mobile phone %D 2022 %7 24.11.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: With an increase in aging population and chronic medical conditions in the United States, the role of informal caregivers has become paramount as they engage in the care of their loved ones. Mounting evidence suggests that such responsibilities place substantial burden on informal caregivers and can negatively impact their health. New wearable health and activity trackers (wearables) are increasingly being used to facilitate and monitor healthy behaviors and to improve health outcomes. Although prior studies have examined the efficacy of wearables in improving health and well-being in the general population, little is known about their benefits among informal caregivers. Objective: This study aimed to examine the association between use of wearables and levels of physical activity (PA) among informal caregivers in the United States. Methods: We used data from the National Cancer Institute’s Health Information National Trends Survey 5 (cycle 3, 2019 and cycle 4, 2020) for a nationally representative sample of 1273 community-dwelling informal caregivers—aged ≥18 years, 60% (757/1273) female, 75.7% (990/1273) had some college or more in education, and 67.3% (885/1273) had ≥1 chronic medical condition—in the United States. Using jackknife replicate weights, a multivariable logistic regression was fit to assess an independent association between the use of wearables and a binary outcome: meeting or not meeting the current World Health Organization’s recommendation of PA for adults (≥150 minutes of at least moderate-intensity PA per week). Results: More than one-third (466/1273, 37.8%) of the informal caregivers met the recommendations for adult PA. However, those who reported using wearables (390/1273, 31.7%) had slightly higher odds of meeting PA recommendations (adjusted odds ratios 1.1, 95% CI 1.04-1.77; P=.04) compared with those who did not use wearables. Conclusions: The results demonstrated a positive association between the use of wearables and levels of PA among informal caregivers in the United States. Therefore, efforts to incorporate wearable technology into the development of health-promoting programs or interventions for informal caregivers could potentially improve their health and well-being. However, any such effort should address the disparities in access to innovative digital technologies, including wearables, to promote health equity. Future longitudinal studies are required to further support the current findings of this study. %M 36422886 %R 10.2196/40391 %U https://mhealth.jmir.org/2022/11/e40391 %U https://doi.org/10.2196/40391 %U http://www.ncbi.nlm.nih.gov/pubmed/36422886 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 2 %P e35796 %T Cardiorespiratory Fitness Estimation Based on Heart Rate and Body Acceleration in Adults With Cardiovascular Risk Factors: Validation Study %A Rissanen,Antti-Pekka E %A Rottensteiner,Mirva %A Kujala,Urho M %A Kurkela,Jari L O %A Wikgren,Jan %A Laukkanen,Jari A %+ Department of Sports and Exercise Medicine, Clinicum, University of Helsinki, Urhea-halli, Mäkelänkatu 47, Helsinki, 00550, Finland, 358 9 434 2100, antti-pekka.rissanen@helsinki.fi %K cardiopulmonary exercise test %K cardiorespiratory fitness %K heart rate variability %K hypertension %K type 2 diabetes %K wearable technology %D 2022 %7 25.10.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Cardiorespiratory fitness (CRF) is an independent risk factor for cardiovascular morbidity and mortality. Adding CRF to conventional risk factors (eg, smoking, hypertension, impaired glucose metabolism, and dyslipidemia) improves the prediction of an individual’s risk for adverse health outcomes such as those related to cardiovascular disease. Consequently, it is recommended to determine CRF as part of individualized risk prediction. However, CRF is not determined routinely in everyday clinical practice. Wearable technologies provide a potential strategy to estimate CRF on a daily basis, and such technologies, which provide CRF estimates based on heart rate and body acceleration, have been developed. However, the validity of such technologies in estimating individual CRF in clinically relevant populations is poorly known. Objective: The objective of this study is to evaluate the validity of a wearable technology, which provides estimated CRF based on heart rate and body acceleration, in working-aged adults with cardiovascular risk factors. Methods: In total, 74 adults (age range 35-64 years; n=56, 76% were women; mean BMI 28.7, SD 4.6 kg/m2) with frequent cardiovascular risk factors (eg, n=64, 86% hypertension; n=18, 24% prediabetes; n=14, 19% type 2 diabetes; and n=51, 69% metabolic syndrome) performed a 30-minute self-paced walk on an indoor track and a cardiopulmonary exercise test on a treadmill. CRF, quantified as peak O2 uptake, was both estimated (self-paced walk: a wearable single-lead electrocardiogram device worn to record continuous beat-to-beat R-R intervals and triaxial body acceleration) and measured (cardiopulmonary exercise test: ventilatory gas analysis). The accuracy of the estimated CRF was evaluated against that of the measured CRF. Results: Measured CRF averaged 30.6 (SD 6.3; range 20.1-49.6) mL/kg/min. In all participants (74/74, 100%), mean difference between estimated and measured CRF was −0.1 mL/kg/min (P=.90), mean absolute error was 3.1 mL/kg/min (95% CI 2.6-3.7), mean absolute percentage error was 10.4% (95% CI 8.5-12.5), and intraclass correlation coefficient was 0.88 (95% CI 0.80-0.92). Similar accuracy was observed in various subgroups (sexes, age, BMI categories, hypertension, prediabetes, and metabolic syndrome). However, mean absolute error was 4.2 mL/kg/min (95% CI 2.6-6.1) and mean absolute percentage error was 16.5% (95% CI 8.6-24.4) in the subgroup of patients with type 2 diabetes (14/74, 19%). Conclusions: The error of the CRF estimate, provided by the wearable technology, was likely below or at least very close to the clinically significant level of 3.5 mL/kg/min in working-aged adults with cardiovascular risk factors, but not in the relatively small subgroup of patients with type 2 diabetes. From a large-scale clinical perspective, the findings suggest that wearable technologies have the potential to estimate individual CRF with acceptable accuracy in clinically relevant populations. %M 36282560 %R 10.2196/35796 %U https://cardio.jmir.org/2022/2/e35796 %U https://doi.org/10.2196/35796 %U http://www.ncbi.nlm.nih.gov/pubmed/36282560 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 10 %P e35628 %T Long-term Effects of the Use of a Step Count–Specific Smartphone App on Physical Activity and Weight Loss: Randomized Controlled Clinical Trial %A Yoshimura,Eiichi %A Tajiri,Eri %A Michiwaki,Ryota %A Matsumoto,Naoyuki %A Hatamoto,Yoichi %A Tanaka,Shigeho %+ Department of Nutrition and Metabolism, National Institutes of Biomedical Innovation, Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo, Tokyo, 162-8636, Japan, 81 0332035725, eyoshi@nibiohn.go.jp %K step counts %K weight loss %K smartphone app %K step count–specific mobile app %K physical activity %K moderate-to-vigorous intensity physical activity %K lifestyle intervention %K mHealth %K mobile app: mobile phone %D 2022 %7 24.10.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Some studies on weight loss promotion using smartphone apps have shown a weight loss effect but not an increase in physical activity. However, the long-term effects of smartphone apps on weight loss and increasing physical activity have not been rigorously examined to date. Objective: The aim of this study was to assess whether the use of a smartphone app will increase physical activity and reduce body weight. Methods: In this parallel randomized clinical trial, participants recruited between April 2018 and June 2019 were randomized in equal proportions to a smartphone app group (n=55) or a control group (n=54). The intention-to-treat approach was used to analyze the data from December 2019 through November 2021. Before the intervention, an hour-long lecture on weight loss instruction and increasing physical activity was conducted once for both groups. Participants in both groups were instructed to weigh themselves immediately after waking up at least once daily from the start of the intervention. Monthly emails were sent advising the participants in both groups on how to lose weight and increase physical activity in order to maintain or increase motivation. Participants in the smartphone app group were instructed to open the app at least once a day to check their step count and rank. The primary outcome was daily accelerometer-measured physical activity (step count) and the secondary outcome was body weight. Since there was a significant difference in the wear time of the accelerometer depending on the intervention period (P<.001), the number of steps and moderate-to-vigorous physical activity were also evaluated per wear time. Results: The mean age of the 109 participants in this study was 47 (SD 8) years. At baseline, the mean daily total steps were 7259 (SD 3256) steps per day for the smartphone app group and 8243 (SD 2815) steps per day for the control group. The difference in the step count per wear time between preintervention and postintervention was significantly different between the app group and the control group (average difference [95% CI], 65 [30 to 101] steps per hour vs –9 [–56 to 39] steps per hour; P=.042). The weight loss was –2.2 kg (SD –3.1%) in the smartphone app group and –2.2 kg (SD –3.1%) in the control group, with no significant difference between the groups. In addition, when divided into weekdays (Monday through Friday) and weekends (Saturday and Sunday), there was a significant interaction between step counts (P=.004) and MVPA (P=.003) during the intervention, with the app group showing higher interaction on weekends than the control group. Conclusions: In this trial, the group with the smartphone app intervention showed increased physical activity, especially on weekends. However, this increased physical activity did not lead to increased weight loss. Trial Registration: University Hospital Medical Information Network UMIN000033397; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000037956 %M 36279159 %R 10.2196/35628 %U https://mhealth.jmir.org/2022/10/e35628 %U https://doi.org/10.2196/35628 %U http://www.ncbi.nlm.nih.gov/pubmed/36279159 %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 %@ 2291-5222 %I JMIR Publications %V 10 %N 10 %P e39150 %T The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System %A Takeuchi,Hiroki %A Suwa,Kaori %A Kishi,Akifumi %A Nakamura,Toru %A Yoshiuchi,Kazuhiro %A Yamamoto,Yoshiharu %+ Graduate School of Education, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan, 81 03 5841 3968, takeuchi@p.u-tokyo.ac.jp %K wearable activity monitor %K smartphone app %K sleep feedback %K ecological momentary assessment %K stabilized sleep timing %K mood and physical symptoms %D 2022 %7 6.10.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sleep is beneficial for physical and mental health. Several mobile and wearable sleep-tracking devices have been developed, and personalized sleep feedback is the most common functionality among these devices. To date, no study has implemented an objective push-type feedback message and investigated the characteristics of habitual sleep behavior and diurnal symptoms when receiving sleep feedback. Objective: We conducted a mobile health intervention trial to examine whether sending objective push-type sleep feedback changes the self-reported mood, physical symptoms, and sleep behavior of Japanese office workers. Methods: In total, 31 office workers (mean age 42.3, SD 7.9 years; male-to-female ratio 21:10) participated in a 2-arm intervention trial from November 30 to December 19, 2020. The participants were instructed to indicate their momentary mood and physical symptoms (depressive mood, anxiety, stress, sleepiness, fatigue, and neck and shoulder stiffness) 5 times a day using a smartphone app. In addition, daily work performance was rated once a day after work. They were randomly assigned to either a feedback or control group, wherein they did or did not receive messages about their sleep status on the app every morning, respectively. All participants wore activity monitors on their nondominant wrists, through which objective sleep data were registered on the web on a server. On the basis of the estimated sleep data on the server, personalized sleep feedback messages were generated and sent to the participants in the feedback group using the app. These processes were fully automated. Results: Using hierarchical statistical models, we examined the differences in the statistical properties of sleep variables (sleep duration and midpoint of sleep) and daily work performance over the trial period. Group differences in the diurnal slopes for mood and physical symptoms were examined using a linear mixed effect model. We found a significant group difference among within-individual residuals at the midpoint of sleep (expected a posteriori for the difference: −15, 95% credible interval −26 to −4 min), suggesting more stable sleep timing in the feedback group. However, there were no significant group differences in daily work performance. We also found significant group differences in the diurnal slopes for sleepiness (P<.001), fatigue (P=.002), and neck and shoulder stiffness (P<.001), which was largely due to better scores in the feedback group at wake-up time relative to those in the control group. Conclusions: This is the first mobile health study to demonstrate that objective push-type sleep feedback improves sleep timing of and physical symptoms in healthy office workers. Future research should incorporate specific behavioral instructions intended to improve sleep habits and examine the effectiveness of these instructions. %M 36201383 %R 10.2196/39150 %U https://mhealth.jmir.org/2022/10/e39150 %U https://doi.org/10.2196/39150 %U http://www.ncbi.nlm.nih.gov/pubmed/36201383 %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 %@ 2291-5222 %I JMIR Publications %V 10 %N 9 %P e30602 %T The Effects of a Lifestyle Intervention Supported by the InterWalk Smartphone App on Increasing Physical Activity Among Persons With Type 2 Diabetes: Parallel-Group, Randomized Trial %A Thorsen,Ida Kær %A Yang,Yanxiang %A Valentiner,Laura Staun %A Glümer,Charlotte %A Karstoft,Kristian %A Brønd,Jan Christian %A Nielsen,Rasmus Oestergaard %A Brøns,Charlotte %A Christensen,Robin %A Nielsen,Jens Steen %A Vaag,Allan Arthur %A Pedersen,Bente Klarlund %A Langberg,Henning %A Ried-Larsen,Mathias %+ Center of Inflammation and Metabolism and Centre for Physical Activity Research, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, Copenhagen, 2100, Denmark, 45 28700785, ida.kaer.thorsen@regionh.dk %K type 2 diabetes mellitus %K exercise %K telemedicine %K primary health care %K accelerometry %K quality of life %K waist circumference %K mHealth %K mobile app %D 2022 %7 28.9.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Effective and sustainable implementation of physical activity (PA) in type 2 diabetes (T2D) health care has in general not been successful. Efficacious and contemporary approaches to support PA adherence and adoption are required. Objective: The primary objective of this study was to investigate the effectiveness of including an app-based (InterWalk) approach in municipality-based rehabilitation to increase moderate-and-vigorous PA (MVPA) across 52 weeks compared with standard care among individuals with T2D. Methods: The study was designed as a parallel-group, randomized trial with 52 weeks’ intervention and subsequent follow-up for effectiveness (52 weeks from baseline). Participants were recruited between January 2015 and December 2016 and randomly allocated (2:1) into 12 weeks of (1) standard care + InterWalk app–based interval walking training (IWT; IWT group; n=140), or (2) standard care + the standard exercise program (StC group; n=74). Following 12 weeks, the IWT group was encouraged to maintain InterWalk app–based IWT (3 times per week for 30-60 minutes) and the StC group was encouraged to maintain exercise without structured support. Moreover, half of the IWT group (IWTsupport group, n=54) received additional motivational support following the 12-week program until 52-week follow-up. The primary outcome was change in objectively measured MVPA time (minutes/day) from baseline to 52-week follow-up. Key secondary outcomes included changes in self-rated physical and mental health–related quality of life (HRQoL), physical fitness, weight, and waist circumference. Results: Participants had a mean age of 59.6 (SD 10.6) years and 128/214 (59.8%) were men. No changes in MVPA time were observed from baseline to 52-week follow-up in the StC and IWT groups (least squares means [95% CI] 0.6 [–4.6 to 5.8] and –0.2 [–3.8 to 3.3], respectively) and no differences were observed between the groups (mean difference [95% CI] –0.8 [–8.1 to 6.4] minutes/day; P=.82). Physical HRQoL increased by a mean of 4.3 (95% CI 1.8 to 6.9) 12-item Short-Form Health Survey (SF-12) points more in the IWT group compared with the StC group (Benjamini-Hochberg adjusted P=.007) and waist circumference apparently decreased a mean of –2.3 (95% CI –4.1 to –0.4) cm more in the IWT group compared with the StC group but with a Benjamini-Hochberg adjusted P=.06. No between-group differences were observed among the remaining key secondary outcomes. Conclusions: Among individuals with T2D referred to municipality-based lifestyle programs, randomization to InterWalk app–based IWT did not increase objectively measured MVPA time over 52 weeks compared with standard health care, although apparent benefits were observed for physical HRQoL. Trial Registration: ClinicalTrials.gov NCT02341690; https://clinicaltrials.gov/ct2/show/NCT02341690 %M 36170002 %R 10.2196/30602 %U https://mhealth.jmir.org/2022/9/e30602 %U https://doi.org/10.2196/30602 %U http://www.ncbi.nlm.nih.gov/pubmed/36170002 %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 %@ 1929-073X %I JMIR Publications %V 11 %N 2 %P e34433 %T The Physical Activity Assessment of Adults With Type 2 Diabetes Using Accelerometer-Based Cut Points: Scoping Review %A Moldovan,Ioana A %A Bragg,Alexa %A Nidhiry,Anna S %A De La Cruz,Barbara A %A Mitchell,Suzanne E %+ Department of Family Medicine and Community Health, University of Massachusetts Chan Medical School, 55 Lake Avenue, Worcester, MA, 01655, United States, 1 9789856033, Suzanne.Mitchell2@umassmed.edu %K accelerometer %K cut points %K type 2 diabetes %K physical activity %D 2022 %7 6.9.2022 %9 Original Paper %J Interact J Med Res %G English %X Background: Incorporating physical activity into lifestyle routines is recommended for individuals with type 2 diabetes. Accelerometers offer a promising method for objectively measuring physical activity and for assessing interventions. However, the existing literature for accelerometer-measured physical activity among middle-aged and older adults with type 2 diabetes is lacking. Objective: This study aims to identify research studies in which accelerometer-based cut points were used to classify the physical activity intensity of middle-aged to older adults with type 2 diabetes as sedentary, light, moderate, vigorous, and very vigorous, and to determine if validated accelerometer cut points specifically for this population exist. Methods: We followed the Joanna Briggs Institute methodology for scoping reviews. Between June 23 and July 12, 2020, two reviewers independently screened records from four databases (PubMed, Web of Science, Embase, Engineering Village) and the ActiGraph Corp web site for eligible studies that included patients with type 2 diabetes with a sample mean age ≥50 years, used research-grade accelerometers, applied cut points to categorize objectively measured physical activity, and were available in English. We excluded studies reporting exclusively steps or step counts measured by accelerometers or pedometers and conference abstracts or other sources that did not have a full text available. Data extraction was completed using Microsoft Excel. Data for the following variables were tabulated based on frequency distributions: study design, accelerometer type, device placement, epoch length, total wear time, and cut points used. Study aims and participant demographic data were summarized. Results: A total of 748 records were screened at the abstract level, and 88 full-text articles were assessed for eligibility. Ultimately, 46 articles were retained and analyzed. Participants’ mean ages ranged from 50 to 79.9 years. The ActiGraph accelerometer and the Freedson et al and Troiano et al counts-per-minute cut points were the most frequently used across the literature. Freedson et al and Troiano et al counts-per-minute cut points for light, moderate, and vigorous activity correspond to <1952, 1952-5724, and ≥5725, and 100-2019, 2020-5998, and ≥5999, respectively. The Lopes et al cut points were developed by calibrating the ActiGraph in middle-aged and older adults with overweight/obesity and type 2 diabetes. These counts-per-minute thresholds are ≥200 (light), ≥1240 (moderate), and ≥2400 (vigorous), and were applied in 1 interventional study. Conclusions: An assortment of accelerometer cut points have been used by researchers to categorize physical activity intensity for middle-aged and older adults with diabetes. Only one set of cut points was validated and calibrated in our population of interest. Additional research is warranted to address the need for diabetes-specific cut points to inform public health recommendations. This includes confirmation that the Lopes et al cut points reflect clinically meaningful changes in physical activity for adults with diabetes who have comorbidities other than overweight/obesity and the development of relative intensity cut points that may be more suitable for those with suboptimal physical functioning. %M 36066937 %R 10.2196/34433 %U https://www.i-jmr.org/2022/2/e34433 %U https://doi.org/10.2196/34433 %U http://www.ncbi.nlm.nih.gov/pubmed/36066937 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 8 %P e36337 %T Association Between Patient Factors and the Effectiveness of Wearable Trackers at Increasing the Number of Steps per Day Among Adults With Cardiometabolic Conditions: Meta-analysis of Individual Patient Data From Randomized Controlled Trials %A Hodkinson,Alexander %A Kontopantelis,Evangelos %A Zghebi,Salwa S %A Grigoroglou,Christos %A McMillan,Brian %A Marwijk,Harm van %A Bower,Peter %A Tsimpida,Dialechti %A Emery,Charles F %A Burge,Mark R %A Esmiol,Hunter %A Cupples,Margaret E %A Tully,Mark A %A Dasgupta,Kaberi %A Daskalopoulou,Stella S %A Cooke,Alexandra B %A Fayehun,Ayorinde F %A Houle,Julie %A Poirier,Paul %A Yates,Thomas %A Henson,Joseph %A Anderson,Derek R %A Grey,Elisabeth B %A Panagioti,Maria %+ Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 (0)161 2753535, alexander.hodkinson@manchester.ac.uk %K systematic review %K individual patient data %K meta-analysis %K steps/day %K wearable tracker %K cardiometabolic conditions %K diabetes %K obesity %K cardiovascular disease %D 2022 %7 30.8.2022 %9 Review %J J Med Internet Res %G English %X Background: Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers. Objective: This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences. Methods: We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI. Results: Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient –668, 95% CI –1157 to –180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient –517, 95% CI –1188 to –11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day. Conclusions: In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity. Trial Registration: PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012 %M 36040779 %R 10.2196/36337 %U https://www.jmir.org/2022/8/e36337 %U https://doi.org/10.2196/36337 %U http://www.ncbi.nlm.nih.gov/pubmed/36040779 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 8 %P e38495 %T Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping %A Chikersal,Prerna %A Venkatesh,Shruthi %A Masown,Karman %A Walker,Elizabeth %A Quraishi,Danyal %A Dey,Anind %A Goel,Mayank %A Xia,Zongqi %+ Department of Neurology, University of Pittsburgh, 3501 Fifth Avenue,, BST3, Suite 7014, Pittsburgh, PA, 15260, United States, 1 412 383 5377, zxia1@pitt.edu %K mobile sensing %K sensor %K sensing %K mobile health %K mHealth %K algorithm %K multiple sclerosis %K disability %K mental health %K depression %K sleep %K fatigue %K tiredness %K predict %K machine learning %K feature selection %K neurological disorder %K COVID-19 %K isolation %K behavior change %K health outcome %K fitness %K movement %K physical activity %K exercise %K tracker %K digital phenotyping %D 2022 %7 24.8.2022 %9 Original Paper %J JMIR Ment Health %G English %X Background: The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). Objective: We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic. Methods: First, we extracted features that capture behavior changes due to the stay-at-home order. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period. Results: Using data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; F1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; F1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; F1-score: 0.84). Conclusions: Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes. %M 35849686 %R 10.2196/38495 %U https://mental.jmir.org/2022/8/e38495 %U https://doi.org/10.2196/38495 %U http://www.ncbi.nlm.nih.gov/pubmed/35849686 %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 %@ 2291-5222 %I JMIR Publications %V 10 %N 8 %P e39520 %T The Use of Digital Health Tools for Health Promotion Among Women With and Without Chronic Diseases: Insights From the 2017-2020 Health Information National Trends Survey %A Ajayi,Kobi V %A Wachira,Elizabeth %A Onyeaka,Henry K %A Montour,Tyra %A Olowolaju,Samson %A Garney,Whitney %+ Department of Health & Kinesiology, Texas A&M University, 2929 Research Parkway, College Station, TX, 77843, United States, 1 9797396250, omo_debare@tamu.edu %K mHealth %K health promotion %K chronic disease %K women %K digital health %K USA %K United States %K patient engagement %D 2022 %7 19.8.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In the United States, almost 90% of women are at risk of at least one chronic condition. However, the awareness, management, and monitoring of these conditions are low and present a substantial public health problem. Digital health tools can be leveraged to reduce the alarmingly high rates of chronic condition–related mortality and morbidity in women. Objective: This study aimed to investigate the 4-year trend of digital health use for health promotion among women with chronic conditions in the United States. Methods: Data for this study were obtained from the 2017 to 2020 iterations of the Health Information Trends Survey 5. Separate weighted logistic regression models were conducted to test the unadjusted and adjusted association of the study variables and each digital health use. The 95% CI, adjusted odds ratio (aOR), and P value (.05) were reported. Analysis was conducted using Stata 17 software. Results: In total, 8573 women were included in this study. The weighted prevalence of the use of a smartphone or tablet for various activities were as follows: track health goals, 50.3% (95% CI 48.4%-52.2%; 3279/7122); make a health decision, 43.6% (95% CI 41.9%-45.3%; 2998/7101); and discuss with a provider, 40% (95% CI 38.2%-41.8%; 2834/7099). In the preceding 12 months, 33% (95% CI 30.9%-35.2%; 1395/4826) of women used an electronic wearable device, 18.7% (95% CI 17.3%-20.2%; 1532/7653) shared health information, and 35.2% (95% CI 33.2%-37.3%; 2262/6349) sent or received an SMS text message with a health professional. Between 2017 and 2020, the weighted prevalence of having 0, 1, and multiple chronic conditions were 37.4% (2718/8564), 33.4% (2776/8564), and 29.3% (3070/8564), respectively. However, slightly above half (52.2%, 95% CI 0.50%-0.53%; 4756/8564) of US women reported having at least one chronic disease. Women with multiple chronic conditions had higher odds of using their tablet or smartphone to achieve a health-related goal (aOR 1.43, 95% CI 1.16-1.77; P=.001) and discuss with their provider (aOR 1.55 95% CI 1.20-2.00; P=.001) than those without any chronic conditions. Correspondingly, in the past 12 months, the odds of using an electronic wearable device (aOR 1.40, 95% CI 1.00-1.96; P=.04), sharing health information (aOR 1.91, 95% CI 1.46-2.51; P<.001), and communicating via SMS text messaging with a provider (aOR 1.31, 95% CI 1.02-1.68; P=.03) were significantly higher among women with chronic conditions than those without a chronic condition. Conclusions: This study suggests that women with chronic conditions accept and integrate digital health tools to manage their care. However, certain subpopulations experience a digital disconnect that may exacerbate existing health inequities. Implications for research and opportunities to leverage and integrate digital health tools to prevent, monitor, manage, and treat chronic conditions in women are discussed. %M 35984680 %R 10.2196/39520 %U https://mhealth.jmir.org/2022/8/e39520 %U https://doi.org/10.2196/39520 %U http://www.ncbi.nlm.nih.gov/pubmed/35984680 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 8 %P e35261 %T Mediators of Effects on Physical Activity and Sedentary Time in an Activity Tracker and Behavior Change Intervention for Adolescents: Secondary Analysis of a Cluster Randomized Controlled Trial %A Verswijveren,Simone Johanna Josefa Maria %A Abbott,Gavin %A Lai,Samuel K %A Salmon,Jo %A Timperio,Anna %A Brown,Helen %A Macfarlane,Susie %A Ridgers,Nicola D %+ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Hwy, Burwood, 3125, Australia, 61 03 9244 6100, s.verswijveren@deakin.edu.au %K movement behavior %K youth %K accelerometry %K Fitbit %K correlates %K correlate %K physical activity %K exercise %K randomized controlled trial %K RCT %K control trial %K Australia %K adolescent %K adolescence %K teenager %K sedentary %K cognitive theory %K behavioral theory %K wearable %K tracker %K tracking device %K clinical trial %D 2022 %7 16.8.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Adolescence is a critical age where steep declines in physical activity and increases in sedentary time occur. Promoting physical activity should therefore be a priority for short- and long-term health benefits. Wearable activity trackers in combination with supportive resources have the potential to influence adolescents’ physical activity levels and sedentary behavior. Examining the pathways through which such interventions work can inform which mediators to target in future studies. Objective: The aim of this paper is to examine the impact of the Raising Awareness of Physical Activity (RAW-PA) intervention on potential mediators of behavior change after intervention, and whether these mediated the intervention effects on physical activity and sedentary time at 6-month follow-up. Methods: RAW-PA was a 12-week intervention, grounded in social cognitive theory and behavioral choice theory, aimed at increasing physical activity among inactive adolescents through combining a wearable activity tracker with digital resources delivered via a private Facebook group (n=159 complete cases). The targeted potential mediators were identified from previous studies conducted in adolescents and included self-efficacy, peer support, family support, teacher support, self-regulation strategies, barriers, and enjoyment. Outcomes included sedentary time as well as light- and moderate-to-vigorous–intensity physical activity. A series of mixed linear models were used to estimate intervention effects on physical activity and sedentary behavior at follow-up and on potential mediators after intervention and to test whether there were indirect effects of the intervention on physical activity and sedentary behavior via mediators. Results: Adolescents in the intervention group (n=75) engaged in higher sedentary time and lower light intensity at 6-month follow-up compared to the wait-list controls (n=84). There were no intervention effects for moderate-to-vigorous–intensity physical activity. The intervention group perceived more barriers to physical activity than the wait-list control group at 6-month follow-up (mean adjusted difference=1.77; 95% CI 0.19-3.34; P=.03). However, indirect effects for each outcome were not statistically significant, indicating that perceived barriers to physical activity did not mediate intervention effects for physical activity or sedentary time. Conclusions: RAW-PA did not beneficially impact hypothesized mediators in these inactive adolescents, despite strategies being designed to target them. This suggests that the lack of overall intervention effects on physical activity and sedentary time observed in the RAW-PA study could be due to the limited impact of the intervention on the targeted mediators. Future studies should consider different strategies to target theoretically informed potential mediators and identify intervention strategies that effectively target key mediators to improve physical activity among inactive adolescents. Finally, intervention effects according to level of wearable tracker use or level of engagement with the intervention should be explored. This may provide important insights for designing successful wearable activity tracker interventions. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12616000899448; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370716&isReview=true International Registered Report Identifier (IRRID): RR2-10.1186/s12889-016-3945-5 %M 35972777 %R 10.2196/35261 %U https://mhealth.jmir.org/2022/8/e35261 %U https://doi.org/10.2196/35261 %U http://www.ncbi.nlm.nih.gov/pubmed/35972777 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 3 %P e33845 %T A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study %A Boateng,George %A Petersen,Curtis L %A Kotz,David %A Fortuna,Karen L %A Masutani,Rebecca %A Batsis,John A %+ Division of Geriatric Medicine, School of Medicine, University of North Carolina at Chapel Hill, 5017 Old Clinic Building, Chapel Hill, NC, 27599, United States, 1 919 843 4096, john.batsis@gmail.com %K step tracking %K step counting %K pedometer %K wearable %K smartwatch %K older adults %K physical activity %K machine learning %K walking %K mHealth %K mobile health %K mobile app %K mobile application %K app %K uHealth %D 2022 %7 10.8.2022 %9 Original Paper %J JMIR Aging %G English %X Background: Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings. Objective: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time. Methods: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions—one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics. Results: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet’s count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669. Conclusions: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults. %M 35947445 %R 10.2196/33845 %U https://aging.jmir.org/2022/3/e33845 %U https://doi.org/10.2196/33845 %U http://www.ncbi.nlm.nih.gov/pubmed/35947445 %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 %@ 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 %@ 2563-3570 %I JMIR Publications %V 3 %N 1 %P e38512 %T Monitoring Physical Behavior in Rehabilitation Using a Machine Learning–Based Algorithm for Thigh-Mounted Accelerometers: Development and Validation Study %A Skovbjerg,Frederik %A Honoré,Helene %A Mechlenburg,Inger %A Lipperts,Matthijs %A Gade,Rikke %A Næss-Schmidt,Erhard Trillingsgaard %+ Research Unit, Hammel Neurorehabilitation Centre & University Research Clinic, Voldbyvej 15, Hammel, 8450, Denmark, 45 28739264, freskv@rm.dk %K activity recognition %K random forest %K acquired brain injury %K biometric monitoring %K machine learning %K physical activity %D 2022 %7 26.7.2022 %9 Original Paper %J JMIR Bioinform Biotech %G English %X Background: Physical activity is emerging as an outcome measure. Accelerometers have become an important tool in monitoring physical behavior, and newer analytical approaches of recognition methods increase the degree of details. Many studies have achieved high performance in the classification of physical behaviors through the use of multiple wearable sensors; however, multiple wearables can be impractical and lower compliance. Objective: The aim of this study was to develop and validate an algorithm for classifying several daily physical behaviors using a single thigh-mounted accelerometer and a supervised machine-learning scheme. Methods: We collected training data by adding the behavior classes—running, cycling, stair climbing, wheelchair ambulation, and vehicle driving—to an existing algorithm with the classes of sitting, lying, standing, walking, and transitioning. After combining the training data, we used a random forest learning scheme for model development. We validated the algorithm through a simulated free-living procedure using chest-mounted cameras for establishing the ground truth. Furthermore, we adjusted our algorithm and compared the performance with an existing algorithm based on vector thresholds. Results: We developed an algorithm to classify 11 physical behaviors relevant for rehabilitation. In the simulated free-living validation, the performance of the algorithm decreased to 57% as an average for the 11 classes (F-measure). After merging classes into sedentary behavior, standing, walking, running, and cycling, the result revealed high performance in comparison to both the ground truth and the existing algorithm. Conclusions: Using a single thigh-mounted accelerometer, we obtained high classification levels within specific behaviors. The behaviors classified with high levels of performance mostly occur in populations with higher levels of functioning. Further development should aim at describing behaviors within populations with lower levels of functioning. %R 10.2196/38512 %U https://bioinform.jmir.org/2022/1/e38512 %U https://doi.org/10.2196/38512 %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 %@ 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 %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e37086 %T Fitbit Use and Activity Levels From Intervention to 2 Years After: Secondary Analysis of a Randomized Controlled Trial %A Hartman,Sheri J %A Chen,Ruohui %A Tam,Rowena M %A Narayan,Hari K %A Natarajan,Loki %A Liu,Lin %+ Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, 3855 Health Sciences Drive, #0901, La Jolla, CA, 92037, United States, 1 8585349235, sjhartman@ucsd.edu %K physical activity %K fitness %K exercise %K Fitbit %K wearable %K health technology %K mHealth %K digital health %K activity tracker %K maintenance %K adherence %K tracker %K use pattern %K activity level %K behavior change %K cancer %K breast %K survivor %K long-term use %K sustained use %D 2022 %7 30.6.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There has been a rapid increase in the use of commercially available activity trackers, such as Fitbit, in physical activity intervention research. However, little is known about the long-term sustained use of trackers and behavior change after short-term interventions. Objective: This study aims to use minute-level data collected from a Fitbit tracker for up to 2 years after the end of a randomized controlled trial to examine patterns of Fitbit use and activity over time. Methods: Participants in this secondary data analysis were 75 female breast cancer survivors who had been enrolled in a 12-week physical activity randomized controlled trial. Participants randomized to the exercise intervention (full intervention arm) received a Fitbit One, which was worn daily throughout the 12-week intervention, and then were followed for 2 years after the intervention. Participants randomized to the waitlist arm, after completing the randomized controlled trial, received a Fitbit One and a minimal version of the exercise intervention (light intervention arm), and then were followed for 2 years after the intervention. Average and daily adherence and MVPA were compared between the 2 groups in the interventional and postinterventional periods using both linear and generalized additive mixed effects models. Results: Adherence to wearing the Fitbit during the 12-week intervention period was significantly higher in the full intervention arm than in the light intervention arm (85% vs 60%; P<.001). Average adherence was significantly lower for both study arms during the follow-up period than in the intervention period; however, there were statistically different patterns of adherence during the follow-up period, with the light intervention arm having steeper declines than the full intervention arm over time (P<.001). Similar to the adherence results, mean minutes of Fitbit-measured MVPA was higher for the full intervention arm than for the light intervention arm during the 12-week intervention period (mean MVPA 27.89 minutes/day, SD 16.38 minutes/day vs 18.35 minutes/day, SD 12.64 minutes/day; P<.001). During the follow-up period, average MVPA was significantly lower than the 12-week intervention period for both the full intervention arm (21.74 minutes/day, SD 24.65 minutes/day; P=.002) and the light intervention arm (15.03 minutes/day, SD 13.27 minutes/day; P=.004). Although the mean MVPA in each arm was similar across the follow-up period (P=.33), the pattern of daily MVPA was significantly different between the 2 groups (P<.001). Conclusions: While adherence to wearing activity trackers and maintaining physical activities declined after completion of a 12-week exercise intervention, a more active interventional strategy resulted in greater wear time and activity levels during the intervention and more stable patterns of adherence and activity in the long term. An improved understanding of long-term maintenance patterns may inform improved exercise interventions that result in sustained increases in physical activity. Trial Registration: ClinicalTrials.gov NCT02332876; https://clinicaltrials.gov/ct2/show/NCT02332876 %M 35771607 %R 10.2196/37086 %U https://mhealth.jmir.org/2022/6/e37086 %U https://doi.org/10.2196/37086 %U http://www.ncbi.nlm.nih.gov/pubmed/35771607 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 6 %P e36870 %T Monitoring and Managing Lifestyle Behaviors Using Wearable Activity Trackers: Mixed Methods Study of Views From the Huntington Disease Community %A Morgan-Jones,Philippa %A Jones,Annabel %A Busse,Monica %A Mills,Laura %A Pallmann,Philip %A Drew,Cheney %A Arnesen,Astri %A Wood,Fiona %A , %+ Division of Population Medicine, Cardiff University, 8th floor, Neuadd Meirionnydd, University Hospital of Wales, Cardiff, CF14 4YS, United Kingdom, 44 2920687185, wood@cardiff.ac.uk %K Huntington disease %K activity tracker %K perceptions %K digital technologies %K physical activity %K qualitative research %K survey %D 2022 %7 29.6.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: There are early indications that lifestyle behaviors, specifically physical activity and sleep, may be associated with the onset and progression of Huntington disease (HD). Wearable activity trackers offer an exciting opportunity to collect long-term activity data to further investigate the role of lifestyle, physical activity, and sleep in disease modification. Given how wearable devices rely on user acceptance and long-term adoption, it is important to understand users’ perspectives on how acceptable any device might be and how users might engage over the longer term. Objective: This study aimed to explore the perceptions, motivators, and potential barriers relating to the adoption of wearable activity trackers by people with HD for monitoring and managing their lifestyle and sleep. This information intended to guide the selection of wearable activity trackers for use in a longitudinal observational clinical study. Methods: We conducted a mixed methods study; this allowed us to draw on the potential strengths of both quantitative and qualitative methods. Opportunistic participant recruitment occurred at 4 Huntington’s Disease Association meetings, including 1 international meeting and 3 United Kingdom–based regional meetings. Individuals with HD, their family members, and carers were invited to complete a user acceptance questionnaire and participate in a focus group discussion. The questionnaire consisted of 35 items across 8 domains using a 0 to 4 Likert scale, along with some additional demographic questions. Average questionnaire responses were recorded as positive (score>2.5), negative (score<1.5), or neutral (score between 1.5 and 2.5) opinions for each domain. Differences owing to demographics were explored using the Kruskal-Wallis and Wilcoxon rank sum tests. Focus group discussions (conducted in English) were driven by a topic guide, a vignette scenario, and an item ranking exercise. The discussions were audio recorded and then analyzed using thematic analysis. Results: A total of 105 completed questionnaires were analyzed (47 people with HD and 58 family members or carers). All sections of the questionnaire produced median scores >2.5, indicating a tendency toward positive opinions on wearable activity trackers, such as the devices being advantageous, easy and enjoyable to use, and compatible with lifestyle and users being able to understand the information from trackers and willing to wear them. People with HD reported a more positive attitude toward wearable activity trackers than their family members or caregivers (P=.02). A total of 15 participants participated in 3 focus groups. Device compatibility and accuracy, data security, impact on relationships, and the ability to monitor and self-manage lifestyle behaviors have emerged as important considerations in device use and user preferences. Conclusions: Although wearable activity trackers were broadly recognized as acceptable for both monitoring and management, various aspects of device design and functionality must be considered to promote acceptance in this clinical cohort. %M 35767346 %R 10.2196/36870 %U https://formative.jmir.org/2022/6/e36870 %U https://doi.org/10.2196/36870 %U http://www.ncbi.nlm.nih.gov/pubmed/35767346 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 5 %N 2 %P e33312 %T A Web-Based, Time-Use App To Assess Children’s Movement Behaviors: Validation Study of My E-Diary for Activities and Lifestyle (MEDAL) %A Tan,Sarah Yi Xuan %A Chia,Airu %A Tai,Bee Choo %A Natarajan,Padmapriya %A Goh,Claire Marie Jie Lin %A Shek,Lynette P %A Saw,Seang Mei %A Chong,Mary Foong-Fong %A Müller-Riemenschneider,Falk %+ Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, #09-01Q, Singapore, 117549, Singapore, 65 6516 4969, mary_chong@nus.edu.sg %K children %K accelerometer %K MEDAL %K web-based app %K self-report %K validity %K physical activity %K movement behavior %K pediatrics %K sleep %K digital health %K behavior %D 2022 %7 24.6.2022 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: Existing modes of collecting self-reported 24-hour movement information from children, including digital assessments, have not been demonstrated to be of acceptable validity when compared to objective measurements. My E-Diary for Activities and Lifestyle (MEDAL) is an interactive web-based diary developed to collect time-use information from children aged 10 years and older. Objective: This study evaluated the validity of MEDAL for assessing children’s movement behaviors by comparing self-reported and accelerometer-measured time spent in movement behavior among children in Singapore aged 10-11 years. Methods: Funding for this study was obtained in October 2017, and data were collected between April and August 2020. Participants recorded their daily activities using MEDAL over 2 specified weekdays and 2 weekend days and wore an Actigraph accelerometer on their nondominant wrist throughout the study to objectively assess movement behaviors. Spearman correlation coefficient and intraclass correlation coefficient (ICC) were used to compare the accelerometer measurements and self-reports for each movement behavior. Bland-Altman plots were generated to investigate trends of bias in the self-reports. Results: Among the participants aged 10-11 years (29/49, 59% boys), we observed that children reported lower light physical activity (LPA) and higher moderate-to-vigorous physical activity (MVPA), inactivity, and night sleep than that measured by the accelerometer. There was a moderate-to-strong correlation between self-reported and accelerometer-measured MVPA (r=0.37; 95% CI 0.20-0.54), inactivity (r=0.36; 95% CI 0.18-0.54), and night sleep (r=0.58; 95% CI 0.43-0.74); the correlation for LPA was poor (r=0.19; 95% CI 0.02-0.36). Agreement was poor for all behaviors (MVPA: ICC=0.24, 95% CI 0.07-0.40; LPA: ICC=0.19, 95% CI 0.01-0.36; inactivity: ICC=0.29, 95% CI 0.11-0.44; night sleep: ICC=0.45, 95% CI 0.29-0.58). There was stronger correlation and agreement on weekdays for inactivity and night sleep; conversely, there was stronger correlation and agreement for MVPA and LPA on weekend days. Finally, based on Bland-Altman plots, we observed that with increasing MVPA, children tended to report higher MVPA than that measured by the accelerometer. There were no clear trends for the other behaviors. Conclusions: MEDAL may be used to assess the movement behaviors of children. Based on self-reports, the children are able to estimate their time spent in MVPA, inactivity, and night sleep although actual time spent in these behaviors may differ from accelerometer-derived estimates; self-reported LPA warrant cautious interpretation. Observable differences in reporting accuracy exist between weekdays and weekend days. %M 35749208 %R 10.2196/33312 %U https://pediatrics.jmir.org/2022/2/e33312 %U https://doi.org/10.2196/33312 %U http://www.ncbi.nlm.nih.gov/pubmed/35749208 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 8 %N 2 %P e35694 %T Self-monitoring of Physical Activity After Hospital Discharge in Patients Who Have Undergone Gastrointestinal or Lung Cancer Surgery: Mixed Methods Feasibility Study %A de Leeuwerk,Marijke Elizabeth %A Botjes,Martine %A van Vliet,Vincent %A Geleijn,Edwin %A de Groot,Vincent %A van Wegen,Erwin %A van der Schaaf,Marike %A Tuynman,Jurriaan %A Dickhoff,Chris %A van der Leeden,Marike %+ Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081 HV, Netherlands, 31 612640672, m.e.deleeuwerk@amsterdamumc.nl %K mobile phone %K physical activity %K self-monitoring %K fitness trackers %K telemedicine %K cancer %K physical therapy %D 2022 %7 24.6.2022 %9 Original Paper %J JMIR Cancer %G English %X Background: Self-monitoring of physical activity (PA) using an accelerometer is a promising intervention to stimulate PA after hospital discharge. Objective: This study aimed to evaluate the feasibility of PA self-monitoring after discharge in patients who have undergone gastrointestinal or lung cancer surgery. Methods: A mixed methods study was conducted in which 41 patients with cancer scheduled for lobectomy, esophageal resection, or hyperthermic intraperitoneal chemotherapy were included. Preoperatively, patients received an ankle-worn accelerometer and the corresponding mobile health app to familiarize themselves with its use. The use was continued for up to 6 weeks after surgery. Feasibility criteria related to the study procedures, the System Usability Scale, and user experiences were established. In addition, 6 patients were selected to participate in semistructured interviews. Results: The percentage of patients willing to participate in the study (68/90, 76%) and the final participation rate (57/90, 63%) were considered good. The retention rate was acceptable (41/57, 72%), whereas the rate of missing accelerometer data was relatively high (31%). The mean System Usability Scale score was good (77.3). Interviewed patients mentioned that the accelerometer and app were easy to use, motivated them to be more physically active, and provided postdischarge support. The technical shortcomings and comfort of the ankle straps should be improved. Conclusions: Self-monitoring of PA after discharge appears to be feasible based on good system usability and predominantly positive user experiences in patients with cancer after lobectomy, esophageal resection, or hyperthermic intraperitoneal chemotherapy. Solving technical problems and improving the comfort of the ankle strap may reduce the number of dropouts and missing data in clinical use and follow-up studies. %M 35749165 %R 10.2196/35694 %U https://cancer.jmir.org/2022/2/e35694 %U https://doi.org/10.2196/35694 %U http://www.ncbi.nlm.nih.gov/pubmed/35749165 %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 %@ 2292-9495 %I JMIR Publications %V 9 %N 2 %P e33972 %T Features and Components Preferred by Adolescents in Smartphone Apps for the Promotion of Physical Activity: Focus Group Study %A Domin,Alex %A Ouzzahra,Yacine %A Vögele,Claus %+ Research Group for 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 mHealth %K physical activity %K mobile phone %K health %K qualitative research %K focus groups %K smartphone apps %K behavior change %K mobile health %K adolescents %D 2022 %7 9.6.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: There is solid evidence that lack of physical activity (PA) is a risk factor for chronic diseases. Sufficient levels of PA in childhood and adolescence are particularly important, as they can set the standards for PA levels in adulthood. The latest reports show that only a small percentage of adolescents reach the recommended levels of PA in European Union countries at the age of 15 years. In view of the scale of the problem, it is crucial to develop interventions that promote and support PA in adolescents. Considering their low implementation costs and ubiquitous presence, smartphone apps could be advantageous as a part of PA interventions. Objective: This study aimed at investigating the attitudes and preferences of adolescents aged 16-18 years toward various PA app features and components that could (1) make the app more attractive for them and consequently (2) increase their interest and engagement with the app. Methods: Two separate focus group discussions were conducted in 2 groups of adolescents (n=4 each) aged 16-18 years. Focus groups were carried out online via video conference. The discussions were conducted using a semistructured interview. Participants (n=8; 4 males and 4 females) had a mean age of 17.25 years (SD 0.82 years). Transcripts were analyzed following the approach by Krueger and Casey, that is, categorizing participants’ answers and comments according to the questions and themes from the focus group schedule. Results: Features, such as “goal setting and planning,” “coaching and training programs,” “activity tracking,” “feedback,” and “location tracking” were appraised as attractive, motivating, and interesting. An “automatic activity recognition” feature was perceived as useful only under the condition that its precision was high. The “reminders” component was also deemed as useful only if a range of conditions was fulfilled (timeliness, opportunity for customization, etc). The features “mood and sleep tracking,” “sharing workout results via social networks,” “digital avatar and coach,” and “rewards” were generally perceived negatively and considered as useless and not motivating. In general, participants preferred features with an easy-to-navigate interface and a clear, simplistic, and straightforward layout with a modern design. Customization and personalization qualities were highly appreciated throughout an app, together with data precision. Conclusions: This study contributes to the understanding of the features and components preferred by adolescents in apps promoting PA. Such apps should provide users with precise data, and have a simplistic modern design and a straightforward easy-to-use interface. Apps should be personalized and customizable. Desired features to be included in an app are goal setting and planning, feedback, coaching and training programs, and activity tracking. The features should involve high levels of data precision and timely delivery while taking into consideration the real-life context. %M 35679113 %R 10.2196/33972 %U https://humanfactors.jmir.org/2022/2/e33972 %U https://doi.org/10.2196/33972 %U http://www.ncbi.nlm.nih.gov/pubmed/35679113 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e30960 %T Impact of the Moderating Effect of National Culture on Adoption Intention in Wearable Health Care Devices: Meta-analysis %A Zhang,Zhenming %A Xia,Enjun %A Huang,Jieping %+ School of Management and Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China, 86 139 1085 0628, cindy@bit.edu.cn %K wearable health care devices %K national culture %K moderating effect %K meta-analysis %D 2022 %7 3.6.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Wearable health care devices have not yet been commercialized on a large scale. Additionally, people in different countries have different utilization rates. Therefore, more in-depth studies on the moderating effect of national culture on adoption intention in wearable health care devices are necessary. Objective: This study aims to explore the summary results of the relationships between perceived usefulness and perceived ease of use with adoption intention in wearable health care devices and the impact of the moderating effect of national culture on these two relationships. Methods: We searched for studies published before September 2021 in the Web of Science, EBSCO, Engineering Village, China National Knowledge Infrastructure, IEEE Xplore, and Wiley Online Library databases. CMA (version 2.0, Biostat Inc) software was used to perform the meta-analysis. We conducted publication bias and heterogeneity tests on the data. The random-effects model was used to estimate the main effect size, and a sensitivity analysis was conducted. A meta-regression analysis was used to test the moderating effect of national culture. Results: This meta-analysis included 20 publications with a total of 6128 participants. Perceived usefulness (r=0.612, P<.001) and perceived ease of use (r=0.462, P<.001) positively affect adoption intention. The relationship between perceived usefulness and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.008, P<.001) and indulgence/restraint (β=.005, P<.001), and negatively moderated by uncertainty avoidance (β=-.005, P<.001). The relationship between perceived ease of use and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.006, P<.001) and indulgence/restraint (β=.009, P<.001), and negatively moderated by uncertainty avoidance (β=-.004, P<.001). Conclusions: This meta-analysis provided comprehensive evidence on the positive relationship between perceived usefulness and perceived ease of use with adoption intention and the moderating effect of national culture on these two relationships. Regarding the moderating effect, perceived usefulness and perceived ease of use have a greater impact on adoption intention for people in individualistic, masculine, low uncertainty avoidance, and indulgence cultures, respectively. %M 35657654 %R 10.2196/30960 %U https://mhealth.jmir.org/2022/6/e30960 %U https://doi.org/10.2196/30960 %U http://www.ncbi.nlm.nih.gov/pubmed/35657654 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 5 %P e27248 %T Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study %A Henriksen,André %A Svartdal,Frode %A Grimsgaard,Sameline %A Hartvigsen,Gunnar %A Hopstock,Laila Arnesdatter %+ Department of Computer Science, UiT The Arctic University of Norway, Hansine Hansens veg 18, Troms, 9019, Norway, 47 77645214, andre.henriksen@uit.no %K actigraphy %K fitness trackers %K motor activity %K energy expenditure %K steps %K activity tracker %D 2022 %7 27.5.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Consumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed. Objective: The objective of this study was to compare the Polar Vantage watch (Polar Electro Oy) and Oura ring (generation 2; Ōura Health Oy) activity trackers to research-based instruments for measuring physical activity, total energy expenditure, resting heart rate, and sleep duration in free-living adults. Methods: A total of 21 participants wore 2 consumer-based activity trackers (Polar watch and Oura ring), an ActiGraph accelerometer (ActiGraph LLC), and an Actiheart accelerometer and heart rate monitor (CamNtech Ltd) and completed a sleep diary for up to 7 days. We assessed Polar watch and Oura ring validity and comparability for measuring physical activity, total energy expenditure, resting heart rate (Oura), and sleep duration. We analyzed repeated measures correlations, Bland-Altman plots, and mean absolute percentage errors. Results: The Polar watch and Oura ring values strongly correlated (P<.001) with the ActiGraph values for steps (Polar: r=0.75, 95% CI 0.54-0.92; Oura: r=0.77, 95% CI 0.62-0.87), moderate-to-vigorous physical activity (Polar: r=0.76, 95% CI 0.62-0.88; Oura: r=0.70, 95% CI 0.49-0.82), and total energy expenditure (Polar: r=0.69, 95% CI 0.48-0.88; Oura: r=0.70, 95% CI 0.51-0.83) and strongly or very strongly correlated (P<.001) with the sleep diary–derived sleep durations (Polar: r=0.74, 95% CI 0.56-0.88; Oura: r=0.82, 95% CI 0.68-0.91). Oura ring–derived resting heart rates had a very strong correlation (P<.001) with the Actiheart-derived resting heart rates (r=0.9, 95% CI 0.85-0.96). However, the mean absolute percentage error was high for all variables except Oura ring–derived sleep duration (10%) and resting heart rate (3%), which the Oura ring underreported on average by 1 beat per minute. Conclusions: The Oura ring can potentially be used as an alternative to the Actiheart to measure resting heart rate. As for sleep duration, the Polar watch and Oura ring can potentially be used as replacements for a manual sleep diary, depending on the acceptable error. Neither the Polar watch nor the Oura ring can replace the ActiGraph when it comes to measuring steps, moderate-to-vigorous physical activity, and total energy expenditure, but they may be used as additional sources of physical activity measures in some settings. On average, the Polar Vantage watch reported higher outputs compared to those reported by the Oura ring for steps, moderate-to-vigorous physical activity, and total energy expenditure. %M 35622397 %R 10.2196/27248 %U https://formative.jmir.org/2022/5/e27248 %U https://doi.org/10.2196/27248 %U http://www.ncbi.nlm.nih.gov/pubmed/35622397 %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 %@ 1438-8871 %I JMIR Publications %V 24 %N 5 %P e31352 %T The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial %A Alley,Stephanie J %A van Uffelen,Jannique %A Schoeppe,Stephanie %A Parkinson,Lynne %A Hunt,Susan %A Power,Deborah %A Waterman,Natasha %A Waterman,Courtney %A To,Quyen G %A Duncan,Mitch J %A Schneiders,Anthony %A Vandelanotte,Corneel %+ Physical Activity Research Group, Appleton Institute, Central Queensland University, Building 7, Bruce Hwy, Rockhampton, 4701, Australia, 61 749232263, s.alley@cqu.edu.au %K internet %K online %K activity trackers %K activity monitors %K wearables %K physical activity %K mobile phone %D 2022 %7 12.5.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Physical activity is an integral part of healthy aging; yet, most adults aged ≥65 years are not sufficiently active. Preliminary evidence suggests that web-based interventions with computer-tailored advice and Fitbit activity trackers may be well suited for older adults. Objective: The aim of this study was to examine the effectiveness of Active for Life, a 12-week web-based physical activity intervention with 6 web-based modules of computer-tailored advice to increase physical activity in older Australians. Methods: Participants were recruited both through the web and offline and were randomly assigned to 1 of 3 trial arms: tailoring+Fitbit, tailoring only, or a wait-list control. The computer-tailored advice was based on either participants’ Fitbit data (tailoring+Fitbit participants) or self-reported physical activity (tailoring-only participants). The main outcome was change in wrist-worn accelerometer (ActiGraph GT9X)–measured moderate to vigorous physical activity (MVPA) from baseline to after the intervention (week 12). The secondary outcomes were change in self-reported physical activity measured by means of the Active Australia Survey at the midintervention point (6 weeks), after the intervention (week 12), and at follow-up (week 24). Participants had a face-to-face meeting at baseline for a demonstration of the intervention and at baseline and week 12 to return the accelerometers. Generalized linear mixed model analyses were conducted with a γ distribution and log link to compare MVPA and self-reported physical activity changes over time within each trial arm and between each of the trial arms. Results: A total of 243 participants were randomly assigned to tailoring+Fitbit (n=78, 32.1%), tailoring only (n=96, 39.5%), and wait-list control (n=69, 28.4%). Attrition was 28.8% (70/243) at 6 weeks, 31.7% (77/243) at 12 weeks, and 35.4% (86/243) at 24 weeks. No significant overall time by group interaction was observed for MVPA (P=.05). There were no significant within-group changes for MVPA over time in the tailoring+Fitbit group (+3%, 95% CI –24% to 40%) or the tailoring-only group (–4%, 95% CI –24% to 30%); however, a significant decline was seen in the control group (–35%, 95% CI –52% to –11%). The tailoring+Fitbit group participants increased their MVPA 59% (95% CI 6%-138%) more than those in the control group. A significant time by group interaction was observed for self-reported physical activity (P=.02). All groups increased their self-reported physical activity from baseline to week 6, week 12, and week 24, and this increase was greater in the tailoring+Fitbit group than in the control group at 6 weeks (+61%, 95% CI 11%-133%). Conclusions: A computer-tailored physical activity intervention with Fitbit integration resulted in improved MVPA outcomes in comparison with a control group in older adults. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618000646246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618000646246 %M 35552166 %R 10.2196/31352 %U https://www.jmir.org/2022/5/e31352 %U https://doi.org/10.2196/31352 %U http://www.ncbi.nlm.nih.gov/pubmed/35552166 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 5 %P e35920 %T mHealth Interventions to Reduce Physical Inactivity and Sedentary Behavior in Children and Adolescents: Systematic Review and Meta-analysis of Randomized Controlled Trials %A Baumann,Hannes %A Fiedler,Janis %A Wunsch,Kathrin %A Woll,Alexander %A Wollesen,Bettina %+ Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, Karlsruhe, 76131, Germany, 49 721 608 454, kathrin.wunsch@kit.edu %K health behavior change %K individualization %K sedentary behavior %K physical activity %K tailored interventions %K personalized medicine %K health app %K mobile phone %D 2022 %7 11.5.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Children and adolescents increasingly do not meet physical activity (PA) recommendations. Hence, insufficient PA (IPA) and sedentary behavior (SB) among children and adolescents are relevant behavior change domains for using individualized mobile health (mHealth) interventions. Objective: This review and meta-analysis investigated the effectiveness of mHealth interventions on IPA and SB, with a special focus on the age and level of individualization. Methods: PubMed, Scopus, Web of Science, SPORTDiscus, and Cochrane Library were searched for randomized controlled trials published between January 2000 and March 2021. mHealth interventions for primary prevention in children and adolescents addressing behavior change related to IPA and SB were included. Included studies were compared for content characteristics and methodological quality and summarized narratively. In addition, a meta-analysis with a subsequent exploratory meta-regression examining the moderating effects of age and individualization on overall effectiveness was performed. Results: On the basis of the inclusion criteria, 1.3% (11/828) of the preliminary identified studies were included in the qualitative synthesis, and 1.2% (10/828) were included in the meta-analysis. Trials included a total of 1515 participants (mean age (11.69, SD 0.788 years; 65% male and 35% female) self-reported (3/11, 27%) or device-measured (8/11, 73%) health data on the duration of SB and IPA for an average of 9.3 (SD 5.6) weeks. Studies with high levels of individualization significantly decreased insufficient PA levels (Cohen d=0.33; 95% CI 0.08-0.58; Z=2.55; P=.01), whereas those with low levels of individualization (Cohen d=−0.06; 95% CI −0.32 to 0.20; Z=0.48; P=.63) or targeting SB (Cohen d=−0.11; 95% CI −0.01 to 0.23; Z=1.73; P=.08) indicated no overall significant effect. The heterogeneity of the studies was moderate to low, and significant subgroup differences were found between trials with high and low levels of individualization (χ21=4.0; P=.04; I2=75.2%). Age as a moderator variable showed a small effect; however, the results were not significant, which might have been because of being underpowered. Conclusions: Evidence suggests that mHealth interventions for children and adolescents can foster moderate reductions in IPA but not SB. Moreover, individualized mHealth interventions to reduce IPA seem to be more effective for adolescents than for children. Although, to date, only a few mHealth studies have addressed inactive and sedentary young people, and their quality of evidence is moderate, these findings indicate the relevance of individualization on the one hand and the difficulties in reducing SB using mHealth interventions on the other. Trial Registration: PROSPERO CRD42020209417; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=209417 %M 35544294 %R 10.2196/35920 %U https://mhealth.jmir.org/2022/5/e35920 %U https://doi.org/10.2196/35920 %U http://www.ncbi.nlm.nih.gov/pubmed/35544294 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 5 %P e37348 %T Effectiveness of an 8-Week Physical Activity Intervention Involving Wearable Activity Trackers and an eHealth App: Mixed Methods Study %A McCormack,Gavin R %A Petersen,Jennie %A Ghoneim,Dalia %A Blackstaffe,Anita %A Naish,Calli %A Doyle-Baker,Patricia K %+ Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive, Calgary, AB, T2N 4Z6, Canada, 1 403 220 8193, gmccorma@ucalgary.ca %K activity tracker %K technology %K eHealth %K physical activity %K intervention %K exercise %K mHealth %K fitness %K wearable %K sensor %K digital health %K COVID-19 %K health promotion %K mixed methods study %K wearable technology %D 2022 %7 3.5.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Health-promotion interventions incorporating wearable technology or eHealth apps can encourage participants to self-monitor and modify their physical activity and sedentary behavior. In 2020, a Calgary (Alberta, Canada) recreational facility developed and implemented a health-promotion intervention (Vivo Play Scientist program) that provided a commercially available wearable activity tracker and a customized eHealth dashboard to participants free of cost. Objective: The aim of this study was to independently evaluate the effectiveness of the Vivo Play Scientist program for modifying physical activity and sedentary behavior during the initial 8 weeks of the piloted intervention. Methods: Our concurrent mixed methods study included a single-arm repeated-measures quasiexperiment and semistructured interviews. Among the 318 eligible participants (≥18 years of age) registered for the program, 87 completed three self-administered online surveys (baseline, T0; 4 weeks, T1; and 8 weeks, T2). The survey captured physical activity, sedentary behavior, use of wearable technology and eHealth apps, and sociodemographic characteristics. Twenty-three participants were recruited using maximal-variation sampling and completed telephone-administered semistructured interviews regarding their program experiences. Self-reported physical activity and sedentary behavior outcomes were statistically compared among the three time points using Friedman tests. Thematic analysis was used to analyze the interview data. Results: The mean age of participants was 39.8 (SD 7.4) years and 75% (65/87) were women. Approximately half of all participants had previously used wearable technology (40/87, 46%) or an eHealth app (43/87, 49%) prior to the intervention. On average, participants reported wearing the activity tracker (Garmin Vivofit4) for 6.4 (SD 1.7) days in the past week at T1 and for 6.0 (SD 2.2) days in the past week at T2. On average, participants reported using the dashboard for 1.6 (SD 2.1) days in the past week at T1 and for 1.0 (SD 1.8) day in the past week at T2. The mean time spent walking at 8 weeks was significantly higher compared with that at baseline (T0 180.34 vs T2 253.79 minutes/week, P=.005), with no significant differences for other physical activity outcomes. Compared to that at baseline, the mean time spent sitting was significantly lower at 4 weeks (T0 334.26 vs T1 260.46 minutes/day, P<.001) and 8 weeks (T0 334.26 vs T2 267.13 minutes/day, P<.001). Significant differences in physical activity and sitting between time points were found among subgroups based on the household composition, history of wearable technology use, and history of eHealth app use. Participants described how wearing the Vivofit4 device was beneficial in helping them to modify physical activity and sedentary behavior. The social support, as a result of multiple members of the same household participating in the program, motivated changes in physical activity. Participants experienced improvements in their mental, physical, and social health. Conclusions: Providing individuals with free-of-cost commercially available wearable technology and an eHealth app has the potential to support increases in physical activity and reduce sedentary behavior in the short term, even under COVID-19 public health restrictions. %M 35404832 %R 10.2196/37348 %U https://formative.jmir.org/2022/5/e37348 %U https://doi.org/10.2196/37348 %U http://www.ncbi.nlm.nih.gov/pubmed/35404832 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 9 %N 2 %P e25494 %T Using Smartwatches to Observe Changes in Activity During Recovery From Critical Illness Following COVID-19 Critical Care Admission: 1-Year, Multicenter Observational Study %A Hunter,Alex %A Leckie,Todd %A Coe,Oliver %A Hardy,Benjamin %A Fitzpatrick,Daniel %A Gonçalves,Ana-Carolina %A Standing,Mary-Kate %A Koulouglioti,Christina %A Richardson,Alan %A Hodgson,Luke %+ Department of Intensive Care Medicine, Worthing Hospital, University Hospitals Sussex National Health Service Trust, Lyndhurst Road, Worthing, United Kingdom, 44 1903 205111, alexander.hunter2@nhs.net %K COVID-19 %K telemedicine %K rehabilitation %K critical illness %K smartphone %K digital health %K mobile health %K remote therapy %K device usability %D 2022 %7 2.5.2022 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: As a sequela of the COVID-19 pandemic, a large cohort of critical illness survivors have had to recover in the context of ongoing societal restrictions. Objective: We aimed to use smartwatches (Fitbit Charge 3; Fitbit LLC) to assess changes in the step counts and heart rates of critical care survivors following hospital admission with COVID-19, use these devices within a remote multidisciplinary team (MDT) setting to support patient recovery, and report on our experiences with this. Methods: We conducted a prospective, multicenter observational trial in 8 UK critical care units. A total of 50 participants with moderate or severe lung injury resulting from confirmed COVID-19 were recruited at discharge from critical care and given a smartwatch (Fitbit Charge 3) between April and June 2020. The data collected included step counts and daily resting heart rates. A subgroup of the overall cohort at one site—the MDT site (n=19)—had their smartwatch data used to inform a regular MDT meeting. A patient feedback questionnaire and direct feedback from the MDT were used to report our experience. Participants who did not upload smartwatch data were excluded from analysis. Results: Of the 50 participants recruited, 35 (70%) used and uploaded data from their smartwatch during the 1-year period. At the MDT site, 74% (14/19) of smartwatch users uploaded smartwatch data, whereas 68% (21/31) of smartwatch users at the control sites uploaded smartwatch data. For the overall cohort, we recorded an increase in mean step count from 4359 (SD 3488) steps per day in the first month following discharge to 7914 (SD 4146) steps per day at 1 year (P=.003). The mean resting heart rate decreased from 79 (SD 7) beats per minute in the first month to 69 (SD 4) beats per minute at 1 year following discharge (P<.001). The MDT subgroup’s mean step count increased more than that of the control group (176% increase vs 42% increase, respectively; +5474 steps vs +2181 steps, respectively; P=.04) over 1 year. Further, 71% (10/14) of smartwatch users at the MDT site and 48% (10/21) of those at the control sites strongly agreed that their Fitbit motivated them to recover, and 86% (12/14) and 48% (10/21), respectively, strongly agreed that they aimed to increase their activity levels over time. Conclusions: This is the first study to use smartwatch data to report on the 1-year recovery of patients who survived a COVID-19 critical illness. This is also the first study to report on smartwatch use within a post–critical care MDT. Future work could explore the role of smartwatches as part of a randomized controlled trial to assess clinical and economic effectiveness. International Registered Report Identifier (IRRID): RR2-10.12968/ijtr.2020.0102 %M 35417402 %R 10.2196/25494 %U https://rehab.jmir.org/2022/2/e25494 %U https://doi.org/10.2196/25494 %U http://www.ncbi.nlm.nih.gov/pubmed/35417402 %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 e34312 %T Promoting Physical Activity in a Spanish-Speaking Latina Population of Low Socioeconomic Status With Chronic Neurological Disorders: Proof-of-Concept Study %A Garbin,Alexander %A Díaz,Jesús %A Bui,Vy %A Morrison,Janina %A Fisher,Beth E %A Palacios,Carina %A Estrada-Darley,Ingrid %A Haase,Danielle %A Wing,David %A Amezcua,Lilyana %A Jakowec,Michael W %A Kaplan,Charles %A Petzinger,Giselle %+ Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Mail Stop C244, 13121 E 17th Ave, Room 310B, Aurora, CO, 80045, United States, 1 303 724 9590, alexander.garbin@cuanschutz.edu %K exercise %K quality of life %K motivation %K promotion %K community study %K clinical trial %D 2022 %7 20.4.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical activity (PA) is known to improve quality of life (QoL) as well as reduce mortality and disease progression in individuals with chronic neurological disorders. However, Latina women are less likely to participate in recommended levels of PA due to common socioeconomic barriers, including limited resources and access to exercise programs. Therefore, we developed a community-based intervention with activity monitoring and behavioral coaching to target these barriers and facilitate sustained participation in an exercise program promoting PA. Objective: The aim of this study was to determine the feasibility and efficacy of a community-based intervention to promote PA through self-monitoring via a Fitbit and behavioral coaching among Latina participants with chronic neurological disorders.  Methods: We conducted a proof-of-concept study among 21 Spanish-speaking Latina participants recruited from the Los Angeles County and University of Southern California (LAC+USC) neurology clinic; participants enrolled in the 16-week intervention at The Wellness Center at The Historic General Hospital in Los Angeles. Demographic data were assessed at baseline. Feasibility was defined by participant attrition and Fitbit adherence. PA promotion was determined by examining change in time spent performing moderate-to-vigorous PA (MVPA) over the 16-week period. The effect of behavioral coaching was assessed by quantifying the difference in MVPA on days when coaching occurred versus on days without coaching. Change in psychometric measures (baseline vs postintervention) and medical center visits (16 weeks preintervention vs during the intervention) were also examined. Results: Participants were of low socioeconomic status and acculturation. A total of 19 out of 21 (90%) participants completed the study (attrition 10%), with high Fitbit wear adherence (mean 90.31%, SD 10.12%). Time performing MVPA gradually increased by a mean of 0.16 (SD 0.23) minutes per day (P<.001), which was equivalent to an increase of approximately 18 minutes in MVPA over the course of the 16-week study period. Behavioral coaching enhanced intervention effectiveness as evidenced by a higher time spent on MVPA on days when coaching occurred via phone (37 min/day, P=.02) and in person (45.5 min/day, P=.01) relative to days without coaching (24 min/day). Participants improved their illness perception (effect size g=0.30) and self-rated QoL (effect size g=0.32). Additionally, a reduction in the number of medical center visits was observed (effect size r=0.44), and this reduction was associated with a positive change in step count during the study period (P.=04). Conclusions: Self-monitoring with behavioral coaching is a feasible community-based intervention for PA promotion among Latina women of low socioeconomic status with chronic neurological conditions. PA is known to be important for brain health in neurological conditions but remains relatively unexplored in minority populations. Trial Registration: ClinicalTrials.gov NCT04820153; https://clinicaltrials.gov/ct2/show/NCT04820153 %M 35442197 %R 10.2196/34312 %U https://formative.jmir.org/2022/4/e34312 %U https://doi.org/10.2196/34312 %U http://www.ncbi.nlm.nih.gov/pubmed/35442197 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 1 %P e30661 %T Physical Activity in Patients With Heart Failure During and After COVID-19 Lockdown: Single-Center Observational Retrospective Study %A Brasca,Francesco Maria Angelo %A Casale,Maria Carla %A Canevese,Fabio Lorenzo %A Tortora,Giovanni %A Pagano,Giulia %A Botto,Giovanni Luca %+ Department of Electrophysiology and Clinical Arrhythmology, Azienda Socio Sanitaria Territoriale Rhodense, Via Forlanini 95, Garbagnate Milanese, Milano, 20024, Italy, 39 02994303391, fmabrasca@gmail.com %K heart failure %K physical activity %K COVID-19 %K remote monitoring %K implantable cardiac device %K monitoring %K exercise %K surveillance %K lockdown %K cardiovascular %K heart %K retrospective %K burden %D 2022 %7 19.4.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: The COVID-19 pandemic forced several European governments to impose severe lockdown measures. The reduction of physical activity during the lockdown could have been deleterious. Objective: The aim of this observational, retrospective study was to investigate the effect of the lockdown strategy on the physical activity burden and subsequent reassessment in a group of patients with heart failure who were followed by means of remote monitoring. Methods: We analyzed remote monitoring transmissions during the 3-month period immediately preceding the lockdown, 69 days of lockdown, and 3-month period after the first lockdown in a cohort of patients with heart failure from a general hospital in Lombardy, Italy. We compared variation of daily physical activity measured by cardiac implantable electrical devices with clinical variables collected in a hospital database. Results: We enrolled 41 patients with heart failure that sent 176 transmissions. Physical activity decreased during the lockdown period (mean 3.4, SD 1.9 vs mean 2.9, SD 1.8 hours/day; P<.001) but no significant difference was found when comparing the period preceding and following the lockdown (–0.0007 hours/day; P=.99). We found a significant correlation between physical activity reduction during and after the lockdown (R2=0.45, P<.001). The only significant predictor of exercise variation in the postlockdown period was the lockdown to prelockdown physical activity ratio. Conclusions: An excessive reduction of exercise in patients with heart failure decreased the tolerance to exercise, especially in patients with more comorbidities. Remote monitoring demonstrated exercise reduction, suggesting its potential utility to encourage patients to maintain their usual physical activity levels. %M 35103602 %R 10.2196/30661 %U https://cardio.jmir.org/2022/1/e30661 %U https://doi.org/10.2196/30661 %U http://www.ncbi.nlm.nih.gov/pubmed/35103602 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e35614 %T Evaluation of the Immediate Effects of Web-Based Intervention Modules for Goals, Planning, and Coping Planning on Physical Activity: Secondary Analysis of a Randomized Controlled Trial on Weight Loss Maintenance %A Mattila,Elina %A Horgan,Graham %A Palmeira,António L %A O'Driscoll,Ruairi %A Stubbs,R James %A Heitmann,Berit L %A Marques,Marta M %+ VTT Technical Research Centre of Finland Ltd, Visiokatu 4, Tampere, 33720, Finland, 358 407162230, elina.m.mattila@vtt.fi %K digital intervention %K Fitbit %K weight %K weight loss maintenance %K physical activity %K fitness %K exercise %K goal setting %K action planning %K coping planning %K control trial %K secondary analysis %K RCT %K randomized controlled trial %K long-term effect %K short-term effect %K immediate effect %K sustained effect %D 2022 %7 14.4.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of digital interventions can be accurately monitored via log files. However, monitoring engagement with intervention goals or enactment of the actual behaviors targeted by the intervention is more difficult and is usually evaluated based on pre-post measurements in a controlled trial. Objective: The objective of this paper is to evaluate if engaging with 2 digital intervention modules focusing on (1) physical activity goals and action plans and (2) coping with barriers has immediate effects on the actual physical activity behavior. Methods: The NoHoW Toolkit (TK), a digital intervention developed to support long-term weight loss maintenance, was evaluated in a 2 x 2 factorial randomized controlled trial. The TK contained various modules based on behavioral self-regulation and motivation theories, as well as contextual emotion regulation approaches, and involved continuous tracking of weight and physical activity through connected commercial devices (Fitbit Aria and Charge 2). Of the 4 trial arms, 2 had access to 2 modules directly targeting physical activity: a module for goal setting and action planning (Goal) and a module for identifying barriers and coping planning (Barriers). Module visits and completion were determined based on TK log files and time spent in the module web page. Seven physical activity metrics (steps; activity; energy expenditure; fairly active, very active and total active minutes; and distance) were compared before and after visiting and completing the modules to examine whether the modules had immediate or sustained effects on physical activity. Immediate effect was determined based on 7-day windows before and after the visit, and sustained effects were evaluated for 1 to 8 weeks after module completion. Results: Out of the 811 participants, 498 (61.4%) visited the Goal module and 406 (50.1%) visited the Barriers module. The Barriers module had an immediate effect on very active and total active minutes (very active minutes: before median 24.2, IQR 10.4-43.0 vs after median 24.9, IQR 10.0-46.3; P=.047; total active minutes: before median 45.1, IQR 22.9-74.9 vs after median 46.9, IQR 22.4-78.4; P=.03). The differences were larger when only completed Barriers modules were considered. The Barriers module completion was also associated with sustained effects in fairly active and total active minutes for most of the 8 weeks following module completion and for 3 weeks in very active minutes. Conclusions: The Barriers module had small, significant, immediate, and sustained effects on active minutes measured by a wrist-worn activity tracker. Future interventions should pay attention to assessing barriers and planning coping mechanisms to overcome them. Trial Registration: ISRCTN Registry ISRCTN88405328; https://www.isrctn.com/ISRCTN88405328 %M 35436232 %R 10.2196/35614 %U https://www.jmir.org/2022/4/e35614 %U https://doi.org/10.2196/35614 %U http://www.ncbi.nlm.nih.gov/pubmed/35436232 %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 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 e35479 %T The Effectiveness of Combining Nonmobile Interventions With the Use of Smartphone Apps With Various Features for Weight Loss: Systematic Review and Meta-analysis %A Antoun,Jumana %A Itani,Hala %A Alarab,Natally %A Elsehmawy,Amir %+ American University of Beirut, Riad El-Solh, Beirut, 110236, Lebanon, 961 3486509, ja46@aub.edu.lb %K obesity %K weight loss %K mobile app %K self-monitoring %K behavioral %K tracker %K behavioral coaching %K coach %K dietitian %K mobile phone %D 2022 %7 8.4.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The effectiveness of smartphone apps for weight loss is limited by the diversity of interventions that accompany such apps. This research extends the scope of previous systematic reviews by including 2 subgroup analyses based on nonmobile interventions that accompanied smartphone use and human-based versus passive behavioral interventions. Objective: The primary objective of this study is to systematically review and perform a meta-analysis of studies that evaluated the effectiveness of smartphone apps on weight loss in the context of other interventions combined with app use. The secondary objective is to measure the impact of different mobile app features on weight loss and mobile app adherence. Methods: We conducted a systematic review and meta-analysis of relevant studies after an extensive search of the PubMed, MEDLINE, and EBSCO databases from inception to January 31, 2022. Gray literature, such as abstracts and conference proceedings, was included. Working independently, 2 investigators extracted the data from the articles, resolving disagreements by consensus. All randomized controlled trials that used smartphone apps in at least 1 arm for weight loss were included. The weight loss outcome was the change in weight from baseline to the 3- and 6-month periods for each arm. Net change estimates were pooled across the studies using random-effects models to compare the intervention group with the control group. The risk of bias was assessed independently by 2 authors using the Cochrane Collaboration tool for assessing the risk of bias in randomized trials. Results: Overall, 34 studies were included that evaluated the use of a smartphone app in at least 1 arm. Compared with controls, the use of a smartphone app–based intervention showed a significant weight loss of –1.99 kg (95% CI –2.19 to –1.79 kg; I2=81%) at 3 months and –2.80 kg (95% CI –3.03 to –2.56 kg; I2=91%) at 6 months. In the subgroup analysis, based on the various intervention components that were added to the mobile app, the combination of the mobile app, tracker, and behavioral interventions showed a statistically significant weight loss of –2.09 kg (95% CI –2.32 to –1.86 kg; I2=91%) and –3.77 kg (95% CI –4.05 to –3.49 kg; I2=90%) at 3 and 6 months, respectively. When a behavioral intervention was present, only the combination of the mobile app with intensive behavior coaching or feedback by a human coach showed a statistically significant weight loss of –2.03 kg (95% CI –2.80 to –1.26 kg; I2=83%) and –2.63 kg (95% CI –2.97 to –2.29 kg; I2=91%) at 3 and 6 months, respectively. Neither the type nor the number of mobile app features was associated with weight loss. Conclusions: Smartphone apps have a role in weight loss management. Nevertheless, the human-based behavioral component remained key to higher weight loss results. %M 35394443 %R 10.2196/35479 %U https://mhealth.jmir.org/2022/4/e35479 %U https://doi.org/10.2196/35479 %U http://www.ncbi.nlm.nih.gov/pubmed/35394443 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e28801 %T Evaluating the Impact of Adaptive Personalized Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool: Results From a 2-Month Randomized Controlled Trial %A Nuijten,Raoul %A Van Gorp,Pieter %A Khanshan,Alireza %A Le Blanc,Pascale %A van den Berg,Pauline %A Kemperman,Astrid %A Simons,Monique %+ Department of Industrial Engineering, Eindhoven University of Technology, Groene Loper 3, Eindhoven, 5612 AE, Netherlands, 31 040 247 2290, r.c.y.nuijten@tue.nl %K mHealth %K health promotion %K physical activity %K personalization %K adaptive goal setting %K gamification %K office workers %D 2022 %7 31.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Although the health benefits of physical activity are well established, it remains challenging for people to adopt a more active lifestyle. Mobile health (mHealth) interventions can be effective tools to promote physical activity and reduce sedentary behavior. Promising results have been obtained by using gamification techniques as behavior change strategies, especially when they were tailored toward an individual’s preferences and goals; yet, it remains unclear how goals could be personalized to effectively promote health behaviors. Objective: In this study, we aim to evaluate the impact of personalized goal setting in the context of gamified mHealth interventions. We hypothesize that interventions suggesting health goals that are tailored based on end users’ (self-reported) current and desired capabilities will be more engaging than interventions with generic goals. Methods: The study was designed as a 2-arm randomized intervention trial. Participants were recruited among staff members of 7 governmental organizations. They participated in an 8-week digital health promotion campaign that was especially designed to promote walks, bike rides, and sports sessions. Using an mHealth app, participants could track their performance on two social leaderboards: a leaderboard displaying the individual scores of participants and a leaderboard displaying the average scores per organizational department. The mHealth app also provided a news feed that showed when other participants had scored points. Points could be collected by performing any of the 6 assigned tasks (eg, walk for at least 2000 m). The level of complexity of 3 of these 6 tasks was updated every 2 weeks by changing either the suggested task intensity or the suggested frequency of the task. The 2 intervention arms—with participants randomly assigned—consisted of a personalized treatment that tailored the complexity parameters based on participants’ self-reported capabilities and goals and a control treatment where the complexity parameters were set generically based on national guidelines. Measures were collected from the mHealth app as well as from intake and posttest surveys and analyzed using hierarchical linear models. Results: The results indicated that engagement with the program inevitably dropped over time. However, engagement was higher for participants who had set themselves a goal in the intake survey. The impact of personalization was especially observed for frequency parameters because the personalization of sports session frequency did foster higher engagement levels, especially when participants set a goal to improve their capabilities. In addition, the personalization of suggested ride duration had a positive effect on self-perceived biking performance. Conclusions: Personalization seems particularly promising for promoting the frequency of physical activity (eg, promoting the number of suggested sports sessions per week), as opposed to the intensity of the physical activity (eg, distance or duration). Replications and variations of our study setup are critical for consolidating and explaining (or refuting) these effects. Trial Registration: ClinicalTrials.gov NCT05264155; https://clinicaltrials.gov/ct2/show/NCT05264155 %M 35357323 %R 10.2196/28801 %U https://mhealth.jmir.org/2022/3/e28801 %U https://doi.org/10.2196/28801 %U http://www.ncbi.nlm.nih.gov/pubmed/35357323 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 5 %N 1 %P e32420 %T A Digital Health Program Targeting Physical Activity Among Adolescents With Overweight or Obesity: Open Trial %A Cummings,Caroline %A Crochiere,Rebecca %A Lansing,Amy Hughes %A Patel,Riya %A Stanger,Catherine %+ Department of Psychological Sciences, Texas Tech University, PO Box 42051, Lubbock, TX, 79409, United States, 1 8068340931, carolicu@ttu.edu %K mHealth program %K physical activity %K adolescent overweight %K adolescent obesity %K incentives %K mobile phone %D 2022 %7 28.3.2022 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: Prior studies suggest that mobile health physical activity programs that provide only weekly or daily text-based health coaching evidence limit the efficacy in improving physical activity in adolescents with overweight or obesity. It is possible that incentives, combined with health coaching and daily feedback on goal success, may increase program efficacy; however, such programs have not yet been tested with adolescents with overweight and obesity. Objective: This study aims to examine the feasibility and acceptability of a 12-week, incentive-based, mobile health physical activity program with text-based health coaching, goal setting, and self-monitoring for adolescents with overweight or obesity. Program adherence and changes in tracked physical activity (ie, steps and active minutes while wearing a Fitbit [Google LLC]), body mass, and body fat are assessed. Methods: A total of 28 adolescents aged 13 to 18 years with a BMI ≥90th percentile participated in the program. Of the 28 participants, 2 (7%) were lost to follow-up; thus, data from 26 (93%) participants were used in analyses. Results: Participant-reported acceptability was high, with all mean ratings of text-based coaching, Fitbit use, and the overall program being >5 on a 7-point scale. In addition, 85% (23/26) of participants reported that they would like to continue to wear the Fitbit. Program adherence was also high, as participants wore the Fitbit on 91.1% (SD 12.6%) of days on average and met their weekly goals for an average of 7 (SD 3.5) of 11 possible weeks. There were no demographic (ie, sex, age, and baseline body mass) differences in the percentage of days participants wore their Fitbit. Across the 12-week study, there were significant improvements in tracked daily active minutes (P=.006) and steps (P<.001) and significant pre- to posttest improvements in body fat percentage (P=.04). Conclusions: The pilot program improved adolescent physical activity and physical health. A larger factorial design trial with adaptive daily goals may clarify the role of each program component in driving physical activity. %M 35343903 %R 10.2196/32420 %U https://pediatrics.jmir.org/2022/1/e32420 %U https://doi.org/10.2196/32420 %U http://www.ncbi.nlm.nih.gov/pubmed/35343903 %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 %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e32212 %T Web-Based Versus Print-Based Physical Activity Intervention for Community-Dwelling Older Adults: Crossover Randomized Trial %A Pischke,Claudia R %A Voelcker-Rehage,Claudia %A Ratz,Tiara %A Peters,Manuela %A Buck,Christoph %A Meyer,Jochen %A von Holdt,Kai %A Lippke,Sonia %+ Institute of Medical Sociology, Centre for Health and Society, Medical Faculty, Heinrich Heine University Duesseldorf, Moorenstrasse 5, Duesseldorf, 40225, Germany, 49 211 81 ext 08599, ClaudiaRuth.Pischke@med.uni-duesseldorf.de %K physical activity %K older adults %K eHealth %K print-based intervention %K web-based intervention %K physical activity promotion %K healthy aging %K preferences %K randomized trial %K mobile phone %D 2022 %7 23.3.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Fewer than half of older German adults engage in the recommended levels of endurance training. Objective: The study aim is to compare the acceptance and effectiveness of two interventions for physical activity (PA) promotion among initially inactive community-dwelling older adults ≥60 years in a 9-month, crossover randomized trial. Methods: Participants were recruited in person and randomized to one of the following interventions for self-monitoring PA: a print-based intervention (PRINT: 113/242, 46.7%) or a web-based intervention (WEB: 129/242, 53.3%). Furthermore, 29.5% (38/129) of those in the web-based intervention group received a PA tracker in addition to WEB (WEB+). After randomization, the participants and researchers were not blinded. The participants’ baseline intervention preferences were retrospectively assessed. All the intervention groups were offered 10 weekly face-to-face group sessions. Afterward, participants could choose to stay in their group or cross over to one of the other groups, and group sessions were continued monthly for another 6 months. 3D accelerometers to assess PA and sedentary behavior (SB) at baseline (T0), 3-month follow-up (T1), and 9-month follow-up (T2) were used. Adherence to PA recommendations, attendance of group sessions, and intervention acceptance were assessed using self-administered paper-based questionnaires. Linear mixed models were used to calculate differences in moderate to vigorous PA (MVPA) and SB between time points and intervention groups. Results: Of the 242 initially recruited participants, 91 (37.6%) were randomized to the WEB group; 38 (15.7%) to the WEB+ group; and 113 (46.7%) to the PRINT group. Overall, 80.6% (195/242) of the participants completed T1. Only 0.4% (1/242) of the participants changed from the WEB group to the PRINT group and 6.2% (15/242) moved from the PRINT group to the WEB group (WEB-WEB: 103/249, (41.4%); PRINT-PRINT: 76/249, 30.5%) when offered to cross over at T1. Furthermore, 66.1% (160/242) of participants completed T2. MVPA in minutes per day increased between baseline and T1, but these within-group changes disappeared after adjusting for covariates. MVPA decreased by 9 minutes per day between baseline and T2 (βtime=−9.37, 95% CI −18.58 to −0.16), regardless of the intervention group (WEB vs PRINT: βgroup*time=−3.76, 95% CI −13.33 to 5.82, WEB+ vs PRINT: βgroup*time=1.40, 95% CI −11.04 to 13.83). Of the participants, 18.6% (38/204) met the PA recommendations at T0, 16.4% (26/159) at T1, and 20.3% (28/138) at T2. For SB, there were no significant group differences or group-by-time interactions at T1 or T2. Intervention acceptance was generally high. The use of intervention material was high to moderate at T1 and decreased by T2. Conclusions: There was little movement between intervention groups at T1 when given the choice, and participation was not associated with increases in PA or decreases in SB over time. Trial Registration: German Clinical Trials Register DRKS00016073; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00016073 %M 35319484 %R 10.2196/32212 %U https://mhealth.jmir.org/2022/3/e32212 %U https://doi.org/10.2196/32212 %U http://www.ncbi.nlm.nih.gov/pubmed/35319484 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 10 %N 1 %P e35040 %T Effect of the Nintendo Ring Fit Adventure Exergame on Running Completion Time and Psychological Factors Among University Students Engaging in Distance Learning During the COVID-19 Pandemic: Randomized Controlled Trial %A Wu,Yi-Syuan %A Wang,Wei-Yun %A Chan,Ta-Chien %A Chiu,Yu-Lung %A Lin,Hung-Che %A Chang,Yu-Tien %A Wu,Hao-Yi %A Liu,Tzu-Chi %A Chuang,Yu-Cheng %A Wu,Jonan %A Chang,Wen-Yen %A Sun,Chien-An %A Lin,Meng-Chiung %A Tseng,Vincent S %A Hu,Je-Ming %A Li,Yuan-Kuei %A Hsiao,Po-Jen %A Chen,Chao-Wen %A Kao,Hao-Yun %A Lee,Chia-Cheng %A Hsieh,Chung-Bao %A Wang,Chih-Hung %A Chu,Chi-Ming %+ School of Public Health, National Defense Medical Center, Rm. 4317, 4F., No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 114201, Taiwan, 886 2 8792 3100 ext 18438, chuchiming@web.de %K exergaming %K cardiac force index %K running %K physical activity %K sleep quality %K mood disorders %K digital health %K physical fitness %K Nintendo Ring Fit Adventure %K COVID-19 pandemic %D 2022 %7 22.3.2022 %9 Original Paper %J JMIR Serious Games %G English %X Background: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. Objective: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). Methods: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. Results: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)_walk, CFIPA_run, or sleep quality. Conclusions: RFA could either maintain or improve an individual’s physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. Trial Registration: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040 %M 35315780 %R 10.2196/35040 %U https://games.jmir.org/2022/1/e35040 %U https://doi.org/10.2196/35040 %U http://www.ncbi.nlm.nih.gov/pubmed/35315780 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 6 %N 1 %P e31501 %T The Effect of Wearable Tracking Devices on Cardiorespiratory Fitness Among Inactive Adults: Crossover Study %A Larsen,Lisbeth Hoejkjaer %A Lauritzen,Maja Hedegaard %A Sinkjaer,Mikkel %A Kjaer,Troels W %+ Department of Neurology, Zealand University Hospital, Sygehusvej 10, Roskilde, 4000, Denmark, 45 41558592, lisbla@regionsjaelland.dk %K activity tracking %K cardiorespiratory fitness %K mHealth %K mobile health %K motivation %K physical activity %K self-monitoring %K wearable %K cardio %K fitness %K cardiorespiratory %K behavior change %D 2022 %7 15.3.2022 %9 Original Paper %J JMIR Cardio %G English %X Background: Modern lifestyle is associated with a high prevalence of physical inactivity. Objective: This study aims to investigate the effect of a wearable tracking device on cardiorespiratory fitness among inactive adults and to explore if personal characteristics and health outcomes can predict adoption of the device. Methods: In total, 62 inactive adults were recruited for this study. A control period (4 weeks) was followed by an intervention period (8 weeks) where participants were instructed to register and follow their physical activity (PA) behavior on a wrist-worn tracking device. Data collected included estimated cardiorespiratory fitness, body composition, blood pressure, perceived stress levels, and self-reported adoption of using the tracking device. Results: In total, 50 participants completed the study (mean age 48, SD 13 years, 84% women). Relative to the control period, participants increased cardiorespiratory fitness by 1.52 mL/kg/minute (95% CI 0.82-2.22; P<.001), self-reported PA by 140 minutes per week (95% CI 93.3-187.1; P<.001), daily step count by 982 (95% CI 492-1471; P<.001), and participants’ fat percentage decreased by 0.48% (95% CI –0.84 to –0.13; P=.009). No difference was observed in blood pressure (systolic: 95% CI –2.16 to 3.57, P=.63; diastolic: 95% CI –0.70 to 2.55; P=.27) or perceived stress (95% CI –0.86 to 1.78; P=.49). No associations were found between adoption of the wearable tracking device and age, gender, personality, or education. However, participants with a low perceived stress at baseline were more likely to rate the use of a wearable tracking device highly motivating. Conclusions: Tracking health behavior using a wearable tracking device increases PA resulting in an improved cardiorespiratory fitness among inactive adults. %M 35289763 %R 10.2196/31501 %U https://cardio.jmir.org/2022/1/e31501 %U https://doi.org/10.2196/31501 %U http://www.ncbi.nlm.nih.gov/pubmed/35289763 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 3 %P e31325 %T Level of Physical Activity, Sedentary Behavior, and Sleep in the Child and Adolescent Population in the Autonomous Community of the Basque Country (6-17 Years Old): Protocol for the Mugikertu Study %A Larrinaga-Undabarrena,Arkaitz %A Albisua,Neritzel %A Río,Xabier %A Angulo-Garay,Garazi %A González-Santamaria,Xabier %A Etxeberria Atxa,Iker %A Martínez de Lahidalga Aguirre,Gorka %A Ruiz de Azua Larrinaga,Malen %A Martínez Aguirre-Betolaza,Aitor %A Gorostegi-Anduaga,Ilargi %A Maldonado-Martín,Sara %A Aldaz Arregui,Juan %A Guerra-Balic,Myriam %A Bringas,Mikel %A Sánchez Isla,José Ramón %A Coca,Aitor %+ Faculty of Education and Sport, University of Deusto, Avenida de las Universidades, 24, Bilbao, 48007, Spain, 34 944139003 ext 3411, a.larrinaga@deusto.es %K physical activity %K sedentary behavior %K sleep %K Basque Autonomous Community %K accelerometry %K adolescents %K children %K healthy behavior %K mobility %D 2022 %7 11.3.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Physical inactivity and sedentary behavior are increasingly common problems in the general population, which can lead to overweight, obesity, diabetes, cardiovascular disease, and decreased motor and cognitive capacity among children and adolescents. Establishing healthy habits in childhood on the basis of the World Health Organization’s 2020 Physical Activity Guidelines is essential for proper physical, motor, and cognitive development. Objective: The primary aim of this study is to describe the level of physical activity (PA), sedentary behavior, and sleep of the child and adolescent population from 6 to 17 years of age in the Basque Autonomous Community (BAC). Our secondary aim is to establish a starting point for future research and intervention protocols to improve the existing reality. Methods: This cross-sectional study aims to recruit 1111 children and adolescents, aged 6 to 17 years from the BAC in a representative random sample. Participants will wear the ActiGraph WGT3X-BT triaxial accelerometer for 7 consecutive days in their nondominant wrist, and fill out a habit diary log of PA, mobility, and sleep routine. PA intensities, sedentary behavior, and sleep parameters (total bedtime, total sleep time, and sleep efficiency) will be calculated from raw accelerometer data using SPSS (IBM Corp). Participants will be randomly selected. Results: The results of this study intend to demonstrate significant differences in PA levels in different age and gender groups since the volume of school PA in the BAC decreases as the age of the schoolchildren increases. The total study sample includes 1111 participants. In April 2021, up to 50% of the sample size was reached, which is expected to increase to 100% by April 2022. This sample will allow us to analyze, discuss, compare, and assess the reality of the school population, in a sensitive period of adherence to behavior patterns, using data from the geographical and administrative area of the BAC. This study will provide a realistic insight into PA levels among children and adolescents in the BAC. It will also offer scientific contributions on the positive relationship between PA levels and sleep quality in this population. Conclusions: This study might highlight the need for the promotion of cross-sectional policies so that children and adolescents may increase their levels of PA, thus improving both the school environment and positive healthy behavior. Trial Registration: ISRCTN Registry ISRCTN65573865; https://www.isrctn.com/ISRCTN65573865 International Registered Report Identifier (IRRID): DERR1-10.2196/31325 %M 35275088 %R 10.2196/31325 %U https://www.researchprotocols.org/2022/3/e31325 %U https://doi.org/10.2196/31325 %U http://www.ncbi.nlm.nih.gov/pubmed/35275088 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 3 %P e32130 %T Increasing the Effectiveness of a Physical Activity Smartphone Intervention With Positive Suggestions: Randomized Controlled Trial %A Skvortsova,Aleksandrina %A Cohen Rodrigues,Talia %A de Buisonjé,David %A Kowatsch,Tobias %A Santhanam,Prabhakaran %A Veldhuijzen,Dieuwke S %A van Middendorp,Henriët %A Evers,Andrea %+ Department of Psychology, McGill University, 1205 avenue du Docteur-Penfield, Montreal, QC, H3A 1B1, Canada, 1 4386303664, a.skvortsova@fsw.leidenuniv.nl %K eHealth %K mobile health %K physical activity %K walking %K positive suggestions %K outcome expectations %K mobile phone %D 2022 %7 1.3.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: eHealth interventions have the potential to increase the physical activity of users. However, their effectiveness varies, and they often have only short-term effects. A possible way of enhancing their effectiveness is to increase the positive outcome expectations of users by giving them positive suggestions regarding the effectiveness of the intervention. It has been shown that when individuals have positive expectations regarding various types of interventions, they tend to benefit from these interventions more. Objective: The main objective of this web-based study is to investigate whether positive suggestions can change the expectations of participants regarding the effectiveness of a smartphone physical activity intervention and subsequently enhance the number of steps the participants take during the intervention. In addition, we study whether suggestions affect perceived app effectiveness, engagement with the app, self-reported vitality, and fatigue of the participants. Methods: This study involved a 21-day fully automated physical activity intervention aimed at helping participants to walk more steps. The intervention was delivered via a smartphone-based app that delivered specific tasks to participants (eg, setting activity goals or looking for social support) and recorded their daily step count. Participants were randomized to either a positive suggestions group (69/133, 51.9%) or a control group (64/133, 48.1%). Positive suggestions emphasizing the effectiveness of the intervention were implemented in a web-based flyer sent to the participants before the intervention. Suggestions were repeated on days 8 and 15 of the intervention via the app. Results: Participants significantly increased their daily step count from baseline compared with 21 days of the intervention (t107=−8.62; P<.001) regardless of the suggestions. Participants in the positive suggestions group had more positive expectations regarding the app (B=−1.61, SE 0.47; P<.001) and higher expected engagement with the app (B=3.80, SE 0.63; P<.001) than the participants in the control group. No effects of suggestions on the step count (B=−22.05, SE 334.90; P=.95), perceived effectiveness of the app (B=0.78, SE 0.69; P=.26), engagement with the app (B=0.78, SE 0.75; P=.29), and vitality (B=0.01, SE 0.11; P=.95) were found. Positive suggestions decreased the fatigue of the participants during the 3 weeks of the intervention (B=0.11, SE 0.02; P<.001). Conclusions: Although the suggestions did not affect the number of daily steps, they increased the positive expectations of the participants and decreased their fatigue. These results indicate that adding positive suggestions to eHealth physical activity interventions might be a promising way of influencing subjective but not objective outcomes of interventions. Future research should focus on finding ways of strengthening the suggestions, as they have the potential to boost the effectiveness of eHealth interventions. Trial Registration: Open Science Framework 10.17605/OSF.IO/CWJES; https://osf.io/cwjes %M 35230245 %R 10.2196/32130 %U https://www.jmir.org/2022/3/e32130 %U https://doi.org/10.2196/32130 %U http://www.ncbi.nlm.nih.gov/pubmed/35230245 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 3 %P e32704 %T Barriers to and Facilitators of Using a One Button Tracker and Web-Based Data Analytics Tool for Personal Science: Exploratory Study %A van de Belt,Tom H %A de Croon,Aimee %A Freriks,Faye %A Blomseth Christiansen,Thomas %A Eg Larsen,Jakob %A de Groot,Martijn %+ Health Innovation Labs, Radboud University Medical Center, Geert Grooteplein Noord 15, Nijmegen, 6525 EZ, Netherlands, 31 613424584, martijn.degroot@radboudumc.nl %K self-tracking %K personal science %K one-button-tracker %K barriers %K facilitators %K quantified self %K health promotion %K button tracker %K usability testing %K One Button Tracker %K health technology %K system usability %D 2022 %7 1.3.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Individuals’ self-tracking of subjectively experienced phenomena related to health can be challenging, as current options for instrumentation often involve too much effort in the moment or rely on retrospective self-reporting, which is likely to impair accuracy and compliance. Objective: This study aims to assess the usability and perceived usefulness of low-effort, in-the-moment self-tracking using simple instrumentation and to establish the amount of support needed when using this approach. Methods: In this exploratory study, the One Button Tracker—a press-button device that records time stamps and durations of button presses—was used for self-tracking. A total of 13 employees of an academic medical center chose a personal research question and used the One Button Tracker to actively track specific subjectively experienced phenomena for 2 to 4 weeks. To assess usability and usefulness, we combined qualitative data from semistructured interviews with quantitative results from the System Usability Scale. Results: In total, 29 barriers and 15 facilitators for using the One Button Tracker were found. Ease of use was the most frequently mentioned facilitator. The One Button Tracker’s usability received a median System Usability Scale score of 75.0 (IQR 42.50), which is considered as good usability. Participants experienced effects such as an increased awareness of the tracked phenomenon, a confirmation of personal knowledge, a gain of insight, and behavior change. Support and guidance during all stages of the self-tracking process were judged as valuable. Conclusions: The low-effort, in-the-moment self-tracking of subjectively experienced phenomena has been shown to support personal knowledge gain and health behavior change for people with an interest in health promotion. After addressing barriers and formally validating the collected data, self-tracking devices may well be helpful for additional user types or health questions. %M 35230247 %R 10.2196/32704 %U https://formative.jmir.org/2022/3/e32704 %U https://doi.org/10.2196/32704 %U http://www.ncbi.nlm.nih.gov/pubmed/35230247 %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 e34059 %T The Effect of a Mobile and Wearable Device Intervention on Increased Physical Activity to Prevent Metabolic Syndrome: Observational Study %A Kim,Hee Jin %A Lee,Kang Hyun %A Lee,Jung Hun %A Youk,Hyun %A Lee,Hee Young %+ Department of Emergency Medicine, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, 26426, Republic of Korea, 82 33 741 1612, ed119@yonsei.ac.kr %K mHealth %K physical activity %K wearable device %K metabolic syndrome %K health care %K exercise %K intervention %K Asia %K Korea %K rural %D 2022 %7 24.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Research on whether wearable devices and app-based interventions can effectively prevent metabolic syndrome (MetS) by increasing physical activity (PA) among middle-aged people living in the rural areas of South Korea remains insufficient. Objective: The aim of this study was to determine whether mobile and wearable device interventions can improve health indicators, including PA, in MetS risk groups in rural South Korea. Methods: In this clinical trial, performed from December 2019 to June 2020, participants were asked to use a wearable device (GalaxyWatch Active1) alone (standard intervention) or the wearable device and mobile app (Yonsei Health Korea) (enhanced intervention). Clinical measures and International Physical Activity Questionnaire (IPAQ) scores were evaluated initially and after 6 months. The number of steps was monitored through the website. The primary outcome was the difference in PA and clinical measures between the enhanced intervention and standard intervention groups. The secondary outcome was the decrease in MetS factors related to the change in PA. Results: A total of 267 participants were randomly selected, 221 of whom completed the 6-month study. Among the 221 participants, 113 were allocated to the enhanced intervention group and 108 were allocated to the standard intervention group. After 6 months, the body weight and BMI for the enhanced intervention group decreased by 0.6 (SD 1.87) and 0.21 (SD 0.76), respectively (P<.001). In both groups, systolic blood pressure, diastolic blood pressure, waist circumference, and glycated hemoglobin A1c (HbA1c) decreased (P<.001). The total PA was approximately 2.8 times lower in the standard intervention group (mean 44.47, SD 224.85) than in the enhanced intervention group (mean 124.36, SD 570.0). Moreover, the enhanced intervention group achieved the recommended level of moderate to vigorous physical activity (MVPA), whereas the standard intervention group did not (188 minutes/week vs 118 minutes/week). Additionally, the number of participants in the enhanced intervention group (n=113) that reached 10,000 daily steps or more after the intervention increased from 9 (8.0%) to 26 (23.1%) (P=.002), whereas this number did not increase significantly in the standard intervention group (n=108), from 8 (7.4%) to 16 (14.8%) (P=.72). The number of participants without any MetS factors increased by 12 (11%) and 8 (7%) in the enhanced and standard intervention group, respectively. Conclusions: PA monitoring and an intervention using wearable devices were effective in preventing MetS in a rural population in Korea. Blood pressure, waist circumference, and HbA1c were improved in both intervention groups, which were effective in reducing MetS factors. However, only the participants in the enhanced intervention group continuously increased their MVPA and step counts above the recommended level to prevent MetS. Body weight and BMI were further improved, and a higher number of participants with zero MetS factors was attained from the enhanced intervention. Trial Registration: Clinical Research Information Service KCT0005783; https://cris.nih.go.kr/cris/search/detailSearch.do/16123 %M 35200145 %R 10.2196/34059 %U https://mhealth.jmir.org/2022/2/e34059 %U https://doi.org/10.2196/34059 %U http://www.ncbi.nlm.nih.gov/pubmed/35200145 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e31363 %T Effectiveness of Digital Forced-Choice Nudges for Voluntary Data Donation by Health Self-trackers in Germany: Web-Based Experiment %A Pilgrim,Katharina %A Bohnet-Joschko,Sabine %+ Department of Management and Entrepreneurship, Faculty of Management, Economics and Society, Witten Herdecke University, Alfred-Herrhausen-Str 50, Witten, 58455, Germany, 49 2302926475, katharina.pilgrim@uni-wh.de %K quantified self %K health self-tracking %K digital nudge %K data donation %K health data %K mobile phone %D 2022 %7 21.2.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Health self-tracking is an evidence-based approach to optimize health and well-being for personal self-improvement through lifestyle changes. At the same time, user-generated health-related data can be of particular value for (health care) research. As longitudinal data, these data can provide evidence for developing better and new medications, diagnosing rare diseases faster, or treating chronic diseases. Objective: This quantitative study aims to investigate the impact of digital forced-choice nudges on the willingness of German health self-trackers to donate self-tracked health-related data for research. This study contributes to the body of knowledge on the effectiveness of nonmonetary incentives. Our study enables a gender-specific statement on influencing factors on the voluntary donation of personal health data and, at the same time, on the effectiveness of digital forced-choice nudges within tracking apps. Methods: We implemented a digital experiment using a web-based questionnaire by graphical manipulation of the Runtastic tracking app interface. We asked 5 groups independently to indicate their willingness to donate tracked data for research. We used a digital forced-choice nudge via a pop-up window, which framed the data donation request with 4 different counter values. We generated the counter values according to the specific target group needs identified from the research literature. Results: A sample of 919 was generated, of which, 625 (68%) were women and 294 (32%) were men. By dividing the sample into male and female participants, we take into account research on gender differences in privacy tendencies on the web and offline, showing that female participants display higher privacy concerns than male participants. A statistical group comparison shows that with a small effect size (r=0.21), men are significantly more likely (P=.04) to donate their self-tracked data for research if the need to take on social responsibility is addressed (the prosocial counter value in this case—contributing to society) compared with the control group without counter value. Selfish or pseudoprosocial counter values had no significant effect on willingness to donate health data among male or female health self-trackers in Germany when presented as a forced-choice nudge within a tracking app. Conclusions: Although surveys regularly reveal an 80% to 95% willingness to donate data on average in the population, our results show that only 41% (377/919) of the health self-trackers would donate their self-collected health data to research. Although selfish motives do not significantly influence willingness to donate, linking data donation to added societal value could significantly increase the likelihood of donating among male self-trackers by 15.5%. Thus, addressing the need to contribute to society promotes the willingness to donate data among male health self-trackers. The implementation of forced-choice framing nudges within tracking apps presented in a pop-up window can add to the accessibility of user-generated health-related data for research. %M 35188472 %R 10.2196/31363 %U https://www.jmir.org/2022/2/e31363 %U https://doi.org/10.2196/31363 %U http://www.ncbi.nlm.nih.gov/pubmed/35188472 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 2 %P e28913 %T The Relation of Attitude Toward Technology and Mastery Experience After an App-Guided Physical Exercise Intervention: Randomized Crossover Trial %A Sassenberg,Kai %A Roesel,Inka %A Sudeck,Gorden %A Bernecker,Katharina %A Durst,Jennifer %A Krauss,Inga %+ Social Processes Lab, Leibniz-Institut für Wissensmedien, Schleichstrasse 6, Tübingen, 72076, Germany, 49 7071 979 220, k.sassenberg@iwm-tuebingen.de %K mobile app %K exercise %K mastery experience %K self-efficacy %K attitudes toward technology %K osteoarthritis %D 2022 %7 18.2.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical exercise has been found to assert a positive impact on many muscular conditions. Exercise under face-to-face supervision is the gold standard, but access to it is limited, for instance, for economic reasons. App-guided therapy is an intervention that is more affordable and easily accessible. However, attitude toward technology is a key predictor for media adoption and is therefore expected to shape user experience during app-guided therapy. This might be of particular importance for mastery experience, which is crucial for promoting exercise-related self-efficacy and perceived usefulness of the interaction. Both should empower patients to continuously exercise. Objective: This study sought to test whether attitudes toward technology predict mastery experience and perceived usefulness of the interaction after an app- versus a physiotherapist-guided treatment. We expect that attitudes toward technology positively predict both outcomes in case of the app-guided but not in case of the physiotherapist-guided treatment. Methods: Patients (n=54) with clinically diagnosed hip osteoarthritis participated in 2 training sessions with the same exercise intervention, once guided by an app on a tablet computer and once guided by a physiotherapist in a German university hospital. The order of the sessions was randomized. Attitude toward technology was assessed as predictor before the first session, while mastery experience and the global perceived usefulness of interaction as self-reported outcomes after each session. Results: In line with our hypotheses, attitude toward technology predicted mastery experience (b=0.16, standard error=0.07, P=.02) and usefulness of interaction (b=0.17, standard error=0.06, P=.01) after the app-based training but not after the training delivered by a physiotherapist (P>.3 in all cases). Mastery experience was lower for the app-based training but reached a very similar level as the physiotherapist-guided training for those holding a very positive attitude toward technology. Conclusions: The attitude toward technology predicts the extent of mastery experience after app-guided exercise therapy. As mastery experience is highly important for self-efficacy and future exercise behavior, attitudes toward technology should be considered when delivering app-guided exercise treatments. Trial Registration: German Clinical Trials Register DRKS00015759; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00015759 %M 35179500 %R 10.2196/28913 %U https://formative.jmir.org/2022/2/e28913 %U https://doi.org/10.2196/28913 %U http://www.ncbi.nlm.nih.gov/pubmed/35179500 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e28886 %T Effectiveness of a Step Counter Smartband and Midwife Counseling Intervention on Gestational Weight Gain and Physical Activity in Pregnant Women With Obesity (Pas and Pes Study): Randomized Controlled Trial %A Gonzalez-Plaza,Elena %A Bellart,Jordi %A Arranz,Ángela %A Luján-Barroso,Leila %A Crespo Mirasol,Esther %A Seguranyes,Gloria %+ Maternal-Fetal Medicine Department at BCNatal, Clinic Hospital of Barcelona, Sabino de Arana, 1, Barcelona, 08028, Spain, 34 932275400 ext 7294, eplaza@clinic.cat %K obesity %K maternal %K pregnancy %K mHealth %K mobile apps %K telemedicine %K telenursing %K physical activity %K gestational weight gain %K lifestyle %K mobile phone %D 2022 %7 15.2.2022 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Women who are pregnant and have obesity and excessive gestational weight gain (GWG) present a higher risk of maternal and perinatal complications. The use of mobile apps and a wristband during pregnancy may contribute to promoting healthy lifestyles and, thus, improving maternal and neonatal health. Objective: This study aims to evaluate the effectiveness of a complex digital health intervention, using a smartband and app with midwife counseling, on GWG and physical activity (PA) in women who are pregnant and have obesity and analyze its impact on maternal and perinatal outcomes. In addition, we aim to study the frequency of use, usability, and satisfaction with the mobile apps used by the women in the intervention group. Methods: A parallel, 2-arm, randomized controlled trial was conducted. A total of 150 women who were pregnant and had obesity were included. The intervention group received a complex combined digital intervention. The intervention was delivered with a smartband (Mi Band 2) linked to the app Mi Fit to measure PA and the Hangouts app with the midwife to provide personal health information. The control group received usual care. The validated Spanish versions of the International Physical Activity Questionnaire–Short Form and the System Usability Scale were used. Satisfaction was measured on a 1- to 5-point Likert scale. Results: We analyzed 120 women, of whom 30 (25%) were withdrawn because of the COVID-19 pandemic. The median GWG in the intervention group was 7.0 (IQR 4-11) kg versus 9.3 (IQR 5.9-13.3) kg in the control group (P=.04). The adjusted mean GWG per week was 0.5 (95% CI 0.4-0.6) kg per week in the control group and 0.3 (95% CI 0.3-0.4) kg per week in the intervention group (df=0.1, 95% CI −0.2 to 0.03; P=.008). During the 35 and 37 gestational weeks, women in the intervention group had higher mean PA than women in the control group (1980 metabolic equivalents of tasks–minutes per week vs 1386 metabolic equivalents of tasks–minutes per week, respectively; P=.01). No differences were observed between the study groups in the incidence of maternal and perinatal outcomes. In the intervention group, 61% (36/59) of the women who were pregnant used the smartband daily, and 75% (44/59) evaluated the usability of the Mi Fit app as excellent. All women in the intervention group used the Hangouts app at least once a week. The mean of the satisfaction scale with the health counseling app and midwife support was 4.8/5 (SD 0.6) points. Conclusions: The use of a complex mobile health intervention was associated with adequate GWG, which was lower in the intervention group than in the control group. In addition, we observed that the intervention group had increases in PA. No differences were observed in maternal perinatal complications. Trial Registration: ClinicalTrials.gov NCT03706872; https://www.clinicaltrials.gov/ct2/show/NCT03706872 %M 35166684 %R 10.2196/28886 %U https://mhealth.jmir.org/2022/2/e28886 %U https://doi.org/10.2196/28886 %U http://www.ncbi.nlm.nih.gov/pubmed/35166684 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 2 %P e34645 %T Risk Factors for COVID-19 in College Students Identified by Physical, Mental, and Social Health Reported During the Fall 2020 Semester: Observational Study Using the Roadmap App and Fitbit Wearable Sensors %A Gilley,Kristen N %A Baroudi,Loubna %A Yu,Miao %A Gainsburg,Izzy %A Reddy,Niyanth %A Bradley,Christina %A Cislo,Christine %A Rozwadowski,Michelle Lois %A Clingan,Caroline Ashley %A DeMoss,Matthew Stephen %A Churay,Tracey %A Birditt,Kira %A Colabianchi,Natalie %A Chowdhury,Mosharaf %A Forger,Daniel %A Gagnier,Joel %A Zernicke,Ronald F %A Cunningham,Julia Lee %A Cain,Stephen M %A Tewari,Muneesh %A Choi,Sung Won %+ Department of Pediatrics, University of Michigan Medical School, 1200 E Hospital Dr, Medical Professional Building D4118, Ann Arbor, MI, 48109, United States, 1 734 615 2263, sungchoi@med.umich.edu %K mHealth %K mobile health %K college student %K mental health %K wearable devices %K wearable %K student %K risk factor %K risk %K COVID-19 %K physical health %K observational %K crisis %K self-report %K outcome %K physical activity %K wellbeing %K well-being %D 2022 %7 10.2.2022 %9 Original Paper %J JMIR Ment Health %G English %X Background: The COVID-19 pandemic triggered a seismic shift in education to web-based learning. With nearly 20 million students enrolled in colleges across the United States, the long-simmering mental health crisis in college students was likely further exacerbated by the pandemic. Objective: This study leveraged mobile health (mHealth) technology and sought to (1) characterize self-reported outcomes of physical, mental, and social health by COVID-19 status; (2) assess physical activity through consumer-grade wearable sensors (Fitbit); and (3) identify risk factors associated with COVID-19 positivity in a population of college students prior to release of the vaccine. Methods: After completing a baseline assessment (ie, at Time 0 [T0]) of demographics, mental, and social health constructs through the Roadmap 2.0 app, participants were instructed to use the app freely, wear the Fitbit, and complete subsequent assessments at T1, T2, and T3, followed by a COVID-19 assessment of history and timing of COVID-19 testing and diagnosis (T4: ~14 days after T3). Continuous measures were described using mean (SD) values, while categorical measures were summarized as n (%) values. Formal comparisons were made on the basis of COVID-19 status. The multivariate model was determined by entering all statistically significant variables (P<.05) in univariable associations at once and then removing one variable at a time through backward selection until the optimal model was obtained. Results: During the fall 2020 semester, 1997 participants consented, enrolled, and met criteria for data analyses. There was a high prevalence of anxiety, as assessed by the State Trait Anxiety Index, with moderate and severe levels in 465 (24%) and 970 (49%) students, respectively. Approximately one-third of students reported having a mental health disorder (n=656, 33%). The average daily steps recorded in this student population was approximately 6500 (mean 6474, SD 3371). Neither reported mental health nor step count were significant based on COVID-19 status (P=.52). Our analyses revealed significant associations of COVID-19 positivity with the use of marijuana and alcohol (P=.02 and P=.046, respectively) and with lower belief in public health measures (P=.003). In addition, graduate students were less likely and those with ≥20 roommates were more likely to report a COVID-19 diagnosis (P=.009). Conclusions: Mental health problems were common in this student population. Several factors, including substance use, were associated with the risk of COVID-19. These data highlight important areas for further attention, such as prioritizing innovative strategies that address health and well-being, considering the potential long-term effects of COVID-19 on college students. Trial Registration: ClinicalTrials.gov NCT04766788; https://clinicaltrials.gov/ct2/show/NCT04766788 International Registered Report Identifier (IRRID): RR2-10.2196/29561 %M 34992051 %R 10.2196/34645 %U https://mental.jmir.org/2022/2/e34645 %U https://doi.org/10.2196/34645 %U http://www.ncbi.nlm.nih.gov/pubmed/34992051 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e27794 %T The Effects of mHealth-Based Gamification Interventions on Participation in Physical Activity: Systematic Review %A Xu,Linqi %A Shi,Hongyu %A Shen,Meidi %A Ni,Yuanyuan %A Zhang,Xin %A Pang,Yue %A Yu,Tianzhuo %A Lian,Xiaoqian %A Yu,Tianyue %A Yang,Xige %A Li,Feng %+ School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130012, China, 86 1 779 008 9009, fli@jlu.edu.cn %K mobile health %K gamification %K physical activity %K systematic review %K mobile phone %D 2022 %7 3.2.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: It is well known that regular physical exercise has associated benefits; yet, participation remains suboptimal. Mobile health (mHealth) has become an indispensable medium to deliver behavior change interventions, and there is a growing interest in the gamification apps in mHealth to promote physical activity (PA) participation. Gamification could use game design elements (such as points, leaderboards, and progress bars), and it has the potential to increase motivation for PA and engagement. However, mHealth-based gamification interventions are still emerging, and little is known about the application status and efficacy of such interventions. Objective: This systematic review aims to investigate gamification apps in mHealth for improving PA levels and simultaneously summarize the impact of gamification interventions on PA participation. Methods: We searched PubMed, Scopus, Web of Science, Embase, CINAHL (EBSCO host), and IEEE Xplore from inception to December 20, 2020. Original empirical research exploring the effects of gamification interventions on PA participation was included. The papers described at least one outcome regarding exercise or PA participation, which could be subjective self-report or objective indicator measurement. Of note, we excluded studies about serious games or full-fledged games. Results: Of 2944 studies identified from the database search, 50 (1.69%) were included, and the information was synthesized. The review revealed that gamification of PA had been applied to various population groups and broadly distributed among young people but less distributed among older adults and patients with a disease. Most of the studies (30/50, 60%) combined gamification with wearable devices to improve PA behavior change, and 50% (25/50) of the studies used theories or principles for designing gamified PA interventions. The most frequently used game elements were goal-setting, followed by progress bars, rewards, points, and feedback. This review demonstrated that gamification interventions could increase PA participation; however, the results were mixed, and modest changes were attained, which could be attributed to the heterogeneity across studies. Conclusions: Overall, this study provides an overview of the existing empirical research in PA gamification interventions and provides evidence for the efficacy of gamification in enhancing PA participation. High-quality empirical studies are needed in the future to assess the efficacy of a combination of gamification and wearable activity devices to promote PA, and further exploration is needed to investigate the optimal implementation of these features of game elements and theories to enhance PA participation. %M 35113034 %R 10.2196/27794 %U https://mhealth.jmir.org/2022/2/e27794 %U https://doi.org/10.2196/27794 %U http://www.ncbi.nlm.nih.gov/pubmed/35113034 %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 %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e26652 %T Usability, Acceptability, and Satisfaction of a Wearable Activity Tracker in Older Adults: Observational Study in a Real-Life Context in Northern Portugal %A Domingos,Célia %A Costa,Patrício %A Santos,Nadine Correia %A Pêgo,José Miguel %+ Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Largo do Paço, Braga, 4710-057, Portugal, 351 253 604 800, jmpego@med.uminho.pt %K user experience %K Technology Acceptance Model %K health monitoring %K fitness trackers %K aging %K seniors %D 2022 %7 26.1.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of activity trackers has significantly increased over the last few years. This technology has the potential to improve the levels of physical activity and health-related behaviors in older adults. However, despite the potential benefits, the rate of adoption remains low among older adults. Therefore, understanding how technology is perceived may potentially offer insight to promote its use. Objective: This study aimed to (1) assess acceptability, usability, and user satisfaction with the Xiaomi Mi Band 2 in Portuguese community-dwelling older adults in a real-world context; (2) explore the mediating effect of the usability on the relationship between user characteristics and satisfaction; and (3) examine the moderating effect of user characteristics on the relationship between usability and user satisfaction. Methods: Older adults used the Xiaomi Mi Band 2 over 15 days. The user experience was evaluated through the Technology Acceptance Model 3, System Usability Scale, and User Satisfaction Evaluation Questionnaire. An integrated framework for usability and user satisfaction was used to explore user experience. Statistical data analysis included descriptive data analysis, reliability analysis, confirmatory factor analysis, and mediation and moderation analyses. Results: A sample of 110 older adults with an average age of 68.41 years (SD 3.11) completed the user experience questionnaires. Mean user acceptance was very high—perceived ease of use: 6.45 (SD 0.78); perceptions of external control: 6.74 (SD 0.55); computer anxiety: 6.85 (SD 0.47); and behavioral intention: 6.60 (SD 0.97). The usability was excellent with an average score of 92.70 (SD 10.73), and user satisfaction was classified as a good experience 23.30 (SD 2.40). The mediation analysis confirmed the direct positive effect of usability on satisfaction (β=.530; P<.01) and the direct negative effect of depression on usability (β=–.369; P<.01). Lastly, the indirect effect of usability on user satisfaction was higher in individuals with lower Geriatric Depression Scale levels. Conclusions: Findings demonstrate that the Xiaomi Mi Band 2 is suitable for older adults. Furthermore, the results confirmed usability as a determinant of satisfaction with the technology and extended the existing knowledge about wearable activity trackers in older adults. %M 35080503 %R 10.2196/26652 %U https://www.jmir.org/2022/1/e26652 %U https://doi.org/10.2196/26652 %U http://www.ncbi.nlm.nih.gov/pubmed/35080503 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 1 %P e33798 %T Active Use and Engagement in an mHealth Initiative Among Young Men With Obesity: Mixed Methods Study %A Gorny,Alexander Wilhelm %A Chee,Wei Chian Douglas %A Müller-Riemenschneider,Falk %+ Centre of Excellence for Soldier Performance, Singapore Armed Forces, Pasir Laba Camp, Blk 130 #03-09, Singapore, 637901, Singapore, 65 81337238, alexander_gorny@u.nus.edu %K mHealth %K physical activity %K health promoting financial incentives %K weight loss maintenance %K young men %D 2022 %7 25.1.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The effectiveness of mobile health (mHealth) approaches that employ wearable technology to promote physical activity have been the subject of concern due to the declining active use observed in trial settings. Objective: To better contextualize active use, this study aimed to identify the barriers and enablers to engagement in a tracker-based mHealth initiative among young men who had recently completed a 19-week residential weight loss program. Methods: A mixed methods study was conducted among 167 young men who had voluntarily enrolled in the national steps challenge (NSC), an mHealth physical activity promotion initiative, following a residential weight loss intervention. A subsample of 29 enrollees with a body mass index of 29.6 (SD 3.1) participated in semistructured interviews and additional follow-up assessments. Quantitative systems data on daily step count rates were used to describe active use. Qualitative data were coded and analyzed to elicit barriers and enablers to microlevel engagement in relation to the NSC, focusing on tracker and smartphone use. We further elicited barriers and enablers to macrolevel engagement by exploring attitudes and behaviors toward the NSC. Using triangulation, we examined how qualitative engagement in the NSC could account for quantitative findings on active use. Using integration of findings, we discussed how the mHealth intervention might have changed physical activity behavior. Results: Among the 167 original enrollees, active use declined from 72 (47%) in week 1 to 27 (17%) in week 21. Mean daily step counts peaked in week 1 at 10,576 steps per day and were variable throughout the NSC. Barriers to engagement had occurred in the form of technical issues leading to abandonment, device switching, and offline tracking. Passive attitudes toward step counting and disinterest in the rewards had also prevented deeper engagement. Enablers of engagement included self-monitoring and coaching features, while system targets and the implicit prospect of reward had fostered new physical activity behaviors. Conclusions: Our study showed that as the NSC is implemented in this population, more emphasis should be placed on technical support and personalized activity targets to promote lasting behavior change. %M 35076399 %R 10.2196/33798 %U https://formative.jmir.org/2022/1/e33798 %U https://doi.org/10.2196/33798 %U http://www.ncbi.nlm.nih.gov/pubmed/35076399 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e30791 %T Accuracy and Acceptability of Wrist-Wearable Activity-Tracking Devices: Systematic Review of the Literature %A Germini,Federico %A Noronha,Noella %A Borg Debono,Victoria %A Abraham Philip,Binu %A Pete,Drashti %A Navarro,Tamara %A Keepanasseril,Arun %A Parpia,Sameer %A de Wit,Kerstin %A Iorio,Alfonso %+ Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4L8, Canada, 1 9055259140 ext 22069, noronn@mcmaster.ca %K diagnosis %K measurement %K wrist-wearable devices %K mobile phone %D 2022 %7 21.1.2022 %9 Review %J J Med Internet Res %G English %X Background: Numerous wrist-wearable devices to measure physical activity are currently available, but there is a need to unify the evidence on how they compare in terms of acceptability and accuracy. Objective: The aim of this study is to perform a systematic review of the literature to assess the accuracy and acceptability (willingness to use the device for the task it is designed to support) of wrist-wearable activity trackers. Methods: We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, and SPORTDiscus for studies measuring physical activity in the general population using wrist-wearable activity trackers. We screened articles for inclusion and, for the included studies, reported data on the studies’ setting and population, outcome measured, and risk of bias. Results: A total of 65 articles were included in our review. Accuracy was assessed for 14 different outcomes, which can be classified in the following categories: count of specific activities (including step counts), time spent being active, intensity of physical activity (including energy expenditure), heart rate, distance, and speed. Substantial clinical heterogeneity did not allow us to perform a meta-analysis of the results. The outcomes assessed most frequently were step counts, heart rate, and energy expenditure. For step counts, the Fitbit Charge (or the Fitbit Charge HR) had a mean absolute percentage error (MAPE) <25% across 20 studies. For heart rate, the Apple Watch had a MAPE <10% in 2 studies. For energy expenditure, the MAPE was >30% for all the brands, showing poor accuracy across devices. Acceptability was most frequently measured through data availability and wearing time. Data availability was ≥75% for the Fitbit Charge HR, Fitbit Flex 2, and Garmin Vivofit. The wearing time was 89% for both the GENEActiv and Nike FuelBand. Conclusions: The Fitbit Charge and Fitbit Charge HR were consistently shown to have a good accuracy for step counts and the Apple Watch for measuring heart rate. None of the tested devices proved to be accurate in measuring energy expenditure. Efforts should be made to reduce the heterogeneity among studies. %M 35060915 %R 10.2196/30791 %U https://www.jmir.org/2022/1/e30791 %U https://doi.org/10.2196/30791 %U http://www.ncbi.nlm.nih.gov/pubmed/35060915 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 1 %P e30682 %T Promoting Physical Activity and Weight Loss With mHealth Interventions Among Workers: Systematic Review and Meta-analysis of Randomized Controlled Trials %A Jung,Jiyeon %A Cho,Inhae %+ College of Nursing, Korea University, 145 Anam-ro Sungbuk-gu, Seoul, 02841, Republic of Korea, 82 2 3290 4912, inhae05@gmail.com %K mHealth %K physical activity %K obesity %K weight loss %K workforce %K workplace health promotion %K mobile phone %D 2022 %7 21.1.2022 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Physical activity (PA) is a vital factor in promoting health in the workforce. Mobile health (mHealth) interventions have recently emerged in workplace health promotion as an effective strategy for inducing changes in health behaviors among workers; however, the effectiveness of mHealth interventions in promoting PA and weight loss for workers is unclear. Objective: This study aims to provide a comprehensive analysis of current evidence on the effectiveness of mHealth interventions in promoting PA and weight loss among workers. Methods: We searched relevant databases, including PubMed, Embase, CINAHL Complete, and the Cochrane Library, for publications on mHealth interventions in the English or Korean language from inception to December 2020. Randomized controlled trials that evaluated the effectiveness of mHealth in improving PA and weight loss were retrieved. A meta-analysis with a random effects model and subgroup analyses was performed on PA types and mHealth intervention characteristics. Results: A total of 8 studies were included in this analysis. More than half of the studies (5/8, 63%) were identified as having a high risk of bias. The mHealth intervention group showed a significant improvement in PA (standardized mean difference [SMD] 0.22, 95% CI 0.03-0.41; P<.001; I2=78%). No significant difference in weight loss was observed when comparing the intervention group with the control groups (SMD 0.02, 95% CI –0.07 to 0.10; P=.48; I2=0%). A subgroup analysis was also performed; walking activity (SMD 0.70, 95% CI 0.21-1.19; P<.001; I2=83.3%), a multicomponent program (SMD 0.19, 95% CI 0.05-0.33; P=.03; I2=57.4%), objective measurement (SMD 0.58, 95% CI 0.05-1.10; P<.001; I2=87.3%), and 2 or more delivery modes (SMD 0.44, 95% CI 0.01-0.87; P<.001; I2=85.1%) were significantly associated with an enhancement in PA. Conclusions: This study suggests that mHealth interventions are effective for improving PA among workers. Future studies that assess long-term efficacy with a larger population are recommended. %M 35060913 %R 10.2196/30682 %U https://mhealth.jmir.org/2022/1/e30682 %U https://doi.org/10.2196/30682 %U http://www.ncbi.nlm.nih.gov/pubmed/35060913 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 9 %N 1 %P e27637 %T Accuracy of Heart Rate Measurement by the Fitbit Charge 2 During Wheelchair Activities in People With Spinal Cord Injury: Instrument Validation Study %A Hoevenaars,Dirk %A Yocarini,Iris E %A Paraschiakos,Stylianos %A Holla,Jasmijn F M %A de Groot,Sonja %A Kraaij,Wessel %A Janssen,Thomas W J %+ Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, Amsterdam, 1081 BT, Netherlands, 31 643259225, dirkhoevenaars@hotmail.com %K Fitbit Charge 2 %K heart rate %K accuracy %K photoplethysmography %K spinal cord injury %D 2022 %7 19.1.2022 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Heart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique. Objective: The objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level. Methods: The HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups—the no SCI group and based on lesion level into the T1 (cervical) group. Mean absolute percentage error (MAPE) and concordance correlation coefficient were determined for each group for each activity type, that is, rest, wheelchair activities, and strength exercise. Results: With an overall MAPEall lesions of 12.99%, the accuracy fell below the standard acceptable MAPE of –10% to +10% with a moderate agreement (concordance correlation coefficient=0.577). The HR accuracy of Fitbit Charge 2 seems to be reduced in those with cervical lesion level in all activities (MAPEno SCI=8.09%; MAPET1=20.43%). The accuracy of the Fitbit Charge 2 decreased with increasing intensity in all lesions (MAPErest=6.5%, MAPEactivity=12.97%, and MAPEstrength=14.2%). Conclusions: HR measured with the PPG technique showed lower accuracy in people with SCI than in those without SCI. The accuracy was just above the acceptable level in people with paraplegia, whereas in people with tetraplegia, a worse accuracy was found. The accuracy seemed to worsen with increasing intensities. Therefore, high-intensity HR data, especially in people with cervical lesions, should be used with caution. %M 35044306 %R 10.2196/27637 %U https://rehab.jmir.org/2022/1/e27637 %U https://doi.org/10.2196/27637 %U http://www.ncbi.nlm.nih.gov/pubmed/35044306 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 1 %P e31989 %T A Remote Health Coaching, Text-Based Walking Program in Ethnic Minority Primary Care Patients With Overweight and Obesity: Feasibility and Acceptability Pilot Study %A Smart,Mary H %A Nabulsi,Nadia A %A Gerber,Ben S %A Gupta,Itika %A Di Eugenio,Barbara %A Ziebart,Brian %A Sharp,Lisa K %+ Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois Chicago, 833 South Wood St, Chicago, IL, 60612, United States, 1 312 355 3569, sharpl@uic.edu %K mHealth %K Fitbit %K SMART goals %K texting %K health coach %K mobile phone %D 2022 %7 19.1.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: Over half of US adults have at least one chronic disease, including obesity. Although physical activity is an important component of chronic disease self-management, few reach the recommended physical activity goals. Individuals who identify as racial and ethnic minorities are disproportionally affected by chronic diseases and physical inactivity. Interventions using consumer-based wearable devices have shown promise for increasing physical activity among patients with chronic diseases; however, populations with the most to gain, such as minorities, have been poorly represented to date. Objective: This study aims to assess the feasibility, acceptability, and preliminary outcomes of an 8-week text-based coaching and Fitbit program aimed at increasing the number of steps in a predominantly overweight ethnic minority population. Methods: Overweight patients (BMI >25 kg/m2) were recruited from an internal medicine clinic located in an inner-city academic medical center. Fitbit devices were provided. Using 2-way SMS text messaging, health coaches (HCs) guided patients to establish weekly step goals that were specific, measurable, attainable, realistic, and time-bound. SMS text messaging and Fitbit activities were managed using a custom-designed app. Program feasibility was assessed via the recruitment rate, retention rate (the proportion of eligible participants completing the 8-week program), and patient engagement (based on the number of weekly text message goals set with the HC across the 8-week period). Acceptability was assessed using a qualitative, summative evaluation. Exploratory statistical analysis included evaluating the average weekly steps in week 1 compared with week 8 using a paired t test (2-tailed) and modeling daily steps over time using a linear mixed model. Results: Of the 33 patients initially screened; 30 (91%) patients were enrolled in the study. At baseline, the average BMI was 39.3 (SD 9.3) kg/m2, with 70% (23/33) of participants presenting as obese. A total of 30% (9/30) of participants self-rated their health as either fair or poor, and 73% (22/30) of participants set up ≥6 weekly goals across the 8-week program. In total, 93% (28/30) of participants completed a qualitative summative evaluation, and 10 themes emerged from the evaluation: patient motivation, convenient SMS text messaging experience, social support, supportive accountability, technology support, self-determined goals, achievable goals, feedback from Fitbit, challenges, and habit formation. There was no significant group change in the average weekly steps for week 1 compared with week 8 (mean difference 7.26, SD 6209.3; P=.99). However, 17% (5/30) of participants showed a significant increase in their daily steps. Conclusions: Overall, the results demonstrate the feasibility and acceptability of a remotely delivered walking study that included an HC; SMS text messaging; a wearable device (Fitbit); and specific, measurable, attainable, realistic, and time-bound goals within an ethnic minority patient population. Results support further development and testing in larger samples to explore efficacy. %M 35044308 %R 10.2196/31989 %U https://formative.jmir.org/2022/1/e31989 %U https://doi.org/10.2196/31989 %U http://www.ncbi.nlm.nih.gov/pubmed/35044308 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e29703 %T Adherence, Efficacy, and Safety of Wearable Technology–Assisted Combined Home-Based Exercise in Chinese Patients With Ankylosing Spondylitis: Randomized Pilot Controlled Clinical Trial %A Wang,Yiwen %A Liu,Xingkang %A Wang,Weimin %A Shi,Yanyun %A Ji,Xiaojian %A Hu,Lidong %A Wang,Lei %A Yin,Yiquan %A Xie,Siyuan %A Zhu,Jian %A Zhang,Jianglin %A Jiao,Wei %A Huang,Feng %+ Department of Rheumatology and Immunology, First Medical Center, Chinese People's Liberation Army General Hospital, 28 Fuxing Road, Beijing, 100853, China, 86 010 5549 9314, fhuang@301hospital.com.cn %K ankylosing spondylitis %K wearable technology %K home-based exercise %K combined exercise %K randomized controlled trial %K RCT %K exercise %K wearable %K photoplethysmography %K spondyloarthritis %D 2022 %7 18.1.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Clinical practice guidelines recommend that exercise is essential in the self-management of ankylosing spondylitis (AS). Attending supervised interventions requiring periodic medical center visits can be difficult and patients may decline participation, whereas effective home-based exercise interventions that do not require regular medical center visits are likely to be more accessible for AS patients. Objective: The goal of the research was to investigate the adherence, efficacy, and safety of a wearable technology–assisted combined home-based exercise program in AS. Methods: This was a 16-week investigator-initiated, assessor-blinded, randomized, pilot controlled trial conducted at Chinese People’s Liberation Army General Hospital. We enrolled patients with AS who had no regular exercise habits and had been stable in drug treatment for the preceding month. Patients were randomly assigned (1:1) using a computer algorithm. An exercise program consisting of moderate-intensity aerobic exercise and functional exercise was given to the patients in the intervention group. The exercise intensity was controlled by a Mio FUSE Heart Rate Monitor wristband, which uses photoplethysmography to measure heart rate. Patients in the control group received usual care. The primary outcome was the difference in the Ankylosing Spondylitis Disease Activity Score (ASDAS). The secondary outcomes were patient global assessment (PGA), physician global assessment (PhGA), total pain, nocturnal pain, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), BAS Functional Index (BASFI), BAS Metrology Index (BASMI), Spondyloarthritis International Society Health Index (ASAS HI), 36-item Short Form Survey (SF-36), maximal oxygen uptake (VO2) max, body composition, range of motion of joints, and muscle endurance tests. Retention rate, adherence rate, barriers to being active, and adverse events were also assessed. Results: A total of 77 patients were screened, of whom 55 (71%) patients were enrolled; 2% (1/55) withdrew without treatment after randomization. Patients were assigned to the intervention (n=26) or control group (n=28). The median adherence rate of the prescribed exercise protocol was 84.2% (IQR 48.7%-97.9%). For the primary outcome, between-group difference of ASDAS was significant, favoring the intervention (–0.2, 95% CI –0.4 to 0.02, P=.03). For the secondary outcomes, significant between-group differences at 16 weeks were detected in PGA, PhGA, total pain, BASDAI, BASDAI-fatigue, BASDAI–spinal pain, BASDAI–morning stiffness intensity, BASFI, and BASMI. Moreover, the frequency of difficulty in ASAS HI-motivation at 16 weeks was less in the intervention group (P=.03). Between-group difference for change from baseline were also detected in VO2 max, SF-36, back extensor endurance test, and the range of motion of cervical lateral flexion at 16 weeks. Lack of time, energy, and willpower were the most distinct barriers to being active. Incidences of adverse events were similar between groups (P=.11). Conclusions: Our pilot study suggests that this technology-assisted combined home-based exercise program can improve the clinical outcomes of patients with AS who have no exercise habit, with good adherence and safety profile. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900024244; http://www.chictr.org.cn/showproj.aspx?proj=40176 %M 35040798 %R 10.2196/29703 %U https://www.jmir.org/2022/1/e29703 %U https://doi.org/10.2196/29703 %U http://www.ncbi.nlm.nih.gov/pubmed/35040798 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e27487 %T Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis %A Cao,Rui %A Azimi,Iman %A Sarhaddi,Fatemeh %A Niela-Vilen,Hannakaisa %A Axelin,Anna %A Liljeberg,Pasi %A Rahmani,Amir M %+ Department of Electrical Engineering and Computer Science, University of California, 1407 Palo Verde Rd, Irvine, CA, 92617, United States, 1 6266883017, caor6@uci.edu %K electrocardiography %K ECG %K wearable device %K heart rate variability %K Oura smart ring %D 2022 %7 18.1.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings. Objective: This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor. Methods: We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices. Results: Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. Conclusions: The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests. %M 35040799 %R 10.2196/27487 %U https://www.jmir.org/2022/1/e27487 %U https://doi.org/10.2196/27487 %U http://www.ncbi.nlm.nih.gov/pubmed/35040799 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e32362 %T Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project %A Thivel,David %A Corteval,Alice %A Favreau,Jean-Marie %A Bergeret,Emmanuel %A Samalin,Ludovic %A Costes,Frédéric %A Toumani,Farouk %A Dualé,Christian %A Pereira,Bruno %A Eschalier,Alain %A Fearnbach,Nicole %A Duclos,Martine %A Tournadre,Anne %+ Clermont Auvergne University, 3 rue de la chabarde, Aubiere, 63170, France, 33 0770398975, david.thivel@uca.fr %K indicator %K fine body motion %K movement behaviors %K decomposition %K structuration %K sequencing %D 2022 %7 14.1.2022 %9 Viewpoint %J J Med Internet Res %G English %X Methods to measure physical activity and sedentary behaviors typically quantify the amount of time devoted to these activities. Among patients with chronic diseases, these methods can provide interesting behavioral information, but generally do not capture detailed body motion and fine movement behaviors. Fine detection of motion may provide additional information about functional decline that is of clinical interest in chronic diseases. This perspective paper highlights the need for more developed and sophisticated tools to better identify and track the decomposition, structuration, and sequencing of the daily movements of humans. The primary goal is to provide a reliable and useful clinical diagnostic and predictive indicator of the stage and evolution of chronic diseases, in order to prevent related comorbidities and complications among patients. %M 35029537 %R 10.2196/32362 %U https://www.jmir.org/2022/1/e32362 %U https://doi.org/10.2196/32362 %U http://www.ncbi.nlm.nih.gov/pubmed/35029537 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 8 %N 1 %P e31576 %T Feasibility and Acceptability of a Physical Activity Tracker and Text Messages to Promote Physical Activity During Chemotherapy for Colorectal Cancer: Pilot Randomized Controlled Trial (Smart Pace II) %A Van Blarigan,Erin L %A Dhruva,Anand %A Atreya,Chloe E %A Kenfield,Stacey A %A Chan,June M %A Milloy,Alexandra %A Kim,Iris %A Steiding,Paige %A Laffan,Angela %A Zhang,Li %A Piawah,Sorbarikor %A Fukuoka,Yoshimi %A Miaskowski,Christine %A Hecht,Frederick M %A Kim,Mi-Ok %A Venook,Alan P %A Van Loon,Katherine %+ Department of Epidemiology and Biostatistics, University of California, San Francisco, UCSF Box 0560, 550 16th St. 2nd Floor, San Francisco, CA, 94158, United States, 1 415 476 1111 ext 13608, erin.vanblarigan@ucsf.edu %K exercise %K treatment %K colon cancer %K rectal cancer %K digital health %K wearables %K SMS %D 2022 %7 11.1.2022 %9 Original Paper %J JMIR Cancer %G English %X Background: We conducted a pilot 2-arm randomized controlled trial to assess the feasibility of a digital health intervention to increase moderate-to-vigorous physical activity in patients with colorectal cancer (CRC) during chemotherapy. Objective: This study aimed to determine whether a digital health physical activity intervention is feasible and acceptable during chemotherapy for CRC. Methods: Potentially eligible patients with CRC expected to receive at least 12 weeks of chemotherapy were identified in person at the University of California, San Francisco, and on the web through advertising. Eligible patients were randomized 1:1 to a 12-week intervention (Fitbit Flex, automated SMS text messages) versus usual care. At 0 and 12 weeks, patients wore an Actigraph GT3X+ accelerometer for 7 days and completed surveys, body size measurements, and an optional 6-minute walk test. Participants could not be masked to their intervention arm, but people assessing the body size and 6-minute walk test outcomes were masked. The primary outcomes were adherence (eg, Fitbit wear and text response rate) and self-assessed acceptability of the intervention. The intervention would be considered feasible if we observed at least 80% complete follow-up and 70% adherence and satisfaction, a priori. Results: From 2018 to 2020, we screened 240 patients; 53.3% (128/240) of patients were ineligible and 26.7% (64/240) declined to participate. A total of 44 patients (44/240, 18%) were randomized to the intervention (n=22) or control (n=22) groups. Of these, 57% (25/44) were women; 68% (30/44) identified as White and 25% (11/44) identified as Asian American or Pacific Islander; and 77% (34/44) had a 4-year college degree. The median age at enrollment was 54 years (IQR 45-62 years). Follow-up at 12 weeks was 91% (40/44) complete. In the intervention arm, patients wore Fitbit devices on a median of 67 out of 84 (80%) study days and responded to a median of 17 out of 27 (63%) questions sent via SMS text message. Among 19 out of 22 (86%) intervention patients who completed the feedback survey, 89% (17/19) were satisfied with the Fitbit device; 63% (12/19) were satisfied with the SMS text messages; 68% (13/19) said the SMS text messages motivated them to exercise; 74% (14/19) said the frequency of SMS text messages (1-3 days) was ideal; and 79% (15/19) said that receiving SMS text messages in the morning and evening was ideal. Conclusions: This pilot study demonstrated that many people receiving chemotherapy for CRC are interested in participating in digital health physical activity interventions. Fitbit adherence was high; however, participants indicated a desire for more tailored SMS text message content. Studies with more socioeconomically diverse patients with CRC are required. Trial Registration: ClinicalTrials.gov NCT03524716; https://clinicaltrials.gov/ct2/show/NCT03524716 %M 35014958 %R 10.2196/31576 %U https://cancer.jmir.org/2022/1/e31576 %U https://doi.org/10.2196/31576 %U http://www.ncbi.nlm.nih.gov/pubmed/35014958 %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 %@ 2291-9694 %I JMIR Publications %V 10 %N 1 %P e32724 %T Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study %A Kraus,Moritz %A Saller,Maximilian Michael %A Baumbach,Sebastian Felix %A Neuerburg,Carl %A Stumpf,Ulla Cordula %A Böcker,Wolfgang %A Keppler,Alexander Martin %+ Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich, Ludwig-Maximilians Universität Munich, Marchioninistr. 15, Munich, 81377, Germany, 49 89 4400 0, alexander.keppler@med.uni-muenchen.de %K wearables %K insole sensors %K orthogeriatric %K artificial intelligence %K prediction models %K machine learning %K gait analysis %K digital sensors %K digital health %K aging %K prediction algorithms %K geriatric %K mobile health %K mobile insoles %D 2022 %7 5.1.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. Objective: The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. Methods: This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. Results: Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). Conclusions: A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients. %M 34989684 %R 10.2196/32724 %U https://medinform.jmir.org/2022/1/e32724 %U https://doi.org/10.2196/32724 %U http://www.ncbi.nlm.nih.gov/pubmed/34989684 %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 e30578 %T A Pragmatic Intervention Using Financial Incentives for Pregnancy Weight Management: Feasibility Randomized Controlled Trial %A Krukowski,Rebecca %A Johnson,Brandi %A Kim,Hyeonju %A Sen,Saunak %A Homsi,Riad %+ Department of Public Health Science, University of Virginia, PO Box 800765, Charlottesville, VA, 22908-0765, United States, 1 434 924 1000, bkrukowski@virginia.edu %K pregnancy %K weight %K physical activity %K self-weighing %D 2021 %7 24.12.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Excessive gestational weight gain (GWG) is common and can result in maternal and child health complications. Pragmatic behavioral interventions that can be incorporated into standard obstetric care are needed, and financial incentives are a promising approach. Objective: The aim of this study is to evaluate the feasibility of recruitment, randomization, and retention, as well as treatment engagement and intervention satisfaction, in a behavioral program. The program provided small incentives for meeting behavioral goals of self-weighing and physical activity as well as larger outcome incentives for meeting GWG goals. Methods: We recruited 40 adult women in their first trimester of pregnancy from February 2019 to September 2019 at an obstetric clinic. Participants were randomized to 3 intervention components using a 2×2×2 factorial design: daily incentives for self-weighing (lottery vs certain loss), incentives for adhering to the Institute of Medicine’s GWG guidelines based on BMI category (monthly vs overall), and incentives for reaching physical activity goals (yes vs no). Participants were asked to complete daily weigh-ins using the Withings Body wireless scale provided by the study, as well as wear a physical activity tracker (Fitbit Flex 2). Feasibility outcomes of recruitment, randomization, and retention, as well as treatment engagement and intervention satisfaction, were assessed. Weight assessments were conducted at baseline, 32-week gestation, and 36-week gestation. Results: Participants were enrolled at, on average, 9.6 (SD 1.8) weeks’ gestation. Of the 39 participants who were oriented to their condition and received the intervention, 24 (62%) were Black or African American, 30 (77%) were not married, and 29 (74%) had an annual household income of less than US $50,000. Of the 39 participants, 35 (90%) completed the follow-up data collection visit. Participants were generally quite positive about the intervention components, with a particular emphasis on the helpfulness of, and the enjoyment of using, the e-scale in both the quantitative and qualitative feedback. Participants who received the loss incentive, on average, had 2.86 times as many days of self-weighing as those who received the lottery incentive. Participants had a relatively low level of activity, with no difference between those who received a physical activity incentive and those who did not. Conclusions: A financial incentive–based pragmatic intervention was feasible and acceptable for pregnant women for promoting self-weighing, physical activity, and healthy GWG. Participants were successfully recruited early in their first trimester of pregnancy and retained for follow-up data collection in the third trimester. Participants demonstrated promising engagement in self-weighing, particularly with loss-based incentives, and reported finding the self-weighing especially helpful. This study supports further investigation of pragmatic, clinic-based financial incentive–based interventions for healthy GWG behaviors. Trial Registration: ClinicalTrials.gov NCT03834194; https://clinicaltrials.gov/ct2/show/NCT03834194 %M 34951594 %R 10.2196/30578 %U https://formative.jmir.org/2021/12/e30578 %U https://doi.org/10.2196/30578 %U http://www.ncbi.nlm.nih.gov/pubmed/34951594 %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 %@ 2371-4379 %I JMIR Publications %V 6 %N 4 %P e29027 %T Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review %A Nagpal,Meghan S %A Barbaric,Antonia %A Sherifali,Diana %A Morita,Plinio P %A Cafazzo,Joseph A %+ Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College St 4th Floor, Toronto, ON, M5T 3M6, Canada, 1 416 978 4326, meghan.nagpal@mail.utoronto.ca %K type 2 diabetes %K obesity management %K health behavior %K machine learning %K artificial intelligence %K big data %K data science %K patient-generated health data %K mobile phone %D 2021 %7 20.12.2021 %9 Review %J JMIR Diabetes %G English %X Background: Complications due to type 2 diabetes (T2D) can be mitigated through proper self-management that can positively change health behaviors. Technological tools are available to help people living with, or at risk of developing, T2D to manage their condition, and such tools provide a large repository of patient-generated health data (PGHD). Analytics can provide insights into the health behaviors of people living with T2D. Objective: The aim of this review is to investigate what can be learned about the health behaviors of those living with, or at risk of developing, T2D through analytics from PGHD. Methods: A scoping review using the Arksey and O’Malley framework was conducted in which a comprehensive search of the literature was conducted by 2 reviewers. In all, 3 electronic databases (PubMed, IEEE Xplore, and ACM Digital Library) were searched using keywords associated with diabetes, behaviors, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted, after which studies were selected. Critical examination took place through a descriptive-analytical narrative method, and data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective. Results: We identified 43 studies that met the inclusion criteria for this review. Although 70% (30/43) of the studies examined PGHD independently, 30% (13/43) combined PGHD with other data sources. Most of these studies used machine learning algorithms to perform their analysis. The themes identified through this review include predicting diabetes or obesity, deriving factors that contribute to diabetes or obesity, obtaining insights from social media or web-based forums, predicting glycemia, improving adherence and outcomes, analyzing sedentary behaviors, deriving behavior patterns, discovering clinical correlations from behaviors, and developing design principles. Conclusions: The increased volume and availability of PGHD have the potential to derive analytical insights into the health behaviors of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavior patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, which constitutes a unique source of data for these applications that would not be possible through the use of other data sources. %M 34783668 %R 10.2196/29027 %U https://diabetes.jmir.org/2021/4/e29027 %U https://doi.org/10.2196/29027 %U http://www.ncbi.nlm.nih.gov/pubmed/34783668 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 12 %P e28128 %T A Wearable Activity Tracker Intervention With and Without Weekly Behavioral Support Emails to Promote Physical Activity Among Women Who Are Overweight or Obese: Randomized Controlled Trial %A Black,Melissa %A Brunet,Jennifer %+ School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, 125 University Private, Ottawa, ON, K1N6N5, Canada, 1 6135625800 ext 3068, jennifer.brunet@uottawa.ca %K behavior change %K motivation %K obesity %K physical activity %K women %K mobile phone %D 2021 %7 16.12.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity (PA) plays a fundamental role in combating the current obesity epidemic; however, most women who are overweight or obese are generally physically inactive. Wearable activity tracker interventions can help increase the PA levels in this population. Supplementing such interventions with behavioral support emails may further improve their effectiveness, but this remains to be confirmed. Objective: This study aims to determine if adding behavioral support emails to a wearable activity tracker intervention can further increase PA levels among women who are overweight or obese in comparison to a wearable activity tracker–only intervention and a control condition. Methods: Women with a BMI ≥25 kg/m2 who were not meeting the Canadian PA guidelines for aerobic and strength training were randomized into 1 of 3 groups. Group 1 received 6 weekly behavioral support emails, a wearable activity tracker, and a copy of the Canadian PA guidelines. Group 2 received a wearable activity tracker and a copy of the Canadian PA guidelines, and group 3 (control condition) received a copy of the Canadian PA guidelines. Self-reported data for walking and moderate to vigorous intensity PA were collected preintervention (week 0; prerandomization), postintervention (7 weeks postrandomization), and at follow-up (21 weeks postrandomization) and analyzed as metabolic equivalent of task minutes per week. In addition, potential mechanisms of behavior change (ie, basic psychological needs satisfaction and motivational regulations) were assessed for within- and between-group differences at all 3 time points. Data were analyzed using nonparametric statistical tests. Results: A total of 49 women were recruited; data from 47 women (mean age 37.57 years, SD 11.78 years; mean BMI 31.69 kg/m2, SD 5.97 kg/m2) were available for analysis. Group 1 reported a significant increase in walking from preintervention to postintervention (χ22=7.5; P=.02) but not in moderate to vigorous intensity PA (P=.24). Group 1 also reported significant increases in perceptions of competence from preintervention to follow-up (χ22=7.6; P=.02) and relatedness from preintervention to follow-up (χ22=8.7; P=.005). Increases in perceived autonomy were observed for group 2 (χ22=7.0) and group 3 (χ22=10.6). There were no significant changes in the motivational regulations within the groups. The difference between the groups was not significant for any outcome variable. Conclusions: The results suggest that adding behavioral support emails to a wearable activity tracker intervention may help to increase time spent walking and perceptions of competence and relatedness for PA among women who are overweight or obese. Trial Registration: ClinicalTrials.gov NCT03601663; http://clinicaltrials.gov/ct2/show/NCT03601663 %M 34927590 %R 10.2196/28128 %U https://mhealth.jmir.org/2021/12/e28128 %U https://doi.org/10.2196/28128 %U http://www.ncbi.nlm.nih.gov/pubmed/34927590 %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 %@ 1929-0748 %I JMIR Publications %V 10 %N 12 %P e32842 %T An App-Based Just-in-Time Adaptive Self-management Intervention for Care Partners (CareQOL): Protocol for a Pilot Trial %A Carlozzi,Noelle E %A Choi,Sung Won %A Wu,Zhenke %A Miner,Jennifer A %A Lyden,Angela K %A Graves,Christopher %A Wang,Jitao %A Sen,Srijan %+ Department of Physical Medicine and Rehabilitation, University of Michigan, 2800 Plymouth Rd, Ann Arbor, MI, 48109, United States, 1 7347638917, carlozzi@med.umich.edu %K caregivers %K quality of life %K spinal cord injuries %K Huntington disease %K hematopoietic stem cell transplantation %K feasibility studies %K self-management %K mobile apps %K outcome assessment %K mobile phone %D 2021 %7 9.12.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Care partners (ie, informal family caregivers) of individuals with health problems face considerable physical and emotional stress, often with a substantial negative impact on the health-related quality of life (HRQOL) of both care partners and care recipients. Given that these individuals are often overwhelmed by their caregiving responsibilities, low-burden self-management interventions are needed to support care partners to ensure better patient outcomes. Objective: The primary objective of this study is to describe an intensive data collection protocol that involves the delivery of a personalized just-in-time adaptive intervention that incorporates passive mobile sensor data feedback (sleep and activity data from a Fitbit [Fitbit LLC]) and real time self-reporting of HRQOL via a study-specific app called CareQOL (University of Michigan) to provide personalized feedback via app alerts. Methods: Participants from 3 diverse care partner groups will be enrolled (care partners of persons with spinal cord injury, care partners of persons with Huntington disease, and care partners of persons with hematopoietic cell transplantation). Participants will be randomized to either a control group, where they will wear the Fitbit and provide daily reports of HRQOL over a 3-month (ie, 90 days) period (without personalized feedback), or the just-in-time adaptive intervention group, where they will wear the Fitbit, provide daily reports of HRQOL, and receive personalized push notifications for 3 months. At the end of the study, participants will complete a feasibility and acceptability questionnaire, and metrics regarding adherence and attrition will be calculated. Results: This trial opened for recruitment in November 2020. Data collection was completed in June 2021, and the primary results are expected to be published in 2022. Conclusions: This trial will determine the feasibility and acceptability of an intensive app-based intervention in 3 distinct care partner groups: care partners for persons with a chronic condition that was caused by a traumatic event (ie, spinal cord injury); care partners for persons with a progressive, fatal neurodegenerative disease (ie, Huntington disease); and care partners for persons with episodic cancer conditions that require intense, prolonged inpatient and outpatient treatment (persons with hematopoietic cell transplantation). Trial Registration: ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591 International Registered Report Identifier (IRRID): DERR1-10.2196/32842 %M 34889775 %R 10.2196/32842 %U https://www.researchprotocols.org/2021/12/e32842 %U https://doi.org/10.2196/32842 %U http://www.ncbi.nlm.nih.gov/pubmed/34889775 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 12 %P e32587 %T Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study) %A Stewart,Callum %A Ranjan,Yatharth %A Conde,Pauline %A Rashid,Zulqarnain %A Sankesara,Heet %A Bai,Xi %A Dobson,Richard J B %A Folarin,Amos A %+ Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom, 44 20 7848 0924, amos.folarin@kcl.ac.uk %K mobile health %K COVID-19 %K digital health %K smartphone %K wearable devices %K mental health %K wearable %K data %K crowdsourced %K monitoring %K surveillance %K observational %K feasibility %K infectious disease %K recovery %K mobile phone %D 2021 %7 8.12.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people’s health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the COVID-19 pandemic. Objective: Covid Collab is a crowdsourced study that was set up to investigate the feasibility of identifying, monitoring, and understanding the stratification of SARS-CoV-2 infection and recovery through remote monitoring technologies. Additionally, we will assess the impacts of the COVID-19 pandemic and associated social measures on people’s behavior, physical health, and mental well-being. Methods: Participants will remotely enroll in the study through the Mass Science app to donate historic and prospective mobile phone data, fitness tracking wearable data, and regular COVID-19–related and mental health–related survey data. The data collection period will cover a continuous period (ie, both before and after any reported infections), so that comparisons to a participant’s own baseline can be made. We plan to carry out analyses in several areas, which will cover symptomatology; risk factors; the machine learning–based classification of illness; and trajectories of recovery, mental well-being, and activity. Results: As of June 2021, there are over 17,000 participants—largely from the United Kingdom—and enrollment is ongoing. Conclusions: This paper introduces a crowdsourced study that will include remotely enrolled participants to record mobile health data throughout the COVID-19 pandemic. The data collected may help researchers investigate a variety of areas, including COVID-19 progression; mental well-being during the pandemic; and the adherence of remote, digitally enrolled participants. International Registered Report Identifier (IRRID): DERR1-10.2196/32587 %M 34784292 %R 10.2196/32587 %U https://www.researchprotocols.org/2021/12/e32587 %U https://doi.org/10.2196/32587 %U http://www.ncbi.nlm.nih.gov/pubmed/34784292 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e29167 %T Gender-Specific Impact of Self-Monitoring and Social Norm Information on Walking Behavior Among Chinese College Students Assessed Using WeChat: Longitudinal Tracking Study %A Xu,Yuepei %A Yue,Ling-Zi %A Wang,Wei %A Wu,Xiao-Ju %A Liang,Zhu-Yuan %+ CAS Key Laboratory of Behavioral Science, Institute of Psychology, 16 Lincui Road, Chaoyang District, Beijing, 100101, China, 86 10 64841536, liangzy@psych.ac.cn %K self-monitoring %K social norm %K group identity %K gender differences %K mHealth %K mobile health %D 2021 %7 7.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Walking is a simple but beneficial form of physical activity (PA). Self-monitoring and providing information about social norms are the 2 most widely used “mobile health (mHealth)” strategies to promote walking behavior. However, previous studies have failed to discriminate the effect of self-monitoring from the combination of the 2 strategies, and provide practical evidence within Chinese culture. Some essential moderators, such as gender and group identity, were also overlooked. Objective: We aimed to investigate the effectiveness of social norm and self-monitoring interventions for walking behavior and assess the moderating effects of gender and group identity, which could guide optimal mHealth intervention projects in China. Methods: In 2 longitudinal tracking studies (study 1, 22 days; study 2, 31 days), Chinese college students wore trackers for at least 8 hours per day (MASAI 3D Pedometer and Xiaomi Wristband 2) to record their daily step counts in baseline, intervention, and follow-up stages. In each study, participants (study 1: n=117, 54% female, mean age 25.60 years; study 2: n=180, 51% female, mean age 22.60 years) were randomly allocated to 1 of the following 3 groups: a self-monitoring group and 2 social norm intervention groups. In the 2 intervention groups and during the intervention stage, participants received different social norm information regarding group member step rankings corresponding to their grouping type of social norm information. In study 1, participants were grouped by within-group member PA levels (PA consistent vs PA inconsistent), and in study 2, participants were grouped by their received gender-specific social norm information (gender consistent vs gender inconsistent). Piece-wise linear mixed models were used to compare the difference in walking steps between groups. Results: In study 1, for males in the self-monitoring group, walking steps significantly decreased from the baseline stage to the intervention stage (change in slope=−1422.16; P=.02). However, additional social norm information regardless of group consistency kept their walking unchanged. For females, social norm information did not provide any extra benefit beyond self-monitoring. Females exposed to PA-inconsistent social norm information even walked less (slope during the intervention=−122.18; P=.03). In study 2, for males, a similar pattern was observed, with a decrease in walking steps in the self-monitoring group (change in slope=−151.33; P=.08), but there was no decrease in the 2 social norm intervention groups. However, for females, gender-consistent social norm information decreased walking steps (slope during the intervention=−143.68; P=.03). Conclusions: Both gender and group identity moderated the effect of social norm information on walking. Among females, social norm information showed no benefit for walking behavior and may have exerted a backfire effect. Among males, while walking behavior decreased with self-monitoring only, the inclusion of social norm information held the level of walking behavior steady. %M 34878992 %R 10.2196/29167 %U https://www.jmir.org/2021/12/e29167 %U https://doi.org/10.2196/29167 %U http://www.ncbi.nlm.nih.gov/pubmed/34878992 %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 11 %P e28577 %T Feasibility, Usability, and Effectiveness of a Machine Learning–Based Physical Activity Chatbot: Quasi-Experimental Study %A To,Quyen G %A Green,Chelsea %A Vandelanotte,Corneel %+ Physical Activity Research Group, Appleton Institute, Central Queensland University, 554-700 Yaamba Rd, Norman Gardens, Rockhampton, 4701, Australia, 61 7 4930 6456, q.to@cqu.edu.au %K conversational agent %K virtual coach %K intervention %K exercise %K acceptability %K mobile phone %D 2021 %7 26.11.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Behavioral eHealth and mobile health interventions have been moderately successful in increasing physical activity, although opportunities for further improvement remain to be discussed. Chatbots equipped with natural language processing can interact and engage with users and help continuously monitor physical activity by using data from wearable sensors and smartphones. However, a limited number of studies have evaluated the effectiveness of chatbot interventions on physical activity. Objective: This study aims to investigate the feasibility, usability, and effectiveness of a machine learning–based physical activity chatbot. Methods: A quasi-experimental design without a control group was conducted with outcomes evaluated at baseline and 6 weeks. Participants wore a Fitbit Flex 1 (Fitbit LLC) and connected to the chatbot via the Messenger app. The chatbot provided daily updates on the physical activity level for self-monitoring, sent out daily motivational messages in relation to goal achievement, and automatically adjusted the daily goals based on physical activity levels in the last 7 days. When requested by the participants, the chatbot also provided sources of information on the benefits of physical activity, sent general motivational messages, and checked participants’ activity history (ie, the step counts/min that were achieved on any day). Information about usability and acceptability was self-reported. The main outcomes were daily step counts recorded by the Fitbit and self-reported physical activity. Results: Among 116 participants, 95 (81.9%) were female, 85 (73.3%) were in a relationship, 101 (87.1%) were White, and 82 (70.7%) were full-time workers. Their average age was 49.1 (SD 9.3) years with an average BMI of 32.5 (SD 8.0) kg/m2. Most experienced technical issues were due to an unexpected change in Facebook policy (93/113, 82.3%). Most of the participants scored the usability of the chatbot (101/113, 89.4%) and the Fitbit (99/113, 87.6%) as at least “OK.” About one-third (40/113, 35.4%) would continue to use the chatbot in the future, and 53.1% (60/113) agreed that the chatbot helped them become more active. On average, 6.7 (SD 7.0) messages/week were sent to the chatbot and 5.1 (SD 7.4) min/day were spent using the chatbot. At follow-up, participants recorded more steps (increase of 627, 95% CI 219-1035 steps/day) and total physical activity (increase of 154.2 min/week; 3.58 times higher at follow-up; 95% CI 2.28-5.63). Participants were also more likely to meet the physical activity guidelines (odds ratio 6.37, 95% CI 3.31-12.27) at follow-up. Conclusions: The machine learning–based physical activity chatbot was able to significantly increase participants’ physical activity and was moderately accepted by the participants. However, the Facebook policy change undermined the chatbot functionality and indicated the need to use independent platforms for chatbot deployment to ensure successful delivery of this type of intervention. %M 34842552 %R 10.2196/28577 %U https://mhealth.jmir.org/2021/11/e28577 %U https://doi.org/10.2196/28577 %U http://www.ncbi.nlm.nih.gov/pubmed/34842552 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 7 %N 4 %P e22931 %T Remote Monitoring of the Performance Status and Burden of Symptoms of Patients With Gastrointestinal Cancer Via a Consumer-Based Activity Tracker: Quantitative Cohort Study %A Ghods,Alireza %A Shahrokni,Armin %A Ghasemzadeh,Hassan %A Cook,Diane %+ Geriatrics / Gastrointestinal Oncology Service, Memorial Sloan-Kettering Cancer Center, Box 205, 1275 York Ave, New York, NY, 10065, United States, 1 646 888 3250, shahroka@mskcc.org %K step count %K performance status %K symptom %K wearable %K activity tracker %K gastrointestinal cancer %K monitoring %K cancer %K gastrointestinal %K burden %D 2021 %7 26.11.2021 %9 Original Paper %J JMIR Cancer %G English %X Background: The number of older patients with gastrointestinal cancer is increasing due to an aging global population. Minimizing reliance on an in-clinic patient performance status test to determine a patient’s prognosis and course of treatment can improve resource utilization. Further, current performance status measurements cannot capture patients' constant changes. These measurements also rely on self-reports, which are subjective and subject to bias. Real-time monitoring of patients' activities may allow for a more accurate assessment of patients’ performance status while minimizing resource utilization. Objective: This study investigates the validity of consumer-based activity trackers for monitoring the performance status of patients with gastrointestinal cancer. Methods: A total of 27 consenting patients (63% male, median age 58 years) wore a consumer-based activity tracker 7 days before chemotherapy and 14 days after receiving their first treatment. The provider assessed patients using the Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scale and Memorial Symptom Assessment Scale-Short Form (MSAS-SF) before and after chemotherapy visits. The statistical correlations between ECOG-PS and MSAS-SF scores and patients’ daily step counts were assessed. Results: The daily step counts yielded the highest correlation with the patients' ECOG-PS scores after chemotherapy (P<.001). The patients with higher ECOG-PS scores experienced a higher fluctuation in their step counts. The patients who walked more prechemotherapy (mean 6071 steps per day) and postchemotherapy (mean 5930 steps per day) had a lower MSAS-SF score (lower burden of symptoms) compared to patients who walked less prechemotherapy (mean 5205 steps per day) and postchemotherapy (mean 4437 steps per day). Conclusions: This study demonstrates the feasibility of using inexpensive, consumer-based activity trackers for the remote monitoring of performance status in the gastrointestinal cancer population. The findings need to be validated in a larger population for generalizability. %M 34842527 %R 10.2196/22931 %U https://cancer.jmir.org/2021/4/e22931 %U https://doi.org/10.2196/22931 %U http://www.ncbi.nlm.nih.gov/pubmed/34842527 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 11 %P e29758 %T Using Methods From Computational Decision-making to Predict Nonadherence to Fitness Goals: Protocol for an Observational Study %A McCarthy,Marie %A Zhang,Lili %A Monacelli,Greta %A Ward,Tomas %+ Insight Centre For Data Analytics, Dublin City University, Collins Ave Ext, Whitehall, Dublin, D9, Ireland, 353 12912500, marie.mccarthy65@mail.dcu.ie %K decision-making games %K computational psychology %K fitness goals %K advanced analytics %K mobile app %K computational modeling %K fitness tracker %K mobile phone %D 2021 %7 26.11.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Can methods from computational models of decision-making be used to build a predictive model to identify individuals most likely to be nonadherent to personal fitness goals? Such a model may have significant value in the global battle against obesity. Despite growing awareness of the impact of physical inactivity on human health, sedentary behavior is increasingly linked to premature death in the developed world. The annual impact of sedentary behavior is significant, causing an estimated 2 million deaths. From a global perspective, sedentary behavior is one of the 10 leading causes of mortality and morbidity. Annually, considerable funding and countless public health initiatives are applied to promote physical fitness, with little impact on sustained behavioral change. Predictive models developed from multimodal methodologies combining data from decision-making tasks with contextual insights and objective physical activity data could be used to identify those most likely to abandon their fitness goals. This has the potential to enable development of more targeted support to ensure that those who embark on fitness programs are successful. Objective: The aim of this study is to determine whether it is possible to use decision-making tasks such as the Iowa Gambling Task to help determine those most likely to abandon their fitness goals. Predictive models built using methods from computational models of decision-making, combining objective data from a fitness tracker with personality traits and modeling from decision-making games delivered via a mobile app, will be used to ascertain whether a predictive algorithm can identify digital personae most likely to be nonadherent to self-determined exercise goals. If it is possible to phenotype these individuals, it may be possible to tailor initiatives to support these individuals to continue exercising. Methods: This is a siteless study design based on a bring your own device model. A total of 200 healthy adults who are novice exercisers and own a Fitbit (Fitbit Inc) physical activity tracker will be recruited via social media for this study. Participants will provide consent via the study app, which they will download from the Google Play store (Alphabet Inc) or Apple App Store (Apple Inc). They will also provide consent to share their Fitbit data. Necessary demographic information concerning age and sex will be collected as part of the recruitment process. Over 12 months, the scheduled study assessments will be pushed to the subjects to complete. The Iowa Gambling Task will be administered via a web app shared via a URL. Results: Ethics approval was received from Dublin City University in December 2020. At manuscript submission, study recruitment was pending. The expected results will be published in 2022. Conclusions: It is hoped that the study results will support the development of a predictive model and the study design will inform future research approaches. Trial Registration: ClinicalTrials.gov NCT04783298; https://clinicaltrials.gov/ct2/show/NCT04783298 %M 34842557 %R 10.2196/29758 %U https://www.researchprotocols.org/2021/11/e29758 %U https://doi.org/10.2196/29758 %U http://www.ncbi.nlm.nih.gov/pubmed/34842557 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 11 %P e23359 %T Objective Measurements of Physical Activity and Sedentary Behavior Using Wearable Devices in Patients With Axial Spondyloarthritis: Protocol for a Systematic Review %A Carlin,Thomas %A Soulard,Julie %A Aubourg,Timothée %A Knitza,Johannes %A Vuillerme,Nicolas %+ AGEIS, Université Grenoble Alpes, Faculty of Medicine, La Tronche, 38706, France, 33 476637104, nicolas.vuillerme@univ-grenoble-alpes.fr %K axial spondyloarthritis %K rheumatology %K physical activity %K sedentary behavior %K objective measures %K wearable %K systematic review %D 2021 %7 25.11.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Axial spondyloarthritis (axSpA) is a subgroup of inflammatory rheumatic diseases. Practicing regular exercise is critical to manage pain and stiffness, reduce disease activity, and improve physical functioning, spinal mobility, and cardiorespiratory function. Accordingly, monitoring physical activity and sedentary behavior in patients with axSpA is relevant for clinical outcomes and disease management. Objective: This review aims to determine which wearable devices, assessment methods, and associated metrics are commonly used to quantify physical activity or sedentary behavior in patients with axSpA. Methods: The PubMed, Physiotherapy Evidence Database (PEDro), and Cochrane electronic databases will be searched, with no limit on publication date, to identify all the studies matching the inclusion criteria. Only original English-language articles published in a peer-reviewed journal will be included. The search strategy will include a combination of keywords related to the study population, wearable devices, physical activity, and sedentary behavior. We will use the Boolean operators “AND” and “OR” to combine keywords as well as Medical Subject Headings terms. Results: Search strategy was completed in June 2020 with 23 records obtained. Data extraction and synthesis are currently ongoing. Dissemination of study results in peer-reviewed journals is expected at the end of 2021. Conclusions: This review will provide a comprehensive and detailed synthesis of published studies that examine the use of wearable devices for objective assessment of physical activity and sedentary behavior in patients with axSpA. Trial Registration: PROSPERO CRD42020182398; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=182398 International Registered Report Identifier (IRRID): PRR1-10.2196/23359 %M 34842559 %R 10.2196/23359 %U https://www.researchprotocols.org/2021/11/e23359 %U https://doi.org/10.2196/23359 %U http://www.ncbi.nlm.nih.gov/pubmed/34842559 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 11 %P e28317 %T Impact of the COVID-19 Pandemic on Objectively Measured Physical Activity and Sedentary Behavior Among Overweight Young Adults: Yearlong Longitudinal Analysis %A Lawhun Costello,Victoria %A Chevance,Guillaume %A Wing,David %A Mansour-Assi,Shadia J %A Sharp,Sydney %A Golaszewski,Natalie M %A Young,Elizabeth A %A Higgins,Michael %A Ibarra,Anahi %A Larsen,Britta %A Godino,Job G %+ Center for Wireless and Population Health Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, United States, 1 8582463302, jobg@fhcsd.org %K COVID-19 %K young adults %K physical activity %K sedentary behavior %K activity monitor %K public health %K wearable %K activity monitors %K wrist worn %K sedentary %K lifestyle %K pandemic %D 2021 %7 24.11.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The COVID-19 pandemic has impacted multiple aspects of daily living, including behaviors associated with occupation, transportation, and health. It is unclear how these changes to daily living have impacted physical activity and sedentary behavior. Objective: In this study, we add to the growing body of research on the health impact of the COVID-19 pandemic by examining longitudinal changes in objectively measured daily physical activity and sedentary behavior among overweight or obese young adults participating in an ongoing weight loss trial in San Diego, California. Methods: Data were collected from 315 overweight or obese (BMI: range 25.0-39.9 kg/m2) participants aged from 18 to 35 years between November 1, 2019, and October 30, 2020, by using the Fitbit Charge 3 (Fitbit LLC). After conducting strict filtering to find valid data on consistent wear (>10 hours per day for ≥250 days), data from 97 participants were analyzed to detect multiple structural changes in time series of physical activity and sedentary behavior. An algorithm was designed to detect multiple structural changes. This allowed for the automatic identification and dating of these changes in linear regression models with CIs. The number of breakpoints in regression models was estimated by using the Bayesian information criterion and residual sum of squares; the optimal segmentation corresponded to the lowest Bayesian information criterion and residual sum of squares. To quantify the changes in each outcome during the periods identified, linear mixed effects analyses were conducted. In terms of key demographic characteristics, the 97 participants included in our analyses did not differ from the 210 participants who were excluded. Results: After the initiation of the shelter-in-place order in California on March 19, 2021, there were significant decreases in step counts (−2872 steps per day; 95% CI −2734 to −3010), light physical activity times (−41.9 minutes; 95% CI −39.5 to −44.3), and moderate-to-vigorous physical activity times (−12.2 minutes; 95% CI −10.6 to −13.8), as well as significant increases in sedentary behavior times (+52.8 minutes; 95% CI 47.0-58.5). The decreases were greater than the expected declines observed during winter holidays, and as of October 30, 2020, they have not returned to the levels observed prior to the initiation of shelter-in-place orders. Conclusions: Among overweight or obese young adults, physical activity times decreased and sedentary behavior times increased concurrently with the implementation of COVID-19 mitigation strategies. The health conditions associated with a sedentary lifestyle may be additional, unintended results of the COVID-19 pandemic. %M 34665759 %R 10.2196/28317 %U https://publichealth.jmir.org/2021/11/e28317 %U https://doi.org/10.2196/28317 %U http://www.ncbi.nlm.nih.gov/pubmed/34665759 %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 %@ 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 %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e23059 %T Fitness Tracker Information and Privacy Management: Empirical Study %A Abdelhamid,Mohamed %+ Department of Information Systems, California State University, Long Beach, 1250 N Bellflower Blvd, Long Beach, CA, 90840, United States, 1 5629852361, mohamed.abdelhamid@csulb.edu %K privacy %K information sharing %K fitness trackers %K wearable devices %D 2021 %7 16.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Fitness trackers allow users to collect, manage, track, and monitor fitness-related activities, such as distance walked, calorie intake, sleep quality, and heart rate. Fitness trackers have become increasingly popular in the past decade. One in five Americans use a device or an app to track their fitness-related activities. These devices generate massive and important data that could help physicians make better assessments of their patients’ health if shared with health providers. This ultimately could lead to better health outcomes and perhaps even lower costs for patients. However, sharing personal fitness information with health care providers has drawbacks, mainly related to the risk of privacy loss and information misuse. Objective: This study investigates the influence of granting users granular privacy control on their willingness to share fitness information. Methods: The study used 270 valid responses collected from Mtrurkers through Amazon Mechanical Turk (MTurk). Participants were randomly assigned to one of two groups. The conceptual model was tested using structural equation modeling (SEM). The dependent variable was the intention to share fitness information. The independent variables were perceived risk, perceived benefits, and trust in the system. Results: SEM explained about 60% of the variance in the dependent variable. Three of the four hypotheses were supported. Perceived risk and trust in the system had a significant relationship with the dependent variable, while trust in the system was not significant. Conclusions: The findings show that people are willing to share their fitness information if they have granular privacy control. This study has practical and theoretical implications. It integrates communication privacy management (CPM) theory with the privacy calculus model. %M 34783672 %R 10.2196/23059 %U https://www.jmir.org/2021/11/e23059 %U https://doi.org/10.2196/23059 %U http://www.ncbi.nlm.nih.gov/pubmed/34783672 %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 %@ 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 %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e28634 %T Home-Based Exercise Program for Patients With Combined Advanced Chronic Cardiac and Pulmonary Diseases: Exploratory Study %A Herkert,Cyrille %A Graat-Verboom,Lidwien %A Gilsing-Fernhout,Judith %A Schols,Manon %A Kemps,Hareld Marijn Clemens %+ Department of Cardiology, Máxima Medical Center, Dominee Theodor Fliednerstraat 1, Eindhoven, 5631 BM, Netherlands, 31 408888220, cyrille.herkert@mmc.nl %K home-based exercise %K cardiac diseases %K pulmonary diseases %K comorbidities %K elderly %D 2021 %7 9.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: As chronic cardiac and pulmonary diseases often coexist, there is a need for combined physical home-based rehabilitation programs, specifically addressing older patients with advanced disease stages. Objective: The primary aim of this study is to evaluate the completion and adherence rates of an 8-week, home-based exercise program for patients with advanced cardiopulmonary disease. The secondary end points include patient satisfaction; adverse events; and program efficacy in terms of change in functional capacity, level of dyspnea, and health-related quality of life. Methods: The participants received a goal-oriented, home-based exercise program, and they used a wrist-worn activity tracker to record their exercise sessions. Activity tracker data were made visible on a digital platform, which was also equipped with several other features such as short instruction videos on how to perform specific exercises. The participants received weekly coaching by a physiotherapist and an occupational therapist through video communication. Results: In all, 10 patients with advanced combined cardiopulmonary disease participated (median age 71, IQR 63-75 years), and 50% (5/10) were men. Of the 10 participants, 9 (90%) completed the 8-week program. Median adherence to the exercise prescription was 75% (IQR 37%-88%), but it declined significantly when the program was divided into 2-week periods (first 2 weeks: 86%, IQR 51%-100%, and final 2 weeks: 57%, IQR 8%-75%; P=.03). The participants were highly satisfied with the program (Client Satisfaction Questionnaire: median score 29, IQR 26-32, and Purpose-Designed Questionnaire: median score 103, IQR 92-108); however, of the 9 participants, 4 (44%) experienced technical issues. The Patient-Specific Complaints Instrument scores declined, indicating functional improvement (from median 7.5, IQR 6.1-8.9, to median 5.7, IQR 3.8-6.7; P=.01). Other program efficacy metrics showed a trend toward improvement. Conclusions: Home-based cardiopulmonary telerehabilitation for patients with severe combined cardiopulmonary disease is feasible in terms of high completion and satisfaction rates. Nevertheless, a decrease in adherence during the program was observed, and some of the participants reported difficulties with the technology, indicating the importance of the integration of behavior change techniques, using appropriate technology. Trial Registration: Netherlands Trial Register NL9182; https://www.trialregister.nl/trial/9182 %M 34751655 %R 10.2196/28634 %U https://formative.jmir.org/2021/11/e28634 %U https://doi.org/10.2196/28634 %U http://www.ncbi.nlm.nih.gov/pubmed/34751655 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 9 %N 4 %P e29044 %T Feasibility of a Sensor-Controlled Digital Game for Heart Failure Self-management: Randomized Controlled Trial %A Radhakrishnan,Kavita %A Julien,Christine %A Baranowski,Tom %A O'Hair,Matthew %A Lee,Grace %A Sagna De Main,Atami %A Allen,Catherine %A Viswanathan,Bindu %A Thomaz,Edison %A Kim,Miyong %+ School of Nursing, The University of Texas Austin, 1710 Red River St, Austin, TX, 78701, United States, 1 512 471 7936, Kradhakrishnan@mail.nur.utexas.edu %K heart failure %K digital game %K sensor %K self-management %K older adults %K weight monitoring %K physical activity %K behaviors %K mobile phone %D 2021 %7 8.11.2021 %9 Original Paper %J JMIR Serious Games %G English %X Background: Poor self-management of heart failure (HF) contributes to devastating health consequences. Our innovative sensor-controlled digital game (SCDG) integrates data from sensors to trigger game rewards, progress, and feedback based on the real-time behaviors of individuals with HF. Objective: The aim of this study is to compare daily weight monitoring and physical activity behavior adherence by older adults using an SCDG intervention versus a sensors-only intervention in a feasibility randomized controlled trial. Methods: English-speaking adults with HF aged 55 years or older who owned a smartphone and could walk unassisted were recruited from Texas and Oklahoma from November 2019 to August 2020. Both groups were given activity trackers and smart weighing scales to track behaviors for 12 weeks. The feasibility outcomes of recruitment, retention, intervention engagement, and satisfaction were assessed. In addition to daily weight monitoring and physical activity adherence, the participants’ knowledge, functional status, quality of life, self-reported HF behaviors, motivation to engage in behaviors, and HF-related hospitalization were also compared between the groups at baseline and at 6, 12, and 24 weeks. Results: A total of 38 participants with HF—intervention group (IG; 19/38, 50%) and control group (CG; 19/38, 50%)—were enrolled in the study. Of the 38 participants, 18 (47%) were women, 18 (47%) were aged 65 years or older, 21 (55%) had been hospitalized with HF in the past 6 months, and 29 (76%) were White. Furthermore, of these 38 participants, 31 (82%)—IG (15/19, 79%) and CG (16/19, 84%)—had both weight monitoring and physical activity data at the end of 12 weeks, and 27 (71%)—IG (14/19, 74%) and CG (13/19, 68%)—participated in follow-up assessments at 24 weeks. For the IG participants who installed the SCDG app (15/19, 79%), the number of days each player opened the game app was strongly associated with the number of days the player engaged in weight monitoring (r=0.72; P=.04) and the number of days with physical activity step data (r=0.9; P<.001). The IG participants who completed the satisfaction survey (13/19, 68%) reported that the SCDG was easy to use. Trends of improvement in daily weight monitoring and physical activity in the IG, as well as within-group improvements in HF functional status, quality of life, knowledge, self-efficacy, and HF hospitalization in both groups, were observed in this feasibility trial. Conclusions: Playing an SCDG on smartphones was feasible and acceptable for older adults with HF for motivating daily weight monitoring and physical activity. A larger efficacy trial of the SCDG intervention will be needed to validate trends of improvement in daily weight monitoring and physical activity behaviors. Trial Registration: ClinicalTrials.gov NCT03947983; https://clinicaltrials.gov/ct2/show/NCT03947983 %M 34747701 %R 10.2196/29044 %U https://games.jmir.org/2021/4/e29044 %U https://doi.org/10.2196/29044 %U http://www.ncbi.nlm.nih.gov/pubmed/34747701 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 4 %P e29788 %T Clinicians and Older Adults’ Perceptions of the Utility of Patient-Generated Health Data in Caring for Older Adults: Exploratory Mixed Methods Study %A Kim,Ben %A Ghasemi,Peyman %A Stolee,Paul %A Lee,Joon %+ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada, 1 403 220 2968, joonwu.lee@ucalgary.ca %K mobile health %K mHealth %K older adults %K wearables %K patient generated health data %K chronic disease management %K home care %K self-care %K activities of daily living %K sleep %D 2021 %7 5.11.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Many people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults’ perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults. Objective: This study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults. Methods: A mixed methods study was conducted to explore clinicians and older adults’ perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics. Results: Of the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (r=0.42; P=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care. Conclusions: This embedded mixed methods study generated several important findings about older adults and clinicians’ perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults. %M 34738913 %R 10.2196/29788 %U https://aging.jmir.org/2021/4/e29788 %U https://doi.org/10.2196/29788 %U http://www.ncbi.nlm.nih.gov/pubmed/34738913 %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 %@ 2291-5222 %I JMIR Publications %V 9 %N 10 %P e24872 %T Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling %A Rykov,Yuri %A Thach,Thuan-Quoc %A Bojic,Iva %A Christopoulos,George %A Car,Josip %+ Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Rd, Clinical Sciences Building, Level 18, Singapore, 308232, Singapore, 65 +85291725838, josip.car@gmail.com %K depression %K digital biomarkers %K screening %K wearable electronic device %K fitness tracker %K circadian rhythm %K rest-activity rhythm %K heart rate %K machine learning %D 2021 %7 25.10.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. Objective: The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. Methods: This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. Results: A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. Conclusions: Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk. %M 34694233 %R 10.2196/24872 %U https://mhealth.jmir.org/2021/10/e24872 %U https://doi.org/10.2196/24872 %U http://www.ncbi.nlm.nih.gov/pubmed/34694233 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 10 %P e30901 %T Function and Emotion in Everyday Life With Type 1 Diabetes (FEEL-T1D): Protocol for a Fully Remote Intensive Longitudinal Study %A Pyatak,Elizabeth Ann %A Hernandez,Raymond %A Pham,Loree T %A Mehdiyeva,Khatira %A Schneider,Stefan %A Peters,Anne %A Ruelas,Valerie %A Crandall,Jill %A Lee,Pey-Jiuan %A Jin,Haomiao %A Hoogendoorn,Claire J %A Crespo-Ramos,Gladys %A Mendez-Rodriguez,Heidy %A Harmel,Mark %A Walker,Martha %A Serafin-Dokhan,Sara %A Gonzalez,Jeffrey S %A Spruijt-Metz,Donna %+ Chan Division of Occupational Science and Occupational Therapy, University of Southern California, 1540 Alcazar St, CHP-133, Los Angeles, CA, 90089-9003, United States, 1 3107741228, beth.pyatak@usc.edu %K ecological momentary assessments %K type 1 diabetes %K patient-centered outcomes research %K actigraphy %K ambulatory monitoring %K continuous glucose monitoring %K EMA %K diabetes %K patient-centered outcome %K outcome %K monitoring %K function %K emotion %K longitudinal %K well-being %D 2021 %7 22.10.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Although short-term blood glucose levels and variability are thought to underlie diminished function and emotional well-being in people with type 1 diabetes (T1D), these relationships are poorly understood. The Function and Emotion in Everyday Life with T1D (FEEL-T1D) study focuses on investigating these short-term dynamic relationships among blood glucose levels, functional ability, and emotional well-being in adults with T1D. Objective: The aim of this study is to present the FEEL-T1D study design, methods, and study progress to date, including adaptations necessitated by the COVID-19 pandemic to implement the study fully remotely. Methods: The FEEL-T1D study will recruit 200 adults with T1D in the age range of 18-75 years. Data collection includes a comprehensive survey battery, along with 14 days of intensive longitudinal data using blinded continuous glucose monitoring, ecological momentary assessments, ambulatory cognitive tasks, and accelerometers. All study procedures are conducted remotely by mailing the study equipment and by using videoconferencing for study visits. Results: The study received institutional review board approval in January 2019 and was funded in April 2019. Data collection began in June 2020 and is projected to end in December 2021. As of June 2021, after 12 months of recruitment, 124 participants have enrolled in the FEEL-T1D study. Approximately 87.6% (7082/8087) of ecological momentary assessment surveys have been completed with minimal missing data, and 82.0% (82/100) of the participants provided concurrent continuous glucose monitoring data, ecological momentary assessment data, and accelerometer data for at least 10 of the 14 days of data collection. Conclusions: Thus far, our reconfiguration of the FEEL-T1D protocol to be implemented remotely during the COVID-19 pandemic has been a success. The FEEL-T1D study will elucidate the dynamic relationships among blood glucose levels, emotional well-being, cognitive function, and participation in daily activities. In doing so, it will pave the way for innovative just-in-time interventions and produce actionable insights to facilitate tailoring of diabetes treatments to optimize the function and well-being of individuals with T1D. International Registered Report Identifier (IRRID): DERR1-10.2196/30901 %M 34463626 %R 10.2196/30901 %U https://www.researchprotocols.org/2021/10/e30901 %U https://doi.org/10.2196/30901 %U http://www.ncbi.nlm.nih.gov/pubmed/34463626 %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 %@ 2561-1011 %I JMIR Publications %V 5 %N 2 %P e27720 %T Clinic Time Required for Remote and In-Person Management of Patients With Cardiac Devices: Time and Motion Workflow Evaluation %A Seiler,Amber %A Biundo,Eliana %A Di Bacco,Marco %A Rosemas,Sarah %A Nicolle,Emmanuelle %A Lanctin,David %A Hennion,Juliette %A de Melis,Mirko %A Van Heel,Laura %+ Medtronic, 8200 Coral Sea Ct NE, Mounds View, MN, 55112, United States, 1 800 633 8766, david.lanctin@medtronic.com %K cardiac implantable electronic devices %K remote monitoring %K patient management %K clinic efficiency %K digital health %K mobile phone %D 2021 %7 15.10.2021 %9 Original Paper %J JMIR Cardio %G English %X Background: The number of patients with cardiac implantable electronic device (CIED) is increasing, creating a substantial workload for device clinics. Objective: This study aims to characterize the workflow and quantify clinic staff time requirements for managing patients with CIEDs. Methods: A time and motion workflow evaluation was performed in 11 US and European CIEDs clinics. Workflow tasks were repeatedly timed during 1 business week of observation at each clinic; these observations included all device models and manufacturers. The mean cumulative staff time required to review a remote device transmission and an in-person clinic visit were calculated, including all necessary clinical and administrative tasks. The annual staff time to manage a patient with a CIED was modeled using CIED transmission volumes, clinical guidelines, and the published literature. Results: A total of 276 in-person clinic visits and 2173 remote monitoring activities were observed. Mean staff time required per remote transmission ranged from 9.4 to 13.5 minutes for therapeutic devices (pacemaker, implantable cardioverter-defibrillator, and cardiac resynchronization therapy) and from 11.3 to 12.9 minutes for diagnostic devices such as insertable cardiac monitors (ICMs). Mean staff time per in-person visit ranged from 37.8 to 51.0 and from 39.9 to 45.8 minutes for therapeutic devices and ICMs, respectively. Including all remote and in-person follow-ups, the estimated annual time to manage a patient with a CIED ranged from 1.6 to 2.4 hours for therapeutic devices and from 7.7 to 9.3 hours for ICMs. Conclusions: The CIED patient management workflow is complex and requires significant staff time. Understanding process steps and time requirements informs the implementation of efficiency improvements, including remote solutions. Future research should examine heterogeneity in patient management processes to identify the most efficient workflow. %M 34156344 %R 10.2196/27720 %U https://cardio.jmir.org/2021/2/e27720 %U https://doi.org/10.2196/27720 %U http://www.ncbi.nlm.nih.gov/pubmed/34156344 %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 e23968 %T Need for Cognition Among Users of Self-Monitoring Systems for Physical Activity: Survey Study %A Halttu,Kirsi %A Oinas-Kukkonen,Harri %+ Oulu Advanced Research on Service and Information Systems Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, P.O. Box 3000, Oulu, FI-90014, Finland, 358 458601190, kirsi.halttu@oulu.fi %K self-monitoring %K wearables %K physical activity tracking %K mHealth %K need for cognition %K persuasive design %K tailoring %K user research %K mobile phone %D 2021 %7 14.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Need for cognition (NFC) is among the most studied personality traits in psychology. Despite its apparent relevance for engaging with technology and the use of information, it has not been studied in the context of self-monitoring systems and wearables for health. This study is the first to explore the relationship between NFC and commercial self-monitoring systems among healthy users. Objective: This study aims to explore the effect of NFC levels on the selection of self-monitoring systems and evaluation of system features of self-monitoring and feedback, as well as perceived credibility and perceived persuasiveness. We also assessed perceived behavior change in the form of self-reported activity after adopting the system. Methods: Survey data were collected in October 2019 among university students and personnel. The invitation to respond to the questionnaire was addressed to those who had used a digital system to monitor their physical activity for at least two months. The web-based questionnaire comprised the following 3 parts: details of system use, partially randomly ordered theoretical measurement items, and user demographics. The data were analyzed using structural equation modeling. The effect of NFC was assessed both as 3 groups (low, moderate, and high) and as a continuous moderator variable. Results: In all, 238 valid responses to the questionnaire were obtained. Individuals with high NFC reported all tested system features with statistically significantly higher scores. The NFC also had some effect on system selection. Hypothesized relationships with perceived credibility gained support in a different way for individuals with low and high NFC; for those with low NFC, credibility increased the persuasiveness of the system, but this effect was absent among individuals with high NFC. For users with high NFC, credibility was related to feedback and self-monitoring and perhaps continuously evaluated during prolonged use instead of being a static system property. Furthermore, the relationship between perceived persuasiveness and self-reported activity after adopting the system had a large effect size (Cohen f2=0.355) for individuals with high NFC, a small effect size for individuals with moderate NFC (Cohen f2=0.107), and a nonsignificant path (P=.16) for those with low NFC. We also detected a moderating effect of NFC in two paths on perceived persuasiveness but only among women. Our research model explained 59.2%, 63.9%, and 47.3% of the variance in perceived persuasiveness of the system among individuals with low, moderate, and high NFC, respectively. Conclusions: The system choices of individuals seem to reflect their intrinsic motivations to engage with rich data, and commercial systems might themselves be a tailoring strategy. Important characteristics of the system, such as perceived credibility, have different roles depending on the NFC levels. Our data demonstrate that NFC as a trait that differentiates information processing has several implications for the selection, design, and tailoring of self-monitoring systems. %M 34647894 %R 10.2196/23968 %U https://formative.jmir.org/2021/10/e23968 %U https://doi.org/10.2196/23968 %U http://www.ncbi.nlm.nih.gov/pubmed/34647894 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e25163 %T Digital Tracking of Physical Activity, Heart Rate, and Inhalation Behavior in Patients With Pulmonary Arterial Hypertension Treated With Inhaled Iloprost: Observational Study (VENTASTEP) %A Stollfuss,Barbara %A Richter,Manuel %A Drömann,Daniel %A Klose,Hans %A Schwaiblmair,Martin %A Gruenig,Ekkehard %A Ewert,Ralf %A Kirchner,Martin C %A Kleinjung,Frank %A Irrgang,Valeska %A Mueller,Christian %+ Bayer Vital GmbH, Building K 56, 1D321, Leverkusen, 51368, Germany, 49 2143046587, christian.mueller4@bayer.com %K 6-minute walk distance %K 6MWD %K Breelib %K daily physical activity %K digital monitoring %K health-related quality of life %K iloprost %K Ventavis %K inhalation behavior %K mobile phone %K pulmonary arterial hypertension %K PAH %K sleeping behavior %K behavior %K sleep %K monitoring %K physical activity %K heart %K cardiology %D 2021 %7 8.10.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Pulmonary arterial hypertension restricts the ability of patients to perform routine physical activities. As part of pulmonary arterial hypertension treatment, inhaled iloprost can be administered via a nebulizer that tracks inhalation behavior. Pulmonary arterial hypertension treatment is guided by intermittent clinical measurements, such as 6-minute walk distance, assessed during regular physician visits. Continuous digital monitoring of physical activity may facilitate more complete assessment of the impact of pulmonary arterial hypertension on daily life. Physical activity tracking with a wearable has not yet been assessed with simultaneous tracking of pulmonary arterial hypertension medication intake. Objective: We aimed to digitally track the physical parameters of patients with pulmonary arterial hypertension who were starting treatment with iloprost using a Breelib nebulizer. The primary objective was to investigate correlations between changes in digital physical activity measures and changes in traditional clinical measures and health-related quality of life over 3 months. Secondary objectives were to evaluate inhalation behavior, adverse events, and changes in heart rate and sleep quality. Methods: We conducted a prospective, multicenter observational study of adults with pulmonary arterial hypertension in World Health Organization functional class III who were adding inhaled iloprost to existing pulmonary arterial hypertension therapy. Daily distance walked, step count, number of standing-up events, heart rate, and 6-minute walk distance were digitally captured using smartwatch (Apple Watch Series 2) and smartphone (iPhone 6S) apps during a 3-month observation period (which began when iloprost treatment began). Before and at the end of the observation period (within 2 weeks), we also evaluated 6-minute walk distance, Borg dyspnea, functional class, B-type natriuretic peptide (or N-terminal pro–B-type natriuretic peptide) levels, health-related quality of life (EQ-5D questionnaire), and sleep quality (Pittsburgh Sleep Quality Index). Results: Of 31 patients, 18 were included in the full analysis (observation period: median 91.5 days, IQR 88.0 to 92.0). Changes from baseline in traditional and digital 6-minute walk distance were moderately correlated (r=0.57). Physical activity (daily distance walked: median 0.4 km, IQR –0.2 to 1.9; daily step count: median 591, IQR −509 to 2413) and clinical measures (traditional 6-minute walk distance: median 26 m, IQR 0 to 40) changed concordantly from baseline to the end of the observation period. Health-related quality of life showed little change. Total sleep score and resting heart rate slightly decreased. Distance walked and step count showed short-term increases after each iloprost inhalation. No new safety signals were identified (safety analysis set: n=30). Conclusions: Our results suggest that despite challenges, parallel monitoring of physical activity, heart rate, and iloprost inhalation is feasible in patients with pulmonary arterial hypertension and may complement traditional measures in guiding treatment; however, the sample size of this study limits generalizability. Trial Registration: ClinicalTrials.gov NCT03293407; https://clinicaltrials.gov/ct2/show/NCT03293407 International Registered Report Identifier (IRRID): RR2-10.2196/12144 %M 34623313 %R 10.2196/25163 %U https://www.jmir.org/2021/10/e25163 %U https://doi.org/10.2196/25163 %U http://www.ncbi.nlm.nih.gov/pubmed/34623313 %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 %@ 2561-326X %I JMIR Publications %V 5 %N 10 %P e27358 %T A Novel Mobile App (“CareFit”) to Support Informal Caregivers to Undertake Regular Physical Activity From Home During and Beyond COVID-19 Restrictions: Co-design and Prototype Development Study %A Egan,Kieren J %A Hodgson,William %A Dunlop,Mark D %A Imperatore,Gennaro %A Kirk,Alison %A Maguire,Roma %+ Department of Computer and Information Science, University of Strathclyde, Livingstone Tower, 26 Richmond Street, Glasgow, G1 1XH, United Kingdom, 44 0141 548 3138, kieren.egan@strath.ac.uk %K physical activity %K Android %K COVID-19 %K intervention %K co-design %K exercise %K app %K development %K support %K caregiver %D 2021 %7 1.10.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Informal caregivers, or carers (unpaid family members and friends), are instrumental to millions worldwide for the ongoing delivery of health and well-being needs. The risk of crisis points (eg, hospitalizations) for caregivers increases with the absence of physical activity. The COVID-19 pandemic is highly likely to have increased the risk of crisis points for caregivers by increasing the amount of time spent indoors due to shielding and lockdown restrictions. Thus, accessible evidence-based tools to facilitate physical activity for caregivers indoors are urgently needed. Objective: The aim of this study was to co-design and develop a novel mobile app to educate and support carers in the undertaking of regular physical activity at home during and beyond COVID-19 restrictions via integration of the transtheoretical model of behavior change and UK physical activity guidelines. Methods: We co-designed a mobile app, “CareFit,” by directly involving caregivers, health care professionals, and social care professionals in the requirements, capturing, and evaluation phases of three Agile Scrum design and development sprints. Seven participants representing multistakeholder views took part in three co-design sessions, each of which was followed by a development sprint. Requirements for CareFit were grounded in a combination of behavioral change science and UK government guidelines for physical activity. Results: Participants identified different barriers and enablers to physical activity, such as a lack of time, recognition of existing activities, and concerns regarding safely undertaking physical activity. Requirements analysis highlighted the importance of simplicity in design and a need to anchor development around the everyday needs of caregivers (eg, easy-to-use video instructions). Our final prototype app integrated guidance for undertaking physical activity at home through educational, physical activity, and communication components. Conclusions: Integrating government guidelines with models of behavioral change into a mobile app to support the physical activity of carers is novel. We found that integrating core physical activity guidelines into a co-designed smartphone app with functionality such as a weekly planner and educational material for users is feasible. This work holds promise to fill the gap of effective physical activity solutions for caregivers both during and beyond the COVID-19 pandemic. Further work is now needed to explore the feasibility, acceptability, and usability of the approach in real-world settings. %M 34406969 %R 10.2196/27358 %U https://formative.jmir.org/2021/10/e27358 %U https://doi.org/10.2196/27358 %U http://www.ncbi.nlm.nih.gov/pubmed/34406969 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e23237 %T Introducing an Integrated Model of Adults’ Wearable Activity Tracker Use and Obesity Information–Seeking Behaviors From a National Quota Sample Survey %A Kim,Bokyung %A Hong,Seoyeon %A Kim,Sungwook %+ Department of Public Relations & Advertising, Ric Edelman College of Communication & Creative Arts, Rowan University, 301 High St, Room 322, Glassboro, NJ, 08028, United States, 1 8562564293, kimb@rowan.edu %K wearable activity tracker %K wearable health technology %K obesity %K health belief %K health belief model %K Technology Acceptance Model %K online information seeking %D 2021 %7 29.9.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Research from multiple perspectives to investigate adults’ use of wearable activity-tracking devices is limited. We offer a multiperspective model and provide empirical evidence of what leads to frequent usage of wearable health technologies from a large, nationally representative survey sample. Objective: This study aims to explore factors affecting the use of wearable activity-tracking devices among health consumers from the perspectives of individual health beliefs (perceived severity, perceived susceptibility, perceived benefits, and self-efficacy) and information-seeking behaviors. Methods: Our Integrated Model of Wearable Activity Tracker (IMWAT) use and proposed hypotheses were validated and tested with data collected from a telephone survey with a national quota sample. The data were analyzed using a variety of statistical techniques, including structural equation analysis. Results: The sample comprised 2006 participants. Our results showed that the perceived benefits of physical activity, perceived susceptibility, and self-efficacy toward obesity were significant predictors of information-seeking behaviors, which, in turn, mediated their effects on the use of wearable activity trackers. Perceptions of obesity severity directly promoted wearable device usage. Conclusions: This study provided a new and powerful theoretical model that combined the health beliefs and information-seeking behaviors behind the use of wearable activity trackers in the adult population. The findings provide meaningful implications for developers and designers of wearable health technology products and will assist health informatics practitioners and obesity prevention communicators. %M 34586076 %R 10.2196/23237 %U https://formative.jmir.org/2021/9/e23237 %U https://doi.org/10.2196/23237 %U http://www.ncbi.nlm.nih.gov/pubmed/34586076 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 9 %P e28524 %T An Activity Tracker–Guided Physical Activity Program for Patients Undergoing Radiotherapy: Protocol for a Prospective Phase III Trial (OnkoFit I and II Trials) %A Hauth,Franziska %A Gehler,Barbara %A Nieß,Andreas Michael %A Fischer,Katharina %A Toepell,Andreas %A Heinrich,Vanessa %A Roesel,Inka %A Peter,Andreas %A Renovanz,Mirjam %A Hartkopf,Andreas %A Stengel,Andreas %A Zips,Daniel %A Gani,Cihan %+ Department of Radiation Oncology, University Hospital Tübingen, Hoppe-Seyler-Str 3, Tuebingen, 72076, Germany, 49 70712985900, franziska.hauth@med.uni-tuebingen.de %K cancer %K fatigue %K physical activity %K quality of life %K activity tracker %K exercise program %K radiotherapy %K digital health %D 2021 %7 22.9.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The positive impact that physical activity has on patients with cancer has been shown in several studies over recent years. However, supervised physical activity programs have several limitations, including costs and availability. Therefore, our study proposes a novel approach for the implementation of a patient-executed, activity tracker–guided exercise program to bridge this gap. Objective: Our trial aims to investigate the impact that an activity tracker–guided, patient-executed exercise program for patients undergoing radiotherapy has on cancer-related fatigue, health-related quality of life, and preoperative health status. Methods: Patients receiving postoperative radiotherapy for breast cancer (OnkoFit I trial) or neoadjuvant, definitive, or postoperative treatment for other types of solid tumors (OnkoFit II trial) will be randomized (1:1:1) into 3-arm studies. Target accrual is 201 patients in each trial (50 patients per year). After providing informed consent, patients will be randomized into a standard care arm (arm A) or 1 of 2 interventional arms (arms B and C). Patients in arms B and C will wear an activity tracker and record their daily step count in a diary. Patients in arm C will receive personalized weekly targets for their physical activity. No further instructions will be given to patients in arm B. The target daily step goals for patients in arm C will be adjusted weekly and will be increased by 10% of the average daily step count of the past week until they reach a maximum of 6000 steps per day. Patients in arm A will not be provided with an activity tracker. The primary end point of the OnkoFit I trial is cancer-related fatigue at 3 months after the completion of radiotherapy. This will be measured by the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire. For the OnkoFit II trial, the primary end point is the overall quality of life, which will be assessed with the Functional Assessment of Cancer Therapy-General sum score at 6 months after treatment to allow for recovery after possible surgery. In parallel, blood samples from before, during, and after treatment will be collected in order to assess inflammatory markers. Results: Recruitment for both trials started on August 1, 2020, and to date, 49 and 12 patients have been included in the OnkoFit I and OnkoFit II trials, respectively. Both trials were approved by the institutional review board prior to their initiation. Conclusions: The OnkoFit trials test an innovative, personalized approach for the implementation of an activity tracker–guided training program for patients with cancer during radiotherapy. The program requires only a limited amount of resources. Trial Registration: ClinicalTrials.gov NCT04506476; https://clinicaltrials.gov/ct2/show/NCT04506476. ClinicalTrials.gov NCT04517019; https://clinicaltrials.gov/ct2/show/NCT04517019. International Registered Report Identifier (IRRID): DERR1-10.2196/28524 %M 34550079 %R 10.2196/28524 %U https://www.researchprotocols.org/2021/9/e28524 %U https://doi.org/10.2196/28524 %U http://www.ncbi.nlm.nih.gov/pubmed/34550079 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e30819 %T Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY): Pilot Evaluation %A Yamagami,Kan %A Nomura,Akihiro %A Kometani,Mitsuhiro %A Shimojima,Masaya %A Sakata,Kenji %A Usui,Soichiro %A Furukawa,Kenji %A Takamura,Masayuki %A Okajima,Masaki %A Watanabe,Kazuyoshi %A Yoneda,Takashi %+ Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takaramachi, Kanazawa, Japan, 81 076 265 2000, anomura@med.kanazawa-u.ac.jp %K COVID-19 %K silent hypoxia %K wearable device %K Fitbit %K estimated oxygen variation %K detection %K infectious disease %K pilot study %K symptom %K outpatient %K oxygen %K sleep %K wearable %D 2021 %7 16.9.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Some patients with COVID-19 experienced sudden death due to rapid symptom deterioration. Thus, it is important to predict COVID-19 symptom exacerbation at an early stage prior to increasing severity in patients. Patients with COVID-19 could experience a unique “silent hypoxia” at an early stage of the infection when they are apparently asymptomatic, but with rather low SpO2 (oxygen saturation) levels. In order to continuously monitor SpO2 in daily life, a high-performance wearable device, such as the Apple Watch or Fitbit, has become commercially available to monitor several biometric data including steps, resting heart rate (RHR), physical activity, sleep quality, and estimated oxygen variation (EOV). Objective: This study aimed to test whether EOV measured by the wearable device Fitbit can predict COVID-19 symptom exacerbation. Methods: We recruited patients with COVID-19 from August to November 2020. Patients were asked to wear the Fitbit for 30 days, and biometric data including EOV and RHR were extracted. EOV is a relative physiological measure that reflects users’ SpO2 levels during sleep. We defined a high EOV signal as a patient’s oxygen level exhibiting a significant dip and recovery within the index period, and a high RHR signal as daily RHR exceeding 5 beats per day compared with the minimum RHR of each patient in the study period. We defined successful prediction as the appearance of those signals within 2 days before the onset of the primary outcome. The primary outcome was the composite of deaths of all causes, use of extracorporeal membrane oxygenation, use of mechanical ventilation, oxygenation, and exacerbation of COVID-19 symptoms, irrespective of readmission. We also assessed each outcome individually as secondary outcomes. We made weekly phone calls to discharged patients to check on their symptoms. Results: We enrolled 23 patients with COVID-19 diagnosed by a positive SARS-CoV-2 polymerase chain reaction test. The patients had a mean age of 50.9 (SD 20) years, and 70% (n=16) were female. Each patient wore the Fitbit for 30 days. COVID-19 symptom exacerbation occurred in 6 (26%) patients. We were successful in predicting exacerbation using EOV signals in 4 out of 5 cases (sensitivity=80%, specificity=90%), whereas the sensitivity and specificity of high RHR signals were 50% and 80%, respectively, both lower than those of high EOV signals. Coincidental obstructive sleep apnea syndrome confirmed by polysomnography was detected in 1 patient via consistently high EOV signals. Conclusions: This pilot study successfully detected early COVID-19 symptom exacerbation by measuring EOV, which may help to identify the early signs of COVID-19 exacerbation. Trial Registration: University Hospital Medical Information Network Clinical Trials Registry UMIN000041421; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047290 %M 34516390 %R 10.2196/30819 %U https://formative.jmir.org/2021/9/e30819 %U https://doi.org/10.2196/30819 %U http://www.ncbi.nlm.nih.gov/pubmed/34516390 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e29875 %T Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review %A Shandhi,Md Mobashir Hasan %A Goldsack,Jennifer C %A Ryan,Kyle %A Bennion,Alexandra %A Kotla,Aditya V %A Feng,Alina %A Jiang,Yihang %A Wang,Will Ke %A Hurst,Tina %A Patena,John %A Carini,Simona %A Chung,Jeanne %A Dunn,Jessilyn %+ Department of Biomedical Engineering, Duke University, 1427 FCIEMAS, Box 90281, Durham, NC, 27708, United States, 1 919 660 5131, jessilyn.dunn@duke.edu %K digital clinical measures %K academic research %K funding %K biosensor %K digital measures %K digital health %K health outcomes %D 2021 %7 15.9.2021 %9 Review %J J Med Internet Res %G English %X Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized. Objective: We performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research. Methods: We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types. Results: The search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded. Conclusions: Specific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust. %M 34524089 %R 10.2196/29875 %U https://www.jmir.org/2021/9/e29875 %U https://doi.org/10.2196/29875 %U http://www.ncbi.nlm.nih.gov/pubmed/34524089 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 3 %P e25356 %T Applying Mobile Technology to Sustain Physical Activity After Completion of Cardiac Rehabilitation: Acceptability Study %A Elnaggar,Abdelaziz %A von Oppenfeld,Julia %A Whooley,Mary A %A Merek,Stephanie %A Park,Linda G %+ Department of Community Health Systems, School of Nursing, University of California San Francisco, 2 Koret Way, Room 531A, San Francisco, CA, 94143-0610, United States, 1 415 502 6616, linda.park@ucsf.edu %K physical activity %K cardiac rehabilitation %K digital health %K mobile app %K wearable device, mHealth %K mobile phone %D 2021 %7 2.9.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Many patients do not meet the recommended levels of physical activity after completing a cardiac rehabilitation (CR) program. Wearable activity trackers and mobile phone apps are promising potential self-management tools for maintaining physical activity after CR completion. Objective: This study aims to evaluate the acceptability of a wearable device, mobile app, and push messages to facilitate physical activity following CR completion. Methods: We used semistructured interviews to assess the acceptability of various mobile technologies after participation in a pilot randomized controlled trial. Intervention patients in the randomized controlled trial wore the Fitbit Charge 2, used the Movn mobile app, and received push messages on cardiovascular disease prevention and physical activity for over 2 months. We asked 26 intervention group participants for feedback about their experience with the technology and conducted semistructured individual interviews with 7 representative participants. We used thematic analysis to create the main themes from individual interviews. Results: Our sample included participants with a mean age of 66.7 (SD 8.6) years; 23% (6/26) were female. Overall, there were varying levels of satisfaction with different technology components. There were 7 participants who completed the satisfaction questionnaires and participated in the interviews. The Fitbit and Movn mobile app received high satisfaction scores of 4.86 and 4.5, respectively, whereas push messages had a score of 3.14 out of 5. We identified four main themes through the interviews: technology use increased motivation to be physically active, technology use served as a reminder to be physically active, recommendations for technology to improve user experience, and desire for personal feedback. Conclusions: By applying a wearable activity tracker, mobile phone app, and push messages, our study showed strong potential for the adoption of new technologies by older adults to maintain physical activity after CR completion. Future research should include a larger sample over a longer period using a mixed methods approach to assess the efficacy of technology use for promoting long-term physical activity behavior in older adults. %M 34473064 %R 10.2196/25356 %U https://humanfactors.jmir.org/2021/3/e25356 %U https://doi.org/10.2196/25356 %U http://www.ncbi.nlm.nih.gov/pubmed/34473064 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e17411 %T Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review %A Patel,Vikas %A Orchanian-Cheff,Ani %A Wu,Robert %+ Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, ON, M5S 1A8, Canada, 1 4169756585, vik.patel@mail.utoronto.ca %K wearable %K inpatient %K continuous monitoring %D 2021 %7 18.8.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The term posthospital syndrome has been used to describe the condition in which older patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep during hospital stay may contribute to posthospital syndrome, the continuous monitoring of such parameters by using affordable wearables may help to reduce the prevalence of this syndrome. Although there have been systematic reviews of wearables for physical activity monitoring in hospital settings, there are limited data on the use of wearables for measuring other health variables in hospitalized patients. Objective: This systematic review aimed to evaluate the validity and utility of wearable devices for monitoring hospitalized patients. Methods: This review involved a comprehensive search of 7 databases and included articles that met the following criteria: inpatients must be aged >18 years, the wearable devices studied in the articles must be used to continuously monitor patients, and wearables should monitor biomarkers other than solely physical activity (ie, heart rate, respiratory rate, blood pressure, etc). Only English-language studies were included. From each study, we extracted basic demographic information along with the characteristics of the intervention. We assessed the risk of bias for studies that validated their wearable readings by using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. Results: Of the 2012 articles that were screened, 14 studies met the selection criteria. All included articles were observational in design. In total, 9 different commercial wearables for various body locations were examined in this review. The devices collectively measured 7 different health parameters across all studies (heart rate, sleep duration, respiratory rate, oxygen saturation, skin temperature, blood pressure, and fall risk). Only 6 studies validated their results against a reference device or standard. There was a considerable risk of bias in these studies due to the low number of patients in most of the studies (4/6, 67%). Many studies that validated their results found that certain variables were inaccurate and had wide limits of agreement. Heart rate and sleep were the parameters with the most evidence for being valid for in-hospital monitoring. Overall, the mean patient completion rate across all 14 studies was >90%. Conclusions: The included studies suggested that wearable devices show promise for monitoring the heart rate and sleep of patients in hospitals. Many devices were not validated in inpatient settings, and the readings from most of the devices that were validated in such settings had wide limits of agreement when compared to gold standards. Even some medical-grade devices were found to perform poorly in inpatient settings. Further research is needed to determine the accuracy of hospitalized patients’ digital biomarker readings and eventually determine whether these wearable devices improve health outcomes. %M 34406121 %R 10.2196/17411 %U https://mhealth.jmir.org/2021/8/e17411 %U https://doi.org/10.2196/17411 %U http://www.ncbi.nlm.nih.gov/pubmed/34406121 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 8 %P e30239 %T Utilization of a Directly Supervised Telehealth-Based Exercise Training Program in Patients With Nonalcoholic Steatohepatitis: Feasibility Study %A Motz,Victoria %A Faust,Alison %A Dahmus,Jessica %A Stern,Benjamin %A Soriano,Christopher %A Stine,Jonathan G %+ Penn State Milton S Hershey Medical Center, 500 University Dr, Hershey, PA, 17033, United States, 1 717 531 1017, jstine@pennstatehealth.psu.edu %K physical activity %K fatty liver %K telemedicine %K liver %K nonalcoholic fatty liver disease %K liver disease %K fatty liver disease %K aerobic training %K telehealth %K fitness %K feasibility %K steatohepatitis %D 2021 %7 17.8.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Most patients with nonalcoholic fatty liver disease (NAFLD) are physically inactive despite the well-known benefits of physical activity. Telehealth offers promise as a novel way to deliver an exercise training program and increase physical activity. However, the feasibility, safety, and efficacy of telehealth-based exercise programs is unknown in patients with NAFLD. Objective: The aim of this study was to determine the feasibility of a directly supervised exercise training program delivered exclusively with telehealth to patients with nonalcoholic steatohepatitis (NASH), the progressive form of NAFLD. Methods: In response to COVID-19 research restrictions, we adapted an existing clinical trial and delivered 20 weeks of moderate-intensity aerobic training 5 days a week under real-time direct supervision using an audio–visual telehealth platform. Aerobic training was completed by walking outdoors or using a home treadmill. Fitness activity trackers with heart rate monitors ensured exercise was completed at the prescribed intensity with real-time feedback from an exercise physiologist. Results: Three female patients with biopsy-proven NASH were enrolled with a mean age of 52 (SD 14) years. The mean body mass index was 31.9 (SD 5.1) kg/m2. All patients had metabolic syndrome. All patients completed over 80% of exercise sessions (mean 84% [SD 3%]) and no adverse events occurred. Body weight (mean –5.1% [SD 3.7%]), body fat (mean –4.4% [SD 2.3%]), and waist circumference (mean –1.3 in. [SD 1.6 in.]) all improved with exercise. The mean relative reduction in magnetic resonance imaging-proton density fat fraction (MRI-PDFF) was 35.1% (SD 8.8%). Mean reductions in hemoglobin A1c and Homeostatic Model Assessment for Insulin Resistance were also observed (–0.5% [SD 0.2%] and –4.0 [SD 1.2], respectively). The mean peak oxygen consumption (VO2peak) improved by 9.9 (SD 6.6) mL/kg/min. Conclusions: This proof-of-concept study found that supervised exercise training delivered via telehealth is feasible and safe in patients with NASH. Telehealth-based exercise training also appears to be highly efficacious in patients with NASH, but this will need to be confirmed by future large-scale trials. Trial Registration: ClinicalTrials.gov NCT03518294; https://clinicaltrials.gov/ct2/show/NCT03518294 %M 34402795 %R 10.2196/30239 %U https://formative.jmir.org/2021/8/e30239 %U https://doi.org/10.2196/30239 %U http://www.ncbi.nlm.nih.gov/pubmed/34402795 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e23896 %T General Practitioners' Perceptions of the Use of Wearable Electronic Health Monitoring Devices: Qualitative Analysis of Risks and Benefits %A Volpato,Lucia %A del Río Carral,María %A Senn,Nicolas %A Santiago Delefosse,Marie %+ Research Centre for Psychology of Health, Aging and Sport Examination, Institute of Psychology, University of Lausanne, Géopolis Quartier UNIL - Mouline, Lausanne, 1015, Switzerland, 41 21 692 32 67, maria.delriocarral@unil.ch %K mHealth %K wearable devices %K health wearables %K activity trackers %K health monitoring %K self-tracking %K general practitioners %K mind maps %K qualitative research %K health psychology %D 2021 %7 9.8.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The rapid diffusion of wearable electronic health monitoring devices (wearable devices or wearables) among lay populations shows that self-tracking and self-monitoring are pervasively expanding, while influencing health-related practices. General practitioners are confronted with this phenomenon, since they often are the expert-voice that patients will seek. Objective: This article aims to explore general practitioners’ perceptions of the role of wearable devices in family medicine and of their benefits, risks, and challenges associated with their use. It also explores their perceptions of the future development of these devices. Methods: Data were collected during a medical conference among 19 Swiss general practitioners through mind maps. Maps were first sketched at the conference and their content was later compared with notes and reports written during the conference, which allowed for further integration of information. This tool represents an innovative methodology in qualitative research that allows for time-efficient data collection and data analysis. Results: Data analysis highlighted that wearable devices were described as user-friendly, adaptable devices that could enable performance monitoring and support medical research. Benefits included support for patients’ empowerment and education, behavior change facilitation, better awareness of personal medical history and body functioning, efficient information transmission, and connection with the patient’s medical network; however, general practitioners were concerned by a lack of scientific validation, lack of clarity over data protection, and the risk of stakeholder-associated financial interests. Other perceived risks included the promotion of an overly medicalized health culture and the risk of supporting patients’ self-diagnosis and self-medication. General practitioners also feared increased pressure on their workload and a compromised doctor–patient relationship. Finally, they raised important questions that can guide wearables’ future design and development, highlighting a need for general practitioners and medical professionals to be involved in the process. Conclusions: Wearables play an increasingly central role in daily health-related practices, and general practitioners expressed a desire to become more involved in the development of such technologies. Described as useful information providers, wearables were generally positively perceived and did not seem to pose a threat to the doctor–patient relationship. However, general practitioners expressed their concern that wearables may fuel a self-monitoring logic, to the detriment of patients’ autonomy and overall well-being. While wearables can contribute to health promotion, it is crucial to clarify the logic underpinning the design of such devices. Through the analysis of group discussions, this study contributes to the existing literature by presenting general practitioners’ perceptions of wearable devices. This paper provides insight on general practitioners’ perception to be considered in the context of product development and marketing. %M 34383684 %R 10.2196/23896 %U https://mhealth.jmir.org/2021/8/e23896 %U https://doi.org/10.2196/23896 %U http://www.ncbi.nlm.nih.gov/pubmed/34383684 %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 %@ 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 %@ 1929-0748 %I JMIR Publications %V 10 %N 7 %P e29013 %T Building on Lessons Learned in a Mobile Intervention to Reduce Pain and Improve Health (MORPH): Protocol for the MORPH-II Trial %A Fanning,Jason %A Brooks,Amber K %A Hsieh,Katherine L %A Kershner,Kyle %A Furlipa,Joy %A Nicklas,Barbara J %A Rejeski,W Jack %+ Department of Health and Exercise Science, Wake Forest University, Worrell Professional Center 2164B, PO Box 7868, Winston-Salem, NC, United States, 1 336 758 5042, fanninjt@wfu.edu %K aging %K physical activity %K sedentary behavior %K weight loss %K chronic pain %K mHealth %D 2021 %7 19.7.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Engaging in sufficient levels of physical activity, guarding against sustained sitting, and maintaining a healthy body weight represent important lifestyle strategies for managing older adults’ chronic pain. Our first Mobile Health Intervention to Reduce Pain and Improve Health (MORPH) randomized pilot study demonstrated that a partially remote group-mediated diet and daylong activity intervention (ie, a focus on moving often throughout the day) can lead to improved physical function, weight loss, less pain intensity, and fewer minutes of sedentary time. We also identified unique delivery challenges that limited the program’s scalability and potential efficacy. Objective: The purpose of the MORPH-II randomized pilot study is to refine the MORPH intervention package based on feedback from MORPH and evaluate the feasibility, acceptability, and preliminary efficacy of this revised package prior to conducting a larger clinical trial. Methods: The MORPH-II study is an iteration on MORPH designed to pilot a refined framework, enhance scalability through fully remote delivery, and increase uptake of the daylong movement protocol through revised education content and additional personalized remote coaching. Older, obese, and low-active adults with chronic multisite pain (n=30) will be randomly assigned to receive a 12-week remote group-mediated physical activity and dietary weight loss intervention followed by a 12-week maintenance period or a control condition. Those in the intervention condition will partake in weekly social cognitive theory–based group meetings via teleconference software plus one-on-one support calls on a tapered schedule. They will also engage with a tablet application paired with a wearable activity monitor and smart scale designed to provide ongoing social and behavioral support throughout the week. Those in the control group will receive only the self-monitoring tools. Results: Recruitment is ongoing as of January 2021. Conclusions: Findings from MORPH-II will help guide other researchers working to intervene on sedentary behavior through frequent movement in older adults with chronic pain. Trial Registration: ClinicalTrials.gov NCT04655001; https://clinicaltrials.gov/ct2/show/NCT04655001 International Registered Report Identifier (IRRID): PRR1-10.2196/29013 %M 34279241 %R 10.2196/29013 %U https://www.researchprotocols.org/2021/7/e29013 %U https://doi.org/10.2196/29013 %U http://www.ncbi.nlm.nih.gov/pubmed/34279241 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 7 %P e26063 %T Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study %A Yang,Yanxiang %A Koenigstorfer,Joerg %+ Chair of Sport and Health Management, Technical University of Munich, Georg-Brauchle-Ring 60/62, Campus D – Uptown Munich, Munich, 80992, Germany, 49 89 289 24559, joerg.koenigstorfer@tum.de %K smartphone %K fitness applications %K mHealth %K technology acceptance %K Unified Theory of Acceptance and Use of Technology 2 %K physical activity %K determinants of app usage %K education-related app features %K motivation-related app features %K gamification-related app features %K mobile phone %D 2021 %7 13.7.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Smartphone fitness apps are considered promising tools for promoting physical activity and health. However, it is unclear which user-perceived factors and app features encourage users to download apps with the intention of being physically active. Objective: Building on the second version of the Unified Theory of Acceptance and Use of Technology, this study aims to examine the association of the seven determinants of the second version of the Unified Theory of Acceptance and Use of Technology with the app usage intentions of the individuals and their behavioral intentions of being physically active as well as the moderating effects of different smartphone fitness app features (ie, education, motivation, and gamification related) and individual differences (ie, age, gender, and experience) on these intentions. Methods: Data from 839 US residents who reported having used at least one smartphone fitness app were collected via a web-based survey. A confirmatory factor analysis was performed, and path modeling was used to test the hypotheses and explore the influence of moderators on structural relationships. Results: The determinants explain 76% of the variance in the behavioral intention to use fitness apps. Habit (β=.42; P<.001), performance expectancy (β=.36; P<.001), facilitating conditions (β=.15; P<.001), price value (β=.13; P<.001), and effort expectancy (β=.09; P=.04) were positively related to behavioral intention to use fitness apps, whereas social influence and hedonic motivation were nonsignificant predictors. Behavioral intentions to use fitness apps were positively related to intentions of being physically active (β=.12; P<.001; R2=0.02). Education-related app features moderated the association between performance expectancy and habit and app usage intentions; motivation-related features moderated the association of performance expectancy, facilitating conditions, and habit with usage intentions; and gamification-related features moderated the association between hedonic motivation and usage intentions. Age moderated the association between effort expectancy and usage intentions, and gender moderated the association between performance expectancy and habit and usage intentions. User experience was a nonsignificant moderator. Follow-up tests were used to describe the nature of significant interaction effects. Conclusions: This study identifies the drivers of the use of fitness apps. Smartphone app features should be designed to increase the likelihood of app usage, and hence physical activity, by supporting users in achieving their goals and facilitating habit formation. Target group–specific preferences for education-, motivation-, and gamification-related app features, as well as age and gender differences, should be considered. Performance expectancy had a high predictive power for intended usage for male (vs female) users who appreciated motivation-related features. Thus, apps targeting these user groups should focus on goal achievement–related features (eg, goal setting and monitoring). Future research could examine the mechanisms of these moderation effects and their long-term influence on physical activity. %M 34255656 %R 10.2196/26063 %U https://www.jmir.org/2021/7/e26063 %U https://doi.org/10.2196/26063 %U http://www.ncbi.nlm.nih.gov/pubmed/34255656 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 3 %P e18130 %T MI-PACE Home-Based Cardiac Telerehabilitation Program for Heart Attack Survivors: Usability Study %A Ding,Eric Y %A Erskine,Nathaniel %A Stut,Wim %A McManus,David D %A Peterson,Amy %A Wang,Ziyue %A Escobar Valle,Jorge %A Albuquerque,Daniella %A Alonso,Alvaro %A Botkin,Naomi F %A Pack,Quinn R %A McManus,David D %+ Division of Cardiology, Department of Medicine, University of Massachusetts Medical School, 55 Lake Ave North, Worcester, MA, 01655, United States, 1 5088561984, eric.ding@umassmed.edu %K cardiac rehabilitation %K telerehabilitation %K health watch %K mHealth %K exercise %D 2021 %7 8.7.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Cardiac rehabilitation programs, consisting of exercise training and disease management interventions, reduce morbidity and mortality after acute myocardial infarction. Objective: In this pilot study, we aimed to developed and assess the feasibility of delivering a health watch–informed 12-week cardiac telerehabilitation program to acute myocardial infarction survivors who declined to participate in center-based cardiac rehabilitation. Methods: We enrolled patients hospitalized after acute myocardial infarction at an academic medical center who were eligible for but declined to participate in center-based cardiac rehabilitation. Each participant underwent a baseline exercise stress test. Participants received a health watch, which monitored heart rate and physical activity, and a tablet computer with an app that displayed progress toward accomplishing weekly walking and exercise goals. Results were transmitted to a cardiac rehabilitation nurse via a secure connection. For 12 weeks, participants exercised at home and also participated in weekly phone counseling sessions with the nurse, who provided personalized cardiac rehabilitation solutions and standard cardiac rehabilitation education. We assessed usability of the system, adherence to weekly exercise and walking goals, counseling session attendance, and disease-specific quality of life. Results: Of 18 participants (age: mean 59 years, SD 7) who completed the 12-week telerehabilitation program, 6 (33%) were women, and 6 (33%) had ST-elevation myocardial infarction. Participants wore the health watch for a median of 12.7 hours (IQR 11.1, 13.8) per day and completed a median of 86% of exercise goals. Participants, on average, walked 121 minutes per week (SD 175) and spent 189 minutes per week (SD 210) in their target exercise heart rate zone. Overall, participants found the system to be highly usable (System Usability Scale score: median 83, IQR 65, 100). Conclusions: This pilot study established the feasibility of delivering cardiac telerehabilitation at home to acute myocardial infarction survivors via a health watch–based program and telephone counseling sessions. Usability and adherence to health watch use, exercise recommendations, and counseling sessions were high. Further studies are warranted to compare patient outcomes and health care resource utilization between center-based rehabilitation and telerehabilitation. %M 34255660 %R 10.2196/18130 %U https://humanfactors.jmir.org/2021/3/e18130 %U https://doi.org/10.2196/18130 %U http://www.ncbi.nlm.nih.gov/pubmed/34255660 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e24915 %T Examining the Impact of an mHealth Behavior Change Intervention With a Brief In-Person Component for Cancer Survivors With Overweight or Obesity: Randomized Controlled Trial %A Walsh,Jane C %A Richmond,Janice %A Mc Sharry,Jenny %A Groarke,AnnMarie %A Glynn,Liam %A Kelly,Mary Grace %A Harney,Owen %A Groarke,Jenny M %+ Centre for Improving Health-Related Quality of Life, School of Psychology, Queen's University Belfast, David Keir Building, Belfast, BT7 1NN, United Kingdom, 44 02890974886, j.groarke@qub.ac.uk %K cancer survivors %K overweight %K obesity %K health behavior %K goals %K accelerometry %K text messaging %K technology %K Ireland %K self-management %K mobile phone %D 2021 %7 5.7.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cancer survivorship in Ireland is increasing in both frequency and longevity. However, a significant proportion of cancer survivors do not reach the recommended physical activity levels and have overweight. This has implications for both physical and psychological health, including an increased risk of subsequent and secondary cancers. Mobile health (mHealth) interventions demonstrate potential for positive health behavior change, but there is little evidence for the efficacy of mobile technology in improving health outcomes in cancer survivors with overweight or obesity. Objective: This study aims to investigate whether a personalized mHealth behavior change intervention improves physical and psychological health outcomes in cancer survivors with overweight or obesity. Methods: A sample of 123 cancer survivors (BMI≥25 kg/m2) was randomly assigned to the standard care control (n=61) or intervention (n=62) condition. Group allocation was unblinded. The intervention group attended a 4-hour tailored lifestyle education and information session with physiotherapists, a dietician, and a clinical psychologist to support self-management of health behavior. Over the following 12 weeks, participants engaged in personalized goal setting to incrementally increase physical activity (with feedback and review of goals through SMS text messaging contact with the research team). Direct measures of physical activity were collected using a Fitbit accelerometer. Data on anthropometric, functional exercise capacity, dietary behavior, and psychological measures were collected at face-to-face assessments in a single hospital site at baseline (T0), 12 weeks (T1; intervention end), and 24 weeks (T2; follow-up). Results: The rate of attrition was 21% (13/61) for the control condition and 14% (9/62) for the intervention condition. Using intent-to-treat analysis, significant reductions in BMI (F2,242=4.149; P=.02; ηp2=0.033) and waist circumference (F2,242=3.342; P=.04; ηp2=0.027) were observed in the intervention group. Over the 24-week study, BMI was reduced by 0.52 in the intervention condition, relative to a nonsignificant reduction of 0.11 in the control arm. Waist circumference was reduced by 3.02 cm in the intervention condition relative to 1.82 cm in the control condition. Physical activity level was significantly higher in the intervention group on 8 of the 12 weeks of the intervention phase and on 5 of the 12 weeks of the follow-up period, accounting for up to 2500 additional steps per day (mean 2032, SD 270). Conclusions: The results demonstrate that for cancer survivors with a BMI≥25 kg/m2, lifestyle education and personalized goal setting using mobile technology can yield significant changes in clinically relevant health indicators. Further research is needed to elucidate the mechanisms of behavior change and explore the capacity for mHealth interventions to improve broader health and well-being outcomes in the growing population of cancer survivors. Trial Registration: ISRCTN Registry ISRCTN18676721; https://www.isrctn.com/ISRCTN18676721 International Registered Report Identifier (IRRID): RR2-10.2196/13214 %M 36260394 %R 10.2196/24915 %U https://mhealth.jmir.org/2021/7/e24915 %U https://doi.org/10.2196/24915 %U http://www.ncbi.nlm.nih.gov/pubmed/36260394 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e15641 %T A Smart Shoe Insole to Monitor Frail Older Adults’ Walking Speed: Results of Two Evaluation Phases Completed in a Living Lab and Through a 12-Week Pilot Study %A Piau,Antoine %A Steinmeyer,Zara %A Charlon,Yoann %A Courbet,Laetitia %A Rialle,Vincent %A Lepage,Benoit %A Campo,Eric %A Nourhashemi,Fati %+ Gerontopole, University Hospital of Toulouse, 24 rue du pont St Pierre, Toulouse, 31400, France, 33 561323010, antoinepiau@hotmail.com %K frail older adults %K walking speed %K outpatient monitoring %K activity tracker %K shoe insert %D 2021 %7 5.7.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Recent World Health Organization reports propose wearable devices to collect information on activity and walking speed as innovative health indicators. However, mainstream consumer-grade tracking devices and smartphone apps are often inaccurate and require long-term acceptability assessment. Objective: Our aim is to assess the user acceptability of an instrumented shoe insole in frail older adults. This device monitors participants’ walking speed and differentiates active walking from shuffling after step length calibration. Methods: A multiphase evaluation has been designed: 9 older adults were evaluated in a living lab for a day, 3 older adults were evaluated at home for a month, and a prospective randomized trial included 35 older adults at home for 3 months. A qualitative research design using face-to-face and phone semistructured interviews was performed. Our hypothesis was that this shoe insole was acceptable in monitoring long-term outdoor and indoor walking. The primary outcome was participants' acceptability, measured by a qualitative questionnaire and average time of insole wearing per day. The secondary outcome described physical frailty evolution in both groups. Results: Living lab results confirmed the importance of a multiphase design study with participant involvement. Participants proposed insole modifications. Overall acceptability had mixed results: low scores for reliability (2.1 out of 6) and high scores for usability (4.3 out of 6) outcomes. The calibration phase raised no particular concern. During the field test, a majority of participants (mean age 79 years) were very (10/16) or quite satisfied (3/16) with the insole's comfort at the end of the follow-up. Participant insole acceptability evolved as follows: 63% (12/19) at 1 month, 50% (9/18) at 2 months, and 75% (12/16) at 3 months. A total of 9 participants in the intervention group discontinued the intervention because of technical issues. All participants equipped for more than a week reported wearing the insole every day at 1 month, 83% (15/18) at 2 months, and 94% (15/16) at 3 months for 5.8, 6.3, and 5.1 hours per day, respectively. Insole data confirmed that participants effectively wore the insole without significant decline during follow-up for an average of 13.5 days per 4 months and 5.6 hours per day. For secondary end points, the change in frailty parameters or quality of life did not differ for those randomly assigned to the intervention group compared to usual care. Conclusions: Our study reports acceptability data on an instrumented insole in indoor and outdoor walking with remote monitoring in frail older adults under real-life conditions. To date, there is limited data in this population set. This thin instrumentation, including a flexible battery, was a technical challenge and seems to provide an acceptable solution over time that is valued by participants. However, users still raised certain acceptability issues. Given the growing interest in wearable health care devices, these results will be useful for future developments. Trial Registration: ClinicalTrials.gov NCT02316600; https://clinicaltrials.gov/ct2/show/NCT02316600 %M 36260404 %R 10.2196/15641 %U https://mhealth.jmir.org/2021/7/e15641 %U https://doi.org/10.2196/15641 %U http://www.ncbi.nlm.nih.gov/pubmed/36260404 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e25591 %T Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS): Cross-sectional Study %A Sardana,Mayank %A Lin,Honghuang %A Zhang,Yuankai %A Liu,Chunyu %A Trinquart,Ludovic %A Benjamin,Emelia J %A Manders,Emily S %A Fusco,Kelsey %A Kornej,Jelena %A Hammond,Michael M %A Spartano,Nicole %A Pathiravasan,Chathurangi H %A Kheterpal,Vik %A Nowak,Christopher %A Borrelli,Belinda %A Murabito,Joanne M %A McManus,David D %+ Department of Medicine, Division of Cardiology, University of California San Francisco, 505 Parnassus Ave, San Francisco, CA, 94143, United States, 1 4157548491, mayank.mamc@gmail.com %K hypertension %K primary prevention %K eCohort %K physical activity %K smartwatch %K Apple Watch %K home blood pressure %D 2021 %7 24.6.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP. Objective: We aimed to study the association of habitual physical activity with home BP. Methods: Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure. Results: We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (P=.004) and 0.36 mmHg lower home diastolic BP (P=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI. Conclusions: In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association. %M 34185019 %R 10.2196/25591 %U https://www.jmir.org/2021/6/e25591/ %U https://doi.org/10.2196/25591 %U http://www.ncbi.nlm.nih.gov/pubmed/34185019 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e22151 %T The Association Between Logging Steps Using a Website, App, or Fitbit and Engaging With the 10,000 Steps Physical Activity Program: Observational Study %A Rayward,Anna T %A Vandelanotte,Corneel %A Van Itallie,Anetta %A Duncan,Mitch J %+ School of Health, Medical and Applied Sciences, Central Queensland University, Bruce Highway, Rockhampton, 4700, Australia, 61 240553239, anna.rayward@newcastle.edu.au %K physical activity intervention %K activity trackers %K engagement %K Fitbit %K pedometer %K eHealth %K mobile phone %D 2021 %7 18.6.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Engagement is positively associated with the effectiveness of digital health interventions. It is unclear whether tracking devices that automatically synchronize data (eg, Fitbit) produce different engagement levels compared with manually entering data. Objective: This study examines how different step logging methods in the freely available 10,000 Steps physical activity program differ according to age and gender and are associated with program engagement. Methods: A subsample of users (n=22,142) of the free 10,000 Steps physical activity program were classified into one of the following user groups based on the step-logging method: Website Only (14,617/22,142, 66.01%), App Only (2100/22,142, 9.48%), Fitbit Only (1705/22,142, 7.7%), Web and App (2057/22,142, 9.29%), and Fitbit Combination (combination of web, app, and Fitbit; 1663/22,142, 7.51%). Generalized linear regression and binary logistic regression were used to examine differences between user groups’ engagement and participation parameters. The time to nonusage attrition was assessed using Cox proportional hazards regression. Results: App Only users were significantly younger and Fitbit user groups had higher proportions of women compared with other groups. The following outcomes were significant and relative to the Website Only group. The App Only group had fewer website sessions (odds ratio [OR] −6.9, 95% CI −7.6 to −6.2), whereas the Fitbit Only (OR 10.6, 95% CI 8.8-12.3), Web and App (OR 1.5, 95% CI 0.4-2.6), and Fitbit Combination (OR 8.0; 95% CI 6.2-9.7) groups had more sessions. The App Only (OR −0.7, 95% CI −0.9 to −0.4) and Fitbit Only (OR −0.5, 95% CI −0.7 to −0.2) groups spent fewer minutes on the website per session, whereas the Fitbit Combination group (OR 0.2, 95% CI 0.0-0.5) spent more minutes. All groups, except the Fitbit Combination group, viewed fewer website pages per session. The mean daily step count was lower for the App Only (OR −201.9, 95% CI −387.7 to −116.0) and Fitbit Only (OR −492.9, 95% CI −679.9 to −305.8) groups but higher for the Web and App group (OR 258.0, 95% CI 76.9-439.2). The Fitbit Only (OR 5.0, 95% CI 3.4-6.6), Web and App (OR 7.2, 95% CI 5.9-8.6), and Fitbit Combination (OR 15.6, 95% CI 13.7-17.5) groups logged a greater number of step entries. The App Only group was less likely (OR 0.65, 95% CI 0.46-0.94) and other groups were more likely to participate in Challenges. The mean time to nonusage attrition was 35 (SD 26) days and was lower than average in the Website Only and App Only groups and higher than average in the Web and App and Fitbit Combination groups. Conclusions: Using a Fitbit in combination with the 10,000 Steps app or website enhanced engagement with a real-world physical activity program. Integrating tracking devices that synchronize data automatically into real-world physical activity interventions is one strategy for improving engagement. %M 34142966 %R 10.2196/22151 %U https://www.jmir.org/2021/6/e22151 %U https://doi.org/10.2196/22151 %U http://www.ncbi.nlm.nih.gov/pubmed/34142966 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e22587 %T Quality of Physical Activity Apps: Systematic Search in App Stores and Content Analysis %A Paganini,Sarah %A Terhorst,Yannik %A Sander,Lasse Bosse %A Catic,Selma %A Balci,Sümeyye %A Küchler,Ann-Marie %A Schultchen,Dana %A Plaumann,Katrin %A Sturmbauer,Sarah %A Krämer,Lena Violetta %A Lin,Jiaxi %A Wurst,Ramona %A Pryss,Rüdiger %A Baumeister,Harald %A Messner,Eva-Maria %+ Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Lise-Meitner-Str 16, Ulm, 89081, Germany, 49 7315032802, eva-maria.messner@uni-ulm.de %K sports %K exercise %K mobile apps %K mHealth %K quality indicators %K systematic review %D 2021 %7 9.6.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical inactivity is a major contributor to the development and persistence of chronic diseases. Mobile health apps that foster physical activity have the potential to assist in behavior change. However, the quality of the mobile health apps available in app stores is hard to assess for making informed decisions by end users and health care providers. Objective: This study aimed at systematically reviewing and analyzing the content and quality of physical activity apps available in the 2 major app stores (Google Play and App Store) by using the German version of the Mobile App Rating Scale (MARS-G). Moreover, the privacy and security measures were assessed. Methods: A web crawler was used to systematically search for apps promoting physical activity in the Google Play store and App Store. Two independent raters used the MARS-G to assess app quality. Further, app characteristics, content and functions, and privacy and security measures were assessed. The correlation between user star ratings and MARS was calculated. Exploratory regression analysis was conducted to determine relevant predictors for the overall quality of physical activity apps. Results: Of the 2231 identified apps, 312 met the inclusion criteria. The results indicated that the overall quality was moderate (mean 3.60 [SD 0.59], range 1-4.75). The scores of the subscales, that is, information (mean 3.24 [SD 0.56], range 1.17-4.4), engagement (mean 3.19 [SD 0.82], range 1.2-5), aesthetics (mean 3.65 [SD 0.79], range 1-5), and functionality (mean 4.35 [SD 0.58], range 1.88-5) were obtained. An efficacy study could not be identified for any of the included apps. The features of data security and privacy were mainly not applied. Average user ratings showed significant small correlations with the MARS ratings (r=0.22, 95% CI 0.08-0.35; P<.001). The amount of content and number of functions were predictive of the overall quality of these physical activity apps, whereas app store and price were not. Conclusions: Apps for physical activity showed a broad range of quality ratings, with moderate overall quality ratings. Given the present privacy, security, and evidence concerns inherent to most rated apps, their medical use is questionable. There is a need for open-source databases of expert quality ratings to foster informed health care decisions by users and health care providers. %M 34106073 %R 10.2196/22587 %U https://mhealth.jmir.org/2021/6/e22587 %U https://doi.org/10.2196/22587 %U http://www.ncbi.nlm.nih.gov/pubmed/34106073 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 8 %N 2 %P e24276 %T Feasibility of an Internet-Based Intervention to Promote Exercise for People With Spinal Cord Injury: Observational Pilot Study %A Ochoa,Christa %A Cole,Maria %A Froehlich-Grobe,Katherine %+ Craig Hospital, 3425 S Clarkson Street, Englewood, CO, 80113, United States, 1 2145317260, KFroehlich-Grobe@Craighospital.org %K spinal cord injury %K lifestyle intervention %K physical activity %K health promotion %K eHealth %D 2021 %7 9.6.2021 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: People with spinal cord injury (SCI) are less likely to be physically active and have higher chronic disease risk than those in the general population due to physical and metabolic changes that occur postinjury. Few studies have investigated approaches to promote increased physical activity (PA) for people with SCI despite evidence that they face unique barriers, including lack of accessible transportation and exercise equipment. To address these obstacles, we adapted an evidence-based phone-delivered intervention that promoted increased PA among people with SCI into a web-based platform, titled the Workout on Wheels internet intervention (WOWii). The adapted program provides participants with weekly skill-building information and activities, basic exercise equipment, and ongoing support through weekly group videoconferencing. Objective: This pilot study was conducted to assess the feasibility of using a web-based and virtual format to deliver the WOWii program in a randomized controlled trial. Methods: We assessed the feasibility of the web-based program by delivering an abbreviated, 4-week version to 10 participants with SCI. Rates of weekly videoconference attendance, activity completion, and exercise activity as tracked by an arm-based activity monitor were recorded for all participants. Results: Participants averaged 3.3 of 4 (83%) weekly group videoconferences attended, 3.4 of 4 (85%) web-based module activities completed, and 2.3 of 4 (58%) weeks of using the arm-based activity monitor. The majority of the sample (9/10, 90%) synced their arm-based PA monitor at least once, and overall engagement as an average of each component across the 4 weeks was 75%. Conclusions: The intervention had sufficiently high levels of engagement to be used in a full randomized controlled trial to test its effectiveness in improving levels of PA among people with SCI. The knowledge we gained from this pilot study informed improvements that were made in the full randomized controlled trial. %M 34106086 %R 10.2196/24276 %U https://rehab.jmir.org/2021/2/e24276 %U https://doi.org/10.2196/24276 %U http://www.ncbi.nlm.nih.gov/pubmed/34106086 %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 %@ 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 %@ 1929-0748 %I JMIR Publications %V 10 %N 6 %P e18410 %T Physical Activity Together for People With Multiple Sclerosis and Their Care Partners: Protocol for a Feasibility Randomized Controlled Trial of a Dyadic Intervention %A Fakolade,Afolasade %A Cameron,Julie %A McKenna,Odessa %A Finlayson,Marcia L %A Freedman,Mark S %A Latimer-Cheung,Amy E %A Pilutti,Lara A %+ School of Rehabilitation Therapy, Queen's University, Louise D Acton Building, 31 George Street, Kingston, ON, K7L 3N6, Canada, 1 613 533 6000 ext 77893, a.fakolade@queensu.ca %K multiple sclerosis %K advanced disability %K care partners %K physical activity %K dyadic intervention %K feasibility randomized controlled trial %D 2021 %7 1.6.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Physical activity (PA) is beneficial for all people; however, people affected by multiple sclerosis (MS) find regular PA challenging. These people may include individuals with advanced disabilities and their care partners. Objective: The objective of this study is to determine the feasibility of a dyadic PA intervention for people with advanced MS and their care partners. Methods: This study is a randomized controlled feasibility trial of a 12-week intervention, with 1:1 allocation into an immediate intervention condition or delayed control condition. A target of 20 people with MS–care partner dyads will be included. The outcomes will be indicators of process, resources, management, and scientific feasibility. Participant satisfaction with the intervention components will be evaluated using a satisfaction survey. The subjective experience of participation in the study will be explored using semistructured interviews. Results: The project is funded by the Consortium of Multiple Sclerosis Centers. This protocol was approved by the Ottawa Hospital Research Ethics Board (20190329-01H) and the University of Ottawa Research Ethics Board (H-09-19-4886). The study protocol was registered with ClinicalTrials.gov in February 2020. The findings of this feasibility trial will be disseminated through presentations at community events to engage the MS population in the interpretation of our results and in the next steps. The results will also be published in peer-reviewed journals and presented to the scientific community at national and international MS conferences. Conclusions: The data collected from this feasibility trial will be used to refine the intervention and materials in preparation for a pilot randomized controlled trial. Trial Registration: ClinicalTrials.gov NCT04267185; https://clinicaltrials.gov/ct2/show/NCT04267185. International Registered Report Identifier (IRRID): PRR1-10.2196/18410 %M 34061040 %R 10.2196/18410 %U https://www.researchprotocols.org/2021/6/e18410 %U https://doi.org/10.2196/18410 %U http://www.ncbi.nlm.nih.gov/pubmed/34061040 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e24076 %T Examining the Impact of a Mobile Health App on Functional Movement and Physical Fitness: Pilot Pragmatic Randomized Controlled Trial %A Stork,Matthew Jordan %A Bell,Ethan Gordon %A Jung,Mary Elizabeth %+ School of Health and Exercise Sciences, The University of British Columbia, 3333 University Way, Kelowna, BC, V1V 1VY, Canada, 1 250 807 9670, mary.jung@ubc.ca %K mHealth %K functional movement %K flexibility %K strength %K cardiovascular fitness %D 2021 %7 28.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Numerous mobile apps available for download are geared toward health and fitness; however, limited research has evaluated the real-world effectiveness of such apps. The movr app is a mobile health app designed to enhance physical functioning by prescribing functional movement training based on individualized movement assessments. The influence of the movr app on functional movement and physical fitness (flexibility, strength, and cardiovascular fitness) has not yet been established empirically. Objective: This study aims to examine the real-world impact of the movr app on functional movement, flexibility, strength, and cardiovascular fitness. Methods: A total of 48 healthy adults (24 women and 24 men; mean age 24, SD 5 years) completed an 8-week pilot pragmatic randomized controlled trial in which they were randomly assigned to either 8-week use of the movr app (n=24) or 8-week waitlist control (n=24). Measures of functional movement (Functional Movement Screen [FMS]), strength (push-ups, handgrip strength, and countermovement jump), flexibility (shoulder flexibility, sit and reach, active straight leg raise [ASLR], and half-kneeling dorsiflexion), and cardiovascular fitness (maximal oxygen uptake []) were collected at baseline and the 8-week follow-up. Results: Repeated measures analyses of variance revealed significant group-by-time interactions for the 100-point FMS (P<.001), shoulder flexibility (P=.01), ASLR (P=.001), half-kneeling dorsiflexion (P<.001), and push-up tests (P=.03). Pairwise comparisons showed that FMS scores increased from pre- to postintervention for those in the movr group (P<.001) and significantly decreased for those in the control group (P=.04). For shoulder flexibility, ASLR, half-kneeling dorsiflexion, and push-up tests, improvements from pre- to postintervention were found in the movr group (all values of P<.05) but not in the control group (all values of P>.05). There were no changes in the sit and reach or handgrip strength test scores for either group (all values of P>.05). A significant main effect of time was found for the countermovement jump (P=.02), such that scores decreased from pre- to postintervention in the control group (P=.02) but not in the movr group (P=.38). Finally, a significant group-by-time interaction was found for (P=.001), revealing that scores decreased pre- to postintervention in the control group (P<.001), but not in the movr group (P=.54). Conclusions: The findings revealed that movr improved indices of functional movement (FMS), flexibility (shoulder, ASLR, and dorsiflexion), and muscular endurance (push-ups) over an 8-week period compared with the control group while maintaining handgrip strength, lower body power (countermovement jump), and cardiovascular fitness (). Thus, this study provides initial evidence of the effectiveness of the movr app for enhancing functional movement and physical fitness among healthy adults. Trial Registration: ClinicalTrials.gov NCT04865666; https://clinicaltrials.gov/ct2/show/NCT04865666 %M 34047704 %R 10.2196/24076 %U https://mhealth.jmir.org/2021/5/e24076 %U https://doi.org/10.2196/24076 %U http://www.ncbi.nlm.nih.gov/pubmed/34047704 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e23411 %T Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review %A St Fleur,Ruth Gaelle %A St George,Sara Mijares %A Leite,Rafael %A Kobayashi,Marissa %A Agosto,Yaray %A Jake-Schoffman,Danielle E %+ Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th St, Miami, FL, 33136, United States, 1 305 2432000, s.stgeorge@med.miami.edu %K physical activity %K Fitbit %K eHealth %K life course %K mobile phone %D 2021 %7 28.5.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Commercial off-the-shelf activity trackers (eg, Fitbit) allow users to self-monitor their daily physical activity (PA), including the number of steps, type of PA, amount of sleep, and other features. Fitbits have been used as both measurement and intervention tools. However, it is not clear how they are being incorporated into PA intervention studies, and their use in specific age groups across the life course is not well understood. Objective: This narrative review aims to characterize how PA intervention studies across the life course use Fitbit devices by synthesizing and summarizing information on device selection, intended use (intervention vs measurement tool), participant wear instructions, rates of adherence to device wear, strategies used to boost adherence, and the complementary use of other PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices. Methods: We conducted a search of the Fitabase Fitbit Research Library, a database of studies published between 2012 and 2018. Of the 682 studies available on the Fitabase research library, 60 interventions met the eligibility criteria and were included in this review. A supplemental search in PubMed resulted in the inclusion of 15 additional articles published between 2019 and 2020. A total of 75 articles were reviewed, which represented interventions conducted in childhood; adolescence; and early, middle, and older adulthood. Results: There was considerable heterogeneity in the use of Fitbit within and between developmental stages. Interventions for adults typically required longer wear periods, whereas studies on children and adolescents tended to have more limited device wear periods. Most studies used developmentally appropriate behavior change techniques and device wear instructions. Regardless of the developmental stage and intended Fitbit use (ie, measurement vs intervention tool), the most common strategies used to enhance wear time included sending participants reminders through texts or emails and asking participants to log their steps or synchronize their Fitbit data daily. The rates of adherence to the wear time criteria were reported using varying metrics. Most studies supplemented the use of Fitbit with additional objective or self-reported measures for PA. Conclusions: Overall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research. As the use of monitoring devices continues to expand in PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for comparability purposes. There is a need for increased transparency in the prospective registration of PA intervention studies. Researchers need to provide a clear rationale for the use of several PA measures and specify the source of their main PA outcome and how additional measures will be used in the context of Fitbit-based interventions. %M 34047705 %R 10.2196/23411 %U https://mhealth.jmir.org/2021/5/e23411 %U https://doi.org/10.2196/23411 %U http://www.ncbi.nlm.nih.gov/pubmed/34047705 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 5 %P e26096 %T Using Biometric Sensor Data to Monitor Cancer Patients During Radiotherapy: Protocol for the OncoWatch Feasibility Study %A Holländer-Mieritz,Cecilie %A Vogelius,Ivan R %A Kristensen,Claus A %A Green,Allan %A Rindum,Judith L %A Pappot,Helle %+ Department of Oncology, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, Copenhagen, 2100, Denmark, 45 35453545, cecilie.hollaender-mieritz@regionh.dk %K biometric sensor technology %K cancer %K head and neck cancer %K home monitoring %K patient-generated health data %K radiotherapy %K sensor %K smartwatch %D 2021 %7 13.5.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Patients with head and neck cancer (HNC) experience severe side effects during radiotherapy (RT). Ongoing technological advances in wearable biometric sensors allow for the collection of objective data (eg, physical activity and heart rate), which might, in the future, help detect and counter side effects before they become severe. A smartwatch such as the Apple Watch allows for objective data monitoring outside the hospital with minimal effort from the patient. To determine whether such tools can be implemented in the oncological setting, feasibility studies are needed. Objective: This protocol describes the design of the OncoWatch 1.0 feasibility study that assesses the adherence of patients with HNC to an Apple Watch during RT. Methods: A prospective, single-cohort trial will be conducted at the Department of Oncology, Rigshospitalet (Copenhagen, Denmark). Patients aged ≥18 years intended for primary or postoperative curatively intended RT for HNC will be recruited. Consenting patients will be asked to wear an Apple Watch on the wrist during and until 2 weeks after RT. The study will include 10 patients. Data on adherence, data acquisition, and biometric data will be collected. Demographic data, objective toxicity scores, and hospitalizations will be documented. Results: The primary outcome is to determine if it is feasible for the patients to wear a smartwatch continuously (minimum 12 hours/day) during RT. Furthermore, we will explore how the heart rate and physical activity change over the treatment course. Conclusions: The study will assess the feasibility of using the Apple Watch for home monitoring of patients with HNC. Our findings may provide novel insights into the patient’s activity levels and variations in heart rate during the treatment course. The knowledge obtained from this study will be essential for further investigating how biometric data can be used as part of symptom monitoring for patients with HNC. Trial Registration: ClinicalTrials.gov NCT04613232; https://clinicaltrials.gov/ct2/show/NCT04613232 International Registered Report Identifier (IRRID): PRR1-10.2196/26096 %M 33983123 %R 10.2196/26096 %U https://www.researchprotocols.org/2021/5/e26096 %U https://doi.org/10.2196/26096 %U http://www.ncbi.nlm.nih.gov/pubmed/33983123 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e13391 %T Predictors of Walking App Users With Comparison of Current Users, Previous Users, and Informed Nonusers in a Sample of Dutch Adults: Questionnaire Study %A De Bruijn,Gert-Jan %A Dallinga,Joan Martine %A Deutekom,Marije %+ Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Nieuwe Achtergracht 166, Amsterdam, 1018 WV, Netherlands, 31 205252636, g.j.debruijn@uva.nl %K technology %K walking %K health %K adult %K survey %K questionnaires %D 2021 %7 12.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The last decade has seen a substantial increase in the use of mobile health apps and research into the effects of those apps on health and health behaviors. In parallel, research has aimed at identifying population subgroups that are more likely to use those health apps. Current evidence is limited by two issues. First, research has focused on broad health apps, and little is known about app usage for a specific health behavior. Second, research has focused on comparing current users and current nonusers, without considering subgroups of nonusers. Objective: We aimed to provide profile distributions of current users, previous users, and informed nonusers, and to identify predictor variables relevant for profile classification. Methods: Data were available from 1683 people who participated in a Dutch walking event in Amsterdam that was held in September 2017. They provided information on demographics, self-reported walking behavior, and walking app usage, as well as items from User Acceptance of Information Technology, in an online survey. Data were analyzed using discriminant function analysis and multinomial logistic regression analysis. Results: Most participants were current walking app users (899/1683, 53.4%), while fewer participants were informed nonusers (663/1683, 39.4%) and very few were previous walking app users (121/1683, 7.2%). Current walking app users were more likely to report walking at least 5 days per week and for at least 30 minutes per bout (odds ratio [OR] 1.44, 95% CI 1.11-1.85; P=.005) and more likely to be overweight (OR 1.72, 95% CI 1.24-2.37; P=.001) or obese (OR 1.49, 95% CI 1.08-2.08; P=.005) as compared with informed nonusers. Further, current walking app users perceived their walking apps to be less boring, easy to use and retrieve information, and more helpful to achieve their goals. Effect sizes ranged from 0.10 (95% CI 0.08-0.30) to 1.58 (95% CI 1.47-1.70). Conclusions: The distributions for walking app usage appeared different from the distributions for more general health app usage. Further, the inclusion of two specific subgroups of nonusers (previous users and informed nonusers) provides important information for health practitioners and app developers to stimulate continued walking app usage, including making information in those apps easy to understand and making it easy to obtain information from the apps, as well as preventing apps from becoming boring and difficult to use for goal attainment. %M 33978595 %R 10.2196/13391 %U https://mhealth.jmir.org/2021/5/e13391 %U https://doi.org/10.2196/13391 %U http://www.ncbi.nlm.nih.gov/pubmed/33978595 %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 %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e26387 %T Exploring Breaks in Sedentary Behavior of Older Adults Immediately After Receiving Personalized Haptic Feedback: Intervention Study %A Compernolle,Sofie %A Van Dyck,Delfien %A Cardon,Greet %A Brondeel,Ruben %+ Department of Movement and Sports Sciences, Ghent University, Watersportlaan 2, Ghent, 9000, Belgium, 32 92646323, sofie.compernolle@ugent.be %K tactile feedback %K sitting behavior %K sedentary behavior %K older adults %K mHealth intervention %K self-monitoring %D 2021 %7 10.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: “Push” components of mobile health interventions may be promising to create conscious awareness of habitual sedentary behavior; however, the effect of these components on the near-time, proximal outcome, being breaks in sedentary behavior immediately after receiving a push notification, is still unknown, especially in older adults. Objective: The aims of this study are to examine if older adults break their sedentary behavior immediately after receiving personalized haptic feedback on prolonged sedentary behavior and if the percentage of breaks differs depending on the time of the day when the feedback is provided. Methods: A total of 26 Flemish older adults (mean age 64.4 years, SD 3.8) wore a triaxial accelerometer (Activator, PAL Technologies Ltd) for 3 weeks. The accelerometer generated personalized haptic feedback by means of vibrations each time a participant sat for 30 uninterrupted minutes. Accelerometer data on sedentary behavior were used to estimate the proximal outcome, which was sedentary behavior breaks immediately (within 1, 3, and 5 minutes) after receiving personalized haptic feedback. Generalized estimating equations were used to investigate whether or not participants broke up their sedentary behavior immediately after receiving haptic feedback. A time-related variable was added to the model to investigate if the sedentary behavior breaks differed depending on the time of day. Results: A total of 2628 vibrations were provided to the participants during the 3-week intervention period. Of these 2628 vibrations, 379 (14.4%), 570 (21.7%), and 798 (30.4%) resulted in a sedentary behavior break within 1, 3 and 5 minutes, respectively. Although the 1-minute interval did not reveal significant differences in the percentage of breaks depending on the time at which the haptic feedback was provided, the 3- and 5-minute intervals did show significant differences in the percentage of breaks depending on the time at which the haptic feedback was provided. Concretely, the percentage of sedentary behavior breaks was significantly higher if personalized haptic feedback was provided between noon and 3 PM compared to if the feedback was provided between 6 and 9 AM (odds ratio 1.58, 95% CI 1.01-2.47, within 3 minutes; odds ratio 1.78, 95% CI 1.11-2.84, within 5 minutes). Conclusions: The majority of haptic vibrations, especially those in the morning, did not result in a break in the sedentary behavior of older adults. As such, simply bringing habitual sedentary behavior into conscious awareness seems to be insufficient to target sedentary behavior. More research is needed to optimize push components in interventions aimed at the reduction of the sedentary behavior of older adults. Trial Registration: ClinicalTrials.gov NCT04003324; https://clinicaltrials.gov/ct2/show/NCT04003324 %M 33970109 %R 10.2196/26387 %U https://mhealth.jmir.org/2021/5/e26387 %U https://doi.org/10.2196/26387 %U http://www.ncbi.nlm.nih.gov/pubmed/33970109 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e22591 %T Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study %A Wu,Chia-Tung %A Li,Guo-Hung %A Huang,Chun-Ta %A Cheng,Yu-Chieh %A Chen,Chi-Hsien %A Chien,Jung-Yien %A Kuo,Ping-Hung %A Kuo,Lu-Cheng %A Lai,Feipei %+ Department of Internal Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan University, No 7 Chung-Shan S Road, Taipei, 100, Taiwan, 886 972651516, jychien@ntu.edu.tw %K chronic obstructive pulmonary disease %K clinical decision support systems %K health risk assessment %K wearable device %D 2021 %7 6.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. Objective: The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. Methods: This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. Results: The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. Conclusions: Using wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data. %M 33955840 %R 10.2196/22591 %U https://mhealth.jmir.org/2021/5/e22591 %U https://doi.org/10.2196/22591 %U http://www.ncbi.nlm.nih.gov/pubmed/33955840 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e20966 %T Usability and Accuracy of a Smartwatch for the Assessment of Physical Activity in the Elderly Population: Observational Study %A Martinato,Matteo %A Lorenzoni,Giulia %A Zanchi,Tommaso %A Bergamin,Alessia %A Buratin,Alessia %A Azzolina,Danila %A Gregori,Dario %+ Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac-Thoracic-Vascular Sciences and Public Health, University of Padova, Via Leonardo Loredan, 18, Padova, , Italy, 39 049 827 5384, dario.gregori@unipd.it %K wearable devices %K elderly %K physical activity %K smartwatches %D 2021 %7 5.5.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Regular physical activity (PA) contributes to the primary and secondary prevention of several chronic diseases and reduces the risk of premature death. Physical inactivity is a modifiable risk factor for cardiovascular disease and a variety of chronic disorders such as diabetes, obesity, hypertension, bone and joint diseases (eg, osteoporosis and osteoarthritis), depression, and colon and breast cancer. Population aging and the related increase in chronic diseases have a major impact on the health care systems of most Western countries and will produce an even more significant effect in the future. Monitoring PA is a valuable method of determining whether people are performing enough PA so as to prevent chronic diseases or are showing early symptoms of those diseases. Objective: The aim of this study was to estimate the accuracy of wearable devices in quantifying the PA of elderly people in a real-life setting. Methods: Participants aged 70 to 90 years with the ability to walk safely without any walking aid for at least 300 meters, who had no walking disabilities or episodes of falling while walking in the last 12 months, were asked to walk 150 meters at their preferred pace wearing a vívoactive HR device (Garmin Ltd) and actual steps were monitored and tallied by a researcher using a hand-tally counter to assess the performance of the device at a natural speed. A Bland-Altman plot was used to analyze the difference between manually counted steps and wearable device–measured steps. The intraclass correlation coefficient (ICC) was computed (with a 95% confidence interval) between step measurements. The generalized linear mixed-model (GLMM) ICCs were estimated, providing a random effect term (random intercept) for the individual measurements (gold standard and device). Both adjusted and conditional ICCs were computed for the GLMM models considering separately the effect of age, sex, BMI, and obesity. Analyses were performed using R software (R Foundation for Statistical Computing) with the rms package. Results: A total of 23 females and 26 males were enrolled in the study. The median age of the participants was 75 years. The Bland-Altman plot revealed that, excluding one observation, all differences across measurements were in the confidence bounds, demonstrating the substantial agreement between the step count measurements. The results were confirmed by an ICC equal to .98 (.96-.99), demonstrating excellent agreement between the two sets of measurements. Conclusions: The level of accuracy of wearable devices in quantifying the PA of elderly people in a real-life setting that was found in this study supports the idea of considering wrist-wearable nonmedical devices (widely available in nonspecialized stores) as reliable tools. Both health care professionals and informal caregivers could monitor the level of PA of their patients. %M 33949953 %R 10.2196/20966 %U https://mhealth.jmir.org/2021/5/e20966 %U https://doi.org/10.2196/20966 %U http://www.ncbi.nlm.nih.gov/pubmed/33949953 %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 %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e26699 %T Long-term Effectiveness of mHealth Physical Activity Interventions: Systematic Review and Meta-analysis of Randomized Controlled Trials %A Mönninghoff,Annette %A Kramer,Jan Niklas %A Hess,Alexander Jan %A Ismailova,Kamila %A Teepe,Gisbert W %A Tudor Car,Lorainne %A Müller-Riemenschneider,Falk %A Kowatsch,Tobias %+ Institute for Customer Insight, University of St. Gallen, Bahnhofstrasse 8, St. Gallen, 9000, Switzerland, 41 76 229 3150, Annette.Moenninghoff@unisg.ch %K mHealth %K physical activity %K systematic review %K meta-analysis %K mobile phone %D 2021 %7 30.4.2021 %9 Review %J J Med Internet Res %G English %X Background: Mobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood. Objective: The primary aim of this study is to understand the immediate and long-term effects of mHealth interventions on PA. The secondary aim is to explore potential effect moderators. Methods: We performed this study according to the Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, the Cochrane Library, SCOPUS, and PsycINFO in July 2020. Eligible studies included randomized controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate-to-vigorous physical activity (MVPA), total physical activity (TPA), and energy expenditure. Where reported, we extracted data for 3 time points (ie, end of intervention, follow-up ≤6 months, and follow-up >6 months). To explore effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration tool. Results: Of the 2828 identified studies, 117 were included. These studies reported on 21,118 participants with a mean age of 52.03 (SD 14.14) years, of whom 58.99% (n=12,459) were female. mHealth interventions significantly increased PA across all the 4 outcome measures at the end of intervention (walking standardized mean difference [SMD] 0.46, 95% CI 0.36-0.55; P<.001; MVPA SMD 0.28, 95% CI 0.21-0.35; P<.001; TPA SMD 0.34, 95% CI 0.20-0.47; P<.001; energy expenditure SMD 0.44, 95% CI 0.13-0.75; P=.01). Only 33 studies reported short-term follow-up measurements, and 8 studies reported long-term follow-up measurements in addition to end-of-intervention results. In the short term, effects were sustained for walking (SMD 0.26, 95% CI 0.09-0.42; P=.002), MVPA (SMD 0.20, 95% CI 0.05-0.35; P=.008), and TPA (SMD 0.53, 95% CI 0.13-0.93; P=.009). In the long term, effects were also sustained for walking (SMD 0.25, 95% CI 0.10-0.39; P=.001) and MVPA (SMD 0.19, 95% CI 0.11-0.27; P<.001). We found the study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and nonscalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 80.3% (94/117) of the studies. Heterogeneity was significant, resulting in low to very low quality of evidence. Conclusions: mHealth interventions can foster small to moderate increases in PA. The effects are maintained long term; however, the effect size decreases over time. The results encourage using mHealth interventions in at-risk and sick populations and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given the low evidence quality, further methodologically rigorous studies are warranted to evaluate the long-term effects. %M 33811021 %R 10.2196/26699 %U https://www.jmir.org/2021/4/e26699 %U https://doi.org/10.2196/26699 %U http://www.ncbi.nlm.nih.gov/pubmed/33811021 %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 %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e21622 %T Effects of an mHealth App (Kencom) With Integrated Functions for Healthy Lifestyles on Physical Activity Levels and Cardiovascular Risk Biomarkers: Observational Study of 12,602 Users %A Hamaya,Rikuta %A Fukuda,Hiroshi %A Takebayashi,Masaki %A Mori,Masaki %A Matsushima,Ryuji %A Nakano,Ken %A Miyake,Kuniaki %A Tani,Yoshiaki %A Yokokawa,Hirohide %+ Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 900 Commonwealth Avenue, Boston, MA, 02215, United States, 1 617 732 4965, rktrocky@gmail.com %K mHealth %K app %K cardiovascular disease %K physical activity %K smartphone %K mobile phone %D 2021 %7 26.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Mobile health (mHealth) apps are considered to be potentially powerful tools for improving lifestyles and preventing cardiovascular disease (CVD), although only few have undergone large, well-designed epidemiological research. “kencom” is a novel mHealth app with integrated functions for healthy lifestyles such as monitoring daily health/step data, providing tailored health information, or facilitating physical activity through group-based game events. The app is linked to large-scale Japanese insurance claims databases and annual health check-up databases, thus comprising a large longitudinal cohort. Objective: We aimed to assess the effects of kencom on physical activity levels and CVD risk factors such as obesity, hypertension, dyslipidemia, and diabetes mellitus in a large population in Japan. Methods: Daily step count, annual health check-up data, and insurance claim data of the kencom users were integrated within the kencom system. Step analysis was conducted by comparing the 1-year average daily step count before and after kencom registration. In the CVD risk analysis, changes in CVD biomarkers following kencom registration were evaluated among the users grouped into the quintile according to their change in step count. Results: A total of 12,602 kencom users were included for the step analysis and 5473 for the CVD risk analysis. The participants were generally healthy and their mean age was 44.1 (SD 10.2) years. The daily step count significantly increased following kencom registration by a mean of 510 steps/day (P<.001). In particular, participation in “Arukatsu” events held twice a year within the app was associated with a remarkable increase in step counts. In the CVD risk analysis, the users of the highest quintile in daily step change had, compared with those of the lowest quartile, a significant reduction in weight (–0.92 kg, P<.001), low-density lipoprotein cholesterol (–2.78 mg/dL, P=.004), hemoglobin A1c (HbA1c; –0.04%, P=.004), and increase in high-density lipoprotein cholesterol (+1.91 mg/dL, P<.001) after adjustment of confounders. Conclusions: The framework of kencom successfully integrated the Japanese health data from multiple data sources to generate a large, longitudinal data set. The use of the kencom app was significantly associated with enhanced physical activity, which might lead to weight loss and improvement in lipid profile. %M 33900203 %R 10.2196/21622 %U https://www.jmir.org/2021/4/e21622 %U https://doi.org/10.2196/21622 %U http://www.ncbi.nlm.nih.gov/pubmed/33900203 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 4 %P e23806 %T Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study %A Henriksen,André %A Johannessen,Erlend %A Hartvigsen,Gunnar %A Grimsgaard,Sameline %A Hopstock,Laila Arnesdatter %+ Department of Community Medicine, UiT The Arctic University of Norway, Postboks 6050 langnes, Tromsø, 9037, Norway, 47 77645214, andre.henriksen@uit.no %K COVID-19 %K energy expenditure %K steps %K smart watch %K fitness tracker %K actigraphy %K public health %K lockdown %K SARS-CoV-2 %K pandemic %K wearables %D 2021 %7 23.4.2021 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems. Objective: The aim of this study is to develop a system to record data on physical activity from different providers of consumer-based activity trackers and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown. Methods: We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from early 2019 and onward. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly changes in the number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure between 2019 and 2020 using bar plots and two-sided paired sample t tests and Wilcoxon signed-rank tests. Results: Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction in steps and activity energy expenditure was temporary, and the following monthly comparisons showed no significant change between 2019 and 2020. A small significant increase in moderate-to-vigorous physical activity was observed for several monthly comparisons after the lockdown period and when comparing March-December 2019 with March-December 2020. Conclusions: mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine changes in physical activity levels during the COVID-19 period. %M 33843598 %R 10.2196/23806 %U https://publichealth.jmir.org/2021/4/e23806 %U https://doi.org/10.2196/23806 %U http://www.ncbi.nlm.nih.gov/pubmed/33843598 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 4 %P e24254 %T A Technology-Based Physical Activity Intervention for Patients With Metastatic Breast Cancer (Fit2ThriveMB): Protocol for a Randomized Controlled Trial %A Phillips,Siobhan %A Solk,Payton %A Welch,Whitney %A Auster-Gussman,Lisa %A Lu,Marilyn %A Cullather,Erin %A Torre,Emily %A Whitaker,Madelyn %A Izenman,Emily %A La,Jennifer %A Lee,Jungwha %A Spring,Bonnie %A Gradishar,William %+ Northwestern University Feinberg School of Medicine, 680 N Lake Shore Drive, Suite 1400, Chicago, IL, 60611-4407, United States, 1 13125034235, smphillips@northwestern.edu %K physical activity %K metastatic breast cancer %K technology %K randomized controlled trial %K mobile phone %D 2021 %7 23.4.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Increased incidence and life expectancy have resulted in a growing population of patients with metastatic breast cancer, and these patients experience high rates of morbidity and premature mortality. Increased physical activity (PA) is consistently associated with improved health and disease outcomes among early-stage survivors. However, there is a paucity of research on PA in patients with metastatic breast cancer, and existing PA interventions have exhibited low feasibility because of their focus on intense PA and/or requirement of on-site visits. Mobile health (mHealth)–based PA interventions may be particularly useful for patients with metastatic breast cancer because they allow for remote monitoring, which facilitates individual tailoring of PA recommendations to patients’ abilities and may minimize participant burden. However, no studies have examined mHealth PA interventions in patients with metastatic breast cancer. Objective: We aim to address these critical research gaps by testing a highly tailored technology-based intervention to promote PA of any intensity (ie, light, moderate, or vigorous) by increasing daily steps in patients with metastatic breast cancer. The primary aim of this study is to test the feasibility and acceptability of the Fit2ThriveMB intervention. We will also examine outcome patterns suggesting the efficacy of Fit2ThriveMB on symptom burden, quality of life, and functional performance. Methods: The Fit2ThriveMB trial is a two-arm pilot randomized controlled trial that will compare the effects of a smartphone-delivered, home-based PA intervention and an attention-control education condition on PA and quality of life in low-active female patients with metastatic breast cancer. A subsample (n=25) will also complete functional performance measures. This innovative trial will recruit 50 participants who will be randomized into the study’s intervention or control arm. The intervention will last 12 weeks. The Fit2ThriveMB intervention consists of a Fitbit, coaching calls, and the Fit2ThriveMB smartphone app that provides self-monitoring, a tailored goal-setting tool, real-time tailored feedback, app notifications, and a group message board. Assessments will occur at baseline and post intervention. Results: The Fit2ThriveMB study is ongoing. Data collection ended in February 2021. Conclusions: Data from this study will provide the preliminary effect sizes needed to assemble an intervention that is to be evaluated in a fully powered trial. In addition, these data will provide essential evidence to support the feasibility and acceptability of using a technology-based PA promotion intervention, a scalable strategy that could be easily integrated into care, among patients with metastatic breast cancer. Trial Registration: ClinicalTrials.gov NCT04129346; https://clinicaltrials.gov/ct2/show/NCT04129346 International Registered Report Identifier (IRRID): DERR1-10.2196/24254 %M 33890857 %R 10.2196/24254 %U https://www.researchprotocols.org/2021/4/e24254 %U https://doi.org/10.2196/24254 %U http://www.ncbi.nlm.nih.gov/pubmed/33890857 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e27383 %T Results of a Culturally Tailored Smartphone-Delivered Physical Activity Intervention Among Midlife African American Women: Feasibility Trial %A Joseph,Rodney P %A Ainsworth,Barbara E %A Hollingshead,Kevin %A Todd,Michael %A Keller,Colleen %+ Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, 500 N 3rd St, Phoenix, AZ, 85004, United States, 1 602 496 0772, rodney.joseph@asu.edu %K exercise %K physical activity %K minority health %K women’s health %K mHealth %K mobile phone %D 2021 %7 22.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Regular aerobic physical activity (PA) is an important component of healthy aging. However, only 27%-40% of African American women achieve national PA guidelines. Available data also show a clear decline in PA as African American women transition from young adulthood (ie, 25-44 years) into midlife. This decline in PA during midlife coincides with an increased risk for African American women developing cardiometabolic disease conditions, including obesity, type 2 diabetes, and cardiovascular disease. Thus, effective efforts are needed to promote PA among sedentary African American women during midlife. Objective: This study aims to examine the acceptability and feasibility of a culturally tailored, smartphone-delivered PA intervention, originally developed to increase PA among African American women aged 24-49 years, among a slightly older sample of midlife African American women aged 50-65 years. Methods: A single-arm pretest-posttest study design was implemented. In total, 20 insufficiently active African American (ie, ≤60 min per week of PA) women between the ages of 50-65 years participated in the 4-month feasibility trial. The Smart Walk intervention was delivered through the study Smart Walk smartphone app and text messages. Features available on the Smart Walk app include personal profile pages, multimedia PA promotion modules, discussion board forums, and an activity tracking feature that integrates with Fitbit activity monitors. Self-reported PA and social cognitive theory mediators targeted by the intervention (ie, self-regulation, behavioral capability, outcome expectations, self-efficacy, and social support) were assessed at baseline and at 4 months. Feasibility and acceptability were assessed using a postintervention satisfaction survey that included multiple-choice and open-ended questions evaluating participant perceptions of the intervention and suggestions for intervention improvement. Wilcoxon signed-rank tests were used to examine pre- and postintervention changes in the PA and social cognitive theory variables. The effect size estimates were calculated using the Pearson r test statistic. Results: Participants increased moderate-to-vigorous PA (median 30 minutes per week increase; r=0.503; P=.002) and reported improvements in 2 theoretical mediators (self-regulation: r=0.397; P=.01; behavioral capability: r=0.440; P=.006). Nearly all participants (14/15, 93% completing the satisfaction survey) indicated that they would recommend the intervention to a friend. Participants’ suggestions for improving the intervention included enhancing the intervention’s provisions of social support for PA. Conclusions: The results provide preliminary support for the feasibility of the smartphone-based approach to increase PA among midlife African American women. However, before larger-scale implementation among midlife African American women, enhancements to the social support components of the intervention are warranted. Trial Registration: ClinicalTrials.gov NCT04073355; https://clinicaltrials.gov/ct2/show/NCT04073355 %M 33885368 %R 10.2196/27383 %U https://mhealth.jmir.org/2021/4/e27383 %U https://doi.org/10.2196/27383 %U http://www.ncbi.nlm.nih.gov/pubmed/33885368 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 4 %P e23432 %T A Mental Health–Informed Physical Activity Intervention for First Responders and Their Partners Delivered Using Facebook: Mixed Methods Pilot Study %A McKeon,Grace %A Steel,Zachary %A Wells,Ruth %A Newby,Jill %A Hadzi-Pavlovic,Dusan %A Vancampfort,Davy %A Rosenbaum,Simon %+ School of Psychiatry, University of New South Wales, Level 1, AGSM, Botany Street, Sydney, 2031, Australia, 61 9065 9097, g.mckeon@unsw.edu.au %K physical activity %K PTSD %K social media %K first responders %K mental health %K families %K online %K exercise %D 2021 %7 22.4.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: First responders (eg, police, firefighters, and paramedics) are at high risk of experiencing poor mental health. Physical activity interventions can help reduce symptoms and improve mental health in this group. More research is needed to evaluate accessible, low-cost methods of delivering programs. Social media may be a potential platform for delivering group-based physical activity interventions. Objective: This study aims to examine the feasibility and acceptability of delivering a mental health–informed physical activity program for first responders and their self-nominated support partners. This study also aims to assess the feasibility of applying a novel multiple time series design and to explore the impact of the intervention on mental health symptoms, sleep quality, quality of life, and physical activity levels. Methods: We co-designed a 10-week web-based physical activity program delivered via a private Facebook group. We provided education and motivation around different topics weekly (eg, goal setting, overcoming barriers to exercise, and reducing sedentary behavior) and provided participants with a Fitbit. A multiple time series design was applied to assess psychological distress levels, with participants acting as their own control before the intervention. Results: In total, 24 participants (12 first responders and 12 nominated support partners) were recruited, and 21 (88%) completed the postassessment questionnaires. High acceptability was observed in the qualitative interviews. Exploratory analyses revealed significant reductions in psychological distress during the intervention. Preintervention and postintervention analysis showed significant improvements in quality of life (P=.001; Cohen d=0.60); total depression, anxiety, and stress scores (P=.047; Cohen d=0.35); and minutes of walking (P=.04; Cohen d=0.55). Changes in perceived social support from family (P=.07; Cohen d=0.37), friends (P=.10; Cohen d=0.38), and sleep quality (P=.28; Cohen d=0.19) were not significant. Conclusions: The results provide preliminary support for the use of social media and a multiple time series design to deliver mental health–informed physical activity interventions for first responders and their support partners. Therefore, an adequately powered trial is required. Trial Registration: Australian New Zealand Clinical Trials Registry (ACTRN): 12618001267246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618001267246. %M 33885376 %R 10.2196/23432 %U https://formative.jmir.org/2021/4/e23432 %U https://doi.org/10.2196/23432 %U http://www.ncbi.nlm.nih.gov/pubmed/33885376 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 4 %P e20468 %T Mobile Health Intervention Promoting Physical Activity in Adults Post Cardiac Rehabilitation: Pilot Randomized Controlled Trial %A Park,Linda G %A Elnaggar,Abdelaziz %A Lee,Sei J %A Merek,Stephanie %A Hoffmann,Thomas J %A Von Oppenfeld,Julia %A Ignacio,Nerissa %A Whooley,Mary A %+ Department of Community Health Systems, School of Nursing, University of California San Francisco, 2 Koret Way, Room 531A, San Francisco, CA, United States, 1 415 221 4810 ext 22573, abdelaziz.elnaggar@ucsf.edu %K physical activity %K cardiac rehabilitation %K digital health %K mobile app %K wearable device %K mHealth %D 2021 %7 16.4.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Cardiac rehabilitation (CR) is an exercise-based program prescribed after cardiac events associated with improved physical, mental, and social functioning; however, many patients return to a sedentary lifestyle leading to deteriorating functional capacity after discharge from CR. Physical activity (PA) is critical to avoid recurrence of cardiac events and mortality and maintain functional capacity. Leveraging mobile health (mHealth) strategies to increase adherence to PA is a promising approach. Based on the social cognitive theory, we sought to determine whether mHealth strategies (Movn mobile app for self-monitoring, supportive push-through messages, and wearable activity tracker) would improve PA and functional capacity over 2 months. Objective: The objectives of this pilot randomized controlled trial were to examine preliminary effects of an mHealth intervention on group differences in PA and functional capacity and group differences in depression and self-efficacy to maintain exercise after CR. Methods: During the final week of outpatient CR, patients were randomized 1:1 to the intervention group or usual care. The intervention group downloaded the Movn mobile app, received supportive push-through messages on motivation and educational messages related to cardiovascular disease (CVD) management 3 times per week, and wore a Charge 2 (Fitbit Inc) activity tracker to track step counts. Participants in the usual care group wore a pedometer and recorded their daily steps in a diary. Data from the 6-minute walk test (6MWT) and self-reported questionnaires were collected at baseline and 2 months. Results: We recruited 60 patients from 2 CR sites at a community hospital in Northern California. The mean age was 68.0 (SD 9.3) years, and 23% (14/60) were female; retention rate was 85% (51/60). Our results from 51 patients who completed follow-up showed the intervention group had a statistically significant higher mean daily step count compared with the control (8860 vs 6633; P=.02). There was no difference between groups for the 6MWT, depression, or self-efficacy to maintain exercise. Conclusions: This intervention addresses a major public health initiative to examine the potential for mobile health strategies to promote PA in patients with CVD. Our technology-based pilot mHealth intervention provides promising results on a pragmatic and contemporary approach to promote PA by increasing daily step counts after completing CR. Trial Registration: ClinicalTrials.gov NCT03446313; https://clinicaltrials.gov/ct2/show/NCT03446313 %M 33861204 %R 10.2196/20468 %U https://formative.jmir.org/2021/4/e20468 %U https://doi.org/10.2196/20468 %U http://www.ncbi.nlm.nih.gov/pubmed/33861204 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e25385 %T Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study %A El Fatouhi,Douae %A Delrieu,Lidia %A Goetzinger,Catherine %A Malisoux,Laurent %A Affret,Aurélie %A Campo,David %A Fagherazzi,Guy %+ Department of Population Health, Luxembourg Institute of Health, 1 A-B Rue Thomas Edison, Strassen, L-1445, Luxembourg, 352 26 97 04 57, guy.fagherazzi@lih.lu %K connected devices %K Withings %K physical activity %K step count %K wearable activity trackers %K digital health %K free-living %K weight loss %K digital scale %K mobile phone %D 2021 %7 15.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity (PA) is a modifiable lifestyle factor that can be targeted to increase energy expenditure and promote weight loss. However, the amount of PA required for weight loss remains inconsistent. Wearable activity trackers constitute a valuable opportunity to obtain objective measurements of PA and study large populations in real-life settings. Objective: We aim to study the associations of initial device-assessed PA characteristics (average step counts and step count variability) and their evolution with 6-month weight change. Methods: We analyzed data from 26,935 Withings-connected device users (wearable activity trackers and digital scales). To assess the initial PA characteristics and their 6-month changes, we used data recorded during the first and sixth 30-day periods of activity tracker use. For each of these periods, we used the monthly mean of daily step values as a proxy for PA level and derived the monthly coefficient of variation (CV) of daily step values to estimate PA level variability. Associations between initial PA characteristics and 6-month weight change were assessed using multivariable linear regression analyses controlled for age, sex, blood pressure, heart rate, and the predominant season. Restricted cubic spline regression was performed to better characterize the continuous shape of the associations between PA characteristics and weight change. Secondary analyses were performed by analyzing the 6-month evolution of PA characteristics in relation to weight change. Results: Our results revealed that both a greater PA level and lower PA level variability were associated with weight loss. Compared with individuals who were initially in the sedentary category (<5000 steps/day), individuals who were low active (5000-7499 steps/day), somewhat active (7500-9999 steps/day), and active (≥10,000 steps/day) had a 0.21-kg, a 0.52-kg, and a 1.17-kg greater decrease in weight, respectively (95% CI −0.36 to −0.06, −0.70 to −0.33, and −1.42 to −0.93, respectively). Compared with users whose PA level CV was >63%, users whose PA level CV ranged from 51% to 63%, 40% to 51%, and was ≤40%, had a 0.19-kg, a 0.23-kg, and a 0.33-kg greater decrease in weight, respectively (95% CI −0.38 to −0.01, −0.41 to −0.04, and −0.53 to −0.13, respectively). We also observed that each 1000 steps/day increase in PA level over the 6-month follow-up was associated with a 0.26-kg (95% CI −0.29 to −0.23) decrease in weight. No association was found between the 6-month changes in PA level variability and weight change. Conclusions: Our results add to the current body of knowledge that health benefits can be observed below the 10,000 steps/day threshold and suggest that not only increased mean PA level but also greater regularity of the PA level may play important roles in short-term weight loss. %M 33856352 %R 10.2196/25385 %U https://mhealth.jmir.org/2021/4/e25385 %U https://doi.org/10.2196/25385 %U http://www.ncbi.nlm.nih.gov/pubmed/33856352 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 4 %P e18803 %T TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study %A Liu,Xiaoli %A Liu,Tongbo %A Zhang,Zhengbo %A Kuo,Po-Chih %A Xu,Haoran %A Yang,Zhicheng %A Lan,Ke %A Li,Peiyao %A Ouyang,Zhenchao %A Ng,Yeuk Lam %A Yan,Wei %A Li,Deyu %+ Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100083, China, 86 010 82339093, deyuli@buaa.edu.cn %K tachycardia onset %K early prediction %K deep neural network %K wearable monitoring system %K electronic health record %D 2021 %7 15.4.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Without timely diagnosis and treatment, tachycardia, also called tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and even death. The predictive performance of conventional clinical diagnostic procedures needs improvement in order to assist physicians in detecting risk early on. Objective: We aimed to develop a deep tachycardia onset prediction (TOP-Net) model based on deep learning (ie, bidirectional long short-term memory) for early tachycardia diagnosis with easily accessible data. Methods: TOP-Net leverages 2 easily accessible data sources: vital signs, including heart rate, respiratory rate, and blood oxygen saturation (SpO2) acquired continuously by wearable embedded systems, and electronic health records, containing age, gender, admission type, first care unit, and cardiovascular disease history. The model was trained with a large data set from an intensive care unit and then transferred to a real-world scenario in the general ward. In this study, 3 experiments incorporated merging patients’ personal information, temporal memory, and different feature combinations. Six metrics (area under the receiver operating characteristic curve [AUROC], sensitivity, specificity, accuracy, F1 score, and precision) were used to evaluate predictive performance. Results: TOP-Net outperformed the baseline models on the large critical care data set (AUROC 0.796, 95% CI 0.768-0.824; sensitivity 0.753, 95% CI 0.663-0.793; specificity 0.720, 95% CI 0.645-0.758; accuracy 0.721; F1 score 0.718; precision 0.686) when predicting tachycardia onset 6 hours in advance. When predicting tachycardia onset 2 hours in advance with data acquired from our hospital using the transferred TOP-Net, the 6 metrics were 0.965, 0.955, 0.881, 0.937, 0.793, and 0.680, respectively. The best performance was achieved using comprehensive vital signs (heart rate, respiratory rate, and SpO2) statistical information. Conclusions: TOP-Net is an early tachycardia prediction model that uses 8 types of data from wearable sensors and electronic health records. When validated in clinical scenarios, the model achieved a prediction performance that outperformed baseline models 0 to 6 hours before tachycardia onset in the intensive care unit and 2 hours before tachycardia onset in the general ward. Because of the model’s implementation and use of easily accessible data from wearable sensors, the model can assist physicians with early discovery of patients at risk in general wards and houses. %M 33856350 %R 10.2196/18803 %U https://medinform.jmir.org/2021/4/e18803 %U https://doi.org/10.2196/18803 %U http://www.ncbi.nlm.nih.gov/pubmed/33856350 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 7 %N 2 %P e18819 %T A Home-Based Mobile Health Intervention to Replace Sedentary Time With Light Physical Activity in Older Cancer Survivors: Randomized Controlled Pilot Trial %A Blair,Cindy K %A Harding,Elizabeth %A Wiggins,Charles %A Kang,Huining %A Schwartz,Matthew %A Tarnower,Amy %A Du,Ruofei %A Kinney,Anita Y %+ Department of Internal Medicine, University of New Mexico, 1 University of New Mexico, MSC07-4025, Albuquerque, NM, United States, 1 5059257907, CiBlair@salud.unm.edu %K light-intensity physical activity %K physical activity %K sedentary behavior %K mobile health %K cancer survivors %K consumer wearable %K activity monitor %K mobile phone %D 2021 %7 13.4.2021 %9 Original Paper %J JMIR Cancer %G English %X Background: Older cancer survivors are at risk of the development or worsening of both age- and treatment-related morbidity. Sedentary behavior increases the risk of or exacerbates these chronic conditions. Light-intensity physical activity (LPA) is more common in older adults and is associated with better health and well-being. Thus, replacing sedentary time with LPA may provide a more successful strategy to reduce sedentary time and increase physical activity. Objective: This study primarily aims to evaluate the feasibility, acceptability, and preliminary efficacy of a home-based mobile health (mHealth) intervention to interrupt and replace sedentary time with LPA (standing and stepping). The secondary objective of this study is to examine changes in objective measures of physical activity, physical performance, and self-reported quality of life. Methods: Overall, 54 cancer survivors (aged 60-84 years) were randomized in a 1:1:1 allocation to the tech support intervention group, tech support plus health coaching intervention group, or waitlist control group. Intervention participants received a Jawbone UP2 activity monitor for use with their smartphone app for 13 weeks. Tech support and health coaching were provided via 5 telephone calls during the 13-week intervention. Sedentary behavior and physical activity were objectively measured using an activPAL monitor for 7 days before and after the intervention. Results: Participants included survivors of breast cancer (21/54, 39%), prostate cancer (16/54, 30%), and a variety of other cancer types; a mean of 4.4 years (SD 1.6) had passed since their cancer diagnosis. Participants, on average, were 70 years old (SD 4.8), 55% (30/54) female, 24% (13/54) Hispanic, and 81% (44/54) overweight or obese. Malfunction of the Jawbone trackers occurred in one-third of the intervention group, resulting in enrollment stopping at 54 rather than the initial goal of 60 participants. Despite these technical issues, the retention in the intervention was high (47/54, 87%). Adherence was high for wearing the tracker (29/29, 100%) and checking the app daily (28/29, 96%) but low for specific aspects related to the sedentary features of the tracker and app (21%-25%). The acceptability of the intervention was moderately high (81%). There were no significant between-group differences in total sedentary time, number of breaks, or number of prolonged sedentary bouts. There were no significant between-group differences in physical activity. The only significant within-group change occurred within the health coaching group, which increased by 1675 daily steps (95% CI 444-2906; P=.009). This increase was caused by moderate-intensity stepping rather than light-intensity stepping (+15.2 minutes per day; 95% CI 4.1-26.2; P=.008). Conclusions: A home-based mHealth program to disrupt and replace sedentary time with stepping was feasible among and acceptable to older cancer survivors. Future studies are needed to evaluate the optimal approach for replacing sedentary behavior with standing and/or physical activity in this population. Trial Registration: ClinicalTrials.gov NCT03632694; https://clinicaltrials.gov/ct2/show/NCT03632694 %M 33847588 %R 10.2196/18819 %U https://cancer.jmir.org/2021/2/e18819 %U https://doi.org/10.2196/18819 %U http://www.ncbi.nlm.nih.gov/pubmed/33847588 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 7 %N 2 %P e24828 %T Associations Among Wearable Activity Tracker Use, Exercise Motivation, and Physical Activity in a Cohort of Cancer Survivors: Secondary Data Analysis of the Health Information National Trends Survey %A De La Torre,Steven %A Spruijt-Metz,Donna %A Farias,Albert J %+ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 N. Soto St., Los Angeles, CA, 90033, United States, 1 323 442 7252, albertfa@usc.edu %K mHealth %K mobile health %K cancer survivors %K exercise %K physical activity %K motivation %K wearable electronic devices %K fitness trackers %D 2021 %7 12.4.2021 %9 Original Paper %J JMIR Cancer %G English %X Background: Cancer survivors who meet physical activity (PA) recommendations (≥150 minutes of moderate-to-vigorous physical activity [MVPA] per week) experience better health outcomes. With the growing availability of wearable activity trackers (WATs), it may be easier to track PA. However, it is unknown what motivates survivors to use these devices. Objective: The aim of this study is to investigate the associations among motivations for exercise, previous WAT use for tracking a health goal or activity, and meeting the recommended amount of PA among a cohort of cancer survivors. Methods: Data on WAT users who reported having a previous cancer diagnosis were analyzed from the National Cancer Institute’s Health Information National Trends Survey 5 Cycle 3. All survivors with complete information on demographics, exercise motivations (internal guilt, external pressure, physical appearance, and exercise enjoyment), previous WAT use (yes or no), and minutes of MVPA per week (N=608) were included. Multivariate logistic regression models were used to test these associations. A separate cluster analysis was conducted to identify the profiles of exercise motivation that were associated with reporting WAT use. Results: The mean age of the cohort was 66.9 years (SD 12.1). The majority were non-Hispanic White (473/608, 78.8%) and female (322/608, 54.9%), and skin cancer was the most commonly reported diagnosed cancer (154/608, 27.8%). Survivors who reported using WATs to track a health goal or activity were 1.6 times more likely to meet MVPA recommendations than those who did not use WATs (odds ratio [OR] 1.65, 95% CI 1.03-2.65; P=.04). When exercise motivations were assessed independently, survivors who reported not feeling any internal guilt as an exercise motivation were 73% less likely to report having used a WAT than those who felt any internal guilt (OR 0.27, 95% CI 0.14-0.54; P<.001). A total of 3 distinct motivational profiles emerged from the cluster analysis. WAT users had an increased probability of membership in profile 3, which was characterized as being strongly motivated to exercise by internal guilt, physical appearance, and exercise enjoyment (OR 4.5, 95% CI 2.1-9.7; P<.001). Conclusions: Among this cohort, survivors who reported using WATs to track a health goal or activity were significantly more likely to report meeting PA recommendations. Survivors who reported feeling internal guilt as an exercise motivation were significantly more likely to report using WATs to track a health goal or activity. When examining clusters of motivation, survivors who reported previous WAT use were more likely to report being motivated to exercise by a mix of intrinsic and extrinsic motivations, including internal guilt, exercise enjoyment, and physical appearance. Given the health benefits of PA for cancer survivors, technology-focused interventions that use WATs and target exercise motivation may aid in cancer survivors meeting the level of recommended PA. %M 33843595 %R 10.2196/24828 %U https://cancer.jmir.org/2021/2/e24828 %U https://doi.org/10.2196/24828 %U http://www.ncbi.nlm.nih.gov/pubmed/33843595 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 4 %P e24455 %T The Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II to Assess the Use of the Apple Watch in Older Emergency Department Patients With Falls: Protocol for a Mixed Methods Study %A Strauss,Daniel H %A Davoodi,Natalie M %A Healy,Margaret %A Metts,Christopher L %A Merchant,Roland C %A Banskota,Swechya %A Goldberg,Elizabeth M %+ Department of Emergency Medicine, Alpert Medical School of Brown University, 55 Claverick Street, Second Floor, Room 203, Providence, RI, 02903, United States, 1 401 527 1740, elizabeth_goldberg@brown.edu %K fall intervention %K geriatric care %K Apple Watch %K wearable technology %D 2021 %7 1.4.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Falls are a common problem among older adults that lead to injury, emergency department (ED) visits, and institutionalization. The Apple Watch can detect falls and alert caregivers and clinicians that help is needed; the device could also be used to objectively collect data on gait, fitness, and falls as part of clinical trials. However, little is known about the ease of use of this technology among older adult ED patients, a population at high risk of recurrent falls. Objective: The goal of this study—the Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II—is to examine the feasibility, acceptability, and usability of the Apple Watch Series 4 paired with the iPhone and our research app Rhode Island FitTest (RIFitTest) among older adult ED patients seeking care for falls. Methods: We will conduct field-testing with older adult ED patients (n=25) who sustained a fall and their caregivers (n=5) to determine whether they can use the Apple Watch, iPhone, and app either (1) continuously or (2) periodically, with or without telephone assistance from the research staff, to assess gait, fitness, and/or falls over time. During the initial encounter, participants will receive training in the Apple Watch, iPhone, and our research app. They will receive an illustrated training manual and a number to call if they have questions about the research protocol or device usage. Participants will complete surveys and cognitive and motor assessments on the app during the study period. At the conclusion of the study, we will solicit participant feedback through semistructured interviews. Qualitative data will be summarized using framework matrix analyses. Sensor and survey response data will be analyzed using descriptive statistics. Results: Recruitment began in December 2019 and was on pause from April 2020 until September 2020 due to the COVID-19 pandemic. Study recruitment will continue until 30 participants are enrolled. This study has been approved by the Rhode Island Hospital Institutional Review Board (approval 1400781-16). Conclusions: GAPcare II will provide insights into the feasibility, acceptability, and usability of the Apple Watch, iPhone, and the RIFitTest app in the population most likely to benefit from the technology: older adults at high risk of recurrent falls. In the future, wearables could be used as part of fall prevention interventions to prevent injury before it occurs. Trial Registration: ClinicalTrials.gov NCT04304495; https://clinicaltrials.gov/ct2/show/NCT04304495 International Registered Report Identifier (IRRID): DERR1-10.2196/24455 %M 33792553 %R 10.2196/24455 %U https://www.researchprotocols.org/2021/4/e24455 %U https://doi.org/10.2196/24455 %U http://www.ncbi.nlm.nih.gov/pubmed/33792553 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 8 %N 1 %P e18942 %T Feasibility and Convergent Validity of an Activity Tracker for Low Back Pain Within a Clinical Study: Cross-sectional Study %A Zhuo,Linda Xiaoqian %A Macedo,Luciana Gazzi %+ School of Rehabilitation Science, McMaster University, IAHS Bldg, 4th fl, 1400 Main St W, Hamilton, ON, L8S 4L8, Canada, 1 289 426 0824, macedol@mcmaster.ca %K activity monitor %K activity tracker %K low back pain %D 2021 %7 26.3.2021 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Low back pain (LBP) is a highly prevalent condition affecting individuals of all ages. To manage the symptoms and prevent recurrences and flare-ups, physical activity in conjunction with self-management education is recommended. Tools such as diaries and questionnaires have been the gold standard for tracking physical activity in clinical studies. However, there are issues with consistency, accuracy, and recall with the use of these outcome measures. Given the growth of technology in today’s society, consumer-grade activity monitors have become a common and convenient method of recording physical activity data. Objective: The aim of this study is to test the feasibility and convergent validity of a Garmin Vivofit 3 activity tracker in evaluating physical activity levels in a clinical trial of patients with LBP. Methods: We recruited 17 individuals with nonspecific LBP referred from health care professionals or self-referred through advertisements in the community. The participants entered into a 12-week physical activity and self-management program. Physical activity was assessed using a self-reported questionnaire and the Garmin activity tracker. Activity tracker data (eg, steps taken, distance walked, and intensity minutes) were extracted weekly from the Garmin Connect online platform. Outcomes of pain and activity limitation were assessed weekly using a mobile app. A linear regression was conducted to evaluate if demographic factors (ie, age, gender, pain level) affected the adherence rates to the activity monitor. We also used Pearson correlations to evaluate the convergent validity of the Garmin activity tracker with the physical activity questionnaire. Results: The mean daily adherence rate for activity monitors was 70% (SD 31%) over the 26 weeks of study. The mean response rate for the weekly physical activity measures using REDCap for the first 12 weeks of the study was 91% (SD 17%). None of the hypothesized variables or questionnaires were predictors of response rate. Conclusions: The majority of participants were compliant with wearing the tracker, and demographic factors were not found to be predictors of adherence to wearing the device. However, there were poor correlations between the modified International Physical Activity Questionnaire Short Form (IPAQ-SF) and the activity monitor, demonstrating problems with convergent validity. %M 33769301 %R 10.2196/18942 %U https://rehab.jmir.org/2021/1/e18942 %U https://doi.org/10.2196/18942 %U http://www.ncbi.nlm.nih.gov/pubmed/33769301 %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 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 %@ 2371-4379 %I JMIR Publications %V 6 %N 1 %P e23364 %T Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort %A Lam,Benjamin %A Catt,Michael %A Cassidy,Sophie %A Bacardit,Jaume %A Darke,Philip %A Butterfield,Sam %A Alshabrawy,Ossama %A Trenell,Michael %A Missier,Paolo %+ School of Computing, Newcastle University, Urban Sciences Building, 1 Science Square, Newcastle upon Tyne, NE4 5TG, United Kingdom, 44 7704111910, b.p.lam1@ncl.ac.uk %K accelerometry %K digital technology %K machine learning %K physical activity %K type 2 diabetes %K digital biomarkers %K digital phenotyping %K mobile phone %D 2021 %7 19.3.2021 %9 Original Paper %J JMIR Diabetes %G English %X Background: Between 2013 and 2015, the UK Biobank collected accelerometer traces from 103,712 volunteers aged between 40 and 69 years using wrist-worn triaxial accelerometers for 1 week. This data set has been used in the past to verify that individuals with chronic diseases exhibit reduced activity levels compared with healthy populations. However, the data set is likely to be noisy, as the devices were allocated to participants without a set of inclusion criteria, and the traces reflect free-living conditions. Objective: This study aims to determine the extent to which accelerometer traces can be used to distinguish individuals with type 2 diabetes (T2D) from normoglycemic controls and to quantify their limitations. Methods: Machine learning classifiers were trained using different feature sets to segregate individuals with T2D from normoglycemic individuals. Multiple criteria, based on a combination of self-assessment UK Biobank variables and primary care health records linked to UK Biobank participants, were used to identify 3103 individuals with T2D in this population. The remaining nondiabetic 19,852 participants were further scored on their physical activity impairment severity based on other conditions found in their primary care data, and those deemed likely physically impaired at the time were excluded. Physical activity features were first extracted from the raw accelerometer traces data set for each participant using an algorithm that extends the previously developed Biobank Accelerometry Analysis toolkit from Oxford University. These features were complemented by a selected collection of sociodemographic and lifestyle features available from UK Biobank. Results: We tested 3 types of classifiers, with an area under the receiver operating characteristic curve (AUC) close to 0.86 (95% CI 0.85-0.87) for all 3 classifiers and F1 scores in the range of 0.80-0.82 for T2D-positive individuals and 0.73-0.74 for T2D-negative controls. Results obtained using nonphysically impaired controls were compared with highly physically impaired controls to test the hypothesis that nondiabetic conditions reduce classifier performance. Models built using a training set that included highly impaired controls with other conditions had worse performance (AUC 0.75-0.77; 95% CI 0.74-0.78; F1 scores in the range of 0.76-0.77 for T2D positives and 0.63-0.65 for controls). Conclusions: Granular measures of free-living physical activity can be used to successfully train machine learning models that are able to discriminate between individuals with T2D and normoglycemic controls, although with limitations because of the intrinsic noise in the data sets. From a broader clinical perspective, these findings motivate further research into the use of physical activity traces as a means of screening individuals at risk of diabetes and for early detection, in conjunction with routinely used risk scores, provided that appropriate quality control is enforced on the data collection protocol. %M 33739298 %R 10.2196/23364 %U https://diabetes.jmir.org/2021/1/e23364 %U https://doi.org/10.2196/23364 %U http://www.ncbi.nlm.nih.gov/pubmed/33739298 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 3 %P e24726 %T eHealth Program to Reduce Hospitalizations Due to Acute Exacerbation of Chronic Obstructive Pulmonary Disease: Retrospective Study %A van Buul,Amanda R %A Derksen,Caroline %A Hoedemaker,Ouke %A van Dijk,Oscar %A Chavannes,Niels H %A Kasteleyn,Marise J %+ Department of Pulmonology, Leiden University Medical Center, Leiden, Netherlands, 31 715297550, a.r.van_buul@lumc.nl %K COPD %K eHealth %K exacerbations %K hospitalizations %K mHealth %D 2021 %7 18.3.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Hospitalization for acute exacerbation of chronic obstructive pulmonary disease (COPD) is associated with poor prognosis. eHealth interventions might improve outcomes and decrease costs. Objective: This study aimed to evaluate the effect of an eHealth program on COPD hospitalizations and exacerbations. Methods: This was a real-world study conducted from April 2018 to December 2019 in the Bravis Hospital, the Netherlands. An eHealth program (EmmaCOPD) was offered to COPD patients at risk of exacerbations. EmmaCOPD consisted of an app that used questionnaires (to monitor symptoms) and a step counter (to monitor the number of steps) to detect exacerbations. Patients and their buddies received feedback when their symptoms worsened or the number of steps declined. Generalized estimating equations were used to compare the number of days admitted to the hospital and the total number of exacerbations 12 months before and (max) 18 months after the start of EmmaCOPD. We additionally adjusted for the potential confounders of age, sex, COPD severity, and inhaled corticosteroid use. Results: The 29 included patients had a mean forced expiratory volume in 1 second of 45.5 (SD 17.7) %predicted. In the year before the intervention, the median total number of exacerbations was 2.0 (IQR 2.0-3.0). The median number of hospitalized days was 8.0 days (IQR 6.0-16.5 days). Afterwards, there was a median 1.0 (IQR 0.0-2.0) exacerbation and 2.0 days (IQR 0.0-4.0 days) of hospitalization. After initiation of EmmaCOPD, both the number of hospitalized days and total number of exacerbations decreased significantly (incidence rate ratio 0.209, 95% CI 0.116-0.382; incidence rate ratio 0.310, 95% CI 0.219-0.438). Adjustment for confounders did not affect the results. Conclusions: The eHealth program seems to reduce the number of total exacerbations and number of days of hospitalization due to exacerbations of COPD. %M 33734091 %R 10.2196/24726 %U https://formative.jmir.org/2021/3/e24726 %U https://doi.org/10.2196/24726 %U http://www.ncbi.nlm.nih.gov/pubmed/33734091 %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 %@ 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 %@ 2561-326X %I JMIR Publications %V 5 %N 3 %P e17993 %T A Rest Quality Metric Using a Cluster-Based Analysis of Accelerometer Data and Correlation With Digital Medicine Ingestion Data: Algorithm Development %A Heidary,Zahra %A Cochran,Jeffrey M %A Peters-Strickland,Timothy %A Knights,Jonathan %+ Otsuka Pharmaceutical Development & Commercialization, Inc, 508 Carnegie Center Dr, Princeton, NJ, 08540, United States, 1 609 524 6788, jeffrey.cochran@otsuka-us.com %K serious mental illness %K rest quality %K actimetry %K behavioral health %K digital medicine %K accelerometer %K medication adherence %D 2021 %7 2.3.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Adherence to medication regimens and patient rest are two important factors in the well-being of patients with serious mental illness. Both of these behaviors are traditionally difficult to record objectively in unsupervised populations. Objective: A digital medicine system that provides objective time-stamped medication ingestion records was used by patients with serious mental illness. Accelerometer data from the digital medicine system was used to assess rest quality and thus allow for investigation into correlations between rest and medication ingestion. Methods: Longest daily rest periods were identified and then evaluated using a k-means clustering algorithm and distance metric to quantify the relative quality of patient rest during these periods. This accelerometer-derived quality-of-rest metric, along with other accepted metrics of rest quality, such as duration and start time of the longest rest periods, was compared to the objective medication ingestion records. Overall medication adherence classification based on rest features was not performed due to a lack of patients with poor adherence in the sample population. Results: Explorations of the relationship between these rest metrics and ingestion did seem to indicate that patients with poor adherence experienced relatively low quality of rest; however, patients with better adherence did not necessarily exhibit consistent rest quality. This sample did not contain sufficient patients with poor adherence to draw more robust correlations between rest quality and ingestion behavior. The correlation of temporal outliers in these rest metrics with daily outliers in ingestion time was also explored. Conclusions: This result demonstrates the ability of digital medicine systems to quantify patient rest quality, providing a framework for further work to expand the participant population, compare these rest metrics to gold-standard sleep measurements, and correlate these digital medicine biomarkers with objective medication ingestion data. %M 33650981 %R 10.2196/17993 %U https://formative.jmir.org/2021/3/e17993 %U https://doi.org/10.2196/17993 %U http://www.ncbi.nlm.nih.gov/pubmed/33650981 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 3 %P e25775 %T Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study %A Yunusova,Asal %A Lai,Jocelyn %A Rivera,Alexander P %A Hu,Sirui %A Labbaf,Sina %A Rahmani,Amir M %A Dutt,Nikil %A Jain,Ramesh C %A Borelli,Jessica L %+ Department of Psychological Science, University of California, Irvine, 4552 Social and Behavioral Sciences Gateway, Irvine, CA, United States, 1 9498243002, Jessica.borelli@uci.edu %K ecological momentary assessment %K stress %K digital mental health %K college student %K mental health %K protocol %K prospective %K feasibility %K individual %K factors %K sleepy %K physiology %K activity %K COVID-19 %D 2021 %7 2.3.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation. Objective: In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health. Methods: We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months. Results: Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19–related stress assessments. Conclusions: Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health. International Registered Report Identifier (IRRID): DERR1-10.2196/25775 %M 33513124 %R 10.2196/25775 %U https://www.researchprotocols.org/2021/3/e25775 %U https://doi.org/10.2196/25775 %U http://www.ncbi.nlm.nih.gov/pubmed/33513124 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 3 %P e22571 %T A Fitness App for Monitoring Walking Behavior and Perception (Runkeeper): Mixed Methods Pilot Study %A Hollander,Justin B %A Folta,Sara C %A Graves,Erin Michelle %A Allen,Jennifer D %A Situ,Minyu %+ Department of Urban and Environmental Policy and Planning, School of Arts and Sciences, Tufts University, 97 Talbot Avenue, Medford, MA, 02155, United States, 1 6176273394, justin.hollander@tufts.edu %K physical activity %K smartphone %K mobile app %K sense of belongingness %K community cohesion %D 2021 %7 1.3.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical activity has a strong positive impact on both physical and mental health, and public health interventions often encourage walking as a means to promote physical activity. Social connectivity, such as that among spouses, families, friends, and colleagues, highly influences physical activity. Although technology-based interventions have some influence on human behavior, they have not been fully implemented and evaluated for their influence on walking through social connectivity. Objective: We aimed to pilot-test the organization of neighborhood walking clubs and use of a mobile app (Runkeeper) to encourage social connectedness and neighborhood cohesion, as well as to increase physical activity. Methods: We used a convenience sampling method to recruit 46 adults from an urban location in Greater Boston, Massachusetts. We assigned participants to teams based on their geographic location and neighborhood and required them to use the app (Runkeeper). Participants completed 2 self-administered web-based surveys before and after the intervention period. The surveys included standard measures to evaluate physical activity, social connectedness, perceived social support, and neighborhood cohesion (Buckner Neighborhood Cohesion Scale) before and after the intervention. Following the intervention, we randomly selected 14 participants to participate in postintervention, in-depth phone interviews to gain an understanding of their experiences. Results: This study was approved by the institutional review board in June 2018 and funded in January 2018. Recruitment started in May 2019 and lasted for 2 months. Data were collected from July 2019 to January 2020. In this study, Runkeeper was of limited feasibility as an app for measuring physical activity or promoting social connectedness. Data from the app recorded sparse and uneven walking behaviors among the participants. Qualitative interviews revealed that users experienced difficulties in using the settings and features of the app. In the questionnaire, there was no change between pre-post assessments in walking minutes (b=−.79; 95% CI −4.0 to 2.4; P=.63) or miles (b=−.07; 95% CI −0.15 to 0.01; P=.09). We observed a pre-post increase in social connectedness and a decrease in neighborhood cohesion. Both quantitative and qualitative results indicated that the psychosocial aspects of walking motivated the participants and helped them relieve stress. Interview results showed that participants felt a greater virtual connection in their assigned groups and enhanced connections with friends and family members. Conclusions: Our study found that Runkeeper created a virtual connection among walking group members and its data sharing and ranking motivated walking. Participants felt that walking improved their mental health, helped to relieve stress, and made them feel more connected with friends or family members. In future studies, it will be important to use an app that integrates with a wearable physical activity device. There is also a need to develop and test intervention components that might be more effective in fostering neighborhood cohesion. %M 33646132 %R 10.2196/22571 %U https://formative.jmir.org/2021/3/e22571 %U https://doi.org/10.2196/22571 %U http://www.ncbi.nlm.nih.gov/pubmed/33646132 %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 %@ 2561-9128 %I JMIR Publications %V 4 %N 1 %P e21571 %T Relationship Between Mobile Digital Sensor Monitoring and Perioperative Outcomes: Systematic Review %A Memon,Ali %A Lec,Patrick %A Lenis,Andrew %A Sharma,Vidit %A Wood,Erika %A Schade,George %A Brisbane,Wayne %+ Department of Urology, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, United States, 1 425 658 9467, almemon98@gmail.com %K mobile sensors %K perioperative %K sensor monitoring %K perioperative outcomes %D 2021 %7 25.2.2021 %9 Review %J JMIR Perioper Med %G English %X Background: Monitoring surgical recovery has traditionally been confined to metrics measurable within the hospital and clinic setting. However, commercially available mobile sensors are now capable of extending measurements into a patient’s home. As these sensors were developed for nonmedical applications, their clinical role has yet to be established. The aim of this systematic review is to evaluate the relationship between data generated by mobile sensors and postoperative outcomes. Objective: The objective of this study is to describe the current use of mobile sensors in the perioperative setting and the correlation between their data and clinical outcomes. Methods: A systematic search of EMBASE, MEDLINE, and Cochrane Library from inception until April 2019 was performed to identify studies of surgical patients monitored with mobile sensors. Sensors were considered if they collected patient metrics such as step count, temperature, or heart rate. Studies were included if patients underwent major surgery (≥1 inpatient postoperative day), patients were monitored using mobile sensors in the perioperative period, and the study reported postoperative outcomes (ie, complications and hospital readmission). For studies including step count, a pooled analysis of the step count per postoperative day was calculated for the complication and noncomplication cohorts using mean and a random-effects linear model. The Grading of Recommendations, Assessment, Development, and Evaluation tool was used to assess study quality. Results: From 2209 abstracts, we identified 11 studies for review. Reviewed studies consisted of either prospective observational cohorts (n=10) or randomized controlled trials (n=1). Activity monitors were the most widely used sensors (n=10), with an additional study measuring temperature, respiratory rate, and heart rate (n=1). Low step count was associated with worse postoperative outcomes. A median step count of around 1000 steps per postoperative day was associated with adverse surgical outcomes. Within the studies, there was heterogeneity between the type of surgery and type of reported postoperative outcome. Conclusions: Despite significant heterogeneity in the type of surgery and sensors, low step count was associated with worse postoperative outcomes across surgical specialties. Further studies and standardization are needed to assess the role of mobile sensors in postoperative care, but a threshold of approximately 1000 steps per postoperative day warrants further investigation. %M 33629966 %R 10.2196/21571 %U https://periop.jmir.org/2021/1/e21571 %U https://doi.org/10.2196/21571 %U http://www.ncbi.nlm.nih.gov/pubmed/33629966 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 1 %P e19859 %T Using Consumer-Grade Physical Activity Trackers to Measure Frailty Transitions in Older Critical Care Survivors: Exploratory Observational Study %A Kim,Ben %A Hunt,Miranda %A Muscedere,John %A Maslove,David M %A Lee,Joon %+ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada, 1 403 220 2968, joonwu.lee@ucalgary.ca %K frailty %K frail elderly %K wearable electronic devices %K fitness trackers %K activity trackers %K heart rate %K sleep monitoring %K critical care outcomes %D 2021 %7 23.2.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Critical illness has been suggested as a sentinel event for frailty development in at-risk older adults. Frail critical illness survivors are affected by increased adverse health outcomes, but monitoring the recovery after intensive care unit (ICU) discharge is challenging. Clinicians and funders of health care systems envision an increased role of wearable devices in monitoring clinically relevant measures, as sensor technology is advancing rapidly. The use of wearable devices has also generated great interest among older patients, and they are the fastest growing group of consumer-grade wearable device users. Recent research studies indicate that consumer-grade wearable devices offer the possibility of measuring frailty. Objective: This study aims to examine the data collected from wearable devices for the progression of frailty among critical illness survivors. Methods: An observational study was conducted with 12 older survivors of critical illness from Kingston General Hospital in Canada. Frailty was measured using the Clinical Frailty Scale (CFS) at ICU admission, hospital discharge, and 4-week follow-up. A wearable device was worn between hospital discharge and 4-week follow-up. The wearable device collected data on step count, physical activity, sleep, and heart rate (HR). Patient assessments were reviewed, including the severity of illness, cognition level, delirium, activities of daily living, and comorbidity. Results: The CFS scores increased significantly following critical illness compared with the pre-ICU frailty level (P=.02; d=−0.53). Survivors who were frail over the 4-week follow-up period had significantly lower daily step counts than survivors who were not frail (P=.02; d=1.81). There was no difference in sleep and HR measures. Daily step count was strongly correlated with the CFS at 4-week follow-up (r=−0.72; P=.04). The average HR was strongly correlated with the CFS at hospital discharge (r=−0.72; P=.046). The HR SD was strongly correlated (r=0.78; P=.02) with the change in CFS from ICU admission to 4-week follow-up. No association was found between the CFS and sleep measures. The pattern of increasing step count over the 4-week follow-up period was correlated with worsening of frailty (r=.62; P=.03). Conclusions: This study demonstrated an association between frailty and data generated from a consumer-grade wearable device. Daily step count and HR showed a strong association with the frailty progression of the survivors of critical illness over time. Understanding this association could unlock a new avenue for clinicians to monitor and identify a vulnerable subset of the older adult population that might benefit from an early intervention. %M 33620323 %R 10.2196/19859 %U https://aging.jmir.org/2021/1/e19859 %U https://doi.org/10.2196/19859 %U http://www.ncbi.nlm.nih.gov/pubmed/33620323 %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 %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e23180 %T Associations Between Digital Health Intervention Engagement, Physical Activity, and Sedentary Behavior: Systematic Review and Meta-analysis %A Mclaughlin,Matthew %A Delaney,Tessa %A Hall,Alix %A Byaruhanga,Judith %A Mackie,Paul %A Grady,Alice %A Reilly,Kathryn %A Campbell,Elizabeth %A Sutherland,Rachel %A Wiggers,John %A Wolfenden,Luke %+ School of Medicine and Public Health, University of Newcastle, University Drive, Callaghan, 2308, Australia, 61 02 4924 6477, Matthew.Mclaughlin1@health.nsw.gov.au %K engagement %K adherence %K digital health intervention %K digital behavior change intervention %K physical activity %K sedentary behavior %K mobile phone %D 2021 %7 19.2.2021 %9 Review %J J Med Internet Res %G English %X Background: The effectiveness of digital health interventions is commonly assumed to be related to the level of user engagement with the digital health intervention, including measures of both digital health intervention use and users’ subjective experience. However, little is known about the relationships between the measures of digital health intervention engagement and physical activity or sedentary behavior. Objective: This study aims to describe the direction and strength of the association between engagement with digital health interventions and physical activity or sedentary behavior in adults and explore whether the direction of association of digital health intervention engagement with physical activity or sedentary behavior varies with the type of engagement with the digital health intervention (ie, subjective experience, activities completed, time, and logins). Methods: Four databases were searched from inception to December 2019. Grey literature and reference lists of key systematic reviews and journals were also searched. Studies were eligible for inclusion if they examined a quantitative association between a measure of engagement with a digital health intervention targeting physical activity and a measure of physical activity or sedentary behavior in adults (aged ≥18 years). Studies that purposely sampled or recruited individuals on the basis of pre-existing health-related conditions were excluded. In addition, studies were excluded if the individual engaging with the digital health intervention was not the target of the physical activity intervention, the study had a non–digital health intervention component, or the digital health interventions targeted multiple health behaviors. A random effects meta-analysis and direction of association vote counting (for studies not included in meta-analysis) were used to address objective 1. Objective 2 used vote counting on the direction of the association. Results: Overall, 10,653 unique citations were identified and 375 full texts were reviewed. Of these, 19 studies (26 associations) were included in the review, with no studies reporting a measure of sedentary behavior. A meta-analysis of 11 studies indicated a small statistically significant positive association between digital health engagement (based on all usage measures) and physical activity (0.08, 95% CI 0.01-0.14, SD 0.11). Heterogeneity was high, with 77% of the variation in the point estimates explained by the between-study heterogeneity. Vote counting indicated that the relationship between physical activity and digital health intervention engagement was consistently positive for three measures: subjective experience measures (2 of 3 associations), activities completed (5 of 8 associations), and logins (6 of 10 associations). However, the direction of associations between physical activity and time-based measures of usage (time spent using the intervention) were mixed (2 of 5 associations supported the hypothesis, 2 were inconclusive, and 1 rejected the hypothesis). Conclusions: The findings indicate a weak but consistent positive association between engagement with a physical activity digital health intervention and physical activity outcomes. No studies have targeted sedentary behavior outcomes. The findings were consistent across most constructs of engagement; however, the associations were weak. %M 33605897 %R 10.2196/23180 %U http://www.jmir.org/2021/2/e23180/ %U https://doi.org/10.2196/23180 %U http://www.ncbi.nlm.nih.gov/pubmed/33605897 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e23936 %T Effect of Sleep and Biobehavioral Patterns on Multidimensional Cognitive Performance: Longitudinal, In-the-Wild Study %A Kalanadhabhatta,Manasa %A Rahman,Tauhidur %A Ganesan,Deepak %+ College of Information and Computer Sciences, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA, 01003, United States, 1 4135453819, manasak@cs.umass.edu %K fitness trackers %K cognitive performance %K alertness %K cognitive throughput %K sleep %K activity %K circadian rhythms %D 2021 %7 18.2.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: With nearly 20% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand how these metrics correlate with cognitive performance measures across a diverse population are lacking, and claims made by device manufacturers are vague. While there has been extensive research leading to a variety of theories on how physiological measures affect cognitive performance, virtually all such studies have been conducted in highly controlled settings and their validity in the real world is poorly understood. Objective: We seek to bridge this gap by evaluating prevailing theories on the effects of a variety of sleep, activity, and heart rate parameters on cognitive performance against data collected in real-world settings. Methods: We used a Fitbit Charge 3 and a smartphone app to collect different physiological and neurobehavioral task data, respectively, as part of our 6-week-long in-the-wild study. We collected data from 24 participants across multiple population groups (shift workers, regular workers, and graduate students) on different performance measures (vigilant attention and cognitive throughput). Simultaneously, we used a fitness tracker to unobtrusively obtain physiological measures that could influence these performance measures, including over 900 nights of sleep and over 1 million minutes of heart rate and physical activity metrics. We performed a repeated measures correlation (rrm) analysis to investigate which sleep and physiological markers show association with each performance measure. We also report how our findings relate to existing theories and previous observations from controlled studies. Results: Daytime alertness was found to be significantly correlated with total sleep duration on the previous night (rrm=0.17, P<.001) as well as the duration of rapid eye movement (rrm=0.12, P<.001) and light sleep (rrm=0.15, P<.001). Cognitive throughput, by contrast, was not found to be significantly correlated with sleep duration but with sleep timing—a circadian phase shift toward a later sleep time corresponded with lower cognitive throughput on the following day (rrm=–0.13, P<.001). Both measures show circadian variations, but only alertness showed a decline (rrm=–0.1, P<.001) as a result of homeostatic pressure. Both heart rate and physical activity correlate positively with alertness as well as cognitive throughput. Conclusions: Our findings reveal that there are significant differences in terms of which sleep-related physiological metrics influence each of the 2 performance measures. This makes the case for more targeted in-the-wild studies investigating how physiological measures from self-tracking data influence, or can be used to predict, specific aspects of cognitive performance. %M 33599622 %R 10.2196/23936 %U http://www.jmir.org/2021/2/e23936/ %U https://doi.org/10.2196/23936 %U http://www.ncbi.nlm.nih.gov/pubmed/33599622 %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 e18288 %T Acceptability of a Mobile Health Behavior Change Intervention for Cancer Survivors With Obesity or Overweight: Nested Mixed Methods Study Within a Randomized Controlled Trial %A Groarke,Jenny M %A Richmond,Janice %A Mc Sharry,Jenny %A Groarke,AnnMarie %A Harney,Owen M %A Kelly,Mary Grace %A Walsh,Jane C %+ Centre for Improving Health-Related Quality of Life, School of Psychology, Queen's University Belfast, 18-30 Malone Road, Belfast, BT71NN, United Kingdom, 44 28 90974886, j.groarke@qub.ac.uk %K mHealth %K self-management %K text messaging %K activity tracker %K exercise %K diet %K overweight %K obesity %K cancer survivors %K qualitative research %K mobile phone %D 2021 %7 16.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A significant proportion of cancer survivors have overweight or obesity. Although this has negative implications for health, weight management is not a standard component of oncology aftercare. Mobile health (mHealth) technology, in combination with behavior change techniques (BCTs), has the potential to support positive lifestyle changes. Few studies have been carried out with cancer survivors; therefore, the acceptability of these tools and techniques requires further investigation. Objective: The aim of this study is to examine the acceptability of a behavior change intervention using mHealth for cancer survivors with a BMI of 25 or more and to gather constructive feedback from participants. Methods: The intervention consisted of educational sessions and an 8-week physical activity goal setting intervention delivered using mobile technology (ie, Fitbit activity monitor plus SMS contact). In the context of a two-arm randomized controlled trial, semistructured interviews were conducted to assess the retrospective acceptability of the intervention from the perspective of the recipients. The theoretical framework for the acceptability of health care interventions was used to inform a topic guide. The interviews were transcribed and analyzed using thematic analysis. A quantitative survey was also conducted to determine the acceptability of the intervention. A total of 13 participants were interviewed, and 36 participants completed the quantitative survey. Results: The results strongly support the acceptability of the intervention. The majority of the survey respondents held a positive attitude toward the intervention (35/36, 97%). In qualitative reports, many of the intervention components were enjoyed and the mHealth components (ie, Fitbit and goal setting through text message contact) were rated especially positively. Responses were mixed as to whether the burden of participating in the intervention was high (6/36, 17%) or low (5/36, 14%). Participants perceived the intervention as having high efficacy in improving health and well-being (34/36, 94%). Most respondents said that they understood how the intervention works (35/36, 97%), and qualitative data show that participants’ understanding of the aim of the intervention was broader than weight management and focused more on moving on psychologically from cancer. Conclusions: On the basis of the coherence of responses with theorized aspects of intervention acceptability, we are confident that this intervention using mHealth and BCTs is acceptable to cancer survivors with obesity or overweight. Participants made several recommendations concerning the additional provision of social support. Future studies are needed to assess the feasibility of delivery in clinical practice and the acceptability of the intervention to those delivering the intervention. International Registered Report Identifier (IRRID): RR2-10.2196/13214 %M 33591290 %R 10.2196/18288 %U http://mhealth.jmir.org/2021/2/e18288/ %U https://doi.org/10.2196/18288 %U http://www.ncbi.nlm.nih.gov/pubmed/33591290 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e24080 %T Effectiveness of Mobile Health–Based Exercise Interventions for Patients with Peripheral Artery Disease: Systematic Review and Meta-Analysis %A Kim,Mihui %A Kim,Changhwan %A Kim,Eunkyo %A Choi,Mona %+ College of Nursing and Mo-Im Kim Nursing Research Institute, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 2 2228 3341, monachoi@yuhs.ac %K peripheral artery disease %K mobile health %K exercise %K adherence %K meta-analysis %D 2021 %7 15.2.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Peripheral artery disease (PAD) affects over 236 million people worldwide, and exercise interventions are commonly used to alleviate symptoms of this condition. However, no previous systematic review has evaluated the effects of mobile health (mHealth)–based exercise interventions for patients with PAD. Objective: This study aimed to assess the effect of mHealth-based exercise interventions on walking performance, functional status, and quality of life in patients with PAD. Methods: A systematic review and meta-analysis were conducted. We searched in seven databases to identify randomized controlled trials of patients with PAD published in English up to December 4, 2020. Studies were included if patients participated in mHealth-based exercise interventions and were assessed for walking performance. We analyzed pooled effect size on walking performance, functional status, and quality of life based on the standardized mean differences between groups. Results: A total of seven studies were selected for the systematic review, and six studies were included in the meta-analysis. The duration of interventions in the included studies was 12 to 48 weeks. In the pooled analysis, when compared with the control groups, the mHealth-based exercise intervention groups were associated with significant improvements in pain-free walking (95% CI 0.13-0.88), maximal walking (95% CI 0.03-0.87), 6-minute walk test (6MWT) distance (95% CI 0.59-1.24), and walking distance (95% CI 0.02-0.49). However, benefits of the interventions on walking speed, stair-climbing ability, and quality of life were not observed. Conclusions: mHealth-based exercise interventions for patients with PAD were beneficial for improving pain-free walking, maximal walking, and 6MWT distance. We found that exercise interventions using mHealth are an important strategy for improving the exercise effectiveness and adherence rate of patients with PAD. Future studies should consider the use of various and suitable functions of mHealth that can increase the adherence rates and improve the effectiveness of exercise. %M 33587042 %R 10.2196/24080 %U http://mhealth.jmir.org/2021/2/e24080/ %U https://doi.org/10.2196/24080 %U http://www.ncbi.nlm.nih.gov/pubmed/33587042 %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 %@ 2291-9279 %I JMIR Publications %V 9 %N 1 %P e23069 %T A Co-Designed Active Video Game for Physical Activity Promotion in People With Chronic Obstructive Pulmonary Disease: Pilot Trial %A Simmich,Joshua %A Mandrusiak,Allison %A Smith,Stuart Trevor %A Hartley,Nicole %A Russell,Trevor Glen %+ Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, Brisbane, 4072, Australia, 61 733655344, joshua.simmich@uqconnect.edu.au %K fitness trackers %K chronic obstructive pulmonary disease %K physical activity %K video games %K smartphone %K mobile phone %D 2021 %7 27.1.2021 %9 Original Paper %J JMIR Serious Games %G English %X Background: People with chronic obstructive pulmonary disease (COPD) who are less active have lower quality of life, greater risk of exacerbations, and greater mortality than those who are more active. The effectiveness of physical activity interventions may facilitate the addition of game elements to improve engagement. The use of a co-design approach with people with COPD and clinicians as co-designers may also improve the effectiveness of the intervention. Objective: The primary aim of this study is to evaluate the feasibility of a co-designed mobile game by examining the usage of the game, subjective measures of game engagement, and adherence to wearing activity trackers. The secondary aim of this study is to estimate the effect of the game on daily steps and daily moderate-to-vigorous physical activity (MVPA). Methods: Participants with COPD who were taking part in the co-design of the active video game (n=9) acted as the experiment group, spending 3 weeks testing the game they helped to develop. Daily steps and MVPA were compared with a control group (n=9) of participants who did not co-design or test the game. Results: Most participants (8/9, 89%) engaged with the game after downloading it. Participants used the game to record physical activity on 58.6% (82/141) of the days the game was available. The highest scores on the Intrinsic Motivation Inventory were seen for the value and usefulness subscale, with a mean of 6.38 (SD 0.6). Adherence to wearing Fitbit was high, with participants in both groups recording steps on >80% of days. Usage of the game was positively correlated with changes in daily steps but not with MVPA. Conclusions: The co-designed mobile app shows promise as an intervention and should be evaluated in a larger-scale trial in this population. %M 33502321 %R 10.2196/23069 %U http://games.jmir.org/2021/1/e23069/ %U https://doi.org/10.2196/23069 %U http://www.ncbi.nlm.nih.gov/pubmed/33502321 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 1 %P e15369 %T A Smartphone App to Support Sedentary Behavior Change by Visualizing Personal Mobility Patterns and Action Planning (SedVis): Development and Pilot Study %A Wang,Yunlong %A König,Laura M. %A Reiterer,Harald %+ Department of Computer and Information Science, University of Konstanz, Universitätsstraße 10, Konstanz, 78457, Germany, 49 7531 88 3704, yunlong.wang@uni-konstanz.de %K sedentary behavior %K data visualization %K mobile app %K action planning %K human mobility patterns %K mobile phone %D 2021 %7 27.1.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Prolonged sedentary behavior is related to a number of risk factors for chronic diseases. Given the high prevalence of sedentary behavior in daily life, simple yet practical solutions for behavior change are needed to avoid detrimental health effects. Objective: The mobile app SedVis was developed based on the health action process approach. The app provides personal mobility pattern visualization (for both physical activity and sedentary behavior) and action planning for sedentary behavior change. The primary aim of the study is to investigate the effect of mobility pattern visualization on users’ action planning for changing their sedentary behavior. The secondary aim is to evaluate user engagement with the visualization and user experience of the app. Methods: A 3-week user study was conducted with 16 participants who had the motivation to reduce their sedentary behavior. Participants were allocated to either an active control group (n=8) or an intervention group (n=8). In the 1-week baseline period, none of the participants had access to the functions in the app. In the following 2-week intervention period, only the intervention group was given access to the visualizations, whereas both groups were asked to make action plans every day and reduce their sedentary behavior. Participants’ sedentary behavior was estimated based on the sensor data of their smartphones, and their action plans and interaction with the app were also recorded by the app. Participants’ intention to change their sedentary behavior and user experience of the app were assessed using questionnaires. Results: The data were analyzed using both traditional null hypothesis significance testing (NHST) and Bayesian statistics. The results suggested that the visualizations in SedVis had no effect on the participants’ action planning according to both the NHST and Bayesian statistics. The intervention involving visualizations and action planning in SedVis had a positive effect on reducing participants’ sedentary hours, with weak evidence according to Bayesian statistics (Bayes factor, BF+0=1.92; median 0.52; 95% CI 0.04-1.25), whereas no change in sedentary time was more likely in the active control condition (BF+0=0.28; median 0.18; 95% CI 0.01-0.64). Furthermore, Bayesian analysis weakly suggested that the more frequently the users checked the app, the more likely they were to reduce their sedentary behavior (BF−0=1.49; r=−0.50). Conclusions: Using a smartphone app to collect data on users’ mobility patterns and provide real-time feedback using visualizations may be a promising method to induce changes in sedentary behavior and may be more effective than action planning alone. Replications with larger samples are needed to confirm these findings. %M 33502322 %R 10.2196/15369 %U http://formative.jmir.org/2021/1/e15369/ %U https://doi.org/10.2196/15369 %U http://www.ncbi.nlm.nih.gov/pubmed/33502322 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e22846 %T Measuring Daily Compliance With Physical Activity Tracking in Ambulatory Surgery Patients: Comparative Analysis of Five Compliance Criteria %A Kelly,Ryan %A Jones,Simon %A Price,Blaine %A Katz,Dmitri %A McCormick,Ciaran %A Pearce,Oliver %+ School of Computing and Communications, The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom, 44 1908653701, b.a.price@open.ac.uk %K activity tracking %K adherence %K compliance %K surgery %K total knee arthroplasty %D 2021 %7 26.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity trackers such as the Fitbit can allow clinicians to monitor the recovery of their patients following surgery. An important issue when analyzing activity tracker data is to determine patients’ daily compliance with wearing their assigned device, using an appropriate criterion to determine a valid day of wear. However, it is currently unclear as to how different criteria can affect the reported compliance of patients recovering from ambulatory surgery. Investigating this issue can help to inform the use of activity data by revealing factors that may impact compliance calculations. Objective: This study aimed to understand how using different criteria can affect the reported compliance with activity tracking in ambulatory surgery patients. It also aimed to investigate factors that explain variation between the outcomes of different compliance criteria. Methods: A total of 62 patients who were scheduled to undergo total knee arthroplasty (TKA, ie, knee replacement) volunteered to wear a commercial Fitbit Zip activity tracker over an 8-week perioperative period. Patients were asked to wear the Fitbit Zip daily, beginning 2 weeks prior to their surgery and ending 6 weeks after surgery. Of the 62 patients who enrolled in the study, 20 provided Fitbit data and underwent successful surgery. The Fitbit data were analyzed using 5 different daily compliance criteria, which consider patients as compliant with daily tracking if they either register >0 steps in a day, register >500 steps in a day, register at least one step in 10 different hours of the day, register >0 steps in 3 distinct time windows, or register >0 steps in 3 out of 4 six-hour time windows. The criteria were compared in terms of compliance outcomes produced for each patient. Data were explored using heatmaps and line graphs. Linear mixed models were used to identify factors that lead to variation between compliance outcomes across the sample. Results: The 5 compliance criteria produce different outcomes when applied to the patients’ data, with an average 24% difference in reported compliance between the most lenient and strictest criteria. However, the extent to which each patient’s reported compliance was impacted by different criteria was not uniform. Some individuals were relatively unaffected, whereas others varied by up to 72%. Wearing the activity tracker as a clip-on device, rather than on the wrist, was associated with greater differences between compliance outcomes at the individual level (P=.004, r=.616). This effect was statistically significant (P<.001) in the first 2 weeks after surgery. There was also a small but significant main effect of age on compliance in the first 2 weeks after surgery (P=.040). Gender and BMI were not associated with differences in individual compliance outcomes. Finally, the analysis revealed that surgery has an impact on patients’ compliance, with noticeable reductions in activity following surgery. These reductions affect compliance calculations by discarding greater amounts of data under strict criteria. Conclusions: This study suggests that different compliance criteria cannot be used interchangeably to analyze activity data provided by TKA patients. Surgery leads to a temporary reduction in patients’ mobility, which affects their reported compliance when strict thresholds are used. Reductions in mobility suggest that the use of lenient compliance criteria, such as >0 steps or windowed approaches, can avoid unnecessary data exclusion over the perioperative period. Encouraging patients to wear the device at their wrist may improve data quality by increasing the likelihood of patients wearing their tracker and ensuring that activity is registered in the 2 weeks after surgery. Trial Registration: ClinicalTrials.gov NCT03518866; https://clinicaltrials.gov/ct2/show/NCT03518866 %M 33496677 %R 10.2196/22846 %U http://mhealth.jmir.org/2021/1/e22846/ %U https://doi.org/10.2196/22846 %U http://www.ncbi.nlm.nih.gov/pubmed/33496677 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 1 %P e19088 %T Physical Activity Evaluation Using a Voice Recognition App: Development and Validation Study %A Namba,Hideyuki %+ Physical Education Lab., College of Science and Technology, Nihon University, 7-24-1 Narashinodai-Funabashi, Chiba, 274-8501, Japan, 81 47 469 5518, nanba.hideyuki@nihon-u.ac.jp %K voice recognition %K smartphone %K physical activity %K accelerometer %K application %D 2021 %7 21.1.2021 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Historically, the evaluation of physical activity has involved a variety of methods such as the use of questionnaires, accelerometers, behavior records, and global positioning systems, each according to the purpose of the evaluation. The use of web-based physical activity evaluation systems has been proposed as an easy method for collecting physical activity data. Voice recognition technology not only eliminates the need for questionnaires during physical activity evaluation but also enables users to record their behavior without physically touching electronic devices. The use of a web-based voice recognition system might be an effective way to record physical activity and behavior. Objective: The purpose of this study was to develop a physical activity evaluation app to record behavior using voice recognition technology and to examine the app’s validity by comparing data obtained using both the app and an accelerometer simultaneously. Methods: A total of 20 participants (14 men, 6 women; mean age 19.1 years, SD 0.9) wore a 3-axis accelerometer and inputted behavioral data into their smartphones for a period of 7 days. We developed a behavior-recording system with a voice recognition function using a voice recognition application programming interface. The exercise intensity was determined from the text data obtained by the voice recognition program. The measure of intensity was metabolic equivalents (METs). Results: From the voice input data of the participants, 601 text-converted data could be confirmed, of which 471 (78.4%) could be automatically converted into behavioral words. In the time-matched analysis, the mean daily METs values measured by the app and the accelerometer were 1.64 (SD 0.20) and 1.63 (SD 0.20), respectively, between which there was no significant difference (P=.57). There was a significant correlation between the average METs obtained from the voice recognition app and the accelerometer in the time-matched analysis (r=0.830, P<.001). In the Bland-Altman plot for METs measured by the voice recognition app as compared with METs measured by accelerometer, the mean difference between the two methods was very small (0.02 METs), with 95% limits of agreement from –0.26 to 0.22 METs between the two methods. Conclusions: The average METs value measured by the voice recognition app was consistent with that measured by the 3-axis accelerometer and, thus, the data gathered by the two measurement methods showed a high correlation. The voice recognition method also demonstrated the ability of the system to measure the physical activity of a large number of people at the same time with less burden on the participants. Although there were still issues regarding the improvement of automatic text data classification technology and user input compliance, this research proposes a new method for evaluating physical activity using voice recognition technology. %M 38907383 %R 10.2196/19088 %U http://biomedeng.jmir.org/2021/1/e19088/ %U https://doi.org/10.2196/19088 %U http://www.ncbi.nlm.nih.gov/pubmed/38907383 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e22488 %T Habit Formation in Wearable Activity Tracker Use Among Older Adults: Qualitative Study %A Peng,Wei %A Li,Lin %A Kononova,Anastasia %A Cotten,Shelia %A Kamp,Kendra %A Bowen,Marie %+ College of Communication Arts and Sciences, Michigan State University, 404 Wilson Room 409, East Lansing, MI, 48824, United States, 1 5174328235, pengwei@msu.edu %K habits %K action planning %K coping planning %K activity trackers %K fitness trackers %K continued use %K mobile phone %K older adults %K health behavior %K mHealth %D 2021 %7 19.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers are popular devices used to motivate behavior change. Wearable activity trackers are especially beneficial for encouraging light physical activity such as walking, which is an ideal behavior for older adults or individuals who cannot be physically active at moderate and vigorous levels. A common problem is that people do not continue to use these wearable devices, with initial behavioral change gains eroding as people disengage. Limited research is available regarding the continued use of wearable activity trackers. The habit formation literature may provide insights into the long-term use of wearables and other health informatics devices. Objective: This study aims to uncover the mechanism underlying the long-term continued use of wearable devices among older adults through the theoretical lens of habit formation. Methods: In-depth interviews were conducted with 20 participants who were aged 65 years or older and had used wearable activity trackers for more than 6 months to understand their experiences and the strategies they employed to support continued use. Results: Thematic analysis of data revealed 8 themes related to habit formation, including aspects in initiation and goal setting, use of contextual cues, action planning, and coping planning. Long-term users tended to have meaningful initiation of wearable activity trackers. They usually started with a small behavioral change goal and gradually increased it. They used consistent time and locational cues to make the use of wearable activity trackers routine. Long-term users also used creative contextual cues and reminders to facilitate action planning, engaged in coping planning to deal with anticipated problems, and had a positive mindset and inventive strategies for managing unfulfillment and lapses. Conclusions: The results of this qualitative study of long-term users of wearable activity trackers suggest specific ways to enhance long-term habit formation among older adults. These best practices by long-term users can inform the future design of technology-based behavior interventions. %M 33464216 %R 10.2196/22488 %U http://mhealth.jmir.org/2021/1/e22488/ %U https://doi.org/10.2196/22488 %U http://www.ncbi.nlm.nih.gov/pubmed/33464216 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e14494 %T Physical Activity Monitoring Using a Fitbit Device in Ischemic Stroke Patients: Prospective Cohort Feasibility Study %A Katzan,Irene %A Schuster,Andrew %A Kinzy,Tyler %+ Neurological Institute Center for Outcomes Research and Evaluation, Cleveland Clinic, 9500 Euclid Avenue, S80, Cleveland, OH, 44195, United States, 1 12164452616, katzani@ccf.org %K physical activity %K accelerometer %K ischemic stroke %K step activity monitor %D 2021 %7 19.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Continuous tracking of ambulatory activity in real-world settings using step activity monitors has many potential uses. However, feasibility, accuracy, and correlation with performance measures in stroke patients have not been well-established. Objective: The primary study objective was to determine adherence with wearing a consumer-grade step activity monitor, the Fitbit Charge HR, in home-going ischemic stroke patients during the first 90 days after hospital discharge. Secondary objectives were to (1) determine accuracy of step counts of the Fitbit Charge HR compared with a manual tally; (2) calculate correlations between the Fitbit step counts and the mobility performance scores at discharge and 30 days after stroke; (3) determine variability and change in weekly step counts over 90 days; and (4) evaluate patient experience with using the Fitbit Charge HR poststroke. Methods: A total of 15 participants with recent mild ischemic stroke wore a Fitbit Charge HR for 90 days after discharge and completed 3 mobility performance tests from the National Institutes of Health Toolbox at discharge and Day 30: (1) Standing Balance Test, (2) 2-Minute Walk Endurance Test, and (3) 4-Meter Walk Gait Speed Test. Accuracy of step activity monitors was assessed by calculating differences in steps recorded on the step activity monitor and a manual tally during 2-minute walk tests. Results: Participants had a mean age of 54 years and a median modified Rankin scale score of 1. Mean daily adherence with step activity monitor use was 83.6%. Mean daily step count in the first week after discharge was 4376. Daily step counts increased slightly during the first 30 days after discharge (average increase of 52.5 steps/day; 95% CI 32.2-71.8) and remained stable during the 30-90 day period after discharge. Mean step count difference between step activity monitor and manual tally was –4.8 steps (–1.8%). Intraclass correlation coefficients for step counts and 2-minute walk, standing balance, and 4-meter gait speed at discharge were 0.41 (95% CI –0.14 to 0.75), –0.12 (95% CI –0.67 to 0.64), and 0.17 (95% CI –0.46 to 0.66), respectively. Values were similarly poor at 30 days. Conclusions: The use of consumer-grade Fitbit Charge HR in patients with recent mild stroke is feasible with reasonable adherence and accuracy. There was poor correlation between step counts and gait speed, balance, and endurance. Further research is needed to evaluate the association between step counts and other outcomes relevant to patients, including patient-reported outcomes and measures of physical function. %M 33464213 %R 10.2196/14494 %U http://mhealth.jmir.org/2021/1/e14494/ %U https://doi.org/10.2196/14494 %U http://www.ncbi.nlm.nih.gov/pubmed/33464213 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 1 %P e21262 %T Step and Distance Measurement From a Low-Cost Consumer-Based Hip and Wrist Activity Monitor: Protocol for a Validity and Reliability Assessment %A Carlin,Thomas %A Vuillerme,Nicolas %+ AGEIS, University Grenoble Alpes, Faculty of Medicine, Grenoble, , France, 33 4 76 63 71 04, nicolas.vuillerme@univ-grenoble-alpes.fr %K activity monitor %K pedometer %K measurement %K validity %K reliability %K walking %K step count %K distance %D 2021 %7 13.1.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Self-tracking via wearable and mobile technologies is becoming an essential part of personal health management. At this point, however, little information is available to substantiate the validity and reliability of low-cost consumer-based hip and wrist activity monitors, with regard more specifically to the measurements of step counts and distance traveled while walking. Objective: The aim of our study is to assess the validity and reliability of step and distance measurement from a low-cost consumer-based hip and wrist activity monitor specific in various walking conditions that are commonly encountered in daily life. Specifically, this study is designed to evaluate whether and to what extent validity and reliability could depend on the sensor placement on the human body and the walking task being performed. Methods: Thirty healthy participants will be instructed to wear four PBN 2433 (Nakosite) activity monitors simultaneously, with one placed on each hip and each wrist. Participants will attend two experimental sessions separated by 1 week. During each experimental session, two separate studies will be performed. In study 1, participants will be instructed to complete a 2-minute walk test along a 30-meter indoor corridor under 3 walking speeds: very slow, slow, and usual speed. In study 2, participants will be required to complete the following 3 conditions performed at usual walking speed: walking on flat ground, upstairs, and downstairs. Activity monitor measured step count and distance values will be computed along with the actual step count (determined from video recordings) and distance (measured using a measuring tape) to determine validity and reliability for each activity monitor placement and each walking condition. Results: Participant recruitment and data collection began in January 2020. As of June 2020, we enrolled 8 participants. Dissemination of study results in peer-reviewed journals is expected in spring 2021. Conclusions: To the best of our knowledge, this is the first study that examines the validity and reliability of step and distance measurement during walking using the PBN 2433 (Nakosite) activity monitor. Results of this study will provide beneficial information on the effects of activity monitor placement, walking speed, and walking tasks on the validity and reliability of step and distance measurement. We believe such information is of utmost importance to general consumers, clinicians, and researchers. International Registered Report Identifier (IRRID): DERR1-10.2196/21262 %M 33439138 %R 10.2196/21262 %U http://www.researchprotocols.org/2021/1/e21262/ %U https://doi.org/10.2196/21262 %U http://www.ncbi.nlm.nih.gov/pubmed/33439138 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e24806 %T Wrist-Worn Activity Trackers in Laboratory and Free-Living Settings for Patients With Chronic Pain: Criterion Validity Study %A Sjöberg,Veronica %A Westergren,Jens %A Monnier,Andreas %A Lo Martire,Riccardo %A Hagströmer,Maria %A Äng,Björn Olov %A Vixner,Linda %+ School of Education, Health and Social Studies, Dalarna University, Högskolegatan 2, Falun, SE-791 88, Sweden, 46 23 77 87 57, vsj@du.se %K chronic pain %K energy expenditure %K heart rate %K physical activity %K step count %K validity %K wearable devices %K wearable %K pain %K rehabilitation %D 2021 %7 12.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity is evidently a crucial part of the rehabilitation process for patients with chronic pain. Modern wrist-worn activity tracking devices seemingly have a great potential to provide objective feedback and assist in the adoption of healthy physical activity behavior by supplying data of energy expenditure expressed as metabolic equivalent of task units (MET). However, no studies of any wrist-worn activity tracking devices’ have examined criterion validity in estimating energy expenditure, heart rate, or step count in patients with chronic pain. Objective: The aim was to determine the criterion validity of wrist-worn activity tracking devices for estimations of energy expenditure, heart rate, and step count in a controlled laboratory setting and free-living settings for patients with chronic pain. Methods: In this combined laboratory and field validation study, energy expenditure, heart rate, and step count were simultaneously estimated by a wrist-worn activity tracker (Fitbit Versa), indirect calorimetry (Jaeger Oxycon Pro), and a research-grade hip-worn accelerometer (ActiGraph GT3X) during treadmill walking at 3 speeds (3.0 km/h, 4.5 km/h, and 6.0 km/h) in the laboratory setting. Energy expenditure and step count were also estimated by the wrist-worn activity tracker in free-living settings for 72 hours. The criterion validity of each measure was determined using intraclass and Spearman correlation, Bland-Altman plots, and mean absolute percentage error. An analysis of variance was used to determine whether there were any significant systematic differences between estimations. Results: A total of 42 patients (age: 25-66 years; male: 10/42, 24%; female: 32/42, 76%), living with chronic pain (duration, in years: mean 9, SD 6.72) were included. At baseline, their mean pain intensity was 3.5 (SD 1.1) out of 6 (Multidimensional Pain Inventory, Swedish version). Results showed that the wrist-worn activity tracking device (Fitbit Versa) systematically overestimated energy expenditure when compared to the criterion standard (Jaeger Oxycon Pro) and the relative criterion standard (ActiGraph GT3X). Poor agreement and poor correlation were shown between Fitbit Versa and both Jaeger Oxycon Pro and ActiGraph GT3X for estimated energy expenditure at all treadmill speeds. Estimations of heart rate demonstrated poor to fair agreement during laboratory-based treadmill walks. For step count, the wrist-worn devices showed fair agreement and fair correlation at most treadmill speeds. In free-living settings; however, the agreement for step count between the wrist-worn device and waist-worn accelerometer was good, and the correlation was excellent. Conclusions: The wrist-worn device systematically overestimated energy expenditure and showed poor agreement and correlation compared to the criterion standard (Jaeger Oxycon Pro) and the relative criterion standard (ActiGraph GT3X), which needs to be considered when used clinically. Step count measured with a wrist-worn device, however, seemed to be a valid estimation, suggesting that future guidelines could include such variables in this group with chronic pain. %M 33433391 %R 10.2196/24806 %U http://mhealth.jmir.org/2021/1/e24806/ %U https://doi.org/10.2196/24806 %U http://www.ncbi.nlm.nih.gov/pubmed/33433391 %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 9 %N 1 %P e18686 %T Effects of Activity Tracker Use With Health Professional Support or Telephone Counseling on Maintenance of Physical Activity and Health Outcomes in Older Adults: Randomized Controlled Trial %A Brickwood,Katie-Jane %A Ahuja,Kiran D K %A Watson,Greig %A O'Brien,Jane A %A Williams,Andrew D %+ School of Health Sciences, College of Health and Medicine, University of Tasmania, Newnham Drive, Launceston, 7250, Australia, 61 0363245487, katiejane.brickwood@utas.edu.au %K physical activity %K fitness trackers %K telemedicine %K feedback %K older adults %K eHealth %K mobile phone %D 2021 %7 5.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Despite a range of efforts to increase physical activity participation in Australia, inactivity levels in older adults have remained high over recent decades, contributing to increased rates of chronic health conditions. Lifestyle interventions, including telephone counseling (TC), improve physical activity participation and associated health outcomes over the short term; however, ongoing feedback and support is required to maintain these changes. Newer technologies such as wearable activity trackers (ATs) may offer an alternative method for providing ongoing support. Objective: This study aims to investigate whether newer technologies such as wearable ATs assist in providing ongoing support to maintain physical activity levels and health outcomes. Methods: Older adults aged >60 years who had just completed a 12-week face-to-face individualized community exercise program in Tasmania, Australia, participated in the study. They were randomized to receive AT, TC, or usual care (UC). All groups received a home exercise program and an optional referral to a community-based exercise program. The AT group also received an AT and text message feedback from an accredited exercise physiologist (AEP). The TC group received phone calls from an AEP throughout the 12-month intervention. The primary outcome was daily steps measured by an ActivPAL (TM) accelerometer at baseline and at 3, 6, and 12 months. Secondary outcome measures included body composition, blood pressure, 10-time sit-to-stand (TTSTS) test, timed up and go test, and cardiorespiratory fitness. This trial was approved by the Tasmanian Health and Medical Human Research Ethics Committee (H0014713). Results: A total of 117 participants were randomized to the study (AT, n=37; TC, n=38; UC, n=42). At baseline, the participants (75/117, 64.1% female; mean age 72.4 years, SD 6.4) completed an average of 6136 steps (SD 2985) per day. Although there were no significant differences between groups, the TC and AT groups maintained daily step counts (mean difference [MD] −79 steps, 95% CI −823 to 663 steps; P=.81; and MD −588 steps, 95% CI −1359 to 182 steps; P=.09), and UC showed a reduction in daily steps (MD 981 steps, 95% CI −1668 to −294 steps; P=.003) during the 12-month period. Diastolic blood pressure was significantly higher after AT than after UC (MD 5.62 mm Hg, 95% CI 1.30 to 9.94 mm Hg; P=.01), and TTSTS was significantly slower on TC compared with UC (MD 2.36 seconds, 95% CI −0.14 to 4.87 seconds; P=.03). Conclusions: The use of an AT with AEP support or TC is effective at maintaining daily step count in older adults over a 12-month period, suggesting that wearable ATs are as effective as TC. Further research to investigate which option is more cost-effective would be beneficial. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12615001104549; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=369118 %M 33399541 %R 10.2196/18686 %U https://mhealth.jmir.org/2021/1/e18686 %U https://doi.org/10.2196/18686 %U http://www.ncbi.nlm.nih.gov/pubmed/33399541 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 12 %P e22201 %T Promotion of Physical Activity in Older People Using mHealth and eHealth Technologies: Rapid Review of Reviews %A McGarrigle,Lisa %A Todd,Chris %+ School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 1613067865, chris.todd@manchester.ac.uk %K physical activity %K mHealth %K eHealth %K app %K accelerometer %K pedometer %K technology %K COVID-19 %D 2020 %7 29.12.2020 %9 Review %J J Med Internet Res %G English %X Background: Older people are at increased risk of adverse health events because of reduced physical activity. There is concern that activity levels are further reduced in the context of the COVID-19 pandemic, as many older people are practicing physical and social distancing to minimize transmission. Mobile health (mHealth) and eHealth technologies may offer a means by which older people can engage in physical activity while physically distancing. Objective: The objective of this study was to assess the evidence for mHealth or eHealth technology in the promotion of physical activity among older people aged 50 years or older. Methods: We conducted a rapid review of reviews using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for systematic reviews published in the English language in 3 electronic databases: MEDLINE, CINAHL Plus, and Scopus. Two reviewers used predefined inclusion criteria to select relevant reviews and extracted data on review characteristics and intervention effectiveness. Two independent raters assessed review quality using the AMSTAR-2 tool. Results: Titles and abstracts (n=472) were screened, and 14 full-text reviews were assessed for eligibility. Initially, we included 5 reviews but excluded 1 from the narrative as it was judged to be of critically low quality. Three reviews concluded that mHealth or eHealth interventions were effective in increasing physical activity. One review found that the evidence was inconclusive. Conclusions: There is low to moderate evidence that interventions delivered via mHealth or eHealth approaches may be effective in increasing physical activity in older adults in the short term. Components of successful interventions include self-monitoring, incorporation of theory and behavior change techniques, and social and professional support. %M 33372894 %R 10.2196/22201 %U http://www.jmir.org/2020/12/e22201/ %U https://doi.org/10.2196/22201 %U http://www.ncbi.nlm.nih.gov/pubmed/33372894 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 12 %P e22090 %T Comparison of the Physical Activity Measured by a Consumer Wearable Activity Tracker and That Measured by Self-Report: Cross-Sectional Analysis of the Health eHeart Study %A Beagle,Alexander J %A Tison,Geoffrey H %A Aschbacher,Kirstin %A Olgin,Jeffrey E %A Marcus,Gregory M %A Pletcher,Mark J %+ Department of Medicine, University of California San Francisco, 505 Parnassus Ave, San Francisco, CA, 94143, United States, 1 9098165831, alexanderjbeagle@gmail.com %K exercise %K body mass index %K overweight %K obesity %K fitness trackers %K self-report %K adult %K mHealth %K public health %K cardiovascular diseases %D 2020 %7 29.12.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Commercially acquired wearable activity trackers such as the Fitbit provide objective, accurate measurements of physically active time and step counts, but it is unclear whether these measurements are more clinically meaningful than self-reported physical activity. Objective: The aim of this study was to compare self-reported physical activity to Fitbit-measured step counts and then determine which is a stronger predictor of BMI by using data collected over the same period reflecting comparable physical activities. Methods: We performed a cross-sectional analysis of data collected by the Health eHeart Study, a large mobile health study of cardiovascular health and disease. Adults who linked commercially acquired Fitbits used in free-living conditions with the Health eHeart Study and completed an International Physical Activity Questionnaire (IPAQ) between 2013 and 2019 were enrolled (N=1498). Fitbit step counts were used to quantify time by activity intensity in a manner comparable to the IPAQ classifications of total active time and time spent being sedentary, walking, or doing moderate activities or vigorous activities. Fitbit steps per day were computed as a measure of the overall activity for exploratory comparisons with IPAQ-measured overall activity (metabolic equivalent of task [MET]-h/wk). Measurements of physical activity were directly compared by Spearman rank correlation. Strengths of associations with BMI for Fitbit versus IPAQ measurements were compared using multivariable robust regression in the subset of participants with BMI and covariates measured. Results: Correlations between synchronous paired measurements from Fitbits and the IPAQ ranged in strength from weak to moderate (0.09-0.48). In the subset with BMI and covariates measured (n=586), Fitbit-derived predictors were generally stronger predictors of BMI than self-reported predictors. For example, an additional hour of Fitbit-measured vigorous activity per week was associated with nearly a full point reduction in BMI (–0.84 kg/m2, 95% CI –1.35 to –0.32) in adjusted analyses, whereas the association between self-reported vigorous activity measured by IPAQ and BMI was substantially smaller in magnitude (–0.17 kg/m2, 95% CI –0.34 to –0.00; P<.001 versus Fitbit) and was dominated by the Fitbit-derived predictor when compared head-to-head in a single adjusted multivariable model. Similar patterns of associations with BMI, with Fitbit dominating self-report, were seen for moderate activity and total active time and in comparisons between overall Fitbit steps per day and IPAQ MET-h/wk on standardized scales. Conclusions: Fitbit-measured physical activity was more strongly associated with BMI than self-reported physical activity, particularly for moderate activity, vigorous activity, and summary measures of total activity. Consumer-marketed wearable activity trackers such as the Fitbit may be useful for measuring health-relevant physical activity in clinical practice and research. %M 33372896 %R 10.2196/22090 %U http://mhealth.jmir.org/2020/12/e22090/ %U https://doi.org/10.2196/22090 %U http://www.ncbi.nlm.nih.gov/pubmed/33372896 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 12 %P e18937 %T Interrelationships Between Patients’ Data Tracking Practices, Data Sharing Practices, and Health Literacy: Onsite Survey Study %A Luo,Yuhan %A Oh,Chi Young %A Jean,Beth St %A Choe,Eun Kyoung %+ College of Information Studies, University of Maryland, 4130 Campus Drive, Hornbake Library South, College Park, MD, , United States, 1 3014051085, choe@umd.edu %K consumer health informatics %K patient-generated health data %K self-tracking %K doctor-patient data sharing %K health literacy %K surveys and questionnaires %D 2020 %7 22.12.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Although the use of patient-generated data (PGD) in the optimization of patient care shows great promise, little is known about whether patients who track their PGD necessarily share the data with their clinicians. Meanwhile, health literacy—an important construct that captures an individual’s ability to manage their health and to engage with their health care providers—has often been neglected in prior studies focused on PGD tracking and sharing. To leverage the full potential of PGD, it is necessary to bridge the gap between patients’ data tracking and data sharing practices by first understanding the interrelationships between these practices and the factors contributing to these practices. Objective: This study aims to systematically examine the interrelationships between PGD tracking practices, data sharing practices, and health literacy among individual patients. Methods: We surveyed 109 patients at the time they met with a clinician at a university health center, unlike prior research that often examined patients’ retrospective experience after some time had passed since their clinic visit. The survey consisted of 39 questions asking patients about their PGD tracking and sharing practices based on their current clinical encounter. The survey also contained questions related to the participants’ health literacy. All the participants completed the survey on a tablet device. The onsite survey study enabled us to collect ecologically valid data based on patients’ immediate experiences situated within their clinic visit. Results: We found no evidence that tracking PGD was related to self-reports of having sufficient information to manage one’s health; however, the number of data types participants tracked positively related to their self-assessed ability to actively engage with health care providers. Participants’ data tracking practices and their health literacy did not relate to their data sharing practices; however, their ability to engage with health care providers positively related to their willingness to share their data with clinicians in the future. Participants reported several benefits of, and barriers to, sharing their PGD with clinicians. Conclusions: Although tracking PGD could help patients better engage with health care providers, it may not provide patients with sufficient information to manage their health. The gaps between tracking and sharing PGD with health care providers call for efforts to inform patients of how their data relate to their health and to facilitate efficient clinician-patient communication. To realize the full potential of PGD and to promote individuals’ health literacy, empowering patients to effectively track and share their PGD is important—both technologies and health care providers can play important roles. %M 33350960 %R 10.2196/18937 %U http://www.jmir.org/2020/12/e18937/ %U https://doi.org/10.2196/18937 %U http://www.ncbi.nlm.nih.gov/pubmed/33350960 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 12 %P e22000 %T ISO/IEEE 11073 Treadmill Interoperability Framework and its Test Method: Design and Implementation %A Huang,Zhi Yong %A Wang,Yujie %A Wang,Linling %+ School of Microelectronics and Communication Engineering, Chongqing University, No 174 Shazhengjie, Shapingba, Chongqing, 400044, China, 86 02365103544, zyhuang@cqu.edu.cn %K ISO/IEEE 11073-PHD %K treadmill %K standard frame model %K test standard %K sports health data %D 2020 %7 9.12.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Regular physical activity is proven to help prevent and treat noncommunicable diseases such as heart disease, stroke, diabetes, and breast and colon cancer. The exercise data generated by health and fitness devices (eg, treadmill, exercise bike) are very important for health management service providers to develop personalized training programs. However, at present, there is little research on a unified interoperability framework in the health and fitness domain, and there are not many solutions; besides, the privatized treadmill data transmission scheme is not conducive to data integration and analysis. Objective: This article will expand the IEEE 11073-PHD standard protocol family, develop standards for health and fitness device (using treadmill as an example) based on the latest version of the 11073-20601 optimized exchange protocol, and design protocol standards compliance testing process and inspection software, which can automatically detect whether the instantiated object of the treadmill meets the standard. Methods: The study includes the following steps: (1) Map the data transmitted by the treadmill to the 11073-PHD objects; (2) Construct a programming language structure corresponding to the 11073-PHD application protocol data unit (APDU) to complete the coding and decoding part of the test software; and (3) Transmit the instantiated simulated treadmill data to the gateway test software through transmission control protocol for standard compliance testing. Results: According to the characteristics of the treadmill, a data exchange framework conforming to 11073-PHD is constructed, and a corresponding testing framework is developed; a treadmill agent simulation is implemented, and the interoperability test is performed. Through the designed testing process, the corresponding testing software was developed to complete the standard compliance testing of the treadmill. Conclusions: The extended research of IEEE 11073-PHD in the field of health and fitness provides a potential new idea for the data transmission framework of sports equipment such as treadmills, which may also provide some help for the development of sports health equipment interoperability standards. %M 33295293 %R 10.2196/22000 %U http://medinform.jmir.org/2020/12/e22000/ %U https://doi.org/10.2196/22000 %U http://www.ncbi.nlm.nih.gov/pubmed/33295293 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 12 %P e19991 %T A Mobile Social Networking App for Weight Management and Physical Activity Promotion: Results From an Experimental Mixed Methods Study %A Laranjo,Liliana %A Quiroz,Juan C %A Tong,Huong Ly %A Arevalo Bazalar,Maria %A Coiera,Enrico %+ Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera road, Sydney, 2113, Australia, 61 413461852, liliana.laranjo@mq.edu.au %K mobile apps %K fitness trackers %K exercise %K social networking %K body weight maintenance %K mobile phone %D 2020 %7 8.12.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users. Objective: This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features. Methods: This was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups—underweight-normal and overweight-obese BMI—using t tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis. Results: In total, 55 participants were recruited (mean age of 23.6, SD 4.6 years; 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants’ BMI decreased by 0.34 kg/m2 (P<.001), with a loss of 0.86 kg/m2 in the overweight-obese group (P=.01). Participants in the overweight-obese group used the app significantly less compared with individuals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app’s self-monitoring and feedback (P=.02) and social (P=.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370; P<.001). Most participants mentioned a desire for a more personalized intervention. Conclusions: This study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match individual preferences and needs. %M 33289670 %R 10.2196/19991 %U http://www.jmir.org/2020/12/e19991/ %U https://doi.org/10.2196/19991 %U http://www.ncbi.nlm.nih.gov/pubmed/33289670 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 12 %P e20926 %T Twitter-Based Social Support Added to Fitbit Self-Monitoring for Decreasing Sedentary Behavior: Protocol for a Randomized Controlled Pilot Trial With Female Patients From a Women’s Heart Clinic %A Oppezzo,Marily %A Tremmel,Jennifer %A Desai,Manisha %A Baiocchi,Michael %A Ramo,Danielle %A Cullen,Mark %A Prochaska,Judith J %+ Stanford Prevention Research Center, Stanford University School of Medicine, Medical School Office Building, 1265 Welch Road, Mail Code 5411, Stanford, CA, 94305-5411, United States, 1 650 724 6152, moppezzo@stanford.edu %K support group %K sedentary behavior %K eHealth %K Twitter %K Fitbit %K intervention %K behavior change theory %K mobile phone %D 2020 %7 4.12.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: Prolonged sitting is an independent risk behavior for the development of chronic disease. With most interventions focusing on physical activity and exercise, there is a separate need for investigation into innovative and accessible interventions to decrease sedentary behavior throughout the day. Twitter is a social media platform with application for health communications and fostering of social support for health behavior change. Objective: This pilot study aims to test the feasibility, acceptability, and preliminary efficacy of delivering daily behavior change strategies within private Twitter groups to foster peer-to-peer support and decrease sedentary behavior throughout the day in women. The Twitter group was combined with a Fitbit for self-monitoring activity and compared to a Fitbit-only control group. Methods: In a 2-group design, participants were randomized to a Twitter + Fitbit treatment group or a Fitbit-only control group. Participants were recruited via the Stanford Research Repository System, screened for eligibility, and then invited to an orientation session. After providing informed consent, they were randomized. All participants received 13 weeks of tailored weekly step goals and a Fitbit. The treatment group participants, placed in a private Twitter support group, received daily automated behavior change “tweets” informed by theory and regular automated encouragement via text to communicate with the group. Fitbit data were collected daily throughout the treatment and follow-up period. Web-based surveys and accelerometer data were collected at baseline, treatment end (13 weeks), and at 8.5 weeks after the treatment. Results: The initial study design funding was obtained from the Women’s Heart Clinic and the Stanford Clayman Institute. Funding to run this pilot study was received from the National Institutes of Health’s National Heart, Lung, and Blood Institute under Award Number K01HL136702. All procedures were approved by Stanford University’s Institutional Review Board, #32127 in 2018, prior to beginning data collection. Recruitment for this study was conducted in May 2019. Of the 858 people screened, 113 met the eligibility criteria, 68 came to an information session, and 45 consented to participate in this pilot study. One participant dropped out of the intervention, and complete follow-up data were obtained from 39 of the 45 participants (87% of the sample). Data were collected over 6 months from June to December 2019. Feasibility, acceptability, and preliminary efficacy results are being analyzed and will be reported in the winter of 2021. Conclusions: This pilot study is assessing the feasibility, acceptability, and preliminary efficacy of delivering behavior change strategies in a Twitter social support group to decrease sedentary behavior in women. These findings will inform a larger evaluation. With an accessible, tailorable, and flexible platform, Twitter-delivered interventions offer potential for many treatment variations and titrations, thereby testing the effects of different behavior change strategies, peer-group makeups, and health behaviors of interest. Trial Registration: ClinicalTrials.gov NCT02958189, https://clinicaltrials.gov/ct2/show/NCT02958189 International Registered Report Identifier (IRRID): DERR1-10.2196/20926 %M 33275104 %R 10.2196/20926 %U https://www.researchprotocols.org/2020/12/e20926 %U https://doi.org/10.2196/20926 %U http://www.ncbi.nlm.nih.gov/pubmed/33275104 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 12 %P e22634 %T Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method %A Minaeva,Olga %A Riese,Harriëtte %A Lamers,Femke %A Antypa,Niki %A Wichers,Marieke %A Booij,Sanne H %+ Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Department of Psychiatry, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, 9713 GZ, Netherlands, 31 50 361 2065, o.minaeva@umcg.nl %K actigraphy %K activity tracker %K depression %K experience sampling method %K prediction model %K screening %D 2020 %7 1.12.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remain unidentified. Introducing additional screening tools may facilitate the diagnostic process. Objective: This study aimed to examine whether experience sampling method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from nondepressed individuals. In addition, the added value of actigraphy-based measures was examined. Methods: We used data from 2 samples to develop and validate prediction models. The development data set included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and nondepressed individuals (n=82). The validation data set included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and nondepressed individuals (n=27). Backward stepwise logistic regression analysis was applied to build the prediction models. Performance of the models was assessed with goodness-of-fit indices, calibration curves, and discriminative ability (area under the receiver operating characteristic curve [AUC]). Results: In the development data set, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for both the ESM (AUC=0.991) and the combined-domains model (AUC=0.993). In the validation data set, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for both the ESM (AUC=0.891) and the combined-domains model (AUC=0.892). Conclusions: ESM is a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor but might still be useful when ESM use is restricted. %M 33258783 %R 10.2196/22634 %U https://www.jmir.org/2020/12/e22634 %U https://doi.org/10.2196/22634 %U http://www.ncbi.nlm.nih.gov/pubmed/33258783 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 5 %N 1 %P e20776 %T Predictors of Walking Activity in Patients With Systolic Heart Failure Equipped With a Step Counter: Randomized Controlled Trial %A Gade,Josefine Dam %A Spindler,Helle %A Hollingdal,Malene %A Refsgaard,Jens %A Dittmann,Lars %A Frost,Lars %A Mahboubi,Kiomars %A Dinesen,Birthe %+ Laboratory for Welfare Technology - Telehealth & Telerehabilitation, Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, Aalborg East, 9220, Denmark, 45 28342888, jdg@hst.aau.dk %K heart failure %K cardiovascular rehabilitation %K step counters %K physical activity %K telerehabilitation %D 2020 %7 30.11.2020 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Physical activity has been shown to decrease cardiovascular mortality and morbidity. Walking, a simple physical activity which is an integral part of daily life, is a feasible and safe activity for patients with heart failure (HF). A step counter, measuring daily walking activity, might be a motivational factor for increased activity. Objective: The aim of this study was to examine the association between walking activity and demographical and clinical data of patients with HF, and whether these associations could be used as predictors of walking activity. Methods: A total of 65 patients with HF from the Future Patient Telerehabilitation (FPT) program were included in this study. The patients monitored their daily activity using a Fitbit step counter for 1 year. This monitoring allowed for continuous and safe data transmission of self-monitored activity data. Results: A higher walking activity was associated with younger age, lower New York Heart Association (NYHA) classification, and higher ejection fraction (EF). There was a statistically significant correlation between the number of daily steps and NYHA classification at baseline (P=.01), between the increase in daily steps and EF at baseline (P<.001), and between the increase in daily steps and improvement in EF (P=.005). The patients’ demographic, clinical, and activity data could predict 81% of the variation in daily steps. Conclusions: This study demonstrated an association between demographic, clinical, and activity data for patients with HF that could predict daily steps. A step counter can thus be a useful tool to help patients monitor their own physical activity. Trial Registration: ClinicalTrials.gov NCT03388918; https://clinicaltrials.gov/ct2/show/NCT03388918 International Registered Report Identifier (IRRID): RR2-10.2196/14517 %R 10.2196/20776 %U http://biomedeng.jmir.org/2020/1/e20776/ %U https://doi.org/10.2196/20776 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e24718 %T Comparison of Mobile Health Technology Use for Self-Tracking Between Older Adults and the General Adult Population in Canada: Cross-Sectional Survey %A Jaana,Mirou %A Paré,Guy %+ Telfer School of Management, University of Ottawa, 55 Laurier Ave East, Ottawa, ON, K1N 6N5, Canada, 1 16135625800 ext 3400, jaana@telfer.uottawa.ca %K mobile health %K older adults %K self-tracking %K wearable technology %K smart devices %K mobile apps %K survey %K mobile phone %K seniors %K elderly %D 2020 %7 27.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The burden of population aging and chronic conditions has been reported worldwide. Older adults, especially those with high needs, experience social isolation and have high rates of emergency visits and limited satisfaction with the care they receive. Mobile health (mHealth) technologies present opportunities to address these challenges. To date, limited information is available on Canadian older adults’ attitudes toward and use of mHealth technologies for self-tracking purposes—an area that is increasingly important and relevant during the COVID-19 era. Objective: This study presents contributions to an underresearched area on older adults and mHealth technology use. The aim of this study was to compare older adults’ use of mHealth technologies to that of the general adult population in Canada and to investigate the factors that affect their use. Methods: A cross-sectional survey on mHealth and digital self-tracking was conducted. A web-based questionnaire was administered to a national sample of 4109 Canadian residents who spoke either English or French. The survey instrument consisted of 3 sections assessing the following items: (1) demographic characteristics, health status, and comorbidities; (2) familiarity with and use of mHealth technologies (ie, mobile apps, consumer smart devices/wearables such as vital signs monitors, bathroom scales, fitness trackers, intelligent clothing); and (3) factors influencing the continued use of mHealth technologies. Results: Significant differences were observed between the older adults and the general adult population in the use of smart technologies and internet (P<.001). Approximately 47.4% (323/682) of the older adults in the community reported using smartphones and 49.8% (340/682) indicated using digital tablets. Only 19.6% (91/463) of the older adults using smartphones/digital tablets reported downloading mobile apps, and 12.3% (47/383) of the older adults who heard of smart devices/wearables indicated using them. The majority of the mobile apps downloaded by older adults was health-related; interestingly, their use was sustained over a longer period of time (P=.007) by the older adults compared to that by the general population. Approximately 62.7% (428/682) of the older adults reported tracking their health measures, but the majority did so manually. Older adults with one or more chronic conditions were mostly nontrackers (odds ratio 0.439 and 0.431 for traditional trackers and digital trackers, respectively). No significant differences were observed between the older adults and the general adult population with regard to satisfaction with mHealth technologies and their intention to continue using them. Conclusions: Leveraging mHealth technologies in partnership with health care providers and sharing of health/well-being data with health care professionals and family members remain very limited. A culture shift in the provision of care to older adults is deemed necessary to keep up with the development of mHealth technologies and the changing demographics and expectations of patients and their caregivers. %M 33104517 %R 10.2196/24718 %U http://mhealth.jmir.org/2020/11/e24718/ %U https://doi.org/10.2196/24718 %U http://www.ncbi.nlm.nih.gov/pubmed/33104517 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e21771 %T Effectiveness of an mHealth Intervention Combining a Smartphone App and Smart Band on Body Composition in an Overweight and Obese Population: Randomized Controlled Trial (EVIDENT 3 Study) %A Lugones-Sanchez,Cristina %A Sanchez-Calavera,Maria Antonia %A Repiso-Gento,Irene %A Adalia,Esther G %A Ramirez-Manent,J Ignacio %A Agudo-Conde,Cristina %A Rodriguez-Sanchez,Emiliano %A Gomez-Marcos,Manuel Angel %A Recio-Rodriguez,Jose I %A Garcia-Ortiz,Luis %A , %+ Institute of Biomedical Research of Salamanca (IBSAL), Primary Care Research Unit of Salamanca (APISAL), Health Service of Castilla y León (SACyL), Av. Portugal 83, 2nd Fl., Salamanca, 37005, Spain, 34 923 291100 ext 54750, crislugsa@gmail.com %K diet records %K mobile app %K telemedicine %K electric impedance %K obesity %K body fat distribution %K weight control %D 2020 %7 26.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) is currently among the supporting elements that may contribute to an improvement in health markers by helping people adopt healthier lifestyles. mHealth interventions have been widely reported to achieve greater weight loss than other approaches, but their effect on body composition remains unclear. Objective: This study aimed to assess the short-term (3 months) effectiveness of a mobile app and a smart band for losing weight and changing body composition in sedentary Spanish adults who are overweight or obese. Methods: A randomized controlled, multicenter clinical trial was conducted involving the participation of 440 subjects from primary care centers, with 231 subjects in the intervention group (IG; counselling with smartphone app and smart band) and 209 in the control group (CG; counselling only). Both groups were counselled about healthy diet and physical activity. For the 3-month intervention period, the IG was trained to use a smartphone app that involved self-monitoring and tailored feedback, as well as a smart band that recorded daily physical activity (Mi Band 2, Xiaomi). Body composition was measured using the InBody 230 bioimpedance device (InBody Co., Ltd), and physical activity was measured using the International Physical Activity Questionnaire. Results: The mHealth intervention produced a greater loss of body weight (–1.97 kg, 95% CI –2.39 to –1.54) relative to standard counselling at 3 months (–1.13 kg, 95% CI –1.56 to –0.69). Comparing groups, the IG achieved a weight loss of 0.84 kg more than the CG at 3 months. The IG showed a decrease in body fat mass (BFM; –1.84 kg, 95% CI –2.48 to –1.20), percentage of body fat (PBF; –1.22%, 95% CI –1.82% to 0.62%), and BMI (–0.77 kg/m2, 95% CI –0.96 to 0.57). No significant changes were observed in any of these parameters in men; among women, there was a significant decrease in BMI in the IG compared with the CG. When subjects were grouped according to baseline BMI, the overweight group experienced a change in BFM of –1.18 kg (95% CI –2.30 to –0.06) and BMI of –0.47 kg/m2 (95% CI –0.80 to –0.13), whereas the obese group only experienced a change in BMI of –0.53 kg/m2 (95% CI –0.86 to –0.19). When the data were analyzed according to physical activity, the moderate-vigorous physical activity group showed significant changes in BFM of –1.03 kg (95% CI –1.74 to –0.33), PBF of –0.76% (95% CI –1.32% to –0.20%), and BMI of –0.5 kg/m2 (95% CI –0.83 to –0.19). Conclusions: The results from this multicenter, randomized controlled clinical trial study show that compared with standard counselling alone, adding a self-reported app and a smart band obtained beneficial results in terms of weight loss and a reduction in BFM and PBF in female subjects with a BMI less than 30 kg/m2 and a moderate-vigorous physical activity level. Nevertheless, further studies are needed to ensure that this profile benefits more than others from this intervention and to investigate modifications of this intervention to achieve a global effect. Trial Registration: Clinicaltrials.gov NCT03175614; https://clinicaltrials.gov/ct2/show/NCT03175614. International Registered Report Identifier (IRRID): RR2-10.1097/MD.0000000000009633 %M 33242020 %R 10.2196/21771 %U http://mhealth.jmir.org/2020/11/e21771/ %U https://doi.org/10.2196/21771 %U http://www.ncbi.nlm.nih.gov/pubmed/33242020 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e20820 %T Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis %A Düking,Peter %A Tafler,Marie %A Wallmann-Sperlich,Birgit %A Sperlich,Billy %A Kleih,Sonja %+ Integrative and Experimental Exercise Science, Department of Sport Science, University of Würzburg, Würzburg, 97082, Germany, 49 931 31 84792, peterdueking@gmx.de %K cardiorespiratory fitness %K innovation %K smartwatch %K technology %K wearable %K eHealth %K mHealth %D 2020 %7 19.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA. Objective: The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users’ PA behavior. Methods: The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature. Results: The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA. Conclusions: Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations. %M 33211023 %R 10.2196/20820 %U http://mhealth.jmir.org/2020/11/e20820/ %U https://doi.org/10.2196/20820 %U http://www.ncbi.nlm.nih.gov/pubmed/33211023 %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 %@ 1929-0748 %I JMIR Publications %V 9 %N 11 %P e20072 %T Using Wearable Devices to Monitor Physical Activity in Patients Undergoing Aortic Valve Replacement: Protocol for a Prospective Observational Study %A Lorenzoni,Giulia %A Azzolina,Danila %A Fraccaro,Chiara %A Di Liberti,Alessandro %A D'Onofrio,Augusto %A Cavalli,Chiara %A Fabris,Tommaso %A D'Amico,Gianpiero %A Cibin,Giorgia %A Nai Fovino,Luca %A Ocagli,Honoria %A Gerosa,Gino %A Tarantini,Giuseppe %A Gregori,Dario %+ Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Leonardo Loredan, 18, Padova, 35131, Italy, 39 0498275384, dario.gregori@unipd.it %K surgical aortic valve replacement %K transcatheter aortic valve replacement %K physical function %K wearable devices %D 2020 %7 12.11.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: In last few decades, several tools have been developed to measure physical function objectively; however, their use has not been well established in clinical practice. Objective: This study aims to describe the preoperative physical function and to assess and compare 6-month postoperative changes in the physical function of patients undergoing treatment for aortic stenosis with either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). The study also aims to evaluate the feasibility of wearable devices in assessing physical function in such patients. Methods: This is a prospective observational study. The enrollment will be conducted 1 month before patients’ SAVR/TAVR. Patients will be provided with the wearable device at baseline (activity tracker device, Garmin vívoactive 3). They will be trained in the use of the device, and they will be requested to wear it on the wrist of their preferred hand until 12 months after SAVR/TAVR. After baseline assessment, they will undergo 4 follow-up assessments at 1, 3, 6, and 12 months after SAVR/TAVR. At baseline and each follow-up, they will undergo a set of standard and validated tests to assess physical function, health-related quality of life, and sleep quality. Results: The ethics committee of Vicenza in Veneto Region in Italy approved the study (Protocol No. 943; January 4, 2019). As of October 2020, the enrollment of participants is ongoing. Conclusions: The use of the wearable devices for real-time monitoring of physical activity of patients undergoing aortic valve replacement is a promising opportunity for improving the clinical management and consequently, the health outcomes of such patients. Trial Registration: Clinicaltrials.gov NCT03843320; https://tinyurl.com/yyareu5y International Registered Report Identifier (IRRID): DERR1-10.2196/20072 %M 33180023 %R 10.2196/20072 %U https://www.researchprotocols.org/2020/11/e20072 %U https://doi.org/10.2196/20072 %U http://www.ncbi.nlm.nih.gov/pubmed/33180023 %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 e19260 %T Asynchronous mHealth Interventions in Rheumatoid Arthritis: Systematic Scoping Review %A Seppen,Bart F %A den Boer,Pim %A Wiegel,Jimmy %A ter Wee,Marieke M %A van der Leeden,Marike %A de Vries,Ralph %A van der Esch,Martin %A Bos,Wouter H %+ Amsterdam Rheumatology and Immunology Center, Reade, Doctor Jan van Breemenstraat 1, Amsterdam, 1056 AB, Netherlands, 31 202421800, b.seppen@reade.nl %K mobile health %K eHealth %K digital health %K telehealth %K telerheumatology %K mHealth %K web app %K smartphone app %K activity tracker %K rheumatoid arthritis %K rheumatology %K review %K telemonitoring %D 2020 %7 5.11.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Mobile devices such as smartphones and tablets have surged in popularity in recent years, generating numerous possibilities for their use in health care as mobile health (mHealth) tools. One advantage of mHealth is that it can be provided asynchronously, signifying that health care providers and patients are not communicating in real time. The integration of asynchronous mHealth into daily clinical practice might therefore help to make health care more efficient for patients with rheumatoid arthritis (RA). The benefits have been reviewed in various medical conditions, such as diabetes and asthma, with promising results. However, to date, it is unclear what evidence exists for the use of asynchronous mHealth in the field of RA. Objective: The objective of this study was to map the different asynchronous mHealth interventions tested in clinical trials in patients with RA and to summarize the effects of the interventions. Methods: A systematic search of Pubmed, Scopus, Cochrane, and PsycINFO was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Studies were initially screened and later assessed by two independent researchers. Disagreements on inclusion or exclusion of studies were resolved by discussion. Results: The literature search yielded 1752 abstracts. After deduplication and screening, 10 controlled intervention studies were included. All studies were assessed to be at risk for bias in at least one domain of the Cochrane risk-of-bias tool. In the 10 selected studies, 4 different types of mHealth interventions were used: SMS reminders (to increase medication adherence or physical activity; n=3), web apps (for disease monitoring and/or to provide medical information; n=5), smartphone apps (for disease monitoring; n=1), and pedometers (to increase and track steps; n=1). Measured outcomes varied widely between studies; improvements were seen in terms of medication compliance (SMS reminders), reaching rapid remission (web app), various domains of physical activity (pedometer, SMS reminders, and web apps), patient-physician interaction (web apps), and self-efficacy (smartphone app). Conclusions: SMS reminders, web apps, smartphone apps, and pedometers have been evaluated in intervention studies in patients with RA. These interventions have been used to monitor patients or to support them in their health behavior. The use of asynchronous mHealth led to desirable outcomes in nearly all studies. However, since all studies were at risk of bias and methods used were very heterogeneous, high-quality research is warranted to corroborate these promising results. %M 33151161 %R 10.2196/19260 %U http://mhealth.jmir.org/2020/11/e19260/ %U https://doi.org/10.2196/19260 %U http://www.ncbi.nlm.nih.gov/pubmed/33151161 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e20465 %T Sleep Tracking of a Commercially Available Smart Ring and Smartwatch Against Medical-Grade Actigraphy in Everyday Settings: Instrument Validation Study %A Asgari Mehrabadi,Milad %A Azimi,Iman %A Sarhaddi,Fatemeh %A Axelin,Anna %A Niela-Vilén,Hannakaisa %A Myllyntausta,Saana %A Stenholm,Sari %A Dutt,Nikil %A Liljeberg,Pasi %A Rahmani,Amir M %+ Department of Electrical Engineering and Computer Science, University of California Irvine, Berk Hall, 1st Floor, Irvine, CA, United States, 1 949 506 8187, masgarim@uci.edu %K sleep %K smart ring %K smartwatch %K actigraphy %K wearable technology %D 2020 %7 2.11.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines. Objective: This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. Methods: We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable. Results: We found significant correlations between the ring’s and actigraphy’s TST (r=0.86; P<.001), WASO (r=0.41; P<.001), and SE (r=0.47; P<.001). Comparing the watch with actigraphy showed a significant correlation in TST (r=0.59; P<.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (P<.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (P<.001) between female and male groups. Conclusions: In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters. %M 33038869 %R 10.2196/20465 %U http://mhealth.jmir.org/2020/10/e20465/ %U https://doi.org/10.2196/20465 %U http://www.ncbi.nlm.nih.gov/pubmed/33038869 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e19542 %T Opportunities and Challenges Surrounding the Use of Data From Wearable Sensor Devices in Health Care: Qualitative Interview Study %A Azodo,Ijeoma %A Williams,Robin %A Sheikh,Aziz %A Cresswell,Kathrin %+ Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, United Kingdom, 44 131 651 4151, kathrin.cresswell@ed.ac.uk %K wearable sensor devices %K health care %K data %K qualitative %D 2020 %7 22.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable sensors connected via networked devices have the potential to generate data that may help to automate processes of care, engage patients, and increase health care efficiency. The evidence of effectiveness of such technologies is, however, nascent and little is known about unintended consequences. Objective: Our objective was to explore the opportunities and challenges surrounding the use of data from wearable sensor devices in health care. Methods: We conducted a qualitative, theoretically informed, interview-based study to purposefully sample international experts in health care, technology, business, innovation, and social sciences, drawing on sociotechnical systems theory. We used in-depth interviews to capture perspectives on development, design, and use of data from wearable sensor devices in health care, and employed thematic analysis of interview transcripts with NVivo to facilitate coding. Results: We interviewed 16 experts. Although the use of data from wearable sensor devices in health and care has significant potential in improving patient engagement, there are a number of issues that stakeholders need to negotiate to realize these benefits. These issues include the current gap between data created and meaningful interpretation in health and care contexts, integration of data into health care professional decision making, negotiation of blurring lines between consumer and medical care, and pervasive monitoring of health across previously disconnected contexts. Conclusions: Stakeholders need to actively negotiate existing challenges to realize the integration of data from wearable sensor devices into electronic health records. Viewing wearables as active parts of a connected digital health and care infrastructure, in which various business, personal, professional, and health system interests align, may help to achieve this. %M 33090107 %R 10.2196/19542 %U http://www.jmir.org/2020/10/e19542/ %U https://doi.org/10.2196/19542 %U http://www.ncbi.nlm.nih.gov/pubmed/33090107 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e18312 %T Comparison of Older and Younger Adults’ Attitudes Toward the Adoption and Use of Activity Trackers %A Kim,Sunyoung %A Choudhury,Abhishek %+ School of Communication and Information, Rutgers University, 4 Huntington Street, New Brunswick, NJ, 08901, United States, 1 8489327585, sunyoung.kim@rutgers.edu %K older adults %K technology acceptance %K activity tracker %K fitness tracker %K mHealth %K health care %K quality of life %D 2020 %7 22.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Activity tracking devices have significant potential in assisting older adults’ health care and quality of life, but this population lags behind in the adoption of these devices. While theoretical frameworks have been introduced to explain and increase the adoption of this technology by older adults, little effort has been made to validate the frameworks with people in other age groups. Objective: The goal of this study was to validate the theoretical framework of technology acceptance by older adults that we previously proposed through a direct comparison of the attitudes to and experiences of activity trackers in older and younger users. Methods: Semistructured interviews were conducted with 2 groups of 15 participants to investigate their experiences of using activity trackers. The recruitment criteria included age (between 18 years and 24 years for the younger participant group or 65 years and older for the older participant group) and prior experiences of using mobile devices or apps for activity tracking for 2 months and longer. Results: Our findings showed that the phase of perceived ease of learning as a significant influencer of the acceptance of activity trackers existed only in the older participant group, but this phase never emerged in the younger participant group. In addition, this study confirmed that other phases exist in both age groups, but 2 distinct patterns emerged according to age groups: (1) the social influence construct influenced the older participants positively but the younger participants negatively and (2) older participants’ exploration in the system experiment phase was purpose-driven by particular needs or benefits but for younger participants, it was a phase to explore a new technology. Conclusions: This study confirms the validity of the proposed theoretical framework to account for the unique aspect of older adults’ technology adoption. This framework can provide theoretical guidelines when designing technology for older adults as well as when generating new investigations and experiments for older adults and technology use. %M 33090116 %R 10.2196/18312 %U https://mhealth.jmir.org/2020/10/e18312 %U https://doi.org/10.2196/18312 %U http://www.ncbi.nlm.nih.gov/pubmed/33090116 %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 e22443 %T Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey %A Chandrasekaran,Ranganathan %A Katthula,Vipanchi %A Moustakas,Evangelos %+ Department of Information & Decision Sciences, University of Illinois at Chicago, 601 S Morgan St, Chicago, IL, 60607, United States, 1 3129962847, ranga@uic.edu %K wearable healthcare devices %K mobile health %K HINTS %K health technology adoption and use %K smart wearables %D 2020 %7 16.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults. Objective: The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults. Methods: Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis. Results: About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health. Conclusions: The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society. %M 33064083 %R 10.2196/22443 %U http://www.jmir.org/2020/10/e22443/ %U https://doi.org/10.2196/22443 %U http://www.ncbi.nlm.nih.gov/pubmed/33064083 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e19227 %T Fitness-Tracker Assisted Frailty-Assessment Before Transcatheter Aortic Valve Implantation: Proof-of-Concept Study %A Mach,Markus %A Watzal,Victoria %A Hasan,Waseem %A Andreas,Martin %A Winkler,Bernhard %A Weiss,Gabriel %A Strouhal,Andreas %A Adlbrecht,Christopher %A Delle Karth,Georg %A Grabenwöger,Martin %+ Division of Cardiac Surgery, Department of Surgery, Medical University of Vienna, Ebene 20A, Waehringer Guertel 18-20, Vienna, 1090, Austria, 43 6645158360, markus.mach@meduniwien.ac.at %K frailty %K activity %K fitness %K tracker %K transcatheter aortic valve implantation %K transcatheter aortic valve repair %D 2020 %7 15.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: While transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of aortic valve stenosis, wearable health-monitoring devices are gradually transforming digital patient care. Objective: The aim of this study was to develop a simple, efficient, and economical method for preprocedural frailty assessment based on parameters measured by a wearable health-monitoring device. Methods: In this prospective study, we analyzed data of 50 consecutive patients with mean (SD) age of 77.5 (5.1) years and a median (IQR) European system for cardiac operative risk evaluation (EuroSCORE) II of 3.3 (4.1) undergoing either transfemoral or transapical TAVR between 2017 and 2018. Every patient was fitted with a wrist-worn health-monitoring device (Garmin Vivosmart 3) for 1 week prior to the procedure. Twenty different parameters were measured, and threshold levels for the 3 most predictive categories (ie, step count, heart rate, and preprocedural stress) were calculated. Patients were assigned 1 point per category for exceeding the cut-off value and were then classified into 4 stages (no, borderline, moderate, and severe frailty). Furthermore, the FItness-tracker assisted Frailty-Assessment Score (FIFA score) was compared with the scores of the preprocedural gait speed category derived from the 6-minute walk test (GSC-6MWT) and the Edmonton Frail Scale classification (EFS-C). The primary study endpoint was hospital mortality. Results: The overall preprocedural stress level (P=.02), minutes of high stress per day (P=.02), minutes of rest per day (P=.045), and daily heart rate maximum (P=.048) as single parameters were the strongest predictors of hospital mortality. When comparing the different frailty scores, the FIFA score demonstrated the greatest predictive power for hospital mortality (FIFA area under the curve [AUC] 0.844, CI 0.656-1.000; P=.048; GSC-6MWT AUC 0.671, CI 0.487-0.855; P=.42; EFS-C AUC 0.636, CI 0.254-1.000; P=.44). Conclusions: This proof-of-concept study demonstrates the strong predictive performance of the FIFA score compared to that of the conventional frailty assessments. %M 33055057 %R 10.2196/19227 %U https://mhealth.jmir.org/2020/10/e19227 %U https://doi.org/10.2196/19227 %U http://www.ncbi.nlm.nih.gov/pubmed/33055057 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e23954 %T Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis %A Ringeval,Mickael %A Wagner,Gerit %A Denford,James %A Paré,Guy %A Kitsiou,Spyros %+ Research Chair in Digital Health, HEC Montreal, 3000, Cote-Sainte-Catherine Road, Montreal, QC, H1Y3K9, Canada, 1 514 882 8672, guy.pare@hec.ca %K Fitbit %K wearables %K healthy lifestyle %K meta-analysis %K literature review %K fuzzy-set qualitative comparative analysis %D 2020 %7 12.10.2020 %9 Review %J J Med Internet Res %G English %X Background: Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good health status. Objective: The objectives of this meta-analysis are (1) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (eg, steps, moderate-to-vigorous physical activity, and weight) and (2) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes. Methods: A systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if (1) they were randomized controlled trials, (2) the intervention involved the use of a Fitbit device, and (3) the reported outcomes were related to healthy lifestyles. The main outcome measures were related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with a control group. We also conducted subgroup analysis and fuzzy-set qualitative comparative analysis (fsQCA) to further disentangle the effects of intervention components. Results: Our final sample comprised 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 950.54, 95% CI 475.89-1425.18; P<.001) and moderate-to-vigorous physical activity (MD 6.16, 95% CI 2.80-9.51; P<.001), a significant decrease in weight (MD −1.48, 95% CI −2.81 to −0.14; P=.03), and a nonsignificant decrease in objectively assessed and self-reported sedentary behavior (MD −10.62, 95% CI −35.50 to 14.27; P=.40 and standardized MD −0.11, 95% CI −0.48 to 0.26; P=.56, respectively). In general, the included studies were at low risk for bias, except for performance bias. Subgroup analysis and fsQCA demonstrated that, in addition to the effects of the Fitbit devices, setting activity goals was the most important intervention component. Conclusions: The use of Fitbit devices in interventions has the potential to promote healthy lifestyles in terms of physical activity and weight. Fitbit devices may be useful to health professionals for patient monitoring and support. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019145450; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019145450 %M 33044175 %R 10.2196/23954 %U http://www.jmir.org/2020/10/e23954/ %U https://doi.org/10.2196/23954 %U http://www.ncbi.nlm.nih.gov/pubmed/33044175 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 10 %P e20590 %T Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study %A Lunsford-Avery,Jessica R %A Keller,Casey %A Kollins,Scott H %A Krystal,Andrew D %A Jackson,Leah %A Engelhard,Matthew M %+ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 2608 Erwin Rd Suite 300, Durham, NC, , United States, 1 919 681 0035, jessica.r.avery@duke.edu %K sleep %K wearable %K mHealth %K adolescents %K EEG %K feasibility %K acceptability %K tolerability %K actigraphy %D 2020 %7 1.10.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Adolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group. Objective: The goal of the research was to investigate the feasibility and acceptability of sleep EEG wearable devices among adolescents aged 11 to 17 years. Methods: A total of 104 adolescents aged 11 to 17 years participated in 7 days of at-home sleep recording using a self-administered wearable sleep EEG device (Zmachine Insight+, General Sleep Corporation) as well as a wristworn actigraph. Feasibility was assessed as the number of full nights of successful recording completed by adolescents, and acceptability was measured by the wearable acceptability survey for sleep. Feasibility and acceptability were assessed separately for the sleep EEG device and wristworn actigraph. Results: A total of 94.2% (98/104) of adolescents successfully recorded at least 1 night of data using the sleep EEG device (mean number of nights 5.42; SD 1.71; median 6, mode 7). A total of 81.6% (84/103) rated the comfort of the device as falling in the comfortable to mildly uncomfortable range while awake. A total of 40.8% (42/103) reported typical sleep while using the device, while 39.8% (41/103) indicated minimal to mild device-related sleep disturbances. A minority (32/104, 30.8%) indicated changes in their sleep position due to device use, and very few (11/103, 10.7%) expressed dissatisfaction with their experience with the device. A similar pattern was observed for the wristworn actigraph device. Conclusions: Wearable sleep EEG appears to represent a feasible, acceptable method for sleep assessment among adolescents and may have utility for assessing and treating sleep disturbances at a population level. Future studies with adolescents should evaluate strategies for further improving usability of such devices, assess relationships between sleep EEG–derived metrics and health outcomes, and investigate methods for incorporating data from these devices into emerging digital interventions and applications. Trial Registration: ClinicalTrials.gov NCT03843762; https://clinicaltrials.gov/ct2/show/NCT03843762 %M 33001035 %R 10.2196/20590 %U https://mhealth.jmir.org/2020/10/e20590 %U https://doi.org/10.2196/20590 %U http://www.ncbi.nlm.nih.gov/pubmed/33001035 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e19223 %T A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study %A Wang,Hailiang %A Zhao,Yang %A Yu,Lisha %A Liu,Jiaxing %A Zwetsloot,Inez Maria %A Cabrera,Javier %A Tsui,Kwok-Leung %+ School of Data Science, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China, 86 34422177, kltsui@cityu.edu.hk %K telehealth monitoring %K personalized health %K technology acceptance %K digital biomarkers %K digital phenotyping %K wearables %K falls detection %K fitness tracker %K sensors %K elderly population %D 2020 %7 30.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. Objective: Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. Methods: The system was designed to capture and record older adults’ health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users’ attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. Results: A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants’ daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). Conclusions: The proposed health monitoring system provides an example design for monitoring older adults’ health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system. %M 32996887 %R 10.2196/19223 %U http://www.jmir.org/2020/9/e19223/ %U https://doi.org/10.2196/19223 %U http://www.ncbi.nlm.nih.gov/pubmed/32996887 %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 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 %@ 1929-0748 %I JMIR Publications %V 9 %N 9 %P e17685 %T Mobile Fitness and Weight Management Apps: Protocol for a Quality Evaluation %A Milne-Ives,Madison %A Lam,Ching %A van Velthoven,Michelle %A Meinert,Edward %+ Centre for Health Technology, Faculty of Health, University of Plymouth, 6 Kirkby Place, Plymouth, PL4 8AA, United Kingdom, 44 1752 600600, edward.meinert@plymouth.ac.uk %K mobile apps %K telemedicine %K smartphone %K exercise %K weight loss %K obesity %K physical fitness %K fitness trackers %D 2020 %7 24.9.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: Obesity is a contributing factor for many noncommunicable diseases and a growing problem worldwide. Many mobile apps have been developed to help users improve their fitness and weight management behaviors. However, the speed at which apps are created and updated means that it is important to periodically assess their quality. Objective: The purpose of this study is to evaluate the quality of fitness and weight management mobile health apps using the Mobile Application Rating Scale (MARS). It will also describe the features of the included apps and compare the results to a previous evaluation conducted in 2015. Methods: Searches for “fitness,” “weight,” “exercise,” “physical activity,” “diet,” “eat*,” and “food” will be conducted in the Apple App Store and Google Play. Apps that have been updated over the past 5 years will be included. Two reviewers will rate the apps’ quality using the MARS objective and subjective quality subscales. Interrater reliability will also be assessed. Features included in high-quality apps will be assessed, and changes in quality, features, and behavior change techniques made during the past 5 years will be described. Results: The results will be included in the evaluation paper, which we aim to publish in 2020. Conclusions: This evaluation will assess the quality of currently available fitness and weight management apps. International Registered Report Identifier (IRRID): PRR1-10.2196/17685 %M 32969830 %R 10.2196/17685 %U http://www.researchprotocols.org/2020/9/e17685/ %U https://doi.org/10.2196/17685 %U http://www.ncbi.nlm.nih.gov/pubmed/32969830 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e16745 %T One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial %A Osborn,Chandra Y %A Hirsch,Ashley %A Sears,Lindsay E %A Heyman,Mark %A Raymond,Jennifer %A Huddleston,Brian %A Dachis,Jeff %+ Lirio, 901 Woodland Street, Nashville, TN, 37206, United States, 1 8604242858, cosborn@lirio.co %K diabetes %K type 1 diabetes %K digital therapy %K mobile app %K coaching %K glucometer %K activity tracker %D 2020 %7 17.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: In 2017, mobile app support for managing diabetes was available to 64% of the global population of adults with diabetes. One Drop’s digital therapeutics solution includes an evidence-based mobile app with global reach, a Bluetooth-connected glucometer, and in-app coaching from Certified Diabetes Educators. Among people with type 1 diabetes and an estimated hemoglobin A1c level≥7.5%, using One Drop for 3 months has been associated with an improved estimated hemoglobin A1c level of 22.2 mg/dL (–0.80%). However, the added value of integrated activity trackers is unknown. Objective: We conducted a pragmatic, remotely administered randomized controlled trial to evaluate One Drop with a new-to-market activity tracker against One Drop only on the 3-month hemoglobin A1c level of adults with type 1 diabetes. Methods: Social media advertisements and online newsletters were used to recruit adults (≥18 years old) diagnosed (≥1 year) with T1D, naïve to One Drop’s full solution and the activity tracker, with a laboratory hemoglobin A1c level≥7%. Participants (N=99) were randomized to receive One Drop and the activity tracker or One Drop only at the start of the study. The One Drop only group received the activity tracker at the end of the study. Multiple imputation, performed separately by group, was used to correct for missing data. Analysis of covariance models, controlling for baseline hemoglobin A1c, were used to evaluate 3-month hemoglobin A1c differences in intent-to-treat (ITT) and per protocol (PP) analyses. Results: The enrolled sample (N=95) had a mean age of 41 (SD 11) years, was 73% female, 88% White, diagnosed for a mean of 20 (SD 11) years, and had a mean hemoglobin A1c level of 8.4% (SD 1.2%); 11% of the participants did not complete follow up. Analysis of covariance assumptions were met for the ITT and PP models. In ITT analysis, participants in the One Drop and activity tracker condition had a significantly lower 3-month hemoglobin A1c level (mean 7.9%, SD 0.60%, 95% CI 7.8-8.2) than that of the participants in the One Drop only condition (mean 8.4%, SD 0.62%, 95% CI 8.2-8.5). In PP analysis, participants in the One Drop and activity tracker condition also had a significantly lower 3-month hemoglobin A1c level (mean 7.9%, SD 0.59%, 95% CI 7.7-8.1) than that of participants in the One Drop only condition (mean 8.2%, SD 0.58%, 95% CI 8.0-8.4). Conclusions: Participants exposed to One Drop and the activity tracker for the 3-month study period had a significantly lower 3-month hemoglobin A1c level compared to that of participants exposed to One Drop only during the same timeframe. One Drop and a tracker may work better together than alone in helping people with type 1 diabetes. Trial Registration: ClinicalTrials.gov NCT03459573; https://clinicaltrials.gov/ct2/show/NCT03459573. %M 32540842 %R 10.2196/16745 %U http://mhealth.jmir.org/2020/9/e16745/ %U https://doi.org/10.2196/16745 %U http://www.ncbi.nlm.nih.gov/pubmed/32540842 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e18986 %T Identification of Patient Perceptions That Can Affect the Uptake of Interventions Using Biometric Monitoring Devices: Systematic Review of Randomized Controlled Trials %A Perlmutter,Alexander %A Benchoufi,Mehdi %A Ravaud,Philippe %A Tran,Viet-Thi %+ Department of Epidemiology, Mailman School of Public Health, Columbia University, Room 720.10, 722 W 168th St, New York, NY, 10032, United States, 1 703 336 9067, asp2183@cumc.columbia.edu %K systematic review %K patient perceptions %K biometric monitoring device %K randomized controlled trials %K accelerometer %K pedometer %K ecological momentary assessment %K electrochemical biosensor %K adoption %K uptake %K real-world %D 2020 %7 11.9.2020 %9 Review %J J Med Internet Res %G English %X Background: Biometric monitoring devices (BMDs) are wearable or environmental trackers and devices with embedded sensors that can remotely collect high-frequency objective data on patients’ physiological, biological, behavioral, and environmental contexts (for example, fitness trackers with accelerometer). The real-world effectiveness of interventions using biometric monitoring devices depends on patients’ perceptions of these interventions. Objective: We aimed to systematically review whether and how recent randomized controlled trials (RCTs) evaluating interventions using BMDs assessed patients’ perceptions toward the intervention. Methods: We systematically searched PubMed (MEDLINE) from January 1, 2017, to December 31, 2018, for RCTs evaluating interventions using BMDs. Two independent investigators extracted the following information: (1) whether the RCT collected information on patient perceptions toward the intervention using BMDs and (2) if so, what precisely was collected, based on items from questionnaires used and/or themes and subthemes identified from qualitative assessments. The two investigators then synthesized their findings in a schema of patient perceptions of interventions using BMDs. Results: A total of 58 RCTs including 10,071 participants were included in the review (the median number of randomized participants was 60, IQR 37-133). BMDs used in interventions were accelerometers/pedometers (n=35, 60%), electrochemical biosensors (eg, continuous glucose monitoring; n=18, 31%), or ecological momentary assessment devices (eg, carbon monoxide monitors for smoking cessation; n=5, 9%). Overall, 26 (45%) trials collected information on patient perceptions toward the intervention using BMDs and allowed the identification of 76 unique aspects of patient perceptions that could affect the uptake of these interventions (eg, relevance of the information provided, alarm burden, privacy and data handling, impact on health outcomes, independence, interference with daily life). Patient perceptions were unevenly collected in trials. For example, only 5% (n=3) of trials assessed how patients felt about privacy and data handling aspects of the intervention using BMDs. Conclusions: Our review showed that less than half of RCTs evaluating interventions using BMDs assessed patients’ perceptions toward interventions using BMDs. Trials that did assess perceptions often only assessed a fraction of them. This limits the extrapolation of the results of these RCTs to the real world. We thus provide a comprehensive schema of aspects of patient perceptions that may affect the uptake of interventions using BMDs and which should be considered in future trials. Trial Registration: PROSPERO CRD42018115522; https://tinyurl.com/y5h8fjgx %M 32915153 %R 10.2196/18986 %U http://www.jmir.org/2020/9/e18986/ %U https://doi.org/10.2196/18986 %U http://www.ncbi.nlm.nih.gov/pubmed/32915153 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e18694 %T Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review %A Fuller,Daniel %A Colwell,Emily %A Low,Jonathan %A Orychock,Kassia %A Tobin,Melissa Ann %A Simango,Bo %A Buote,Richard %A Van Heerden,Desiree %A Luan,Hui %A Cullen,Kimberley %A Slade,Logan %A Taylor,Nathan G A %+ School of Human Kinetics and Recreation, Memorial University of Newfoundland, St. John's, NL, A1C 5S7, Canada, 1 7098647270, dfuller@mun.ca %K commercial wearable devices %K systematic review %K heart rate %K energy expenditure %K step count %K Fitbit %K Apple Watch %K Garmin %K Polar %D 2020 %7 8.9.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures. Objective: The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. Methods: We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability. Results: We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Conclusions: Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research. %M 32897239 %R 10.2196/18694 %U http://mhealth.jmir.org/2020/9/e18694/ %U https://doi.org/10.2196/18694 %U http://www.ncbi.nlm.nih.gov/pubmed/32897239 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e18142 %T Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study %A Mohammadi,Ramin %A Atif,Mursal %A Centi,Amanda Jayne %A Agboola,Stephen %A Jethwani,Kamal %A Kvedar,Joseph %A Kamarthi,Sagar %+ Northeastern University, 360 Huntington Ave, Boston, MA, 02115, United States, 1 6173733070, sagar@coe.neu.edu %K activity tracker %K exercise engagement %K dynamic activity target %K neural network %K activity target prediction %K machine learning %D 2020 %7 8.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective: The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods: We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results: Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions: Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm. %M 32897235 %R 10.2196/18142 %U https://mhealth.jmir.org/2020/9/e18142 %U https://doi.org/10.2196/18142 %U http://www.ncbi.nlm.nih.gov/pubmed/32897235 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e19732 %T Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study %A Kim,Ben %A McKay,Sandra M %A Lee,Joon %+ Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada, 1 403 220 2968, joonwu.lee@ucalgary.ca %K frailty %K mobile health %K wearables %K physical activity %K home care %K prediction %K predictive modeling, older adults %K activities of daily living, sleep %D 2020 %7 3.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population. Objective: The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible. Methods: From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty. Results: A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88). Conclusions: We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services. %M 32880582 %R 10.2196/19732 %U https://www.jmir.org/2020/9/e19732 %U https://doi.org/10.2196/19732 %U http://www.ncbi.nlm.nih.gov/pubmed/32880582 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e18509 %T Clusters of Adolescent Physical Activity Tracker Patterns and Their Associations With Physical Activity Behaviors in Finland and Ireland: Cross-Sectional Study %A Ng,Kwok %A Kokko,Sami %A Tammelin,Tuija %A Kallio,Jouni %A Belton,Sarahjane %A O'Brien,Wesley %A Murphy,Marie %A Powell,Cormac %A Woods,Catherine %+ School of Educational Sciences and Psychology, University of Eastern Finland, PO Box 111, Joensuu, 80101, Finland, 358 504724051, kwok.ng@hbsc.org %K wearables %K children %K activity trackers %K active travel %K organised sport %K self-quantification %D 2020 %7 1.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Physical activity trackers (PATs) such as apps and wearable devices (eg, sports watches, heart rate monitors) are increasingly being used by young adolescents. Despite the potential of PATs to help monitor and improve moderate-to-vigorous physical activity (MVPA) behaviors, there is a lack of research that confirms an association between PAT ownership or use and physical activity behaviors at the population level. Objective: The purpose of this study was to examine the ownership and use of PATs in youth and their associations with physical activity behaviors, including daily MVPA, sports club membership, and active travel, in 2 nationally representative samples of young adolescent males and females in Finland and Ireland. Methods: Comparable data were gathered in the 2018 Finnish School-aged Physical Activity (F-SPA 2018, n=3311) and the 2018 Irish Children’s Sport Participation and Physical Activity (CSPPA 2018, n=4797) studies. A cluster analysis was performed to obtain the patterns of PAT ownership and usage by adolescents (age, 11-15 years). Four similar clusters were identified across Finnish and Irish adolescents: (1) no PATs, (2) PAT owners, (3) app users, and (4) wearable device users. Adjusted binary logistic regression analyses were used to evaluate how PAT clusters were associated with physical activity behaviors, including daily MVPA, membership of sports clubs, and active travel, after stratification by gender. Results: The proportion of app ownership among Finnish adolescents (2038/3311, 61.6%) was almost double that of their Irish counterparts (1738/4797, 36.2%). Despite these differences, the clustering patterns of PATs were similar between the 2 countries. App users were more likely to take part in daily MVPA (males, odds ratio [OR] 1.27, 95% CI 1.04-1.55; females, OR 1.49, 95% CI 1.20-1.85) and be members of sports clubs (males, OR 1.37, 95% CI 1.15-1.62; females, OR 1.25, 95% CI 1.07-1.50) compared to the no PATs cluster, after adjusting for country, age, family affluence, and disabilities. These associations, after the same adjustments, were even stronger for wearable device users to participate in daily MVPA (males, OR 1.83, 95% CI 1.49-2.23; females, OR 2.25, 95% CI 1.80-2.82) and be members of sports clubs (males, OR 1.88, 95% CI 1.55-2.88; females, OR 2.07, 95% CI 1.71-2.52). Significant associations were observed between male users of wearable devices and taking part in active travel behavior (OR 1.39, 95% CI 1.04-1.86). Conclusions: Although Finnish adolescents report more ownership of PATs than Irish adolescents, the patterns of use and ownership remain similar among the cohorts. The findings of our study show that physical activity behaviors were positively associated with wearable device users and app users. These findings were similar between males and females. Given the cross-sectional nature of this data, the relationship between using apps or wearable devices and enhancing physical activity behaviors requires further investigation. %M 32667894 %R 10.2196/18509 %U https://www.jmir.org/2020/9/e18509 %U https://doi.org/10.2196/18509 %U http://www.ncbi.nlm.nih.gov/pubmed/32667894 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 8 %P e16727 %T Calibrating Wrist-Worn Accelerometers for Physical Activity Assessment in Preschoolers: Machine Learning Approaches %A Li,Shiyu %A Howard,Jeffrey T %A Sosa,Erica T %A Cordova,Alberto %A Parra-Medina,Deborah %A Yin,Zenong %+ The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, United States, 1 210 204 7621, lis9@livemail.uthscsa.edu %K preschoolers %K accelerometer %K physical activity %K obesity %K machine learning %D 2020 %7 31.8.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical activity (PA) level is associated with multiple health benefits during early childhood. However, inconsistency in the methods for quantification of PA levels among preschoolers remains a problem. Objective: This study aimed to develop PA intensity cut points for wrist-worn accelerometers by using machine learning (ML) approaches to assess PA in preschoolers. Methods: Wrist- and hip-derived acceleration data were collected simultaneously from 34 preschoolers on 3 consecutive preschool days. Two supervised ML models, receiver operating characteristic curve (ROC) and ordinal logistic regression (OLR), and one unsupervised ML model, k-means cluster analysis, were applied to establish wrist-worn accelerometer vector magnitude (VM) cut points to classify accelerometer counts into sedentary behavior, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA). Physical activity intensity levels identified by hip-worn accelerometer VM cut points were used as reference to train the supervised ML models. Vector magnitude counts were classified by intensity based on three newly established wrist methods and the hip reference to examine classification accuracy. Daily estimates of PA were compared to the hip-reference criterion. Results: In total, 3600 epochs with matched hip- and wrist-worn accelerometer VM counts were analyzed. All ML approaches performed differently on developing PA intensity cut points for wrist-worn accelerometers. Among the three ML models, k-means cluster analysis derived the following cut points: ≤2556 counts per minute (cpm) for sedentary behavior, 2557-7064 cpm for LPA, 7065-14532 cpm for MPA, and ≥14533 cpm for VPA; in addition, k-means cluster analysis had the highest classification accuracy, with more than 70% of the total epochs being classified into the correct PA categories, as examined by the hip reference. Additionally, k-means cut points exhibited the most accurate estimates on sedentary behavior, LPA, and VPA as the hip reference. None of the three wrist methods were able to accurately assess MPA. Conclusions: This study demonstrates the potential of ML approaches in establishing cut points for wrist-worn accelerometers to assess PA in preschoolers. However, the findings from this study warrant additional validation studies. %M 32667893 %R 10.2196/16727 %U https://formative.jmir.org/2020/8/e16727 %U https://doi.org/10.2196/16727 %U http://www.ncbi.nlm.nih.gov/pubmed/32667893 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 8 %P e16537 %T Formative Evaluation of Consumer-Grade Activity Monitors Worn by Older Adults: Test-Retest Reliability and Criterion Validity of Step Counts %A Maganja,Stephanie A %A Clarke,David C %A Lear,Scott A %A Mackey,Dawn C %+ Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Shrum Science Centre Building K, 8888 University Drive, Burnaby, BC, V5A 4Z2, Canada, 1 778 782 9330, dmackey@sfu.ca %K aged %K gait %K mobility limitation %K exercise %K movement %K wearable electronic devices %K mobile phone %K reproducibility of results %K bias %K dimensional measurement accuracy %D 2020 %7 18.8.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: To assess whether commercial-grade activity monitors are appropriate for measuring step counts in older adults, it is essential to evaluate their measurement properties in this population. Objective: This study aimed to evaluate test-retest reliability and criterion validity of step counting in older adults with self-reported intact and limited mobility from 6 commercial-grade activity monitors: Fitbit Charge, Fitbit One, Garmin vívofit 2, Jawbone UP2, Misfit Shine, and New-Lifestyles NL-1000. Methods: For test-retest reliability, participants completed two 100-step overground walks at a usual pace while wearing all monitors. We tested the effects of the activity monitor and mobility status on the absolute difference in step count error (%) and computed the standard error of measurement (SEM) between repeat trials. To assess criterion validity, participants completed two 400-meter overground walks at a usual pace while wearing all monitors. The first walk was continuous; the second walk incorporated interruptions to mimic the conditions of daily walking. Criterion step counts were from the researcher tally count. We estimated the effects of the activity monitor, mobility status, and walk interruptions on step count error (%). We also generated Bland-Altman plots and conducted equivalence tests. Results: A total of 36 individuals participated (n=20 intact mobility and n=16 limited mobility; 19/36, 53% female) with a mean age of 71.4 (SD 4.7) years and BMI of 29.4 (SD 5.9) kg/m2. Considering test-retest reliability, there was an effect of the activity monitor (P<.001). The Fitbit One (1.0%, 95% CI 0.6% to 1.3%), the New-Lifestyles NL-1000 (2.6%, 95% CI 1.3% to 3.9%), and the Garmin vívofit 2 (6.0%, 95 CI 3.2% to 8.8%) had the smallest mean absolute differences in step count errors. The SEM values ranged from 1.0% (Fitbit One) to 23.5% (Jawbone UP2). Regarding criterion validity, all monitors undercounted the steps. Step count error was affected by the activity monitor (P<.001) and walk interruptions (P=.02). Three monitors had small mean step count errors: Misfit Shine (−1.3%, 95% CI −19.5% to 16.8%), Fitbit One (−2.1%, 95% CI −6.1% to 2.0%), and New-Lifestyles NL-1000 (−4.3%, 95 CI −18.9% to 10.3%). Mean step count error was larger during interrupted walking than continuous walking (−5.5% vs −3.6%; P=.02). Bland-Altman plots illustrated nonsystematic bias and small limits of agreement for Fitbit One and Jawbone UP2. Mean step count error lay within an equivalence bound of ±5% for Fitbit One (P<.001) and Misfit Shine (P=.001). Conclusions: Test-retest reliability and criterion validity of step counting varied across 6 consumer-grade activity monitors worn by older adults with self-reported intact and limited mobility. Walk interruptions increased the step count error for all monitors, whereas mobility status did not affect the step count error. The hip-worn Fitbit One was the only monitor with high test-retest reliability and criterion validity. %M 32651956 %R 10.2196/16537 %U http://formative.jmir.org/2020/8/e16537/ %U https://doi.org/10.2196/16537 %U http://www.ncbi.nlm.nih.gov/pubmed/32651956 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 8 %P e19624 %T App Technology to Support Physical Activity and Intake of Vitamins and Minerals After Bariatric Surgery (the PromMera Study): Protocol of a Randomized Controlled Clinical Trial %A Bonn,Stephanie Erika %A Hult,Mari %A Spetz,Kristina %A Löf,Marie %A Andersson,Ellen %A Wiren,Mikael %A Trolle Lagerros,Ylva %+ Clinical Epidemiology Division, Department of Medicine (Solna), Karolinska Institutet, Karolinska University Hospital, KEP T2:02, Stockholm, 171 76, Sweden, 46 851779173, stephanie.bonn@ki.se %K adults %K body composition %K exercise %K metabolic health %K obesity %K randomized controlled trial %K smartphones %K vitamin intake %K mobile phone %D 2020 %7 14.8.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: To optimize postoperative outcomes after bariatric surgery, lifestyle changes including increased physical activity are needed. Micronutrient deficiency after surgery is also common and daily supplementation is recommended. Objective: The aim of the PromMera study is to evaluate the effects of a 12-week smartphone app intervention on promotion of physical activity (primary outcome) and adherence to postsurgery vitamin and mineral supplementation, as well as on other lifestyle factors and overall health in patients undergoing bariatric surgery. Methods: The PromMera study is a two-arm, randomized controlled trial comprising patients undergoing bariatric surgery. Participants are randomized postsurgery 1:1 to either the intervention group (ie, use of the PromMera app for 12 weeks) or the control group receiving only standard care. Clinical and lifestyle variables are assessed pre- and postsurgery after 18 weeks (postintervention assessment), 6 months, 1 year, and 2 years. Assessments include body composition using Tanita or BOD POD analyzers, muscle function using handgrip, biomarkers in blood, and an extensive questionnaire on lifestyle factors. Physical activity is objectively measured using the ActiGraph wGT3X-BT triaxial accelerometer. Results: A total of 154 participants have been enrolled in the study. The last study participant was recruited in May 2019. Data collection will be complete in May 2021. Conclusions: Implementing lifestyle changes are crucial after bariatric surgery and new ways to reach patients and support such changes are needed. An app-based intervention is easily delivered at any time and can be a key factor in the adoption of healthier behavioral patterns in this rapidly growing group of patients. Trial Registration: ClinicalTrials.gov NCT03480464; https://clinicaltrials.gov/ct2/show/NCT03480464 International Registered Report Identifier (IRRID): DERR1-10.2196/19624 %M 32795990 %R 10.2196/19624 %U http://www.researchprotocols.org/2020/8/e19624/ %U https://doi.org/10.2196/19624 %U http://www.ncbi.nlm.nih.gov/pubmed/32795990 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e17803 %T Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study %A Lee,JeeEun %A Yoo,Sun K %+ Department of Medical Engineering, Yonsei University College of Medicine, Yonsei-ro, Seodaemun-gu, Seoul, , Republic of Korea, 82 10 3458 2435, sunkyoo@yuhs.ac %K respiration rate %K accelerometer %K smartphone %K independent component analysis %K quefrency %K mobile phone %D 2020 %7 10.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As the mobile environment has developed recently, there have been studies on continuous respiration monitoring. However, it is not easy for general users to access the sensors typically used to measure respiration. There is also random noise caused by various environmental variables when respiration is measured using noncontact methods in a mobile environment. Objective: In this study, we aimed to estimate the respiration rate using an accelerometer sensor in a smartphone. Methods: First, data were acquired from an accelerometer sensor by a smartphone, which can easily be accessed by the general public. Second, an independent component was extracted to calibrate the three-axis accelerometer. Lastly, the respiration rate was estimated using quefrency selection reflecting the harmonic component because respiration has regular patterns. Results: From April 2018, we enrolled 30 male participants. When the independent component and quefrency selection were used to estimate the respiration rate, the correlation with respiration acquired from a chest belt was 0.7. The statistical results of the Wilcoxon signed-rank test were used to determine whether the differences in the respiration counts acquired from the chest belt and from the accelerometer sensor were significant. The P value of the difference in the respiration counts acquired from the two sensors was .27, which was not significant. This indicates that the number of respiration counts measured using the accelerometer sensor was not different from that measured using the chest belt. The Bland-Altman results indicated that the mean difference was 0.43, with less than one breath per minute, and that the respiration rate was at the 95% limits of agreement. Conclusions: There was no relevant difference in the respiration rate measured using a chest belt and that measured using an accelerometer sensor. The accelerometer sensor approach could solve the problems related to the inconvenience of chest belt attachment and the settings. It could be used to detect sleep apnea through constant respiration rate estimation in an internet-of-things environment. %M 32773384 %R 10.2196/17803 %U https://mhealth.jmir.org/2020/8/e17803 %U https://doi.org/10.2196/17803 %U http://www.ncbi.nlm.nih.gov/pubmed/32773384 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e18370 %T Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation %A Liu,Jiaxing %A Zhao,Yang %A Lai,Boya %A Wang,Hailiang %A Tsui,Kwok Leung %+ Centre for Systems Informatics Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, 999077, China (Hong Kong), 852 34425792, yang.zhao@my.cityu.edu.hk %K sleep/wake identification %K hidden Markov model %K personalized health %K unsupervised learning %K sleep %K physical activity %K wearables %K heart rate %D 2020 %7 5.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns. Objective: We aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach’s output with that of an existing commercial wearable device’s algorithms. Methods: In this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual’s data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified. Results: When applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants. Conclusions: Our unsupervised approach can be effectively implemented based on an individual’s multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters. %M 32755887 %R 10.2196/18370 %U https://mhealth.jmir.org/2020/8/e18370 %U https://doi.org/10.2196/18370 %U http://www.ncbi.nlm.nih.gov/pubmed/32755887 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e17699 %T Using Smart Bracelets to Assess Heart Rate Among Students During Physical Education Lessons: Feasibility, Reliability, and Validity Study %A Sun,Jiangang %A Liu,Yang %+ School of Physical Education and Sport Training, Shanghai University of Sport, 650 Qingyuanhuan Rd, Shanghai, 200438, China, 86 21 6550 7989, docliuyang@hotmail.com %K physical education %K heart rate %K validation %K feasibility %K reliability %K Fizzo %K Polar %K wrist-worn devices %K physical education lesson %K monitoring %D 2020 %7 5.8.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: An increasing number of wrist-worn wearables are being examined in the context of health care. However, studies of their use during physical education (PE) lessons remain scarce. Objective: We aim to examine the reliability and validity of the Fizzo Smart Bracelet (Fizzo) in measuring heart rate (HR) in the laboratory and during PE lessons. Methods: In Study 1, 11 healthy subjects (median age 22.0 years, IQR 3.75 years) twice completed a test that involved running on a treadmill at 6 km/h for 12 minutes and 12 km/h for 5 minutes. During the test, participants wore two Fizzo devices, one each on their left and right wrists, to measure their HR. At the same time, the Polar Team2 Pro (Polar), which is worn on the chest, was used as the standard. In Study 2, we went to 10 schools and measured the HR of 24 students (median age 14.0 years, IQR 2.0 years) during PE lessons. During the PE lessons, each student wore a Polar device on their chest and a Fizzo on their right wrist to measure HR data. At the end of the PE lessons, the students and their teachers completed a questionnaire where they assessed the feasibility of Fizzo. The measurements taken by the left wrist Fizzo and the right wrist Fizzo were compared to estimate reliability, while the Fizzo measurements were compared to the Polar measurements to estimate validity. To measure reliability, intraclass correlation coefficients (ICC), mean difference (MD), standard error of measurement (SEM), and mean absolute percentage errors (MAPE) were used. To measure validity, ICC, limits of agreement (LOA), and MAPE were calculated and Bland-Altman plots were constructed. Percentage values were used to estimate the feasibility of Fizzo. Results: The Fizzo showed excellent reliability and validity in the laboratory and moderate validity in a PE lesson setting. In Study 1, reliability was excellent (ICC>0.97; MD<0.7; SEM<0.56; MAPE<1.45%). The validity as determined by comparing the left wrist Fizzo and right wrist Fizzo was excellent (ICC>0.98; MAPE<1.85%). Bland-Altman plots showed a strong correlation between left wrist Fizzo measurements (bias=0.48, LOA=–3.94 to 4.89 beats per minute) and right wrist Fizzo measurements (bias=0.56, LOA=–4.60 to 5.72 beats per minute). In Study 2, the validity of the Fizzo was lower compared to that found in Study 1 but still moderate (ICC>0.70; MAPE<9.0%). The Fizzo showed broader LOA in the Bland-Altman plots during the PE lessons (bias=–2.60, LOA=–38.89 to 33.69 beats per minute). Most participants considered the Fizzo very comfortable and easy to put on. All teachers thought the Fizzo was helpful. Conclusions: When participants ran on a treadmill in the laboratory, both left and right wrist Fizzo measurements were accurate. The validity of the Fizzo was lower in PE lessons but still reached a moderate level. The Fizzo is feasible for use during PE lessons. %M 32663136 %R 10.2196/17699 %U http://mhealth.jmir.org/2020/8/e17699/ %U https://doi.org/10.2196/17699 %U http://www.ncbi.nlm.nih.gov/pubmed/32663136 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 7 %P e16471 %T Developing Effective Methods for Electronic Health Personalization: Protocol for Health Telescope, a Prospective Interventional Study %A Willemse,Bastiaan Johannes Paulus Cornelis %A Kaptein,Maurits Clemens %A Hasaart,Fleur %+ Jheronimus Academy of Data Science, Sint Janssingel 92, 's-Hertogenbosch, 5211 DA, Netherlands, 31 073 614 5515, b.j.p.c.willemse@tilburguniversity.edu %K eHealth %K mHealth %K personalization %K longitudinal study %K wearables %K panel study %K persuasive technology %K gdpr %D 2020 %7 31.7.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: Existing evaluations of the effects of mobile apps to encourage physical activity have been criticized owing to their common lack of external validity, their short duration, and their inability to explain the drivers of the observed effects. This protocol describes the setup of Health Telescope, a longitudinal panel study in which the long-term effects of mobile electronic health (eHealth) apps are investigated. By setting up Health Telescope, we aim to (1) understand more about the long-term use of eHealth apps in an externally valid setting, (2) understand the relationships between short-term and long-term outcomes of the usage of eHealth apps, and (3) test different ways in which eHealth app allocation can be personalized. Objective: The objectives of this paper are to (1) demonstrate and motivate the validity of the many choices that we made in setting up an intensive longitudinal study, (2) provide a resource for researchers interested in using data generated by our study, and (3) act as a guideline for researchers interested in setting up their own longitudinal data collection using wearable devices. For the third objective, we explicitly discuss the General Data Protection Regulation and ethical requirements that need to be addressed. Methods: In this 4-month study, a group of approximately 450 participants will have their daily step count measured and will be asked daily about their mood using experience sampling. Once per month, participants will receive an intervention containing a recommendation to download an app that focuses on increasing physical activity. The mechanism for assigning recommendations to participants will be personalized over time, using contextual data obtained from previous interventions. Results: The data collection software has been developed, and all the legal and ethical checks are in place. Recruitment will start in Q4 of 2020. The initial results will be published in 2021. Conclusions: The aim of Health Telescope is to investigate how different individuals respond to different ways of being encouraged to increase their physical activity. In this paper, we detail the setup, methods, and analysis plan that will enable us to reach this aim. International Registered Report Identifier (IRRID): PRR1-10.2196/16471 %M 32734930 %R 10.2196/16471 %U http://www.researchprotocols.org/2020/7/e16471/ %U https://doi.org/10.2196/16471 %U http://www.ncbi.nlm.nih.gov/pubmed/32734930 %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 %@ 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 %@ 2368-7959 %I JMIR Publications %V 7 %N 7 %P e17075 %T Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study %A Kumar,Shefali %A Tran,Jennifer L A %A Ramirez,Ernesto %A Lee,Wei-Nchih %A Foschini,Luca %A Juusola,Jessie L %+ Evidation Health, 167 2nd Ave, San Mateo, CA, 94401, United States, 1 650 279 8855, jjuusola@evidation.com %K mental health %K anxiety %K depression %K behavioral data %D 2020 %7 23.7.2020 %9 Original Paper %J JMIR Ment Health %G English %X Background: Depression and anxiety greatly impact daily behaviors, such as sleep and activity levels. With the increasing use of activity tracking wearables among the general population, there has been a growing interest in how data collected from these devices can be used to further understand the severity and progression of mental health conditions. Objective: This virtual 1-year observational study was designed with the objective of creating a longitudinal data set combining self-reported health outcomes, health care utilization, and quality of life data with activity tracker and app-based behavioral data for individuals with depression and anxiety. We provide an overview of the study design, report on baseline health and behavioral characteristics of the study population, and provide initial insights into how behavioral characteristics differ between groups of individuals with varying levels of disease severity. Methods: Individuals who were existing members of an online health community (Achievement, Evidation Health Inc) and were 18 years or older who had self-reported a diagnosis of depression or anxiety were eligible to enroll in this virtual 1-year study. Participants agreed to connect wearable activity trackers that captured data related to physical activity and sleep behavior. Mental health outcomes such as the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder Questionnaire (GAD-7), mental health hospitalizations, and medication use were captured with surveys completed at baseline and months 3, 6, 9, and 12. In this analysis, we report on baseline characteristics of the sample, including mental health disease severity and health care utilization. Additionally, we explore the relationship between passively collected behavioral data and baseline mental health status and health care utilization. Results: Of the 1304 participants enrolled in the study, 1277 individuals completed the baseline survey and 1068 individuals had sufficient activity tracker data. Mean age was 33 (SD 9) years, and the majority of the study population was female (77.2%, 994/1288) and identified as Caucasian (88.3%, 1137/1288). At baseline, 94.8% (1211/1277) of study participants reported experiencing depression or anxiety symptoms in the last year. This baseline analysis found that some passively tracked behavioral traits are associated with more severe forms of anxiety or depression. Individuals with depressive symptoms were less active than those with minimal depressive symptoms. Severe forms of depression were also significantly associated with inconsistent sleep patterns and more disordered sleep. Conclusions: These initial findings suggest that longitudinal behavioral and health outcomes data may be useful for developing digital measures of health for mental health symptom severity and progression. %M 32706712 %R 10.2196/17075 %U http://mental.jmir.org/2020/7/e17075/ %U https://doi.org/10.2196/17075 %U http://www.ncbi.nlm.nih.gov/pubmed/32706712 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e15576 %T Effectiveness of Wearable Trackers on Physical Activity in Healthy Adults: Systematic Review and Meta-Analysis of Randomized Controlled Trials %A Tang,Matilda Swee Sun %A Moore,Katherine %A McGavigan,Andrew %A Clark,Robyn A %A Ganesan,Anand N %+ College of Medicine and Public Health, Flinders University, Level 5, Room 5E209 Flinders Medical Centre, Adelaide, Australia, 61 (08) 7221 8200, anand.ganesan@flinders.edu.au %K wearable activity tracker %K physical activity %K healthy adults %K randomized controlled trials %D 2020 %7 22.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable trackers are an increasingly popular tool among healthy adults and are used to facilitate self-monitoring of physical activity. Objective: We aimed to systematically review the effectiveness of wearable trackers for improving physical activity and weight reduction among healthy adults. Methods: This review used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology and reporting criteria. English-language randomized controlled trials with more than 20 participants from MEDLINE, CINAHL, Cochrane Library, Web of Science, PubMed, and Scopus (2000-2017) were identified. Studies were eligible for inclusion if they reported an intervention group using wearable trackers, reporting steps per day, total moderate-to-vigorous physical activity, activity, physical activity, energy expenditure, and weight reduction. Results: Twelve eligible studies with a total of 1693 participants met the inclusion criteria. The weighted average age was 40.7 years (95% CI 31.1-50.3), with 64.4% women. The mean intervention duration was 21.4 weeks (95% CI 6.1-36.7). The usage of wearable trackers was associated with increased physical activity (standardized mean difference 0.449, 95% CI 0.10-0.80; P=.01). In the subgroup analyses, however, wearable trackers demonstrated no clear benefit for physical activity or weight reduction. Conclusions: These data suggest that the use of wearable trackers in healthy adults may be associated with modest short-term increases in physical activity. Further data are required to determine if a sustained benefit is associated with wearable tracker usage. %M 32706685 %R 10.2196/15576 %U http://mhealth.jmir.org/2020/7/e15576/ %U https://doi.org/10.2196/15576 %U http://www.ncbi.nlm.nih.gov/pubmed/32706685 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e15331 %T Usability of Wearable Devices With a Novel Cardiac Force Index for Estimating the Dynamic Cardiac Function: Observational Study %A Hsiao,Po-Jen %A Chiu,Chih-Chien %A Lin,Ke-Hsin %A Hu,Fu-Kang %A Tsai,Pei-Jan %A Wu,Chun-Ting %A Pang,Yuan-Kai %A Lin,Yu %A Kuo,Ming-Hao %A Chen,Kang-Hua %A Wu,Yi-Syuan %A Wu,Hao-Yi %A Chang,Ya-Ting %A Chang,Yu-Tien %A Cheng,Chia-Shiang %A Chuu,Chih-Pin %A Lin,Fu-Huang %A Chang,Chi-Wen %A Li,Yuan-Kuei %A Chan,Jenq-Shyong %A Chu,Chi-Ming %+ Division of Biostatistics and Medical Informatics, Department of Epidemiology, School of Public Health, National Defense Medical Center, Neihu 114, Taipei, Taiwan, 1 886 2 87923100, chuchiming@web.de %K cardiac force %K running %K acceleration %K physical activity %K heart rate %D 2020 %7 21.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Long-distance running can be a form of stress to the heart. Technological improvements combined with the public’s gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. Objective: We investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. Methods: This study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. Results: The results showed that waist circumference, smoking, and CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = –2.702 – 0.096 × [waist circumference] – 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner’s CFR varies with changes in activity. Since the runner’s acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner’s explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. Conclusions: In conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance. %M 32706725 %R 10.2196/15331 %U https://mhealth.jmir.org/2020/7/e15331 %U https://doi.org/10.2196/15331 %U http://www.ncbi.nlm.nih.gov/pubmed/32706725 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e15873 %T Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study %A Andersen,Tariq Osman %A Langstrup,Henriette %A Lomborg,Stine %+ Department of Computer Science, University of Copenhagen, Universitetsparken 5, Copenhagen, 2100, Denmark, 45 26149169, tariq@di.ku.dk %K consumer health information %K wearable electronic devices %K self-care %K chronic illness %K patient experiences %D 2020 %7 20.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Most commercial activity trackers are developed as consumer devices and not as clinical devices. The aim is to monitor and motivate sport activities, healthy living, and similar wellness purposes, and the devices are not designed to support care management in a clinical context. There are great expectations for using wearable sensor devices in health care settings, and the separate realms of wellness tracking and disease self-monitoring are increasingly becoming blurred. However, patients’ experiences with activity tracking technologies designed for use outside the clinical context have received little academic attention. Objective: This study aimed to contribute to understanding how patients with a chronic disease experience activity data from consumer self-tracking devices related to self-care and their chronic illness. Our research question was: “How do patients with heart disease experience activity data in relation to self-care and chronic illness?” Methods: We conducted a qualitative interview study with patients with chronic heart disease (n=27) who had an implanted cardioverter-defibrillator. Patients were invited to wear a FitBit Alta HR wearable activity tracker for 3-12 months and provide their perspectives on their experiences with step, sleep, and heart rate data. The average age was 57.2 years (25 men and 2 women), and patients used the tracker for 4-49 weeks (mean 26.1 weeks). Semistructured interviews (n=66) were conducted with patients 2–3 times and were analyzed iteratively in workshops using thematic analysis and abductive reasoning logic. Results: Of the 27 patients, 18 related the heart rate, sleep, and step count data directly to their heart disease. Wearable activity trackers actualized patients’ experiences across 3 dimensions with a spectrum of contrasting experiences: (1) knowing, which spanned gaining insight and evoking doubts; (2) feeling, which spanned being reassured and becoming anxious; and (3) evaluating, which spanned promoting improvements and exposing failure. Conclusions: Patients’ experiences could reside more on one end of the spectrum, could reside across all 3 dimensions, or could combine contrasting positions and even move across the spectrum over time. Activity data from wearable devices may be a resource for self-care; however, the data may simultaneously constrain and create uncertainty, fear, and anxiety. By showing how patients experience self-tracking data across dimensions of knowing, feeling, and evaluating, we point toward the richness and complexity of these data experiences in the context of chronic illness and self-care. %M 32706663 %R 10.2196/15873 %U https://www.jmir.org/2020/7/e15873 %U https://doi.org/10.2196/15873 %U http://www.ncbi.nlm.nih.gov/pubmed/32706663 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 7 %P e14370 %T Measurement of Physical Activity and Sedentary Behavior by Accelerometry Among a Nationwide Sample from the KiGGS and MoMo Study: Study Protocol %A Burchartz,Alexander %A Manz,Kristin %A Anedda,Bastian %A Niessner,Claudia %A Oriwol,Doris %A Schmidt,Steffen CE %A Woll,Alexander %+ Institute for Sports and Sports Science, Karlsruhe Institute of Technology, Building 40.40, Engler-Bunte-Ring 15, Karlsruhe, 76131, Germany, 49 721 608 46952, alexander.burchartz@kit.edu %K processing criteria %K wear time protocol %K epoch length %K sampling frequency %K intensity classification %K Motorik-Modul study %D 2020 %7 14.7.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: Currently, no nationwide objective physical activity data exists for children and adolescents living in Germany. The German Health Interview and Examination Survey for Children and Adolescents (KiGGS) and the Motorik-Modul study (MoMo) is a national cohort study that has incorporated accelerometers in its most recent data collection wave (wave 2, since 2014). This wave 2 marks the first nationwide collection of objective data on the physical activity of children and adolescents living in Germany. Objective: The purpose of this protocol is to describe the methods used in the KiGGS and MoMo study to capture the intensity, frequency, and duration of physical activity with accelerometers. Methods: Participants (N=11,003, aged 6 to 31 years) were instructed to wear an ActiGraph GT3X+ or wGT3X-BT accelerometer laterally on the right hip. Accelerometers were worn on consecutive days during waking hours, including at least 4 valid weekdays and 1 weekend day (wear time >8 hours) in the evaluation. A nonwear time protocol was also implemented. Results: Data collection was completed by October 2017. Data harmonization took place in 2018. The first accelerometer results from this wave were published in 2019, and detailed analyses are ready to be submitted in 2020. Conclusions: This study protocol provides an overview of technical details and basic choices when using accelerometers in large-scale epidemiological studies. At the same time, the restrictions imposed by the specified filters and the evaluation routines must be taken into account. International Registered Report Identifier (IRRID): DERR1-10.2196/14370 %M 32459648 %R 10.2196/14370 %U https://www.researchprotocols.org/2020/7/e14370 %U https://doi.org/10.2196/14370 %U http://www.ncbi.nlm.nih.gov/pubmed/32459648 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e16925 %T Mobile Health Apps in Pediatric Obesity Treatment: Process Outcomes From a Feasibility Study of a Multicomponent Intervention %A Browne,Sarah %A Kechadi,M-Tahar %A O'Donnell,Shane %A Dow,Mckenzie %A Tully,Louise %A Doyle,Gerardine %A O'Malley,Grace %+ Division of Population Health Sciences, School of Physiotherapy, Royal College of Surgeons in Ireland, Beaux Lane House, Dublin, D02 YN77, Ireland, 353 014028591, graceomalley@rcsi.ie %K childhood obesity %K diet therapy %K mHealth %K mobile phones %K smartphones %K appetite %K satiety %K rate of eating %K accelerometer %K physical activity %D 2020 %7 8.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Multicomponent family interventions underline current best practice in childhood obesity treatment. Mobile health (mHealth) adjuncts that address eating and physical activity behaviors have shown promise in clinical studies. Objective: This study aimed to describe process methods for applying an mHealth intervention to reduce the rate of eating and monitor physical activity among children with obesity. Methods: The study protocol was designed to incorporate 2 mHealth apps as an adjunct to usual care treatment for obesity. Children and adolescents (aged 9-16 years) with obesity (BMI ≥98th centile) were recruited in person from a weight management service at a tertiary health care center in the Republic of Ireland. Eligible participants and their parents received information leaflets, and informed consent and assent were signed. Participants completed 2 weeks of baseline testing, including behavioral and quality of life questionnaires, anthropometry, rate of eating by Mandolean, and physical activity level using a smart watch and the myBigO smartphone app. Thereafter, participants were randomized to the (1) intervention (usual clinical care+Mandolean training to reduce the rate of eating) or (2) control (usual clinical care) groups. Gender and age group (9.0-12.9 years and 13.0-16.9 years) stratifications were applied. At the end of a 4-week treatment period, participants repeated the 2-week testing period. Process evaluation measures included recruitment, study retention, fidelity parameters, acceptability, and user satisfaction. Results: A total of 20 participants were enrolled in the study. A web-based randomization system assigned 8 participants to the intervention group and 12 participants to the control group. Attrition rates were higher among the participants in the intervention group (5/8, 63%) than those in the control group (3/12, 25%). Intervention participants undertook a median of 1.0 training meal using Mandolean (25th centile 0, 75th centile 9.3), which represented 19.2% of planned intervention exposure. Only 50% (9/18) of participants with smart watches logged physical activity data. Significant differences in psychosocial profile were observed at baseline between the groups. The Child Behavior Checklist (CBCL) mean total score was 71.7 (SD 3.1) in the intervention group vs 57.6 (SD 6.6) in the control group, t-test P<.001, and also different among those who completed the planned protocol compared with those who withdrew early (CBCL mean total score 59.0, SD 9.3, vs 67.9, SD 5.6, respectively; t-test P=.04). Conclusions: A high early attrition rate was a key barrier to full study implementation. Perceived task burden in combination with behavioral issues may have contributed to attrition. Low exposure to the experimental intervention was explained by poor acceptability of Mandolean as a home-based tool for treatment. Self-monitoring using myBigO and the smartwatch was acceptable among this cohort. Further technical and usability studies are needed to improve adherence in our patient group in the tertiary setting. %M 32673267 %R 10.2196/16925 %U https://mhealth.jmir.org/2020/7/e16925 %U https://doi.org/10.2196/16925 %U http://www.ncbi.nlm.nih.gov/pubmed/32673267 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 7 %P e17841 %T Adherence Tracking With Smart Watches for Shoulder Physiotherapy in Rotator Cuff Pathology: Protocol for a Longitudinal Cohort Study %A Burns,David %A Razmjou,Helen %A Shaw,James %A Richards,Robin %A McLachlin,Stewart %A Hardisty,Michael %A Henry,Patrick %A Whyne,Cari %+ Holland Bone and Joint Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, 2075 Bayview Ave Room S-621, Toronto, ON, Canada, 1 4164806100, d.burns@utoronto.ca %K rehabilitation %K treatment adherence and compliance %K wearable electronic devices %K machine learning %K rotator cuff %D 2020 %7 5.7.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: Physiotherapy is essential for the successful rehabilitation of common shoulder injuries and following shoulder surgery. Patients may receive some training and supervision for shoulder physiotherapy through private pay or private insurance, but they are typically responsible for performing most of their physiotherapy independently at home. It is unknown how often patients perform their home exercises and if these exercises are performed correctly without supervision. There are no established tools for measuring this. It is, therefore, unclear if the full benefit of shoulder physiotherapy treatments is being realized. Objective: The proposed research will (1) validate a smartwatch and machine learning (ML) approach for evaluating adherence to shoulder exercise participation and technique in a clinical patient population with rotator cuff pathology; (2) quantify the rate of home physiotherapy adherence, determine the effects of adherence on recovery, and identify barriers to successful adherence; and (3) develop and pilot test an ethically conscious adherence-driven rehabilitation program that individualizes patient care based on their capacity to effectively participate in their home physiotherapy. Methods: This research will be conducted in 2 phases. The first phase is a prospective longitudinal cohort study, involving 120 patients undergoing physiotherapy for rotator cuff pathology. Patients will be issued a smartwatch that will record 9-axis inertial sensor data while they perform physiotherapy exercises both in the clinic and in the home setting. The data collected in the clinic under supervision will be used to train and validate our ML algorithms that classify shoulder physiotherapy exercise. The validated algorithms will then be used to assess home physiotherapy adherence from the inertial data collected at home. Validated outcome measures, including the Disabilities of the Arm, Shoulder, and Hand questionnaire; Numeric Pain Rating Scale; range of motion; shoulder strength; and work status, will be collected pretreatment, monthly through treatment, and at a final follow-up of 12 months. We will then relate improvement in patient outcomes to measured physiotherapy adherence and patient baseline variables in univariate and multivariate analyses. The second phase of this research will involve the evaluation of a novel rehabilitation program in a cohort of 20 patients. The program will promote patient physiotherapy engagement via the developed technology and support adherence-driven care decisions. Results: As of December 2019, 71 patients were screened for enrollment in the noninterventional validation phase of this study; 65 patients met the inclusion and exclusion criteria. Of these, 46 patients consented and 19 declined to participate in the study. Only 2 patients de-enrolled from the study and data collection is ongoing for the remaining 44. Conclusions: This study will provide new and important insights into shoulder physiotherapy adherence, the relationship between adherence and recovery, barriers to better adherence, and methods for addressing them. International Registered Report Identifier (IRRID): DERR1-10.2196/17841 %M 32623366 %R 10.2196/17841 %U https://www.researchprotocols.org/2020/7/e17841 %U https://doi.org/10.2196/17841 %U http://www.ncbi.nlm.nih.gov/pubmed/32623366 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 7 %P e19116 %T Effects of a 12-Week Multifaceted Wearable-Based Program for People With Knee Osteoarthritis: Randomized Controlled Trial %A Li,Linda C %A Feehan,Lynne M %A Xie,Hui %A Lu,Na %A Shaw,Christopher D %A Gromala,Diane %A Zhu,Siyi %A Aviña-Zubieta,J Antonio %A Hoens,Alison M %A Koehn,Cheryl %A Tam,Johnathan %A Therrien,Stephanie %A Townsend,Anne F %A Noonan,Gregory %A Backman,Catherine L %+ Department of Physical Therapy, University of British Columbia, 2177 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada, 1 6042074020, lli@arthritisresearch.ca %K physical activity %K counseling %K knee osteoarthritis %K physiotherapy %K wearables %D 2020 %7 3.7.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Current guidelines emphasize an active lifestyle in the management of knee osteoarthritis (OA), but up to 90% of patients with OA are inactive. In a previous study, we demonstrated that an 8-week physiotherapist (PT)-led counseling intervention, with the use of a Fitbit, improved step count and quality of life in patients with knee OA, compared with a control. Objective: This study aimed to examine the effect of a 12-week, multifaceted wearable-based program on physical activity and patient outcomes in patients with knee OA. Methods: This was a randomized controlled trial with a delay-control design. The immediate group (IG) received group education, a Fitbit, access to FitViz (a Fitbit-compatible app), and 4 biweekly phone calls from a PT over 8 weeks. Participants then continued using Fitbit and FitViz independently up to week 12. The delay group (DG) received a monthly electronic newsletter in weeks 1 to 12 and started the same intervention in week 14. Participants were assessed in weeks 13, 26, and 39. The primary outcome was time spent in daily moderate-to-vigorous physical activity (MVPA; in bouts ≥10 min) measured with a SenseWear Mini. Secondary outcomes included daily steps, time spent in purposeful activity and sedentary behavior, Knee Injury and OA Outcome Score, Patient Health Questionnaire-9, Partners in Health Scale, Theory of Planned Behavior Questionnaire, and Self-Reported Habit Index. Results: We enrolled 51 participants (IG: n=26 and DG: n=25). Compared with the IG, the DG accumulated significantly more MVPA time at baseline. The adjusted mean difference in MVPA was 13.1 min per day (95% CI 1.6 to 24.5). A significant effect was also found in the adjusted mean difference in perceived sitting habit at work (0.7; 95% CI 0.2 to 1.2) and during leisure activities (0.7; 95% CI 0.2 to 1.2). No significant effect was found in the remaining secondary outcomes. Conclusions: A 12-week multifaceted program with the use of a wearable device, an app, and PT counseling improved physical activity in people with knee OA. Trial Registration: ClinicalTrials.gov NCT02585323; https://clinicaltrials.gov/ct2/show/NCT02585323 %M 32618578 %R 10.2196/19116 %U https://mhealth.jmir.org/2020/7/e19116 %U https://doi.org/10.2196/19116 %U http://www.ncbi.nlm.nih.gov/pubmed/32618578 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e17118 %T Accuracy of Distance Recordings in Eight Positioning-Enabled Sport Watches: Instrument Validation Study %A Gilgen-Ammann,Rahel %A Schweizer,Theresa %A Wyss,Thomas %+ Swiss Federal Institute of Sport Magglingen, Hauptstrasse 247, Magglingen/Macolin, , Switzerland, 41 584676321, rahel.gilgen@baspo.admin.ch %K geographic information systems %K GPS measurement error %K sports %K geographic locations %K monitoring physical training %K movement analysis %D 2020 %7 24.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Elite athletes and recreational runners rely on the accuracy of global navigation satellite system (GNSS)–enabled sport watches to monitor and regulate training activities. However, there is a lack of scientific evidence regarding the accuracy of such sport watches. Objective: The aim was to investigate the accuracy of the recorded distances obtained by eight commercially available sport watches by Apple, Coros, Garmin, Polar, and Suunto when assessed in different areas and at different speeds. Furthermore, potential parameters that affect the measurement quality were evaluated. Methods: Altogether, 3 × 12 measurements in urban, forest, and track and field areas were obtained while walking, running, and cycling under various outdoor conditions. Results: The selected reference distances ranged from 404.0 m to 4296.9 m. For all the measurement areas combined, the recorded systematic errors (±limits of agreements) ranged between 3.7 (±195.6) m and –101.0 (±231.3) m, and the mean absolute percentage errors ranged from 3.2% to 6.1%. Only the GNSS receivers from Polar showed overall errors <5%. Generally, the recorded distances were significantly underestimated (all P values <.04) and less accurate in the urban and forest areas, whereas they were overestimated but with good accuracy in 75% (6/8) of the sport watches in the track and field area. Furthermore, the data assessed during running showed significantly higher error rates in most devices compared with the walking and cycling activities. Conclusions: The recorded distances might be underestimated by up to 9%. However, the use of all investigated sport watches can be recommended, especially for distance recordings in open areas. %M 32396865 %R 10.2196/17118 %U http://mhealth.jmir.org/2020/6/e17118/ %U https://doi.org/10.2196/17118 %U http://www.ncbi.nlm.nih.gov/pubmed/32396865 %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 %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e14116 %T Continuous Measurement of Reconnaissance Marines in Training With Custom Smartphone App and Watch: Observational Cohort Study %A Saxon,Leslie %A DiPaula,Brooks %A Fox,Glenn R %A Ebert,Rebecca %A Duhaime,Josiah %A Nocera,Luciano %A Tran,Luan %A Sobhani,Mona %+ University of Southern California, Center for Body Computing, Keck School of Medicine, 12015 East Waterfront Drive, Playa Vista, CA, 90094, United States, 1 310 448 5373, saxon@usc.edu %K military %K marines %K wearable devices %K wearable technology %K smartphone %K mobile app %D 2020 %7 15.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Specialized training for elite US military units is associated with high attrition due to intense psychological and physical demands. The need to graduate more service members without degrading performance standards necessitates the identification of factors to predict success or failure in targeted training interventions. Objective: The aim of this study was to continuously quantify the mental and physical status of trainees of an elite military unit to identify novel predictors of success in training. Methods: A total of 3 consecutive classes of a specialized training course were provided with an Apple iPhone, Watch, and specially designed mobile app. Baseline personality assessments and continuous daily measures of mental status, physical pain, heart rate, activity, sleep, hydration, and nutrition were collected from the app and Watch data. Results: A total of 115 trainees enrolled and completed the study (100% male; age: mean 22 years, SD 4 years) and 64 (55.7%) successfully graduated. Most training withdrawals (27/115, 23.5%) occurred by day 7 (mean 5.5 days, SD 3.4 days; range 1-22 days). Extraversion, positive affect personality traits, and daily psychological profiles were associated with course completion; key psychological factors could predict withdrawals 1-2 days in advance (P=.009). Conclusions: Gathering accurate and continuous mental and physical status data during elite military training is possible with early predictors of withdrawal providing an opportunity for intervention. %M 32348252 %R 10.2196/14116 %U https://mhealth.jmir.org/2020/6/e14116 %U https://doi.org/10.2196/14116 %U http://www.ncbi.nlm.nih.gov/pubmed/32348252 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e17730 %T Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis %A Yang,Qing %A Hatch,Daniel %A Crowley,Matthew J %A Lewinski,Allison A %A Vaughn,Jacqueline %A Steinberg,Dori %A Vorderstrasse,Allison %A Jiang,Meilin %A Shaw,Ryan J %+ School of Nursing, Duke University, 307 Trent Drive, Durham, NC, 27710, United States, 1 9196139768, qing.yang@duke.edu %K digital phenotype %K latent class growth analysis %K type 2 diabetes %K self-management %K self-monitoring %K Mobile Health %D 2020 %7 11.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sustained self-monitoring and self-management behaviors are crucial to maintain optimal health for individuals with type 2 diabetes mellitus (T2DM). As smartphones and mobile health (mHealth) devices become widely available, self-monitoring using mHealth devices is an appealing strategy in support of successful self-management of T2DM. However, research indicates that engagement with mHealth devices decreases over time. Thus, it is important to understand engagement trajectories to provide varying levels of support that can improve self-monitoring and self-management behaviors. Objective: The aims of this study were to develop (1) digital phenotypes of the self-monitoring behaviors of patients with T2DM based on their engagement trajectory of using multiple mHealth devices, and (2) assess the association of individual digital phenotypes of self-monitoring behaviors with baseline demographic and clinical characteristics. Methods: This longitudinal observational feasibility study included 60 participants with T2DM who were instructed to monitor their weight, blood glucose, and physical activity using a wireless weight scale, phone-tethered glucometer, and accelerometer, respectively, over 6 months. We used latent class growth analysis (LCGA) with multitrajectory modeling to associate the digital phenotypes of participants’ self-monitoring behaviors based on their engagement trajectories with multiple mHealth devices. Associations between individual characteristics and digital phenotypes on participants’ self-monitoring behavior were assessed by analysis of variance or the Chi square test. Results: The engagement with accelerometers to monitor daily physical activities was consistently high for all participants over time. Three distinct digital phenotypes were identified based on participants’ engagement with the wireless weight scale and glucometer: (1) low and waning engagement group (24/60, 40%), (2) medium engagement group (20/60, 33%), and (3) consistently high engagement group (16/60, 27%). Participants that were younger, female, nonwhite, had a low income, and with a higher baseline hemoglobin A1c level were more likely to be in the low and waning engagement group. Conclusions: We demonstrated how to digitally phenotype individuals’ self-monitoring behavior based on their engagement trajectory with multiple mHealth devices. Distinct self-monitoring behavior groups were identified. Individual demographic and clinical characteristics were associated with different self-monitoring behavior groups. Future research should identify methods to provide tailored support for people with T2DM to help them better monitor and manage their condition. International Registered Report Identifier (IRRID): RR2-10.2196/13517 %M 32525492 %R 10.2196/17730 %U https://mhealth.jmir.org/2020/6/e17730 %U https://doi.org/10.2196/17730 %U http://www.ncbi.nlm.nih.gov/pubmed/32525492 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e16721 %T The Mediating Role of Organizational Reputation and Trust in the Intention to Use Wearable Health Devices: Cross-Country Study %A Adebesin,Funmi %A Mwalugha,Revingstone %+ Department of Informatics, University of Pretoria, Corner of Lynwood and Roper Street, Hatfield, Pretoria, 0083, South Africa, 27 0124205667, funmi.adebesin@up.ac.za %K fitness trackers %K intention %K Kenya %K physical activity %K privacy %K South Africa %K trust %K regression analysis %D 2020 %7 9.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of consumer wearable health devices for fitness tracking has seen an upward trend across the globe. Previous studies have shown that trust is an important factor in the adoption and use of new technologies. However, little is known about the influence of organizational reputation and trust on the intention to use wearable health devices. Objective: This study aimed to investigate the mediating role of organizational reputation and trust in the intention to use wearable health devices and to examine the extent to which the country of residence influenced the effect of organizational reputation on consumers’ trust in and intention to use wearable health devices. Methods: We conducted a cross-country survey with participants from Kenya and South Africa using a Google Forms questionnaire derived from previously validated items. A series of mediation regression analyses were carried out using the PROCESS macro with the bootstrap CI procedure. A one-way, between-group multivariate analysis of variance (MANOVA) was also used to determine the key factors that distinguish Kenyans and South Africans in their intention to use wearable health devices. Results: A total of 232 questionnaire responses were collected. The results revealed that organizational reputation significantly mediates the relationship between trust propensity and trust, with an indirect effect of 0.22 (95% CI 0.143-0.309). Organizational reputation also plays a significant direct role in the intention to use a wearable health device, with a direct effect of 0.32 (95% CI 0.175-0.483). This role is regardless of participants’ country of residence. Furthermore, there is a significant mediating effect of trust on the relationship between trust propensity and the intention to use a wearable health device, with an indirect effect of 0.26 (95% CI 0.172-0.349); between perceived security and the intention to use a wearable health device, with an indirect effect of 0.36 (95% CI 0.255-0.461); and between perceived privacy and the intention to use a wearable health device, with an indirect effect of 0.42 (95% CI 0.282-0.557). The MANOVA test shows statistically significant differences in all variables for both groups, with the exception of organizational reputation where there is no significant difference between the two cohorts. Conclusions: Organizational reputation has a significant direct influence on participants’ trust in and the intention to use a wearable health device irrespective of their country of residence. Even in the presence of perceived security and perceived privacy, trust has a significant mediating effect on the intention to use a wearable health device. %M 32348260 %R 10.2196/16721 %U http://mhealth.jmir.org/2020/6/e16721/ %U https://doi.org/10.2196/16721 %U http://www.ncbi.nlm.nih.gov/pubmed/32348260 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 6 %P e18703 %T A Mind-Body Physical Activity Program for Chronic Pain With or Without a Digital Monitoring Device: Proof-of-Concept Feasibility Randomized Controlled Trial %A Greenberg,Jonathan %A Popok,Paula J %A Lin,Ann %A Kulich,Ronald J %A James,Peter %A Macklin,Eric A %A Millstein,Rachel A %A Edwards,Robert R %A Vranceanu,Ana-Maria %+ Integrated Brain Health Clinical and Research Program, Massachusetts General Hospital, One Bowdoin Square, 1st Floor, Suite 100, Boston, MA, , United States, 1 617 724 4977, avranceanu@mgh.harvard.edu %K chronic pain %K meditation %K walking %K feasibility studies %K actigraphy %D 2020 %7 8.6.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Chronic pain is associated with poor physical and emotional functioning. Nonpharmacological interventions can help, but improvements are small and not sustained. Previous clinical trials do not follow recommendations to comprehensively target objectively measured and performance-based physical function in addition to self-reported physical function. Objective: This study aimed to establish feasibility benchmarks and explore improvements in physical (self-reported, performance based, and objectively measured) and emotional function, pain outcomes, and coping through a pilot randomized controlled trial of a mind-body physical activity program (GetActive) with and without a digital monitoring device (GetActive-Fitbit), which were iteratively refined through mixed methods. Methods: Patients with chronic pain were randomized to the GetActive (n=41) or GetActive-Fitbit (n=41) programs, which combine relaxation, cognitive behavioral, and physical restoration skills and were delivered in person. They completed in-person assessments before and after the intervention. Performance-based function was assessed with the 6-min walk test, and step count was measured with an ActiGraph. Results: Feasibility benchmarks (eg, recruitment, acceptability, credibility, therapist adherence, adherence to practice at home, ActiGraph wear, and client satisfaction) were good to excellent and similar in both programs. Within each program, we observed improvement in the 6-min walk test (mean increase=+41 m, SD 41.15; P<.001; effect size of 0.99 SD units for the GetActive group and mean increase=+50 m, SD 58.63; P<.001; effect size of 0.85 SD units for the GetActive-Fitbit group) and self-reported physical function (P=.001; effect size of 0.62 SD units for the GetActive group and P=.02; effect size of 0.38 SD units for the GetActive-Fitbit group). The mean step count increased only among sedentary patients (mean increase=+874 steps for the GetActive group and +867 steps for the GetActive-Fitbit group). Emotional function, pain intensity, pain coping, and mindfulness also improved in both groups. Participants rated themselves as much improved at the end of the program, and those in the GetActive-Fitbit group noted that Fitbit greatly helped with increasing their activity. Conclusions: These preliminary findings support a fully powered efficacy trial of the two programs against an education control group. We present a model for successfully using the Initiative on the Methods, Measurement, and Pain Assessment in Clinical Trials criteria for a comprehensive assessment of physical function and following evidence-based models to maximize feasibility before formal efficacy testing. Trial Registration: ClinicalTrial.gov NCT03412916; https://clinicaltrials.gov/ct2/show/NCT03412916 %M 32348281 %R 10.2196/18703 %U https://formative.jmir.org/2020/6/e18703 %U https://doi.org/10.2196/18703 %U http://www.ncbi.nlm.nih.gov/pubmed/32348281 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e16203 %T The Use of a Smartphone App and an Activity Tracker to Promote Physical Activity in the Management of Chronic Obstructive Pulmonary Disease: Randomized Controlled Feasibility Study %A Bentley,Claire L %A Powell,Lauren %A Potter,Stephen %A Parker,Jack %A Mountain,Gail A %A Bartlett,Yvonne Kiera %A Farwer,Jochen %A O'Connor,Cath %A Burns,Jennifer %A Cresswell,Rachel L %A Dunn,Heather D %A Hawley,Mark S %+ School of Health and Related Research, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, United Kingdom, 44 114 222 1726, mark.hawley@sheffield.ac.uk %K mobile health %K mHealth %K chronic obstructive pulmonary disease %K feasibility %K physical activity %K activity tracker %K Fitbit %K self-management %K health behavior change %K pulmonary rehabilitation %D 2020 %7 3.6.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Chronic obstructive pulmonary disease (COPD) is highly prevalent and significantly affects the daily functioning of patients. Self-management strategies, including increasing physical activity, can help people with COPD have better health and a better quality of life. Digital mobile health (mHealth) techniques have the potential to aid the delivery of self-management interventions for COPD. We developed an mHealth intervention (Self-Management supported by Assistive, Rehabilitative, and Telehealth technologies-COPD [SMART-COPD]), delivered via a smartphone app and an activity tracker, to help people with COPD maintain (or increase) physical activity after undertaking pulmonary rehabilitation (PR). Objective: This study aimed to determine the feasibility and acceptability of using the SMART-COPD intervention for the self-management of physical activity and to explore the feasibility of conducting a future randomized controlled trial (RCT) to investigate its effectiveness. Methods: We conducted a randomized feasibility study. A total of 30 participants with COPD were randomly allocated to receive the SMART-COPD intervention (n=19) or control (n=11). Participants used SMART-COPD throughout PR and for 8 weeks afterward (ie, maintenance) to set physical activity goals and monitor their progress. Questionnaire-based and physical activity–based outcome measures were taken at baseline, the end of PR, and the end of maintenance. Participants, and health care professionals involved in PR delivery, were interviewed about their experiences with the technology. Results: Overall, 47% (14/30) of participants withdrew from the study. Difficulty in using the technology was a common reason for withdrawal. Participants who completed the study had better baseline health and more prior experience with digital technology, compared with participants who withdrew. Participants who completed the study were generally positive about the technology and found it easy to use. Some participants felt their health had benefitted from using the technology and that it assisted them in achieving physical activity goals. Activity tracking and self-reporting were both found to be problematic as outcome measures of physical activity for this study. There was dissatisfaction among some control group members regarding their allocation. Conclusions: mHealth shows promise in helping people with COPD self-manage their physical activity levels. mHealth interventions for COPD self-management may be more acceptable to people with prior experience of using digital technology and may be more beneficial if used at an earlier stage of COPD. Simplicity and usability were more important for engagement with the SMART-COPD intervention than personalization; therefore, the intervention should be simplified for future use. Future evaluation will require consideration of individual factors and their effect on mHealth efficacy and use; within-subject comparison of step count values; and an opportunity for control group participants to use the intervention if an RCT were to be carried out. Sample size calculations for a future evaluation would need to consider the high dropout rates. %M 32490838 %R 10.2196/16203 %U https://mhealth.jmir.org/2020/6/e16203 %U https://doi.org/10.2196/16203 %U http://www.ncbi.nlm.nih.gov/pubmed/32490838 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e15112 %T A Pedometer-Guided Physical Activity Intervention for Obese Pregnant Women (the Fit MUM Study): Randomized Feasibility Study %A Darvall,Jai N %A Wang,Andrew %A Nazeem,Mohamed Nusry %A Harrison,Cheryce L %A Clarke,Lauren %A Mendoza,Chennelle %A Parker,Anna %A Harrap,Benjamin %A Teale,Glyn %A Story,David %A Hessian,Elizabeth %+ Department of Anaesthesia and Pain Management, Royal Melbourne Hospital, 300 Grattan St, Parkville, Melbourne, 3050, Australia, 61 393427000, jai.darvall@mh.org.au %K gestational weight gain %K pregnancy %K maternal obesity %K lifestyle intervention %K pedometer %D 2020 %7 26.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Obesity in pregnancy is a growing problem worldwide, with excessive gestational weight gain (GWG) occurring in the majority of pregnancies. This significantly increases risks to both mother and child. A major contributor to both prepregnancy obesity and excessive GWG is physical inactivity; however, past interventions targeting maternal weight gain and activity levels during the antenatal period have been ineffective in women who are already overweight. Pedometer-guided activity may offer a novel solution for increasing activity levels in this population. Objective: This initial feasibility randomized controlled trial aimed to test a pedometer-based intervention to increase activity and reduce excessive GWG in pregnant women. Methods: We supplied 30 pregnant women with obesity a Fitbit Zip pedometer and randomized them into 1 of 3 groups: control (pedometer only), app (pedometer synced to patients’ personal smartphone, with self-monitoring of activity), or app-coach (addition of a health coach–delivered behavioral change program). Feasibility outcomes included participant compliance with wearing pedometers (days with missing pedometer data), data syncing, and data integrity. Activity outcomes (step counts and active minutes) were analyzed using linear mixed models and generalized estimating equations. Results: A total of 30 participants were recruited within a 10-week period, with a dropout rate of 10% (3/30; 2 withdrawals and 1 stillbirth); 27 participants thus completed the study. Mean BMI in all groups was ≥35 kg/m2. Mean (SD) percentage of missing data days were 23.4% (20.6%), 39.5% (32.4%), and 21.1% (16.0%) in control, app group, and app-coach group patients, respectively. Estimated mean baseline activity levels were 14.5 active min/day and 5455 steps/day, with no significant differences found in activity levels between groups, with mean daily step counts in all groups remaining in the sedentary (5000 steps/day) or low activity (5000-7499 steps/day) categories for the entire study duration. There was a mean decrease of 7.8 steps/day for each increase in gestation day over the study period (95% CI 2.91 to 12.69, P=.002). Conclusions: Activity data syncing with a personal smartphone is feasible in a cohort of pregnant women with obesity. However, our results do not support a future definitive study in its present form. Recruitment and retention rates were adequate, as was activity data syncing to participants’ smartphones. A follow-up interventional trial seeking to reduce GWG and improve activity in this population must focus on improving compliance with activity data recording and behavioral interventions delivered. Trial Registration: Australian and New Zealand Clinical Trials Registry ACTRN12617000038392; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370884 %M 32348280 %R 10.2196/15112 %U http://mhealth.jmir.org/2020/5/e15112/ %U https://doi.org/10.2196/15112 %U http://www.ncbi.nlm.nih.gov/pubmed/32348280 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e14453 %T A Mobile Phone–Based Gait Assessment App for the Elderly: Development and Evaluation %A Zhong,Runting %A Rau,Pei-Luen Patrick %+ Department of Industrial Engineering, Tsinghua University, Shunde Building, 5th Floor, Beijing, 100084, China, 86 62776664, rpl@mail.tsinghua.edu.cn %K aged %K gait %K mHealth %K telemedicine %K falls prevention %D 2020 %7 26.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Gait disorders are common among older adults. With an increase in the use of technology among older adults, a mobile phone app provides a solution for older adults to self-monitor their gait quality in daily life. Objective: This study aimed to develop a gait-monitoring mobile phone app (Pocket Gait) and evaluate its acceptability and usability among potential older users. Methods: The app was developed to allow older adults to track their gait quality, including step frequency, acceleration root mean square (RMS), step regularity, step symmetry, and step variability. We recruited a total of 148 community-dwelling older adults aged 60 years and older from two cities in China: Beijing and Chongqing. They walked in three ways (single task, dual task, and fast walking) using a smartphone with the gait-monitoring app installed and completed an acceptability and usability survey after the walk test. User acceptability was measured by a questionnaire including four quantitative measures: perceived ease of use, perceived usefulness, ease of learning, and intention to use. Usability was measured using the System Usability Scale (SUS). Interviews were conducted with participants to collect open-ended feedback questions. Results: Task type had a significant effect on all gait parameters, namely, step frequency, RMS, step variability, step regularity, and step symmetry (all P values <.001). Age had a significant effect on step frequency (P=.01), and region had a significant effect on step regularity (P=.04). The acceptability of the gait-monitoring app was positive among older adults. Participants identified the usability of the system with an overall score of 59.7 (SD 10.7) out of 100. Older adults from Beijing scored significantly higher SUS compared with older adults from Chongqing (P<.001). The age of older adults was significantly associated with their SUS score (P=.048). Older adults identified improvements such as a larger font size, inclusion of reference values for gait parameters, and inclusion of heart rate and blood pressure monitoring. Conclusions: This mobile phone app is a health management tool for older adults to self-manage their gait quality and prevent adverse outcomes. In the future, it will be important to take factors such as age and region into consideration while designing a mobile phone–based gait assessment app. The feedback of the participants would help to design more elderly-friendly products. %M 32473005 %R 10.2196/14453 %U https://mhealth.jmir.org/2020/5/e14453 %U https://doi.org/10.2196/14453 %U http://www.ncbi.nlm.nih.gov/pubmed/32473005 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e16674 %T Low-Cost Consumer-Based Trackers to Measure Physical Activity and Sleep Duration Among Adults in Free-Living Conditions: Validation Study %A Degroote,Laurent %A Hamerlinck,Gilles %A Poels,Karolien %A Maher,Carol %A Crombez,Geert %A De Bourdeaudhuij,Ilse %A Vandendriessche,Ann %A Curtis,Rachel G %A DeSmet,Ann %+ Department of Movement and Sports Sciences, Ghent University, Watersportlaan 2, Ghent, Belgium, 32 9 264 62 99, laurent.degroote@ugent.be %K fitness trackers %K mobile phone %K accelerometry %K physical activity %K sleep %D 2020 %7 19.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. Objective: This study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. Methods: A free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. Results: Large variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. Conclusions: This study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers. %M 32282332 %R 10.2196/16674 %U http://mhealth.jmir.org/2020/5/e16674/ %U https://doi.org/10.2196/16674 %U http://www.ncbi.nlm.nih.gov/pubmed/32282332 %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 e15015 %T A Lower Leg Physical Activity Intervention for Individuals With Chronic Venous Leg Ulcers: Randomized Controlled Trial %A Kelechi,Teresa J %A Prentice,Margaret A %A Mueller,Martina %A Madisetti,Mohan %A Vertegel,Alexey %+ College of Nursing, Medical University of South Carolina, 99 Jonathan Lucas Street, MSC 160, Charleston, SC, 29425, United States, 1 843 792 4602, kelechtj@musc.edu %K leg ulcer %K physical activity %K exercise %K mHealth %K adherence %K randomized controlled trial %K feasibility %D 2020 %7 15.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Individuals with venous leg ulcers (VLUs) suffer disproportionately with multiple chronic conditions, are often physically deconditioned, and demonstrate high levels of physical inactivity. Objective: The primary objective of this randomized controlled trial was to establish the feasibility of a mobile health (mHealth) physical activity exercise app for individuals with VLUs to improve lower leg function. Methods: In a 6-week study, adults with VLUs were recruited from 2 wound centers in South Carolina, United States, and enrolled if they were aged 18 years or older with impaired functional mobility and an ankle-brachial index between 0.8 and 1.3. Participants were randomized 1:1 to receive evidence-based, phased, nonexertive physical conditioning activities for lower leg function (FOOTFIT) or FOOTFIT+ with an added patient-provider communication feature. The mHealth Conditioning Activities for Lower Leg Function app also provided automated educational and motivational messages and user reports. Foot movement on the VLU-affected leg was tracked by a Bluetooth-enabled triaxial accelerometer. The study was guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework to assess the feasibility of reach, adherence, acceptability, implementation, and maintenance. Results: A total of 24 patients were recruited, enrolled, and randomized in the study. Most patients reported difficulty following the protocol for exercising and using the accelerometer and mobile phone and did not use the provider contact feature. However, all patients were adherent to the 6-week exercise program more than 85% of the time for duration, whereas 33% (8/24) of patients adhered more than 85% for the frequency of performing the exercises. Across the three exercise levels, adherence did not differ between the two groups. Confidence limits around the difference in proportions ranged from −0.4 to 0.7. Providers in FOOTFIT+ were inconsistent in checking participant progress reports because of lack of time from competing work commitments. The technology became outdated quickly, making maintenance problematic. Participants said they would continue to exercise their foot and legs and liked being able to follow along with the demonstrations of each level of exercise provided through the app. Conclusions: The findings of this study suggest that despite initial interest in using the app, several components of the program as originally designed had limited acceptability and feasibility. Future refinements should include the use of more modern technology including smaller wearable accelerometers, mobile phones or tablets with larger screens, an app designed with larger graphics, automated reporting for providers, and more engaging user features. Trial Registration: ClinicalTrials.gov NTC02632695; https://clinicaltrials.gov/ct2/show/NCT02632695 %M 32412419 %R 10.2196/15015 %U https://mhealth.jmir.org/2020/5/e15015 %U https://doi.org/10.2196/15015 %U http://www.ncbi.nlm.nih.gov/pubmed/32412419 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e16716 %T Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study %A Düking,Peter %A Giessing,Laura %A Frenkel,Marie Ottilie %A Koehler,Karsten %A Holmberg,Hans-Christer %A Sperlich,Billy %+ Integrative and Experimental Exercise Science, Department of Sport Science, University of Würzburg, Judenbühlweg 11, Würzburg, 97082, Germany, 49 931 31 ext 8479, peterdueking@gmx.de %K cardiorespiratory fitness %K innovation %K smartwatch %K technology %K wearable %K digital health %D 2020 %7 6.5.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity. Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa). Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE. Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively. Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested. %M 32374274 %R 10.2196/16716 %U https://mhealth.jmir.org/2020/5/e16716 %U https://doi.org/10.2196/16716 %U http://www.ncbi.nlm.nih.gov/pubmed/32374274 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e14841 %T Step-Based Metrics and Overall Physical Activity in Children With Overweight or Obesity: Cross-Sectional Study %A Migueles,Jairo H %A Cadenas-Sanchez,Cristina %A Aguiar,Elroy J %A Molina-Garcia,Pablo %A Solis-Urra,Patricio %A Mora-Gonzalez,Jose %A García-Mármol,Eduardo %A Shiroma,Eric J %A Labayen,Idoia %A Chillón,Palma %A Löf,Marie %A Tudor-Locke,Catrine %A Ortega,Francisco B %+ PROFITH (PROmoting FITness and Health through physical activity) Research Group, Department of Physical and Sports Education, Faculty of Sport Sciences, University of Granada, Carretera Alfacar s/n, Granada, 18011, Spain, 34 958244353, jairohm@ugr.es %K motion sensor %K pedometer %K sedentary behavior %K MVPA %K cadence %D 2020 %7 28.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Best-practice early interventions to increase physical activity (PA) in children with overweight and obesity should be both feasible and evidence based. Walking is a basic human movement pattern that is practical, cost-effective, and does not require complex movement skills. However, there is still a need to investigate how much walking—as a proportion of total PA level—is performed by children who are overweight and obese in order to determine its utility as a public health strategy. Objective: This study aimed to (1) investigate the proportion of overall PA indicators that are explained by step-based metrics and (2) study step accumulation patterns relative to achievement of public health recommendations in children who are overweight and obese. Methods: A total of 105 overweight and obese children (mean 10.1 years of age [SD 1.1]; 43 girls) wore hip-worn accelerometers for 7 days. PA volumes were derived using the daily average of counts per 15 seconds, categorized using standard cut points for light-moderate-vigorous PA (LMVPA) and moderate-to-vigorous PA (MVPA). Derived step-based metrics included volume (steps/day), time in cadence bands, and peak 1-minute, 30-minute, and 60-minute cadences. Results: Steps per day explained 66%, 40%, and 74% of variance for counts per 15 seconds, LMVPA, and MVPA, respectively. The variance explained was increased up to 80%, 92%, and 77% by including specific cadence bands and peak cadences. Children meeting the World Health Organization recommendation of 60 minutes per day of MVPA spent less time at zero cadence and more time in cadence bands representing sporadic movement to brisk walking (ie, 20-119 steps/min) than their less-active peers. Conclusions: Step-based metrics, including steps per day and various cadence-based metrics, seem to capture a large proportion of PA for children who are overweight and obese. Given the availability of pedometers, step-based metrics could be useful in discriminating between those children who do or do not achieve MVPA recommendations. Trial Registration: ClinicalTrials.gov NCT02295072; https://clinicaltrials.gov/ct2/show/NCT02295072 %M 32343251 %R 10.2196/14841 %U http://mhealth.jmir.org/2020/4/e14841/ %U https://doi.org/10.2196/14841 %U http://www.ncbi.nlm.nih.gov/pubmed/32343251 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e14707 %T Accuracy of Optical Heart Rate Sensing Technology in Wearable Fitness Trackers for Young and Older Adults: Validation and Comparison Study %A Chow,Hsueh-Wen %A Yang,Chao-Ching %+ Graduate Institute of Physical Education, Health & Leisure Studies, National Cheng Kung University, No. 1 University Rd, East District, Tainan City, Taiwan, 886 62757575 ext 81806, hwchow@mail.ncku.edu.tw %K pulse %K photoplethysmography %K wearable device %K aerobic exercise %D 2020 %7 28.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable fitness trackers are devices that can record and enhance physical activity among users. Recently, photoplethysmography (PPG) devices that use optical heart rate sensors to detect heart rate in real time have become popular and help in monitoring and controlling exercise intensity. Although the benefits of using optical heart rate monitors have been highlighted through studies, the accuracy of the readouts these commercial devices generate has not been widely assessed for different age groups, especially for the East Asian population with Fitzpatrick skin type III or IV. Objective: This study aimed to examine the accuracy of 2 wearable fitness trackers with PPG to monitor heart rate in real time during moderate exercise in young and older adults. Methods: A total of 20 young adults and 20 older adults were recruited for this study. All participants were asked to undergo a series of sedentary and moderate physical activities using indoor aerobic exercise equipment. In this study, the Polar H7 chest-strapped heart rate monitor was used as the criterion measure in 2 fitness trackers, namely Xiaomi Mi Band 2 and Garmin Vivosmart HR+. The real-time, second-by-second heart rate data obtained from both devices were recorded using the broadcast heart rate mode. To critically analyze the results, multiple statistical parameters including the mean absolute percentage error (MAPE), Lin concordance correlation coefficient (CCC), intraclass correlation coefficient, the Pearson product moment correlation coefficient, and the Bland-Altman coefficient were determined to examine the performances of the devices. Results: Both test devices exhibited acceptable overall accuracy as heart rate sensors based on several statistical tests. Notably, the MAPE values were below 10% (the designated threshold) in both devices (GarminYoung=3.77%; GarminSenior=4.73%; XiaomiYoung=7.69%; and XiaomiSenior=6.04%). The scores for reliability test of CCC for Garmin were 0.92 (Young) and 0.80 (Senior), whereas those for Xiaomi were 0.76 (Young) and 0.73 (Senior). However, the results obtained using the Bland-Altman analysis indicated that both test optical devices underestimated the average heart rate. More importantly, the study documented some unexpected outlier readings reported by these devices when used on certain participants. Conclusions: The study reveals that commonly used optical heart rate sensors, such as the ones used herein, generally produce accurate heart rate readings irrespective of the age of the user. However, users should avoid relying entirely on these readings to indicate exercise intensities, as these devices have a tendency to produce erroneous, extreme readings, which might misinterpret the real-time exercise intensity. Future studies should therefore emphasize the occurrence rate of such errors, as this will likely benefit the development of improved models of heart rate sensors. %M 32343255 %R 10.2196/14707 %U http://mhealth.jmir.org/2020/4/e14707/ %U https://doi.org/10.2196/14707 %U http://www.ncbi.nlm.nih.gov/pubmed/32343255 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e14306 %T Objective Characterization of Activity, Sleep, and Circadian Rhythm Patterns Using a Wrist-Worn Actigraphy Sensor: Insights Into Posttraumatic Stress Disorder %A Tsanas,Athanasios %A Woodward,Elizabeth %A Ehlers,Anke %+ Usher Institute, University of Edinburgh, NINE Edinburgh BioQuarter, 9 Little France Road, Edinburgh, EH164UX, United Kingdom, 44 131 651 7887, Athanasios.Tsanas@ed.ac.uk %K actigraphy %K sleep %K Geneactiv %K posttraumatic stress disorder %K wearable technology %D 2020 %7 20.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearables have been gaining increasing momentum and have enormous potential to provide insights into daily life behaviors and longitudinal health monitoring. However, to date, there is still a lack of principled algorithmic framework to facilitate the analysis of actigraphy and objectively characterize day-by-day data patterns, particularly in cohorts with sleep problems. Objective: This study aimed to propose a principled algorithmic framework for the assessment of activity, sleep, and circadian rhythm patterns in people with posttraumatic stress disorder (PTSD), a mental disorder with long-lasting distressing symptoms such as intrusive memories, avoidance behaviors, and sleep disturbance. In clinical practice, these symptoms are typically assessed using retrospective self-reports that are prone to recall bias. The aim of this study was to develop objective measures from patients’ everyday lives, which could potentially considerably enhance the understanding of symptoms, behaviors, and treatment effects. Methods: Using a wrist-worn sensor, we recorded actigraphy, light, and temperature data over 7 consecutive days from three groups: 42 people diagnosed with PTSD, 43 traumatized controls, and 30 nontraumatized controls. The participants also completed a daily sleep diary over 7 days and the standardized Pittsburgh Sleep Quality Index questionnaire. We developed a novel approach to automatically determine sleep onset and offset, which can also capture awakenings that are crucial for assessing sleep quality. Moreover, we introduced a new intuitive methodology facilitating actigraphy exploration and characterize day-by-day data across 49 activity, sleep, and circadian rhythm patterns. Results: We demonstrate that the new sleep detection algorithm closely matches the sleep onset and offset against the participants' sleep diaries consistently outperforming an existing open-access widely used approach. Participants with PTSD exhibited considerably more fragmented sleep patterns (as indicated by greater nocturnal activity, including awakenings) and greater intraday variability compared with traumatized and nontraumatized control groups, showing statistically significant (P<.05) and strong associations (|R|>0.3). Conclusions: This study lays the foundation for objective assessment of activity, sleep, and circadian rhythm patterns using passively collected data from a wrist-worn sensor, facilitating large community studies to monitor longitudinally healthy and pathological cohorts under free-living conditions. These findings may be useful in clinical PTSD assessment and could inform therapy and monitoring of treatment effects. %M 32310142 %R 10.2196/14306 %U http://mhealth.jmir.org/2020/4/e14306/ %U https://doi.org/10.2196/14306 %U http://www.ncbi.nlm.nih.gov/pubmed/32310142 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e17544 %T Consumer Perceptions of Wearable Technology Devices: Retrospective Review and Analysis %A Chong,Kimberly P L %A Guo,Julia Z %A Deng,Xiaomeng %A Woo,Benjamin K P %+ University of California, Los Angeles, 14445 Olive View Drive, Sylmar, CA, 91342, United States, 1 747 210 3830, juliaguo@mednet.ucla.edu %K wearable technology devices %K Fitbit %K Amazon %K sleep %D 2020 %7 20.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Individuals of all ages are becoming more health conscious, and wearable technology devices (eg, Fitbit and Apple Watch) are becoming increasingly popular in encouraging healthy lifestyles. Objective: The aim of this paper was to explore how consumers use wearable devices. Methods: A retrospective review was done on the top-rated verified purchase reviews of the Fitbit One posted on Amazon.com between January 2014 and August 2018. Relevant themes were identified by qualitatively analyzing open-ended reviews. Results: On retrieval, there were 9369 reviews with 7706 positive reviews and 1663 critical reviews. The top 100 positive and top 100 critical comments were subsequently analyzed. Four major themes were identified: sleep hygiene (“charts when you actually fall asleep, when you wake up during the night, when you're restless--and gives you a cumulative time of “actual sleep” as well as weekly averages.”), motivation (“25 lbs lost after 8 months – best motivator ever!”), accountability (“platform to connect with people you know and set little competitions or group…fun accountability if you set a goal with a friend/family.”), and discretion (“able to be clipped to my bra without being seen.”). Alternatively, negative reviewers felt that the wearable device’s various tracking functions, specifically steps and sleep, were inaccurate. Conclusions: Wearable technology devices are an affordable, user-friendly application that can support all individuals throughout their everyday lives and potentially be implemented into medical surveillance, noninvasive medical care, and mobile health and wellness monitoring. This study is the first to explore wearable technology device use among consumers, and further studies are needed to examine the limitless possibilities of wearable devices in health care. %M 32310148 %R 10.2196/17544 %U http://mhealth.jmir.org/2020/4/e17544/ %U https://doi.org/10.2196/17544 %U http://www.ncbi.nlm.nih.gov/pubmed/32310148 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e14969 %T A Social Group-Based Information-Motivation-Behavior Skill Intervention to Promote Acceptability and Adoption of Wearable Activity Trackers Among Middle-Aged and Older Adults: Cluster Randomized Controlled Trial %A Liao,Jing %A Xiao,Hai-Yan %A Li,Xue-Qi %A Sun,Shu-Hua %A Liu,Shi-Xing %A Yang,Yung-Jen %A Xu,Dong (Roman) %+ Sun Yat-sen Global Health Institute, School of Public Health and Institute of State Governance, Sun Yat-sen University, #135 Xingang West Road, Guangzhou, 510275, China, 86 84112657, xudong5@mail.sysu.edu.cn %K mobile health %K group exercise %K social influence %K behavior change %K cluster randomized controlled trial %D 2020 %7 9.4.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers offer potential to optimize behavior and support self-management. To assist older adults in benefiting from mobile technologies, theory-driven deployment strategies are needed to overcome personal, technological, and sociocontextual barriers in technology adoption. Objective: To test the effectiveness of a social group–based strategy to improve the acceptability and adoption of activity trackers by middle-aged and older adults. Methods: A cluster randomized controlled trial was conducted among 13 groups of middle-aged and older adults (≥45 years) performing group dancing (ie, square dancing) as a form of exercise in Guangzhou from November 2017 to October 2018. These dancing groups were randomized 1:1 into two arms, and both received wrist-worn activity trackers and instructions at the baseline face-to-face assessment. Based on the Information-Motivation-Behavior Skill framework, the intervention arm was also given a tutorial on the purpose of exercise monitoring (Information), encouraged to participate in exercise and share their exercise records with their dancing peers (Motivation), and were further assisted with the use of the activity tracker (Behavior Skill). We examined two process outcomes: acceptability evaluated by a 14-item questionnaire, and adoption assessed by the uploaded step count data. Intention-to-treat analysis was applied, with the treatment effects estimated by multilevel models. Results: All dancing groups were followed up for the postintervention reassessment, with 61/69 (88%) participants of the intervention arm (7 groups) and 56/80 (70%) participants of the control arm (6 groups). Participants’ sociodemographic characteristics (mean age 62 years, retired) and health status were comparable between the two arms, except the intervention arm had fewer female participants and lower cognitive test scores. Our intervention significantly increased the participants’ overall acceptability by 6.8 points (95% CI 2.2-11.4), mainly driven by promoted motivation (adjusted group difference 2.0, 95% CI 0.5-3.6), increased usefulness (adjusted group difference 2.5, 95% CI 0.9-4.1), and better perceived ease of use (adjusted group difference 1.2, 95% CI 0.1-2.4), whereas enjoyment and comfort were not increased (adjusted group difference 0.9, 95% CI –0.4-2.3). Higher adoption was also observed among participants in the intervention arm, who were twice as likely to have valid daily step account data than their controlled counterparts (adjusted incidence relative risk [IRR]=2.0, 95% CI 1.2-3.3). The average daily step counts (7803 vs 5653 steps/day for the intervention and control, respectively) were similar between the two arms (adjusted IRR=1.4, 95% CI 0.7-2.5). Conclusions: Our social group–based deployment strategy incorporating information, motivation, and behavior skill components effectively promoted acceptability and adoption of activity trackers among community-dwelling middle-aged and older adults. Future studies are needed to examine the long-term effectiveness and apply this social engagement strategy in other group settings or meeting places. Trial Registration: Chinese Clinical Trial Registry ChiCTR-IOC-17013185; https://tinyurl.com/vedwc7h. %M 32271151 %R 10.2196/14969 %U https://mhealth.jmir.org/2020/4/e14969 %U https://doi.org/10.2196/14969 %U http://www.ncbi.nlm.nih.gov/pubmed/32271151 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 4 %P e10733 %T Clinical Applications of Mobile Health Wearable–Based Sleep Monitoring: Systematic Review %A Guillodo,Elise %A Lemey,Christophe %A Simonnet,Mathieu %A Walter,Michel %A Baca-García,Enrique %A Masetti,Vincent %A Moga,Sorin %A Larsen,Mark %A , %A Ropars,Juliette %A Berrouiguet,Sofian %+ Urci Mental Health Department, Brest Medical University Hospital, Brest, 29200, France, 33 0298223333, elise.guillodo@chu-brest.fr %K sleep %K eHealth %K telemedicine %K review %K medicine %K wearable electronic devices %D 2020 %7 1.4.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health. Objective: The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic. Methods: We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults. Results: The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable–based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography. Conclusions: This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring. %M 32234707 %R 10.2196/10733 %U https://mhealth.jmir.org/2020/4/e10733 %U https://doi.org/10.2196/10733 %U http://www.ncbi.nlm.nih.gov/pubmed/32234707 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e17001 %T Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study %A Wang,Chaofan %A Sarsenbayeva,Zhanna %A Chen,Xiuge %A Dingler,Tilman %A Goncalves,Jorge %A Kostakos,Vassilis %+ School of Computing and Information Systems, The University of Melbourne, Doug McDonell Building (Building 168), Parkville, 3010, Australia, 61 3903 58966, chaofan.wang@unimelb.edu.au %K hand hygiene %K wearable devices %K machine learning %D 2020 %7 26.3.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Hand hygiene is a crucial and cost-effective method to prevent health care–associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation. Objective: We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing. Methods: We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping. Results: Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance. Conclusions: A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands. %M 32213469 %R 10.2196/17001 %U http://mhealth.jmir.org/2020/3/e17001/ %U https://doi.org/10.2196/17001 %U http://www.ncbi.nlm.nih.gov/pubmed/32213469 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 3 %N 1 %P e15995 %T Actual Use of Multiple Health Monitors Among Older Adults With Diabetes: Pilot Study %A Zheng,Yaguang %A Weinger,Katie %A Greenberg,Jordan %A Burke,Lora E %A Sereika,Susan M %A Patience,Nicole %A Gregas,Matt C %A Li,Zhuoxin %A Qi,Chenfang %A Yamasaki,Joy %A Munshi,Medha N. %+ School of Nursing, University of Pittsburgh, 360D Victoria Building, Pittsburgh, PA, 15261, United States, 1 4126242305, yaz100@pitt.edu %K mobile health %K aged %K lifestyle %K self-management %K diabetes mellitus, type 2 %D 2020 %7 23.3.2020 %9 Original Paper %J JMIR Aging %G English %X Background: Previous studies have reported older adults’ perceptions of using health monitors; however, no studies have examined the actual use of multiple health monitors for lifestyle changes over time among older adults with type 2 diabetes (T2D). Objective: The primary aim of this study was to examine the actual use of multiple health monitors for lifestyle changes over 3 months among older adults with T2D. The secondary aim was to explore changes in caloric intake and physical activity (PA) over 3 months. Methods: This was a single-group study lasting 3 months. The study sample included participants who were aged ≥65 years with a diagnosis of T2D. Participants were recruited through fliers posted at the Joslin Diabetes Center in Boston. Participants attended five 60-min, biweekly group sessions, which focused on self-monitoring, goal setting, self-regulation to achieve healthy eating and PA habits, and the development of problem-solving skills. Participants were provided with the Lose It! app to record daily food intake and devices such as a Fitbit Alta for monitoring PA, a Bluetooth-enabled blood glucose meter, and a Bluetooth-enabled digital scale. Descriptive statistics were used for analysis. Results: Of the enrolled participants (N=9), the sample was white (8/9, 89%) and female (4/9, 44%), with a mean age of 76.4 years (SD 6.0; range 69-89 years), 15.7 years (SD 2.0) of education, 33.3 kg/m2 (SD 3.1) BMI, and 7.4% (SD 0.8) hemoglobin A1c. Over the 84 days of self-monitoring, the mean percentage of days using the Lose It!, Fitbit Alta, blood glucose meter, and scale were 82.7 (SD 17.6), 85.2 (SD 19.7), 65.3 (SD 30.1), and 53.0 (SD 34.5), respectively. From baseline to completion of the study, the mean daily calorie intake was 1459 (SD 661) at week 1, 1245 (SD 554) at week 11, and 1333 (SD 546) at week 12, whereas the mean daily step counts were 5618 (SD 3654) at week 1, 5792 (SD 3814) at week 11, and 4552 (SD 3616) at week 12. The mean percentage of weight loss from baseline was 4.92% (SD 0.25). The dose of oral hypoglycemic agents or insulin was reduced in 55.6% (5/9) of the participants. Conclusions: The results from the pilot study are encouraging and suggest the need for a larger study to confirm the outcomes. In addition, a study design that includes a control group with educational sessions but without the integration of technology would offer additional insight to understand the value of mobile health in behavior changes and the health outcomes observed during this pilot study. %M 32202506 %R 10.2196/15995 %U http://aging.jmir.org/2020/1/e15995/ %U https://doi.org/10.2196/15995 %U http://www.ncbi.nlm.nih.gov/pubmed/32202506 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 3 %N 1 %P e17292 %T A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Usability and Feasibility Study %A Low,Carissa A %A Danko,Michaela %A Durica,Krina C %A Kunta,Abhineeth Reddy %A Mulukutla,Raghu %A Ren,Yiyi %A Bartlett,David L %A Bovbjerg,Dana H %A Dey,Anind K %A Jakicic,John M %+ University of Pittsburgh, 3347 Forbes Avenue, Suite 200, Pittsburgh, PA, 15213, United States, 1 4126235973, lowca@upmc.edu %K sedentary behavior %K mobile health %K smartphone %K mobile phone %K wearable device %K surgical oncology %K physical activity %D 2020 %7 23.3.2020 %9 Original Paper %J JMIR Perioper Med %G English %X Background: Sedentary behavior (SB) is common after cancer surgery and may negatively affect recovery and quality of life, but postoperative symptoms such as pain can be a significant barrier to patients achieving recommended physical activity levels. We conducted a single-arm pilot trial evaluating the usability and acceptability of a real-time mobile intervention that detects prolonged SB in the perioperative period and delivers prompts to walk that are tailored to daily self-reported symptom burden. Objective: The aim of this study is to develop and test a mobile technology-supported intervention to reduce SB before and after cancer surgery, and to evaluate the usability and feasibility of the intervention. Methods: A total of 15 patients scheduled for abdominal cancer surgery consented to the study, which involved using a Fitbit smartwatch with a companion smartphone app across the perioperative period (from a minimum of 2 weeks before surgery to 30 days postdischarge). Participants received prompts to walk after any SB that exceeded a prespecified threshold, which varied from day to day based on patient-reported symptom severity. Participants also completed weekly semistructured interviews to collect information on usability, acceptability, and experience using the app and smartphone; in addition, smartwatch logs were examined to assess participant study compliance. Results: Of eligible patients approached, 79% (15/19) agreed to participate. Attrition was low (1/15, 7%) and due to poor health and prolonged hospitalization. Participants rated (0-100) the smartphone and smartwatch apps as very easy (mean 92.3 and 93.2, respectively) and pleasant to use (mean 93.0 and 93.2, respectively). Overall satisfaction with the whole system was 89.9, and the mean System Usability Scale score was 83.8 out of 100. Overall compliance with symptom reporting was 51% (469/927 days), decreasing significantly from before surgery (264/364, 73%) to inpatient recovery (32/143, 22%) and postdischarge (173/420, 41%). Overall Fitbit compliance was 70% (653/927 days) but also declined from before surgery (330/364, 91%) to inpatient (51/143, 36%) and postdischarge (272/420, 65%). Conclusions: Perioperative patients with cancer were willing to use a smartwatch- and smartphone-based real-time intervention to reduce SB, and they rated the apps as very easy and pleasant to use. Compliance with the intervention declined significantly after surgery. The effects of the intervention on postoperative activity patterns, recovery, and quality of life will be evaluated in an ongoing randomized trial. %M 33393915 %R 10.2196/17292 %U http://periop.jmir.org/2020/1/e17292/ %U https://doi.org/10.2196/17292 %U http://www.ncbi.nlm.nih.gov/pubmed/33393915 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e14538 %T Patterns of Sedentary Time and Quality of Life in Women With Fibromyalgia: Cross-Sectional Study From the al-Ándalus Project %A Gavilán-Carrera,Blanca %A Segura-Jiménez,Víctor %A Acosta-Manzano,Pedro %A Borges-Cosic,Milkana %A Álvarez-Gallardo,Inmaculada C %A Delgado-Fernández,Manuel %+ Department of Physical Education, Faculty of Education Sciences, University of Cádiz, Av República Saharaui s/n, 11519, Puerto Real, Cádiz, Spain, 34 956016219, victor.segura@uca.es %K GT3X+ %K accelerometry %K sedentary behavior %K symptomatology %D 2020 %7 19.3.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sedentary time (ST) has been associated with detrimental health outcomes in fibromyalgia. Previous evidence in the general population has shown that not only is the total amount of ST harmful but the pattern of accumulation of sedentary behaviors is also relevant to health, with prolonged unbroken periods (ie, bouts) being particularly harmful. Objective: To examine the association of the patterns of ST with health-related quality of life (HRQoL) in women with fibromyalgia and to test whether these associations are independent of moderate-to-vigorous physical activity (MVPA). Methods: A total of 407 women (mean 51.4 years of age [SD 7.6]) with fibromyalgia participated. ST and MVPA were measured with triaxial accelerometry. The percentage of ST accumulated in bouts and the frequency of sedentary bouts of different lengths (≥10 min, ≥20 min, ≥30 min, and ≥60 min) were obtained. Four groups combining total ST and sedentary bout duration (≥30 min) were created. We assessed HRQoL using the 36-item Short-Form Health Survey (SF-36). Results: A greater percentage of ST spent in all bout lengths was associated with worsened physical function, bodily pain, vitality, social function, and physical component summary (PCS) (all P<.05). In addition, a higher percentage of ST in bouts of 60 minutes or more was related to worsened physical role (P=.04). A higher frequency of bouts was negatively associated with physical function, social function, the PCS (≥30 min and ≥60 min), physical role (≥60 min), bodily pain (≥60 min), and vitality (≥20 min, ≥30 min, and ≥60 min) (all P<.05). Overall, for different domains of HRQoL, these associations were independent of MVPA for higher bout lengths. Patients with high total ST and high sedentary bout duration had significantly worsened physical function (mean difference 8.73 units, 95% CI 2.31-15.15; independent of MVPA), social function (mean difference 10.51 units, 95% CI 2.59-18.44; not independent of MVPA), and PCS (mean difference 2.71 units, 95% CI 0.36-5.06; not independent of MVPA) than those with low ST and low sedentary bout duration. Conclusions: Greater ST in prolonged periods of any length and a higher frequency of ST bouts, especially in longer bout durations, are associated with worsened HRQoL in women with fibromyalgia. These associations were generally independent of MVPA. %M 32191211 %R 10.2196/14538 %U http://mhealth.jmir.org/2020/3/e14538/ %U https://doi.org/10.2196/14538 %U http://www.ncbi.nlm.nih.gov/pubmed/32191211 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 3 %P e15790 %T Patterns in Weight and Physical Activity Tracking Data Preceding a Stop in Weight Monitoring: Observational Analysis %A Frie,Kerstin %A Hartmann-Boyce,Jamie %A Jebb,Susan %A Oke,Jason %A Aveyard,Paul %+ Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 01865 ext 289592, kerstin.frie@phc.ox.ac.uk %K self-monitoring %K self-regulation %K weight loss %K activity trackers %K mobile applications %D 2020 %7 17.3.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Self-regulation for weight loss requires regular self-monitoring of weight, but the frequency of weight tracking commonly declines over time. Objective: This study aimed to investigate whether it is a decline in weight loss or a drop in motivation to lose weight (using physical activity tracking as a proxy) that may be prompting a stop in weight monitoring. Methods: We analyzed weight and physical activity data from 1605 Withings Health Mate app users, who had set a weight loss goal and stopped tracking their weight for at least six weeks after a minimum of 16 weeks of continuous tracking. Mixed effects models compared weight change, average daily steps, and physical activity tracking frequency between a 4-week period of continuous tracking and a 4-week period preceding the stop in weight tracking. Additional mixed effects models investigated subsequent changes in physical activity data during 4 weeks of the 6-week long stop in weight tracking. Results: People lost weight during continuous tracking (mean −0.47 kg, SD 1.73) but gained weight preceding the stop in weight tracking (mean 0.25 kg, SD 1.62; difference 0.71 kg; 95% CI 0.60 to 0.81). Average daily steps (beta=−220 daily steps per time period; 95% CI −320 to −120) and physical activity tracking frequency (beta=−3.4 days per time period; 95% CI −3.8 to −3.1) significantly declined from the continuous tracking to the pre-stop period. From pre-stop to post-stop, physical activity tracking frequency further decreased (beta=−6.6 days per time period; 95% CI −7.12 to −6.16), whereas daily step count on the day’s activity was measured increased (beta=110 daily steps per time period; 95% CI 50 to 170). Conclusions: In the weeks before people stop tracking their weight, their physical activity and physical activity monitoring frequency decline. At the same time, weight increases, suggesting that declining motivation for weight control and difficulties with making use of negative weight feedback might explain why people stop tracking their weight. The increase in daily steps but decrease in physical activity tracking frequency post-stop might result from selective measurement of more active days. %M 32181749 %R 10.2196/15790 %U http://www.jmir.org/2020/3/e15790/ %U https://doi.org/10.2196/15790 %U http://www.ncbi.nlm.nih.gov/pubmed/32181749 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e14435 %T Efficacy and Safety of an mHealth App and Wearable Device in Physical Performance for Patients With Hepatocellular Carcinoma: Development and Usability Study %A Kim,Yoon %A Seo,Jinserk %A An,So-Yeon %A Sinn,Dong Hyun %A Hwang,Ji Hye %+ Department of Physical and Rehabilitation Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-Ro 81, Gangnam-Gu, Seoul, 06351, Republic of Korea, 82 2 3410 2818, jhlee.hwang@samsung.com %K mHealth %K hepatocellular carcinoma %K rehabilitation %K exercise %K physical fitness %K physical activity %D 2020 %7 11.3.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Exercise is predicted to have a positive effect among hepatocellular carcinoma (HCC) patients. However, these patients are hesitant to start and build up an exercise program for one major reason: the vague fear of developing hepatic decompensation, a potentially fatal condition that can lead to death. Integrating mobile health (mHealth) with individualized exercise programs could be a possible option for promoting physical capacity among HCC patients. Objective: The aim of this study was to evaluate the efficacy and safety of rehabilitation exercises, which have been individually prescribed via an mHealth app, on physical fitness, body composition, biochemical profile, and quality of life among HCC patients. Methods: A total of 37 HCC patients were enrolled in a 12-week course with an mHealth app program targeted to HCC patients. The wearable wristband device Neofit (Partron Co) was provided to participants, and recorded daily physical data, such as the number of steps, calorie expenditure, exercise time, and heart rate. Each participant was given an individualized rehabilitation exercise program that was prescribed and adjusted at the 6-week midintervention period based on the assessment results. At baseline, 6-week, and 12-week sessions, participants’ physical fitness levels (ie, 6-minute walk test, grip strength test, and 30-second chair stand test) were measured. Physical activity levels, as measured by the International Physical Activity Questionnaire-Short Form (IPAQ-SF); body composition (ie, body mass index, body fat percentage, and muscle mass); biochemical profiles; and quality of life, as measured by the European Organization for Research and Treatment of Cancer Quality-of-Life Questionnaire C30, were assessed at baseline and at the end point. At the 6-week midpoint, exercise intensity was individually adjusted. Results: Of the 37 patients, 31 (84%) completed the 12-week intervention. Grip strength improved significantly after 12 weeks of the intervention. The 30-second chair stand test and the 6-minute walk test showed significant improvement from 0 to 6 weeks, from 0 to 12 weeks, and from 6 to 12 weeks. Muscle mass and the IPAQ-SF score increased significantly after 12 weeks of the intervention without biochemical deterioration. Conclusions: Following 12 weeks of mHealth care, including an individually prescribed rehabilitation exercise program, we saw significant improvements in physical fitness, body composition, and physical activity without any complication or biochemical deterioration among compensated HCC patients who had completed therapy. %M 32159517 %R 10.2196/14435 %U http://mhealth.jmir.org/2020/3/e14435/ %U https://doi.org/10.2196/14435 %U http://www.ncbi.nlm.nih.gov/pubmed/32159517 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 3 %P e13900 %T A Walking Intervention Supplemented With Mobile Health Technology in Low-Active Urban African American Women With Asthma: Proof-of-Concept Study %A Nyenhuis,Sharmilee M %A Balbim,Guilherme Moraes %A Ma,Jun %A Marquez,David X %A Wilbur,JoEllen %A Sharp,Lisa K %A Kitsiou,Spyros %+ Department of Medicine, University of Illinois at Chicago, 840 S Wood St, MC 719, Chicago, IL, 60612, United States, 1 312 413 1655, snyenhui@uic.edu %K activity trackers %K text message %K physical activity %K asthma %K African-American %K women %K mHealth %K smartphone %K mobile phone %D 2020 %7 11.3.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Physical inactivity is associated with worse asthma outcomes. African American women experience disparities in both physical inactivity and asthma relative to their white counterparts. We conducted a modified evidence-based walking intervention supplemented with mobile health (mHealth) technologies to increase physical activity (PA). Objective: This study aimed to assess the preliminary feasibility of a 7-week walking intervention modified for African American women with asthma. Methods: African American women with suboptimally controlled asthma were identified from a health system serving low-income minorities. At a baseline data collection visit, participants performed spirometry and incremental shuttle walk test, completed questionnaires, and were given an accelerometer to wear for 1 week. The intervention comprised an informational study manual and 3 in-person group sessions over 7 weeks, led by a nurse interventionist, in a community setting. The supplemental mHealth tools included a wearable activity tracker device (Fitbit Charge HR) and one-way text messages related to PA and asthma 3 times per week. A secure Web-based research platform, iCardia, was used to obtain Fitbit data in real time (wear time, moderate-to-vigorous physical activity [MVPA] and sedentary time) and send text messages. The feasibility of the intervention was assessed in the domains of recruitment capability, acceptability (adherence, retention, engagement, text messaging, acceptability, complaints, and concerns), and preliminary outcome effects on PA behavior (change in steps, duration, and intensity). Results: We approached 22 women, of whom 10 were eligible; 7 consented, enrolled and completed the study. Group session attendance was 71% (5/7), 86% (6/7), and 86% (6/7), respectively, across the 3 sessions. All participants completed evaluations at each group session. The women reported being satisfied or very satisfied with the program (eg, location, time, and materials). None of them had concerns about using, charging, or syncing the Fitbit device and app. Participants wore their Fitbit device for at least 10 hours per day in 44 out of the 49 intervention days. There was an increase in Fitbit-measured MVPA from week 1 (19 min/week, SD 14 min/week) to the last week of intervention (22 min/week, SD 12 min/week; Cohen d=0.24, 95% CI 0.1 to 6.4). A slight decrease in step count was observed from week 1 (8926 steps/day, SD 2156 steps/day) to the last week of intervention (8517 steps/day, SD 1612 steps/day; Cohen d=−0.21, 95% CI −876.9 to 58.9). Conclusions: The initial feasibility results of a 7-week community-based walking intervention tailored for African American women with asthma and supplemented with mHealth tools are promising. Modifications to recruitment, retention, and the intervention itself are needed. These findings support the need to conduct a further modified pilot trial to collect additional data on feasibility and estimate the efficacy of the intervention on asthma and PA outcomes. %M 32159520 %R 10.2196/13900 %U https://formative.jmir.org/2020/3/e13900 %U https://doi.org/10.2196/13900 %U http://www.ncbi.nlm.nih.gov/pubmed/32159520 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 3 %P e13461 %T Long-Term Weight Management Using Wearable Technology in Overweight and Obese Adults: Systematic Review %A Fawcett,Emily %A Van Velthoven,Michelle Helena %A Meinert,Edward %+ Department of Paediatrics, University of Oxford, John Radcliffe Hospital, Children's Hospital, Oxford, OX3 9DU, United Kingdom, 44 7824446808, e.meinert14@imperial.ac.uk %K telemedicine %K mHealth %K eHealth %K mobile health %K obesity %K wearable electronic devices %K wearable technology %K wearable device %K digital technology %K weight loss %K overweight %K fitness trackers %D 2020 %7 10.3.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Although there are many wearable devices available to help people lose weight and decrease the rising prevalence of obesity, the effectiveness of these devices in long-term weight management has not been established. Objective: This study aimed to systematically review the literature on using wearable technology for long-term weight loss in overweight and obese adults. Methods: We searched the following databases: Medical Literature Analysis and Retrieval System Online, EMBASE, Compendex, ScienceDirect, Cochrane Central, and Scopus. The inclusion criteria were studies that took measurements for a period of ≥1 year (long-term) and had adult participants with a BMI >24. A total of 2 reviewers screened titles and abstracts and assessed the selected full-text papers for eligibility. The risk of bias assessment was performed using the following tools appropriate for different study types: the Cochrane risk of bias tool, Risk Of Bias In Nonrandomized Studies-of Interventions, A MeaSurement Tool to Assess systematic Reviews, and 6 questions to trigger critical thinking. The results of the studies have been provided in a narrative summary. Results: We included five intervention studies: four randomized controlled trials and one nonrandomized study. In addition, we used insights from six systematic reviews, four commentary papers, and a dissertation. The interventions delivered by wearable devices did not show a benefit over comparator interventions, but overweight and obese participants still lost weight over time. The included intervention studies were likely to suffer from bias. Significant variances in objectives, methods, and results of included studies prevented meta-analysis. Conclusions: This review showed some evidence that wearable devices can improve long-term physical activity and weight loss outcomes, but there was not enough evidence to show a benefit over the comparator methods. A major issue is the challenge of separating the effect of decreasing use of wearable devices over time from the effect of the wearable devices on the outcomes. Consistency in study methods is needed in future long-term studies on the use of wearable devices for weight loss. Trial Registration: PROSPERO CRD42018096932; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=96932 %M 32154788 %R 10.2196/13461 %U https://mhealth.jmir.org/2020/3/e13461 %U https://doi.org/10.2196/13461 %U http://www.ncbi.nlm.nih.gov/pubmed/32154788 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 3 %P e15552 %T Using the Technology Acceptance Model to Explore Adolescents’ Perspectives on Combining Technologies for Physical Activity Promotion Within an Intervention: Usability Study %A Drehlich,Mark %A Naraine,Michael %A Rowe,Katie %A Lai,Samuel K %A Salmon,Jo %A Brown,Helen %A Koorts,Harriet %A Macfarlane,Susie %A Ridgers,Nicola D %+ Institute for Physical Activity and Nutrition, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia, 61 392446718, nicky.ridgers@deakin.edu.au %K fitness trackers %K social media %K physical activity %K youth %D 2020 %7 6.3.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable activity trackers and social media have been identified as having the potential to increase physical activity among adolescents, yet little is known about the perceived ease of use and perceived usefulness of the technology by adolescents. Objective: The aim of this study was to use the technology acceptance model to explore adolescents’ acceptance of wearable activity trackers used in combination with social media within a physical activity intervention. Methods: The Raising Awareness of Physical Activity study was a 12-week physical activity intervention that combined a wearable activity tracker (Fitbit Flex) with supporting digital materials that were delivered using social media (Facebook). A total of 124 adolescents aged 13 to 14 years randomized to the intervention group (9 schools) participated in focus groups immediately post intervention. Focus groups explored adolescents’ perspectives of the intervention and were analyzed using pen profiles using a coding framework based on the technology acceptance model. Results: Adolescents reported that Fitbit Flex was useful as it motivated them to be active and provided feedback about their physical activity levels. However, adolescents typically reported that Fitbit Flex required effort to use, which negatively impacted on their perceived ease of use. Similarly, Facebook was considered to be a useful platform for delivering intervention content. However, adolescents generally noted preferences for using alternative social media websites, which may have impacted on negative perceptions concerning Facebook’s ease of use. Perceptions of technological risks included damage to or loss of the device, integrity of data, and challenges with both Fitbit and Facebook being compatible with daily life. Conclusions: Wearable activity trackers and social media have the potential to impact adolescents’ physical activity levels. The findings from this study suggest that although the adolescents recognized the potential usefulness of the wearable activity trackers and the social media platform, the effort required to use these technologies, as well as the issues concerning risks and compatibility, may have influenced overall engagement and technology acceptance. As wearable activity trackers and social media platforms can change rapidly, future research is needed to examine the factors that may influence the acceptance of specific forms of technology by using the technology acceptance model. Trial Registration: Australian and New Zealand Clinical Trials Registry ACTRN12616000899448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370716 %M 32141834 %R 10.2196/15552 %U https://www.jmir.org/2020/3/e15552 %U https://doi.org/10.2196/15552 %U http://www.ncbi.nlm.nih.gov/pubmed/32141834 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 2 %P e12665 %T A Mobile Health Team Challenge to Promote Stepping and Stair Climbing Activities: Exploratory Feasibility Study %A Liew,Seaw Jia %A Gorny,Alex Wilhelm %A Tan,Chuen Seng %A Müller-Riemenschneider,Falk %+ Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Tahir Foundation Building (Block MD1), 12 Science Drive 2, #09-01v, Singapore, 117549, Singapore, 65 6601 3122, ephmf@nus.edu.sg %K behavior %K health %K physical activity %K wearables %D 2020 %7 4.2.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile health (mHealth) approaches are growing in popularity as a means of addressing low levels of physical activity (PA). Objective: This study aimed to determine the validity of wearables in measuring step count and floor count per day and assess the feasibility and effects of a 6-week team challenge intervention delivered through smartphone apps. Methods: Staff and students from a public university were recruited between 2015 and 2016. In phase 1, everyone wore a Fitbit tracker (Charge or Charge HR) and an ActiGraph for 7 days to compare daily step count estimated by the two devices under free-living conditions. They were also asked to climb 4 bouts of floors in an indoor stairwell to measure floor count which was compared against direct observation. In phase 2, participants were allocated to either a control or intervention group and received a Fitbit tracker synced to the Fitbit app. Furthermore, the intervention group participants were randomized to 4 teams and competed in 6 weekly (Monday to Friday) real-time challenges. A valid day was defined as having 1500 steps or more per day. The outcomes were as follows: (1) adherence to wearing the Fitbit (ie, number of days in which all participants in each group were classified as valid users aggregated across the entire study period), (2) mean proportion of valid participants over the study period, and (3) the effects of the intervention on step count and floor count determined using multiple linear regression models and generalized estimating equations (GEEs) for longitudinal data analysis. Results: In phase 1, 32 of 40 eligible participants provided valid step count data, whereas all 40 participants provided valid floor count data. The Fitbit trackers demonstrated high correlations (step count: Spearman ρ=0.89; P<.001; floor count: Spearman ρ=0.98; P<.001). The trackers overestimated step count (median absolute error: 17%) but accurately estimated floor count. In phase 2, 20 participants each were allocated to an intervention or control group. Overall, 24 participants provided complete covariates and valid PA data for analyses. Multiple linear regressions revealed that the average daily steps was 15.9% higher for the intervention group (95% CI −8.9 to 47.6; P=.21) during the final two intervention weeks; the average daily floors climbed was 39.4% higher (95% CI 2.4 to 89.7; P=.04). GEE results indicated no significant interaction effects between groups and the intervention week for weekly step count, whereas a significant effect (P<.001) was observed for weekly floor count. Conclusions: The consumer wearables used in this study provided acceptable validity in estimating stepping and stair climbing activities, and the mHealth-based team challenge interventions were feasible. Compared with the control group, the participants in the intervention group climbed more stairs, so this can be introduced as an additional PA promotion target in the context of mHealth strategies. Methodologically rigorous studies are warranted to further strengthen this study’s findings. %M 32014845 %R 10.2196/12665 %U https://mhealth.jmir.org/2020/2/e12665 %U https://doi.org/10.2196/12665 %U http://www.ncbi.nlm.nih.gov/pubmed/32014845 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e16409 %T Activity Tracker–Based Metrics as Digital Markers of Cardiometabolic Health in Working Adults: Cross-Sectional Study %A Rykov,Yuri %A Thach,Thuan-Quoc %A Dunleavy,Gerard %A Roberts,Adam Charles %A Christopoulos,George %A Soh,Chee-Kiong %A Car,Josip %+ Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, CSB, 18th Floor, 11 Mandalay Rd, Singapore, 308232, Singapore, 65 87660342, yuri.rykov@ntu.edu.sg %K mobile health %K metabolic cardiovascular syndrome %K fitness trackers %K wearable electronic devices %K Fitbit %K steps %K heart rate %K physical activity %K circadian rhythms %K sedentary behavior %D 2020 %7 31.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Greater adoption of wearable devices with multiple sensors may enhance personalized health monitoring, facilitate early detection of some diseases, and further scale up population health screening. However, few studies have explored the utility of data from wearable fitness trackers in cardiovascular and metabolic disease risk prediction. Objective: This study aimed to investigate the associations between a range of activity metrics derived from a wearable consumer-grade fitness tracker and major modifiable biomarkers of cardiometabolic disease in a working-age population. Methods: This was a cross-sectional study of 83 working adults. Participants wore Fitbit Charge 2 for 21 consecutive days and went through a health assessment, including fasting blood tests. The following clinical biomarkers were collected: BMI, waist circumference, waist-to-hip ratio, blood pressure, triglycerides (TGs), high-density lipoprotein (HDL) and low-density lipoprotein cholesterol, and blood glucose. We used a range of wearable-derived metrics based on steps, heart rate (HR), and energy expenditure, including measures of stability of circadian activity rhythms, sedentary time, and time spent at various intensities of physical activity. Spearman rank correlation was used for preliminary analysis. Multiple linear regression adjusted for potential confounders was used to determine the extent to which each metric of activity was associated with continuous clinical biomarkers. In addition, pairwise multiple regression was used to investigate the significance and mutual dependence of activity metrics when two or more of them had significant association with the same outcome from the previous step of the analysis. Results: The participants were predominantly middle aged (mean age 44.3 years, SD 12), Chinese (62/83, 75%), and male (64/83, 77%). Blood biomarkers of cardiometabolic disease (HDL cholesterol and TGs) were significantly associated with steps-based activity metrics independent of age, gender, ethnicity, education, and shift work, whereas body composition biomarkers (BMI, waist circumference, and waist-to-hip ratio) were significantly associated with energy expenditure–based and HR-based metrics when adjusted for the same confounders. Steps-based interdaily stability of circadian activity rhythm was strongly associated with HDL (beta=5.4 per 10% change; 95% CI 1.8 to 9.0; P=.005) and TG (beta=−27.7 per 10% change; 95% CI −48.4 to −7.0; P=.01). Average daily steps were negatively associated with TG (beta=−6.8 per 1000 steps; 95% CI −13.0 to −0.6; P=.04). The difference between average HR and resting HR was significantly associated with BMI (beta=−.5; 95% CI −1.0 to −0.1; P=.01) and waist circumference (beta=−1.3; 95% CI −2.4 to −0.2; P=.03). Conclusions: Wearable consumer-grade fitness trackers can provide acceptably accurate and meaningful information, which might be used in the risk prediction of cardiometabolic disease. Our results showed the beneficial effects of stable daily patterns of locomotor activity for cardiometabolic health. Study findings should be further replicated with larger population studies. %M 32012098 %R 10.2196/16409 %U http://mhealth.jmir.org/2020/1/e16409/ %U https://doi.org/10.2196/16409 %U http://www.ncbi.nlm.nih.gov/pubmed/32012098 %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 e12538 %T The Use and Effects of an App-Based Physical Activity Intervention “Active2Gether” in Young Adults: Quasi-Experimental Trial %A Middelweerd,Anouk %A Mollee,Julia %A Klein,Michel MCA %A Manzoor,Adnan %A Brug,Johannes %A te Velde,Saskia J %+ Research Center for Healthy and Sustainable Living, Hogeschool Utrecht University of Applied Sciences Utrecht, Heidelberglaan 7, Utrecht, 3584 CS, Netherlands, 31 614752495, saskia.tevelde@hu.nl %K physical activity %K smartphone %K mobile app %D 2020 %7 21.1.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Insufficient physical activity (PA) is highly prevalent and associated with adverse health conditions and the risk of noncommunicable diseases. To increase levels of PA, effective interventions to promote PA are needed. Present-day technologies such as smartphones, smartphone apps, and activity trackers offer several possibilities in health promotion. Objective: This study aimed to explore the use and short-term effects of an app-based intervention (Active2Gether) to increase the levels of PA in young adults. Methods: Young adults aged 18-30 years were recruited (N=104) using diverse recruitment strategies. The participants were allocated to the Active2Gether-Full condition (tailored coaching messages, self-monitoring, and social comparison), Active2Gether-Light condition (self-monitoring and social comparison), and the Fitbit-only control condition (self-monitoring). All participants received a Fitbit One activity tracker, which could be synchronized with the intervention apps, to monitor PA behavior. A 12-week quasi-experimental trial was conducted to explore the intervention effects on weekly moderate-to-vigorous PA (MVPA) and relevant behavioral determinants (ie, self-efficacy, outcome expectations, social norm, intentions, satisfaction, perceived barriers, and long-term goals). The ActiGraph wGT3XBT and GT3X+ were used to assess baseline and postintervention follow-up PA. Results: Compared with the Fitbit condition, the Active2Gether-Light condition showed larger effect sizes for minutes of MVPA per day (regression coefficient B=3.1; 95% CI −6.7 to 12.9), and comparatively smaller effect sizes were seen for the Active2Gether-Full condition (B=1.2; 95% CI −8.7 to 11.1). Linear and logistic regression analyses for the intervention effects on the behavioral determinants at postintervention follow-up showed no significant intervention effects of the Active2Gether-Full and Active2Gether-Light conditions. The overall engagement with the Fitbit activity tracker was high (median 88% (74/84) of the days), but lower in the Fitbit condition. Participants in the Active2Gether conditions reported more technical problems than those in the Fitbit condition. Conclusions: This study showed no statistically significant differences in MVPA or determinants of MVPA after exposure to the Active2Gether-Full condition compared with the Active2Gether-Light or Fitbit condition. This might partly be explained by the small sample size and the low rates of satisfaction in the participants in the two Active2Gether conditions that might be because of the high rates of technical problems. %M 31961330 %R 10.2196/12538 %U http://formative.jmir.org/2020/1/e12538/ %U https://doi.org/10.2196/12538 %U http://www.ncbi.nlm.nih.gov/pubmed/31961330 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 7 %N 1 %P e14059 %T Accuracy and Precision of Three Consumer-Grade Motion Sensors During Overground and Treadmill Walking in People With Parkinson Disease: Cross-Sectional Comparative Study %A Lai,Byron %A Sasaki,Jeffer E %A Jeng,Brenda %A Cederberg,Katie L %A Bamman,Marcas M %A Motl,Robert W %+ Department of Physical Therapy, University of Alabama at Birmingham, 1720 2nd Ave S, Birmingham, Birmingham, AL, 35294, United States, 1 6263762852, byronlai@uab.edu %K wearable electronic devices %K wearable %K fitness tracker %K accelerometer %K reproducibility %K Parkinson disease %K disabled persons %K exercise %D 2020 %7 16.1.2020 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Wearable motion sensors are gaining popularity for monitoring free-living physical activity among people with Parkinson disease (PD), but more evidence supporting the accuracy and precision of motion sensors for capturing step counts is required in people with PD. Objective: This study aimed to examine the accuracy and precision of 3 common consumer-grade motion sensors for measuring actual steps taken during prolonged periods of overground and treadmill walking in people with PD. Methods: A total of 31 ambulatory participants with PD underwent 6-min bouts of overground and treadmill walking at a comfortable speed. Participants wore 3 devices (Garmin Vivosmart 3, Fitbit One, and Fitbit Charge 2 HR), and a single researcher manually counted the actual steps taken. Accuracy and precision were based on absolute and relative metrics, including intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results: Participants walked 628 steps over ground based on manual counting, and Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR devices had absolute (relative) error values of 6 (6/628, 1.0%), 8 (8/628, 1.3%), and 30 (30/628, 4.8%) steps, respectively. ICC values demonstrated excellent agreement between manually counted steps and steps counted by both Garmin Vivosmart (0.97) and Fitbit One (0.98) but poor agreement for Fitbit Charge 2 HR (0.47). The absolute (relative) precision values for Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR were 11.1 (11.1/625, 1.8%), 14.7 (14.7/620, 2.4%), and 74.4 (74.4/598, 12.4%) steps, respectively. ICC confidence intervals demonstrated low variability for Garmin Vivosmart (0.96 to 0.99) and Fitbit One (0.93 to 0.99) but high variability for Fitbit Charge 2 HR (–0.57 to 0.74). The Fitbit One device maintained high accuracy and precision values for treadmill walking, but both Garmin Vivosmart and Fitbit Charge 2 HR (the wrist-worn devices) had worse accuracy and precision for treadmill walking. Conclusions: The waist-worn sensor (Fitbit One) was accurate and precise in measuring steps with overground and treadmill walking. The wrist-worn sensors were accurate and precise only during overground walking. Similar research should inform the application of these devices in clinical research and practice involving patients with PD. %M 31944182 %R 10.2196/14059 %U https://rehab.jmir.org/2020/1/e14059 %U https://doi.org/10.2196/14059 %U http://www.ncbi.nlm.nih.gov/pubmed/31944182 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e12651 %T Perspectives of People Who Are Overweight and Obese on Using Wearable Technology for Weight Management: Systematic Review %A Hu,Ruiqi %A van Velthoven,Michelle Helena %A Meinert,Edward %+ Digitally Enabled PrevenTative Health Research Group, Department of Paediatrics, University of Oxford, Level 2, Children's Hospital, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom, 44 7824446808, e.meinert14@imperial.ac.uk %K wearable electronic devices %K wearable technology %K wearable device %K mobile health %K digital technology %K weight loss %K wearable %K activity tracker %K obesity %K overweight %D 2020 %7 13.1.2020 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Obesity is a large contributor to preventable chronic diseases and health care costs. The efficacy of wearable devices for weight management has been researched; however, there is limited knowledge on how these devices are perceived by users. Objective: This study aimed to review user perspectives on wearable technology for weight management in people who are overweight and obese. Methods: We searched the online databases Pubmed, Scopus, Embase, and the Cochrane library for literature published from 2008 onward. We included all types of studies using a wearable device for delivering weight-loss interventions in adults who are overweight or obese, and qualitative data were collected about participants' perspectives on the device. We performed a quality assessment using criteria relevant to different study types. The Cochrane risk of bias tool was used for randomized controlled trials. The Risk of Bias in Non-randomized Studies - of Interventions (ROBINS-I) was used for nonrandomized studies. The Oxman and Guyatt Criteria were used for systematic reviews. We used the critical appraisal checklist for qualitative studies. Data were extracted into a data extraction sheet and thematically analyzed. Results: We included 19 studies: 5 randomized controlled trials, 6 nonrandomized studies, 5 qualitative studies, and 3 reviews. Mixed perceptions existed for different constructs of wearable technologies, which reflects the differences in the suitability of wearable technology interventions for different individuals in different contexts. This also indicates that interventions were not often tailored to participants' motivations. In addition, very few wearable technology interventions included a thorough qualitative analysis of the participants' view on important features of the intervention that made it successful. Conclusions: This study highlights the importance of determining the type of intervention most suitable for an individual before the intervention is used. Our findings could help participants find a suitable intervention that is most effective for them. Further research needs to develop a user-centered tool for obtaining comprehensive user feedback. Trial Registration: PROSPERO CRD42018096932; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=96932 %M 31929104 %R 10.2196/12651 %U https://mhealth.jmir.org/2020/1/e12651 %U https://doi.org/10.2196/12651 %U http://www.ncbi.nlm.nih.gov/pubmed/31929104 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e15359 %T Development of a Living Lab for a Mobile-Based Health Program for Korean-Chinese Working Women in South Korea: Mixed Methods Study %A Kim,Youlim %A Lee,Hyeonkyeong %A Lee,Mi Kyung %A Lee,Hyeyeon %A Jang,Hyoeun %+ Mo-im Kim Nursing Research Institute, College of Nursing, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 222283373, hlee39@yuhs.ac %K mHealth %K living lab %K intervention mapping %K health promotion %D 2020 %7 8.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Korean-Chinese (KC) women make up the largest group of female migrants in South Korea. To prevent and manage chronic diseases in middle-aged KC women working full time, it is necessary to develop health promotion programs that utilize an online platform because such a platform would allow individuals to participate in health promotion interventions at their convenience. Objective: This study aimed to develop a living lab for a mobile-based health (LLm Health) program focused on improving the physical activity and cultural adaptation of KC women workers. Methods: We used a mixed methods design. Living lab principles were factored into the LLm Health program, including the use of multiple methods, user engagement, multistakeholder participants, real-life settings, and cocreation. The program was developed using the 4 steps of the intervention mapping method: needs assessment, setting of objectives, identification of intervention strategies, and intervention design. Needs assessment was conducted through a literature review, focus group interviews with a total of 16 middle-aged KC women, and an online survey related to health promotion of migrant workers given to 38 stakeholders. KC middle-aged women participated in the early stages of program development and provided the idea of developing programs and mobile apps to enhance physical activity and acculturation. The mobile app developed in the program was validated with the help of 12 KC women and 4 experts, including 3 nursing professors and a professor of physical education. They were asked to rate each item based on content, interface design, and technology on a 4-point scale using a 23-item Smartphone App Evaluation Tool for Health Care. Results: The LLm Health program comprised a 24-week walking program using Fitbit devices, the mobile app, and social cognitive interventions. The mobile app contained 6 components: a step counter, an exercise timer, an online chat function, health information, level of cardiovascular risk, and health status. The cultural aspects and lifestyles of KC women were accommodated in the entire process of program development. The content validity of the mobile app was found to be 0.90 and 0.96 according to the 12 KC women and 4 experts, respectively. Conclusions: The mobile app was found to be valid and acceptable for KC women. The living lab approach was a useful strategy for developing a culturally adaptive LLm Health program for KC women workers, leading to their active participation in the overall research process, including needs assessment, program composition, and pre-evaluation. %M 31913134 %R 10.2196/15359 %U https://mhealth.jmir.org/2020/1/e15359 %U https://doi.org/10.2196/15359 %U http://www.ncbi.nlm.nih.gov/pubmed/31913134 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 4 %N 1 %P e14963 %T Using Mobile Health Tools to Assess Physical Activity Guideline Adherence and Smoking Urges: Secondary Analysis of mActive-Smoke %A Shan,Rongzi %A Yanek,Lisa R %A Silverman-Lloyd,Luke G %A Kianoush,Sina %A Blaha,Michael J %A German,Charles A %A Graham,Garth N %A Martin,Seth S %+ Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe Street, Carnegie 591, Baltimore, MD, 21287, United States, 1 4105020469, smart100@jhmi.edu %K physical activity %K smoking %K mHealth %K fitness trackers %K short message service %D 2020 %7 6.1.2020 %9 Original Paper %J JMIR Cardio %G English %X Background: Rates of cigarette smoking are decreasing because of public health initiatives, pharmacological aids, and clinician focus on smoking cessation. However, a sedentary lifestyle increases cardiovascular risk, and therefore, inactive smokers have a particularly enhanced risk of cardiovascular disease. Objective: In this secondary analysis of mActive-Smoke, a 12-week observational study, we investigated adherence to guideline-recommended moderate-to-vigorous physical activity (MVPA) in smokers and its association with the urge to smoke. Methods: We enrolled 60 active smokers (≥3 cigarettes per day) and recorded continuous step counts with the Fitbit Charge HR. MVPA was defined as a cadence of greater than or equal to 100 steps per minute. Participants were prompted to report instantaneous smoking urges via text message 3 times a day on a Likert scale from 1 to 9. We used a mixed effects linear model for repeated measures, controlling for demographics and baseline activity level, to investigate the association between MVPA and urge. Results: A total of 53 participants (mean age 40 [SD 12] years, 57% [30/53] women, 49% [26/53] nonwhite, and 38% [20/53] obese) recorded 6 to 12 weeks of data. Data from 3633 person-days were analyzed, with a mean of 69 days per participant. Among all participants, median daily MVPA was 6 min (IQR 2-13), which differed by sex (12 min [IQR 3-20] for men vs 3.5 min [IQR 1-9] for women; P=.004) and BMI (2.5 min [IQR 1-8.3] for obese vs 10 min [IQR 3-15] for nonobese; P=.04). The median total MVPA minutes per week was 80 (IQR 31-162). Only 10% (5/51; 95% CI 4% to 22%) of participants met national guidelines of 150 min per week of MVPA on at least 50% of weeks. Adjusted models showed no association between the number of MVPA minutes per day and mean daily smoking urge (P=.72). Conclusions: The prevalence of MVPA was low in adult smokers who rarely met national guidelines for MVPA. Given the poor physical activity attainment in smokers, more work is required to enhance physical activity in this population. %M 31904575 %R 10.2196/14963 %U https://cardio.jmir.org/2020/1/e14963 %U https://doi.org/10.2196/14963 %U http://www.ncbi.nlm.nih.gov/pubmed/31904575 %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 e15707 %T Adults’ Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study %A DeSmet,Ann %A De Bourdeaudhuij,Ilse %A Chastin,Sebastien %A Crombez,Geert %A Maddison,Ralph %A Cardon,Greet %+ Clinical and Health Psychology, Université Libre de Bruxelles, Franklin Rooseveltlaan 50, Brussels, 1050, Belgium, 32 2 650 32 82, Ann.DeSmet@ulb.be %K physical activity %K sleep %K sedentary behavior %K 24-hour movement %K mobile health %K mobile apps %K behavior change technique %K engagement %K adult %D 2019 %7 20.12.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features. Objective: This study aimed to examine adult users’ preferences for techniques and features in mobile apps for 24-hour movement behaviors. Methods: A total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample t tests were used to examine associations and differences between and within users by the type of health domain and users’ behavioral intention and adoption. Results: Preferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users’ intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (P=.03), information on behavior health outcome (P=.048), and feedback (P=.04) and incorporate social support (P=.048) to help those who are further removed from healthy sleep. A virtual coach (P<.001) and video modeling (P=.004) may provide appreciated support to those who are physically less active. PA self-monitoring appealed more to those with an intention to change PA (P=.03). Social comparison and support features are not high on users’ agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement but should be examined in future research with a less reflective method. Conclusions: The findings of this study provide guidance for the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines. %M 31859680 %R 10.2196/15707 %U http://mhealth.jmir.org/2019/12/e15707/ %U https://doi.org/10.2196/15707 %U http://www.ncbi.nlm.nih.gov/pubmed/31859680 %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 e15045 %T Usefulness of Modern Activity Trackers for Monitoring Exercise Behavior in Chronic Cardiac Patients: Validation Study %A Herkert,Cyrille %A Kraal,Jos Johannes %A van Loon,Eline Maria Agnes %A van Hooff,Martijn %A Kemps,Hareld Marijn Clemens %+ Máxima Medical Center, Flow, Center for Prevention, Telemedicine and Rehabilitation in Chronic Disease, Dominee Theodor Fliednerstraat 1, Eindhoven, 5631 BM, Netherlands, 31 408888200, cyrille.herkert@mmc.nl %K cardiac diseases %K activity trackers %K energy metabolism %K physical activity %K validation studies %D 2019 %7 19.12.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Improving physical activity (PA) is a core component of secondary prevention and cardiac (tele)rehabilitation. Commercially available activity trackers are frequently used to monitor and promote PA in cardiac patients. However, studies on the validity of these devices in cardiac patients are scarce. As cardiac patients are being advised and treated based on PA parameters measured by these devices, it is highly important to evaluate the accuracy of these parameters in this specific population. Objective: The aim of this study was to determine the accuracy and responsiveness of 2 wrist-worn activity trackers, Fitbit Charge 2 (FC2) and Mio Slice (MS), for the assessment of energy expenditure (EE) in cardiac patients. Methods: EE assessed by the activity trackers was compared with indirect calorimetry (Oxycon Mobile [OM]) during a laboratory activity protocol. Two groups were assessed: patients with stable coronary artery disease (CAD) with preserved left ventricular ejection fraction (LVEF) and patients with heart failure with reduced ejection fraction (HFrEF). Results: A total of 38 patients were included: 19 with CAD and 19 with HFrEF (LVEF 31.8%, SD 7.6%). The CAD group showed no significant difference in total EE between FC2 and OM (47.5 kcal, SD 112 kcal; P=.09), in contrast to a significant difference between MS and OM (88 kcal, SD 108 kcal; P=.003). The HFrEF group showed significant differences in EE between FC2 and OM (38 kcal, SD 57 kcal; P=.01), as well as between MS and OM (106 kcal, SD 167 kcal; P=.02). Agreement of the activity trackers was low in both groups (CAD: intraclass correlation coefficient [ICC] FC2=0.10, ICC MS=0.12; HFrEF: ICC FC2=0.42, ICC MS=0.11). The responsiveness of FC2 was poor, whereas MS was able to detect changes in cycling loads only. Conclusions: Both activity trackers demonstrated low accuracy in estimating EE in cardiac patients and poor performance to detect within-patient changes in the low-to-moderate exercise intensity domain. Although the use of activity trackers in cardiac patients is promising and could enhance daily exercise behavior, these findings highlight the need for population-specific devices and algorithms. %M 31855191 %R 10.2196/15045 %U http://mhealth.jmir.org/2019/12/e15045/ %U https://doi.org/10.2196/15045 %U http://www.ncbi.nlm.nih.gov/pubmed/31855191 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 12 %P e14854 %T Improving Pacific Adolescents’ Physical Activity Toward International Recommendations: Exploratory Study of a Digital Education App Coupled With Activity Trackers %A Galy,Olivier %A Yacef,Kalina %A Caillaud,Corinne %+ Interdisciplinary Laboratory for Research in Education, EA 7483, School of Education, The University of New Caledonia, Campus de Nouville, Noumea, New Caledonia, 687 815 602, olivier.galy@unc.nc %K exercise %K eHealth %K adolescents %K health education %K noncommunicable diseases %K iEngage %K data mining %K movement %K food %K Melanesia %D 2019 %7 11.12.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The prevalence of overweight and obesity in children and adolescents has dramatically increased in the Pacific Island countries and territories over the last decade. Childhood overweight and obesity not only have short-term consequences but are also likely to lead to noncommunicable diseases in adulthood. A major factor contributing to the rising prevalence is an insufficient amount of daily moderate-to-vigorous physical activity (MVPA). In the Pacific region, less than 50% of children and adolescents meet the international recommendations of 11,000 steps and 60 min of MVPA per day. Although studies have shown the potential of digital technologies to change behaviors, none has been proposed to guide adolescents toward achieving these recommendations. Objective: The aims of this study were (1) to investigate whether a technology-based educational program that combines education, objective measures of physical activity (PA), and self-assessment of goal achievement would be well received by Pacific adolescents and help change their PA behaviors toward the international PA recommendations and (2) to create more insightful data analysis methods to better understand PA behavior change. Methods: A total of 24 adolescents, aged 12 to 14 years, participated in a 4-week program comprising 8 1-hour modules designed to develop health literacy and physical skills. This self-paced user-centered program was delivered via an app and provided health-related learning content as well as goal setting and self-assessment tasks. PA performed during the 4-week program was captured by an activity tracker to support learning and help the adolescents self-assess their achievements against personal goals. The data were analyzed using a consistency rate and daily behavior clustering to reveal any PA changes, particularly regarding adherence to international recommendations. Results: The consistency rate of daily steps revealed that the adolescents reached 11,000 steps per day 48% (approximately 3.4 days per week) of the time in the first week of the program, and this peaked at 59% (approximately 4.1 days per week) toward the end of the program. PA data showed an overall increase during the program, particularly in the less active adolescents, who increased their daily steps by 15% and ultimately reached 11,000 steps more frequently. The consistency of daily behavior clustering showed a 27% increase in adherence to international recommendations in the least active adolescents. Conclusions: Technology-supported educational programs that include self-monitored PA via activity trackers can be successfully delivered to adolescents in schools in remote Pacific areas. New data mining techniques enable innovative analyses of PA engagement based on the international recommendations. %M 31825319 %R 10.2196/14854 %U http://mhealth.jmir.org/2019/12/e14854/ %U https://doi.org/10.2196/14854 %U http://www.ncbi.nlm.nih.gov/pubmed/31825319 %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 %@ 2369-2529 %I JMIR Publications %V 6 %N 2 %P e14468 %T Wrist-Based Accelerometers and Visual Analog Scales as Outcome Measures for Shoulder Activity During Daily Living in Patients With Rotator Cuff Tendinopathy: Instrument Validation Study %A Larrivée,Samuel %A Balg,Frédéric %A Léonard,Guillaume %A Bédard,Sonia %A Tousignant,Michel %A Boissy,Patrick %+ Research Center on Aging, Centre intégré universitaire de santé et de services sociaux de l'Estrie, Centre Hospitalier Universitaire de Sherbrooke, 1036 Rue Belvédère Sud, Sherbrooke, QC, J1H 4C4, Canada, 1 819 780 2220 ext 45628, patrick.boissy@usherbrooke.ca %K shoulder %K wearable sensors %K activity count %K validation %K test-retest %K sensitivity to change %D 2019 %7 3.12.2019 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Shoulder pain secondary to rotator cuff tendinopathy affects a large proportion of patients in orthopedic surgery practices. Corticosteroid injections are a common intervention proposed for these patients. The clinical evaluation of a response to corticosteroid injections is usually based only on the patient’s self-evaluation of his function, activity, and pain by multiple questionnaires with varying metrological qualities. Objective measures of upper extremity functions are lacking, but wearable sensors are emerging as potential tools to assess upper extremity function and activity. Objective: This study aimed (1) to evaluate and compare test-retest reliability and sensitivity to change of known clinical assessments of shoulder function to wrist-based accelerometer measures and visual analog scales (VAS) of shoulder activity during daily living in patients with rotator cuff tendinopathy convergent validity and (2) to determine the acceptability and compliance of using wrist-based wearable sensors. Methods: A total of 38 patients affected by rotator cuff tendinopathy wore wrist accelerometers on the affected side for a total of 5 weeks. Western Ontario Rotator Cuff (WORC) index; Short version of the Disability of the Arm, Shoulder, and Hand questionnaire (QuickDASH); and clinical examination (range of motion and strength) were performed the week before the corticosteroid injections, the day of the corticosteroid injections, and 2 and 4 weeks after the corticosteroid injections. Daily Single Assessment Numeric Evaluation (SANE) and VAS were filled by participants to record shoulder pain and activity. Accelerometer data were processed to extract daily upper extremity activity in the form of active time; activity counts; and ratio of low-intensity activities, medium-intensity activities, and high-intensity activities. Results: Daily pain measured using VAS and SANE correlated well with the WORC and QuickDASH questionnaires (r=0.564-0.815) but not with accelerometry measures, amplitude, and strength. Daily activity measured with VAS had good correlation with active time (r=0.484, P=.02). All questionnaires had excellent test-retest reliability at 1 week before corticosteroid injections (intraclass correlation coefficient [ICC]=0.883-0.950). Acceptable reliability was observed with accelerometry (ICC=0.621-0.724), apart from low-intensity activities (ICC=0.104). Sensitivity to change was excellent at 2 and 4 weeks for all questionnaires (standardized response mean=1.039-2.094) except for activity VAS (standardized response mean=0.50). Accelerometry measures had low sensitivity to change at 2 weeks, but excellent sensitivity at 4 weeks (standardized response mean=0.803-1.032). Conclusions: Daily pain VAS and SANE had good correlation with the validated questionnaires, excellent reliability at 1 week, and excellent sensitivity to change at 2 and 4 weeks. Daily activity VAS and accelerometry-derived active time correlated well together. Activity VAS had excellent reliability, but moderate sensitivity to change. Accelerometry measures had moderate reliability and acceptable sensitivity to change at 4 weeks. %M 31793896 %R 10.2196/14468 %U http://rehab.jmir.org/2019/2/e14468/ %U https://doi.org/10.2196/14468 %U http://www.ncbi.nlm.nih.gov/pubmed/31793896 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 11 %P e16273 %T Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis %A Haghayegh,Shahab %A Khoshnevis,Sepideh %A Smolensky,Michael H %A Diller,Kenneth R %A Castriotta,Richard J %+ Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, 107 W Dean Keeton St, Austin, TX, , United States, 1 5129543436, shahab@utexas.edu %K Fitbit %K polysomnography %K sleep tracker %K wearable %K actigraphy %K sleep diary %K sleep stages %K accuracy %K validation %K comparison of performance %D 2019 %7 28.11.2019 %9 Review %J J Med Internet Res %G English %X Background: Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. Objective: We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. Methods: In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria. Results: The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. Conclusions: Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG. %M 31778122 %R 10.2196/16273 %U http://www.jmir.org/2019/11/e16273/ %U https://doi.org/10.2196/16273 %U http://www.ncbi.nlm.nih.gov/pubmed/31778122 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 11 %P e15191 %T Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial %A Mueller,Arne %A Hoefling,Holger Alfons %A Muaremi,Amir %A Praestgaard,Jens %A Walsh,Lorcan C %A Bunte,Ola %A Huber,Roland Martin %A Fürmetz,Julian %A Keppler,Alexander Martin %A Schieker,Matthias %A Böcker,Wolfgang %A Roubenoff,Ronenn %A Brachat,Sophie %A Rooks,Daniel S %A Clay,Ieuan %+ Novartis Institutes for BioMedical Research, Campus St Johan, Basel, Switzerland, 41 61 324 1111, ieuan.clay@novartis.com %K gait %K walking speed %K mobility limitation %K accelerometry %K clinical trials %K frailty %K wearable electronic devices %K algorithms %K open source data %K data collection %K dataset %D 2019 %7 27.11.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health-relevant data from patients. However, only limited data and results have been published that demonstrate accuracy in target indications, real-world feasibility, or the validity and value of these novel approaches. Objective: This study aimed to establish accuracy, feasibility, and validity of continuous digital monitoring of walking speed in frail, elderly patients with sarcopenia and to create an open source repository of raw, derived, and reference data as a resource for the community. Methods: Data described here were collected as a part of 2 clinical studies: an independent, noninterventional validation study and a phase 2b interventional clinical trial in older adults with sarcopenia. In both studies, participants were monitored by using a waist-worn inertial sensor. The cross-sectional, independent validation study collected data at a single site from 26 naturally slow-walking elderly subjects during a parcours course through the clinic, designed to simulate a real-world environment. In the phase 2b interventional clinical trial, 217 patients with sarcopenia were recruited across 32 sites globally, where patients were monitored over 25 weeks, both during and between visits. Results: We have demonstrated that our approach can capture in-clinic gait speed in frail slow-walking adults with a residual standard error of 0.08 m per second in the independent validation study and 0.08, 0.09, and 0.07 m per second for the 4 m walk test (4mWT), 6-min walk test (6MWT), and 400 m walk test (400mWT) standard gait speed assessments, respectively, in the interventional clinical trial. We demonstrated the feasibility of our approach by capturing 9668 patient-days of real-world data from 192 patients and 32 sites, as part of the interventional clinical trial. We derived inferred contextual information describing the length of a given walking bout and uncovered positive associations between the short 4mWT gait speed assessment and gait speed in bouts between 5 and 20 steps (correlation of 0.23) and longer 6MWT and 400mWT assessments with bouts of 80 to 640 steps (correlations of 0.48 and 0.59, respectively). Conclusions: This study showed, for the first time, accurate capture of real-world gait speed in slow-walking older adults with sarcopenia. We demonstrated the feasibility of long-term digital monitoring of mobility in geriatric populations, establishing that sufficient data can be collected to allow robust monitoring of gait behaviors outside the clinic, even in the absence of feedback or incentives. Using inferred context, we demonstrated the ecological validity of in-clinic gait assessments, describing positive associations between in-clinic performance and real-world walking behavior. We make all data available as an open source resource for the community, providing a basis for further study of the relationship between standardized physical performance assessment and real-world behavior and independence. %M 31774406 %R 10.2196/15191 %U http://mhealth.jmir.org/2019/11/e15191/ %U https://doi.org/10.2196/15191 %U http://www.ncbi.nlm.nih.gov/pubmed/31774406 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 11 %P e13858 %T Parental Perspectives of a Wearable Activity Tracker for Children Younger Than 13 Years: Acceptability and Usability Study %A Mackintosh,Kelly A %A Chappel,Stephanie E %A Salmon,Jo %A Timperio,Anna %A Ball,Kylie %A Brown,Helen %A Macfarlane,Susie %A Ridgers,Nicola D %+ Institute for Physical Activity and Nutrition, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia, 61 3 9244 6718, nicky.ridgers@deakin.edu.au %K mobile applications %K physical activity %K child %K monitoring, ambulatory %K wearable electronic devices %D 2019 %7 4.11.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is increasing availability of, and interest in, wearable activity trackers for children younger than 13 years. However, little is known about how children and parents use these activity trackers or perceive their acceptability. Objective: This study primarily aimed to ascertain parental perspectives on the acceptability and usability of wearables designed to monitor children’s physical activity levels. Secondary aims were to (1) identify practical considerations for future use in physical activity interventions and promotion initiatives; (2) determine use of different features and functions incorporated into the accompanying app; and (3) identify parents’ awareness of their child’s current physical activity levels. Methods: In total, 36 children (18 boys and 18 girls) aged 7-12 years were asked to wear a wrist-worn activity tracker (KidFit) for 4 consecutive weeks and to use the accompanying app with parental assistance and guidance. Each week, one parent from each family (n=25; 21 mothers and 4 fathers) completed a Web-based survey to record their child’s activity tracker use, app interaction, and overall experiences. At the end of the 4-week period, a subsample of 10 parents (all mothers) participated in face-to-face interviews exploring perceptions of the acceptability and usability of wearable activity trackers and accompanying apps. Quantitative and qualitative data were analyzed descriptively and thematically, respectively. Thematic data are presented using pen profiles, which were constructed from verbatim transcripts. Results: Parents reported that they and their children typically found the associated app easy to use for activity tracking, though only step or distance information was generally accessed and some difficulties interpreting the data were reported. Children were frustrated with not being able to access real-time feedback, as the features and functions were only available through the app, which was typically accessed by, or in the presence of, parents. Parents identified that children wanted additional functions including a visual display to track and self-monitor activity, access to the app for goal setting, and the option of undertaking challenges against schools or significant others. Other barriers to the use of wearable activity trackers included discomfort of wearing the monitor because of the design and the inability to wear for water- or contact-based sports. Conclusions: Most parents reported that the wearable activity tracker was easy for their child or children to use and a useful tool for tracking their children’s daily activity. However, several barriers were identified, which may impact sustained use over time; both the functionality and wearability of the activity tracker should therefore be considered. Overall, wearable activity trackers for children have the potential to be integrated into targeted physical activity promotion initiatives. %M 31682585 %R 10.2196/13858 %U https://mhealth.jmir.org/2019/11/e13858 %U https://doi.org/10.2196/13858 %U http://www.ncbi.nlm.nih.gov/pubmed/31682585 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e11603 %T Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study %A Centi,Amanda Jayne %A Atif,Mursal %A Golas,Sara Bersche %A Mohammadi,Ramin %A Kamarthi,Sagar %A Agboola,Stephen %A Kvedar,Joseph C %A Jethwani,Kamal %+ Pivot Labs, Partners Healthcare, 25 New Chardon St, Ste 300, 3rd Floor, Boston, MA, 02114, United States, 1 6177242158, acenti@partners.org %K activity trackers %K exercise %K engagement %D 2019 %7 24.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement. Objective: The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers. Methods: Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker. Results: The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker. Conclusions: This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers. %M 31651405 %R 10.2196/11603 %U https://mhealth.jmir.org/2019/10/e11603 %U https://doi.org/10.2196/11603 %U http://www.ncbi.nlm.nih.gov/pubmed/31651405 %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 %@ 1438-8871 %I JMIR Publications %V 21 %N 10 %P e13219 %T Results of MyPlan 2.0 on Physical Activity in Older Belgian Adults: Randomized Controlled Trial %A Van Dyck,Delfien %A Herman,Karel %A Poppe,Louise %A Crombez,Geert %A De Bourdeaudhuij,Ilse %A Gheysen,Freja %+ Department of Movement and Sports Sciences, Faculty of Medicine and Health Sciences, Ghent University, Watersportlaan 2, Ghent, 9000, Belgium, 32 92646323, delfien.vandyck@ugent.be %K self-regulation %K exercise %K elderly %K eHealth %D 2019 %7 7.10.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: The beneficial effects of physical activity (PA) for older adults are well known. However, few older adults reach the health guideline of 150 min per week of moderate-to-vigorous PA (MVPA). Electronic health (eHealth) interventions are effective in increasing PA levels in older adults in the short term but, rarely, intermediate-term effects after a period without the support of a website or an app have been examined. Furthermore, current theory-based interventions focus mainly on preintentional determinants, although postintentional determinants should also be included to increase the likelihood of successful behavior change. Objective: This study aimed to investigate the effect of the theory-based eHealth intervention, MyPlan 2.0, focusing on pre- and postintentional determinants on both accelerometer-based and self-reported PA levels in older Belgian adults in the short and intermediate term. Methods: This study was a randomized controlled trial with three data collection points: baseline (N=72), post (five weeks after baseline; N=65), and follow-up (three months after baseline; N=65). The study took place in Ghent, and older adults (aged ≥65 years) were recruited through a combination of random and convenience sampling. At all the time points, participants were visited by the research team. Self-reported domain-specific PA was assessed using the International Physical Activity Questionnaire, and accelerometers were used to objectively assess PA. Participants in the intervention group got access to the eHealth intervention, MyPlan 2.0, and used it independently for five consecutive weeks after baseline. MyPlan 2.0 was based on the self-regulatory theory and focused on both pre- and postintentional processes to increase PA. Multilevel mixed-models repeated measures analyses were performed in R (R Foundation for Statistical Computing). Results: Significant (borderline) positive intervention effects were found for accelerometer-based MVPA (baseline−follow-up: intervention group +5 min per day and control group −5 min per day; P=.07) and for accelerometer-based total PA (baseline−post: intervention group +20 min per day and control group −24 min per day; P=.05). MyPlan 2.0 was also effective in increasing self-reported PA, mainly in the intermediate term. A positive intermediate-term intervention effect was found for leisure-time vigorous PA (P=.02), moderate household-related PA (P=.01), and moderate PA in the garden (P=.04). Negative intermediate-term intervention effects were found for leisure-time moderate PA (P=.01) and cycling for transport (P=.07). Conclusions: The findings suggest that theory-based eHealth interventions focusing on pre- and postintentional determinants have the potential for behavior change in older adults. If future studies including larger samples and long-term follow-up can confirm and clarify these findings, researchers and practitioners should be encouraged to use a self-regulation perspective for eHealth intervention development. Trial Registration: Clinicaltrials.gov NCT03194334; https://clinicaltrials.gov/ct2/show/NCT03783611. %M 31593541 %R 10.2196/13219 %U https://www.jmir.org/2019/10/e13219 %U https://doi.org/10.2196/13219 %U http://www.ncbi.nlm.nih.gov/pubmed/31593541 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 10 %P e14534 %T Accuracy of the Multisensory Wristwatch Polar Vantage's Estimation of Energy Expenditure in Various Activities: Instrument Validation Study %A Gilgen-Ammann,Rahel %A Schweizer,Theresa %A Wyss,Thomas %+ Swiss Federal Institute of Sport Magglingen, Hauptstrasse 247, Magglingen, 2532, Switzerland, 41 584676321, rahel.gilgen@baspo.admin.ch %K validation %K mHealth and eHealth %K activity monitor %D 2019 %7 2.10.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Sport watches and fitness trackers provide a feasible way of obtaining energy expenditure (EE) estimations in daily life as well as during exercise. However, today’s popular wrist-worn technologies show only poor-to-moderate EE accuracy. Recently, the invention of optical heart rate measurement and the further development of accelerometers in wrist units have opened up the possibility of measuring EE. Objective: This study aimed to validate the new multisensory wristwatch Polar Vantage and its EE estimation in healthy individuals during low-to-high-intensity activities against indirect calorimetry. Methods: Overall, 30 volunteers (15 females; mean age 29.5 [SD 5.1] years; mean height 1.7 [SD 0.8] m; mean weight 67.5 [SD 8.7] kg; mean maximal oxygen uptake 53.4 [SD 6.8] mL/min·kg) performed 7 activities—ranging in intensity from sitting to playing floorball—in a semistructured indoor environment for 10 min each, with 2-min breaks in between. These activities were performed while wearing the Polar Vantage M wristwatch and the MetaMax 3B spirometer. Results: After EE estimation, a mean (SD) of 69.1 (42.7) kcal and 71.4 (37.8) kcal per 10-min activity were reported for the MetaMax 3B and the Polar Vantage, respectively, with a strong correlation of r=0.892 (P<.001). The systematic bias was 2.3 kcal (3.3%), with 37.8 kcal limits of agreement. The lowest mean absolute percentage errors were reported during the sitting and reading activities (9.1%), and the highest error rates during household chores (31.4%). On average, 59.5% of the mean EE values obtained by the Polar Vantage were within ±20% of accuracy when compared with the MetaMax 3B. The activity intensity quantified by perceived exertion (odds ratio [OR] 2.028; P<.001) and wrist circumference (OR −1.533; P=.03) predicted 29% of the error rates within the Polar Vantage. Conclusions: The Polar Vantage has a statistically moderate-to-good accuracy in EE estimation that is activity dependent. During sitting and reading activities, the EE estimation is very good, whereas during nonsteady activities that require wrist and arm movement, the EE accuracy is only moderate. However, compared with other available wrist-worn EE monitors, the Polar Vantage can be recommended, as it performs among the best. %M 31579020 %R 10.2196/14534 %U https://mhealth.jmir.org/2019/10/e14534 %U https://doi.org/10.2196/14534 %U http://www.ncbi.nlm.nih.gov/pubmed/31579020 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 9 %P e12861 %T Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions %A Dinh-Le,Catherine %A Chuang,Rachel %A Chokshi,Sara %A Mann,Devin %+ Department of Population Health, New York University School of Medicine, 227 E 30th Street, New York, NY, 10016, United States, 1 6465012503, devin.mann@nyulangone.org %K wearable electronic devices %K electronic health records %K data collection %K mobile health %K patient monitoring %D 2019 %7 11.09.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers. Objective: This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations. Methods: We used a scoping process to survey existing efforts through Epic’s Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms. Results: Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock. Conclusions: The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry. %M 31512582 %R 10.2196/12861 %U https://mhealth.jmir.org/2019/9/e12861/ %U https://doi.org/10.2196/12861 %U http://www.ncbi.nlm.nih.gov/pubmed/31512582 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 9 %P e13463 %T An Assessment of Physical Activity Data Collected via a Smartphone App and a Smart Band in Breast Cancer Survivors: Observational Study %A Chung,Il Yong %A Jung,Miyeon %A Lee,Sae Byul %A Lee,Jong Won %A Park,Yu Rang %A Cho,Daegon %A Chung,Haekwon %A Youn,Soyoung %A Min,Yul Ha %A Park,Hye Jin %A Lee,Minsun %A Chung,Seockhoon %A Son,Byung Ho %A Ahn,Sei-Hyun %+ Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, 05505, Seoul,, Republic of Korea, 82 10 7209 4620, newstar153@hanmail.net %K telemedicine %K breast neoplasms %K mobile apps %K quality of life %K stress, psychological %K patient compliance %K smartphone %K mobile phone %K wearable electronic devices %K survivorship %D 2019 %7 06.09.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Although distress screening is crucial for cancer survivors, it is not easy for clinicians to recognize distress. Physical activity (PA) data collected by mobile devices such as smart bands and smartphone apps have the potential to be used to screen distress in breast cancer survivors. Objective: The aim of this study was to assess data collection rates of smartphone apps and smart bands in terms of PA data, investigate the correlation between PA data from mobile devices and distress-related questionnaires from smartphone apps, and demonstrate factors associated with data collection with smart bands and smartphone apps in breast cancer survivors. Methods: In this prospective observational study, patients who underwent surgery for breast cancer at Asan Medical Center, Seoul, Republic of Korea, between June 2017 and March 2018 were enrolled and asked to use both a smartphone app and smart band for 6 months. The overall compliance rates of the daily PA data collection via the smartphone walking apps and wearable smart bands were analyzed in a within-subject manner. The longitudinal daily collection rates were calculated to examine the dropout pattern. We also performed multivariate linear regression analysis to examine factors associated with compliance with daily collection. Finally, we tested the correlation between the count of daily average steps and distress level using Pearson correlation analysis. Results: A total of 160 female patients who underwent breast cancer surgeries were enrolled. The overall compliance rates for using a smartphone app and smart bands were 88.0% (24,224/27,513) and 52.5% (14,431/27,513), respectively. The longitudinal compliance rate for smartphone apps was 77.8% at day 180, while the longitudinal compliance rate for smart bands rapidly decreased over time, reaching 17.5% at day 180. Subjects who were young, with other comorbidities, or receiving antihormonal therapy or targeted therapy showed significantly higher compliance rates to the smartphone app. However, no factor was associated with the compliance rate to the smart band. In terms of the correlation between the count of daily steps and distress level, step counts collected via smart band showed a significant correlation with distress level. Conclusions: Smartphone apps or smart bands are feasible tools to collect data on the physical activity of breast cancer survivors. PA data from mobile devices are correlated with participants’ distress data, which suggests the potential role of mobile devices in the management of distress in breast cancer survivors. Trial Registration: ClinicalTrials.gov NCT03072966; https://clinicaltrials.gov/ct2/show/NCT03072966 %M 31493319 %R 10.2196/13463 %U https://www.jmir.org/2019/9/e13463 %U https://doi.org/10.2196/13463 %U http://www.ncbi.nlm.nih.gov/pubmed/31493319 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 3 %P e14438 %T Validity of the Polar M430 Activity Monitor in Free-Living Conditions: Validation Study %A Henriksen,André %A Grimsgaard,Sameline %A Horsch,Alexander %A Hartvigsen,Gunnar %A Hopstock,Laila %+ Department of Community Medicine, UiT The Arctic University of Norway, Postboks 6050 Langnes, Tromsø, 9037, Norway, 47 77645214, andre.henriksen@uit.no %K actigraphy %K fitness trackers %K motor activity %K validation studies %D 2019 %7 16.08.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Accelerometers, often in conjunction with heart rate sensors, are extensively used to track physical activity (PA) in research. Research-grade instruments are often expensive and have limited battery capacity, limited storage, and high participant burden. Consumer-based activity trackers are equipped with similar technology and designed for long-term wear, and can therefore potentially be used in research. Objective: We aimed to assess the criterion validity of the Polar M430 sport watch, compared with 2 research-grade instruments (ActiGraph and Actiheart), worn on 4 different locations using 1- and 3-axis accelerometers. Methods: A total of 50 participants wore 2 ActiGraphs (wrist and hip), 2 Actihearts (upper and lower chest position), and 1 Polar M430 sport watch for 1 full day. We compared reported time (minutes) spent in sedentary behavior and in light, moderate, vigorous, and moderate to vigorous PA, step counts, activity energy expenditure, and total energy expenditure between devices. We used Pearson correlations, intraclass correlations, mean absolute percentage errors (MAPEs), and Bland-Altman plots to assess criterion validity. Results: Pearson correlations between the Polar M430 and all research-grade instruments were moderate or stronger for vigorous PA (r range .59-.76), moderate to vigorous PA (r range .51-.75), steps (r range .85-.87), total energy expenditure (r range .88-.94), and activity energy expenditure (r range .74-.79). Bland-Altman plots showed higher agreement for higher intensities of PA. MAPE was high for most outcomes. Only total energy expenditure measured by the hip-worn ActiGraph and both Actiheart positions had acceptable or close to acceptable errors with MAPEs of 6.94% (ActiGraph, 3 axes), 8.26% (ActiGraph, 1 axis), 14.54% (Actiheart, upper position), and 14.37% (Actiheart, lower position). The wrist-worn ActiGraph had a MAPE of 15.94% for measuring steps. All other outcomes had a MAPE of 22% or higher. For most outcomes, the Polar M430 was most strongly correlated with the hip-worn triaxial ActiGraph, with a moderate or strong Pearson correlation for sedentary behavior (r=.52) and for light (r=.7), moderate (r=.57), vigorous (r=.76), and moderate to vigorous (r=.75) PA. In addition, correlations were strong or very strong for activity energy expenditure (r=.75), steps (r=.85), and total energy expenditure (r=.91). Conclusions: The Polar M430 can potentially be used as an addition to established research-grade instruments to collect some PA variables over a prolonged period. However, due to the high MAPE of most outcomes, only total energy expenditure can be trusted to provide close to valid results. Depending on the variable, the Polar M430 over- or underreported most metrics, and may therefore be better suited to report changes in PA over time for some outcomes, rather than as an accurate instrument for PA status in a population. %M 31420958 %R 10.2196/14438 %U http://formative.jmir.org/2019/3/e14438/ %U https://doi.org/10.2196/14438 %U http://www.ncbi.nlm.nih.gov/pubmed/31420958 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 8 %P e13938 %T Accuracy of 12 Wearable Devices for Estimating Physical Activity Energy Expenditure Using a Metabolic Chamber and the Doubly Labeled Water Method: Validation Study %A Murakami,Haruka %A Kawakami,Ryoko %A Nakae,Satoshi %A Yamada,Yosuke %A Nakata,Yoshio %A Ohkawara,Kazunori %A Sasai,Hiroyuki %A Ishikawa-Takata,Kazuko %A Tanaka,Shigeho %A Miyachi,Motohiko %+ Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, 1-23-1 Toyama, Shinjuku, Tokyo, 162-8636, Japan, 81 3 3203 8061 ext 4201, miyachi@nibiohn.go.jp %K physical activity %K accelerometry %K energy expenditure %K indirect calorimetry %K doubly labeled water %D 2019 %7 02.08.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. Objective: We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. Methods: A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. Results: On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw. Conclusions: Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE. %M 31376273 %R 10.2196/13938 %U https://mhealth.jmir.org/2019/8/e13938/ %U https://doi.org/10.2196/13938 %U http://www.ncbi.nlm.nih.gov/pubmed/31376273 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 8 %P e13652 %T Information Literacy in Food and Activity Tracking Among Parkrunners, People With Type 2 Diabetes, and People With Irritable Bowel Syndrome: Exploratory Study %A McKinney,Pamela %A Cox,Andrew Martin %A Sbaffi,Laura %+ Information School, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP, United Kingdom, 44 0114 2222650, p.mckinney@sheffield.ac.uk %K activity logging %K food logging %K information literacy %K irritable bowel syndrome %K personal informatics %K quantified self %K running %K self-tracking %K type 2 diabetes %D 2019 %7 01.08.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: The tracking, or logging, of food intake and physical activity is increasing among people, and as a result there is increasing evidence of a link to improvement in health and well-being. Crucial to the effective and safe use of logging is a user’s information literacy. Objective: The aim of this study was to analyze food and activity tracking from an information literacy perspective. Methods: An online survey was distributed to three communities via parkrun, diabetes.co.uk and the Irritable Bowel Syndrome Network. Results: The data showed that there were clear differences in the logging practices of the members of the three different communities, as well as differences in motivations for tracking and the extent of sharing of said tracked data. Respondents showed a good understanding of the importance of information accuracy and were confident in their ability to understand tracked data, however, there were differences in the extent to which food and activity data were shared and also a lack of understanding of the potential reuse and sharing of data by third parties. Conclusions: Information literacy in this context involves developing awareness of the issues of accurate information recording, and how tracked information can be applied to support specific health goals. Developing awareness of how and when to share data, as well as of data ownership and privacy, are also important aspects of information literacy. %M 31373277 %R 10.2196/13652 %U https://www.jmir.org/2019/8/e13652/ %U https://doi.org/10.2196/13652 %U http://www.ncbi.nlm.nih.gov/pubmed/31373277 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 7 %P e13209 %T Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data %A Doryab,Afsaneh %A Villalba,Daniella K %A Chikersal,Prerna %A Dutcher,Janine M %A Tumminia,Michael %A Liu,Xinwen %A Cohen,Sheldon %A Creswell,Kasey %A Mankoff,Jennifer %A Creswell,John D %A Dey,Anind K %+ School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United States, 1 4123045320, adoryab@gmail.com %K mobile health %K loneliness %K machine learning %K statistical data analysis %K data mining %K digital phenotyping %D 2019 %7 24.07.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. Objective: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. Methods: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. Results: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). Conclusions: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being. %M 31342903 %R 10.2196/13209 %U http://mhealth.jmir.org/2019/7/e13209/ %U https://doi.org/10.2196/13209 %U http://www.ncbi.nlm.nih.gov/pubmed/31342903 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 7 %P e12768 %T Behavior Change Techniques Incorporated in Fitness Trackers: Content Analysis %A Chia,Gladys Lai Cheng %A Anderson,Angelika %A McLean,Louise Anne %+ Monash University, Wellington Road, Clayton,, Australia, 61 412965684, lai-cheng.chia@monash.edu %K behavioral medicine %K self-management %K fitness tracker %K physical activity %K sedentary behavior %D 2019 %7 23.07.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of fitness trackers as tools of self-management to promote physical activity is increasing. However, the content of fitness trackers remains unexplored. Objective: The aim of this study was to use the Behavior Change Technique Taxonomy v1 (BCTTv1) to examine if swim-proof fitness trackers below Aus $150 (US$ 105) incorporate behavior change techniques (BCTs) that relate to self-management strategies to increase physical activity and reduce sedentary behavior and to determine if content of the fitness trackers correspond to physical activity guidelines. Methods: A total of two raters used the BCTTv1 to code 6 fitness trackers that met the inclusion criteria. The inclusion criteria were the ability to track activity, be swim proof, be compatible with Android and Apple operating systems, and cost below Aus $150. Results: All fitness trackers contained BCTs known to promote physical activity, with the most frequently used BCTs overlapping with self-management strategies, including goal setting, self-monitoring, and feedback on behavior. Fitbit Flex 2 (Fitbit Inc) contained the most BCTs at 20. Huawei Band 2 Pro (Huawei Technologies) and Misfit Shine 2 (Fossil Group) contained the least BCTs at 11. Conclusions: Fitness trackers contain evidence-based BCTs that overlap with self-management strategies, which have been shown to increase physical activity and reduce sedentary behavior. Fitness trackers offer the prospect for physical activity interventions that are cost-effective and easily accessed by a wide population. %M 31339101 %R 10.2196/12768 %U http://mhealth.jmir.org/2019/7/e12768/ %U https://doi.org/10.2196/12768 %U http://www.ncbi.nlm.nih.gov/pubmed/31339101 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 4 %N 3 %P e12936 %T An Evaluation of Digital Health Tools for Diabetes Self-Management in Hispanic Adults: Exploratory Study %A Yingling,Leah %A Allen,Nancy A %A Litchman,Michelle L %A Colicchio,Vanessa %A Gibson,Bryan S %+ Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way #140, Salt Lake City, UT, 84108, United States, 1 801 582 1565, bryan.gibson@utah.edu %K type 2 diabetes %K Hispanic %K blood glucose self-monitoring %K culturally appropriate technology %K mobile app %D 2019 %7 16.07.2019 %9 Original Paper %J JMIR Diabetes %G English %X Background: Although multiple self-monitoring technologies for type 2 diabetes mellitus (T2DM) show promise for improving T2DM self-care behaviors and clinical outcomes, they have been understudied in Hispanic adult populations who suffer disproportionately from T2DM. Objective: The objective of this study was to evaluate the acceptability, feasibility, and potential integration of wearable sensors for diabetes self-monitoring among Hispanic adults with self-reported T2DM. Methods: We conducted a pilot study of T2DM self-monitoring technologies among Hispanic adults with self-reported T2DM. Participants (n=21) received a real-time continuous glucose monitor (RT-CGM), a wrist-worn physical activity (PA) tracker, and a tablet-based digital food diary to self-monitor blood glucose, PA, and food intake, respectively, for 1 week. The RT-CGM captured viewable blood glucose concentration (mg/dL) and PA trackers collected accelerometer-based data, viewable on the device or an associated tablet app. After 1 week of use, we conducted a semistructured interview with each participant to understand experiences and thoughts on integration of the data from the devices into a technology-facilitated T2DM self-management intervention. We also conducted a brief written questionnaire to understand participants’ self-reported T2DM history and past experience using digital health tools for T2DM self-management. Feasibility was measured by device utilization and objective RT-CGM, PA tracker, and diet logging data. Acceptability and potential integration were evaluated through thematic analysis of verbatim interview transcripts. Results: Participants (n=21, 76% female, 50.4 [SD 11] years) had a mean self-reported hemoglobin A1c of 7.4 [SD 1.8] mg/dL and had been diagnosed with T2DM for 7.4 [SD 5.2] years (range: 1-16 years). Most (89%) were treated with oral medications, whereas the others self-managed through diet and exercise. Nearly all participants (n=20) used both the RT-CGM and PA tracker, and 52% (11/21) logged at least one meal, with 33% (7/21) logging meals for 4 or more days. Of the 8 possible days, PA data were recorded for 7.1 [SD 1.8] days (range: 2-8), and participants averaged 7822 [SD 3984] steps per day. Interview transcripts revealed that participants felt most positive about the RT-CGM as it unveiled previously unknown relationships between lifestyle and health and contributed to changes in T2DM-related thoughts and behaviors. Participants felt generally positive about incorporating the wearable sensors and mobile apps into a future intervention if support were provided by a health coach or health care provider, device training were provided, apps were tailored to their language and culture, and content were both actionable and delivered on a single platform. Conclusions: Sensor-based tools for facilitating T2DM self-monitoring appear to be a feasible and acceptable technology among low-income Hispanic adults. We identified barriers to acceptability and highlighted preferences for wearable sensor integration in a community-based intervention. These findings have implications for the design of T2DM interventions targeting Hispanic adults. %M 31313657 %R 10.2196/12936 %U http://diabetes.jmir.org/2019/3/e12936/ %U https://doi.org/10.2196/12936 %U http://www.ncbi.nlm.nih.gov/pubmed/31313657 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 7 %P e14090 %T A Combination of Indoor Localization and Wearable Sensor–Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results %A Ramezani,Ramin %A Zhang,Wenhao %A Xie,Zhuoer %A Shen,John %A Elashoff,David %A Roberts,Pamela %A Stanton,Annette %A Eslami,Michelle %A Wenger,Neil %A Sarrafzadeh,Majid %A Naeim,Arash %+ Center for Smart Health, University of California, Los Angeles, , Los Angeles, CA,, United States, 1 4242997051, raminr@ucla.edu %K rehabilitation %K frailty %K remote sensing technology %K wearable electronic devices %K fitness trackers %K monitoring ambulatory %K smartwatches %K bluetooth low energy beacons %D 2019 %7 10.07.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Health care, in recent years, has made great leaps in integrating wireless technology into traditional models of care. The availability of ubiquitous devices such as wearable sensors has enabled researchers to collect voluminous datasets and harness them in a wide range of health care topics. One of the goals of using on-body wearable sensors has been to study and analyze human activity and functional patterns, thereby predicting harmful outcomes such as falls. It can also be used to track precise individual movements to form personalized behavioral patterns, to standardize the concept of frailty, well-being/independence, etc. Most wearable devices such as activity trackers and smartwatches are equipped with low-cost embedded sensors that can provide users with health statistics. In addition to wearable devices, Bluetooth low-energy sensors known as BLE beacons have gained traction among researchers in ambient intelligence domain. The low cost and durability of newer versions have made BLE beacons feasible gadgets to yield indoor localization data, an adjunct feature in human activity recognition. In the studies by Moatamed et al and the patent application by Ramezani et al, we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extracting indoor localization using BLE beacons, in concert. Objective: The study aimed to examine the ability of combination of physical activity and indoor location features, extracted at baseline, on a cohort of 154 rehabilitation-dwelling patients to discriminate between subacute care patients who are re-admitted to the hospital versus the patients who are able to stay in a community setting. Methods: We analyzed physical activity sensor features to assess activity time and intensity. We also analyzed activities with regard to indoor localization. Chi-square and Kruskal-Wallis tests were used to compare demographic variables and sensor feature variables in outcome groups. Random forests were used to build predictive models based on the most significant features. Results: Standing time percentage (P<.001, d=1.51), laying down time percentage (P<.001, d=1.35), resident room energy intensity (P<.001, d=1.25), resident bed energy intensity (P<.001, d=1.23), and energy percentage of active state (P=.001, d=1.24) are the 5 most statistically significant features in distinguishing outcome groups at baseline. The energy intensity of the resident room (P<.001, d=1.25) was achieved by capturing indoor localization information. Random forests revealed that the energy intensity of the resident room, as a standalone attribute, is the most sensitive parameter in the identification of outcome groups (area under the curve=0.84). Conclusions: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features at baseline, a subset of which can better distinguish between at-risk patients that can gain independence versus the patients that are rehospitalized. %M 31293244 %R 10.2196/14090 %U http://mhealth.jmir.org/2019/7/e14090/ %U https://doi.org/10.2196/14090 %U http://www.ncbi.nlm.nih.gov/pubmed/31293244 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 2 %N 1 %P e14300 %T Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study %A Venkataramanan,Revathy %A Thirunarayan,Krishnaprasad %A Jaimini,Utkarshani %A Kadariya,Dipesh %A Yip,Hong Yung %A Kalra,Maninder %A Sheth,Amit %+ Ohio Center of Excellence in Knowledge-enabled Computing, Wright State University, 377 Joshi Research Center, 3640 Colonel Glenn Highway, Dayton, OH, 45435, United States, 1 9372390625, amit@knoesis.org %K personalized digital health %K medical internet of things %K asthma management %K patient-generated health data %K pediatric asthma %K asthma control %K medication adherence %K childhood asthma %K understanding and treatment of asthma %D 2019 %7 27.06.2019 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: Asthma is a chronic pulmonary disease with multiple triggers. It can be managed by strict adherence to an asthma care plan and by avoiding these triggers. Clinicians cannot continuously monitor their patients’ environment and their adherence to an asthma care plan, which poses a significant challenge for asthma management. Objective: In this study, pediatric patients were continuously monitored using low-cost sensors to collect asthma-relevant information. The objective of this study was to assess whether kHealth kit, which contains low-cost sensors, can identify personalized triggers and provide actionable insights to clinicians for the development of a tailored asthma care plan. Methods: The kHealth asthma kit was developed to continuously track the symptoms of asthma in pediatric patients and monitor the patients’ environment and adherence to their care plan for either 1 or 3 months. The kit consists of an Android app–based questionnaire to collect information on asthma symptoms and medication intake, Fitbit to track sleep and activity, the Peak Flow meter to monitor lung functions, and Foobot to monitor indoor air quality. The data on the patient’s outdoor environment were collected using third-party Web services based on the patient’s zip code. To date, 107 patients consented to participate in the study and were recruited from the Dayton Children’s Hospital, of which 83 patients completed the study as instructed. Results: Patient-generated health data from the 83 patients who completed the study were included in the cohort-level analysis. Of the 19% (16/83) of patients deployed in spring, the symptoms of 63% (10/16) and 19% (3/16) of patients suggested pollen and particulate matter (PM2.5), respectively, to be their major asthma triggers. Of the 17% (14/83) of patients deployed in fall, symptoms of 29% (4/17) and 21% (3/17) of patients suggested pollen and PM2.5, respectively, to be their major triggers. Among the 28% (23/83) of patients deployed in winter, PM2.5 was identified as the major trigger for 83% (19/23) of patients. Similar correlations were not observed between asthma symptoms and factors such as ozone level, temperature, and humidity. Furthermore, 1 patient from each season was chosen to explain, in detail, his or her personalized triggers by observing temporal associations between triggers and asthma symptoms gathered using the kHealth asthma kit. Conclusions: The continuous monitoring of pediatric asthma patients using the kHealth asthma kit generates insights on the relationship between their asthma symptoms and triggers across different seasons. This can ultimately inform personalized asthma management and intervention plans. %M 31518318 %R 10.2196/14300 %U http://pediatrics.jmir.org/2019/1/e14300/ %U https://doi.org/10.2196/14300 %U http://www.ncbi.nlm.nih.gov/pubmed/31518318 %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 e14265 %T Persuasive System Design Principles and Behavior Change Techniques to Stimulate Motivation and Adherence in Electronic Health Interventions to Support Weight Loss Maintenance: Scoping Review %A Asbjørnsen,Rikke Aune %A Smedsrød,Mirjam Lien %A Solberg Nes,Lise %A Wentzel,Jobke %A Varsi,Cecilie %A Hjelmesæth,Jøran %A van Gemert-Pijnen,Julia EWC %+ Center for eHealth and Wellbeing Research, Department of Psychology, Health, and Technology, University of Twente, De Zul 10, Enschede, 7522 NJ, Netherlands, 31 534899111, r.a.asbjornsen@utwente.nl %K eHealth %K weight loss maintenance %K weight loss %K behavior change %K persuasive technology %K review %K motivation %K adherence %D 2019 %7 21.06.2019 %9 Review %J J Med Internet Res %G English %X Background: Maintaining weight after weight loss is a major health challenge, and eHealth (electronic health) solutions may be a way to meet this challenge. Application of behavior change techniques (BCTs) and persuasive system design (PSD) principles in eHealth development may contribute to the design of technologies that positively influence behavior and motivation to support the sustainable health behavior change needed. Objective: This review aimed to identify BCTs and PSD principles applied in eHealth interventions to support weight loss and weight loss maintenance, as well as techniques and principles applied to stimulate motivation and adherence for long-term weight loss maintenance. Methods: A systematic literature search was conducted in PsycINFO, Ovid MEDLINE (including PubMed), EMBASE, Scopus, Web of Science, and AMED, from January 1, 2007 to June 30, 2018. Arksey and O’Malley’s scoping review methodology was applied. Publications on eHealth interventions were included if focusing on weight loss or weight loss maintenance, in combination with motivation or adherence and behavior change. Results: The search identified 317 publications, of which 45 met the inclusion criteria. Of the 45 publications, 11 (24%) focused on weight loss maintenance, and 34 (76%) focused on weight loss. Mobile phones were the most frequently used technology (28/45, 62%). Frequently used wearables were activity trackers (14/45, 31%), as well as other monitoring technologies such as wireless or digital scales (8/45, 18%). All included publications were anchored in behavior change theories. Feedback and monitoring and goals and planning were core behavior change technique clusters applied in the majority of included publications. Social support and associations through prompts and cues to support and maintain new habits were more frequently used in weight loss maintenance than weight loss interventions. In both types of interventions, frequently applied persuasive principles were self-monitoring, goal setting, and feedback. Tailoring, reminders, personalization, and rewards were additional principles frequently applied in weight loss maintenance interventions. Results did not reveal an ideal combination of techniques or principles to stimulate motivation, adherence, and weight loss maintenance. However, the most frequently mentioned individual techniques and principles applied to stimulate motivation were, personalization, simulation, praise, and feedback, whereas associations were frequently mentioned to stimulate adherence. eHealth interventions that found significant effects for weight loss maintenance all applied self-monitoring, feedback, goal setting, and shaping knowledge, combined with a human social support component to support healthy behaviors. Conclusions: To our knowledge, this is the first review examining key BCTs and PSD principles applied in weight loss maintenance interventions compared with those of weight loss interventions. This review identified several techniques and principles applied to stimulate motivation and adherence. Future research should aim to examine which eHealth design combinations can be the most effective in support of long-term behavior change and weight loss maintenance. %M 31228174 %R 10.2196/14265 %U http://www.jmir.org/2019/6/e14265/ %U https://doi.org/10.2196/14265 %U http://www.ncbi.nlm.nih.gov/pubmed/31228174 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e13084 %T Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort %A Tedesco,Salvatore %A Sica,Marco %A Ancillao,Andrea %A Timmons,Suzanne %A Barton,John %A O'Flynn,Brendan %+ Tyndall National Institute, University College Cork, Lee Maltings, Prospect Row, Cork, T12R5CP, Ireland, 353 212346286, salvatore.tedesco@tyndall.ie %K aging %K fitness trackers %K wristbands %K older adults %K wearable activity trackers %K Fitbit %K Garmin %K energy expenditure %K physical activity %K sleep %D 2019 %7 19.06.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings. Objective: This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment. Methods: Overall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered. Results: For step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36%), the Garmin and ActiGraph undercounted (MPE 9.36% and 11.53%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7%. Conclusions: The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes. %M 31219048 %R 10.2196/13084 %U https://mhealth.jmir.org/2019/6/e13084/ %U https://doi.org/10.2196/13084 %U http://www.ncbi.nlm.nih.gov/pubmed/31219048 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 7 %N 2 %P e13051 %T Effects of the FIT Game on Physical Activity in Sixth Graders: A Pilot Reversal Design Intervention Study %A Joyner,Damon %A Wengreen,Heidi %A Aguilar,Sheryl %A Madden,Gregory %+ Utah State University, 8700 Old Main Hill, Logan, UT,, United States, 1 435 797 1806, heidi.wengreen@usu.edu %K children %K accelerometer %K step count %D 2019 %7 18.06.2019 %9 Original Paper %J JMIR Serious Games %G English %X Background: The FIT Game is a low-cost intervention that increases fruit and vegetable consumption in elementary school children. For this study, the FIT Game was adapted into an intervention designed to increase children’s physical activity at school. Objective: We aimed to evaluate if the FIT Game could increase children’s physical activity relative to their baseline levels. Methods: A total of 29 participants were recruited from a sixth-grade classroom. An ABAB reversal design was used. Participants wore an accelerometer while at school during pre/postintervention baseline (A) and intervention (B) phases. During the FIT Game intervention, daily physical activity goals encouraged the class to increase their median daily step count above the 60th percentile of the previous 10 days. When daily goals were met, game-based accomplishments were realized. Results: Children met their activity goals 80% of the time during the intervention phases. Physical activity at school increased from a median of 3331 steps per day during the baseline to 4102 steps during the FIT Game phases (P<.001, Friedman test). Conclusions: Preliminary evidence showed that playing the FIT Game could positively influence children’s physical activity at school. %M 31215508 %R 10.2196/13051 %U http://games.jmir.org/2019/2/e13051/ %U https://doi.org/10.2196/13051 %U http://www.ncbi.nlm.nih.gov/pubmed/31215508 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e13327 %T Estimating Maximal Oxygen Uptake From Daily Activity Data Measured by a Watch-Type Fitness Tracker: Cross-Sectional Study %A Kwon,Soon Bin %A Ahn,Joong Woo %A Lee,Seung Min %A Lee,Joonnyong %A Lee,Dongheon %A Hong,Jeeyoung %A Kim,Hee Chan %A Yoon,Hyung-Jin %+ Interdisciplinary Program in Bioengineering, Seoul National University, 103 Daehak-ro, Jongro-gu, Seoul, 03080, Republic of Korea, 82 2 740 8596, hjyoon@snu.ac.kr %K cardiorespiratory fitness %K oxygen consumption %K fitness tracker %D 2019 %7 13.6.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cardiorespiratory fitness (CRF), an important index of physical fitness, is the ability to inhale and provide oxygen to the exercising muscle. However, despite its importance, the current gold standard for measuring CRF is impractical, requiring maximal exercise from the participants. Objective: This study aimed to develop a convenient and practical estimation model for CRF using data collected from daily life with a wristwatch-type device. Methods: A total of 191 subjects, aged 20 to 65 years, participated in this study. Maximal oxygen uptake (VO2 max), a standard measure of CRF, was measured with a maximal exercise test. Heart rate (HR) and physical activity data were collected using a commercial wristwatch-type fitness tracker (Fitbit; Fitbit Charge; Fitbit) for 3 consecutive days. Maximal activity energy expenditure (aEEmax) and slope between HR and physical activity were calculated using a linear regression. A VO2 max estimation model was built using multiple linear regression with data on age, sex, height, percent body fat, aEEmax, and the slope. The result was validated with 2 different cross-validation methods. Results: aEEmax showed a moderate correlation with VO2 max (r=0.50). The correlation coefficient for the multiple linear regression model was 0.81, and the SE of estimate (SEE) was 3.518 mL/kg/min. The regression model was cross-validated through the predicted residual error sum of square (PRESS). The PRESS correlation coefficient was 0.79, and the PRESS SEE was 3.667 mL/kg/min. The model was further validated by dividing it into different subgroups and calculating the constant error (CE) where a low CE showed that the model does not significantly overestimate or underestimate VO2 max. Conclusions: This study proposes a CRF estimation method using data collected by a wristwatch-type fitness tracker without any specific protocol for a wide range of the population. %M 31199336 %R 10.2196/13327 %U https://mhealth.jmir.org/2019/6/e13327/ %U https://doi.org/10.2196/13327 %U http://www.ncbi.nlm.nih.gov/pubmed/31199336 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 2 %N 1 %P e12153 %T Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study %A Yang,Yang %A Hirdes,John P %A Dubin,Joel A %A Lee,Joon %+ Faculty of Applied Health Sciences, School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 (226) 317 3726, y24yang@uwaterloo.ca %K falls %K elderly %K wearable devices %K machine learning %K interRAI %D 2019 %7 07.06.2019 %9 Original Paper %J JMIR Aging %G English %X Background:  Little is known about whether off-the-shelf wearable sensor data can contribute to fall risk classification or complement clinical assessment tools such as the Resident Assessment Instrument-Home Care (RAI-HC). Objective:  This study aimed to (1) investigate the similarities and differences in physical activity (PA), heart rate, and night sleep in a sample of community-dwelling older adults with varying fall histories using a smart wrist-worn device and (2) create and evaluate fall risk classification models based on (i) wearable data, (ii) the RAI-HC, and (iii) the combination of wearable and RAI-HC data. Methods:  A prospective, observational study was conducted among 3 faller groups (G0, G1, G2+) based on the number of previous falls (0, 1, ≥2 falls) in a sample of older community-dwelling adults. Each participant was requested to wear a smart wristband for 7 consecutive days while carrying out day-to-day activities in their normal lives. The wearable and RAI-HC assessment data were analyzed and utilized to create fall risk classification models, with 3 supervised machine learning algorithms: logistic regression, decision tree, and random forest (RF). Results:  Of 40 participants aged 65 to 93 years, 16 (40%) had no previous falls, whereas 8 (20%) and 16 (40%) had experienced 1 and multiple (≥2) falls, respectively. Level of PA as measured by average daily steps was significantly different between groups (P=.04). In the 3 faller group classification, RF achieved the best accuracy of 83.8% using both wearable and RAI-HC data, which is 13.5% higher than that of using the RAI-HC data only and 18.9% higher than that of using wearable data exclusively. In discriminating between {G0+G1} and G2+, RF achieved the best area under the receiver operating characteristic curve of 0.894 (overall accuracy of 89.2%) based on wearable and RAI-HC data. Discrimination between G0 and {G1+G2+} did not result in better classification performance than that between {G0+G1} and G2+. Conclusions:  Both wearable data and the RAI-HC assessment can contribute to fall risk classification. All the classification models revealed that RAI-HC outperforms wearable data, and the best performance was achieved with the combination of 2 datasets. Future studies in fall risk assessment should consider using wearable technologies to supplement resident assessment instruments. %M 31518278 %R 10.2196/12153 %U http://aging.jmir.org/2019/1/e12153/ %U https://doi.org/10.2196/12153 %U http://www.ncbi.nlm.nih.gov/pubmed/31518278 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e13384 %T Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors %A Liang,Zilu %A Chapa-Martell,Mario Alberto %+ School of Engineering, Kyoto University of Advanced Science, 18 Yamanouchi Gotanda-Cho, Kyoto, 6158577, Japan, 81 8040866433, z.liang@cnl.t.u-tokyo.ac.jp %K wearable electronic devices %K sleep %K validation studies %D 2019 %7 06.06.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. Objective: This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. Methods: A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night’s sleep in participants’ homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed–rank test was performed to investigate the effect of user-specific factors. Results: Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. Conclusions: Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE. %M 31172956 %R 10.2196/13384 %U https://mhealth.jmir.org/2019/6/e13384/ %U https://doi.org/10.2196/13384 %U http://www.ncbi.nlm.nih.gov/pubmed/31172956 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 5 %P e12691 %T Development of Comprehensive Personal Health Records Integrating Patient-Generated Health Data Directly From Samsung S-Health and Apple Health Apps: Retrospective Cross-Sectional Observational Study %A Jung,Se Young %A Kim,Jeong-Whun %A Hwang,Hee %A Lee,Keehyuck %A Baek,Rong-Min %A Lee,Ho-Young %A Yoo,Sooyoung %A Song,Wongeun %A Han,Jong Soo %+ Office of eHealth Research and Business, Seoul National University Bundang Hospital, 172 Dolma-ro, Bundang-gu, Seongnam-si, 13620, Republic of Korea, 82 31 787 0114, epilepsyguy@gmail.com %K personal health record %K patient generated health data %K lifelog %K electronic medical record %K mobile phone %D 2019 %7 28.05.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Patient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective tool to engage patients more actively in the management of their chronic diseases. However, no PHRs currently integrate PGHD directly from Samsung S-Health and Apple Health apps. Objective: The purposes of this study were (1) to demonstrate the development of an electronic medical record (EMR)–tethered PHR system (Health4U) that integrates lifelog data from Samsung S-Health and Apple Health apps and (2) to explore the factors associated with the use rate of the functions. Methods: To upgrade conventional EMR-tethered PHRs, a task-force team (TFT) defined the functions necessary for users. After implementing a new system, we enrolled adults aged 19 years and older with prior experience of accessing Health4U in the 7-month period after November 2017, when the service was upgraded. Results: Of the 17,624 users, 215 (1.22%) integrated daily steps data, 175 (0.99%) integrated weight data, 51 (0.29%) integrated blood sugar data, and 90 (0.51%) integrated blood pressure data. Overall, 61.95% (10,919/17,624) had one or more chronic diseases. For integration of daily steps data, 48.3% (104/215) of patients used the Apple Health app, 43.3% (93/215) used the S-Health app, and 8.4% (18/215) entered data manually. To retrieve medical documentation, 324 (1.84%) users downloaded PDF files and 31 (0.18%) users integrated their medical records into the Samsung S-Health app via the Consolidated-Clinical Document Architecture download function. We found a consistent increase in the odds ratios for PDF downloads among patients with a higher number of chronic diseases. The age groups of ≥60 years and ≥80 years tended to use the download function less frequently than the others. Conclusions: This is the first study to examine the factors related to integration of lifelog data from Samsung S-Health and Apple Health apps into EMR-tethered PHRs and factors related to the retrieval of medical documents from PHRs. Our findings on the lifelog data integration can be used to design PHRs as a platform to integrate lifelog data in the future. %M 31140446 %R 10.2196/12691 %U http://mhealth.jmir.org/2019/5/e12691/ %U https://doi.org/10.2196/12691 %U http://www.ncbi.nlm.nih.gov/pubmed/31140446 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 5 %P e13547 %T Automatic Identification of Physical Activity Type and Duration by Wearable Activity Trackers: A Validation Study %A Dorn,Diana %A Gorzelitz,Jessica %A Gangnon,Ronald %A Bell,David %A Koltyn,Kelli %A Cadmus-Bertram,Lisa %+ Department of Kinesiology, University of Wisconsin - Madison, 2000 Observatory Drive, Madison, WI, 53562, United States, 1 608 265 5946, cadmusbertra@wisc.edu %K fitness trackers %K exercise %K accelerometry %K data accuracy %D 2019 %7 23.05.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Activity trackers are now ubiquitous in certain populations, with potential applications for health promotion and monitoring and chronic disease management. Understanding the accuracy of this technology is critical to the appropriate and productive use of wearables in health research. Although other peer-reviewed validations have examined other features (eg, steps and heart rate), no published studies to date have addressed the accuracy of automatic activity type detection and duration accuracy in wearable trackers. Objective: The aim of this study was to examine the ability of 4 commercially available wearable activity trackers (Fitbits Flex 2, Fitbit Alta HR, Fitbit Charge 2, and Garmin Vívosmart HR), in a controlled setting, to correctly and automatically identify the type and duration of the physical activity being performed. Methods: A total of 8 activity types, including walking and running (on both a treadmill and outdoors), a run embedded in walking bouts, elliptical use, outdoor biking, and pool lap swimming, were tested by 28 to 34 healthy adult participants (69 total participants who participated in some to all activity types). Actual activity type and duration were recorded by study personnel and compared with tracker data using descriptive statistics and mean absolute percent error (MAPE). Results: The proportion of trials in which the activity type was correctly identified was 93% to 97% (depending on the tracker) for treadmill walking, 93% to 100% for treadmill running, 36% to 62% for treadmill running when preceded and followed by a walk, 97% to 100% for outdoor walking, 100% for outdoor running, 3% to 97% for using an elliptical, 44% to 97% for biking, and 87.5% for swimming. When activities were correctly identified, the MAPE of the detected duration versus the actual activity duration was between 7% and 7.9% for treadmill walking, 8.7% and 144.8% for treadmill running, 23.6% and 28.9% for treadmill running when preceded and followed by a walk, 4.9% and 11.8% for outdoor walking, 5.6% and 9.6% for outdoor running, 9.7% and 13% for using an elliptical, 9.5% and 17.7% for biking, and was 26.9% for swimming. Conclusions: In a controlled setting, wearable activity trackers provide accurate recognition of the type of some common physical activities, especially outdoor walking and running and walking on a treadmill. The accuracy of measurement of activity duration varied considerably by activity type and tracker model and was poor for complex sets of activity, such as a run embedded within 2 walking segments. %M 31124470 %R 10.2196/13547 %U http://mhealth.jmir.org/2019/5/e13547/ %U https://doi.org/10.2196/13547 %U http://www.ncbi.nlm.nih.gov/pubmed/31124470 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 3 %N 1 %P e12122 %T Use of Free-Living Step Count Monitoring for Heart Failure Functional Classification: Validation Study %A Baril,Jonathan-F %A Bromberg,Simon %A Moayedi,Yasbanoo %A Taati,Babak %A Manlhiot,Cedric %A Ross,Heather Joan %A Cafazzo,Joseph %K exercise physiology %K heart rate tracker %K wrist worn devices %K Fitbit %K heart failure %K steps %K cardiopulmonary exercise test %K ambulatory monitoring %D 2019 %7 17.05.2019 %9 Original Paper %J JMIR Cardio %G English %X Background: The New York Heart Association (NYHA) functional classification system has poor inter-rater reproducibility. A previously published pilot study showed a statistically significant difference between the daily step counts of heart failure (with reduced ejection fraction) patients classified as NYHA functional class II and III as measured by wrist-worn activity monitors. However, the study’s small sample size severely limits scientific confidence in the generalizability of this finding to a larger heart failure (HF) population. Objective: This study aimed to validate the pilot study on a larger sample of patients with HF with reduced ejection fraction (HFrEF) and attempt to characterize the step count distribution to gain insight into a more objective method of assessing NYHA functional class. Methods: We repeated the analysis performed during the pilot study on an independently recorded dataset comprising a total of 50 patients with HFrEF (35 NYHA II and 15 NYHA III) patients. Participants were monitored for step count with a Fitbit Flex for a period of 2 weeks in a free-living environment. Results: Comparing group medians, patients exhibiting NYHA class III symptoms had significantly lower recorded 2-week mean daily total step count (3541 vs 5729 [steps], P=.04), lower 2-week maximum daily total step count (10,792 vs 5904 [steps], P=.03), lower 2-week recorded mean daily mean step count (4.0 vs 2.5 [steps/minute], P=.04,), and lower 2-week mean and 2-week maximum daily per minute step count maximums (88.1 vs 96.1 and 111.0 vs 123.0 [steps/minute]; P=.02 and .004, respectively). Conclusions: Patients with NYHA II and III symptoms differed significantly by various aggregate measures of free-living step count including the (1) mean and (2) maximum daily total step count as well as by the (3) mean of daily mean step count and by the (4) mean and (5) maximum of the daily per minute step count maximum. These findings affirm that the degree of exercise intolerance of NYHA II and III patients as a group is quantifiable in a replicable manner. This is a novel and promising finding that suggests the existence of a possible, completely objective measure of assessing HF functional class, something which would be a great boon in the continuing quest to improve patient outcomes for this burdensome and costly disease. %M 31758777 %R 10.2196/12122 %U http://cardio.jmir.org/2019/1/e12122/ %U https://doi.org/10.2196/12122 %U http://www.ncbi.nlm.nih.gov/pubmed/31758777 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 5 %P e10737 %T Change in Waist Circumference With Continuous Use of a Smart Belt: An Observational Study %A Lee,Myeonggyun %A Shin,Jaeyong %+ Department of Preventive Medicine, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-Gu, Suwon, 16499, Republic of Korea, 82 312197455, drshin@ajou.ac.kr %K smart health care %K wearable device %K obesity %K internet of things %K mHealth %K digital health care %K lifestyle modification %K metabolic syndrome %D 2019 %7 02.05.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Health insurers and policymakers are trying to prevent and reduce cardiovascular diseases due to obesity. A smart belt that monitors activity and waist circumference is a new concept for conquering obesity and may be a promising new strategy for health insurers and policymakers. Objective: This preliminary study evaluated whether the use of a smart belt was associated with a decrease in waist circumference. Methods: In the manufacturer’s database, there were data on a total of 427 men at baseline. A total of 223, 81, and 27 users kept using the smart belt for 4, 8, and 12 weeks, respectively. Paired t tests and repeated measures analysis of variance (ANOVA) were used to identify the change in waist circumference at specified time intervals (at 4, 8, and 12 weeks). In addition, a linear mixed model was used to incorporate all users’ waist circumference data at each time point. Preexisting data on waist circumference and self-reported demographics were obtained from the manufacturer of the smart belt (WELT Corporation, South Korea). Results: Compared with baseline, the waist circumference (cm) decreased significantly at all time points: –0.270 for week 4, –0.761 for week 8, and –1.972 for week 12 (all P<.01). Although each paired t test had a different sample size because of loss to follow-up, the differences between baseline and each subsequent week increased. Equal continuous reduction in waist circumference was observed with the ANOVA and mixed model analysis (beta=–0.158 every week). Conclusions: The smart belt is a newly developed, wearable device that measures real-time steps, sedentary time, and waist circumference. In this study, we showed that wearing the smart belt was associated with reducing waist circumference over 12 weeks. This direct-to-consumer smart health device may contribute toward reducing the risk of obesity and related conditions and controlling increasing health costs for health insurers. %M 31045500 %R 10.2196/10737 %U http://mhealth.jmir.org/2019/5/e10737/ %U https://doi.org/10.2196/10737 %U http://www.ncbi.nlm.nih.gov/pubmed/31045500 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e8298 %T Effects of Mobile Health Including Wearable Activity Trackers to Increase Physical Activity Outcomes Among Healthy Children and Adolescents: Systematic Review %A Böhm,Birgit %A Karwiese,Svenja D %A Böhm,Harald %A Oberhoffer,Renate %+ Institute of Preventive Pediatrics, Technical University of Munich, Georg-Brauchle-Ring 60/62, Munich, 80992, Germany, 49 89 289 ext 24571, birgit.boehm@tum.de %K children %K adolescent %K mHealth %K fitness tracker %K physical activity %K physical fitness %D 2019 %7 30.04.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Children and adolescents do not meet the current recommendations on physical activity (PA), and as such, the health-related benefits of regular PA are not achieved. Nowadays, technology-based programs represent an appealing and promising option for children and adolescents to promote PA. Objective: The aim of this review was to systematically evaluate the effects of mobile health (mHealth) and wearable activity trackers on PA-related outcomes in this target group. Methods: Electronic databases such as the Cochrane Central Register of Controlled Trials, PubMed, Scopus, SPORTDiscus, and Web of Science were searched to retrieve English language articles published in peer-reviewed journals from January 2012 to June 2018. Those included were articles that contained descriptions of interventions designed to increase PA among children (aged 6 to 12 years) only, or adolescents (aged 13 to 18 years) only, or articles that include both populations, and also, articles that measured at least 1 PA-related cognitive, psychosocial, or behavioral outcome. The interventions had to be based on mHealth tools (mobile phones, smartphones, tablets, or mobile apps) or wearable activity trackers. Randomized controlled trials (RCTs) and non-RCTs, cohort studies, before-and-after studies, and cross-sectional studies were considered, but only controlled studies with a PA comparison between groups were assessed for methodological quality. Results: In total, 857 articles were identified. Finally, 7 studies (5 with tools of mHealth and 2 with wearable activity trackers) met the inclusion criteria. All studies with tools of mHealth used an RCT design, and 3 were of high methodological quality. Intervention delivery ranged from 4 weeks to 12 months, whereby mainly smartphone apps were used as a tool. Intervention delivery in studies with wearable activity trackers covered a period from 22 sessions during school recess and 8 weeks. Trackers were used as an intervention and evaluation tool. No evidence was found for the effect of mHealth tools, respectively wearable activity trackers, on PA-related outcomes. Conclusions: Given the small number of studies, poor compliance with accelerometers as a measuring instrument for PA, risk of bias, missing RCTs in relation to wearable activity trackers, and the heterogeneity of intervention programs, caution is warranted regarding the comparability of the studies and their effects. There is a clear need for future studies to develop PA interventions grounded on intervention mapping with a high methodological study design for specific target groups to achieve meaningful evidence. %M 31038460 %R 10.2196/mhealth.8298 %U http://mhealth.jmir.org/2019/4/e8298/ %U https://doi.org/10.2196/mhealth.8298 %U http://www.ncbi.nlm.nih.gov/pubmed/31038460 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 4 %P e12910 %T Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches %A Rozet,Alan %A Kronish,Ian M %A Schwartz,Joseph E %A Davidson,Karina W %+ Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center, Presbyterian Hospital Building, 9th Floor, 622 W 168th Street, New York, NY, 10032, United States, 1 212 342 4493, ar3793@cumc.columbia.edu %K ecological momentary assessment %K machine learning %K stress-behavior pathway %K personal informatics %K self-quantification %K exercise %K weather %K just-in-time interventions %D 2019 %7 26.04.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Investigations into person-specific predictors of stress have typically taken either a population-level nomothetic approach or an individualized ideographic approach. Nomothetic approaches can quickly identify predictors but can be hindered by the heterogeneity of these predictors across individuals and time. Ideographic approaches may result in more predictive models at the individual level but require a longer period of data collection to identify robust predictors. Objective: Our objectives were to compare predictors of stress identified through nomothetic and ideographic models and to assess whether sequentially combining nomothetic and ideographic models could yield more accurate and actionable predictions of stress than relying on either model. At the same time, we sought to maintain the interpretability necessary to retrieve individual predictors of stress despite using nomothetic models. Methods: Data collected in a 1-year observational study of 79 participants performing low levels of exercise were used. Physical activity was continuously and objectively monitored by actigraphy. Perceived stress was recorded by participants via daily ecological momentary assessments on a mobile app. Environmental variables including daylight time, temperature, and precipitation were retrieved from the public archives. Using these environmental, actigraphy, and mobile assessment data, we built machine learning models to predict individual stress ratings using linear, decision tree, and neural network techniques employing nomothetic and ideographic approaches. The accuracy of the approaches for predicting individual stress ratings was compared based on classification errors. Results: Across the group of patients, an individual’s recent history of stress ratings was most heavily weighted in predicting a future stress rating in the nomothetic recurrent neural network model, whereas environmental factors such as temperature and daylight, as well as duration and frequency of bouts of exercise, were more heavily weighted in the ideographic models. The nomothetic recurrent neural network model was the highest performing nomothetic model and yielded 72% accuracy for an 80%/20% train/test split. Using the same 80/20 split, the ideographic models yielded 75% accuracy. However, restricting ideographic models to participants with more than 50 valid days in the training set, with the same 80/20 split, yielded 85% accuracy. Conclusions: We conclude that for some applications, nomothetic models may be useful for yielding higher initial performance while still surfacing personalized predictors of stress, before switching to ideographic models upon sufficient data collection. %M 31025942 %R 10.2196/12910 %U http://www.jmir.org/2019/4/e12910/ %U https://doi.org/10.2196/12910 %U http://www.ncbi.nlm.nih.gov/pubmed/31025942 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 3 %N 2 %P e11489 %T Measuring Free-Living Physical Activity With Three Commercially Available Activity Monitors for Telemonitoring Purposes: Validation Study %A Breteler,Martine JM %A Janssen,Joris H %A Spiering,Wilko %A Kalkman,Cor J %A van Solinge,Wouter W %A Dohmen,Daan AJ %+ Department of Anesthesiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Utrecht,, Netherlands, 31 647396841, m.j.m.breteler@umcutrecht.nl %K activity trackers %K telemedicine %K exercise %D 2019 %7 24.04.2019 %9 Original Paper %J JMIR Form Res %G English %X Background: Remote monitoring of physical activity in patients with chronic conditions could be useful to offer care professionals real-time assessment of their patient’s daily activity pattern to adjust appropriate treatment. However, the validity of commercially available activity trackers that can be used for telemonitoring purposes is limited. Objective: The purpose of this study was to test usability and determine the validity of 3 consumer-level activity trackers as a measure of free-living activity. Methods: A usability evaluation (study 1) and validation study (study 2) were conducted. In study 1, 10 individuals wore one activity tracker for a period of 30 days and filled in a questionnaire on ease of use and wearability. In study 2, we validated three selected activity trackers (Apple Watch, Misfit Shine, and iHealth Edge) and a fourth pedometer (Yamax Digiwalker) against the reference standard (Actigraph GT3X) in 30 healthy participants for 72 hours. Outcome measures were 95% limits of agreement (LoA) and bias (Bland-Altman analysis). Furthermore, median absolute differences (MAD) were calculated. Correction for bias was estimated and validated using leave-one-out cross validation. Results: Usability evaluation of study 1 showed that iHealth Edge and Apple Watch were more comfortable to wear as compared with the Misfit Flash. Therefore, the Misfit Flash was replaced by Misfit Shine in study 2. During study 2, the total number of steps of the reference standard was 21,527 (interquartile range, IQR 17,475-24,809). Bias and LoA for number of steps from the Apple Watch and iHealth Edge were 968 (IQR −5478 to 7414) and 2021 (IQR −4994 to 9036) steps. For Misfit Shine and Yamax Digiwalker, bias was −1874 and 2004, both with wide LoA of (13,869 to 10,121) and (−10,932 to 14,940) steps, respectively. The Apple Watch noted the smallest MAD of 7.7% with the Actigraph, whereas the Yamax Digiwalker noted the highest MAD (20.3%). After leave-one-out cross validation, accuracy estimates of MAD of the iHealth Edge and Misfit Shine were within acceptable limits with 10.7% and 11.3%, respectively. Conclusions: Overall, the Apple Watch and iHealth Edge were positively evaluated after wearing. Validity varied widely between devices, with the Apple Watch being the most accurate and Yamax Digiwalker the least accurate for step count in free-living conditions. The iHealth Edge underestimates number of steps but can be considered reliable for activity monitoring after correction for bias. Misfit Shine overestimated number of steps and cannot be considered suitable for step count because of the low agreement. Future studies should focus on the added value of remotely monitoring activity patterns over time in chronic patients. %M 31017587 %R 10.2196/11489 %U http://formative.jmir.org/2019/2/e11489/ %U https://doi.org/10.2196/11489 %U http://www.ncbi.nlm.nih.gov/pubmed/31017587 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e11989 %T Wearable-Based Mobile Health App in Gastric Cancer Patients for Postoperative Physical Activity Monitoring: Focus Group Study %A Wu,Jin-Ming %A Ho,Te-Wei %A Chang,Yao-Ting %A Hsu,ChungChieh %A Tsai,Chia Jui %A Lai,Feipei %A Lin,Ming-Tsan %+ Department of Surgery, National Taiwan University Hospital, National Taiwan University, No.7 Chung Shan South Road, Taipei,, Taiwan, 886 223123456, linmt@ntu.edu.tw %K telemedicine %K exercise %K perioperative care %K gastrectomy %K stomach neoplasms %D 2019 %7 23.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Surgical cancer patients often have deteriorated physical activity (PA), which in turn, contributes to poor outcomes and early recurrence of cancer. Mobile health (mHealth) platforms are progressively used for monitoring clinical conditions in medical subjects. Despite prevalent enthusiasm for the use of mHealth, limited studies have applied these platforms to surgical patients who are in much need of care because of acutely significant loss of physical function during the postoperative period. Objective: The aim of our study was to determine the feasibility and clinical value of using 1 wearable device connected with the mHealth platform to record PA among patients with gastric cancer (GC) who had undergone gastrectomy. Methods: We enrolled surgical GC patients during their inpatient stay and trained them to use the app and wearable device, enabling them to automatically monitor their walking steps. The patients continued to transmit data until postoperative day 28. The primary aim of this study was to validate the feasibility of this system, which was defined as the proportion of participants using each element of the system (wearing the device and uploading step counts) for at least 70% of the 28-day study. “Definitely feasible,” “possibly feasible,” and “not feasible” were defined as ≥70%, 50%-69%, and <50% of participants meeting the criteria, respectively. Moreover, the secondary aim was to evaluate the clinical value of measuring walking steps by examining whether they were associated with early discharge (length of hospital stay <9 days). Results: We enrolled 43 GC inpatients for the analysis. The weekly submission rate at the first, second, third, and fourth week was 100%, 93%, 91%, and 86%, respectively. The overall daily submission rate was 95.5% (1150 days, with 43 subjects submitting data for 28 days). These data showed that this system met the definition of “definitely feasible.” Of the 54 missed transmission days, 6 occurred in week 2, 12 occurred in week 3, and 36 occurred in week 4. The primary reason for not sending data was that patients or caregivers forgot to charge the wearable devices (>90%). Furthermore, we used a multivariable-adjusted model to predict early discharge, which demonstrated that every 1000-step increment of walking on postoperative day 5 was associated with early discharge (odds ratio 2.72, 95% CI 1.17-6.32; P=.02). Conclusions: Incorporating the use of mobile phone apps with wearable devices to record PA in patients of postoperative GC was feasible in patients undergoing gastrectomy in this study. With the support of the mHealth platform, this app offers seamless tracing of patients’ recovery with a little extra burden and turns subjective PA into an objective, measurable parameter. %M 31012858 %R 10.2196/11989 %U http://mhealth.jmir.org/2019/4/e11989/ %U https://doi.org/10.2196/11989 %U http://www.ncbi.nlm.nih.gov/pubmed/31012858 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e11819 %T Consumer-Based Wearable Activity Trackers Increase Physical Activity Participation: Systematic Review and Meta-Analysis %A Brickwood,Katie-Jane %A Watson,Greig %A O'Brien,Jane %A Williams,Andrew D %+ School of Health Science, College of Health and Medicine, University of Tasmania, Newnham Drive, Newnham, 7250, Australia, 61 0363245487, katiejane.brickwood@utas.edu.au %K exercise %K fitness trackers %K telemedicine %K meta-analysis %D 2019 %7 12.04.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The range of benefits associated with regular physical activity participation is irrefutable. Despite the well-known benefits, physical inactivity remains one of the major contributing factors to ill-health throughout industrialized countries. Traditional lifestyle interventions such as group education or telephone counseling are effective at increasing physical activity participation; however, physical activity levels tend to decline over time. Consumer-based wearable activity trackers that allow users to objectively monitor activity levels are now widely available and may offer an alternative method for assisting individuals to remain physically active. Objective: This review aimed to determine the effects of interventions utilizing consumer-based wearable activity trackers on physical activity participation and sedentary behavior when compared with interventions that do not utilize activity tracker feedback. Methods: A systematic review was performed searching the following databases for studies that included the use of a consumer-based wearable activity tracker to improve physical activity participation: Cochrane Controlled Register of Trials, MEDLINE, PubMed, Scopus, Web of Science, Cumulative Index of Nursing and Allied Health Literature, SPORTDiscus, and Health Technology Assessments. Controlled trials of adults comparing the use of a consumer-based wearable activity tracker with other nonactivity tracker–based interventions were included. The main outcome measures were physical activity participation and sedentary behavior. All studies were assessed for risk of bias, and the Grades of Recommendation, Assessment, Development, and Evaluation system was used to rank the quality of evidence. The guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement were followed. A random-effects meta-analysis was completed on the included outcome measures to estimate the treatment effect of interventions that included an activity tracker compared with a control group. Results: There was a significant increase in daily step count (standardized mean difference [SMD] 0.24; 95% CI 0.16 to 0.33; P<.001), moderate and vigorous physical activity (SMD 0.27; 95% CI 0.15 to 0.39; P<.001), and energy expenditure (SMD 0.28; 95% CI 0.03 to 0.54; P=.03) and a nonsignificant decrease in sedentary behavior (SMD −0.20; 95% CI −0.43 to 0.03; P=.08) following the intervention versus control comparator across all studies in the meta-analyses. In general, included studies were at low risk of bias, except for performance bias. Heterogeneity varied across the included meta-analyses ranging from low (I2=3%) for daily step count through to high (I2=67%) for sedentary behavior. Conclusions: Utilizing a consumer-based wearable activity tracker as either the primary component of an intervention or as part of a broader physical activity intervention has the potential to increase physical activity participation. As the effects of physical activity interventions are often short term, the inclusion of a consumer-based wearable activity tracker may provide an effective tool to assist health professionals to provide ongoing monitoring and support. %M 30977740 %R 10.2196/11819 %U https://mhealth.jmir.org/2019/4/e11819/ %U https://doi.org/10.2196/11819 %U http://www.ncbi.nlm.nih.gov/pubmed/30977740 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e9832 %T The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change %A Kononova,Anastasia %A Li,Lin %A Kamp,Kendra %A Bowen,Marie %A Rikard,RV %A Cotten,Shelia %A Peng,Wei %+ Department of Advertising and Public Relations, Michigan State University, Room 319, 404 Wilson Road, East Lansing, MI, 48824, United States, 1 5174325129, kononova@msu.edu %K aging %K wearable electronic devices %K biobehavioral sciences %K transtheoretical model of behavior change %K exercise %K physical activity %D 2019 %7 05.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers offer the opportunity to increase physical activity through continuous monitoring. Viewing tracker use as a beneficial health behavior, we explored the factors that facilitate and hinder long-term activity tracker use, applying the transtheoretical model of behavior change with the focus on the maintenance stage and relapse. Objective: The aim of this study was to investigate older adults’ perceptions and uses of activity trackers at different points of use: from nonuse and short-term use to long-term use and abandoned use to determine the factors to maintain tracker use and prevent users from discontinuing tracker usage. Methods: Data for the research come from 10 focus groups. Of them, 4 focus groups included participants who had never used activity trackers (n=17). These focus groups included an activity tracker trial. The other 6 focus groups (without the activity tracker trial) were conducted with short-term (n=9), long-term (n=11), and former tracker users (n=11; 2 focus groups per user type). Results: The results revealed that older adults in different tracker use stages liked and wished for different tracker features, with long-term users (users in the maintenance stage) being the most diverse and sophisticated users of the technology. Long-term users had developed a habit of tracker use whereas other participants made an effort to employ various encouragement strategies to ensure behavior maintenance. Social support through collaboration was the primary motivator for long-term users to maintain activity tracker use. Short-term and former users focused on competition, and nonusers engaged in vicarious tracker use experiences. Former users, or those who relapsed by abandoning their trackers, indicated that activity tracker use was fueled by curiosity in quantifying daily physical activity rather than the desire to increase physical activity. Long-term users saw a greater range of pros in activity tracker use whereas others focused on the cons of this behavior. Conclusions: The results suggest that activity trackers may be an effective technology to encourage physical activity among older adults, especially those who have never tried it. However, initial positive response to tracker use does not guarantee tracker use maintenance. Maintenance depends on recognizing the long-term benefits of tracker use, social support, and internal motivation. Nonadoption and relapse may occur because of technology’s limitations and gaining awareness of one’s physical activity without changing the physical activity level itself. %M 30950807 %R 10.2196/mhealth.9832 %U https://mhealth.jmir.org/2019/4/e9832/ %U https://doi.org/10.2196/mhealth.9832 %U http://www.ncbi.nlm.nih.gov/pubmed/30950807 %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 %@ 1438-8871 %I JMIR Publications %V 21 %N 3 %P e12374 %T Acceptability and Feasibility of Implementing Accelorometry-Based Activity Monitors and a Linked Web Portal in an Exercise Referral Scheme: Feasibility Randomized Controlled Trial %A Hawkins,Jemma %A Charles,Joanna M %A Edwards,Michelle %A Hallingberg,Britt %A McConnon,Linda %A Edwards,Rhiannon Tudor %A Jago,Russell %A Kelson,Mark %A Morgan,Kelly %A Murphy,Simon %A Oliver,Emily J %A Simpson,Sharon A %A Moore,Graham %+ Centre for the Development and Evaluation of Complex Interventions for Public Health Improvement, School of Social Sciences, Cardiff University, 1-3 Museum Place, Cardiff, CF10 3BD, United Kingdom, 44 02920875184, hawkinsj10@cardiff.ac.uk %K exercise referral %K physical activity %K feasibility studies %K wearable technologies %K costs %K economic evaluation %K fitness trackers %K activity trackers %K exercise %K physical activity %K accelerometry %D 2019 %7 29.03.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Exercise referral schemes (ERSs) are recommended for patients with health conditions or risk factors. Evidence points to the initial effectiveness and cost-effectiveness of such schemes for increasing physical activity, but effects often diminish over time. Techniques such as goal setting, self-monitoring, and personalized feedback may support motivation for physical activity and maintenance of effects. Wearable technologies could provide an opportunity to integrate motivational techniques into exercise schemes. However, little is known about acceptability to exercise referral populations or implementation feasibility within exercise referral services. Objective: To determine the feasibility and acceptability of implementing an activity-monitoring device within the Welsh National ERS to inform a decision on whether and how to proceed to an effectiveness trial. Methods: We conducted a feasability randomized controlled trial with embedded mixed-methods process evaluation and an exploratory economic analysis. Adults (N=156) were randomized to intervention (plus usual practice; n=88) or usual practice only (n=68). Usual practice was a 16-week structured exercise program. The intervention group additionally received an accelerometry-based activity monitor (MyWellnessKey) and associated Web platform (MyWellnessCloud). The primary outcomes were predefined progression criteria assessing acceptability and feasibility of the intervention and proposed evaluation. Postal questionnaires were completed at baseline (time 0:T0), 16 weeks (T1), and 12 months after T0 (T2). Routine data were accessed at the same time-points. A subsample of intervention participants and scheme staff were interviewed following the initiation of intervention delivery and at T2. Results: Participants were on average aged 56.6 (SD 16.3) years and mostly female (101/156, 64.7%) and white (150/156, 96.2%). Only 2 of 5 progression criteria were met; recruitment and randomization methods were acceptable to participants, and contamination was low. However, recruitment and retention rates (11.3% and 67.3%, respectively) fell substantially short of target criteria (20% and 80%, respectively), and disproportionally recruited from the least deprived quintile. Only 57.4% of intervention participants reported receipt of the intervention (below the 80% progression threshold). Less than half reported the intervention to be acceptable at T2. Participant and staff interviews revealed barriers to intervention delivery and engagement related to the device design as well as context-specific technological challenges, all of which made it difficult to integrate the technology into the service. Routinely collected health economic measures had substantial missing data, suggesting that other methods for collecting these should be used in future. Conclusions: To our knowledge, this is the first study to evaluate short- and long-term feasibility and acceptability of integrating wearable technologies into community-based ERSs. The findings highlight device- and context-specific barriers to doing this in routine practice, with typical exercise referral populations. Key criteria for progression to a full-scale evaluation were not met. Trial Registration: ISRCTN Registry ISRCTN85785652; http://www.isrctn.com/ISRCTN85785652 %M 30924791 %R 10.2196/12374 %U http://www.jmir.org/2019/3/e12374/ %U https://doi.org/10.2196/12374 %U http://www.ncbi.nlm.nih.gov/pubmed/30924791 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e12181 %T Efficacy of a Mobile Social Networking Intervention in Promoting Physical Activity: Quasi-Experimental Study %A Tong,Huong Ly %A Coiera,Enrico %A Tong,William %A Wang,Ying %A Quiroz,Juan C %A Martin,Paige %A Laranjo,Liliana %+ Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, 2109, Australia, 61 29850 ext 2475, huong-ly.tong@students.mq.edu.au %K mobile apps %K fitness trackers %K exercise %K social networking %D 2019 %7 28.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Technological interventions such as mobile apps, Web-based social networks, and wearable trackers have the potential to influence physical activity; yet, only a few studies have examined the efficacy of an intervention bundle combining these different technologies. Objective: This study aimed to pilot test an intervention composed of a social networking mobile app, connected with a wearable tracker, and investigate its efficacy in improving physical activity, as well as explore participant engagement and the usability of the app. Methods: This was a pre-post quasi-experimental study with 1 arm, where participants were subjected to the intervention for a 6-month period. The primary outcome measure was the difference in daily step count between baseline and 6 months. Secondary outcome measures included engagement with the intervention and system usability. Descriptive and inferential statistical tests were conducted; posthoc subgroup analyses were carried out for participants with different levels of steps at baseline, app usage, and social features usage. Results: A total of 55 participants were enrolled in the study; the mean age was 23.6 years and 28 (51%) were female. There was a nonstatistically significant increase in the average daily step count between baseline and 6 months (mean change=14.5 steps/day, P=.98, 95% CI –1136.5 to 1107.5). Subgroup analysis comparing the higher and lower physical activity groups at baseline showed that the latter had a statistically significantly higher increase in their daily step count (group difference in mean change from baseline to 6 months=3025 steps per day, P=.008, 95% CI 837.9-5211.8). At 6 months, the retention rate was 82% (45/55); app usage decreased over time. The mean system usability score was 60.1 (SD 19.2). Conclusions: This study showed the preliminary efficacy of a mobile social networking intervention, integrated with a wearable tracker to promote physical activity, particularly for less physically active subgroups of the population. Future research should explore how to address challenges faced by physically inactive people to provide tailored advices. In addition, users’ perspectives should be explored to shed light on factors that might influence their engagement with the intervention. %M 30920379 %R 10.2196/12181 %U http://mhealth.jmir.org/2019/3/e12181/ %U https://doi.org/10.2196/12181 %U http://www.ncbi.nlm.nih.gov/pubmed/30920379 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 3 %P e12808 %T Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study %A Jauhiainen,Milla %A Puustinen,Juha %A Mehrang,Saeed %A Ruokolainen,Jari %A Holm,Anu %A Vehkaoja,Antti %A Nieminen,Hannu %+ Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 1, Tampere, 33720, Finland, 358 504478380, milla.jauhiainen@tuni.fi %K Parkinson disease %K movement analysis %K gait %K wearable sensors %K smartphone %K home monitoring %K mobile phone %D 2019 %7 27.03.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders. Trial Registration: ClinicalTrials.gov NCT03366558; https://clinicaltrials.gov/ct2/show/NCT03366558  International Registered Report Identifier (IRRID): DERR1-10.2196/12808 %M 30916665 %R 10.2196/12808 %U http://www.researchprotocols.org/2019/3/e12808/ %U https://doi.org/10.2196/12808 %U http://www.ncbi.nlm.nih.gov/pubmed/30916665 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 3 %P e11486 %T The Association Between Medication Adherence for Chronic Conditions and Digital Health Activity Tracking: Retrospective Analysis %A Quisel,Tom %A Foschini,Luca %A Zbikowski,Susan M %A Juusola,Jessie L %+ Evidation Health, 167 2nd Avenue, San Mateo, CA,, United States, 1 650 279 8855, jjuusola@evidation.com %K activity tracker %K health behavior %K eHealth %K mHealth %K medication adherence %D 2019 %7 20.03.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Chronic diseases have a widespread impact on health outcomes and costs in the United States. Heart disease and diabetes are among the biggest cost burdens on the health care system. Adherence to medication is associated with better health outcomes and lower total health care costs for individuals with these conditions, but the relationship between medication adherence and health activity behavior has not been explored extensively. Objective: The aim of this study was to examine the relationship between medication adherence and health behaviors among a large population of insured individuals with hypertension, diabetes, and dyslipidemia. Methods: We conducted a retrospective analysis of health status, behaviors, and medication adherence from medical and pharmacy claims and health behavior data. Adherence was measured in terms of proportion of days covered (PDC), calculated from pharmacy claims using both a fixed and variable denominator methodology. Individuals were considered adherent if their PDC was at least 0.80. We used step counts, sleep, weight, and food log data that were transmitted through devices that individuals linked. We computed metrics on the frequency of tracking and the extent to which individuals engaged in each tracking activity. Finally, we used logistic regression to model the relationship between adherent status and the activity-tracking metrics, including age and sex as fixed effects. Results: We identified 117,765 cases with diabetes, 317,340 with dyslipidemia, and 673,428 with hypertension between January 1, 2015 and June 1, 2016 in available data sources. Average fixed and variable PDC for all individuals ranged from 0.673 to 0.917 for diabetes, 0.756 to 0.921 for dyslipidemia, and 0.756 to 0.929 for hypertension. A subgroup of 8553 cases also had health behavior data (eg, activity-tracker data). On the basis of these data, individuals who tracked steps, sleep, weight, or diet were significantly more likely to be adherent to medication than those who did not track any activities in both the fixed methodology (odds ratio, OR 1.33, 95% CI 1.29-1.36) and variable methodology (OR 1.37, 95% CI 1.32-1.43), with age and sex as fixed effects. Furthermore, there was a positive association between frequency of activity tracking and medication adherence. In the logistic regression model, increasing the adjusted tracking ratio by 0.5 increased the fixed adherent status OR by a factor of 1.11 (95% CI 1.06-1.16). Finally, we found a positive association between number of steps and adherent status when controlling for age and sex. Conclusions: Adopters of digital health activity trackers tend to be more adherent to hypertension, diabetes, and dyslipidemia medications, and adherence increases with tracking frequency. This suggests that there may be value in examining new ways to further promote medication adherence through programs that incentivize health tracking and leveraging insights derived from connected devices to improve health outcomes. %M 30892271 %R 10.2196/11486 %U http://www.jmir.org/2019/3/e11486/ %U https://doi.org/10.2196/11486 %U http://www.ncbi.nlm.nih.gov/pubmed/30892271 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 3 %P e12053 %T Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis %A Romeo,Amelia %A Edney,Sarah %A Plotnikoff,Ronald %A Curtis,Rachel %A Ryan,Jillian %A Sanders,Ilea %A Crozier,Alyson %A Maher,Carol %+ Alliance for Research in Exercise, Nutrition and Activity, School of Health Sciences, University of South Australia, GPO Box 2471, Adelaide, 5001, Australia, 61 8 830 22315, carol.maher@unisa.edu.au %K physical activity %K smartphone %K mobile phone %K app %K mobile apps %K program %K health behavior %K systematic review %K meta-analysis %D 2019 %7 19.03.2019 %9 Review %J J Med Internet Res %G English %X Background: Smartphone apps are a promising tool for delivering accessible and appealing physical activity interventions. Given the large growth of research in this field, there are now enough studies using the “gold standard” of experimental design—the randomized controlled trial design—and employing objective measurements of physical activity, to support a meta-analysis of these scientifically rigorous studies. Objective: This systematic review and meta-analysis aimed to determine the effectiveness of smartphone apps for increasing objectively measured physical activity in adults. Methods: A total of 7 electronic databases (EMBASE, EmCare, MEDLINE, Scopus, Sport Discus, The Cochrane Library, and Web of Science) were searched from 2007 to January 2018. Following the Population, Intervention, Comparator, Outcome and Study Design format, studies were eligible if they were randomized controlled trials involving adults, used a smartphone app as the primary or sole component of the physical activity intervention, used a no- or minimal-intervention control condition, and measured objective physical activity either in the form of moderate-to-vigorous physical activity minutes or steps. Study quality was assessed using a 25-item tool based on the Consolidated Standards of Reporting Trials checklist. A meta-analysis of study effects was conducted using a random effects model approach. Sensitivity analyses were conducted to examine whether intervention effectiveness differed on the basis of intervention length, target behavior (physical activity alone vs physical activity in combination with other health behaviors), or target population (general adult population vs specific health populations). Results: Following removal of duplicates, a total of 6170 studies were identified from the original database searches. Of these, 9 studies, involving a total of 1740 participants, met eligibility criteria. Of these, 6 studies could be included in a meta-analysis of the effects of physical activity apps on steps per day. In comparison with the control conditions, smartphone apps produced a nonsignificant (P=.19) increase in participants’ average steps per day, with a mean difference of 476.75 steps per day (95% CI −229.57 to 1183.07) between groups. Sensitivity analyses suggested that physical activity programs with a duration of less than 3 months were more effective than apps evaluated across more than 3 months (P=.01), and that physical activity apps that targeted physical activity in isolation were more effective than apps that targeted physical activity in combination with diet (P=.04). Physical activity app effectiveness did not appear to differ on the basis of target population. Conclusions: This meta-analysis provides modest evidence supporting the effectiveness of smartphone apps to increase physical activity. To date, apps have been most effective in the short term (eg, up to 3 months). Future research is needed to understand the time course of intervention effects and to investigate strategies to sustain intervention effects over time. %M 30888321 %R 10.2196/12053 %U http://www.jmir.org/2019/3/e12053/ %U https://doi.org/10.2196/12053 %U http://www.ncbi.nlm.nih.gov/pubmed/30888321 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11075 %T Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data %A Faust,Louis %A Wang,Cheng %A Hachen,David %A Lizardo,Omar %A Chawla,Nitesh V %+ Department of Computer Science and Engineering, University of Notre Dame, 257 Fitzpatrick Hall, Notre Dame, IN, 46556, United States, 1 5746317095, lfaust@nd.edu %K mHealth %K fitness trackers %K activity trackers %K exercise %K health behavior %K physical activity %K health %K mental health %K perception %K social network %D 2019 %7 12.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data. Objective: The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies. Methods: Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks’ output in supporting MVPA behavior change studies, we applied it to 2 case studies. Results: Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes. Conclusions: By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA. %M 30860488 %R 10.2196/11075 %U http://mhealth.jmir.org/2019/3/e11075/ %U https://doi.org/10.2196/11075 %U http://www.ncbi.nlm.nih.gov/pubmed/30860488 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e10828 %T Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study %A Nelson,Benjamin W %A Allen,Nicholas B %+ Department of Psychology, University of Oregon, 1227 University Street, Eugene, OR, 97403, United States, 1 3108014595, bwn@uoregon.edu %K electrocardiography %K Apple Watch 3 %K digital health %K Fitbit Charge 2 %K heart rate %K mobile health %K passive sensing %K photoplethysmography %K wearables %D 2019 %7 11.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wrist-worn smart watches and fitness monitors (ie, wearables) have become widely adopted by consumers and are gaining increased attention from researchers for their potential contribution to naturalistic digital measurement of health in a scalable, mobile, and unobtrusive way. Various studies have examined the accuracy of these devices in controlled laboratory settings (eg, treadmill and stationary bike); however, no studies have investigated the heart rate accuracy of wearables during a continuous and ecologically valid 24-hour period of actual consumer device use conditions. Objective: The aim of this study was to determine the heart rate accuracy of 2 popular wearable devices, the Apple Watch 3 and Fitbit Charge 2, as compared with the gold standard reference method, an ambulatory electrocardiogram (ECG), during consumer device use conditions in an individual. Data were collected across 5 daily conditions, including sitting, walking, running, activities of daily living (ADL; eg, chores, brushing teeth), and sleeping. Methods: One participant, (first author; 29-year-old Caucasian male) completed a 24-hour ecologically valid protocol by wearing 2 popular wrist wearable devices (Apple Watch 3 and Fitbit Charge 2). In addition, an ambulatory ECG (Vrije Universiteit Ambulatory Monitoring System) was used as the gold standard reference method, which resulted in the collection of 102,740 individual heartbeats. A single-subject design was used to keep all variables constant except for wearable devices while providing a rapid response design to provide initial assessment of wearable accuracy for allowing the research cycle to keep pace with technological advancements. Accuracy of these devices compared with the gold standard ECG was assessed using mean error, mean absolute error, and mean absolute percent error. These data were supplemented with Bland-Altman analyses and concordance class correlation to assess agreement between devices. Results: The Apple Watch 3 and Fitbit Charge 2 were generally highly accurate across the 24-hour condition. Specifically, the Apple Watch 3 had a mean difference of −1.80 beats per minute (bpm), a mean absolute error percent of 5.86%, and a mean agreement of 95% when compared with the ECG across 24 hours. The Fitbit Charge 2 had a mean difference of −3.47 bpm, a mean absolute error of 5.96%, and a mean agreement of 91% when compared with the ECG across 24 hours. These findings varied by condition. Conclusions: The Apple Watch 3 and the Fitbit Charge 2 provided acceptable heart rate accuracy (<±10%) across the 24 hour and during each activity, except for the Apple Watch 3 during the daily activities condition. Overall, these findings provide preliminary support that these devices appear to be useful for implementing ambulatory measurement of cardiac activity in research studies, especially those where the specific advantages of these methods (eg, scalability, low participant burden) are particularly suited to the population or research question. %M 30855232 %R 10.2196/10828 %U https://mhealth.jmir.org/2019/3/e10828/ %U https://doi.org/10.2196/10828 %U http://www.ncbi.nlm.nih.gov/pubmed/30855232 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 2 %N 1 %P e12303 %T Relevance of Activity Tracking With Mobile Devices in the Relationship Between Physical Activity Levels and Satisfaction With Physical Fitness in Older Adults: Representative Survey %A Schlomann,Anna %A Seifert,Alexander %A Rietz,Christian %+ Department of Special Education and Rehabilitation Science, University of Cologne, Herbert-Lewin-Straße 2, Cologne, 50931, Germany, 49 221470 ext 3343, anna.schlomann@uni-koeln.de %K physical fitness %K wearable electronic devices %K smartphone %K mobile phone %K aged %K satisfaction %K fitness trackers %D 2019 %7 06.03.2019 %9 Original Paper %J JMIR Aging %G English %X Background: Physical activity has been shown to positively affect many aspects of life, and the positive relationship between physical activity levels and health is well established. Recently, research on the interrelationship between physical activity levels and subjective experiences has gained attention. However, the underlying mechanisms that link physical activity levels with subjective experiences of physical fitness have not been sufficiently explained. Objective: This study aimed to explore the role of physical activity tracking (PAT) in the relationship between physical activity levels and satisfaction with physical fitness in older adults. It is hypothesized that higher levels of physical activity are associated with a higher satisfaction with physical fitness in older adults and that this positive association is stronger for older people who use mobile devices for PAT. Methods: As part of this study, 1013 participants aged 50 years or older and living in Switzerland were interviewed via computer-assisted telephone interviews. Bivariate and multivariate analyses were applied. The interaction effects between physical activity levels and PAT were evaluated using multiple linear regression analysis. Results: Descriptive analyses showed that 719 participants used at least 1 mobile device and that 136 out of 719 mobile device users (18.9%) used mobile devices for PAT. In the multivariate regression analysis, frequent physical activity was found to have a positive effect on satisfaction with physical fitness (beta=.24, P<.001). A significant interaction effect between physical activity levels and PAT (beta=.30, P=.03) provides some first evidence that the positive effects of physical activity on satisfaction with physical fitness can be enhanced by PAT. Conclusions: The results indicate the potential of PAT to enhance the physical fitness of older adults. However, the results also raise new issues in this context. Recommendations for further research and practice include the acquisition of longitudinal data, a more detailed observation of durations of use, and the development of devices for PAT considering health psychology and gerontology theories. %M 31518263 %R 10.2196/12303 %U http://aging.jmir.org/2019/1/e12303/ %U https://doi.org/10.2196/12303 %U http://www.ncbi.nlm.nih.gov/pubmed/31518263 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e10820 %T Breast Cancer Survivors’ Experiences With an Activity Tracker Integrated Into a Supervised Exercise Program: Qualitative Study %A Wu,Hoi San %A Gal,Roxanne %A van Sleeuwen,Niek C %A Brombacher,Aarnout C %A IJsselsteijn,Wijnand A %A May,Anne M %A Monninkhof,Evelyn M %+ Julius Center of Health Sciences and Primary Care, University Medical Center Utrecht, University of Utrecht, STR 6.131, PO Box 85500, Utrecht, 3508 GA, Netherlands, 31 887569624, R.Gal@umcutrecht.nl %K breast cancer %K activity trackers %K physical activity %K sedentary behavior %K qualitative research %D 2019 %7 21.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There is growing evidence that physical activity is related to a better prognosis after a breast cancer diagnosis, whereas sedentary behavior is associated with worse outcomes. It is therefore important to stimulate physical activity and reduce sedentary time among patients with breast cancer. Activity trackers offer a new opportunity for interventions directed at stimulating physical activity behavior change. Objective: This study aimed to explore the experience of patients with breast cancer who used an activity tracker in addition to a supervised exercise intervention in the randomized UMBRELLA Fit trial. Methods: A total of 10 patients with breast cancer who completed cancer treatment participated in semistructured in-depth interviews about their experience with and suggestions for improvements for the Jawbone UP2 activity tracker. Results: The activity tracker motivated women to be physically active and created more awareness of their (sedentary) lifestyles. The women indicated that the automatically generated advice (received via the Jawbone UP app) lacked individualization and was not applicable to their personal situations (ie, having been treated for cancer). Furthermore, women felt that the daily step goal was one-dimensional, and they preferred to incorporate other physical activity goals. The activity tracker’s inability to measure strength exercises was a noted shortcoming. Finally, women valued personal feedback about the activity tracker from the physiotherapist. Conclusions: Wearing an activity tracker raised lifestyle awareness in patients with breast cancer. The women also reported additional needs not addressed by the system. Potential improvements include a more realistic total daily physical activity representation, personalized advice, and personalized goals. %M 30789349 %R 10.2196/10820 %U https://mhealth.jmir.org/2019/2/e10820/ %U https://doi.org/10.2196/10820 %U http://www.ncbi.nlm.nih.gov/pubmed/30789349 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 2 %N 1 %P e10658 %T Use of Physical Activity Monitoring Devices by Families in Rural Communities: Qualitative Approach %A Sharaievska,Iryna %A Battista,Rebecca A %A Zwetsloot,Jennifer %+ Department of Recreation Management and Physical Education, Appalachian State University, 111 Rivers Str, ASU Box 32181, Boone, NC, 28608, United States, 1 8282626327, sharaievskai@appstate.edu %K motion sensors %K physical activity %K family %K rural community %D 2019 %7 20.02.2019 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: Several studies support the impact of information communication technology–based interventions to promote physical activity among youth. However, little is known on how technology can be used by the entire family to encourage healthy behavior. Previous studies showed that children and youth rely and are dependent upon the decisions and values of their caregivers when it comes to having a healthy lifestyle. Thus, the exploration of behavior and attitudes of the entire family is needed. Objective: The study aimed to explore (1) perceptions of how the use of physical activity tracking devices (Fitbit Zip) by families in rural communities influence their patterns of participation in physical activity, (2) how attitudes toward physical activity change as a result of using physical activity tracking devices as a family, and (3) what factors influence participation in physical activity among families in rural communities. Methods: A total of 11 families with 1 to 3 children of different ages (7-13 years) took part in semistructured group interviews following 2 weeks of using physical activity tracking devices (Fitbit Zip) as a family. The participants were asked to discuss their experience using the Fitbit Zip as a family, the motivation to be physically active, the changes in their pattern of participation in those activities, the level of engagement by different family members, and the factors that affected their participation. All interviews were voice-recorded with the participants’ permission and later transcribed verbatim using pseudonyms. To analyze the data, the principal investigator (IS) used open, axial, and selective coding techniques. Results: A total of 3 themes and several subthemes appeared from the data. The families in rural communities reported no or minimal changes in physical activities as a result of using physical activity tracking devices (Fitbit Zip) because of a lack of interest or an already active lifestyle. However, the attitude toward physical activity was altered. The family members reported an increased awareness of their activity level, introduced more conversations about active and healthy lifestyles, and changed their view of physical activity to a more positive one. The participants described the changes they were able to make and the constraining factors that stopped them from making further changes in their lifestyle. Conclusions: Technology might serve as a facilitator to participation in physical activity among families. Technology can motivate the change in attitude toward active recreation. As long-term changes in lifestyle require internal motivation, the change in the attitude might have a more long-lasting impact than the change in the immediate behavior. More longitudinal studies are needed to further explore long-term change in both behavior and attitude toward physical activity. Additional exploration of constraints to participation in physical activity among families is also an important area of exploration. %M 31518327 %R 10.2196/10658 %U http://pediatrics.jmir.org/2019/1/e10658/ %U https://doi.org/10.2196/10658 %U http://www.ncbi.nlm.nih.gov/pubmed/31518327 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e10988 %T Evaluating Motivational Interviewing and Habit Formation to Enhance the Effect of Activity Trackers on Healthy Adults’ Activity Levels: Randomized Intervention %A Ellingson,Laura D %A Lansing,Jeni E %A DeShaw,Kathryn J %A Peyer,Karissa L %A Bai,Yang %A Perez,Maria %A Phillips,L Alison %A Welk,Gregory J %+ Department of Kinesiology, Iowa State University, 534 Wallace Road, Ames, IA, 50011, United States, 1 5154990663, ellingl@iastate.edu %K activity tracker %K habit %K mHealth %K motivational interviewing %K mobile phone %K physical activity %K wearable electronic devices %D 2019 %7 14.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: While widely used and endorsed, there is limited evidence supporting the benefits of activity trackers for increasing physical activity; these devices may be more effective when combined with additional strategies that promote sustained behavior change like motivational interviewing (MI) and habit development. Objective: This study aims to determine the utility of wearable activity trackers alone or in combination with these behavior change strategies for promoting improvements in active and sedentary behaviors. Methods: A sample of 91 adults (48/91 female, 53%) was randomized to receive a Fitbit Charge alone or in combination with MI and habit education for 12 weeks. Active and sedentary behaviors were assessed pre and post using research-grade activity monitors (ActiGraph and activPAL), and the development of habits surrounding the use of the trackers was assessed postintervention with the Self-Reported Habit Index. During the intervention, Fitbit wear time and activity levels were monitored with the activity trackers. Linear regression analyses were used to determine the influence of the trial on outcomes of physical activity and sedentary time. The influence of habits was examined using correlation coefficients relating habits of tracker use (wearing the tracker and checking data on the tracker and associated app) to Fitbit wear time and activity levels during the intervention and at follow-up. Results: Regression analyses revealed no significant differences by group in any of the primary outcomes (all P>.05). However, personal characteristics, including lower baseline activity levels (beta=–.49, P=.01) and lack of previous experience with pedometers (beta=–.23, P=.03) were predictive of greater improvements in moderate and vigorous physical activity. Furthermore, for individuals with higher activity levels at the baseline, MI and habit education were more effective for maintaining these activity levels when compared with receiving a Fitbit alone (eg, small increase of ~48 steps/day, d=0.01, vs large decrease of ~1830 steps/day, d=0.95). Finally, habit development was significantly related to steps/day during (r=.30, P=.004) and following the intervention (r=.27, P=.03). Conclusions: This study suggests that activity trackers may have beneficial effects on physical activity in healthy adults, but benefits vary based on individual factors. Furthermore, this study highlights the importance of habit development surrounding the wear and use of activity trackers and the associated software to promote increases in physical activity. Trial Registration: ClinicalTrials.gov NCT03837366; https://clinicaltrials.gov/ct2/show/NCT03837366 %M 30762582 %R 10.2196/10988 %U https://mhealth.jmir.org/2019/2/e10988/ %U https://doi.org/10.2196/10988 %U http://www.ncbi.nlm.nih.gov/pubmed/30762582 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e11201 %T Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data %A Li,Kenan %A Habre,Rima %A Deng,Huiyu %A Urman,Robert %A Morrison,John %A Gilliland,Frank D %A Ambite,José Luis %A Stripelis,Dimitris %A Chiang,Yao-Yi %A Lin,Yijun %A Bui,Alex AT %A King,Christine %A Hosseini,Anahita %A Vliet,Eleanne Van %A Sarrafzadeh,Majid %A Eckel,Sandrah P %+ Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Soto Building Room 202-09, 2001 North Soto Street, Los Angeles, CA, 90089, United States, 1 2256102559, kenanl@usc.edu %K machine learning %K physical activity %K smartphone %K statistical data analysis wearable devices %D 2019 %7 07.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. Objective: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. Methods: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. Results: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. Conclusions: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data. %M 30730297 %R 10.2196/11201 %U http://mhealth.jmir.org/2019/2/e11201/ %U https://doi.org/10.2196/11201 %U http://www.ncbi.nlm.nih.gov/pubmed/30730297 %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 %@ 2291-5222 %I JMIR Publications %V 7 %N 2 %P e11190 %T Middle-Aged Men With HIV Have Diminished Accelerometry-Based Activity Profiles Despite Similar Lab-Measured Gait Speed: Pilot Study %A Hale,Timothy M %A Guardigni,Viola %A Roitmann,Eva %A Vegreville,Matthieu %A Brawley,Brooke %A Woodbury,Erin %A Storer,Thomas W %A Sax,Paul E %A Montano,Monty %+ Brigham & Women's Hospital, 221 Longwood Avenue, Boston, MA, 02115, United States, 1 6175259046, mmontano@bwh.harvard.edu %K aging %K digital biomarker %K gait speed %K HIV %D 2019 %7 01.02.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: People aging with HIV are living with increased risk for functional decline compared with uninfected adults of the same age. Early preclinical changes in biomarkers in middle-aged individuals at risk for mobility and functional decline are needed. Objective: This pilot study aims to compare measures of free-living activity with lab-based measures. In addition, we aim to examine differences in the activity level and patterns by HIV status. Methods: Forty-six men (23 HIV+, 23 HIV−) currently in the MATCH (Muscle and Aging Treated Chronic HIV) cohort study wore a consumer-grade wristband accelerometer continuously for 3 weeks. We used free-living activity to calculate the gait speed and time spent at different activity intensities. Accelerometer data were compared with lab-based gait speed using the 6-minute walk test (6-MWT). Plasma biomarkers were measured and biobehavioral questionnaires were administered. Results: HIV+ men more often lived alone (P=.02), reported more pain (P=.02), and fatigue (P=.048). In addition, HIV+ men had lower blood CD4/CD8 ratios (P<.001) and higher Veterans Aging Cohort Study Index scores (P=.04) and T-cell activation (P<.001) but did not differ in levels of inflammation (P=.30) or testosterone (P=.83). For all participants, accelerometer-based gait speed was significantly lower than the lab-based 6-MWT gait speed (P<.001). Moreover, accelerometer-based gait speed was significantly lower in HIV+ participants (P=.04) despite the absence of differences in the lab-based 6-MWT (P=.39). HIV+ participants spent more time in the lowest quartile of activity compared with uninfected (P=.01), who spent more time in the middle quartiles of activity (P=.02). Conclusions: Accelerometer-based assessment of gait speed and activity patterns are lower for asymptomatic men living with HIV compared with uninfected controls and may be useful as preclinical digital biomarkers that precede differences captured in lab-based measures. %M 30707104 %R 10.2196/11190 %U http://mhealth.jmir.org/2019/2/e11190/ %U https://doi.org/10.2196/11190 %U http://www.ncbi.nlm.nih.gov/pubmed/30707104 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e9865 %T Assessment of Physical Activity by Wearable Technology During Rehabilitation After Cardiac Surgery: Explorative Prospective Monocentric Observational Cohort Study %A Thijs,Isabeau %A Fresiello,Libera %A Oosterlinck,Wouter %A Sinnaeve,Peter %A Rega,Filip %+ Research Unit of Cardiac Surgery, Department of Cardiovascular Sciences, University Hospitals Leuven, Herestraat 49, Bus 7003, Leuven, 3000, Belgium, 32 +3216344219, isabeau.thijs@outlook.com %K fitness trackers %K coronary artery bypass %K cardiac surgery %K cardiac rehabilitation %K postoperative care %K wearable %K physical activity %K exercise %D 2019 %7 31.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable technology is finding its way into clinical practice. Physical activity describes patients’ functional status after cardiac surgery and can be monitored remotely by using dedicated trackers. Objective: The aim of this study was to compare the progress of physical activity in cardiac rehabilitation by using wearable fitness trackers in patients undergoing coronary artery bypass surgery by either the conventional off-pump coronary artery bypass (OPCAB) or the robotically assisted minimally invasive coronary artery bypass (RA-MIDCAB). We hypothesized faster recovery of physical activity after RA-MIDCAB in the first weeks after discharge as compared to OPCAB. Methods: Patients undergoing RA-MIDCAB or OPCAB were included in the study. Each patient received a Fitbit Charge HR (Fitbit Inc, San Francisco, CA) physical activity tracker following discharge. Rehabilitation progress was assessed by measuring the number of steps and physical activity level daily. The physical activity level was calculated as energy expenditure divided by the basic metabolic rate. Results: A total of 10 RA-MIDCAB patients with a median age of 68 (min, 55; max, 83) years and 12 OPCAB patients with a median age of 69 (min, 50; max, 82) years were included. Baseline characteristics were comparable except for body mass index (RA-MIDCAB: 26 kg/m²; min, 22; max, 28 versus OPCAB: 29 kg/m²; min, 27; max, 33; P<.001). Intubation time (P<.05) was significantly lower in the RA-MIDCAB group. A clear trend, although not statistically significant, was observed towards a higher number of steps in RA-MIDCAB patients in the first week following discharge. Conclusions: RA-MIDCAB patients have an advantage in recovery in the first weeks of revalidation, which is reflected by the number of steps and physical activity level measured by the Fitbit Charge HR, as compared to OPCAB patients. However, unsupervised assessment of daily physical activity varied widely and could have consequences with regard to the use of these trackers as research tools. %M 30702433 %R 10.2196/mhealth.9865 %U http://mhealth.jmir.org/2019/1/e9865/ %U https://doi.org/10.2196/mhealth.9865 %U http://www.ncbi.nlm.nih.gov/pubmed/30702433 %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 %@ 1929-0748 %I JMIR Publications %V 8 %N 1 %P e12526 %T Understanding the Effect of Adding Automated and Human Coaching to a Mobile Health Physical Activity App for Afghanistan and Iraq Veterans: Protocol for a Randomized Controlled Trial of the Stay Strong Intervention %A Buis,Lorraine R %A McCant,Felicia A %A Gierisch,Jennifer M %A Bastian,Lori A %A Oddone,Eugene Z %A Richardson,Caroline R %A Kim,Hyungjin Myra %A Evans,Richard %A Hooks,Gwendolyn %A Kadri,Reema %A White-Clark,Courtney %A Damschroder,Laura J %+ VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, PO Box 130170, Ann Arbor, MI, 48113, United States, 1 734 845 3603, laura.damschroder@va.gov %K exercise %K veterans %K cell phones %K mobile phone %K telemedicine %D 2019 %7 29.01.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Although maintaining a healthy weight and physical conditioning are requirements of active military duty, many US veterans rapidly gain weight and lose conditioning when they separate from active-duty service. Mobile health (mHealth) interventions that incorporate wearables for activity monitoring have become common, but it is unclear how to optimize engagement over time. Personalized health coaching, either through tailored automated messaging or by individual health coaches, has the potential to increase the efficacy of mHealth programs. In an attempt to preserve conditioning and ward off weight gain, we developed Stay Strong, a mobile app that is tailored to veterans of recent conflicts and tracks physical activity monitored by Fitbit Charge 2 devices and weight measured on a Bluetooth-enabled scale. Objective: The goal of this study is to determine the effect of activity monitoring plus health coaching compared with activity monitoring alone. Methods: In this randomized controlled trial, with Stay Strong, a mobile app designed specifically for veterans, we plan to enroll 350 veterans to engage in an mHealth lifestyle intervention that combines the use of a wearable physical activity tracker and a Bluetooth-enabled weight scale. The Stay Strong app displays physical activity and weight data trends over time. Enrolled participants are randomized to receive the Stay Strong app (active comparator arm) or Stay Strong + Coaching, an enhanced version of the program that adds coaching features (automated tailored messaging with weekly physical activity goals and up to 3 telephone calls with a health coach—intervention arm) for 1 year. Our primary outcome is change in physical activity at 12 months, with weight, pain, patient activation, and depression serving as secondary outcome measures. All processes related to recruitment, eligibility screening, informed consent, Health Insurance Portability and Accountability Act authorization, baseline assessment, randomization, the bulk of intervention delivery, and outcome assessment will be accomplished via the internet or smartphone app. Results: The study recruitment began in September 2017, and data collection is expected to conclude in 2019. A total of 465 participants consented to participate and 357 (357/465, 77%) provided baseline levels of physical activity and were randomized to 1 of the 2 interventions. Conclusions: This novel randomized controlled trial will provide much-needed findings about whether the addition of telephone-based human coaching and other automated supportive-coaching features will improve physical activity compared with using a smartphone app linked to a wearable device alone. Trial Registration: ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293 (Archived by WebCite at http://www.webcitation.org/75KQeIFwh) International Registered Report Identifier (IRRID): DERR1-10.2196/12526 %M 30694208 %R 10.2196/12526 %U http://www.researchprotocols.org/2019/1/e12526/ %U https://doi.org/10.2196/12526 %U http://www.ncbi.nlm.nih.gov/pubmed/30694208 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e10978 %T Design, Development, and Evaluation of an Injury Surveillance App for Cricket: Protocol and Qualitative Study %A Soomro,Najeebullah %A Chhaya,Meraj %A Soomro,Mariam %A Asif,Naukhez %A Saurman,Emily %A Lyle,David %A Sanders,Ross %+ Broken Hill University Department of Rural Health, University of Sydney, Corrindah Court, Broken Hill, 2880, Australia, 61 880801282, naj.soomro@sydney.edu.au %K cricket %K injury surveillance %K mobile app %K mobile phone %K TeamDoc %K mHealth %D 2019 %7 22.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Injury surveillance and workload monitoring are important aspects of professional sports, including cricket. However, at the community level, there is a dearth of accessible and intelligent surveillance tools. Mobile apps are an accessible tool for monitoring cricket-related injuries at all levels. Objective: The objective of this paper is to share the novel methods associated with the development of the free TeamDoc app and provide evidence from an evaluation of the user experience and perception of the app regarding its functionality, utility, and design. Methods: TeamDoc mobile app for Android and Apple smartphones was developed using 3 languages: C++, Qt Modeling Language, and JavaScript. For the server-side connectivity, Hypertext Preprocessor (PHP) was used as it is a commonly used cross-platform language. PHP includes components that interact with popular database management systems, allowing for secure interaction with databases on a server level. The app was evaluated by administrating a modified user version of the Mobile App Rating Scale (uMARS; maximum score: 5). Results: TeamDoc is the first complementary, standalone mobile app that records cricket injuries through a smartphone. It can also record cricketing workloads, which is a known risk factor for injury. The app can be used without the need for supplementary computer devices for synchronization. The uMARS scores showed user satisfaction (overall mean score 3.6 [SD 0.5]), which demonstrates its acceptability by cricketers. Conclusions: Electronic injury surveillance systems have been shown to improve data collection during competitive sports. Therefore, TeamDoc may assist in improving injury reporting and may also act as a monitoring system for coaching staff to adjust individual training workloads. The methods described in this paper provide a template for researchers to develop similar apps for other sports. %M 30668516 %R 10.2196/10978 %U http://mhealth.jmir.org/2019/1/e10978/ %U https://doi.org/10.2196/10978 %U http://www.ncbi.nlm.nih.gov/pubmed/30668516 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e9929 %T Health Care Provider Perceptions of Consumer-Grade Devices and Apps for Tracking Health: A Pilot Study %A Holtz,Bree %A Vasold,Kerri %A Cotten,Shelia %A Mackert,Michael %A Zhang,Mi %+ Department of Advertising and Public Relations, College of Communication Arts & Sciences, Michigan State University, 404 Wilson Road, Room 309, East Lansing, MI, 48824, United States, 1 517 884 4537, bholtz@msu.edu %K physicians %K primary care %K APRN %K nurse practitioners %K technology %D 2019 %7 22.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of Web- or mobile phone–based apps for tracking health indicators has increased greatly. However, provider perceptions of consumer-grade devices have not been widely explored. Objective: The purpose of this study was to determine primary care physicians’ and advanced practice registered nurses’ perceptions of consumer-grade sensor devices and Web- or mobile phone–based apps that allow patients to track physical activity, diet, and sleep. Methods: We conducted a cross-sectional mailed survey with a random sample of 300 primary care physicians and 300 advanced practice registered nurses from Michigan, USA. Providers’ use and recommendation of these types of technologies, and their perceptions of the benefits of and barriers to patients’ use of the technologies for physical activity, diet, and sleep tracking were key outcomes assessed. Results: Most of the respondents (189/562, 33.6% response rate) were advanced practice registered nurses (107/189, 56.6%). Almost half of the sample (93/189, 49.2%) owned or used behavioral tracking technologies. Providers found these technologies to be helpful in clinical encounters, trusted the data, perceived their patients to be interested in them, and did not have concerns over the privacy of the data. However, the providers did perceive patient barriers to using these technologies. Additionally, those who owned or used these technologies were up to 6.5 times more likely to recommend them to their patients. Conclusions: Our study demonstrated that many providers perceived benefits for their patients to use these technologies, including improved communication. Providers’ concerns included their patients’ access and the usability of these technologies. Providers who encountered data from these technologies during patient visits generally perceive this to be helpful. We additionally discuss the barriers perceived by the providers and offer suggestions and future research to realize the potential benefits to using these data in clinical encounters. %M 30668515 %R 10.2196/mhealth.9929 %U https://mhealth.jmir.org/2018/1/e9929/ %U https://doi.org/10.2196/mhealth.9929 %U http://www.ncbi.nlm.nih.gov/pubmed/30668515 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e11098 %T User Models for Personalized Physical Activity Interventions: Scoping Review %A Ghanvatkar,Suparna %A Kankanhalli,Atreyi %A Rajan,Vaibhav %+ Department of Information Systems and Analytics, School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore, 65 65166737, vaibhav.rajan@nus.edu.sg %K review %K exercise %K physical fitness %K automation %K mobile apps %K web browser %K health communication %K health promotion %D 2019 %7 16.01.2019 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior. Objective: This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions. Methods: A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization. Results: The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized. Conclusions: This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously. %M 30664474 %R 10.2196/11098 %U http://mhealth.jmir.org/2019/1/e11098/ %U https://doi.org/10.2196/11098 %U http://www.ncbi.nlm.nih.gov/pubmed/30664474 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e11267 %T A Mobile Phone-Based Program to Promote Healthy Behaviors Among Adults With Prediabetes Who Declined Participation in Free Diabetes Prevention Programs: Mixed-Methods Pilot Randomized Controlled Trial %A Griauzde,Dina %A Kullgren,Jeffrey T %A Liestenfeltz,Brad %A Ansari,Tahoora %A Johnson,Emily H %A Fedewa,Allison %A Saslow,Laura R %A Richardson,Caroline %A Heisler,Michele %+ University of Michigan School of Dentistry, 1011 North University Avenue, Ann Arbor, MI, 48109, United States, 1 734 647 4844, mheisler@umich.edu %K autonomous motivation %K behavioral change %K mHealth %K mobile phone %K prediabetes %K prevention %K type 2 diabetes mellitus %D 2019 %7 09.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Despite evidence that Diabetes Prevention Programs (DPPs) can delay or prevent progression to type 2 diabetes mellitus (T2DM), few individuals with prediabetes enroll in offered programs. This may be in part because many individuals with prediabetes have low levels of autonomous motivation (ie, motivation that arises from internal sources) to prevent T2DM. Objective: This study aims to examine the feasibility and acceptability of a mobile health (mHealth) intervention designed to increase autonomous motivation and healthy behaviors among adults with prediabetes who previously declined participation free DPPs. In addition, the study aims to examine changes in autonomous motivation among adults offered 2 versions of the mHealth program compared with an information-only control group. Methods: In this 12-week, parallel, 3-arm, mixed-methods pilot randomized controlled trial, participants were randomized to (1) a group that received information about prediabetes and strategies to prevent T2DM (control); (2) a group that received a mHealth app that aims to increase autonomous motivation among users (app-only); or (3) a group that received the app plus a physical activity tracker and wireless-enabled digital scale for self-monitoring (app-plus). Primary outcome measures included rates of intervention uptake (number of individuals enrolled/number of individuals assessed for eligibility), retention (number of 12-week survey completers/number of participants), and adherence (number of device-usage days). The secondary outcome measure was change in autonomous motivation (measured using the Treatment Self-Regulation Questionnaire), which was examined using difference-in-difference analysis. Furthermore, we conducted postintervention qualitative interviews with participants. Results: Overall, 28% (69/244) of eligible individuals were randomized; of these, 80% (55/69) completed the 12-week survey. Retention rates were significantly higher among app-plus participants than participants in the other 2 study arms combined (P=.004, χ2). No significant differences were observed in adherence rates between app-only and app-plus participants (43 days vs 37 days; P=.34). Among all participants, mean autonomous motivation measures were relatively high at baseline (6.0 of 7.0 scale), with no statistically significant within- or between-group differences in follow-up scores. In qualitative interviews (n=15), participants identified reasons that they enjoyed using the app (eg, encouraged self-reflection), reasons that they did not enjoy using the app (eg, did not consider personal circumstances), and strategies to improve the intervention (eg, increased interpersonal contact). Conclusions: Among individuals with prediabetes who did not engage in free DPPs, this mHealth intervention was feasible and acceptable. Future work should (1) examine the effectiveness of a refined intervention on clinically relevant outcomes (eg, weight loss) among a larger population of DPP nonenrollees with low baseline autonomous motivation and (2) identify other factors associated with DPP nonenrollment, which may serve as additional potential targets for interventions. Trial Registration: ClinicalTrials.gov NCT03025607; https://clinicaltrials.gov/ct2/show/NCT03025607 (Archived by WebCite at http://www.webcitation.org/73cvaSAie) %M 30626566 %R 10.2196/11267 %U http://mhealth.jmir.org/2019/1/e11267/ %U https://doi.org/10.2196/11267 %U http://www.ncbi.nlm.nih.gov/pubmed/30626566 %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 %@ 2291-5222 %I JMIR Publications %V 7 %N 1 %P e12070 %T Impact of Personal Health Records and Wearables on Health Outcomes and Patient Response: Three-Arm Randomized Controlled Trial %A Kim,Jeong-Whun %A Ryu,Borim %A Cho,Seoyoon %A Heo,Eunyoung %A Kim,Yoojung %A Lee,Joongseek %A Jung,Se Young %A Yoo,Sooyoung %+ Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, 82 173rd Street, Gumi-ro, Bundang-gu, Seongnam, 13620, Republic of Korea, 82 317878980, yoosoo0@snubh.org %K personal health record %K lifestyle %K sleep apnea, obstructive %K delivery of health care %K electronic health record %K mobile health %D 2019 %7 04.01.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Although using the technologies for a variety of chronic health conditions such as personal health record (PHR) is reported to be acceptable and useful, there is a lack of evidence on the associations between the use of the technologies and the change of health outcome and patients’ response to a digital health app. Objective: This study aimed to examine the impact of the use of PHR and wearables on health outcome improvement and sustained use of the health app that can be associated with patient engagement. Methods: We developed an Android-based mobile phone app and used a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across 4 weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to 3 subgroups including 2 intervention groups: (1) mobile app and wearable device users (n=20), (2) mobile app–only users (n=20), and (3) controls (n=20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA–related respiratory parameters such as respiratory disturbance, apnea-hypopnea, and oxygenation desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant. Results: We collected the individual data for each group during the trial, specifically anthropometric measurement and laboratory test results for health outcomes, and the app usage logs for patient response were collected and analyzed. The body weight showed a significant reduction in the 2 intervention groups after intervention, and the mobile app–only group showed more weight loss compared with the controls (P=.01). There were no significant changes in sleep-related health outcomes. From a patient response point of view, the average daily step counts (8165 steps) from the app plus wearable group were significantly higher than those (6034 steps) from the app-only group because they collected step count data from different devices (P=.02). The average rate of data collection was not different in physical activity (P=.99), food intake (P=.98), sleep (P=.95), stress (P=.70), and weight (P=.90) in the app plus wearable and app-only groups, respectively. Conclusions: We tried to integrate PHR data that allow clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results suggest that a PHR–based intervention may be successful in losing body weight and improvement in lifestyle behavior. Trial Registration: ClinicalTrials.gov NCT03200223; https://clinicaltrials.gov/ct2/show/NCT03200223 (Archived by WebCite at http://www.webcitation.org/74baZmnCX). %M 30609978 %R 10.2196/12070 %U https://mhealth.jmir.org/2019/1/e12070/ %U https://doi.org/10.2196/12070 %U http://www.ncbi.nlm.nih.gov/pubmed/30609978 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 4 %N 2 %P e11978 %T An Interactive Web Portal for Tracking Oncology Patient Physical Activity and Symptoms: Prospective Cohort Study %A Marthick,Michael %A Dhillon,Haryana M %A Alison,Jennifer A %A Cheema,Bobby S %A Shaw,Tim %+ Chris O'Brien Lifehouse, 119-143 Missenden Road, Camperdown,, Australia, 61 028 514 0763, michael.marthick@lh.org.au %K physical activity %K fitness trackers %K eHealth %K neoplasms %D 2018 %7 21.12.2018 %9 Original Paper %J JMIR Cancer %G English %X Background: Physical activity levels typically decline during cancer treatment and often do not return to prediagnosis or minimum recommended levels. Interventions to promote physical activity are needed. Support through the use of digital health tools may be helpful in this situation. Objective: The goal of the research was to evaluate the feasibility, usability, and acceptability of an interactive Web portal developed to support patients with cancer to increase daily physical activity levels. Methods: A Web portal for supportive cancer care which was developed to act as a patient-clinician information and coaching tool focused on integrating wearable device data and remote symptom reporting. Patients currently receiving or who had completed intensive anticancer therapy were recruited to 3 cohorts. All cohorts were given access to the Web portal and an activity monitor over a 10-week period. Cohort 2 received additional summative messaging, and cohort 3 received personalized coaching messaging. Qualitative semistructured interviews were completed following the intervention. The primary outcome was feasibility of the use of the portal assessed as both the number of log-ins to the portal to record symptoms and the completion of post-program questionnaires. Results: Of the 49 people were recruited, 40 completed the intervention. Engagement increased with more health professional contact and was highest in cohort 3. The intervention was found to be acceptable by participants. Conclusions: The portal was feasible for use by people with a history of cancer. Further research is needed to determine optimal coaching methods. %M 30578217 %R 10.2196/11978 %U http://cancer.jmir.org/2018/2/e11978/ %U https://doi.org/10.2196/11978 %U http://www.ncbi.nlm.nih.gov/pubmed/30578217 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 12 %P e193 %T An Activity Tracker and Its Accompanying App as a Motivator for Increased Exercise and Better Sleeping Habits for Youths in Need of Social Care: Field Study %A Rönkkö,Kari %+ Department of Design, Faculty of Business, Kristianstad University, Elmetorpsvägen 15, Kristianstad, SE-291 88, Sweden, 46 0442503192, kari.ronkko@hkr.se %K mHealth %K social work %K youths %K activity trackers %K mobile applications %K motivation %K self-care %K sleep hygiene %K goals %D 2018 %7 21.12.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The number of mobile self-tracking devices connected to the Web has exploded in today’s society. With these wearable activity trackers related to Web 2.0 apps and social media have come new ways of monitoring, measuring, representing, and sharing experiences of the human body. New opportunities related to health and new areas of implementation for professionals have appeared, and one identified area that can benefit from mobile health technologies is social work. Objective: There are still only a small number of papers reporting the results from studying wearable activity trackers and accompanying apps in the context of agency-based social work. This study aimed to contribute to the identified shortage by presenting results from a research project framed by the following overarching question: What effects will the studied youths in need of social care experience in relation to exercise and sleep as the result of using a wearable activity tracker and its accompanying app? Methods: A field study framed by action research was performed. The study concerned vulnerable youths living in a Swedish municipality’s care and accommodation home that tried out an activity tracker and its accompanying app. Results: The results from the study confirm previously published research results reporting that instant graphical feedback, sharing information, and being part of a social community can have a positive impact on lifestyle changes. In addition, this study’s main results are that (1) the most important factor for positive health-related lifestyle changes was the establishment of personal long-term goals and (2) professional social workers found the studied technology to function as a valuable counseling tool, opening up avenues for lifestyle talks that otherwise were hard to undertake. Conclusions: This study demonstrates how an activity tracker and its accompanying app can open up a topic for discussion regarding how vulnerable youths can achieve digital support for changing unhealthy lifestyle patterns, and it shows that the technology might be a valuable counseling tool for professionals in social work. %M 30578186 %R 10.2196/mhealth.9286 %U https://mhealth.jmir.org/2018/12/e193/ %U https://doi.org/10.2196/mhealth.9286 %U http://www.ncbi.nlm.nih.gov/pubmed/30578186 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 5 %N 2 %P e11748 %T Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study %A Barbareschi,Giulia %A Holloway,Catherine %A Bianchi-Berthouze,Nadia %A Sonenblum,Sharon %A Sprigle,Stephen %+ University College London Interaction Centre, 2nd Floor, 66-72 Gower Street, London, WC1E 6EA, United Kingdom, 44 20 31087192, giulia.barbareschi.14@ucl.ac.uk %K wheelchair transfers %K movement evaluation %K machine learning %K activity monitoring %K accelerometer %D 2018 %7 20.12.2018 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Transfers are an important skill for many wheelchair users (WU). However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories, and these methods of assessment are poorly suited to evaluation in real and unconstrained world settings where transfers take place. Objective: The objective of this paper is to test the feasibility of a system based on a wearable low-cost sensor to monitor transfer skills in real-world settings. Methods: We collected data from 9 WU wearing triaxial accelerometer on their chest while performing transfers to and from car seats and home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data. Results: Results show a good predictive accuracy of support vector machine classifiers when determining the use of head-hip relationship (75.9%) and smoothness of landing (79.6%) when the starting and ending of the transfer are known. Automatic transfer detection reaches performances that are similar to state of the art in this context (multinomial logistic regression accuracy 87.8%). However, we achieve these results using only a single sensor and collecting data in a more ecological manner. Conclusions: The use of a single chest-placed accelerometer shows good predictive accuracy for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous-technology based personalized skill development interventions for WU. However, monitoring transfers still require the use of external inputs or extra sensors to identify the start and end of the transfer, which is needed to perform an accurate evaluation. %M 30573447 %R 10.2196/11748 %U http://rehab.jmir.org/2018/2/e11748/ %U https://doi.org/10.2196/11748 %U http://www.ncbi.nlm.nih.gov/pubmed/30573447 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 12 %P e11321 %T The Effectiveness of a Web-Based Computer-Tailored Physical Activity Intervention Using Fitbit Activity Trackers: Randomized Trial %A Vandelanotte,Corneel %A Duncan,Mitch J %A Maher,Carol A %A Schoeppe,Stephanie %A Rebar,Amanda L %A Power,Deborah A %A Short,Camille E %A Doran,Christopher M %A Hayman,Melanie J %A Alley,Stephanie J %+ Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Building 7, Bruce Highway, Rockhampton, 4700, Australia, 61 749232183, c.vandelanotte@cqu.edu.au %K online, internet, tracking, health behavior change, advanced activity trackers, wearables %D 2018 %7 18.12.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Web-based interventions that provide personalized physical activity advice have demonstrated good effectiveness but rely on self-reported measures of physical activity, which are prone to overreporting, potentially reducing the accuracy and effectiveness of the advice provided. Objective: This study aimed to examine whether the effectiveness of a Web-based computer-tailored intervention could be improved by integrating Fitbit activity trackers. Methods: Participants received the 3-month TaylorActive intervention, which included 8 modules of theory-based, personally tailored physical activity advice and action planning. Participants were randomized to receive the same intervention either with or without Fitbit tracker integration. All intervention materials were delivered on the Web, and there was no face-to-face contact at any time point. Changes in physical activity (Active Australia Survey), sitting time (Workforce Sitting Questionnaire), and body mass index (BMI) were assessed 1 and 3 months post baseline. Advice acceptability, website usability, and module completion were also assessed. Results: A total of 243 Australian adults participated. Linear mixed model analyses showed a significant increase in total weekly physical activity (adjusted mean increase=163.2; 95% CI 52.0-274.5; P=.004) and moderate-to-vigorous physical activity (adjusted mean increase=78.6; 95% CI 24.4-131.9; P=.004) in the Fitbit group compared with the non-Fitbit group at the 3-month follow-up. The sitting time and BMI decreased more in the Fitbit group, but no significant group × time interaction effects were found. The physical activity advice acceptability and the website usability were consistently rated higher by participants in the Fitbit group. Non-Fitbit group participants completed 2.9 (SD 2.5) modules, and Fitbit group participants completed 4.4 (SD 3.1) modules. Conclusions: Integrating physical activity trackers into a Web-based computer-tailored intervention significantly increased intervention effectiveness. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12616001555448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371793 (Archived by WebCite at http://www.webcitation.org/73ioTxQX2) %M 30563808 %R 10.2196/11321 %U http://www.jmir.org/2018/12/e11321/ %U https://doi.org/10.2196/11321 %U http://www.ncbi.nlm.nih.gov/pubmed/30563808 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 12 %P e11315 %T Inferring Physical Function From Wearable Activity Monitors: Analysis of Free-Living Activity Data From Patients With Knee Osteoarthritis %A Agarwal,Vibhu %A Smuck,Matthew %A Tomkins-Lane,Christy %A Shah,Nigam H %+ Center for Biomedical Informatics Research, Stanford University, Medical School Office Building, X225, 1265 Welch Road, Stanford, CA, 94305, United States, 1 (650) 724 3979, vibhua@stanford.edu %K physical function %K passive monitoring %K physical function profile %K wearable activity data %K statistical learning %D 2018 %7 18.12.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Clinical assessments for physical function do not objectively quantify routine daily activities. Wearable activity monitors (WAMs) enable objective measurement of daily activities, but it remains unclear how these map to clinically measured physical function measures. Objective: This study aims to derive a representation of physical function from daily measurements of free-living activity obtained through a WAM. In addition, we evaluate our derived measure against objectively measured function using an ordinal classification setup. Methods: We defined function profiles representing average time spent in a set of pattern classes over consecutive days. We constructed a function profile using minute-level activity data from a WAM available from the Osteoarthritis Initiative. Using the function profile as input, we trained statistical models that classified subjects into quartiles of objective measurements of physical function as measured through the 400-m walk test, 20-m walk test, and 5 times sit-stand test. Furthermore, we evaluated model performance on held-out data. Results: The function profile derived from minute-level activity data can accurately predict physical performance as measured through clinical assessments. Using held-out data, the Goodman-Kruskal Gamma statistic obtained in classifying performance values in the first quartile, interquartile range, and the fourth quartile was 0.62, 0.53, and 0.51 for the 400-m walk, 20-m walk, and 5 times sit-stand tests, respectively. Conclusions: Function profiles accurately represent physical function, as demonstrated by the relationship between the profiles and clinically measured physical performance. The estimation of physical performance through function profiles derived from free-living activity data may enable remote functional monitoring of patients. %M 30394876 %R 10.2196/11315 %U http://mhealth.jmir.org/2018/12/e11315/ %U https://doi.org/10.2196/11315 %U http://www.ncbi.nlm.nih.gov/pubmed/30394876 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 12 %P e201 %T Technology Adoption, Motivational Aspects, and Privacy Concerns of Wearables in the German Running Community: Field Study %A Wiesner,Martin %A Zowalla,Richard %A Suleder,Julian %A Westers,Maximilian %A Pobiruchin,Monika %+ Department of Medical Informatics, Heilbronn University, Max-Planck-Straße 39, Heilbronn, D-74081, Germany, 49 71315046947, martin.wiesner@hs-heilbronn.de %K athlete %K wearables %K mobile phones %K physical activity %K activity monitoring %D 2018 %7 14.12.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Despite the availability of a great variety of consumer-oriented wearable devices, perceived usefulness, user satisfaction, and privacy concerns have not been fully investigated in the field of wearable applications. It is not clear why healthy, active citizens equip themselves with wearable technology for running activities, and what privacy and data sharing features might influence their individual decisions. Objective: The primary aim of the study was to shed light on motivational and privacy aspects of wearable technology used by healthy, active citizens. A secondary aim was to reevaluate smart technology adoption within the running community in Germany in 2017 and to compare it with the results of other studies and our own study from 2016. Methods: A questionnaire was designed to assess what wearable technology is used by runners of different ages and sex. Data on motivational factors were also collected. The survey was conducted at a regional road race event in May 2017, paperless via a self-implemented app. The demographic parameters of the sample cohort were compared with the event’s official starter list. In addition, the validation included comparison with demographic parameters of the largest German running events in Berlin, Hamburg, and Frankfurt/Main. Binary logistic regression analysis was used to investigate whether age, sex, or course distance were associated with device use. The same method was applied to analyze whether a runner’s age was predictive of privacy concerns, openness to voluntary data sharing, and level of trust in one’s own body for runners not using wearables (ie, technological assistance considered unnecessary in this group). Results: A total of 845 questionnaires were collected. Use of technology for activity monitoring during events or training was prevalent (73.0%, 617/845) in this group. Male long-distance runners and runners in younger age groups (30-39 years: odds ratio [OR] 2.357, 95% CI 1.378-4.115; 40-49 years: OR 1.485, 95% CI 0.920-2.403) were more likely to use tracking devices, with ages 16 to 29 years as the reference group (OR 1). Where wearable technology was used, 42.0% (259/617) stated that they were not concerned if data might be shared by a device vendor without their consent. By contrast, 35.0% (216/617) of the participants would not accept this. In the case of voluntary sharing, runners preferred to exchange tracked data with friends (51.7%, 319/617), family members (43.4%, 268/617), or a physician (32.3%, 199/617). A large proportion (68.0%, 155/228) of runners not using technology stated that they preferred to trust what their own body was telling them rather than trust a device or an app (50-59 years: P<.001; 60-69 years: P=.008). Conclusions: A total of 136 distinct devices by 23 vendors or manufacturers and 17 running apps were identified. Out of 4, 3 runners (76.8%, 474/617) always trusted in the data tracked by their personal device. Data privacy concerns do, however, exist in the German running community, especially for older age groups (30-39 years: OR 1.041, 95% CI 0.371-0.905; 40-49 years: OR 1.421, 95% CI 0.813-2.506; 50-59 years: OR 2.076, 95% CI 1.813-3.686; 60-69 years: OR 2.394, 95% CI 0.957-6.183). %M 30552085 %R 10.2196/mhealth.9623 %U http://mhealth.jmir.org/2018/12/e201/ %U https://doi.org/10.2196/mhealth.9623 %U http://www.ncbi.nlm.nih.gov/pubmed/30552085 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 12 %P e10972 %T The Accuracy of Smart Devices for Measuring Physical Activity in Daily Life: Validation Study %A Degroote,Laurent %A De Bourdeaudhuij,Ilse %A Verloigne,Maïté %A Poppe,Louise %A Crombez,Geert %+ Physical Activity & Health, Department of Movement and Sports Sciences, Ghent University, Watersportlaan 2, Ghent, 9000, Belgium, 32 9 264 62 99, laurent.degroote@ugent.be %K physical activity %K fitness trackes %K accelerometry %D 2018 %7 13.12.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearables for monitoring physical activity (PA) are increasingly popular. These devices are not only used by consumers to monitor their own levels of PA but also by researchers to track the behavior of large samples. Consequently, it is important to explore how accurately PA can be tracked via these devices. Objectives: The aim of this study was, therefore, to investigate convergent validity of 3 Android Wear smartwatches—Polar M600 (Polar Electro Oy, Kempele, Finland), Huawei Watch (Huawei Technologies Co, Ltd, Shenzhen, Guangdong, China), Asus Zenwatch3 (AsusTek Computer Inc, Taipei, Taiwan)—and Fitbit Charge with an ActiGraph accelerometer for measuring steps and moderate to vigorous physical activity (MVPA) on both a day level and 15-min level. Methods: A free-living protocol was used in which 36 adults engaged in usual daily activities over 2 days while wearing 2 different wearables on the nondominant wrist and an ActiGraph GT3X+ accelerometer on the hip. Validity was evaluated on both levels by comparing each wearable with the ActiGraph GT3X+ accelerometer using correlations and Bland-Altman plots in IBM SPSS 24.0. Results: On a day level, all devices showed strong correlations (Spearman r=.757-.892) and good agreement (interclass correlation coefficient, ICC=.695-.885) for measuring steps, whereas moderate correlations (Spearman r=.557-.577) and low agreement (ICC=.377-.660) for measuring MVPA. Bland-Altman revealed a systematic overestimation of the wearables for measuring steps but a variation between over- and undercounting of MVPA. On a 15-min level, all devices showed strong correlations (Spearman r=.752-.917) and good agreement (ICC=.792-.887) for measuring steps, whereas weak correlations (Spearman r=.116-.208) and low agreement (ICC=.461-.577) for measuring MVPA. Bland-Altman revealed a systematic overestimation of the wearables for steps but under- or overestimation for MVPA depending on the device. Conclusions: In sum, all 4 consumer-level devices can be considered accurate step counters in free-living conditions. This study, however, provides evidence of systematic bias for all devices in measurement of MVPA. The results on a 15-min level also indicate that these devices are not sufficiently accurate to provide correct real-time feedback. %M 30545810 %R 10.2196/10972 %U https://mhealth.jmir.org/2018/12/e10972/ %U https://doi.org/10.2196/10972 %U http://www.ncbi.nlm.nih.gov/pubmed/30545810 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 12 %P e10338 %T Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study %A Reddy,Ravi Kondama %A Pooni,Rubin %A Zaharieva,Dessi P %A Senf,Brian %A El Youssef,Joseph %A Dassau,Eyal %A Doyle III,Francis J %A Clements,Mark A %A Rickels,Michael R %A Patton,Susana R %A Castle,Jessica R %A Riddell,Michael C %A Jacobs,Peter G %+ Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Avenue, Portland, OR, 97239, United States, 1 503 358 2291, jacobsp@ohsu.edu %K heart rate %K energy metabolism %K fitness trackers %K high-intensity interval training %K artificial pancreas %D 2018 %7 10.12.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. Objective: The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. Methods: We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. Results: Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures. Conclusions: Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done. %M 30530451 %R 10.2196/10338 %U https://mhealth.jmir.org/2018/12/e10338/ %U https://doi.org/10.2196/10338 %U http://www.ncbi.nlm.nih.gov/pubmed/30530451 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 12 %P e10911 %T Creating Engaging Health Promotion Campaigns on Social Media: Observations and Lessons From Fitbit and Garmin %A Edney,Sarah %A Bogomolova,Svetlana %A Ryan,Jillian %A Olds,Tim %A Sanders,Ilea %A Maher,Carol %+ Alliance for Research in Exercise, Nutrition and Activity, University of South Australia, GPO Box 2471, Adelaide, 5000, Australia, 61 +61402371792, sarah.edney@mymail.unisa.edu.au %K social media %K engagement %K physical activity %D 2018 %7 10.12.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: The popularity and reach of social media make it an ideal delivery platform for interventions targeting health behaviors, such as physical inactivity. Research has identified a dose-response relationship whereby greater engagement and exposure are positively associated with intervention effects, hence enhancing engagement will maximize the potential of these interventions. Objective: This study examined the social media activity of successful commercial activity tracker brands to understand which creative elements (message content and design) they use in their communication to their audience, which social media platforms attract the most engagement, and which creative elements prompted the most engagement. Methods: Posts (n=509) made by Fitbit and Garmin on Facebook, Twitter, and Instagram over a 3-month period were coded for the presence of creative elements. User engagement regarding the total number of likes, comments, or shares per post was recorded. Negative binomial regression analyses were used to identify creative elements associated with higher engagement. Results: Engagement on Instagram was 30-200 times higher than on Facebook, or Twitter. Fitbit and Garmin tended to use different creative elements from one another. A higher engagement was achieved by posts featuring an image of the product, highlighting new product features and with themes of self-improvement (P<.01). Conclusions: Findings suggest that Instagram may be a particularly promising platform for delivering engaging health messaging. Health messages which incorporate inspirational imagery and focus on a tangible product appear to achieve the highest engagement. Fitbit and Garmin employed difference creative elements, which is likely to reflect differences in their target markets. This underscores the importance of market segmentation in health messaging campaigns. %M 30530449 %R 10.2196/10911 %U https://www.jmir.org/2018/12/e10911/ %U https://doi.org/10.2196/10911 %U http://www.ncbi.nlm.nih.gov/pubmed/30530449 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 11 %P e10523 %T Feasibility of Using a Commercial Fitness Tracker as an Adjunct to Family-Based Weight Management Treatment: Pilot Randomized Trial %A Phan,Thao-Ly Tam %A Barnini,Nadia %A Xie,Sherlly %A Martinez,Angelica %A Falini,Lauren %A Abatemarco,Atiera %A Waldron,Maura %A Benton,Jane M %A Frankenberry,Steve %A Coleman,Cassandra %A Nguyen,Linhda %A Bo,Cindy %A Datto,George A %A Werk,Lloyd N %+ Center for Healthcare Delivery Science, Nemours Children's Health System, 1600 Rockland Road, Wilmington, DE, 19803, United States, 1 3023317342, tphan@nemours.org %K fitness trackers %K pediatric obesity %K health behavior %K accelerometry %D 2018 %7 27.11.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Fitness trackers can engage users through automated self-monitoring of physical activity. Studies evaluating the utility of fitness trackers are limited among adolescents, who are often difficult to engage in weight management treatment and are heavy technology users. Objective: We conducted a pilot randomized trial to describe the impact of providing adolescents and caregivers with fitness trackers as an adjunct to treatment in a tertiary care weight management clinic on adolescent fitness tracker satisfaction, fitness tracker utilization patterns, and physical activity levels. Methods: Adolescents were randomized to 1 of 2 groups (adolescent or dyad) at their initial weight management clinic visit. Adolescents received a fitness tracker and counseling around activity data in addition to standard treatment. A caregiver of adolescents in the dyad group also received a fitness tracker. Satisfaction with the fitness tracker, fitness tracker utilization patterns, and physical activity patterns were evaluated over 3 months. Results: A total of 88 adolescents were enrolled, with 69% (61/88) being female, 36% (32/88) black, 23% (20/88) Hispanic, and 63% (55/88) with severe obesity. Most adolescents reported that the fitness tracker was helping them meet their healthy lifestyle goals (69%) and be more motivated to achieve a healthy weight (66%). Despite this, 68% discontinued use of the fitness tracker by the end of the study. There were no significant differences between the adolescent and the dyad group in outcomes, but adolescents in the dyad group were 12.2 times more likely to discontinue using their fitness tracker if their caregiver also discontinued use of their fitness tracker (95% CI 2.4-61.6). Compared with adolescents who discontinued use of the fitness tracker during the study, adolescents who continued to use the fitness tracker recorded a higher number of daily steps in months 2 and 3 of the study (mean 5760 vs 4148 in month 2, P=.005, and mean 5942 vs 3487 in month 3, P=.002). Conclusions: Despite high levels of satisfaction with the fitness trackers, fitness tracker discontinuation rates were high, especially among adolescents whose caregivers also discontinued use of their fitness tracker. More studies are needed to determine how to sustain the use of fitness trackers among adolescents with obesity and engage caregivers in adolescent weight management interventions. %M 30482743 %R 10.2196/10523 %U http://mhealth.jmir.org/2018/11/e10523/ %U https://doi.org/10.2196/10523 %U http://www.ncbi.nlm.nih.gov/pubmed/30482743 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 11 %P e11066 %T Usability Study of Mainstream Wearable Fitness Devices: Feature Analysis and System Usability Scale Evaluation %A Liang,Jun %A Xian,Deqiang %A Liu,Xingyu %A Fu,Jing %A Zhang,Xingting %A Tang,Buzhou %A Lei,Jianbo %+ Center for Medical Informatics, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China, 86 8280 5901, jblei@hsc.pku.edu.cn %K wearable devices %K usability %K System Usability Scale %K function comparison %K fitness %D 2018 %7 08.11.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable devices have the potential to promote a healthy lifestyle because of their real-time data monitoring capabilities. However, device usability is a critical factor that determines whether they will be adopted on a large scale. Usability studies on wearable devices are still scarce. Objective: This study aims to compare the functions and attributes of seven mainstream wearable devices and to evaluate their usability. Methods: The wearable devices selected were the Apple Watch, Samsung Gear S, Fitbit Surge, Jawbone Up3, Mi Band, Huawei Honor B2, and Misfit Shine. A mixed method of feature comparison and a System Usability Scale (SUS) evaluation based on 388 participants was applied; the higher the SUS score, the better the usability of the product. Results: For features, all devices had step counting, an activity timer, and distance recording functions. The Samsung Gear S had a unique sports track recording feature and the Huawei Honor B2 had a unique wireless earphone. The Apple Watch, Samsung Gear S, Jawbone Up3, and Fitbit Surge could measure heart rate. All the devices were able to monitor sleep, except the Apple Watch. For product characteristics, including attributes such as weight, battery life, price, and 22 functions such as step counting, activity time, activity type identification, sleep monitoring, and expandable new features, we found a very weak negative correlation between the SUS scores and price (r=−.10, P=.03) and devices that support expandable new features (r=−.11, P=.02), and a very weak positive correlation between the SUS scores and devices that support the activity type identification function (r=.11, P=.02). The Huawei Honor B2 received the highest score of mean 67.6 (SD 16.1); the lowest Apple Watch score was only 61.4 (SD 14.7). No significant difference was observed among brands. The SUS score had a moderate positive correlation with the user’s experience (length of time the device was used) (r=.32, P<.001); participants in the medical and health care industries gave a significantly higher score (mean 61.1, SD 17.9 vs mean 68.7, SD 14.5, P=.03). Conclusions: The functions of wearable devices tend to be homogeneous and usability is similar across various brands. Overall, Mi Band had the lowest price and the lightest weight. Misfit Shine had the longest battery life and most functions, and participants in the medical and health care industries had the best evaluation of wearable devices. The perceived usability of mainstream wearable devices is unsatisfactory and customer loyalty is not high. A consumer’s SUS rating for a wearable device is related to their personal situation instead of the device brand. Device manufacturers should put more effort into developing innovative functions and improving the usability of their products by integrating more cognitive behavior change techniques. %M 30409767 %R 10.2196/11066 %U http://mhealth.jmir.org/2018/11/e11066/ %U https://doi.org/10.2196/11066 %U http://www.ncbi.nlm.nih.gov/pubmed/30409767 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 2 %N 2 %P e10945 %T Barriers and Opportunities for Using Wearable Devices to Increase Physical Activity Among Veterans: Pilot Study %A Kim,Rebecca H %A Patel,Mitesh S %+ Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA,, United States, 1 2678720364, rebecca.kim@uphs.upenn.edu %K veterans %K wearable devices %K connected health device %K physical activity %K mobile phone %D 2018 %7 06.11.2018 %9 Short Paper %J JMIR Formativ Res %G English %X Background: Few studies have examined the use of wearable devices among the veteran population. Objective: The objective of this study was to evaluate veterans’ perceptions of and experiences with wearable devices and identify the potential barriers and opportunities to using such devices to increase physical activity levels in this population. Methods: Veterans able to ambulate with or without assistance completed surveys about their mobile technology use and physical activity levels. They were then given the option of using a wearable device to monitor their activity levels. Follow-up telephone interviews were conducted after 2 months. Results: A total of 16 veterans were enrolled in this study, and all of them agreed to take home and use the wearable device to monitor their activity levels. At follow-up, 91% (10/11) veterans were still using the device daily. Veterans identified both opportunities and barriers for incorporating these devices into interventions to increase physical activity. Conclusions: Veterans engaged in using wearable devices at high rates. %M 30684414 %R 10.2196/10945 %U http://formative.jmir.org/2018/2/e10945/ %U https://doi.org/10.2196/10945 %U http://www.ncbi.nlm.nih.gov/pubmed/30684414 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 10 %P e11281 %T Features, Behavioral Change Techniques, and Quality of the Most Popular Mobile Apps to Measure Physical Activity: Systematic Search in App Stores %A Simões,Patrícia %A Silva,Anabela G %A Amaral,João %A Queirós,Alexandra %A Rocha,Nelson P %A Rodrigues,Mário %+ School of Health Sciences, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-193, Portugal, 351 234401558 ext 27120, asilva@ua.pt %K behavioral change techniques %K mobile phone app %K physical activity %K quality %K technical features %K mobile phone %D 2018 %7 26.10.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: It is estimated that 23% of adults and 55% of older adults do not meet the recommended levels of physical activity. Thus, improving the levels of physical activity is of paramount importance, but it requires the use of low-cost resources that facilitate universal access without depleting the health system. The high number of apps available constitutes an opportunity, but it also makes it quite difficult for the layperson to select the most appropriate app. Furthermore, the information available in the app stores is often insufficient, lacks quality, and is not evidence based, and the systematic reviews fail to assess app quality using standardized and validated instruments. Objective: The objective of this study was to systematically assess the features, content, and quality of the most popular apps that can be used to measure and, potentially, promote physical activity. Methods: Systematic searches were conducted on Apple App Store, Google Play, and Windows Phone Store between December 2017 and January 2018. Apps were included if their primary objective was to assess the aspects of physical activity, if they had a user rating of at least 4, if their number of ratings was ≥100, and if they were free. Apps meeting these criteria were independently assessed by two reviewers regarding their general and technical information, aspects of physical activity, presence of behavioral change techniques, and quality. Data were analyzed using means and SDs or frequencies and percentages. Results: Of 51 apps included, none specified the age of the target group and only one mentioned the involvement of health professionals. Most apps offered the possibility to work in background (n=50) and allowed data sharing (n=40). Regarding physical activity, most apps measured steps and distance (n=11) or steps, distance, and time (n=17). Only 18 apps, all of which measured number of steps, followed the guidelines on recommendations for physical activity. On average, 5.5 (SD 1.8) behavioral change techniques were identified per app; the most frequently used techniques were “provide feedback on performance” (n=50) and “prompt self-monitoring of behavior” (n=50). The overall quality score was 3.88 (SD 0.34). Conclusions: Although the overall quality of the apps was moderate, the quality of their content, particularly the use of international guidelines on physical activity, should be improved. Additionally, a more in-depth assessment of apps should be performed before releasing them for public use, particularly regarding their reliability and validity. %M 30368438 %R 10.2196/11281 %U http://mhealth.jmir.org/2018/10/e11281/ %U https://doi.org/10.2196/11281 %U http://www.ncbi.nlm.nih.gov/pubmed/30368438 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 9 %P e10706 %T Medical-Grade Physical Activity Monitoring for Measuring Step Count and Moderate-to-Vigorous Physical Activity: Validity and Reliability Study %A O'Brien,Myles William %A Wojcik,William Robert %A Fowles,Jonathon Richard %+ Centre of Lifestyle Studies, School of Kinesiology, Acadia University, 550 Main Street, Wolfville, NS,, Canada, 1 9025851560, jonathon.fowles@acadiau.ca %K pedometer %K accelerometry %K exercise prescription %K validity %K reliability %D 2018 %7 05.09.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of physical activity (PA) monitors is commonly associated with an increase in habitual PA level in healthy and clinical populations. The PiezoRx is a medical-grade PA monitor that uses adjustable step rate thresholds to estimate moderate-to-vigorous physical activity (MVPA) and is a valid indicator of free-living PA in adults. Laboratory validation of step count derived MVPA in adults is needed to justify the use of these monitors in clinical practice to track individuals’ progress toward meeting PA guidelines that are based on MVPA, not steps. Objective: The objective of our study was to assess the validity and interinstrument reliability of the PiezoRx to derive step count and MVPA in a laboratory setting compared with criterion measures and other frequently used PA monitors in a diverse sample of adults. Methods: The adult participants (n=43; 39.4 years, SD 15.2) wore an Omron HJ-320 pedometer, an ActiGraph GT3X accelerometer, and four PiezoRx monitors during a progressive treadmill protocol conducted for 6 minutes at speeds of 2.4, 3.2, 4.0, 5.6, 6.4, and 7.2 km/hour, respectively. The four PiezoRx monitors were set at different MVPA step rate thresholds (MPA in steps/minute/VPA in steps/minute) 100/120, 110/130, height adjusted, and height+fitness adjusted. Results: The PiezoRx was more correlated (intraclass correlation, ICC=.97; P<.001) to manual step counting than the ActiGraph (ICC=.72; P<.001) and Omron (ICC=.62; P<.001). The PiezoRxs absolute percent error in measuring steps was 2.2% (ActiGraph=15.9%; Omron=15.0%). Compared with indirect calorimetry, the height-adjusted PiezoRx and ActiGraph were accurate measures of the time spent in MVPA (both ICC=.76; P<.001). Conclusions: The PiezoRx PA monitor appears to be a valid and reliable measure of step count and MVPA in this diverse sample of adults. The device’s ability to measure MVPA may be improved when anthropometric differences are considered, performing at par or better than a research grade accelerometer. %M 30185406 %R 10.2196/10706 %U http://mhealth.jmir.org/2018/9/e10706/ %U https://doi.org/10.2196/10706 %U http://www.ncbi.nlm.nih.gov/pubmed/30185406 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 8 %P e10487 %T A Personalized Physical Activity Program With Activity Trackers and a Mobile Phone App for Patients With Metastatic Breast Cancer: Protocol for a Single-Arm Feasibility Trial %A Delrieu,Lidia %A Pérol,Olivia %A Fervers,Béatrice %A Friedenreich,Christine %A Vallance,Jeff %A Febvey-Combes,Olivia %A Pérol,David %A Canada,Brice %A Roitmann,Eva %A Dufresne,Armelle %A Bachelot,Thomas %A Heudel,Pierre-Etienne %A Trédan,Olivier %A Touillaud,Marina %A Pialoux,Vincent %+ Inter-University Laboratory of Human Movement Biology, University Claude Bernard Lyon 1, University of Lyon, Faculté de Médecine Lyon Est, 8 Avenue Rockefeller, Lyon, 69008, France, 33 472431625, vincent.pialoux@univ-lyon1.fr %K metastatic breast cancer %K physical activity %K oxidative stress %K activity trackers %K feasibility %D 2018 %7 30.08.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: About 5% of breast cancer cases are metastatic at diagnosis, and 20%-30% of localized breast cancer cases become secondarily metastatic. Patients frequently report many detrimental symptoms related to metastasis and treatments. The physical, biological, psychological, and clinical benefits of physical activity during treatment in patients with localized breast cancer have been demonstrated; however, limited literature exists regarding physical activity and physical activity behavior change in patients with metastatic breast cancer. Objective: The primary objective of this study is to assess the feasibility of a 6-month physical activity intervention with activity trackers in patients with metastatic breast cancer (the Advanced stage Breast cancer and Lifestyle Exercise, ABLE Trial). Secondary objectives are to examine the effects of physical activity on physical, psychological, anthropometrics, clinical, and biological parameters. Methods: We plan to conduct a single-center, single-arm trial with 60 patients who are newly diagnosed with metastatic breast cancer. Patients will receive an unsupervised and personalized 6-month physical activity program that includes an activity tracker Nokia Go and is based on the physical activity recommendation. Patients will be encouraged to accumulate at least 150 minutes per week of moderate-to-vigorous intensity physical activity. Baseline and 6-month assessments will include anthropometric measures, functional tests (eg, 6-minute walk test and upper and lower limb strength), blood draws, patient-reported surveys (eg, quality of life and fatigue), and clinical markers of tumor progression (eg, Response Evaluation Criteria In Solid Tumors criteria). Results: Data collection occurred between October 2016 and January 2018, and the results are expected in August 2018. Conclusions: The ABLE Trial will be the first study to assess the feasibility and effectiveness of an unsupervised and personalized physical activity intervention performed under real-life conditions with activity trackers in patients with metastatic breast cancer. Trial Registration: ClinicalTrials.gov NCT03148886; https://clinicaltrials.gov/ct2/show/NCT03148886 (Accessed by WebCite at http://www.webcitation.org/71yabi0la) Registered Report Identifier: RR1-10.2196/10487 %M 30166274 %R 10.2196/10487 %U http://www.researchprotocols.org/2018/8/e10487/ %U https://doi.org/10.2196/10487 %U http://www.ncbi.nlm.nih.gov/pubmed/30166274 %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 e10270 %T mHealth Self-Report Monitoring in Competitive Middle- and Long-Distance Runners: Qualitative Study of Long-Term Use Intentions Using the Technology Acceptance Model %A Rönnby,Sara %A Lundberg,Oscar %A Fagher,Kristina %A Jacobsson,Jenny %A Tillander,Bo %A Gauffin,Håkan %A Hansson,Per-Olof %A Dahlström,Örjan %A Timpka,Toomas %+ Athletics Research Center, Department of Medical and Health Sciences, Linköping University, Hus 511-001, ingång 76, plan 14, Campus US, Linköping, 581 83, Sweden, 46 4705364357, toomas.timpka@liu.se %K running %K mHealth %K health technology %K diagnostic self-evaluation %K remote sensing technology %K self-evaluation programs %K qualitative research %D 2018 %7 13.08.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: International middle- and long-distance running competitions attract millions of spectators in association with city races, world championships, and Olympic Games. It is therefore a major concern that ill health and pain, as a result of sports overuse, lead to numerous hours of lost training and decreased performance in competitive runners. Despite its potential for sustenance of performance, approval of mHealth self-report monitoring (mHSM) in this group of athletes has not been investigated. Objective: The objective of our study was to explore individual and situational factors associated with the acceptance of long-term mHSM in competitive runners. Methods: The study used qualitative research methods with the Technology Acceptance Model as the theoretical foundation. The study population included 20 middle- and long-distance runners competing at national and international levels. Two mHSM apps asking for health and training data from track and marathon runners were created on a platform for web survey development (Briteback AB). Data collection for the technology acceptance analysis was performed via personal interviews before and after a 6-week monitoring period. Preuse interviews investigated experience and knowledge of mHealth monitoring and thoughts on benefits and possible side effects. The postuse interviews addressed usability and usefulness, attitudes toward nonfunctional issues, and intentions to adhere to long-term monitoring. In addition, the runners’ trustworthiness when providing mHSM data was discussed. The interview data were investigated using a deductive thematic analysis. Results: The mHSM apps were considered technically easy to use. Although the runners read the instructions and entered data effortlessly, some still perceived mHSM as problematic. Concerns were raised about the selection of items for monitoring (eg, recording training load as running distance or time) and about interpretation of concepts (eg, whether subjective well-being should encompass only the running context or daily living on the whole). Usefulness of specific mHSM apps was consequently not appraised on the same bases in different subcategories of runners. Regarding nonfunctional issues, the runners competing at the international level requested detailed control over who in their sports club and national federation should be allowed access to their data; the less competitive runners had no such issues. Notwithstanding, the runners were willing to adhere to long-term mHSM, provided the technology was adjusted to their personal routines and the output was perceived as contributing to running performance. Conclusions: Adoption of mHSM by competitive runners requires clear definitions of monitoring purpose and populations, repeated in practice tests of monitoring items and terminology, and meticulousness regarding data-sharing routines. Further naturalistic studies of mHSM use in routine sports practice settings are needed with nonfunctional ethical and legal issues included in the evaluation designs. %M 30104183 %R 10.2196/10270 %U http://mhealth.jmir.org/2018/8/e10270/ %U https://doi.org/10.2196/10270 %U http://www.ncbi.nlm.nih.gov/pubmed/30104183 %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 %@ 2291-5222 %I JMIR Publications %V 6 %N 8 %P e10527 %T Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data %A Feehan,Lynne M %A Geldman,Jasmina %A Sayre,Eric C %A Park,Chance %A Ezzat,Allison M %A Yoo,Ju Young %A Hamilton,Clayon B %A Li,Linda C %+ Department of Physical Therapy, University of British Columbia, Friedman Building, 2177 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada, 1 604 822 7408, lynnefeehan@gmail.com %K wearable activity tracker %K accuracy %K Fitbit %K steps %K sleep %K energy expenditure %K distance %K time in activity %K systematic review %K fitness trackers %K data accuracy %K energy metabolism %K review %D 2018 %7 09.08.2018 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Although designed as a consumer product to help motivate individuals to be physically active, Fitbit activity trackers are becoming increasingly popular as measurement tools in physical activity and health promotion research and are also commonly used to inform health care decisions. Objective: The objective of this review was to systematically evaluate and report measurement accuracy for Fitbit activity trackers in controlled and free-living settings. Methods: We conducted electronic searches using PubMed, EMBASE, CINAHL, and SPORTDiscus databases with a supplementary Google Scholar search. We considered original research published in English comparing Fitbit versus a reference- or research-standard criterion in healthy adults and those living with any health condition or disability. We assessed risk of bias using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. We explored measurement accuracy for steps, energy expenditure, sleep, time in activity, and distance using group percentage differences as the common rubric for error comparisons. We conducted descriptive analyses for frequency of accuracy comparisons within a ±3% error in controlled and ±10% error in free-living settings and assessed for potential bias of over- or underestimation. We secondarily explored how variations in body placement, ambulation speed, or type of activity influenced accuracy. Results: We included 67 studies. Consistent evidence indicated that Fitbit devices were likely to meet acceptable accuracy for step count approximately half the time, with a tendency to underestimate steps in controlled testing and overestimate steps in free-living settings. Findings also suggested a greater tendency to provide accurate measures for steps during normal or self-paced walking with torso placement, during jogging with wrist placement, and during slow or very slow walking with ankle placement in adults with no mobility limitations. Consistent evidence indicated that Fitbit devices were unlikely to provide accurate measures for energy expenditure in any testing condition. Evidence from a few studies also suggested that, compared with research-grade accelerometers, Fitbit devices may provide similar measures for time in bed and time sleeping, while likely markedly overestimating time spent in higher-intensity activities and underestimating distance during faster-paced ambulation. However, further accuracy studies are warranted. Our point estimations for mean or median percentage error gave equal weighting to all accuracy comparisons, possibly misrepresenting the true point estimate for measurement bias for some of the testing conditions we examined. Conclusions: Other than for measures of steps in adults with no limitations in mobility, discretion should be used when considering the use of Fitbit devices as an outcome measurement tool in research or to inform health care decisions, as there are seemingly a limited number of situations where the device is likely to provide accurate measurement. %M 30093371 %R 10.2196/10527 %U http://mhealth.jmir.org/2018/8/e10527/ %U https://doi.org/10.2196/10527 %U http://www.ncbi.nlm.nih.gov/pubmed/30093371 %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 %@ 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 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 e10042 %T Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial %A Fukuoka,Yoshimi %A Lindgren,Teri G %A Mintz,Yonatan Dov %A Hooper,Julie %A Aswani,Anil %+ Department of Physiological Nursing/Institute for Health & Aging, School of Nursing, University of California, San Francisco, 2 Koret Way, N631, San Francisco, CA, 94143, United States, 1 (415) 476 8419, Yoshimi.Fukuoka@ucsf.edu %K mobile apps %K physical activity %K fitness trackers %K women %K maintenance %K accelerometer %K randomized controlled trial %K motivation %K barriers %K behavioral change %D 2018 %7 20.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Regular physical activity is associated with reduced risk of chronic illnesses. Despite various types of successful physical activity interventions, maintenance of activity over the long term is extremely challenging. Objective: The aims of this original paper are to 1) describe physical activity engagement post intervention, 2) identify motivational profiles using natural language processing (NLP) and clustering techniques in a sample of women who completed the physical activity intervention, and 3) compare sociodemographic and clinical data among these identified cluster groups. Methods: In this cross-sectional analysis of 203 women completing a 12-month study exit (telephone) interview in the mobile phone-based physical activity education study were examined. The mobile phone-based physical activity education study was a randomized, controlled trial to test the efficacy of the app and accelerometer intervention and its sustainability over a 9-month period. All subjects returned the accelerometer and stopped accessing the app at the last 9-month research office visit. Physical engagement and motivational profiles were assessed by both closed and open-ended questions, such as “Since your 9-month study visit, has your physical activity been more, less, or about the same (compared to the first 9 months of the study)?” and, “What motivates you the most to be physically active?” NLP and cluster analysis were used to classify motivational profiles. Descriptive statistics were used to compare participants’ baseline characteristics among identified groups. Results: Approximately half of the 2 intervention groups (Regular and Plus) reported that they were still wearing an accelerometer and engaging in brisk walking as they were directed during the intervention phases. These numbers in the 2 intervention groups were much higher than the control group (overall P=.01 and P=.003, respectively). Three clusters were identified through NLP and named as the Weight Loss group (n=19), the Illness Prevention group (n=138), and the Health Promotion group (n=46). The Weight Loss group was significantly younger than the Illness Prevention and Health Promotion groups (overall P<.001). The Illness Prevention group had a larger number of Caucasians as compared to the Weight Loss group (P=.001), which was composed mostly of those who identified as African American, Hispanic, or mixed race. Additionally, the Health Promotion group tended to have lower BMI scores compared to the Illness Prevention group (overall P=.02). However, no difference was noted in the baseline moderate-to-vigorous intensity activity level among the 3 groups (overall P>.05). Conclusions: The findings could be relevant to tailoring a physical activity maintenance intervention. Furthermore, the findings from NLP and cluster analysis are useful methods to analyze short free text to differentiate motivational profiles. As more sophisticated NL tools are developed in the future, the potential of NLP application in behavioral research will broaden. Trial Registration: ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/70IkGagAJ) %M 29925491 %R 10.2196/10042 %U http://mhealth.jmir.org/2018/6/e10042/ %U https://doi.org/10.2196/10042 %U http://www.ncbi.nlm.nih.gov/pubmed/29925491 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 6 %P e143 %T Analysis of the Features Important for the Effectiveness of Physical Activity–Related Apps for Recreational Sports: Expert Panel Approach %A Dallinga,Joan %A Janssen,Mark %A van der Werf,Jet %A Walravens,Ruben %A Vos,Steven %A Deutekom,Marije %+ Faculty of Sports and Nutrition, Amsterdam University of Applied Sciences, Dr Meurerlaan 8, Amsterdam, 1067 SM, Netherlands, 31 621156682, j.m.dallinga@hva.nl %K mobile applications %K exercise %K healthy lifestyle %K mHealth %K measures %K health behavior %K features %D 2018 %7 18.06.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A large number of people participate in individual or unorganized sports on a recreational level. Furthermore, many participants drop out because of injury or lowered motivation. Potentially, physical activity–related apps could motivate people during sport participation and help them to follow and maintain a healthy active lifestyle. It remains unclear what the quality of running, cycling, and walking apps is and how it can be assessed. Quality of these apps was defined as having a positive influence on participation in recreational sports. This information will show which features need to be assessed when rating physical activity–related app quality. Objective: The aim of this study was to identify expert perception on which features are important for the effectiveness of physical activity–related apps for participation in individual, recreational sports. Methods: Data were gathered via an expert panel approach using the nominal group technique. Two expert panels were organized to identify and rank app features relevant for sport participation. Experts were researchers or professionals in the field of industrial design and information technology (technology expert panel) and in the field of behavior change, health, and human movement sciences who had affinity with physical activity–related apps (health science expert panel). Of the 24 experts who were approached, 11 (46%) agreed to participate. Each panel session consisted of three consultation rounds. The 10 most important features per expert were collected. We calculated the frequency of the top 10 features and the mean importance score per feature (0-100). The sessions were taped and transcribed verbatim; a thematic analysis was conducted on the qualitative data. Results: In the technology expert panel, applied feedback and feedforward (91.3) and fun (91.3) were found most important (scale 0-100). Together with flexibility and look and feel, these features were mentioned most often (all n=4 [number of experts]; importance scores=41.3 and 43.8, respectively). The experts in the health science expert panels a and b found instructional feedback (95.0), motivating or challenging (95.0), peer rating and use (92.0), motivating feedback (91.3), and monitoring or statistics (91.0) most important. Most often ranked features were monitoring or statistics, motivating feedback, works good technically, tailoring starting point, fun, usability anticipating or context awareness, and privacy (all n=3-4 [number of experts]; importance scores=16.7-95.0). The qualitative analysis resulted in four overarching themes: (1) combination behavior change, technical, and design features needed; (2) extended feedback and tailoring is advised; (3) theoretical or evidence base as standard; and (4) entry requirements related to app use. Conclusions: The results show that a variety of features, including design, technical, and behavior change, are considered important for the effectiveness of physical activity–related apps by experts from different fields of expertise. These insights may assist in the development of an improved app rating scale. %M 29914863 %R 10.2196/mhealth.9459 %U http://mhealth.jmir.org/2018/6/e143/ %U https://doi.org/10.2196/mhealth.9459 %U http://www.ncbi.nlm.nih.gov/pubmed/29914863 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 5 %N 2 %P e10415 %T Veterans’ Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study %A Ng,Ada %A Reddy,Madhu %A Zalta,Alyson K %A Schueller,Stephen M %+ People, Information, and Technology Changing Health Lab, Technology and Social Behavior Program, Northwestern University, 2240 Campus Drive, Evanston, IL, 60208, United States, 1 847 491 7023, adang@u.northwestern.edu %K fitness trackers %K patient generated health data %K consumer health informatics %K stress disorders, post-traumatic %K PTSD %K mental health %K veterans %D 2018 %7 15.06.2018 %9 Original Paper %J JMIR Ment Health %G English %X Background: The increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data. Objective: The aim of this study was to gain an understanding of patients’ motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit? Methods: We conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. Results: We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. Conclusions: To integrate wearable data into mental health treatment programs, it is important to understand the patient’s perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified. %M 29907556 %R 10.2196/10415 %U http://mental.jmir.org/2018/2/e10415/ %U https://doi.org/10.2196/10415 %U http://www.ncbi.nlm.nih.gov/pubmed/29907556 %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 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 e121 %T mActive-Smoke: A Prospective Observational Study Using Mobile Health Tools to Assess the Association of Physical Activity With Smoking Urges %A Silverman-Lloyd,Luke G %A Kianoush,Sina %A Blaha,Michael J %A Sabina,Alyse B %A Graham,Garth N %A Martin,Seth S %+ Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Carnegie 591, 600 North Wolfe Street, Baltimore, MD, 21287, United States, 1 410 502 0469, smart100@jhmi.edu %K activity trackers %K cigarette smoking %K exercise %K fitness trackers %K mobile health %K mHealth %K physical activity %K smartphone %K smoking %K text messaging %K texting %D 2018 %7 11.05.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Evidence that physical activity can curb smoking urges is limited in scope to acute effects and largely reliant on retrospective self-reported measures. Mobile health technologies offer novel mechanisms for capturing real-time data of behaviors in the natural environment. Objective: This study aimed to explore this in a real-world longitudinal setting by leveraging mobile health tools to assess the association between objectively measured physical activity and concurrent smoking urges in a 12-week prospective observational study. Methods: We enrolled 60 active smokers (≥3 cigarettes per day) and recorded baseline demographics, physical activity, and smoking behaviors using a Web-based questionnaire. Step counts were measured continuously using the Fitbit Charge HR. Participants reported instantaneous smoking urges via text message using a Likert scale ranging from 1 to 9. On study completion, participants reported follow-up smoking behaviors in an online exit survey. Results: A total of 53 participants (aged 40 [SD 12] years, 57% [30/53] women, 49% [26/53] nonwhite) recorded at least 6 weeks of data and were thus included in the analysis. We recorded 15,365 urge messages throughout the study, with a mean of 290 (SD 62) messages per participant. Mean urge over the course of the study was positively associated with daily cigarette consumption at follow-up (Pearson r=.33; P=.02). No association existed between daily steps and mean daily urge (beta=−6.95×10−3 per 1000 steps; P=.30). Regression models of acute effects, however, did reveal modest inverse associations between steps within 30-, 60-, and 120-min time windows of a reported urge (beta=−.0191 per 100 steps, P<.001). Moreover, 6 individuals (approximately 10% of the study population) exhibited a stronger and consistent inverse association between steps and urge at both the day level (mean individualized beta=−.153 per 1000 steps) and 30-min level (mean individualized beta=−1.66 per 1000 steps). Conclusions: Although there was no association between objectively measured daily physical activity and concurrently self-reported smoking urges, there was a modest inverse relationship between recent step counts (30-120 min) and urge. Approximately 10% of the individuals appeared to have a stronger and consistent inverse association between physical activity and urge, a provocative finding warranting further study. %M 29752250 %R 10.2196/mhealth.9292 %U http://mhealth.jmir.org/2018/5/e121/ %U https://doi.org/10.2196/mhealth.9292 %U http://www.ncbi.nlm.nih.gov/pubmed/29752250 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 5 %P e114 %T Using Digital Health Technologies to Understand the Association Between Movement Behaviors and Interstitial Glucose: Exploratory Analysis %A Kingsnorth,Andrew P %A Whelan,Maxine E %A Sanders,James P %A Sherar,Lauren B %A Esliger,Dale W %+ National Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Loughborough, LE11 3TU, United Kingdom, 44 01509 225454, a.kingsnorth@lboro.ac.uk %K accelerometry %K glucose %K physical activity %K physiological monitoring %K sedentary time %D 2018 %7 03.05.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Acute reductions in postprandial glucose excursions because of movement behaviors have been demonstrated in experimental studies but less so in free-living settings. Objective: The objective of this study was to explore the nature of the acute stimulus-response model between accelerometer-assessed physical activity, sedentary time, and glucose variability over 13 days in nondiabetic adults. Methods: This study measured physical activity, sedentary time, and interstitial glucose continuously over 13 days in 29 participants (mean age in years: 44.9 [SD 9.1]; female: 59%, 17/29; white: 90%, 26/29; mean body mass index: 25.3 [SD 4.1]) as part of the Sensing Interstitial Glucose to Nudge Active Lifestyles (SIGNAL) research program. Daily minutes spent sedentary, in light activity, and moderate to vigorous physical activity were associated with daily mean glucose, SD of glucose, and mean amplitude of glycemic excursions (MAGE) using generalized estimating equations. Results: After adjustment for covariates, sedentary time in minutes was positively associated with a higher daily mean glucose (mmol/L; beta=0.0007; 95% CI 0.00030-0.00103; P<.001), SD of glucose (mmol/L; beta=0.0006; 95% CI 0.00037-0.00081; P<.001), and MAGE (mmol/L; beta=0.002; 95% CI 0.00131-0.00273; P<.001) for those of a lower fitness. Additionally, light activity was inversely associated with mean glucose (mmol/L; beta=−0.0004; 95% CI −0.00078 to −0.00006; P=.02), SD of glucose (mmol/L; beta=−0.0006; 95% CI −0.00085 to −0.00039; P<.001), and MAGE (mmol/L; beta=−0.002; 95% CI −0.00285 to −0.00146; P<.001) for those of a lower fitness. Moderate to vigorous physical activity was only inversely associated with mean glucose (mmol/L; beta=−0.002; 95% CI −0.00250 to −0.00058; P=.002). Conclusions: Evidence of an acute stimulus-response model was observed between sedentary time, physical activity, and glucose variability in low fitness individuals, with sedentary time and light activity conferring the most consistent changes in glucose variability. Further work is required to investigate the coupling of movement behaviors and glucose responses in larger samples and whether providing these rich data sources as feedback could induce lifestyle behavior change. %M 29724703 %R 10.2196/mhealth.9471 %U http://mhealth.jmir.org/2018/5/e114/ %U https://doi.org/10.2196/mhealth.9471 %U http://www.ncbi.nlm.nih.gov/pubmed/29724703 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 2 %N 1 %P e10057 %T Mobile Phone Apps to Support Heart Failure Self-Care Management: Integrative Review %A Athilingam,Ponrathi %A Jenkins,Bradlee %+ University of South Florida, 12901 Bruce B Downs Blvd, MDN 22, Tampa, FL,, United States, 1 813 974 7526, pathilin@health.usf.edu %K heart failure %K self-care management %K mobile health %D 2018 %7 02.05.2018 %9 Review %J JMIR Cardio %G English %X Background: With an explosive growth in mobile health, an estimated 500 million patients are potentially using mHealth apps for supporting health and self-care of chronic diseases. Therefore, this review focused on mHealth apps for use among patients with heart failure. Objective: The aim of this integrative review was to identify and assess the functionalities of mHealth apps that provided usability and efficacy data and apps that are commercially available without supporting data, all of which are to support heart failure self-care management and thus impact heart failure outcomes. Methods: A search of published, peer-reviewed literature was conducted for studies of technology-based interventions that used mHealth apps specific for heart failure. The initial database search yielded 8597 citations. After filters for English language and heart failure, the final 487 abstracts was reviewed. After removing duplicates, a total of 18 articles that tested usability and efficacy of mobile apps for heart failure self-management were included for review. Google Play and Apple App Store were searched with specified criteria to identify mHealth apps for heart failure. A total of 26 commercially available apps specific for heart failure were identified and rated using the validated Mobile Application Rating Scale. Results: The review included studies with low-quality design and sample sizes ranging from 7 to 165 with a total sample size of 847 participants from all 18 studies. Nine studies assessed usability of the newly developed mobile health system. Six of the studies included are randomized controlled trials, and 4 studies are pilot randomized controlled trials with sample sizes of fewer than 40. There were inconsistencies in the self-care components tested, increasing bias. Thus, risk of bias was assessed using the Cochrane Collaboration’s tool for risk of selection, performance, detection, attrition, and reporting biases. Most studies included in this review are underpowered and had high risk of bias across all categories. Three studies failed to provide enough information to allow for a complete assessment of bias, and thus had unknown or unclear risk of bias. This review on the commercially available apps demonstrated many incomplete apps, many apps with bugs, and several apps with low quality. Conclusions: The heterogeneity of study design, sample size, intervention components, and outcomes measured precluded the performance of a systematic review or meta-analysis, thus introducing bias of this review. Although the heart failure–related outcomes reported in this review vary, they demonstrated trends toward making an impact and offer a potentially cost-effective solution with 24/7 access to symptom monitoring as a point of care solution, promoting patient engagement in their own home care. %M 31758762 %R 10.2196/10057 %U http://cardio.jmir.org/2018/1/e10057/ %U https://doi.org/10.2196/10057 %U http://www.ncbi.nlm.nih.gov/pubmed/31758762 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e102 %T Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity %A Düking,Peter %A Fuss,Franz Konstantin %A Holmberg,Hans-Christer %A Sperlich,Billy %+ Integrative & Experimental Exercise Science & Training, Institute for Sport Sciences, University of Würzburg, Judenbühlweg 11, Würzburg, 97082, Germany, 49 931 31 ext 8479, peterdueking@gmx.de %K activity tracker %K data mining %K Internet of Things %K load management %K physical activity %K smartwatch %D 2018 %7 30.04.2018 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies. %M 29712629 %R 10.2196/mhealth.9341 %U http://mhealth.jmir.org/2018/4/e102/ %U https://doi.org/10.2196/mhealth.9341 %U http://www.ncbi.nlm.nih.gov/pubmed/29712629 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 4 %P e110 %T The Unanticipated Challenges Associated With Implementing an Observational Study Protocol in a Large-Scale Physical Activity and Global Positioning System Data Collection %A McCrorie,Paul %A Walker,David %A Ellaway,Anne %+ MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, 200 Renfield Street, Glasgow,, United Kingdom, 44 0141 353 7620, paul.mccrorie@glasgow.ac.uk %K physical activity %K children %K data collection %K postal survey %D 2018 %7 30.04.2018 %9 Viewpoint %J JMIR Res Protoc %G English %X Background: Large-scale primary data collections are complex, costly, and time-consuming. Study protocols for trial-based research are now commonplace, with a growing number of similar pieces of work being published on observational research. However, useful additions to the literature base are publications that describe the issues and challenges faced while conducting observational studies. These can provide researchers with insightful knowledge that can inform funding proposals or project development work. Objectives: In this study, we identify and reflectively discuss the unforeseen or often unpublished issues associated with organizing and implementing a large-scale objectively measured physical activity and global positioning system (GPS) data collection. Methods: The SPACES (Studying Physical Activity in Children’s Environments across Scotland) study was designed to collect objectively measured physical activity and GPS data from 10- to 11-year-old children across Scotland, using a postal delivery method. The 3 main phases of the project (recruitment, delivery of project materials, and data collection and processing) are described within a 2-stage framework: (1) intended design and (2) implementation of the intended design. Results: Unanticipated challenges arose, which influenced the data collection process; these encompass four main impact categories: (1) cost, budget, and funding; (2) project timeline; (3) participation and engagement; and (4) data challenges. The main unforeseen issues that impacted our timeline included the informed consent process for children under the age of 18 years; the use of, and coordination with, the postal service to deliver study information and equipment; and the variability associated with when participants began data collection and the time taken to send devices and consent forms back (1-12 months). Unanticipated budgetary issues included the identification of some study materials (AC power adapter) not fitting through letterboxes, as well as the employment of fieldworkers to increase recruitment and the return of consent forms. Finally, we encountered data issues when processing physical activity and GPS data that had been initiated across daylight saving time. Conclusions: We present learning points and recommendations that may benefit future studies of similar methodology in their early stages of development. %M 29712624 %R 10.2196/resprot.9537 %U http://www.researchprotocols.org/2018/4/e110/ %U https://doi.org/10.2196/resprot.9537 %U http://www.ncbi.nlm.nih.gov/pubmed/29712624 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e101 %T Digital Inequalities in the Use of Self-Tracking Diet and Fitness Apps: Interview Study on the Influence of Social, Economic, and Cultural Factors %A Régnier,Faustine %A Chauvel,Louis %+ Institut National de la Recherche Agronomique, Alimentation et Sciences Sociales Unité de Recherche 1303, University of Paris Saclay, 65 Boulevard de Brandebourg, Ivry sur Seine Cedex, 94205, France, 33 149596914, faustine.regnier@inra.fr %K diet %K digital divide %K fitness trackers %K France %K healthy diet %K physical activity %K social networking %K social participation %K weight loss %D 2018 %7 20.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Digital devices are driving economic and social transformations, but assessing the uses, perceptions, and impact of these new technologies on diet and physical activity remains a major societal challenge. Objective: We aimed to determine under which social, economic, and cultural conditions individuals in France were more likely to be actively invested in the use of self-tracking diet and fitness apps for better health behaviors. Methods: Existing users of 3 diet and fitness self-tracking apps (Weight Watchers, MyFitnessPal, and sport apps) were recruited from 3 regions of France. We interviewed 79 individuals (Weight Watchers, n=37; MyFitnessPal, n=20; sport apps, n=22). In-depth semistructured interviews were conducted with each participant, using open-ended questions about their use of diet and fitness apps. A triangulation of methods (content, textual, and quantitative analyses) was performed. Results: We found 3 clusters of interviewees who differed by social background and curative goal linked to use under constraint versus preventive goal linked to chosen use, and intensity of their self-quantification efforts and participation in social networks. Interviewees used the apps for a diversity of uses, including measurement, tracking, quantification, and participation in digital communities. A digital divide was highlighted, comprising a major social gap. Social conditions for appropriation of self-tracking devices included sociodemographic factors, life course stages, and cross-cutting factors of heterogeneity. Conclusions: Individuals from affluent or intermediate social milieus were most likely to use the apps and to participate in the associated online social networks. These interviewees also demonstrated a preventive approach to a healthy lifestyle. Individuals from lower milieus were more reluctant to use digital devices relating to diet and physical activity or to participate in self-quantification. The results of the study have major implications for public health: the digital self-quantification device is intrinsically less important than the way the individual uses it, in terms of adoption of successful health behaviors. %M 29678807 %R 10.2196/mhealth.9189 %U http://mhealth.jmir.org/2018/4/e101/ %U https://doi.org/10.2196/mhealth.9189 %U http://www.ncbi.nlm.nih.gov/pubmed/29678807 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e100 %T A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation %A Magistro,Daniele %A Sessa,Salvatore %A Kingsnorth,Andrew P %A Loveday,Adam %A Simeone,Alessandro %A Zecca,Massimiliano %A Esliger,Dale W %+ School of Sport, Exercise, and Health Sciences, Loughborough University, Epinal Way, Loughborough, LE11 3TU, United Kingdom, 44 0 7541 703272, D.Magistro@lboro.ac.uk %K context %K indoor location %K activity monitor %K behavior %K wearable sensor %K beacons/proximity %K algorithm %K physical activity %K sedentary behavior %D 2018 %7 20.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Unfortunately, global efforts to promote “how much” physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets. Objective: The aim of this study was to develop and implement an innovative algorithm to determine “where” physical activity occurs using proximity sensors coupled with a widely used physical activity monitor. Methods: A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters. Results: Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R2=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R2=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer. Conclusions: This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of “context sensing” will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction). %M 29678806 %R 10.2196/mhealth.8516 %U http://mhealth.jmir.org/2018/4/e100/ %U https://doi.org/10.2196/mhealth.8516 %U http://www.ncbi.nlm.nih.gov/pubmed/29678806 %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 e94 %T Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study %A Xie,Junqing %A Wen,Dong %A Liang,Lizhong %A Jia,Yuxi %A Gao,Li %A Lei,Jianbo %+ Center for Medical Informatics, Peking University, 38 Xueyuan Rd, Haidian District,, Beijing, 100191, China, 86 10 8280 5901, jblei@hsc.pku.edu.cn %K wearable electronic devices %K fitness trackers %K data accuracy %K physical activity %D 2018 %7 12.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. Objective: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. Methods: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Results: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). Conclusions: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states. %M 29650506 %R 10.2196/mhealth.9754 %U http://mhealth.jmir.org/2018/4/e94/ %U https://doi.org/10.2196/mhealth.9754 %U http://www.ncbi.nlm.nih.gov/pubmed/29650506 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 1 %N 1 %P e3 %T Combining Activity Trackers With Motivational Interviewing and Mutual Support to Increase Physical Activity in Parent-Adolescent Dyads: Longitudinal Observational Feasibility Study %A Bianchi-Hayes,Josette %A Schoenfeld,Elinor %A Cataldo,Rosa %A Hou,Wei %A Messina,Catherine %A Pati,Susmita %+ Department of Pediatrics, Stony Brook University & Stony Brook Children's Hospital, HSC T-11, Rm 060, Stony Brook, NY, 11794-8111, United States, 1 631 444 7203, josette.bianchi-hayes@stonybrookmedicine.edu %K adolescent obesity %K activity trackers %K dyads %K motivation %K physical activity %K adolescent health %K pediatric obesity %K fitness trackers %K parent-child relations %K motivation %K exercise %D 2018 %7 12.04.2018 %9 Original Paper %J JMIR Pediatr Parent %G English %X Background: An essential component of any effective adolescent weight management program is physical activity (PA). PA levels drop dramatically in adolescence, contributing to the rising prevalence of adolescent obesity. Therefore, finding innovative interventions to address this decline in PA may help adolescents struggling with weight issues. The growing field of health technology provides potential solutions for addressing chronic health issues and lifestyle change, such as adolescent obesity. Activity trackers, used in conjunction with smartphone apps, can engage, motivate, and foster support among users while simultaneously providing feedback on their PA progress. Objective: The objective of our study was to evaluate the effect of a 10-week pilot study using smartphone-enabled activity tracker data to tailor motivation and goal setting on PA for overweight and obese adolescents and their parents. Methods: We queried enrolled adolescents, aged 14 to 16 years, with a body mass index at or above the 85th percentile, and 1 of their parents as to behaviors, barriers to change, and perceptions about exercise and health before and after the intervention. We captured daily step count and active minutes via activity trackers. Staff made phone calls to dyads at weeks 1, 2, 4, and 8 after enrollment to set daily personalized step-count and minutes goals based on their prior data and age-specific US national guidelines. We evaluated dyad correlations using nonparametric Spearman rank order correlations. Results: We enrolled 9 parent-adolescent dyads. Mean adolescent age was 15 (SD 0.9) years (range 14-16 years; 4 female and 5 male participants); mean parent age was 47 (SD 8.0) years (range 36-66 years). On average, adolescents met their personalized daily step-count goals on 35% (range 11%-62%) of the days they wore their trackers; parents did so on 39% (range 3%-68%) of the days they wore their trackers. Adolescents met their active-minutes goals on 55% (range 27%-85%) of the days they wore their trackers; parents did so on 83% (range 52%-97%) of the days. Parent and adolescent success was strongly correlated (step count: r=.36, P=.001; active minutes: r=.30, P=.007). Parental age was inversely correlated with step-count success (r=–.78, P=.01). Conclusions: Our findings illustrate that parent-adolescent dyads have highly correlated PA success rates. This supports further investigation of family-centered weight management interventions for adolescents, particularly those that involve the parent and the adolescent working together. %M 31518313 %R 10.2196/pediatrics.8878 %U http://pediatrics.jmir.org/2018/1/e3/ %U https://doi.org/10.2196/pediatrics.8878 %U http://www.ncbi.nlm.nih.gov/pubmed/31518313 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e84 %T Findings of the Chronic Obstructive Pulmonary Disease-Sitting and Exacerbations Trial (COPD-SEAT) in Reducing Sedentary Time Using Wearable and Mobile Technologies With Educational Support: Randomized Controlled Feasibility Trial %A Orme,Mark W %A Weedon,Amie E %A Saukko,Paula M %A Esliger,Dale W %A Morgan,Mike D %A Steiner,Michael C %A Downey,John W %A Sherar,Lauren B %A Singh,Sally J %+ Centre for Exercise and Rehabilitation Science, National Institute for Health Research Leicester Biomedical Research Centre - Respiratory, Glenfield Hospital, Groby Road, Leicester, LE39QP, United Kingdom, 44 1162502762, mark.orme@uhl-tr.nhs.uk %K chronic obstructive pulmonary disease %K feasibility %K fitness trackers %K intervention %K physical activity %K sedentary lifestyle %K sedentary time %K self-monitoring %K wearable electronic devices %D 2018 %7 11.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Targeting sedentary time post exacerbation may be more relevant than targeting structured exercise for individuals with chronic obstructive pulmonary disease. Focusing interventions on sitting less and moving more after an exacerbation may act as a stepping stone to increase uptake to pulmonary rehabilitation. Objective: The aim of this paper was to conduct a randomized trial examining trial feasibility and the acceptability of an education and self-monitoring intervention using wearable technology to reduce sedentary behavior for individuals with chronic obstructive pulmonary disease admitted to hospital for an acute exacerbation. Methods: Participants were recruited and randomized in hospital into 3 groups, with the intervention lasting 2 weeks post discharge. The Education group received verbal and written information about reducing their time in sedentary behavior, sitting face-to-face with a study researcher. The Education+Feedback group received the same education component along with real-time feedback on their sitting time, stand-ups, and steps at home through a waist-worn inclinometer linked to an app. Patients were shown how to use the technology by the same study researcher. The inclinometer also provided vibration prompts to encourage movement at patient-defined intervals of time. Patients and health care professionals involved in chronic obstructive pulmonary disease exacerbation care were interviewed to investigate trial feasibility and acceptability of trial design and methods. Main quantitative outcomes of trial feasibility were eligibility, uptake, and retention, and for acceptability, were behavioral responses to the vibration prompts. Results: In total, 111 patients were approached with 33 patients recruited (11 Control, 10 Education, and 12 Education+Feedback). Retention at 2-week follow-up was 52% (17/33; n=6 for Control, n=3 for Education, and n=8 for Education+Feedback). No study-related adverse events occurred. Collectively, patients responded to 106 out of 325 vibration prompts from the waist-worn inclinometer (32.62%). Within 5 min of the prompt, 41% of responses occurred, with patients standing for a mean 1.4 (SD 0.8) min and walking for 0.4 (SD 0.3) min (21, SD 11, steps). Interviews indicated that being unwell and overwhelmed after an exacerbation was the main reason for not engaging with the intervention. Health care staff considered reducing sedentary behavior potentially attractive for patients but suggested starting the intervention as an inpatient. Conclusions: Although the data support that it was feasible to conduct the trial, modifications are needed to improve participant retention. The intervention was acceptable to most patients and health care professionals. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 13790881; http://www.isrctn.com/ISRCTN13790881 (Archived by WebCite at http://www.webcitation.org/6xmnRGjFf) %M 29643055 %R 10.2196/mhealth.9398 %U http://mhealth.jmir.org/2018/4/e84/ %U https://doi.org/10.2196/mhealth.9398 %U http://www.ncbi.nlm.nih.gov/pubmed/29643055 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e86 %T Wearable Activity Tracker Use Among Australian Adolescents: Usability and Acceptability Study %A Ridgers,Nicola D %A Timperio,Anna %A Brown,Helen %A Ball,Kylie %A Macfarlane,Susie %A Lai,Samuel K %A Richards,Kara %A Mackintosh,Kelly A %A McNarry,Melitta A %A Foster,Megan %A Salmon,Jo %+ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, 3125, Australia, 61 3 9244 6718, nicky.ridgers@deakin.edu.au %K qualitative research %K fitness trackers %K physical activity %D 2018 %7 11.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers have the potential to be integrated into physical activity interventions, yet little is known about how adolescents use these devices or perceive their acceptability. Objective: The aim of this study was to examine the usability and acceptability of a wearable activity tracker among adolescents. A secondary aim was to determine adolescents’ awareness and use of the different functions and features in the wearable activity tracker and accompanying app. Methods: Sixty adolescents (aged 13-14 years) in year 8 from 3 secondary schools in Melbourne, Australia, were provided with a wrist-worn Fitbit Flex and accompanying app, and were asked to use it for 6 weeks. Demographic data (age, sex) were collected via a Web-based survey completed during week 1 of the study. At the conclusion of the 6-week period, all adolescents participated in focus groups that explored their perceptions of the usability and acceptability of the Fitbit Flex, accompanying app, and Web-based Fitbit profile. Qualitative data were analyzed using pen profiles, which were constructed from verbatim transcripts. Results: Adolescents typically found the Fitbit Flex easy to use for activity tracking, though greater difficulties were reported for monitoring sleep. The Fitbit Flex was perceived to be useful for tracking daily activities, and adolescents used a range of features and functions available through the device and the app. Barriers to use included the comfort and design of the Fitbit Flex, a lack of specific feedback about activity levels, and the inability to wear the wearable activity tracker for water-based sports. Conclusions: Adolescents reported that the Fitbit Flex was easy to use and that it was a useful tool for tracking daily activities. A number of functions and features were used, including the device’s visual display to track and self-monitor activity, goal-setting in the accompanying app, and undertaking challenges against friends. However, several barriers to use were identified, which may impact on sustained use over time. Overall, wearable activity trackers have the potential to be integrated into physical activity interventions targeted at adolescents, but both the functionality and wearability of the monitor should be considered. %M 29643054 %R 10.2196/mhealth.9199 %U http://mhealth.jmir.org/2018/4/e86/ %U https://doi.org/10.2196/mhealth.9199 %U http://www.ncbi.nlm.nih.gov/pubmed/29643054 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e70 %T Counting Steps in Activities of Daily Living in People With a Chronic Disease Using Nine Commercially Available Fitness Trackers: Cross-Sectional Validity Study %A Ummels,Darcy %A Beekman,Emmylou %A Theunissen,Kyra %A Braun,Susy %A Beurskens,Anna J %+ Research Centre for Autonomy and Participation of Persons with a Chronic Illness, Zuyd University of Applied Sciences, PO Box 550, Heerlen,, Netherlands, 31 45 400 63 78, darcy.ummels@zuyd.nl %K activity tracker %K accelerometer %K wearable %K chronic disease %K validity %K physical therapy %K physical activity %D 2018 %7 02.04.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Measuring physical activity with commercially available activity trackers is gaining popularity. People with a chronic disease can especially benefit from knowledge about their physical activity pattern in everyday life since sufficient physical activity can contribute to wellbeing and quality of life. However, no validity data are available for this population during activities of daily living. Objective: The aim of this study was to investigate the validity of 9 commercially available activity trackers for measuring step count during activities of daily living in people with a chronic disease receiving physiotherapy. Methods: The selected activity trackers were Accupedo (Corusen LLC), Activ8 (Remedy Distribution Ltd), Digi-Walker CW-700 (Yamax), Fitbit Flex (Fitbit inc), Lumoback (Lumo Bodytech), Moves (ProtoGeo Oy), Fitbit One (Fitbit inc), UP24 (Jawbone), and Walking Style X (Omron Healthcare Europe BV). In total, 130 persons with chronic diseases performed standardized activity protocols based on activities of daily living that were recorded on video camera and analyzed for step count (gold standard). The validity of the trackers’ step count was assessed by correlation coefficients, t tests, scatterplots, and Bland-Altman plots. Results: The correlations between the number of steps counted by the activity trackers and the gold standard were low (range: –.02 to .33). For all activity trackers except for Fitbit One, a significant systematic difference with the gold standard was found for step count. Plots showed a wide range in scores for all activity trackers; Activ8 showed an average overestimation and the other 8 trackers showed underestimations. Conclusions: This study showed that the validity of 9 commercially available activity trackers is low measuring steps while individuals with chronic diseases receiving physiotherapy engage in activities of daily living. %M 29610110 %R 10.2196/mhealth.8524 %U http://mhealth.jmir.org/2018/4/e70/ %U https://doi.org/10.2196/mhealth.8524 %U http://www.ncbi.nlm.nih.gov/pubmed/29610110 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 4 %P e10009 %T Connecting Smartphone and Wearable Fitness Tracker Data with a Nationally Used Electronic Health Record System for Diabetes Education to Facilitate Behavioral Goal Monitoring in Diabetes Care: Protocol for a Pragmatic Multi-Site Randomized Trial %A Wang,Jing %A Coleman,Deidra Carroll %A Kanter,Justin %A Ummer,Brad %A Siminerio,Linda %+ Cizik School of Nursing, The University of Texas Health Science Center at Houston, 6901 Bertner Avenue, SON 580C, Houston, TX, 77030, United States, 1 7135009022, jing.wang@uth.tmc.edu %K wearable devices %K connected health %K mobile health %K diabetes %K randomized clinical trial %K goal setting %K lifestyle intervention %K electronic health record %K self-monitoring %K behavior modification %D 2018 %7 02.04.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Mobile and wearable technology have been shown to be effective in improving diabetes self-management; however, integrating data from these technologies into clinical diabetes care to facilitate behavioral goal monitoring has not been explored. Objective: The objective of this paper is to report on a study protocol for a pragmatic multi-site trial along with the intervention components, including the detailed connected health interface. This interface was developed to integrate patient self-monitoring data collected from a wearable fitness tracker and its companion smartphone app to an electronic health record system for diabetes self-management education and support (DSMES) to facilitate behavioral goal monitoring. Methods: A 3-month multi-site pragmatic clinical trial was conducted with eligible patients with diabetes mellitus from DSMES programs. The Chronicle Diabetes system is currently freely available to diabetes educators through American Diabetes Association–recognized DSMES programs to set patient nutrition and physical activity goals. To integrate the goal-setting and self-monitoring intervention into the DSMES process, a connected interface in the Chronicle Diabetes system was developed. With the connected interface, patient self-monitoring information collected from smartphones and wearable fitness trackers can facilitate educators’ monitoring of patients’ adherence to their goals. Feasibility outcomes of the 3-month trial included hemoglobin A1c levels, weight, and the usability of the connected system. Results: An interface designed to connect data from a wearable fitness tracker with a companion smartphone app for nutrition and physical activity self-monitoring into a diabetes education electronic health record system was successfully developed to enable diabetes educators to facilitate goal setting and monitoring. A total of 60 eligible patients with type 2 diabetes mellitus were randomized into either group 1) standard diabetes education or 2) standard education enhanced with the connected system. Data collection for the 3-month pragmatic trial is completed. Data analysis is in progress. Conclusions: If results of the pragmatic multi-site clinical trial show preliminary efficacy and usability of the connected system, a large-scale implementation trial will be conducted. Trial Registration: ClinicalTrials.gov NCT02664233; https://clinicaltrials.gov/ct2/show/NCT02664233 (Archived by WebCite at http://www.webcitation.org/6yDEwXHo5) %M 29610111 %R 10.2196/10009 %U http://www.researchprotocols.org/2018/4/e10009/ %U https://doi.org/10.2196/10009 %U http://www.ncbi.nlm.nih.gov/pubmed/29610111 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 3 %P e67 %T Monitoring Energy Balance in Breast Cancer Survivors Using a Mobile App: Reliability Study %A Lozano-Lozano,Mario %A Galiano-Castillo,Noelia %A Martín-Martín,Lydia %A Pace-Bedetti,Nicolás %A Fernández-Lao,Carolina %A Arroyo-Morales,Manuel %A Cantarero-Villanueva,Irene %+ Department of Physical Therapy, University of Granada, Avenida de la Ilustración, 60, Granada, 18016, Spain, 34 958248765, marroyo@ugr.es %K telemedicine %K breast neoplasms %K survivors %K life style %K exercise %K diet %K mhealth %D 2018 %7 27.03.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The majority of breast cancer survivors do not meet recommendations in terms of diet and physical activity. To address this problem, we developed a mobile health (mHealth) app for assessing and monitoring healthy lifestyles in breast cancer survivors, called the Energy Balance on Cancer (BENECA) mHealth system. The BENECA mHealth system is a novel and interactive mHealth app, which allows breast cancer survivors to engage themselves in their energy balance monitoring. BENECA was designed to facilitate adherence to healthy lifestyles in an easy and intuitive way. Objective: The objective of the study was to assess the concurrent validity and test-retest reliability between the BENECA mHealth system and the gold standard assessment methods for diet and physical activity. Methods: A reliability study was conducted with 20 breast cancer survivors. In the study, tri-axial accelerometers (ActiGraphGT3X+) were used as gold standard for 8 consecutive days, in addition to 2, 24-hour dietary recalls, 4 dietary records, and sociodemographic questionnaires. Two-way random effect intraclass correlation coefficients, a linear regression-analysis, and a Passing-Bablok regression were calculated. Results: The reliability estimates were very high for all variables (alpha≥.90). The lowest reliability was found in fruit and vegetable intakes (alpha=.94). The reliability between the accelerometer and the dietary assessment instruments against the BENECA system was very high (intraclass correlation coefficient=.90). We found a mean match rate of 93.51% between instruments and a mean phantom rate of 3.35%. The Passing-Bablok regression analysis did not show considerable bias in fat percentage, portions of fruits and vegetables, or minutes of moderate to vigorous physical activity. Conclusions: The BENECA mHealth app could be a new tool to measure energy balance in breast cancer survivors in a reliable and simple way. Our results support the use of this technology to not only to encourage changes in breast cancer survivors' lifestyles, but also to remotely monitor energy balance. Trial Registration: ClinicalTrials.gov NCT02817724; https://clinicaltrials.gov/ct2/show/NCT02817724 (Archived by WebCite at http://www.webcitation.org/6xVY1buCc) %M 29588273 %R 10.2196/mhealth.9669 %U http://mhealth.jmir.org/2018/3/e67/ %U https://doi.org/10.2196/mhealth.9669 %U http://www.ncbi.nlm.nih.gov/pubmed/29588273 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 3 %P e58 %T Evaluating the Impact of Physical Activity Apps and Wearables: Interdisciplinary Review %A McCallum,Claire %A Rooksby,John %A Gray,Cindy M %+ Institute of Health and Wellbeing, University of Glasgow, Room 142, 25-29 Bute Gardens, Glasgow, G12 8RS, United Kingdom, 44 141 330 4615, c.mccallum.2@research.gla.ac.uk %K mobile health %K physical activity %K smartphone %K fitness trackers %K wearable electronic devices %K research design %K evaluation studies as topic %K efficiency %D 2018 %7 23.03.2018 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines. %M 29572200 %R 10.2196/mhealth.9054 %U http://mhealth.jmir.org/2018/3/e58/ %U https://doi.org/10.2196/mhealth.9054 %U http://www.ncbi.nlm.nih.gov/pubmed/29572200 %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 %@ 1438-8871 %I JMIR Publications %V 20 %N 3 %P e106 %T Just-in-Time Feedback in Diet and Physical Activity Interventions: Systematic Review and Practical Design Framework %A Schembre,Susan M %A Liao,Yue %A Robertson,Michael C %A Dunton,Genevieve Fridlund %A Kerr,Jacqueline %A Haffey,Meghan E %A Burnett,Taylor %A Basen-Engquist,Karen %A Hicklen,Rachel S %+ Department of Behavioral Science, Division of Cancer Control and Population Sciences, The University of Texas MD Anderson Cancer Center, Unit 1330, PO Box 301439, Houston, TX, 77230-1439, United States, 1 713 563 5858, sschembre@mdanderson.org %K health behavior %K diet %K exercise %K task performance and analysis %K Internet %K mHealth %K accelerometer %K activity monitor %K self-tracking %K wearable sensors %D 2018 %7 22.03.2018 %9 Review %J J Med Internet Res %G English %X Background: The integration of body-worn sensors with mobile devices presents a tremendous opportunity to improve just-in-time behavioral interventions by enhancing bidirectional communication between investigators and their participants. This approach can be used to deliver supportive feedback at critical moments to optimize the attainment of health behavior goals. Objective: The goals of this systematic review were to summarize data on the content characteristics of feedback messaging used in diet and physical activity (PA) interventions and to develop a practical framework for designing just-in-time feedback for behavioral interventions. Methods: Interventions that included just-in-time feedback on PA, sedentary behavior, or dietary intake were eligible for inclusion. Feedback content and efficacy data were synthesized descriptively. Results: The review included 31 studies (15/31, 48%, targeting PA or sedentary behavior only; 13/31, 42%, targeting diet and PA; and 3/31, 10%, targeting diet only). All studies used just-in-time feedback, 30 (97%, 30/31) used personalized feedback, and 24 (78%, 24/31) used goal-oriented feedback, but only 5 (16%, 5/31) used actionable feedback. Of the 9 studies that tested the efficacy of providing feedback to promote behavior change, 4 reported significant improvements in health behavior. In 3 of these 4 studies, feedback was continuously available, goal-oriented, or actionable. Conclusions: Feedback that was continuously available, personalized, and actionable relative to a known behavioral objective was prominent in intervention studies with significant behavior change outcomes. Future research should determine whether all or some of these characteristics are needed to optimize the effect of feedback in just-in-time interventions. %M 29567638 %R 10.2196/jmir.8701 %U http://www.jmir.org/2018/3/e106/ %U https://doi.org/10.2196/jmir.8701 %U http://www.ncbi.nlm.nih.gov/pubmed/29567638 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 7 %N 1 %P e5 %T Usage, Acceptability, and Effectiveness of an Activity Tracker in a Randomized Trial of a Workplace Sitting Intervention: Mixed-Methods Evaluation %A Brakenridge,Charlotte L %A Healy,Genevieve N %A Winkler,Elisabeth AH %A Fjeldsoe,Brianna S %+ School of Public Health, The University of Queensland, Herston Rd, Brisbane, 4006, Australia, 61 733655163, c.brakenridge@uq.edu.au %K wearable electronic devices %K fitness trackers %K sedentary lifestyle %K exercise %K workplace %K adult %D 2018 %7 02.03.2018 %9 Original Paper %J Interact J Med Res %G English %X Background: Wearable activity trackers are now a common feature of workplace wellness programs; however, their ability to impact sitting time (the behavior in which most of the desk-based workday is spent) is relatively unknown. This study evaluated the LUMOback, an activity tracker that targets sitting time, as part of a cluster-randomized workplace sitting intervention in desk-based office workers. Objective: Study objectives were to explore: (1) office workers’ self-directed LUMOback use, (2) individual-level characteristics associated with LUMOback use, (3) the impact of LUMOback use on activity and sitting behaviors, and (4) office workers’ perceived LUMOback acceptability. Methods: Exploratory analyses were conducted within the activity tracker intervention group (n=66) of a 2-arm cluster-randomized trial (n=153) with follow-up at 3 and 12 months. The intervention, delivered from within the workplace, consisted of organizational support strategies (eg, manager support, emails) to stand up, sit less, and move more, plus the provision of a LUMOback activity tracker. The LUMOback, worn belted around the waist, provides real-time sitting feedback through a mobile app. LUMOback usage data (n=62), Web-based questionnaires (n=33), activPAL-assessed sitting, prolonged (≥30 min bouts) and nonprolonged (<30 min bouts) sitting, standing and stepping time (7-day, 24 h/day protocol; n=40), and telephone interviews (n=27) were used to evaluate study aims. LUMOback usage data were downloaded and described. Associations between user characteristics and LUMOback usage (in the first 3 months) were analyzed using zero-inflated negative binomial models. Associations between LUMOback usage and 3-month activity outcomes were analyzed using mixed models, correcting for cluster. LUMOback acceptability was explored using 3-month questionnaire data and thematic analysis of telephone interviews (conducted 6 to 10 months post intervention commencement). Results: Tracker uptake was modest (43/61, 70%), and among users, usage over the first 3 months was low (1-48 days, median 8). Usage was greatest among team leaders and those with low self-perceived scores for job control and supervisor relationships. Greater tracker use (≥5 days vs <5 days) was significantly associated only with changes in prolonged unbroken sitting (−50.7 min/16 h; 95% CI −94.0 to −7.3; P=.02) during all waking hours, and changes in nonprolonged sitting (+32.5 min/10 h; 95% CI 5.0 to 59.9; P=.02) during work hours. Participants found the LUMOback easy to use but only somewhat comfortable. Qualitatively, participants valued the real-time app feedback. Nonuptake was attributed to being busy and setup issues. Low usage was attributed to discomfort wearing the LUMOback. Conclusions: The LUMOback—although able to reduce prolonged sitting time—was only used to a limited extent, and its low usage may provide a partial explanation for the limited behavior changes that occurred. Discomfort limited the feasibility of the LUMOback for ongoing use. Such findings yield insight into how to improve upon implementing activity trackers in workplace settings. %M 29500158 %R 10.2196/ijmr.9001 %U http://www.i-jmr.org/2018/1/e5/ %U https://doi.org/10.2196/ijmr.9001 %U http://www.ncbi.nlm.nih.gov/pubmed/29500158 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 2 %N 1 %P e6 %T Measuring Moderate-Intensity Exercise with the Apple Watch: Validation Study %A Abt,Grant %A Bray,James %A Benson,Amanda Clare %+ School of Life Sciences, The University of Hull, Cottingham Road, Kingston upon Hull, HU6 7RX, United Kingdom, 44 01482463397, g.abt@hull.ac.uk %K smartwatch %K wearables %K technology %K physical activity %K cardiovascular health, Apple Watch %D 2018 %7 28.02.2018 %9 Original Paper %J JMIR Cardio %G English %X Background: Moderate fitness levels and habitual exercise have a protective effect for cardiovascular disease, stroke, type 2 diabetes, and all-cause mortality. The Apple Watch displays exercise completed at an intensity of a brisk walk or above using a green “exercise” ring. However, it is unknown if the exercise ring accurately represents an exercise intensity comparable to that defined as moderate-intensity. In order for health professionals to prescribe exercise intensity with confidence, consumer wearable devices need to be accurate and precise if they are to be used as part of a personalized medicine approach to disease management. Objective: The aim of this study was to examine the validity and reliability of the Apple Watch for measuring moderate-intensity exercise, as defined as 40-59% oxygen consumption reserve (VO2R). Methods: Twenty recreationally active participants completed resting oxygen consumption (VO2rest) and maximal oxygen consumption (VO2 max) tests prior to a series of 5-minute bouts of treadmill walking at increasing speed while wearing an Apple Watch on both wrists, and with oxygen consumption measured continuously. Five-minute exercise bouts were added until the Apple Watch advanced the green “exercise” ring by 5 minutes (defined as the treadmill inflection speed). Validity was examined using a one-sample t-test, with interdevice and intradevice reliability reported as the standardized typical error and intraclass correlation. Results: The mean %VO2R at the treadmill inflection speed was 30% (SD 7) for both Apple Watches. There was a large underestimation of moderate-intensity exercise (left hand: mean difference = -10% [95% CI -14 to -7], d=-1.4; right hand: mean difference = -10% [95% CI -13 to -7], d=-1.5) when compared to the criterion of 40% VO2R. Standardized typical errors for %VO2R at the treadmill inflection speed were small to moderate, with intraclass correlations higher within trials compared to between trials. Conclusions: The Apple Watch threshold for moderate-intensity exercise was lower than the criterion, which would lead to an overestimation of moderate-intensity exercise minutes completed throughout the day. %M 31758766 %R 10.2196/cardio.8574 %U http://cardio.jmir.org/2018/1/e6/ %U https://doi.org/10.2196/cardio.8574 %U http://www.ncbi.nlm.nih.gov/pubmed/31758766 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 2 %P e49 %T Clinical Feasibility of Continuously Monitored Data for Heart Rate, Physical Activity, and Sleeping by Wearable Activity Trackers in Patients with Thyrotoxicosis: Protocol for a 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, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic Of Korea, 82 31 787 7068, jaemoon76@gmail.com %K activity tracker %K pulse rate %K thyrotoxicosis %K hyperthyroidism %K Graves’ disease %D 2018 %7 21.02.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Thyrotoxicosis is a common disease caused by an excess of thyroid hormones. The prevalence of thyrotoxicosis about 2% and 70-90% of thyrotoxicosis cases are caused by Graves' disease, an autoimmune disease, which has a high recurrence rate when treated with antithyroid drugs such as methimazole or propylthiouracil. The clinical symptoms and signs of thyrotoxicosis include palpitation, weight loss, restlessness, and difficulty sleeping. Although these clinical changes in thyrotoxicosis can be detected by currently available wearable activity trackers, there have been few trials of the clinical application of wearable devices in patients with thyrotoxicosis. Objective: The aim of this study is to investigate the clinical applicability of wearable device-generated data to the management of thyrotoxicosis. We are analyzing continuously monitored data for heart rate, physical activity, and sleep in patients with thyrotoxicosis during their clinical course after treatment. Methods: Thirty thyrotoxic patients and 10 control subjects were enrolled in this study at Seoul National University Bundang Hospital. Heart rate, physical activity, and sleep are being monitored using a Fitbit Charge HR or Fitbit Charge 2. Clinical data including anthropometric measures, thyroid function test, and hyperthyroidism symptom scale are recorded. Results: Study enrollment began in December 2016, and the intervention and follow-up phases are ongoing. The results of the data analysis are expected to be available by September 2017. Conclusions: This study will provide a foundational feasibility trial of the clinical applications of biosignal measurements to the differential diagnosis, prediction of clinical course, early detection of recurrence, and treatment in patients with thyrotoxicosis. Trial Registration: ClinicalTrials.gov NCT03009357; https://clinicaltrials.gov/ct2/show/NCT03009357 (Archived by WebCite at http://www.webcitation.org/6wh4MWPm2) %M 29467121 %R 10.2196/resprot.8119 %U http://www.researchprotocols.org/2018/2/e49/ %U https://doi.org/10.2196/resprot.8119 %U http://www.ncbi.nlm.nih.gov/pubmed/29467121 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 2 %P e42 %T Peer Coaching Through mHealth Targeting Physical Activity in People With Parkinson Disease: Feasibility Study %A Colón-Semenza,Cristina %A Latham,Nancy K %A Quintiliani,Lisa M %A Ellis,Terry D %+ Center for Neurorehabilitation, Department of Physical Therapy & Athletic Training, College of Health & Rehabilitation Sciences: Sargent College, Boston University, 635 Commonwealth Ave, Boston, MA, 02215, United States, 1 6173537571, tellis@bu.edu %K Parkinson disease %K exercise %K telemedicine %K social support %K fitness tracker %D 2018 %7 15.02.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Long-term engagement in exercise and physical activity mitigates the progression of disability and increases quality of life in people with Parkinson disease (PD). Despite this, the vast majority of individuals with PD are sedentary. There is a critical need for a feasible, safe, acceptable, and effective method to assist those with PD to engage in active lifestyles. Peer coaching through mobile health (mHealth) may be a viable approach. Objective: The purpose of this study was to develop a PD-specific peer coach training program and a remote peer-mentored walking program using mHealth technology with the goal of increasing physical activity in persons with PD. We set out to examine the feasibility, safety, and acceptability of the programs along with preliminary evidence of individual-level changes in walking activity, self-efficacy, and disability in the peer mentees. Methods: A peer coach training program and a remote peer-mentored walking program using mHealth was developed and tested in 10 individuals with PD. We matched physically active persons with PD (peer coaches) with sedentary persons with PD (peer mentees), resulting in 5 dyads. Using both Web-based and in-person delivery methods, we trained the peer coaches in basic knowledge of PD, exercise, active listening, and motivational interviewing. Peer coaches and mentees wore FitBit Zip activity trackers and participated in daily walking over 8 weeks. Peer dyads interacted daily via the FitBit friends mobile app and weekly via telephone calls. Feasibility was determined by examining recruitment, participation, and retention rates. Safety was assessed by monitoring adverse events during the study period. Acceptability was assessed via satisfaction surveys. Individual-level changes in physical activity were examined relative to clinically important differences. Results: Four out of the 5 peer pairs used the FitBit activity tracker and friends function without difficulty. A total of 4 of the 5 pairs completed the 8 weekly phone conversations. There were no adverse events over the course of the study. All peer coaches were “satisfied” or “very satisfied” with the training program, and all participants were “satisfied” or “very satisfied” with the peer-mentored walking program. All participants would recommend this program to others with PD. Increases in average steps per day exceeding the clinically important difference occurred in 4 out of the 5 mentees. Conclusions: Remote peer coaching using mHealth is feasible, safe, and acceptable for persons with PD. Peer coaching using mHealth technology may be a viable method to increase physical activity in individuals with PD. Larger controlled trials are necessary to examine the effectiveness of this approach. %M 29449201 %R 10.2196/mhealth.8074 %U http://mhealth.jmir.org/2018/2/e42/ %U https://doi.org/10.2196/mhealth.8074 %U http://www.ncbi.nlm.nih.gov/pubmed/29449201 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 2 %P e29 %T Patterns of Fitbit Use and Activity Levels Throughout a Physical Activity Intervention: Exploratory Analysis from a Randomized Controlled Trial %A Hartman,Sheri J %A Nelson,Sandahl H %A Weiner,Lauren S %+ Department of Family Medicine and Public Health, University of California San Diego, 3855 Health Sciences Drive, La Jolla, CA, 92093, United States, 1 8585349235, sjhartman@ucsd.edu %K physical activity %K technology %K activity tracker %K self-monitoring %K adherence %D 2018 %7 05.02.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: There has been a rapid increase in the use of technology-based activity trackers to promote behavior change. However, little is known about how individuals use these trackers on a day-to-day basis or how tracker use relates to increasing physical activity. Objective: The aims were to use minute level data collected from a Fitbit tracker throughout a physical activity intervention to examine patterns of Fitbit use and activity and their relationships with success in the intervention based on ActiGraph-measured moderate to vigorous physical activity (MVPA). Methods: Participants included 42 female breast cancer survivors randomized to the physical activity intervention arm of a 12-week randomized controlled trial. The Fitbit One was worn daily throughout the 12-week intervention. ActiGraph GT3X+ accelerometer was worn for 7 days at baseline (prerandomization) and end of intervention (week 12). Self-reported frequency of looking at activity data on the Fitbit tracker and app or website was collected at week 12. Results: Adherence to wearing the Fitbit was high and stable, with a mean of 88.13% of valid days over 12 weeks (SD 14.49%). Greater adherence to wearing the Fitbit was associated with greater increases in ActiGraph-measured MVPA (binteraction=0.35, P<.001). Participants averaged 182.6 minutes/week (SD 143.9) of MVPA on the Fitbit, with significant variation in MVPA over the 12 weeks (F=1.91, P=.04). The majority (68%, 27/40) of participants reported looking at their tracker or looking at the Fitbit app or website once a day or more. Changes in Actigraph-measured MVPA were associated with frequency of looking at one’s data on the tracker (b=−1.36, P=.07) but not significantly associated with frequency of looking at one’s data on the app or website (P=.36). Conclusions: This is one of the first studies to explore the relationship between use of a commercially available activity tracker and success in a physical activity intervention. A deeper understanding of how individuals engage with technology-based trackers may enable us to more effectively use these types of trackers to promote behavior change. Trial Registration: ClinicalTrials.gov NCT02332876; https://clinicaltrials.gov/ct2/show/NCT02332876?term=NCT02332876 &rank=1 (Archived by WebCite at http://www.webcitation.org/6wplEeg8i). %M 29402761 %R 10.2196/mhealth.8503 %U https://mhealth.jmir.org/2018/2/e29/ %U https://doi.org/10.2196/mhealth.8503 %U http://www.ncbi.nlm.nih.gov/pubmed/29402761 %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 2 %P e34 %T Activity Monitors as Support for Older Persons’ Physical Activity in Daily Life: Qualitative Study of the Users’ Experiences %A Ehn,Maria %A Eriksson,Lennie Carlén %A Åkerberg,Nina %A Johansson,Ann-Christin %+ School of Innovation, Design and Engineering, Mälardalen University, Box 883, Västerås, S-721 23, Sweden, 46 21 107093 ext 107093, maria.ehn@mdh.se %K exercise %K behavior %K aged %K seniors %K mobile applications %K fitness trackers %D 2018 %7 01.02.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Falls are a major threat to the health and independence of seniors. Regular physical activity (PA) can prevent 40% of all fall injuries. The challenge is to motivate and support seniors to be physically active. Persuasive systems can constitute valuable support for persons aiming at establishing and maintaining healthy habits. However, these systems need to support effective behavior change techniques (BCTs) for increasing older adults’ PA and meet the senior users’ requirements and preferences. Therefore, involving users as codesigners of new systems can be fruitful. Prestudies of the user’s experience with similar solutions can facilitate future user-centered design of novel persuasive systems. Objective: The aim of this study was to investigate how seniors experience using activity monitors (AMs) as support for PA in daily life. The addressed research questions are as follows: (1) What are the overall experiences of senior persons, of different age and balance function, in using wearable AMs in daily life?; (2) Which aspects did the users perceive relevant to make the measurements as meaningful and useful in the long-term perspective?; and (3) What needs and requirements did the users perceive as more relevant for the activity monitors to be useful in a long-term perspective? Methods: This qualitative interview study included 8 community-dwelling older adults (median age: 83 years). The participants’ experiences in using two commercial AMs together with tablet-based apps for 9 days were investigated. Activity diaries during the usage and interviews after the usage were exploited to gather user experience. Comments in diaries were summarized, and interviews were analyzed by inductive content analysis. Results: The users (n=8) perceived that, by using the AMs, their awareness of own PA had increased. However, the AMs’ impact on the users’ motivation for PA and activity behavior varied between participants. The diaries showed that self-estimated physical effort varied between participants and varied for each individual over time. Additionally, participants reported different types of accomplished activities; talking walks was most frequently reported. To be meaningful, measurements need to provide the user with a reliable receipt of whether his or her current activity behavior is sufficient for reaching an activity goal. Moreover, praise when reaching a goal was described as motivating feedback. To be useful, the devices must be easy to handle. In this study, the users perceived wearables as easy to handle, whereas tablets were perceived difficult to maneuver. Users reported in the diaries that the devices had been functional 78% (58/74) of the total test days. Conclusions: Activity monitors can be valuable for supporting seniors’ PA. However, the potential of the solutions for a broader group of seniors can significantly be increased. Areas of improvement include reliability, usability, and content supporting effective BCTs with respect to increasing older adults’ PA. %M 29391342 %R 10.2196/mhealth.8345 %U http://mhealth.jmir.org/2018/2/e34/ %U https://doi.org/10.2196/mhealth.8345 %U http://www.ncbi.nlm.nih.gov/pubmed/29391342 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 1 %P e33 %T A Wearable Sensor-Based Exercise Biofeedback System: Mixed Methods Evaluation of Formulift %A O'Reilly,Martin Aidan %A Slevin,Patrick %A Ward,Tomas %A Caulfield,Brian %+ Insight Centre for Data Analytics, University College Dublin, O'Brien Centre for Science, 3rd Fl, Belfield, Dublin, D4, Ireland, 353 871245972, martin.oreilly@insight-centre.org %K mHealth %K feedback %K posture %K exercise therapy %K biomedical technology %K lower extremity %K physical therapy specialty %D 2018 %7 31.01.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Formulift is a newly developed mobile health (mHealth) app that connects to a single inertial measurement unit (IMU) worn on the left thigh. The IMU captures users’ movements as they exercise, and the app analyzes the data to count repetitions in real time and classify users’ exercise technique. The app also offers feedback and guidance to users on exercising safely and effectively. Objective: The aim of this study was to assess the Formulift system with three different and realistic types of potential users (beginner gym-goers, experienced gym-goers, and qualified strength and conditioning [S&C] coaches) under a number of categories: (1) usability, (2) functionality, (3) the perceived impact of the system, and (4) the subjective quality of the system. It was also desired to discover suggestions for future improvements to the system. Methods: A total of 15 healthy volunteers participated (12 males; 3 females; age: 23.8 years [SD 1.80]; height: 1.79 m [SD 0.07], body mass: 78.4 kg [SD 9.6]). Five participants were beginner gym-goers, 5 were experienced gym-goers, and 5 were qualified and practicing S&C coaches. IMU data were first collected from each participant to create individualized exercise classifiers for them. They then completed a number of nonexercise-related tasks with the app. Following this, a workout was completed using the system, involving squats, deadlifts, lunges, and single-leg squats. Participants were then interviewed about their user experience and completed the System Usability Scale (SUS) and the user version of the Mobile Application Rating Scale (uMARS). Thematic analysis was completed on all interview transcripts, and survey results were analyzed. Results: Qualitative and quantitative analysis found the system has “good” to “excellent” usability. The system achieved a mean (SD) SUS usability score of 79.2 (8.8). Functionality was also deemed to be good, with many users reporting positively on the systems repetition counting, technique classification, and feedback. A number of bugs were found, and other suggested changes to the system were also made. The overall subjective quality of the app was good, with a median star rating of 4 out of 5 (interquartile range, IQR: 3-5). Participants also reported that the system would aid their technique, provide motivation, reassure them, and help them avoid injury. Conclusions: This study demonstrated an overall positive evaluation of Formulift in the categories of usability, functionality, perceived impact, and subjective quality. Users also suggested a number of changes for future iterations of the system. These findings are the first of their kind and show great promise for wearable sensor-based exercise biofeedback systems. %M 29386171 %R 10.2196/mhealth.8115 %U http://mhealth.jmir.org/2018/1/e33/ %U https://doi.org/10.2196/mhealth.8115 %U http://www.ncbi.nlm.nih.gov/pubmed/29386171 %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 %@ 2291-5222 %I JMIR Publications %V 6 %N 1 %P e28 %T Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial %A Zhou,Mo %A Fukuoka,Yoshimi %A Mintz,Yonatan %A Goldberg,Ken %A Kaminsky,Philip %A Flowers,Elena %A Aswani,Anil %+ Department of Industrial Engineering and Operations Research, University of California, 4119 Etcheverry Hall, Berkeley, CA, 94720, United States, 1 510 664 9114, aaswani@berkeley.edu %K physical activity %K cell phone %K fitness tracker %K clinical trial %D 2018 %7 25.01.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. Objective: The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone–based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000. Methods: In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session. The participants were randomized into either the intervention or active control group with a one-to-one ratio after a run-in period for data collection. A study-developed mobile phone app (which delivers daily step goals using push notifications and allows real-time physical activity monitoring) was installed on each participant’s mobile phone, and participants were asked to keep their phone in a pocket throughout the entire day. Through the app, the intervention group received fully automated adaptively personalized daily step goals, and the control group received constant step goals of 10,000 steps per day. Daily step count was objectively measured by the study-developed mobile phone app. Results: The mean (SD) age of participants was 41.1 (11.3) years, and 83% (53/64) of participants were female. The baseline demographics between the 2 groups were similar (P>.05). Participants in the intervention group (n=34) had a decrease in mean (SD) daily step count of 390 (490) steps between run-in and 10 weeks, compared with a decrease of 1350 (420) steps among control participants (n=30; P=.03). The net difference in daily steps between the groups was 960 steps (95% CI 90-1830 steps). Both groups had a decrease in daily step count between run-in and 10 weeks because interventions were also provided during run-in and no natural baseline was collected. Conclusions: The results showed the short-term efficacy of this intervention, which should be formally evaluated in a full-scale RCT with a longer follow-up period. Trial Registration: ClinicalTrials.gov: NCT02886871; https://clinicaltrials.gov/ct2/show/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng). %M 29371177 %R 10.2196/mhealth.9117 %U http://mhealth.jmir.org/2018/1/e28/ %U https://doi.org/10.2196/mhealth.9117 %U http://www.ncbi.nlm.nih.gov/pubmed/29371177 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 2 %N 1 %P e1 %T Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison %A Rozanski,Gabriela M %A Aqui,Anthony %A Sivakumaran,Shajicaa %A Mansfield,Avril %+ Mobility Team, Toronto Rehabilitation Institute-University Health Network, University Centre, 550 University Avenue, Toronto, ON, M5G 2A2, Canada, 1 416 597 3422 ext 3872, gabriela.rozanski@uhn.ca %K physical activity %K heart rate %K accelerometry %K stroke rehabilitation %K walking %D 2018 %7 04.01.2018 %9 Original Paper %J JMIR Cardio %G English %X Background: Activity monitoring is necessary to investigate sedentary behavior after a stroke. Consumer wearable devices are an attractive alternative to research-grade technology, but measurement properties have not been established. Objective: The purpose of this study was to determine the accuracy of 2 wrist-worn fitness trackers: Fitbit Charge HR (FBT) and Garmin Vivosmart (GAR). Methods: Adults attending in- or outpatient therapy for stroke (n=37) wore FBT and GAR each on 2 separate days, in addition to an X6 accelerometer and Actigraph chest strap monitor. Step counts and heart rate data were extracted, and the agreement between devices was determined using Pearson or Spearman correlation and paired t or Wilcoxon signed rank tests (one- and two-sided). Subgroup analyses were conducted. Results: Step counts from FBT and GAR positively correlated with the X6 accelerometer (ρ=.78 and ρ=.65, P<.001, respectively) but were significantly lower (P<.01). For individuals using a rollator, there was no significant correlation between step counts from the X6 accelerometer and either FBT (ρ=.42, P=.12) or GAR (ρ=.30, P=.27). Heart rate from Actigraph, FBT, and GAR demonstrated responsiveness to changes in activity. Both FBT and GAR positively correlated with Actigraph for average heart rate (r=.53 and .75, P<.01, respectively) and time in target zone (ρ=.49 and .74, P<.01, respectively); these measures were not significantly different, but nonequivalence was found. Conclusions: FBT and GAR had moderate to strong correlation with best available reference measures of walking activity in individuals with subacute stroke. Accuracy appears to be lower among rollator users and varies according to heart rhythm. Consumer wearables may be a viable option for large-scale studies of physical activity. %M 31758760 %R 10.2196/cardio.8199 %U http://cardio.jmir.org/2018/1/e1/ %U https://doi.org/10.2196/cardio.8199 %U http://www.ncbi.nlm.nih.gov/pubmed/31758760 %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 %@ 1929-0748 %I JMIR Publications %V 6 %N 12 %P e255 %T Daily Activity Measured With Wearable Technology as a Novel Measurement of Treatment Effect in Patients With Coronary Microvascular Dysfunction: Substudy of a Randomized Controlled Crossover Trial %A Birkeland,Kade %A Khandwalla,Raj M %A Kedan,Ilan %A Shufelt,Chrisandra L %A Mehta,Puja K %A Minissian,Margo B %A Wei,Janet %A Handberg,Eileen M %A Thomson,Louise EJ %A Berman,Daniel S %A Petersen,John W %A Anderson,R David %A Cook-Wiens,Galen %A Pepine,Carl J %A Bairey Merz,C Noel %+ Barbra Streisand Women’s Heart Center, Cedars-Sinai Heart Institute, 127 S San Vicente Blvd, Advanced Health Sciences Pavilion, A3206, Los Angeles, CA, 90048, United States, 1 310 423 9680, noel.baireymerz@cshs.org %K angina %K coronary microvascular dysfunction %K physical activity %D 2017 %7 20.12.2017 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Digital wearable devices provide a “real-world” assessment of physical activity and quantify intervention-related changes in clinical trials. However, the value of digital wearable device-recorded physical activity as a clinical trial outcome is unknown. Objective: Because late sodium channel inhibition (ranolazine) improves stress laboratory exercise duration among angina patients, we proposed that this benefit could be quantified and translated during daily life by measuring digital wearable device-determined step count in a clinical trial. Methods: We conducted a substudy in a randomized, double-blinded, placebo-controlled, crossover trial of participants with angina and coronary microvascular dysfunction (CMD) with no obstructive coronary artery disease to evaluate the value of digital wearable device monitoring. Ranolazine or placebo were administered (500-1000 mg twice a day) for 2 weeks with a subsequent 2-week washout followed by crossover to ranolazine or placebo (500-1000 mg twice a day) for an additional 2 weeks. The outcome of interest was within-subject difference in Fitbit Flex daily step count during week 2 of ranolazine versus placebo during each treatment period. Secondary outcomes included within-subject differences in angina, quality of life, myocardial perfusion reserve, and diastolic function. Results: A total of 43 participants were enrolled in the substudy and 30 successfully completed the substudy for analysis. Overall, late sodium channel inhibition reduced within-subject daily step count versus placebo (mean 5757 [SD 3076] vs mean 6593 [SD 339], P=.01) but did not improve angina (Seattle Angina Questionnaire-7 [SAQ-7]) (P=.83). Among the subgroup with improved angina (SAQ-7), a direct correlation with increased step count (r=.42, P=.02) was observed. Conclusions: We report one of the first studies to use digital wearable device-determined step count as an outcome variable in a placebo-controlled crossover trial of late sodium channel inhibition in participants with CMD. Our substudy demonstrates that late sodium channel inhibition was associated with a decreased step count overall, although the subgroup with angina improvement had a step count increase. Our findings suggest digital wearable device technology may provide new insights in clinical trial research. Trial Registration: Clinicaltrials.gov NCT01342029; https://clinicaltrials.gov/ct2/show/NCT01342029 (Archived by WebCite at http://www.webcitation.org/6uyd6B2PO) %M 29263019 %R 10.2196/resprot.8057 %U http://www.researchprotocols.org/2017/12/e255/ %U https://doi.org/10.2196/resprot.8057 %U http://www.ncbi.nlm.nih.gov/pubmed/29263019 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 12 %P e420 %T Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study %A Low,Carissa A %A Dey,Anind K %A Ferreira,Denzil %A Kamarck,Thomas %A Sun,Weijing %A Bae,Sangwon %A Doryab,Afsaneh %+ Department of Medicine, University of Pittsburgh, 5200 Centre Avenue, Suite 614, Pittsburgh, PA, 15232, United States, 1 4126235973, lowca@upmc.edu %K patient reported outcome measures %K cancer %K mobile health %D 2017 %7 19.12.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden. Objective: The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy. Methods: A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability. Results: Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants. Conclusions: Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms. %M 29258977 %R 10.2196/jmir.9046 %U http://www.jmir.org/2017/12/e420/ %U https://doi.org/10.2196/jmir.9046 %U http://www.ncbi.nlm.nih.gov/pubmed/29258977 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 1 %N 2 %P e8 %T Assessing the Use of Wrist-Worn Devices in Patients With Heart Failure: Feasibility Study %A Moayedi,Yasbanoo %A Abdulmajeed,Raghad %A Duero Posada,Juan %A Foroutan,Farid %A Alba,Ana Carolina %A Cafazzo,Joseph %A Ross,Heather Joan %+ Ted Rogers Centre of Excellence in Heart Function, University Health Network, Toronto General Hospital, 190 Elizabeth St, Toronto, ON, M5G 2C4, Canada, 1 416 340 3482, heather.ross@uhn.ca %K MeSH: exercise physiology %K heart rate tracker %K wrist worn devices %K Fitbit %K Apple watch %K heart failure %K steps %D 2017 %7 19.12.2017 %9 Original Paper %J JMIR Cardio %G English %X Background: Exercise capacity and raised heart rate (HR) are important prognostic markers in patients with heart failure (HF). There has been significant interest in wrist-worn devices that track activity and HR. Objective: We aimed to assess the feasibility and accuracy of HR and activity tracking of the Fitbit and Apple Watch. Methods: We conducted a two-phase study assessing the accuracy of HR by Apple Watch and Fitbit in healthy participants. In Phase 1, 10 healthy individuals wore a Fitbit, an Apple Watch, and a GE SEER Light 5-electrode Holter monitor while exercising on a cycle ergometer with a 10-watt step ramp protocol from 0-100 watts. In Phase 2, 10 patients with HF and New York Heart Association (NYHA) Class II-III symptoms wore wrist devices for 14 days to capture overall step count/exercise levels. Results: Recorded HR by both wrist-worn devices had the best agreement with Holter readings at a workload of 60-100 watts when the rate of change of HR is less dynamic. Fitbit recorded a mean 8866 steps/day for NYHA II patients versus 4845 steps/day for NYHA III patients (P=.04). In contrast, Apple Watch recorded a mean 7027 steps/day for NYHA II patients and 4187 steps/day for NYHA III patients (P=.08). Conclusions: Both wrist-based devices are best suited for static HR rate measurements. In an outpatient setting, these devices may be adequate for average HR in patients with HF. When assessing exercise capacity, the Fitbit better differentiated patients with NYHA II versus NYHA III by the total number of steps recorded. This exploratory study indicates that these wrist-worn devices show promise in prognostication of HF in the continuous monitoring of outpatients. %M 31758789 %R 10.2196/cardio.8301 %U http://cardio.jmir.org/2017/2/e8/ %U https://doi.org/10.2196/cardio.8301 %U http://www.ncbi.nlm.nih.gov/pubmed/31758789 %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 11 %P e173 %T User Acceptance of Wrist-Worn Activity Trackers Among Community-Dwelling Older Adults: Mixed Method Study %A Puri,Arjun %A Kim,Ben %A Nguyen,Olivier %A Stolee,Paul %A Tung,James %A Lee,Joon %+ Health Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Lyle Hallman North, 200 University Avenue West, Waterloo, ON, N2L3G1, Canada, 1 5198884567 ext 31567, joon.lee@uwaterloo.ca %K health %K mHealth %K fitness trackers %K older adults %D 2017 %7 15.11.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers are newly emerging technologies with the anticipation for successfully supporting aging-in-place. Consumer-grade wearable activity trackers are increasingly ubiquitous in the market, but the attitudes toward, as well as acceptance and voluntary use of, these trackers in older population are poorly understood. Objective: The aim of this study was to assess acceptance and usage of wearable activity trackers in Canadian community-dwelling older adults, using the potentially influential factors as identified in literature and technology acceptance model. Methods: A mixed methods design was used. A total of 20 older adults aged 55 years and older were recruited from Southwestern Ontario. Participants used 2 different wearable activity trackers (Xiaomi Mi Band and Microsoft Band) separately for each segment in the crossover design study for 21 days (ie, 42 days total). A questionnaire was developed to capture acceptance and experience at the end of each segment, representing 2 different devices. Semistructured interviews were conducted with 4 participants, and a content analysis was performed. Results: Participants ranged in age from 55 years to 84 years (mean age: 64 years). The Mi Band gained higher levels of acceptance (16/20, 80%) compared with the Microsoft Band (10/20, 50%). The equipment characteristics dimension scored significantly higher for the Mi Band (P<.05). The amount a participant was willing to pay for the device was highly associated with technology acceptance (P<.05). Multivariate logistic regression with 3 covariates resulted in an area under the curve of 0.79. Content analysis resulted in the formation of the following main themes: (1) smartphones as facilitators of wearable activity trackers; (2) privacy is less of a concern for wearable activity trackers, (3) value proposition: self-awareness and motivation; (4) subjective norm, social support, and sense of independence; and (5) equipment characteristics matter: display, battery, comfort, and aesthetics. Conclusions: Older adults were mostly accepting of wearable activity trackers, and they had a clear understanding of its value for their lives. Wearable activity trackers were uniquely considered more personal than other types of technologies, thereby the equipment characteristics including comfort, aesthetics, and price had a significant impact on the acceptance. Results indicated that privacy was less of concern for older adults, but it may have stemmed from a lack of understanding of the privacy risks and implications. These findings add to emerging research that investigates acceptance and factors that may influence acceptance of wearable activity trackers among older adults. %M 29141837 %R 10.2196/mhealth.8211 %U http://mhealth.jmir.org/2017/11/e173/ %U https://doi.org/10.2196/mhealth.8211 %U http://www.ncbi.nlm.nih.gov/pubmed/29141837 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 11 %P e378 %T Development and Validation of a Taxonomy for Characterizing Measurements in Health Self-Quantification %A Almalki,Manal %A Gray,Kathleen %A Martin-Sanchez,Fernando %+ Faculty of Public Health and Tropical Medicine, Health Informatics Department, Jazan University, Level 1, Prince Ahmed Bin Abdulaziz Street, Jazan, 82726, Saudi Arabia, 966 173210584 ext 102, manal.almalki1@gmail.com %K health %K self-management %K self-experimentation %K wearables %K quantified self %K taxonomy %K classification %D 2017 %7 03.11.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of wearable tools for health self-quantification (SQ) introduces new ways of thinking about one’s body and about how to achieve desired health outcomes. Measurements from individuals, such as heart rate, respiratory volume, skin temperature, sleep, mood, blood pressure, food consumed, and quality of surrounding air can be acquired, quantified, and aggregated in a holistic way that has never been possible before. However, health SQ still lacks a formal common language or taxonomy for describing these kinds of measurements. Establishing such taxonomy is important because it would enable systematic investigations that are needed to advance in the use of wearable tools in health self-care. For a start, a taxonomy would help to improve the accuracy of database searching when doing systematic reviews and meta-analyses in this field. Overall, more systematic research would contribute to build evidence of sufficient quality to determine whether and how health SQ is a worthwhile health care paradigm. Objective: The aim of this study was to investigate a sample of SQ tools and services to build and test a taxonomy of measurements in health SQ, titled: the classification of data and activity in self-quantification systems (CDA-SQS). Methods: Eight health SQ tools and services were selected to be examined: Zeo Sleep Manager, Fitbit Ultra, Fitlinxx Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, and uBiome. An open coding analytical approach was used to find all the themes related to the research aim. Results: This study distinguished three types of measurements in health SQ: body structures and functions, body actions and activities, and around the body. Conclusions: The CDA-SQS classification should be applicable to align health SQ measurement data from people with many different health objectives, health states, and health conditions. CDA-SQS is a critical contribution to a much more consistent way of studying health SQ. %M 29101092 %R 10.2196/jmir.6903 %U http://www.jmir.org/2017/11/e378/ %U https://doi.org/10.2196/jmir.6903 %U http://www.ncbi.nlm.nih.gov/pubmed/29101092 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e164 %T Determinants for Sustained Use of an Activity Tracker: Observational Study %A Hermsen,Sander %A Moons,Jonas %A Kerkhof,Peter %A Wiekens,Carina %A De Groot,Martijn %+ Institute for Communication, Research Group Crossmedial Communication in the Public Domain, Utrecht University of Applied Sciences, Bolognalaan 101, Utrecht, 3584 CJ, Netherlands, 31 884813953, sander.hermsen@hu.nl %K mobile health %K mHealth %K physical activity %K machine learning %K habits %D 2017 %7 30.10.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time. Objective: The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience–related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity. Methods: A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique. Results: The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience–related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn. Conclusions: This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers. %M 29084709 %R 10.2196/mhealth.7311 %U http://mhealth.jmir.org/2017/10/e164/ %U https://doi.org/10.2196/mhealth.7311 %U http://www.ncbi.nlm.nih.gov/pubmed/29084709 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e166 %T iPhone Sensors in Tracking Outcome Variables of the 30-Second Chair Stand Test and Stair Climb Test to Evaluate Disability: Cross-Sectional Pilot Study %A Adusumilli,Gautam %A Joseph,Solomon Eben %A Samaan,Michael A %A Schultz,Brooke %A Popovic,Tijana %A Souza,Richard B %A Majumdar,Sharmila %+ Musculoskeletal Quantitative Imaging Research Group, Department of Radiology and Imaging, University of California San Francisco, Lobby 6, Suite 350, 185 Berry St, San Francisco, CA, 94107, United States, 1 919 576 3243, gautam.adusumilli@wustl.edu %K osteoarthritis %K telemedicine %K mobile phone %K mobile apps %K algorithms %K medical informatics %D 2017 %7 27.10.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Performance tests are important to characterize patient disabilities and functional changes. The Osteoarthritis Research Society International and others recommend the 30-second Chair Stand Test and Stair Climb Test, among others, as core tests that capture two distinct types of disability during activities of daily living. However, these two tests are limited by current protocols of testing in clinics. There is a need for an alternative that allows remote testing of functional capabilities during these tests in the osteoarthritis patient population. Objective: Objectives are to (1) develop an app for testing the functionality of an iPhone’s accelerometer and gravity sensor and (2) conduct a pilot study objectively evaluating the criterion validity and test-retest reliability of outcome variables obtained from these sensors during the 30-second Chair Stand Test and Stair Climb Test. Methods: An iOS app was developed with data collection capabilities from the built-in iPhone accelerometer and gravity sensor tools and linked to Google Firebase. A total of 24 subjects performed the 30-second Chair Stand Test with an iPhone accelerometer collecting data and an external rater manually counting sit-to-stand repetitions. A total of 21 subjects performed the Stair Climb Test with an iPhone gravity sensor turned on and an external rater timing the duration of the test on a stopwatch. App data from Firebase were converted into graphical data and exported into MATLAB for data filtering. Multiple iterations of a data processing algorithm were used to increase robustness and accuracy. MATLAB-generated outcome variables were compared to the manually determined outcome variables of each test. Pearson’s correlation coefficients (PCCs), Bland-Altman plots, intraclass correlation coefficients (ICCs), standard errors of measurement, and repeatability coefficients were generated to evaluate criterion validity, agreement, and test-retest reliability of iPhone sensor data against gold-standard manual measurements. Results: App accelerometer data during the 30-second Chair Stand Test (PCC=.890) and gravity sensor data during the Stair Climb Test (PCC=.865) were highly correlated to gold-standard manual measurements. Greater than 95% of values on Bland-Altman plots comparing the manual data to the app data fell within the 95% limits of agreement. Strong intraclass correlation was found for trials of the 30-second Chair Stand Test (ICC=.968) and Stair Climb Test (ICC=.902). Standard errors of measurement for both tests were found to be within acceptable thresholds for MATLAB. Repeatability coefficients for the 30-second Chair Stand Test and Stair Climb Test were 0.629 and 1.20, respectively. Conclusions: App-based performance testing of the 30-second Chair Stand Test and Stair Climb Test is valid and reliable, suggesting its applicability to future, larger-scale studies in the osteoarthritis patient population. %M 29079549 %R 10.2196/mhealth.8656 %U http://mhealth.jmir.org/2017/10/e166/ %U https://doi.org/10.2196/mhealth.8656 %U http://www.ncbi.nlm.nih.gov/pubmed/29079549 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 10 %P e336 %T Effectiveness of Two Web-Based Interventions for Chronic Cancer-Related Fatigue Compared to an Active Control Condition: Results of the “Fitter na kanker” Randomized Controlled Trial %A Bruggeman-Everts,Fieke Z %A Wolvers,Marije D J %A van de Schoot,Rens %A Vollenbroek-Hutten,Miriam M R %A Van der Lee,Marije L %+ Helen Dowling Instituut, Scientific Research Department, Professor Bronkhorstlaan 20, Bilthoven, 3723 MB, Netherlands, 31 30 252 40 20, bruggeman.everts@gmail.com %K fatigue %K cancer survivors %K Internet interventions %K mindfulness-based cognitive therapy %K physiotherapy %K accelerometry %K latent growth analysis %K implementation %K RCT %D 2017 %7 19.10.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Approximately one third of all patients who have been successfully treated for cancer suffer from chronic cancer-related fatigue (CCRF). Effective and easily accessible interventions are needed for these patients. Objective: The current paper reports on the results of a 3-armed randomized controlled trial investigating the clinical effectiveness of two different guided Web-based interventions for reducing CCRF compared to an active control condition. Methods: Severely fatigued cancer survivors were recruited via online and offline channels, and self-registered on an open-access website. After eligibility checks, 167 participants were randomized via an embedded automated randomization function into: (1) physiotherapist-guided Ambulant Activity Feedback (AAF) therapy encompassing the use of an accelerometer (n=62); (2) psychologist-guided Web-based mindfulness-based cognitive therapy (eMBCT; n=55); or (3) an unguided active control condition receiving psycho-educational emails (n=50). All interventions lasted nine weeks. Fatigue severity was self-assessed using the Checklist Individual Strength - Fatigue Severity subscale (primary outcome) six times from baseline (T0b) to six months (T2). Mental health was self-assessed three times using the Hospital Anxiety and Depression Scale and Positive and Negative Affect Schedule (secondary outcome). Treatment dropout was investigated. Results: Multiple group latent growth curve analysis, corrected for individual time between assessments, showed that fatigue severity decreased significantly more in the AAF and eMBCT groups compared to the psycho-educational group. The analyses were checked by a researcher who was blind to allocation. Clinically relevant changes in fatigue severity were observed in 66% (41/62) of patients in AAF, 49% (27/55) of patients in eMBCT, and 12% (6/50) of patients in psycho-education. Dropout was 18% (11/62) in AAF, mainly due to technical problems and poor usability of the accelerometer, and 38% (21/55) in eMBCT, mainly due to the perceived high intensity of the program. Conclusions: Both the AAF and eMBCT interventions are effective for managing fatigue severity compared to receiving psycho-educational emails. Trial Registration: Trialregister.nl NTR3483; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3483 (Archived by WebCite at http://www.webcitation.org/6NWZqon3o) %M 29051138 %R 10.2196/jmir.7180 %U http://www.jmir.org/2017/10/e336/ %U https://doi.org/10.2196/jmir.7180 %U http://www.ncbi.nlm.nih.gov/pubmed/29051138 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e146 %T Feasibility of Gamified Mobile Service Aimed at Physical Activation in Young Men: Population-Based Randomized Controlled Study (MOPO) %A Leinonen,Anna-Maiju %A Pyky,Riitta %A Ahola,Riikka %A Kangas,Maarit %A Siirtola,Pekka %A Luoto,Tim %A Enwald,Heidi %A Ikäheimo,Tiina M %A Röning,Juha %A Keinänen-Kiukaanniemi,Sirkka %A Mäntysaari,Matti %A Korpelainen,Raija %A Jämsä,Timo %+ Research Unit of Medical Imaging, Physics and Technology, University of Oulu, PO Box 5000, Oulu, 90014 University of, Finland, 358 29 448 600, anna.jauho@oulu.fi %K accelerometry %K adolescent %K behavior change %K health %K Internet %K self-monitoring %K wearable %D 2017 %7 10.10.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The majority of young people do not meet the recommendations on physical activity for health. New innovative ways to motivate young people to adopt a physically active lifestyle are needed. Objective: The study aimed to study the feasibility of an automated, gamified, tailored Web-based mobile service aimed at physical and social activation among young men. Methods: A population-based sample of 496 young men (mean age 17.8 years [standard deviation 0.6]) participated in a 6-month randomized controlled trial (MOPO study). Participants were randomized to an intervention (n=250) and a control group (n=246). The intervention group was given a wrist-worn physical activity monitor (Polar Active) with physical activity feedback and access to a gamified Web-based mobile service, providing fitness guidelines, tailored health information, advice of youth services, social networking, and feedback on physical activity. Through the trial, the physical activity of the men in the control group was measured continuously with an otherwise similar monitor but providing only the time of day and no feedback. The primary outcome was the feasibility of the service based on log data and questionnaires. Among completers, we also analyzed the change in anthropometry and fitness between baseline and 6 months and the change over time in weekly time spent in moderate to vigorous physical activity. Results: Mobile service users considered the various functionalities related to physical activity important. However, compliance of the service was limited, with 161 (64.4%, 161/250) participants visiting the service, 118 (47.2%, 118/250) logging in more than once, and 41 (16.4%, 41/250) more than 5 times. Baseline sedentary time was higher in those who uploaded physical activity data until the end of the trial (P=.02). A total of 187 (74.8%, 187/250) participants in the intervention and 167 (67.9%, 167/246) in the control group participated in the final measurements. There were no differences in the change in anthropometry and fitness from baseline between the groups, whereas waist circumference was reduced in the most inactive men within the intervention group (P=.01). Among completers with valid physical activity data (n=167), there was a borderline difference in the change in mean daily time spent in moderate to vigorous physical activity between the groups (11.9 min vs −9.1 min, P=.055, linear mixed model). Within the intervention group (n=87), baseline vigorous physical activity was inversely associated with change in moderate to vigorous physical activity during the trial (R=−.382, P=.01). Conclusions: The various functionalities related to physical activity of the gamified tailored mobile service were considered important. However, the compliance was limited. Within the current setup, the mobile service had no effect on anthropometry or fitness, except reduced waist circumference in the most inactive men. Among completers with valid physical activity data, the trial had a borderline positive effect on moderate to vigorous physical activity. Further development is needed to improve the feasibility and adherence of an integrated multifunctional service. Trial registration: Clinicaltrials.gov NCT01376986; http://clinicaltrials.gov/ct2/show/NCT01376986 (Archived by WebCite at http://www.webcitation.org/6tjdmIroA) %M 29017991 %R 10.2196/mhealth.6675 %U https://mhealth.jmir.org/2017/10/e146/ %U https://doi.org/10.2196/mhealth.6675 %U http://www.ncbi.nlm.nih.gov/pubmed/29017991 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e137 %T Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study %A DeMasi,Orianna %A Feygin,Sidney %A Dembo,Aluma %A Aguilera,Adrian %A Recht,Benjamin %+ Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, 593-5 Soda Hall, MC-1776, Berkeley, CA, 94720, United States, 1 5107769028, odemasi@eecs.berkeley.edu %K depression %K mobile health %K smartphones %D 2017 %7 05.10.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Automatically tracking mental well-being could facilitate personalization of treatments for mood disorders such as depression and bipolar disorder. Smartphones present a novel and ubiquitous opportunity to track individuals’ behavior and may be useful for inferring and automatically monitoring mental well-being. Objective: The aim of this study was to assess the extent to which activity and sleep tracking with a smartphone can be used for monitoring individuals’ mental well-being. Methods: A cohort of 106 individuals was recruited to install an app on their smartphone that would track their well-being with daily surveys and track their behavior with activity inferences from their phone’s accelerometer data. Of the participants recruited, 53 had sufficient data to infer activity and sleep measures. For this subset of individuals, we related measures of activity and sleep to the individuals’ well-being and used these measures to predict their well-being. Results: We found that smartphone-measured approximations for daily physical activity were positively correlated with both mood (P=.004) and perceived energy level (P<.001). Sleep duration was positively correlated with mood (P=.02) but not energy. Our measure for sleep disturbance was not found to be significantly related to either mood or energy, which could imply too much noise in the measurement. Models predicting the well-being measures from the activity and sleep measures were found to be significantly better than naive baselines (P<.01), despite modest overall improvements. Conclusions: Measures of activity and sleep inferred from smartphone activity were strongly related to and somewhat predictive of participants’ well-being. Whereas the improvement over naive models was modest, it reaffirms the importance of considering physical activity and sleep for predicting mood and for making automatic mood monitoring a reality. %M 28982643 %R 10.2196/mhealth.7820 %U https://mhealth.jmir.org/2017/10/e137/ %U https://doi.org/10.2196/mhealth.7820 %U http://www.ncbi.nlm.nih.gov/pubmed/28982643 %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 %@ 1438-8871 %I JMIR Publications %V 19 %N 9 %P e328 %T Prescribing of Electronic Activity Monitors in Cardiometabolic Diseases: Qualitative Interview-Based Study %A Bellicha,Alice %A Macé,Sandrine %A Oppert,Jean-Michel %+ Laboratory of Bioengineering, Tissues and Neuroplasticity, University Paris-Est, 8 rue Jean Sarrail, Créteil, 94010, France, 33 42175782, alice.bellicha@u-pec.fr %K cardiometabolic diseases %K physical activity %K physicians’ perspectives %K prescriptions %K mobile health %K telemedicine %K mHealth %K electronic activity monitors %K fitness tracker %K accelerometer %K smart pedometer %D 2017 %7 23.9.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The prevalence of noncommunicable diseases, including those such as type 2 diabetes, obesity, dyslipidemia, and hypertension, so-called cardiometabolic diseases, is high and is increasing worldwide. Strong evidence supports the role of physical activity in management of these diseases. There is general consensus that mHealth technology, including electronic activity monitors, can potentially increase physical activity in patients, but their use in clinical settings remains limited. Practitioners’ requirements when prescribing electronic activity monitors have been poorly described. Objective: The aims of this qualitative study were (1) to explore how specialist physicians prescribe electronic activity monitors to patients presenting with cardiometabolic conditions, and (2) to better understand their motivation for and barriers to prescribing such monitors. Methods: We conducted qualitative semistructured interviews in March to May 2016 with 11 senior physicians from a public university hospital in France with expertise in management of cardiometabolic diseases (type 1 and type 2 diabetes, obesity, hypertension, and dyslipidemia). Interviews lasted 45 to 60 minutes and were audiotaped, transcribed verbatim, and analyzed using directed content analysis. We report our findings following the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. Results: Most physicians we interviewed had never prescribed electronic activity monitors, whereas they frequently prescribed blood glucose or blood pressure self-monitoring devices. Reasons for nonprescription included lack of interest in the data collected, lack of evidence for data accuracy, concern about work overload possibly resulting from automatic data transfer, and risk of patients becoming addicted to data. Physicians expected future marketing of easy-to-use monitors that will accurately measure physical activity duration and intensity and provide understandable motivating feedback. Conclusions: Features of electronic activity monitors, although popular among the general public, do not meet the needs of physicians. In-depth understanding of physicians’ expectations is a first step toward designing technologies that can be widely used in clinical settings and facilitate physical activity prescription. Physicians should have a role, along with key health care stakeholders—patients, researchers, information technology firms, the public, and private payers—in developing the most effective methods for integrating activity monitors into patient care. %M 28947415 %R 10.2196/jmir.8107 %U http://www.jmir.org/2017/9/e328/ %U https://doi.org/10.2196/jmir.8107 %U http://www.ncbi.nlm.nih.gov/pubmed/28947415 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 3 %N 1 %P e41 %T Why Did It Fail? Surveying Employees to Improve a Tracker-Based Corporate Wellness Initiative %A Gualtieri,Lisa %A Bradley,Danielle %A Hassounah,Marwah %A Kahn-Boesel,Olivia %A Kim,Dowon %+ Department of Public Health and Community Medicine, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA,, United States, 1 6176360438, lisa.gualtieri@tufts.edu %K employee wellness %K physical activity %K wellness programs %K workplace health promotion %K tracker %K activity tracker %K wearable activity tracker %D 2017 %7 22.09.2017 %9 Abstract %J iproc %G English %X Background: In 2016, a medium-size, private company in the United States implemented a program to distribute activity trackers to its employees in order to enhance their physical activity; the company distributed 150 trackers in total. However, many employees stopped using the trackers shortly after they received them. This study explores reasons why the initiative failed, as well as ways that the company may improve future wellness initiatives. Objective: A study was designed to gain insight into why corporate wellness programs may be unsuccessful, as well as investigate ways that they can be improved in the future. This is especially relevant as trackers in the workplace and corporate wellness programs have grown in popularity in recent years. Methods: We used a mix of cross-sectional surveys and open-ended interviews to grasp both the quantitative and qualitative aspects of employee perceptions on trackers and tracker data. We sent an online survey to company employees via email, inquiring about the employee's current physical activity behaviors, attitudes, and expressed interests in activity trackers. In addition, we held structured interviews and follow-up phone meetings with administrative figures. Results: Of 204 employees surveyed, 116 completed the survey and three administrators were interviewed. Employees were dissatisfied with the initiative largely due to lack of tracker choice and lack of other wellness activities offered with the trackers. While many participants reported positive feelings about tracking in general, 60.7% of respondents wanted options relating to brand and model, as 51.9% were dissatisfied with the model that they received (Jawbone). Some employees mentioned they wanted one that was waterproof, while others stated that they needed one with a longer lasting battery. Additionally, 62% of respondents expressed interest in wellness classes in the workplace, such as fitness classes or lecture-based nutrition and sleep classes, to go along with the trackers. Furthermore, 44% of respondents said that they would like to receive customized fitness advice from the program. We also gauged interest in the possibility of providing incentives to employees for reaching goals or completing challenges, as incentives may be a successful way to engage employees in a wellness initiative. However, more than half of respondents (58%) were not interested in any form of reward, incentive, or recognition. Consequently, we concluded that incentive was not among the major factors that affected employee adoption of the wellness initiative. Conclusions: The corporate wellness program was unsuccessful largely due to the following: dissatisfaction with the specific tracker model that was selected for distribution to employees; lack of employee choice in tracker model and features; and because there were no programs implemented to support the use of the trackers to increase fitness. Based on study results, in order to increase employee participation and satisfaction, future initiatives should incorporate other workplace wellness activities (walking groups, nutrition classes) into tracker-based programs, and should provide a set of tracker options to employees, so that employees are able to select trackers that fit their individual needs. %R 10.2196/iproc.8711 %U http://www.iproc.org/2017/1/e41/ %U https://doi.org/10.2196/iproc.8711 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 9 %P e133 %T Effectiveness of SmartMoms, a Novel eHealth Intervention for Management of Gestational Weight Gain: Randomized Controlled Pilot Trial %A Redman,Leanne M %A Gilmore,L. Anne %A Breaux,Jeffrey %A Thomas,Diana M %A Elkind-Hirsch,Karen %A Stewart,Tiffany %A Hsia,Daniel S %A Burton,Jeffrey %A Apolzan,John W %A Cain,Loren E %A Altazan,Abby D %A Ragusa,Shelly %A Brady,Heather %A Davis,Allison %A Tilford,J. Mick %A Sutton,Elizabeth F %A Martin,Corby K %+ Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA, 70808, United States, 1 2257630947, leanne.redman@pbrc.edu %K pregnancy %K gestational weight gain %K lifestyle modification %K intervention %D 2017 %7 13.09.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Two-thirds of pregnant women exceed gestational weight gain (GWG) recommendations. Because excess GWG is associated with adverse outcomes for mother and child, development of scalable and cost-effective approaches to deliver intensive lifestyle programs during pregnancy is urgent. Objective: The aim of this study was to decrease the proportion of women who exceed the Institute of Medicine (IOM) 2009 GWG guidelines. Methods: In a parallel-arm randomized controlled trial, 54 pregnant women (age 18-40 years) who were overweight (n=25) or obese (n=29) were enrolled to test whether an intensive lifestyle intervention (called SmartMoms) decreased the proportion of women with excess GWG, defined as exceeding the 2009 IOM guidelines, compared to no intervention (usual care group). The SmartMoms intervention was delivered through mobile phone (remote group) or in a traditional in-person, clinic-based setting (in-person group), and included a personalized dietary intake prescription, self-monitoring weight against a personalized weight graph, activity tracking with a pedometer, receipt of health information, and continuous personalized feedback from counselors. Results: A significantly smaller proportion of women exceeded the IOM 2009 GWG guidelines in the SmartMoms intervention groups (in-person: 56%, 10/18; remote: 58%, 11/19) compared to usual care (85%, 11/13; P=.02). The remote intervention was a lower cost to participants (mean US $97, SD $6 vs mean US $347, SD $40 per participant; P<.001) and clinics (US $215 vs US $419 per participant) and with increased intervention adherence (76.5% vs 60.8%; P=.049). Conclusions: An intensive lifestyle intervention for GWG can be effectively delivered via a mobile phone, which is both cost-effective and scalable. Trial Registration: Clinicaltrials.gov NCT01610752; https://clinicaltrials.gov/ct2/show/NCT01610752 (Archived by WebCite at http://www.webcitation.org/6sarNB4iW) %M 28903892 %R 10.2196/mhealth.8228 %U http://mhealth.jmir.org/2017/9/e133/ %U https://doi.org/10.2196/mhealth.8228 %U http://www.ncbi.nlm.nih.gov/pubmed/28903892 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 8 %P e295 %T The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review %A Rathbone,Amy Leigh %A Prescott,Julie %+ School of Education and Psychology, University of Bolton, Deane Road, Bolton, BL3 5AB, United Kingdom, 44 01204903676, alr3wss@bolton.ac.uk %K mHealth %K smartphone %K health %K review %K systematic %K short message service %K treatment efficacy %K portable electronic applications %K intervention study %D 2017 %7 24.08.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The initial introduction of the World Wide Web in 1990 brought around the biggest change in information acquisition. Due to the abundance of devices and ease of access they subsequently allow, the utility of mobile health (mHealth) has never been more endemic. A substantial amount of interactive and psychoeducational apps are readily available to download concerning a wide range of health issues. mHealth has the potential to reduce waiting times for appointments; eradicate the need to meet in person with a clinician, successively diminishing the workload of mental health professionals; be more cost effective to practices; and encourage self-care tactics. Previous research has given valid evidence with empirical studies proving the effectiveness of physical and mental health interventions using mobile apps. Alongside apps, there is evidence to show that receiving short message service (SMS) messages, which entail psychoeducation, medication reminders, and links to useful informative Web pages can also be advantageous to a patient’s mental and physical well-being. Available mHealth apps and SMS services and their ever improving quality necessitates a systematic review in the area in reference to reduction of symptomology, adherence to intervention, and usability. Objective: The aim of this review was to study the efficacy, usability, and feasibility of mobile apps and SMS messages as mHealth interventions for self-guided care. Methods: A systematic literature search was carried out in JMIR, PubMed, PsychINFO, PsychARTICLES, Google Scholar, MEDLINE, and SAGE. The search spanned from January 2008 to January 2017. The primary outcome measures consisted of weight management, (pregnancy) smoking cessation, medication adherence, depression, anxiety and stress. Where possible, adherence, feasibility, and usability outcomes of the apps or SMS services were evaluated. Between-group and within-group effect sizes (Cohen d) for the mHealth intervention method group were determined. Results: A total of 27 studies, inclusive of 4658 participants were reviewed. The papers included randomized controlled trials (RCTs) (n=19), within-group studies (n=7), and 1 within-group study with qualitative aspect. Studies show improvement in physical health and significant reductions of anxiety, stress, and depression. Within-group and between-group effect sizes ranged from 0.05-3.37 (immediately posttest), 0.05-3.25 (1-month follow-up), 0.08-3.08 (2-month follow-up), 0.00-3.10 (3-month follow-up), and 0.02-0.27 (6-month follow-up). Usability and feasibility of mHealth interventions, where reported, also gave promising, significant results. Conclusions: The review shows the promising and emerging efficacy of using mobile apps and SMS text messaging as mHealth interventions. %M 28838887 %R 10.2196/jmir.7740 %U http://www.jmir.org/2017/8/e295/ %U https://doi.org/10.2196/jmir.7740 %U http://www.ncbi.nlm.nih.gov/pubmed/28838887 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 4 %N 2 %P e9 %T Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation %A O'Reilly,Martin %A Duffin,Joe %A Ward,Tomas %A Caulfield,Brian %+ Insight Centre for Data Analytics, University College Dublin, 3rd Floor, O'Brien Centre for Science, Science Centre EAST, Belfield, D4, Ireland, 353 871245972, martin.oreilly@insight-centre.org %K exercise therapy %K biomedical technology %K lower extremity %K physical therapy specialty %D 2017 %7 21.08.2017 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Biofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers. Objective: We aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation. Methods: A tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated. Results: The tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh. Conclusions: A tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The personalized classification model displayed excellent system accuracy even when assessing acute deviations in compound exercises with a single IMU. %M 28827210 %R 10.2196/rehab.7259 %U http://rehab.jmir.org/2017/2/e9/ %U https://doi.org/10.2196/rehab.7259 %U http://www.ncbi.nlm.nih.gov/pubmed/28827210 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 8 %P e122 %T Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study %A Maddison,Ralph %A Gemming,Luke %A Monedero,Javier %A Bolger,Linda %A Belton,Sarahjane %A Issartel,Johann %A Marsh,Samantha %A Direito,Artur %A Solenhill,Madeleine %A Zhao,Jinfeng %A Exeter,Daniel John %A Vathsangam,Harshvardhan %A Rawstorn,Jonathan Charles %+ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, 3125, Australia, 61 3 924 68461, jonathan.rawstorn@deakin.edu.au %K telemedicine %K smartphone %K validation studies %K geographic information systems %K locomotion %K physical activity %K humans %D 2017 %7 17.08.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data—which includes billions of digital traces—offers scientists a new lens to examine PA in fine-grained detail and allows us to track people’s geocoded movement patterns to determine their interaction with the environment. Objective: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. Methods: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. Results: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [−0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. Conclusions: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics. %M 28818819 %R 10.2196/mhealth.7167 %U http://mhealth.jmir.org/2017/8/e122/ %U https://doi.org/10.2196/mhealth.7167 %U http://www.ncbi.nlm.nih.gov/pubmed/28818819 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 6 %N 2 %P e13 %T Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors %A Duncan,Mitch %A Murawski,Beatrice %A Short,Camille E %A Rebar,Amanda L %A Schoeppe,Stephanie %A Alley,Stephanie %A Vandelanotte,Corneel %A Kirwan,Morwenna %+ School of Medicine & Public Health, Priority Research Centre for Physical Activity and Nutrition, Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan, 2308, Australia, 61 024921 7805, Mitch.Duncan@newcastle.edu.au %K health behavior %K public health %K exercise %K sleep %K behavior change %K fitness trackers %K adult, mobile applications %D 2017 %7 14.08.2017 %9 Original Paper %J Interact J Med Res %G English %X Background: Several studies have examined how the implementation of behavior change techniques (BCTs) varies between different activity trackers. However, activity trackers frequently allow tracking of activity, sleep, and sedentary behaviors; yet, it is unknown how the implementation of BCTs differs between these behaviors. Objective: The aim of this study was to assess the number and type of BCTs that are implemented by wearable activity trackers (self-monitoring systems) in relation to activity, sleep, and sedentary behaviors and to determine whether the number and type of BCTs differ between behaviors. Methods: Three self-monitoring systems (Fitbit [Charge HR], Garmin [Vivosmart], and Jawbone [UP3]) were each used for a 1-week period in August 2015. Each self-monitoring system was used by two of the authors (MJD and BM) concurrently. The Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy was used to assess the implementation of 40 BCTs in relation to activity, sleep, and sedentary behaviors. Discrepancies in ratings were resolved by discussion, and interrater agreement in the number of BCTs implemented was assessed using kappa statistics. Results: Interrater agreement ranged from 0.64 to 1.00. From a possible range of 40 BCTs, the number of BCTs present for activity ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to 29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for sedentary behavior. The average number of BCTs implemented was greatest for activity (n=26) and smaller for sleep (n=14) and sedentary behavior (n=6). Conclusions: The number and type of BCTs implemented varied between each of the systems and between activity, sleep, and sedentary behaviors. This provides an indication of the potential of these systems to change these behaviors, but the long-term effectiveness of these systems to change activity, sleep, and sedentary behaviors remains unknown. %M 28807889 %R 10.2196/ijmr.6685 %U http://www.i-jmr.org/2017/2/e13/ %U https://doi.org/10.2196/ijmr.6685 %U http://www.ncbi.nlm.nih.gov/pubmed/28807889 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 8 %P e106 %T How Accurate Is Your Activity Tracker? A Comparative Study of Step Counts in Low-Intensity Physical Activities %A Alinia,Parastoo %A Cain,Chris %A Fallahzadeh,Ramin %A Shahrokni,Armin %A Cook,Diane %A Ghasemzadeh,Hassan %+ School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, Pullman, WA, 99164, United States, 1 509 335 3564, parastoo.alinia@wsu.edu %K activities of daily living %K activity tracker %K mobility limitations %K mobile health %D 2017 %7 11.08.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As commercially available activity trackers are being utilized in clinical trials, the research community remains uncertain about reliability of the trackers, particularly in studies that involve walking aids and low-intensity activities. While these trackers have been tested for reliability during walking and running activities, there has been limited research on validating them during low-intensity activities and walking with assistive tools. Objective: The aim of this study was to (1) determine the accuracy of 3 Fitbit devices (ie, Zip, One, and Flex) at different wearing positions (ie, pants pocket, chest, and wrist) during walking at 3 different speeds, 2.5, 5, and 8 km/h, performed by healthy adults on a treadmill; (2) determine the accuracy of the mentioned trackers worn at different sites during activities of daily living; and (3) examine whether intensity of physical activity (PA) impacts the choice of optimal wearing site of the tracker. Methods: We recruited 15 healthy young adults to perform 6 PAs while wearing 3 Fitbit devices (ie, Zip, One, and Flex) on their chest, pants pocket, and wrist. The activities include walking at 2.5, 5, and 8 km/h, pushing a shopping cart, walking with aid of a walker, and eating while sitting. We compared the number of steps counted by each tracker with gold standard numbers. We performed multiple statistical analyses to compute descriptive statistics (ie, ANOVA test), intraclass correlation coefficient (ICC), mean absolute error rate, and correlation by comparing the tracker-recorded data with that of the gold standard. Results: All the 3 trackers demonstrated good-to-excellent (ICC>0.75) correlation with the gold standard step counts during treadmill experiments. The correlation was poor (ICC<0.60), and the error rate was significantly higher in walker experiment compared to other activities. There was no significant difference between the trackers and the gold standard in the shopping cart experiment. The wrist worn tracker, Flex, counted several steps when eating (P<.01). The chest tracker was identified as the most promising site to capture steps in more intense activities, while the wrist was the optimal wearing site in less intense activities. Conclusions: This feasibility study focused on 6 PAs and demonstrated that Fitbit trackers were most accurate when walking on a treadmill and least accurate during walking with a walking aid and for low-intensity activities. This may suggest excluding participants with assistive devices from studies that focus on PA interventions using commercially available trackers. This study also indicates that the wearing site of the tracker is an important factor impacting the accuracy performance. A larger scale study with a more diverse population, various activity tracker vendors, and a larger activity set are warranted to generalize our results. %M 28801304 %R 10.2196/mhealth.6321 %U http://mhealth.jmir.org/2017/8/e106/ %U https://doi.org/10.2196/mhealth.6321 %U http://www.ncbi.nlm.nih.gov/pubmed/28801304 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 4 %N 2 %P e8 %T Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models %A Lonini,Luca %A Gupta,Aakash %A Deems-Dluhy,Susan %A Hoppe-Ludwig,Shenan %A Kording,Konrad %A Jayaraman,Arun %+ Shirley Ryan Ability Lab, Max Näder Lab, 355 E Erie St, Suite 11-1401, Chicago, IL, 60611, United States, 1 312 238 1619, llonini@ricres.org %K activities of daily living %K machine learning %K wearables %K rehabilitation %K orthotic devices %D 2017 %7 10.08.2017 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Wearable sensors gather data that machine-learning models can convert into an identification of physical activities, a clinically relevant outcome measure. However, when individuals with disabilities upgrade to a new walking assistive device, their gait patterns can change, which could affect the accuracy of activity recognition. Objective: The objective of this study was to assess whether we need to train an activity recognition model with labeled data from activities performed with the new assistive device, rather than data from the original device or from healthy individuals. Methods: Data were collected from 11 healthy controls as well as from 11 age-matched individuals with disabilities who used a standard stance control knee-ankle-foot orthosis (KAFO), and then a computer-controlled adaptive KAFO (Ottobock C-Brace). All subjects performed a structured set of functional activities while wearing an accelerometer on their waist, and random forest classifiers were used as activity classification models. We examined both global models, which are trained on other subjects (healthy or disabled individuals), and personal models, which are trained and tested on the same subject. Results: Median accuracies of global and personal models trained with data from the new KAFO were significantly higher (61% and 76%, respectively) than those of models that use data from the original KAFO (55% and 66%, respectively) (Wilcoxon signed-rank test, P=.006 and P=.01). These models also massively outperformed a global model trained on healthy subjects, which only achieved a median accuracy of 53%. Device-specific models conferred a major advantage for activity recognition. Conclusions: Our results suggest that when patients use a new assistive device, labeled data from activities performed with the specific device are needed for maximal precision activity recognition. Personal device-specific models yield the highest accuracy in such scenarios, whereas models trained on healthy individuals perform poorly and should not be used in patient populations. %M 28798008 %R 10.2196/rehab.7317 %U http://rehab.jmir.org/2017/2/e8/ %U https://doi.org/10.2196/rehab.7317 %U http://www.ncbi.nlm.nih.gov/pubmed/28798008 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 8 %P e277 %T Key Components in eHealth Interventions Combining Self-Tracking and Persuasive eCoaching to Promote a Healthier Lifestyle: A Scoping Review %A Lentferink,Aniek J %A Oldenhuis,Hilbrand KE %A de Groot,Martijn %A Polstra,Louis %A Velthuijsen,Hugo %A van Gemert-Pijnen,Julia EWC %+ Centre for eHealth & Wellbeing Research, Departement of Psychology, Health, and Technology, University of Twente, Cubicus Bldg, 10 De Zul, Enschede, 7522 NJ, Netherlands, 31 505956217, a.j.lentferink@utwente.nl %K telemedicine %K review %K health promotion %K remote sensing technology %D 2017 %7 01.08.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The combination of self-tracking and persuasive eCoaching in automated interventions is a new and promising approach for healthy lifestyle management. Objective: The aim of this study was to identify key components of self-tracking and persuasive eCoaching in automated healthy lifestyle interventions that contribute to their effectiveness on health outcomes, usability, and adherence. A secondary aim was to identify the way in which these key components should be designed to contribute to improved health outcomes, usability, and adherence. Methods: The scoping review methodology proposed by Arskey and O’Malley was applied. Scopus, EMBASE, PsycINFO, and PubMed were searched for publications dated from January 1, 2013 to January 31, 2016 that included (1) self-tracking, (2) persuasive eCoaching, and (3) healthy lifestyle intervention. Results: The search resulted in 32 publications, 17 of which provided results regarding the effect on health outcomes, 27 of which provided results regarding usability, and 13 of which provided results regarding adherence. Among the 32 publications, 27 described an intervention. The most commonly applied persuasive eCoaching components in the described interventions were personalization (n=24), suggestion (n=19), goal-setting (n=17), simulation (n=17), and reminders (n=15). As for self-tracking components, most interventions utilized an accelerometer to measure steps (n=11). Furthermore, the medium through which the user could access the intervention was usually a mobile phone (n=10). The following key components and their specific design seem to influence both health outcomes and usability in a positive way: reduction by setting short-term goals to eventually reach long-term goals, personalization of goals, praise messages, reminders to input self-tracking data into the technology, use of validity-tested devices, integration of self-tracking and persuasive eCoaching, and provision of face-to-face instructions during implementation. In addition, health outcomes or usability were not negatively affected when more effort was requested from participants to input data into the technology. The data extracted from the included publications provided limited ability to identify key components for adherence. However, one key component was identified for both usability and adherence, namely the provision of personalized content. Conclusions: This scoping review provides a first overview of the key components in automated healthy lifestyle interventions combining self-tracking and persuasive eCoaching that can be utilized during the development of such interventions. Future studies should focus on the identification of key components for effects on adherence, as adherence is a prerequisite for an intervention to be effective. %M 28765103 %R 10.2196/jmir.7288 %U http://www.jmir.org/2017/8/e277/ %U https://doi.org/10.2196/jmir.7288 %U http://www.ncbi.nlm.nih.gov/pubmed/28765103 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 7 %P e105 %T Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents %A Bian,Jiang %A Guo,Yi %A Xie,Mengjun %A Parish,Alice E %A Wardlaw,Isaac %A Brown,Rita %A Modave,François %A Zheng,Dong %A Perry,Tamara T %+ Department of Health Outcomes and Policy, University of Florida, 2004 Mowry Road, Suite 3228, PO Box 100219, Gainesville, FL, 32610, United States, 1 (352) 273 8878, bianjiang@ufl.edu %K mobile health %K mHealth %K asthma %K Fitbit %K physical activity %K sleep %K sleep quality %D 2017 %7 25.07.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients’ asthma outcomes have not been established. Objective: The objective of this study was to explore the association between self‑reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI). Methods: We conducted an 8‑week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient‑reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep‑related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F‑SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest. Results: We have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=−.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=−.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62). Conclusions: Our findings support the potential of using wrist-worn devices to continuously monitor two important factors—physical activity and sleep—associated with patients’ asthma outcomes and to develop a personalized asthma management platform. %M 28743679 %R 10.2196/mhealth.7346 %U http://mhealth.jmir.org/2017/7/e105/ %U https://doi.org/10.2196/mhealth.7346 %U http://www.ncbi.nlm.nih.gov/pubmed/28743679 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 7 %P e250 %T Youth Oriented Activity Trackers: Comprehensive Laboratory- and Field-Based Validation %A Sirard,John R %A Masteller,Brittany %A Freedson,Patty S %A Mendoza,Albert %A Hickey,Amanda %+ Department of Kinesiology, University of Massachusetts Amherst, 30 Eastman Lane, Totman 110, Amherst, MA, 01003, United States, 1 4135457898, jsirard@kin.umass.edu %K child %K movement %K fitness trackers %D 2017 %7 19.07.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Commercial activity trackers are growing in popularity among adults and some are beginning to be marketed to children. There is, however, a paucity of independent research examining the validity of these devices to detect physical activity of different intensity levels. Objectives: The purpose of this study was to determine the validity of the output from 3 commercial youth-oriented activity trackers in 3 phases: (1) orbital shaker, (2) structured indoor activities, and (3) 4 days of free-living activity. Methods: Four units of each activity tracker (Movband [MB], Sqord [SQ], and Zamzee [ZZ]) were tested in an orbital shaker for 5-minutes at three frequencies (1.3, 1.9, and 2.5 Hz). Participants for Phase 2 (N=14) and Phase 3 (N=16) were 6-12 year old children (50% male). For Phase 2, participants completed 9 structured activities while wearing each tracker, the ActiGraph GT3X+ (AG) research accelerometer, and a portable indirect calorimetry system to assess energy expenditure (EE). For Phase 3, participants wore all 4 devices for 4 consecutive days. Correlation coefficients, linear models, and non-parametric statistics evaluated the criterion and construct validity of the activity tracker output. Results: Output from all devices was significantly associated with oscillation frequency (r=.92-.99). During Phase 2, MB and ZZ only differentiated sedentary from light intensity (P<.01), whereas the SQ significantly differentiated among all intensity categories (all comparisons P<.01), similar to AG and EE. During Phase 3, AG counts were significantly associated with activity tracker output (r=.76, .86, and .59 for the MB, SQ, and ZZ, respectively). Conclusions: Across study phases, the SQ demonstrated stronger validity than the MB and ZZ. The validity of youth-oriented activity trackers may directly impact their effectiveness as behavior modification tools, demonstrating a need for more research on such devices. %M 28724509 %R 10.2196/jmir.6360 %U http://www.jmir.org/2017/7/e250/ %U https://doi.org/10.2196/jmir.6360 %U http://www.ncbi.nlm.nih.gov/pubmed/28724509 %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 %@ 1438-8871 %I JMIR Publications %V 19 %N 7 %P e246 %T Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial %A Frias,Juan %A Virdi,Naunihal %A Raja,Praveen %A Kim,Yoona %A Savage,George %A Osterberg,Lars %+ Proteus Digital Health, 2600 Bridge Parkway, Redwood City, CA, 94065, United States, 1 4158285009, nvirdi@proteus.com %K digital medicine %K hypertension %K type 2 diabetes %K patient engagement, medication adherence %K therapeutic inertia %D 2017 %7 11.07.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Hypertension and type 2 diabetes mellitus are major modifiable risk factors for cardiac, cerebrovascular, and kidney diseases. Reasons for poor disease control include nonadherence, lack of patient engagement, and therapeutic inertia. Objective: The aim of this study was to assess the impact on clinic-measured blood pressure (BP) and glycated hemoglobin (HbA1c) using a digital medicine offering (DMO) that measures medication ingestion adherence, physical activity, and rest using digital medicines (medication taken with ingestible sensor), wearable sensor patches, and a mobile device app. Methods: Participants with elevated systolic BP (SBP ≥140 mm Hg) and HbA1c (≥7%) failing antihypertensive (≥2 medications) and oral diabetes therapy were enrolled in this three-arm, 12-week, cluster-randomized study. Participants used DMO (includes digital medicines, the wearable sensor patch, and the mobile device app) for 4 or 12 weeks or received usual care based on site randomization. Providers in the DMO arms could review the DMO data via a Web portal. In all three arms, providers were instructed to make medical decisions (medication titration, adherence counseling, education, and lifestyle coaching) on all available clinical information at each visit. Primary outcome was change in SBP at week 4. Other outcomes included change in SBP and HbA1c at week 12, and low-density lipoprotein cholesterol (LDL-C) and diastolic blood pressure (DBP) at weeks 4 and 12, as well as proportion of patients at BP goal (<140/90 mm Hg) at weeks 4 and 12, medical decisions, and medication adherence patterns. Results: Final analysis included 109 participants (12 sites; age: mean 58.7, SD years; female: 49.5%, 54/109; Hispanic: 45.9%, 50/109; income ≤ US $20,000: 56.9%, 62/109; and ≤ high school education: 52.3%, 57/109). The DMO groups had 80 participants (7 sites) and usual care had 29 participants (5 sites). At week 4, DMO resulted in a statistically greater SBP reduction than usual care (mean –21.8, SE 1.5 mm Hg vs mean –12.7, SE 2.8 mmHg; mean difference –9.1, 95% CI –14.0 to –3.3 mm Hg) and maintained a greater reduction at week 12. The DMO groups had greater reductions in HbA1c, DBP, and LDL-C, and a greater proportion of participants at BP goal at weeks 4 and 12 compared with usual care. The DMO groups also received more therapeutic interventions than usual care. Medication adherence was ≥80% while using the DMO. The most common adverse event was a self-limited rash at the wearable sensor site (12%, 10/82). Conclusions: For patients failing hypertension and diabetes oral therapy, this DMO, which provides dose-by-dose feedback on medication ingestion adherence, can help lower BP, HbA1c, and LDL-C, and promote patient engagement and provider decision making. Trial Registration: Clinicaltrials.gov NCT02827630; https://clinicaltrials.gov/show/NCT02827630 (Archived by WebCite at http://www.webcitation.org/6rL8dW2VF) %M 28698169 %R 10.2196/jmir.7833 %U http://www.jmir.org/2017/7/e246/ %U https://doi.org/10.2196/jmir.7833 %U http://www.ncbi.nlm.nih.gov/pubmed/28698169 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 7 %P e92 %T A Bit of Fit: Minimalist Intervention in Adolescents Based on a Physical Activity Tracker %A Gaudet,Jeffrey %A Gallant,François %A Bélanger,Mathieu %+ Centre de formation médicale du Nouveau-Brunswick, 18 Antonine Maillet street, Moncton, NB,, Canada, 1 506 863 2221, mathieu.f.belanger@usherbrooke.ca %K health behavior %K health promotion %K mHealth %K physical activity tracker %D 2017 %7 06.07.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Only 5% of Canadian youth meet the recommended 60 minutes of moderate to vigorous physical activity (MVPA) per day, with leisure time being increasingly allocated to technology usage. Direct-to-consumer mHealth devices that promote physical activity, such as wrist-worn physical activity trackers, have features with potential appeal to youth. Objective: The primary purpose of this study was to determine whether a minimalist physical activity tracker-based intervention would lead to an increase in physical activity in young adolescents. A secondary aim of this study was to assess change in physical activity across a 7-week intervention, as measured by the tracker. Methods: Using a quasi-experimental crossover design, two groups of 23 young adolescents (aged 13-14 years) were randomly assigned to immediate intervention or delayed intervention. The intervention consisted of wearing a Fitbit-Charge-HR physical activity tracker over a 7-week period. Actical accelerometers were used to measure participants’ levels of MVPA before and at the end of intervention periods for each group. Covariates such as age, sex, stage of change for physical activity behavior, and goal commitment were also measured. Results: There was an increase in physical activity over the course of the study period, though it was not related to overall physical activity tracker use. An intervention response did, however, occur in a subset of participants. Specifically, exposure to the physical activity tracker was associated with an average daily increase in MVPA by more than 15 minutes (P=.01) among participants who reported being in the action and maintenance stages of behavior change in relation to participation in physical activity. Participants in the precontemplation, contemplation, and preparation stages of behavior change had no change in their level of MVPA (P=.81). Conclusions: These results suggest that physical activity trackers may elicit improved physical activity related behavior in young adolescents demonstrating a readiness to be active. Future studies should seek to investigate if integrating physical activity trackers as part of more intensive interventions leads to greater increases in physical activity across different levels of stages of behavior change and if these changes can be sustained over longer periods of time. %M 28684384 %R 10.2196/mhealth.7647 %U http://mhealth.jmir.org/2017/7/e92/ %U https://doi.org/10.2196/mhealth.7647 %U http://www.ncbi.nlm.nih.gov/pubmed/28684384 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 6 %P e88 %T Mobile Device Accuracy for Step Counting Across Age Groups %A Modave,François %A Guo,Yi %A Bian,Jiang %A Gurka,Matthew J %A Parish,Alice %A Smith,Megan D %A Lee,Alexandra M %A Buford,Thomas W %+ University of Florida, Department of Health Outcomes and Policy, 2004 Mowry Road, Suite 2243 PO Box 100177, Gainesville, FL, 32610-0177, United States, 1 3522945984, modavefp@ufl.edu %K mobile %K devices %K physical activity %K weight reduction %K adults %D 2017 %7 28.06.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Only one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm. Objective: We aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups. Methods: We recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant. Results: Among the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years. Conclusions: Our analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program. %M 28659255 %R 10.2196/mhealth.7870 %U https://mhealth.jmir.org/2017/6/e88/ %U https://doi.org/10.2196/mhealth.7870 %U http://www.ncbi.nlm.nih.gov/pubmed/28659255 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 6 %P e86 %T A Community-Based Physical Activity Counselling Program for People With Knee Osteoarthritis: Feasibility and Preliminary Efficacy of the Track-OA Study %A Li,Linda C %A Sayre,Eric C %A Xie,Hui %A Clayton,Cam %A Feehan,Lynne M %+ Arthritis Research Canada, 5591 No. 3 Road, Richmond, BC, V6X 2C7, Canada, 1 604 207 4020, lli@arthritisresearch.ca %K osteoarthritis %K physical activity %K sedentary behavior %K sedentary lifestyle %K wearables %K digital technology %K fitness trackers %K exercise %D 2017 %7 26.06.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity can improve health outcomes in people with knee osteoarthritis (OA); however, participation in physical activity is very low in this population. Objective: The objective of our study was to assess the feasibility and preliminary efficacy of the use of wearables (Fitbit Flex) and telephone counselling by a physical therapist (PT) for improving physical activity in people with a physician-confirmed diagnosis of knee OA, or who have passed 2 validated criteria for early OA. Methods: We conducted a community-based feasibility randomized controlled trial. The immediate group (n=17) received a brief education session by a physical therapist, a Fitbit Flex activity tracker, and a weekly telephone call for activity counselling with the physical therapist. The delayed group (n=17) received the same intervention 1 month later. All participants were assessed at baseline (T0), and the end of 1 month (T1) and 2 months (T2). Outcomes were (1) mean moderate to vigorous physical activity time, (2) mean time spent on sedentary behavior, (3) Knee Injury and Osteoarthritis Outcome Score (KOOS), and (4) Partners in Health Scale. Feasibility data were summarized with descriptive statistics. We used analysis of covariance to evaluate the effect of the group type on the outcome measures at T1 and T2, after adjusting for blocking and T0. We assessed planned contrasts of changes in outcome measures over measurement periods. Results: We identified 46 eligible individuals; of those, 34 (74%) enrolled and no one dropped out. All but 1 participant adhered to the intervention protocol. We found a significant effect, with the immediate intervention group having improved in the moderate to vigorous physical activity time and in the Partners in Health Scale at T0 to T1 compared with the delayed intervention group. The planned contrast of the immediate intervention group at T0 to T1 versus the delayed group at T1 to T2 showed a significant effect in the sedentary time and the KOOS symptoms subscale, favoring the delayed group. Conclusions: This study demonstrated the feasibility of a behavioral intervention, supported by the use of a wearable device, to promote physical activity among people with knee OA. Trial Registration: ClinicalTrials.gov NCT02313506; https://clinicaltrials.gov/ct2/show/NCT02313506 (Archived by WebCite at http://www.webcitation.org/6r4P3Bub0) %M 28652228 %R 10.2196/mhealth.7863 %U http://mhealth.jmir.org/2017/6/e86/ %U https://doi.org/10.2196/mhealth.7863 %U http://www.ncbi.nlm.nih.gov/pubmed/28652228 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 5 %P e105 %T PREventive Care Infrastructure based On Ubiquitous Sensing (PRECIOUS): A Study Protocol %A Castellano-Tejedor,Carmina %A Moreno,Jordi %A Ciudin,Andrea %A Parramón,Gemma %A Lusilla-Palacios,Pilar %+ University Hospital Vall d’Hebron - Vall d'Hebron Research Institute, Department of Psychiatry, CIBERSAM, Autonomous University of Barcelona, Passeig Vall d'Hebron 119-129, Barcelona, 08035, Spain, 34 934893649 ext 3649, ninacastej@yahoo.es %K mHealth %K motivational interviewing %K physical activity %K diet %K sustained motivation %K adherence %D 2017 %7 31.05.2017 %9 Protocol %J JMIR Res Protoc %G English %X Background: mHealth has experienced a huge growth during the last decade. It has been presented as a new and promising pathway to increase self-management of health and chronic conditions in several populations. One of the most prolific areas of mHealth has been healthy lifestyles promotion. However, few mobile apps have succeeded in engaging people and ensuring sustained use. Objective: This paper describes the pilot test protocol of the PReventive Care Infrastructure based on Ubiquitous Sensing (PRECIOUS) project, aimed at validating the PRECIOUS system with end users. This system includes, within a motivational framework, the Bodyguard2 sensor (accelerometer with heart rate monitoring) and the PRECIOUS app. Methods: This is a pilot experimental study targeting morbidly obese prediabetic patients who will be randomized to three conditions: (1) Group 1 - Control group (Treatment as usual with the endocrinologist and the nurse + Bodyguard2), (2) Group 2 - PRECIOUS system (Bodyguard2 + PRECIOUS app), and (3) Group 3 - PRECIOUS system (Bodyguard2 + PRECIOUS app + Motivational Interviewing). The duration of the study will be 3 months with scheduled follow-up appointments within the scope of the project at Weeks 3, 5, 8, and 12. During the study, several measures related to healthy lifestyles, weight management, and health-related quality of life will be collected to explore the effectiveness of PRECIOUS to foster behavior change, as well as user acceptance, usability, and satisfaction with the solution. Results: Because of the encouraging results shown in similar scientific work analyzing health apps acceptance in clinical settings, we expect patients to widely accept and express satisfaction with PRECIOUS. We also expect to find acceptable usability of the preventive health solution. The recruitment of the pilot study has concluded with the inclusion of 31 morbidly obese prediabetic patients. Results are expected to be available in mid-2017. Conclusions: Adopting and maintaining healthy habits may be challenging in people with chronic conditions who usually need regular support to ensure mid/long-term adherence to recommendations and behavior change. Thus, mHealth could become a powerful and efficient tool since it allows continuous communication with users and immediate feedback. The PRECIOUS system is an innovative preventive health care solution aimed at enhancing inner motivation from users to change their lifestyles and adopt healthier habits. PRECIOUS includes ubiquitous sensors and a scientifically grounded app to address three main components of health: physical activity, diet, and stress levels. Trial Registration: Clinicaltrials.gov NCT02818790; https://clinicaltrials.gov/ct2/show/NCT02818790 (Archived by WebCite at http://www.webcitation.org/6qfzdfMoU) %M 28566263 %R 10.2196/resprot.6973 %U http://www.researchprotocols.org/2017/5/e105/ %U https://doi.org/10.2196/resprot.6973 %U http://www.ncbi.nlm.nih.gov/pubmed/28566263 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 3 %N 2 %P e32 %T Understanding Environmental and Contextual Influences of Physical Activity During First-Year University: The Feasibility of Using Ecological Momentary Assessment in the MovingU Study %A Bedard,Chloe %A King-Dowling,Sara %A McDonald,Madeline %A Dunton,Genevieve %A Cairney,John %A Kwan,Matthew %+ INfant and Child Health (INCH) Lab, Department of Family Medicine, McMaster University, DBHSC, 5th Floor, 1280 Main Street West, Hamilton, ON, L8S 4L8, Canada, 1 9055259140 ext 20303, kwanmy@mcmaster.ca %K exercise %K compliance %K feasibility studies %K young adult %K students %D 2017 %7 31.05.2017 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: It is well established that drastic declines in physical activity (PA) occur during young adults’ transition into university; however, our understanding of contextual and environmental factors as it relates to young adults’ PA is limited. Objective: The purpose of our study was to examine the feasibility of using wrist-worn accelerometers and the use of ecological momentary assessment (EMA) to assess the context and momentary correlates of PA on multiple occasions each day during first-year university. Methods: First-year university students were asked to participate in the study. The participants completed a brief questionnaire and were subsequently asked to wear an ActiGraph GT9X-Link accelerometer and respond to a series of EMA prompts (7/day) via their phones for 5 consecutive days. Results: A total of 96 first-year university students with smartphones agreed to participate in the study (mean age 18.3 [SD 0.51]; n=45 females). Overall, there was good compliance for wearing the accelerometers, with 91% (78/86) of the participants having ≥2 days of ≥10 hours of wear time (mean=3.53 valid days). Students were generally active, averaging 10,895 steps/day (SD 3413) or 1123.23 activity counts/min (SD 356.10). Compliance to EMA prompts was less desirable, with 64% (55/86) of the participants having usable EMA data (responding to a minimum of ≥3 days of 3 prompts/day or ≥4 days of 2 prompts/day), and only 47% (26/55) of these participants were considered to have excellent EMA compliance (responding to ≥5 days of 4 prompts/day or ≥ 4 days of 5 prompts/day). Conclusions: This study represents one of the first studies to use an intensive real-time data capture strategy to examine time-varying correlates of PA among first-year university students. These data will aim to describe the physical and social contexts in which PA occurs and examine the relationships between momentary correlates of PA among the first-year university students. Overall, current results suggest that wrist-worn accelerometers and EMA are feasible methods for data collection among the young adult population; however, more work is needed to understand how to improve upon compliance to a real-time data capture method such as EMA. %M 28566264 %R 10.2196/publichealth.7010 %U http://publichealth.jmir.org/2017/2/e32/ %U https://doi.org/10.2196/publichealth.7010 %U http://www.ncbi.nlm.nih.gov/pubmed/28566264 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e186 %T Design and Evaluation of a Computer-Based 24-Hour Physical Activity Recall (cpar24) Instrument %A Kohler,Simone %A Behrens,Gundula %A Olden,Matthias %A Baumeister,Sebastian E %A Horsch,Alexander %A Fischer,Beate %A Leitzmann,Michael F %+ Department of Epidemiology and Preventive Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, Regensburg, 93053, Germany, 49 941 944 ext 5217, Gundula.Behrens@klinik.uni-regensburg.de %K web-based method %K validity %K reliability %K usability %K lifestyle behavior %K physical activity %K sedentary behavior %D 2017 %7 30.05.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Widespread access to the Internet and an increasing number of Internet users offers the opportunity of using Web-based recalls to collect detailed physical activity data in epidemiologic studies. Objective: The aim of this investigation was to evaluate the validity and reliability of a computer-based 24-hour physical activity recall (cpar24) instrument with respect to the recalled 24-h period. Methods: A random sample of 67 German residents aged 22 to 70 years was instructed to wear an ActiGraph GT3X+ accelerometer for 3 days. Accelerometer counts per min were used to classify activities as sedentary (<100 counts per min), light (100-1951 counts per min), and moderate to vigorous (≥1952 counts per min). On day 3, participants were also requested to specify the type, intensity, timing, and context of all activities performed during day 2 using the cpar24. Using metabolic equivalent of task (MET), the cpar24 activities were classified as sedentary (<1.5 MET), light (1.5-2.9 MET), and moderate to vigorous (≥3.0 MET). The cpar24 was administered twice at a 3-h interval. The Spearman correlation coefficient (r) was used as primary measure of concurrent validity and test-retest reliability. Results: As compared with accelerometry, the cpar24 underestimated light activity by −123 min (median difference, P difference <.001) and overestimated moderate to vigorous activity by 89 min (P difference <.001). By comparison, time spent sedentary assessed by the 2 methods was similar (median difference=+7 min, P difference=.39). There was modest agreement between the cpar24 and accelerometry regarding sedentary (r=.54), light (r=.46), and moderate to vigorous (r=.50) activities. Reliability analyses revealed modest to high intraclass correlation coefficients for sedentary (r=.75), light (r=.65), and moderate to vigorous (r=.92) activities and no statistically significant differences between replicate cpar24 measurements (median difference for sedentary activities=+10 min, for light activities=−5 min, for moderate to vigorous activities=0 min, all P difference ≥.60). Conclusion: These data show that the cpar24 is a valid and reproducible Web-based measure of physical activity in adults. %M 28559229 %R 10.2196/jmir.7620 %U http://www.jmir.org/2017/5/e186/ %U https://doi.org/10.2196/jmir.7620 %U http://www.ncbi.nlm.nih.gov/pubmed/28559229 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e184 %T Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting %A O'Brien,Megan K %A Shawen,Nicholas %A Mummidisetty,Chaithanya K %A Kaur,Saninder %A Bo,Xiao %A Poellabauer,Christian %A Kording,Konrad %A Jayaraman,Arun %+ Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, 345 E. Superior St., Chicago, IL, 60611, United States, 1 312 238 6875, a-jayaraman@northwestern.edu %K smartphone %K activities of daily living %K ambulatory monitoring %K machine learning %K stroke rehabilitation %D 2017 %7 25.05.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions. Objective: In this study, we sought to evaluate AR performance in a home setting for individuals who had suffered a stroke, by using different sets of training activities. Specifically, we compared AR performance for persons with stroke while varying the origin of training data, based on either population (healthy persons or persons with stoke) or environment (laboratory or home setting). Methods: Thirty individuals with stroke and fifteen healthy subjects performed a series of mobility-related activities, either in a laboratory or at home, while wearing a smartphone. A custom-built app collected signals from the phone’s accelerometer, gyroscope, and barometer sensors, and subjects self-labeled the mobility activities. We trained a random forest AR model using either healthy or stroke activity data. Primary measures of AR performance were (1) the mean recall of activities and (2) the misclassification of stationary and ambulatory activities. Results: A classifier trained on stroke activity data performed better than one trained on healthy activity data, improving average recall from 53% to 75%. The healthy-trained classifier performance declined with gait impairment severity, more often misclassifying ambulatory activities as stationary ones. The classifier trained on in-lab activities had a lower average recall for at-home activities (56%) than for in-lab activities collected on a different day (77%). Conclusions: Stroke-based training data is needed for high quality AR among gait-impaired individuals with stroke. Additionally, AR systems for home and community monitoring would likely benefit from including at-home activities in the training data. %M 28546137 %R 10.2196/jmir.7385 %U http://www.jmir.org/2017/5/e184/ %U https://doi.org/10.2196/jmir.7385 %U http://www.ncbi.nlm.nih.gov/pubmed/28546137 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 5 %P e65 %T What to Build for Middle-Agers to Come? Attractive and Necessary Functions of Exercise-Promotion Mobile Phone Apps: A Cross-Sectional Study %A Liao,Gen-Yih %A Chien,Yu-Tai %A Chen,Yu-Jen %A Hsiung,Hsiao-Fang %A Chen,Hsiao-Jung %A Hsieh,Meng-Hua %A Wu,Wen-Jie %+ Department of Information Management, Chang Gung University, 259 Wen-Hwa 1st Road, Guishan District, Taoyuan City, 333, Taiwan, 886 32118800 ext 5852, gyliao@acm.org %K physical exercise %K middle aged %K mobile application %K self efficacy %K consumer preference %D 2017 %7 25.05.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity is important for middle-agers to maintain health both in middle age and in old age. Although thousands of exercise-promotion mobile phone apps are available for download, current literature offers little understanding regarding which design features can enhance middle-aged adults’ quality perception toward exercise-promotion apps and which factor may influence such perception. Objectives: The aims of this study were to understand (1) which design features of exercise-promotion apps can enhance quality perception of middle-agers, (2) whether their needs are matched by current functions offered in app stores, and (3) whether physical activity (PA) and mobile phone self-efficacy (MPSE) influence quality perception. Methods: A total of 105 middle-agers participated and filled out three scales: the International Physical Activity Questionnaire (IPAQ), the MPSE scale, and the need for design features questionnaire. The design features were developed based on the Coventry, Aberdeen, and London—Refined (CALO-RE) taxonomy. Following the Kano quality model, the need for design features questionnaire asked participants to classify design features into five categories: attractive, one-dimensional, must-be, indifferent, and reverse. The quality categorization was conducted based on a voting approach and the categorization results were compared with the findings of a prevalence study to realize whether needs match current availability. In total, 52 multinomial logistic regression models were analyzed to evaluate the effects of PA level and MPSE on quality perception of design features. Results: The Kano analysis on the total sample revealed that visual demonstration of exercise instructions is the only attractive design feature, whereas the other 51 design features were perceived with indifference. Although examining quality perception by PA level, 21 features are recommended to low level, 6 features to medium level, but none to high-level PA. In contrast, high-level MPSE is recommended with 14 design features, medium level with 6 features, whereas low-level participants are recommended with 1 feature. The analysis suggests that the implementation of demanded features could be low, as the average prevalence of demanded design features is 20% (4.3/21). Surprisingly, social comparison and social support, most implemented features in current apps, were categorized into the indifferent category. The magnitude of effect is larger for MPSE because it effects quality perception of more design features than PA. Delving into the 52 regression models revealed that high MPSE more likely induces attractive or one- dimensional categorization, suggesting the importance of technological self-efficacy on eHealth care promotion. Conclusions: This study is the first to propose middle-agers’ needs in relation to mobile phone exercise-promotion. In addition to the tailor-made recommendations, suggestions are offered to app designers to enhance the performance of persuasive features. An interesting finding on change of quality perception attributed to MPSE is proposed as future research. %M 28546140 %R 10.2196/mhealth.6233 %U http://mhealth.jmir.org/2017/5/e65/ %U https://doi.org/10.2196/mhealth.6233 %U http://www.ncbi.nlm.nih.gov/pubmed/28546140 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 5 %P e64 %T Assessing the Impact of a Novel Smartphone Application Compared With Standard Follow-Up on Mobility of Patients With Knee Osteoarthritis Following Treatment With Hylan G-F 20: A Randomized Controlled Trial %A Skrepnik,Nebojsa %A Spitzer,Andrew %A Altman,Roy %A Hoekstra,John %A Stewart,John %A Toselli,Richard %+ Tucson Orthopaedic Institute, 5301 E. Grant Road, Tucson, AZ,, United States, 1 520 784 6140, NSkrepnik@tucsonortho.com %K mobile health %K mHealth %K mobile apps %K osteoarthritis %K osteoarthritis, knee %K hylan G-F 20 %K Synvisc %D 2017 %7 09.05.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Osteoarthritis (OA) is a leading cause of disability in the United States. Although no disease-modifying therapies exist, patients with knee OA who increase walking may reduce risk of functional limitations. Objective: The objective of the study is to evaluate the impact of a mobile app (OA GO) plus wearable activity monitor/pedometer (Jawbone UP 24) used for 90 days on the mobility of patients with knee OA treated with hylan G-F 20. Methods: Patients with knee OA aged 30 to 80 years who were eligible to receive hylan G-F 20 and were familiar with smartphone technology were enrolled in this randomized, multicenter, open-label study. Patients who had a body mass index above 35 kg/m2 were excluded. All patients received a single 6-mL injection of hylan G-F 20 and wore the Jawbone monitor. The patients were then randomized 1:1 to Jawbone and OA GO (Group A; n=107) with visible feedback (unblinded) or Jawbone only (Group B; n=104) with no visible feedback (blinded). The primary endpoint was mean change from baseline in steps per day at day 90 between Groups A and B. Results: Baseline characteristics were similar between groups. There were significant differences between the increases in least squares (LS) mean number of steps per day (1199 vs 467, P=.03) and the mean percentage change (35.8% vs 11.5%, P=.02) from baseline in favor of Group A over Group B. There was a greater reduction in pain from baseline during the 6-minute walk test in Group A versus Group B. (LS mean change: −55.3 vs −33.8, P=.007). Most patients (65.4%) and surveys of physicians (67.3%) reported they would be likely or very likely to use/recommend the devices. Patient Activity Measure-13 scores improved from baseline (LS mean change for Groups A and B: 5.0 vs 6.9), with no significant differences between groups. The occurrence of adverse events was similar in the 2 groups. Conclusions: Use of a novel smartphone app in conjunction with a wearable activity monitor provided additional improvement on mobility parameters such as steps per day and pain with walking in the 6-minute walk test in patients with knee OA who were treated with hylan G-F 20. Results also highlight the amenability of patients and physicians to using mobile health technology in the treatment of OA and suggest further study is warranted. %M 28487266 %R 10.2196/mhealth.7179 %U http://mhealth.jmir.org/2017/5/e64/ %U https://doi.org/10.2196/mhealth.7179 %U http://www.ncbi.nlm.nih.gov/pubmed/28487266 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 5 %P e61 %T Ownership and Use of Commercial Physical Activity Trackers Among Finnish Adolescents: Cross-Sectional Study %A Ng,Kwok %A Tynjälä,Jorma %A Kokko,Sami %+ Research Centre for Health Promotion, Faculty of Sport and Health Sciences, University of Jyvaskyla, PO Box 35 (L), Jyväskylä, 40014, Finland, 358 451499919, kwok.ng@jyu.fi %K social determinants of health %K mobile phone %K health promotion %K disabled children %K physical activity %K adolescent %D 2017 %7 04.05.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Mobile phone apps for monitoring and promoting physical activity (PA) are extremely popular among adults. Devices, such as heart rate monitors or sports watches (HRMs/SWs) that work with these apps are at sufficiently low costs to be available through the commercial markets. Studies have reported an increase in PA levels among adults with devices; however, it is unknown whether the phenomena are similar during early adolescence. At a time when adolescents start to develop their own sense of independence and build friendship, the ease of smartphone availability in developed countries needs to be investigated in important health promoting behaviors such as PA. Objective: The objective of this study was to investigate the ownership and usage of PA trackers (apps and HRM/SW) among adolescents in a national representative sample and to examine the association between use of devices and PA levels. Methods: The Finnish school-aged physical activity (SPA) study consisted of 4575 adolescents, aged 11-, 13-, and 15-years, who took part in a web-based questionnaire during school time about PA behaviors between April and May 2016. Binary logistic regression analyses were used to test the associations between moderate to vigorous physical activity (MVPA) and devices, after controlling for gender, age, disability, and family affluence. Results: PA tracking devices have been categorized into two types, which are accessible to adolescents: (1) apps and (2) HRM/SW. Half the adolescents (2351/4467; 52.63%) own apps for monitoring PA, yet 16.12% (720/4467) report using apps. Fewer adolescents (782/4413; 17.72%) own HRM/SW and 9.25% (408/4413) use HRM/SW. In this study, users of HRM/SW were 2.09 times (95% CI 1.64-2.67), whereas users of apps were 1.4 times (95% CI 1.15-1.74) more likely to meet PA recommendations of daily MVPA for at least 60 min compared with adolescents without HRM/SW or without apps. Conclusions: To our knowledge, this is the first study that describes the situation in Finland with adolescents using PA trackers and its association with PA levels. Implications of the use of apps and HRM/SW by adolescents are discussed. %M 28473304 %R 10.2196/mhealth.6940 %U http://mhealth.jmir.org/2017/5/e61/ %U https://doi.org/10.2196/mhealth.6940 %U http://www.ncbi.nlm.nih.gov/pubmed/28473304 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 4 %P e55 %T The Physical Activity Tracker Testing in Youth (P.A.T.T.Y.) Study: Content Analysis and Children’s Perceptions %A Masteller,Brittany %A Sirard,John %A Freedson,Patty %+ Department of Kinesiology, University of Massachusetts Amherst, 30 Eastman Lane, Totman 110, Amherst, MA, 01003, United States, 1 4135457898, bmasteller@kin.umass.edu %K child %K physical activity %K qualitative research %D 2017 %7 28.4.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Activity trackers are widely used by adults and several models are now marketed for children. Objective: The aims of this study were to (1) perform a content analysis of behavioral change techniques (BCTs) used by three commercially available youth-oriented activity trackers and (2) obtain feedback describing children’s perception of these devices and the associated websites. Methods: A content analysis recorded the presence of 36 possible BCTs for the MovBand (MB), Sqord (SQ), and Zamzee (ZZ) activity trackers. In addition, 16 participants (mean age 8.6 years [SD 1.6]; 50% female [8/16]) received all three trackers and were oriented to the devices and websites. Participants were instructed to wear the trackers on 4 consecutive days and spend ≥10 min/day on each website. A cognitive interview and survey were administered when the participant returned the devices. Qualitative data analysis was used to analyze the content of the cognitive interviews. Chi-square analyses were used to determine differences in behavioral monitoring and social interaction features between websites. Results: The MB, SQ, and ZZ devices or websites included 8, 15, and 14 of the possible 36 BCTs, respectively. All of the websites had a behavioral monitoring feature (charts for tracking activity), but the percentage of participants indicating that they “liked” those features varied by website (MB: 8/16, 50%; SQ: 6/16, 38%; ZZ: 11/16, 69%). Two websites (SQ and ZZ) included an “avatar” that the user could create to represent themselves on the website. Participants reported that they “liked” creating and changing their avatar (SQ: 12/16, 75%, ZZ: 15/16, 94%), which was supported by the qualitative analyses of the cognitive interviews. Most participants (75%) indicated that they would want to wear the devices more if their friends were wearing a tracker. No significant differences were observed between SQ and ZZ devices in regards to liking or use of social support interaction features (P=.21 to .37). Conclusions: The websites contained several BCTs consistent with previously identified strategies. Children “liked” the social aspects of the websites more than the activity tracking features. Developers of commercial activity trackers for youth may benefit from considering a theoretical perspective during the website design process. %M 28455278 %R 10.2196/mhealth.6347 %U http://mhealth.jmir.org/2017/4/e55/ %U https://doi.org/10.2196/mhealth.6347 %U http://www.ncbi.nlm.nih.gov/pubmed/28455278 %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 %@ 2369-1999 %I JMIR Publications %V 3 %N 1 %P e5 %T The Fitbit One Physical Activity Tracker in Men With Prostate Cancer: Validation Study %A Van Blarigan,Erin L %A Kenfield,Stacey A %A Tantum,Lucy %A Cadmus-Bertram,Lisa A %A Carroll,Peter R %A Chan,June M %+ Department of Epidemiology and Biostatistics, Department of Urology, University of California, San Francisco, 550 16th St. 2nd Floor, San Francisco, CA, 94158, United States, 1 415 476 1111 ext 13608, erin.vanblarigan@ucsf.edu %K prostatic neoplasms %K exercise %D 2017 %7 18.04.2017 %9 Original Paper %J JMIR Cancer %G English %X Background: Physical activity after cancer diagnosis improves quality of life and may lengthen survival. However, objective data in cancer survivors are limited and no physical activity tracker has been validated for use in this population. Objective: The aim of this study was to validate the Fitbit One’s measures of physical activity over 7 days in free-living men with localized prostate cancer. Methods: We validated the Fitbit One against the gold-standard ActiGraph GT3X+ accelerometer in 22 prostate cancer survivors under free-living conditions for 7 days. We also compared these devices with the HJ-322U Tri-axis USB Omron pedometer and a physical activity diary. We used descriptive statistics (eg, mean, standard deviation, median, interquartile range) and boxplots to examine the distribution of average daily light, moderate, and vigorous physical activity and steps measured by each device and the diary. We used Pearson and Spearman rank correlation coefficients to compare measures of physical activity and steps between the devices and the diary. Results: On average, the men wore the devices for 5.8 days. The mean (SD) moderate-to-vigorous physical activity (MVPA; minutes/day) measured was 100 (48) via Fitbit, 51 (29) via ActiGraph, and 110 (78) via diary. The mean (SD) steps/day was 8724 (3535) via Fitbit, 8024 (3231) via ActiGraph, and 6399 (3476) via pedometer. Activity measures were well correlated between the Fitbit and ActiGraph: 0.85 for MPVA and 0.94 for steps (all P<.001). The Fitbit’s step measurements were well correlated with the pedometer (0.67, P=.001), and the Fitbit’s measure of MVPA was well correlated with self-reported activity in the diary (0.84; P<.001). Conclusions: Among prostate cancer survivors, the Fitbit One’s activity and step measurements were well correlated with the ActiGraph GT3X+ and Omron pedometer. However, the Fitbit One measured two times more MVPA on average compared with the ActiGraph. %M 28420602 %R 10.2196/cancer.6935 %U http://cancer.jmir.org/2017/1/e5/ %U https://doi.org/10.2196/cancer.6935 %U http://www.ncbi.nlm.nih.gov/pubmed/28420602 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 3 %P e44 %T Biometrics and Policing: A Protocol for Multichannel Sensor Data Collection and Exploratory Analysis of Contextualized Psychophysiological Response During Law Enforcement Operations %A Furberg,Robert D %A Taniguchi,Travis %A Aagaard,Brian %A Ortiz,Alexa M %A Hegarty-Craver,Meghan %A Gilchrist,Kristin H %A Ridenour,Ty A %+ Digital Health & Clinical Informatics, RTI International, 3040 Cornwallis Rd, Research Triangle Park, NC, 27709, United States, 1 919 316 3726, rfurberg@rti.org %K psychophysiology %K law enforcement %K sensor, wearable %K clinical trial %K digital health %D 2017 %7 17.03.2017 %9 Protocol %J JMIR Res Protoc %G English %X Background: Stress experienced by law enforcement officers is often extreme and is in many ways unique among professions. Although past research on officer stress is informative, it is limited, and most studies measure stress using self-report questionnaires or observational studies that have limited generalizability. We know of no research studies that have attempted to track direct physiological stress responses in high fidelity, especially within an operational police setting. The outcome of this project will have an impact on both practitioners and policing researchers. To do so, we will establish a capacity to obtain complex, multisensor data; process complex datasets; and establish the methods needed to conduct idiopathic clinical trials on behavioral interventions in similar contexts. Objective: The objective of this pilot study is to demonstrate the practicality and utility of wrist-worn biometric sensor-based research in a law enforcement agency. Methods: We will use nonprobability convenience-based sampling to recruit 2-3 participants from the police department in Durham, North Carolina, USA. Results: Data collection was conducted in 2016. We will analyze data in early 2017 and disseminate our results via peer reviewed publications in late 2017. Conclusions: We developed the Biometrics & Policing Demonstration project to provide a proof of concept on collecting biometric data in a law enforcement setting. This effort will enable us to (1) address the regulatory approvals needed to collect data, including human participant considerations, (2) demonstrate the ability to use biometric tracking technology in a policing setting, (3) link biometric data to law enforcement data, and (4) explore project results for law enforcement policy and training. %M 28314707 %R 10.2196/resprot.7499 %U http://www.researchprotocols.org/2017/3/e44/ %U https://doi.org/10.2196/resprot.7499 %U http://www.ncbi.nlm.nih.gov/pubmed/28314707 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 3 %P e34 %T Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices %A Dooley,Erin E %A Golaszewski,Natalie M %A Bartholomew,John B %+ Department of Kinesiology and Health Education, University of Texas at Austin, UT Mail Code: D3700 2109 San Jacinto Blvd, Austin, TX, 78712-1415, United States, 1 512 232 6021, jbart@austin.utexas.edu %K motor activity %K physical exertion %K exercise %K monitoring, physiologic %K energy metabolism %K heart rate %K photoplethysmography %D 2017 %7 16.03.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown. Objective: The objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities. Methods: A total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE). Results: For the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (P<.01). For the Fitbit device, the HR MAPE was between 2.38% and 16.99%. HR was not significantly different at the start (P=.67) or during moderate intensity (P=.34); lower HR readings were measured during baseline, vigorous intensity, and recovery (P<.001) and higher HR during light intensity (P<.001). EE MAPE was between 16.85% and 84.98%. The device measured higher EE at baseline (P=.003), light intensity (P<.001), and moderate intensity (P=.001). EE was not significantly different at vigorous (P=.70) or recovery (P=.10). For Garmin Forerunner 225, HR MAPE was between 7.87% and 24.38%. HR was not significantly different at vigorous intensity (P=.35). The device measured higher HR readings at start, baseline, light intensity, moderate intensity (P<.001), and recovery (P=.04). EE MAPE was between 30.77% and 155.05%. The device measured higher EE at all stages (P<.001). Conclusions: This study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific physical activity intensities. Establishing validity of wearable devices is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist. %M 28302596 %R 10.2196/mhealth.7043 %U http://mhealth.jmir.org/2017/3/e34/ %U https://doi.org/10.2196/mhealth.7043 %U http://www.ncbi.nlm.nih.gov/pubmed/28302596 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 3 %P e68 %T Evaluating the Consistency of Current Mainstream Wearable Devices in Health Monitoring: A Comparison Under Free-Living Conditions %A Wen,Dong %A Zhang,Xingting %A Liu,Xingyu %A Lei,Jianbo %+ Center for Medical Informatics, Peking University, 38 Xueyuan Rd., Haidian District, Beijing, 100191, China, 86 (10) 8280 5901, jblei@hsc.pku.edu.cn %K fitness trackers %K monitoring, physiologic %K motor activity %K activities of daily living %K health status %D 2017 %7 07.03.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Wearable devices are gaining increasing market attention; however, the monitoring accuracy and consistency of the devices remains unknown. Objective: The purpose of this study was to assess the consistency of the monitoring measurements of the latest wearable devices in the state of normal activities to provide advice to the industry and support to consumers in making purchasing choices. Methods: Ten pieces of representative wearable devices (2 smart watches, 4 smart bracelets of Chinese brands or foreign brands, and 4 mobile phone apps) were selected, and 5 subjects were employed to simultaneously use all the devices and the apps. From these devices, intact health monitoring data were acquired for 5 consecutive days and analyzed on the degree of differences and the relationships of the monitoring measurements ​​by the different devices. Results: The daily measurements by the different devices fluctuated greatly, and the coefficient of variation (CV) fluctuated in the range of 2-38% for the number of steps, 5-30% for distance, 19-112% for activity duration, .1-17% for total energy expenditure (EE), 22-100% for activity EE, 2-44% for sleep duration, and 35-117% for deep sleep duration. After integrating the measurement data of 25 days among the devices, the measurements of the number of steps (intraclass correlation coefficient, ICC=.89) and distance (ICC=.84) displayed excellent consistencies, followed by those of activity duration (ICC=.59) and the total EE (ICC=.59) and activity EE (ICC=.57). However, the measurements for sleep duration (ICC=.30) and deep sleep duration (ICC=.27) were poor. For most devices, there was a strong correlation between the number of steps and distance measurements (R2>.95), and for some devices, there was a strong correlation between activity duration measurements and EE measurements (R2>.7). A strong correlation was observed in the measurements of steps, distance and EE from smart watches and mobile phones of the same brand, Apple or Samsung (r>.88). Conclusions: Although wearable devices are developing rapidly, the current mainstream devices are only reliable in measuring the number of steps and distance, which can be used as health assessment indicators. However, the measurement consistencies of activity duration, EE, sleep quality, and so on, are still inadequate, which require further investigation and improved algorithms. %M 28270382 %R 10.2196/jmir.6874 %U http://www.jmir.org/2017/3/e68/ %U https://doi.org/10.2196/jmir.6874 %U http://www.ncbi.nlm.nih.gov/pubmed/28270382 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 3 %P e28 %T Feasibility and Acceptability of a Wearable Technology Physical Activity Intervention With Telephone Counseling for Mid-Aged and Older Adults: A Randomized Controlled Pilot Trial %A Lyons,Elizabeth J %A Swartz,Maria C %A Lewis,Zakkoyya H %A Martinez,Eloisa %A Jennings,Kristofer %+ Department of Nutrition and Metabolism, The University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, United States, 1 4097722575, ellyons@utmb.edu %K physical activity %K technology %K mobile health %K health behavior %K self-control %D 2017 %7 06.03.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As adults age, their physical activity decreases and sedentary behavior increases, leading to increased risk of negative health outcomes. Wearable electronic activity monitors have shown promise for delivering effective behavior change techniques. However, little is known about the feasibility and acceptability of non-Fitbit wearables (Fitbit, Inc, San Francisco, California) combined with telephone counseling among adults aged more than 55 years. Objective: The purpose of our study was to determine the feasibility, acceptability, and effect on physical activity of an intervention combining a wearable physical activity monitor, tablet device, and telephone counseling among adults aged 55-79 years. Methods: Adults (N=40, aged 55-79 years, body mass index=25-35, <60 min of activity per week) were randomized to receive a 12-week intervention or to a wait list control. Intervention participants received a Jawbone Up24 monitor, a tablet with the Jawbone Up app installed, and brief weekly telephone counseling. Participants set daily and weekly step goals and used the monitor’s idle alert to notify them when they were sedentary for more than 1 h. Interventionists provided brief counseling once per week by telephone. Feasibility was measured using observation and study records, and acceptability was measured by self-report using validated items. Physical activity and sedentary time were measured using ActivPAL monitors following standard protocols. Body composition was measured using dual-energy x-ray absorptiometry scans, and fitness was measured using a 6-min walk test. Results: Participants were 61.48 years old (SD 5.60), 85% (34/40) female, 65% (26/40) white. Average activity monitor wear time was 81.85 (SD 3.73) of 90 days. Of the 20 Up24 monitors, 5 were reported broken and 1 lost. No related adverse events were reported. Acceptability items were rated at least 4 on a scale of 1-5. Effect sizes for most outcomes were small, including stepping time per day (d=0.35), steps per day (d=0.26), sitting time per day (d=0.21), body fat (d=0.17), and weight (d=0.33). Conclusions: The intervention was feasible and acceptable in this population. Effect sizes were similar to the sizes found using other wearable electronic activity monitors, indicating that when combined with telephone counseling, wearable activity monitors are a potentially effective tool for increasing physical activity and decreasing sedentary behavior. Trial registration: Clinicaltrials.gov NCT01869348; https://clinicaltrials.gov/ct2/show/NCT01869348 (Archived by WebCite at http://www.webcitation.org/6odlIolqy) %M 28264796 %R 10.2196/mhealth.6967 %U http://mhealth.jmir.org/2017/3/e28/ %U https://doi.org/10.2196/mhealth.6967 %U http://www.ncbi.nlm.nih.gov/pubmed/28264796 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 2 %P e24 %T Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study %A Pobiruchin,Monika %A Suleder,Julian %A Zowalla,Richard %A Wiesner,Martin %+ GECKO Institute for Medicine, Informatics & Economics, Heilbronn University, Max-Planck-Str 39, Heilbronn, 74081, Germany, 49 7131 504 633, monika.pobiruchin@hs-heilbronn.de %K athlete %K wearables %K mobile phones %K physical activity %K activity monitoring %D 2017 %7 28.02.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Today, runners use wearable technology such as global positioning system (GPS)–enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors. Objective: The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially “smart” devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices. Methods: A pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other. Results: A total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P<.001; 70-79 year: P=.004) were less likely to use wearables. The mean of the track distances recorded by mobile phones with combined app (mean absolute error, MAE=0.35 km) and GPS-enabled sport watches (MAE=0.12 km) was significantly different (P=.002) for the half-marathon event. Conclusions: A great variety of vendors (n=36) and devices (n=156) were identified. Under real-world conditions, GPS-enabled devices, especially sport watches and mobile phones, were found to be accurate in terms of recorded course distances. %M 28246070 %R 10.2196/mhealth.6395 %U http://mhealth.jmir.org/2017/2/e24/ %U https://doi.org/10.2196/mhealth.6395 %U http://www.ncbi.nlm.nih.gov/pubmed/28246070 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 2 %P e10 %T Clinical Evaluation of the Measurement Performance of the Philips Health Watch: A Within-Person Comparative Study %A Hendrikx,Jos %A Ruijs,Loes S %A Cox,Lieke GE %A Lemmens,Paul MC %A Schuijers,Erik GP %A Goris,Annelies HC %+ Philips Research, High Tech Campus 34, Eindhoven, 5656AE, Netherlands, 31 40 27 91111, lieke.cox@philips.com %K sedentary lifestyle %K monitoring, ambulatory %K monitoring, physiologic %K accelerometry %K actigraphy %K photoplethysmography %K heart rate %K energy metabolism %K adult %K humans %D 2017 %7 02.02.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Physical inactivity is an important modifiable risk factor for chronic diseases. A new wrist-worn heart rate and activity monitor has been developed for unobtrusive data collection to aid prevention and management of lifestyle-related chronic diseases by means of behavioral change programs. Objective: The objective of the study was to evaluate the performance of total energy expenditure and resting heart rate measures of the Philips health watch. Secondary objectives included the assessment of accuracy of other output parameters of the monitor: heart rate, respiration rate at rest, step count, and activity type recognition. Methods: A within-person comparative study was performed to assess the performance of the health watch against (medical) reference measures. Participants executed a protocol including 15 minutes of rest and various activities of daily life. A two one-sided tests approach was adopted for testing equivalence. In addition, error metrics such as mean error and mean absolute percentage error (MAPE) were calculated. Results: A total of 29 participants (14 males; mean age 41.2, SD 14.4, years; mean weight 77.2, SD 10.2, kg; mean height 1.8, SD 0.1, m; mean body mass index 25.1, SD 3.1, kg/m2) completed the 81-minute protocol. Their mean resting heart rate in beats per minute (bpm) was 64 (SD 7.3). With a mean error of −10 (SD 38.9) kcal and a MAPE of 10% (SD 8.7%), total energy expenditure estimation of the health watch was found to be within the 15% predefined equivalence margin in reference to a portable indirect calorimeter. Resting heart rate determined during a 15-minute rest protocol was found to be within a 10% equivalence margin in reference to a wearable electrocardiogram (ECG) monitor, with a mean deviation of 0 bpm and a maximum deviation of 3 bpm. Heart rate was within 10 bpm and 10% of the ECG monitor reference for 93% of the duration of the protocol. Step count estimates were on average 21 counts lower than a waist-mounted step counter over all walking activities combined, with a MAPE of 3.5% (SD 2.4%). Resting respiration rate was on average 0.7 (SD 1.1) breaths per minute lower than the reference measurement by the spirometer embedded in the indirect calorimeter during the 15-minute rest, resulting in a MAPE of 8.3% (SD 7.0%). Activity type recognition of walking, running, cycling, or other was overall 90% accurate in reference to the activities performed. Conclusions: The health watch can serve its medical purpose of measuring resting heart rate and total energy expenditure over time in an unobtrusive manner, thereby providing valuable data for the prevention and management of lifestyle-related chronic diseases. Trial Registration: Netherlands trial register NTR5552; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5552 (Archived by WebCite at http://www.webcitation.org/6neYJgysl) %M 28153815 %R 10.2196/mhealth.6893 %U http://mhealth.jmir.org/2017/2/e10/ %U https://doi.org/10.2196/mhealth.6893 %U http://www.ncbi.nlm.nih.gov/pubmed/28153815 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 2 %N 1 %P e1 %T Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group %A Gualtieri,Lisa %A Rosenbluth,Sandra %A Phillips,Jeffrey %+ Department of Public Health and Community Medicine, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA, 02111, United States, 1 617 636 0438, lisa.gualtieri@tufts.edu %K wearable activity trackers %K fitness trackers %K trackers %K physical activity %K chronic disease %K behavior change %K wellness group %K wellness %K older adults %K digital health %D 2016 %7 15.12.2016 %9 Poster %J iproc %G English %X Background: Wearable activity trackers (trackers) are increasingly popular devices used to track step count and other health indicators. Trackers have the potential to benefit those in need of increased physical activity, such as adults who are older and who face significant health challenges. These populations are least likely to purchase trackers and most likely to face challenges in using them, yet may derive educational, motivational, and health benefits from their use once these barriers are removed. Objective: The aim of this research was to investigate the use of trackers by older adults with chronic medical conditions who had never used trackers previously. Our primary research questions were (1) if participants would accept and use trackers to increase their physical activity; (2) if there were barriers to use besides cost and training; (3) if trackers would educate participants on their baseline and ongoing activity levels and support behavior change; and (4) if clinical outcomes would show improvements in participants’ health. Methods: This study was conducted with 10 patients in a 12 week physician-led wellness group offered by Family Doctors, LLC. Patients were given trackers in the second week of the wellness group and were interviewed 2-4 weeks after it ended. The study investigators analyzed the interview notes to extract themes about the participants’ attitudes and behavior changes and collected and analyzed participants’ clinical data, including weight and LDL-Cholesterol (LDL), over the course of the study. Results: Over the 12-14 weeks of tracker use, improvements were seen in clinical outcomes, attitudes towards the trackers, and physical activity behaviors. Participants lost an average of a half-pound per week (SD=0.408), with a mean total weight loss of 5.97 pounds (P=.0038). Other short-term clinical outcomes included a 9.2% decrease in LDL levels (P=.0377). All participants reported an increase in well-being and confidence in their ability to lead more active lives. We identified 6 major attitudinal themes from our qualitative analysis of the interview notes: (1) barriers to tracker purchase included cost, perceived value, and choice confusion; (2) attitudes towards the trackers shifted for many, from half of the participants expressing excitement and hope and half expressing hesitation or trepidation, to all participants feeling positive towards their tracker at the time of the interviews; (3) trackers served as educational tools for baseline activity levels; (4) trackers provided concrete feedback on physical activity, which motivated behavior change; (5) tracker use reinforced wellness group activities and goals; and (6) although commitment to tracker use did not waver, external circumstances influenced some participants’ ongoing use. Conclusions: Our findings suggest that adding trackers to wellness groups comprising older adults with chronic medical conditions can support education and behavior change to be more physically active. The trackers increased participant self-efficacy by providing a tangible, visible reminder of a commitment to increasing activity and immediate feedback on step count and progress towards a daily step goal. While acceptance was high and attitudes ultimately positive, training and support are needed and short-term drop-off in participant use is to be expected. Future research will further consider the potential of trackers in older adults with chronic medical conditions who are unlikely to purchase them, and studies will use larger samples, continue over a longer period of time, and evaluate outcomes independent of a wellness group. %R 10.2196/iproc.6245 %U http://www.iproc.org/2016/1/e1/ %U https://doi.org/10.2196/iproc.6245 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 12 %P e315 %T Influence of Pokémon Go on Physical Activity: Study and Implications %A Althoff,Tim %A White,Ryen W %A Horvitz,Eric %+ Computer Science Department, Stanford University, 94 Thoburn Ct., Apt 108, Stanford, CA, 94305, United States, 1 6504850758, althoff@cs.stanford.edu %K physical activity %K Pokémon Go %K mobile health %K mHealth %K wearable devices %K mobile applications %K games %K exergames %K public health %D 2016 %7 06.12.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Physical activity helps people maintain a healthy weight and reduces the risk for several chronic diseases. Although this knowledge is widely recognized, adults and children in many countries around the world do not get recommended amounts of physical activity. Although many interventions are found to be ineffective at increasing physical activity or reaching inactive populations, there have been anecdotal reports of increased physical activity due to novel mobile games that embed game play in the physical world. The most recent and salient example of such a game is Pokémon Go, which has reportedly reached tens of millions of users in the United States and worldwide. Objective: The objective of this study was to quantify the impact of Pokémon Go on physical activity. Methods: We study the effect of Pokémon Go on physical activity through a combination of signals from large-scale corpora of wearable sensor data and search engine logs for 32,000 Microsoft Band users over a period of 3 months. Pokémon Go players are identified through search engine queries and physical activity is measured through accelerometers. Results: We find that Pokémon Go leads to significant increases in physical activity over a period of 30 days, with particularly engaged users (ie, those making multiple search queries for details about game usage) increasing their activity by 1473 steps a day on average, a more than 25% increase compared with their prior activity level (P<.001). In the short time span of the study, we estimate that Pokémon Go has added a total of 144 billion steps to US physical activity. Furthermore, Pokémon Go has been able to increase physical activity across men and women of all ages, weight status, and prior activity levels showing this form of game leads to increases in physical activity with significant implications for public health. In particular, we find that Pokémon Go is able to reach low activity populations, whereas all 4 leading mobile health apps studied in this work largely draw from an already very active population. Conclusions: Mobile apps combining game play with physical activity lead to substantial short-term activity increases and, in contrast to many existing interventions and mobile health apps, have the potential to reach activity-poor populations. Future studies are needed to investigate potential long-term effects of these applications. %M 27923778 %R 10.2196/jmir.6759 %U http://www.jmir.org/2016/12/e315/ %U https://doi.org/10.2196/jmir.6759 %U http://www.ncbi.nlm.nih.gov/pubmed/27923778 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 4 %P e237 %T Can a Free Wearable Activity Tracker Change Behavior? The Impact of Trackers on Adults in a Physician-Led Wellness Group %A Gualtieri,Lisa %A Rosenbluth,Sandra %A Phillips,Jeffrey %+ Department of Public Health and Community Medicine, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA, 02111, United States, 1 617 636 0438, lisa.gualtieri@tufts.edu %K wearable activity trackers %K fitness trackers %K trackers %K physical activity %K chronic disease %K behavior change %K wellness group %K wellness %K older adults %K digital health %D 2016 %7 30.11.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Wearable activity trackers (trackers) are increasingly popular devices used to track step count and other health indicators. Trackers have the potential to benefit those in need of increased physical activity, such as adults who are older and face significant health challenges. These populations are least likely to purchase trackers and most likely to face challenges in using them, yet may derive educational, motivational, and health benefits from their use once these barriers are removed. Objective: The aim of this pilot research is to investigate the use of trackers by adults with chronic medical conditions who have never used trackers previously. Specifically, we aim to determine (1) if participants would accept and use trackers to increase their physical activity; (2) if there were barriers to use besides cost and training; (3) if trackers would educate participants on their baseline and ongoing activity levels and support behavior change; and (4) if clinical outcomes would show improvements in participants’ health. Methods: This study was conducted with patients (N=10) in a 12-week physician-led wellness group offered by Family Doctors, LLC. Patients were given trackers in the second week of The Wellness Group and were interviewed 2 to 4 weeks after it ended. The study investigators analyzed the interview notes to extract themes about the participants’ attitudes and behavior changes and collected and analyzed participants’ clinical data, including weight and low-density lipoprotein (LDL) cholesterol over the course of the study. Results: Over the 12 to 14 weeks of tracker use, improvements were seen in clinical outcomes, attitudes towards the trackers, and physical activity behaviors. Participants lost an average of 0.5 lbs per week (SD 0.4), with a mean total weight loss of 5.97 lbs (P=.004). Other short-term clinical outcomes included a 9.2% decrease in LDL levels (P=.038). All participants reported an increase in well-being and confidence in their ability to lead more active lives. We identified the following 6 major attitudinal themes from our qualitative analysis of the interview notes: (1) barriers to tracker purchase included cost, perceived value, and choice confusion; (2) attitudes towards the trackers shifted for many, from half of the participants expressing excitement and hope and half expressing hesitation or trepidation, to all participants feeling positive towards their tracker at the time of the interviews; (3) trackers served as educational tools for baseline activity levels; (4) trackers provided concrete feedback on physical activity, which motivated behavior change; (5) tracker use reinforced wellness group activities and goals; and (6) although commitment to tracker use did not waver, external circumstances influenced some participants’ ongoing use. Conclusions: Our findings suggest that adding trackers to wellness groups comprising primarily older adults with chronic medical conditions can support education and behavior change to be more physically active. The trackers increased participant self-efficacy by providing a tangible, visible reminder of a commitment to increasing activity and immediate feedback on step count and progress towards a daily step goal. While acceptance was high and attitudes ultimately positive, training and support are needed and short-term drop-off in participant use is to be expected. Future research will further consider the potential of trackers in older adults with chronic medical conditions who are unlikely to purchase them, and studies will use larger samples, continue over a longer period of time, and evaluate outcomes independent of a wellness group. %M 27903490 %R 10.2196/resprot.6534 %U http://www.researchprotocols.org/2016/4/e237/ %U https://doi.org/10.2196/resprot.6534 %U http://www.ncbi.nlm.nih.gov/pubmed/27903490 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 11 %P e305 %T Clinical Relevance of the First Domomedicine Platform Securing Multidrug Chronotherapy Delivery in Metastatic Cancer Patients at Home: The inCASA European Project %A Innominato,Pasquale F %A Komarzynski,Sandra %A Mohammad-Djafari,Ali %A Arbaud,Alexandre %A Ulusakarya,Ayhan %A Bouchahda,Mohamed %A Haydar,Mazen %A Bossevot-Desmaris,Rachel %A Plessis,Virginie %A Mocquery,Magali %A Bouchoucha,Davina %A Afshar,Mehran %A Beau,Jacques %A Karaboué,Abdoulaye %A Morère,Jean-François %A Fursse,Joanna %A Rovira Simon,Jordi %A Levi,Francis %+ Cancer Chronotherapy Unit, Cancer Research Centre, Warwick Medical School, Gibbet Hill Road, Coventry, CV4 7AL, United Kingdom, 44 2476575132, F.Levi@warwick.ac.uk %K domomedicine %K chronotherapy %K actigraphy %K MDASI %K telemonitoring %D 2016 %7 25.11.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Telehealth solutions can improve the safety of ambulatory chemotherapy, contributing to the maintenance of patients at their home, hence improving their well-being, all the while reducing health care costs. There is, however, need for a practicable multilevel monitoring solution, encompassing relevant outputs involved in the pathophysiology of chemotherapy-induced toxicity. Domomedicine embraces the delivery of complex care and medical procedures at the patient’s home based on modern technologies, and thus it offers an integrated approach for increasing the safety of cancer patients on chemotherapy. Objective: The objective was to evaluate patient compliance and clinical relevance of a novel integrated multiparametric telemonitoring domomedicine platform in cancer patients receiving multidrug chemotherapy at home. Methods: Self-measured body weight, self-rated symptoms using the 19-item MD Anderson Symptom Inventory (MDASI), and circadian rest-activity rhythm recording with a wrist accelerometer (actigraph) were transmitted daily by patients to a server via the Internet, using a dedicated platform installed at home. Daily body weight changes, individual MDASI scores, and relative percentage of activity in-bed versus out-of-bed (I7 were enrolled in-person to participate in the study for 6 months and were randomized into either the intervention arm that received the full complement of the intervention or a control arm that received only pedometers. The primary outcome was change in physical activity. We also assessed the effect of the intervention on HbA1c, weight, and participant engagement. Results: All participants (intervention: n=64; control: n=62) were included in the analyses. The intervention group had significantly higher monthly step counts in the third (risk ratio [RR] 4.89, 95% CI 1.20 to 19.92, P=.03) and fourth (RR 6.88, 95% CI 1.21 to 39.00, P=.03) months of the study compared to the control group. However, over the 6-month follow-up period, monthly step counts did not differ statistically by group (intervention group: 9092 steps; control group: 3722 steps; RR 2.44, 95% CI 0.68 to 8.74, P=.17). HbA1c decreased by 0.07% (95% CI –0.47 to 0.34, P=.75) in the TTM group compared to the control group. Within groups, HbA1c decreased significantly from baseline in the TTM group by –0.43% (95% CI –0.75 to –0.12, P=.01), but nonsignificantly in the control group by –0.21% (95% CI –0.49 to 0.06, P=.13). Similar changes were observed for other secondary outcomes. Conclusion: Personalized text messaging can be used to improve outcomes in patients with T2DM by employing optimal patient engagement measures. %M 27864165 %R 10.2196/jmir.6439 %U http://www.jmir.org/2016/11/e307/ %U https://doi.org/10.2196/jmir.6439 %U http://www.ncbi.nlm.nih.gov/pubmed/27864165 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 11 %P e292 %T Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviors %A Kim,Ju Young %A Wineinger,Nathan E %A Taitel,Michael %A Radin,Jennifer M %A Akinbosoye,Osayi %A Jiang,Jenny %A Nikzad,Nima %A Orr,Gregory %A Topol,Eric %A Steinhubl,Steve %+ Department of Digital Health, Scripps Translational Science Institute, 3344 North Torrey Pines Court, Suite 300, La Jolla, CA, 92037, United States, 1 858 554 5757, steinhub@scripps.edu %K health behavior %K mobile health %K mobile apps %K reward %K self blood pressure monitoring %K blood glucose self-monitoring %D 2016 %7 17.11.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: The advent of digital technology has enabled individuals to track meaningful biometric data about themselves. This novel capability has spurred nontraditional health care organizations to develop systems that aid users in managing their health. One of the most prolific systems is Walgreens Balance Rewards for healthy choices (BRhc) program, an incentivized, Web-based self-monitoring program. Objective: This study was performed to evaluate health data self-tracking characteristics of individuals enrolled in the Walgreens’ BRhc program, including the impact of manual versus automatic data entries through a supported device or apps. Methods: We obtained activity tracking data from a total of 455,341 BRhc users during 2014. Upon identifying users with sufficient follow-up data, we explored temporal trends in user participation. Results: Thirty-four percent of users quit participating after a single entry of an activity. Among users who tracked at least two activities on different dates, the median length of participating was 8 weeks, with an average of 5.8 activities entered per week. Furthermore, users who participated for at least twenty weeks (28.3% of users; 33,078/116,621) consistently entered 8 to 9 activities per week. The majority of users (77%; 243,774/315,744) recorded activities through manual data entry alone. However, individuals who entered activities automatically through supported devices or apps participated roughly four times longer than their manual activity-entering counterparts (average 20 and 5 weeks, respectively; P<.001). Conclusions: This study provides insights into the utilization patterns of individuals participating in an incentivized, Web-based self-monitoring program. Our results suggest automated health tracking could significantly improve long-term health engagement. %M 27856407 %R 10.2196/jmir.6371 %U http://www.jmir.org/2016/11/e292/ %U https://doi.org/10.2196/jmir.6371 %U http://www.ncbi.nlm.nih.gov/pubmed/27856407 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 4 %P e125 %T Sleep Quality Prediction From Wearable Data Using Deep Learning %A Sathyanarayana,Aarti %A Joty,Shafiq %A Fernandez-Luque,Luis %A Ofli,Ferda %A Srivastava,Jaideep %A Elmagarmid,Ahmed %A Arora,Teresa %A Taheri,Shahrad %+ Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Foundation, HBKU Research Complex, Doha, 5825, Qatar, 974 50173040, lluque@qf.org.qa %K wearables %K sleep quality %K sleep efficiency %K actigraphy %K body sensor networks %K mobile health %K connected health %K accelerometer %K physical activity %K pervasive health %K consumer health informatics %K deep learning %D 2016 %7 04.11.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective: The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods: Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results: Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). Conclusions: Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. %M 27815231 %R 10.2196/mhealth.6562 %U http://mhealth.jmir.org/2016/4/e125/ %U https://doi.org/10.2196/mhealth.6562 %U http://www.ncbi.nlm.nih.gov/pubmed/27815231 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 10 %P e259 %T The Effectiveness of Lower-Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: A Systematic Review %A Powell,Lauren %A Parker,Jack %A Martyn St-James,Marrissa %A Mawson,Susan %+ School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S14DA, United Kingdom, 44 1142228275, l.a.powell@sheffield.ac.uk %K wearable technology %K stroke %K gait %K rehabilitation %D 2016 %7 07.10.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Regaining ambulation is a top priority for an increasing number of stroke survivors. However, despite an increase in research exploring these devices for lower limb rehabilitation, little is known of the effectiveness. Objective: This review aims to assess the effectiveness of lower limb wearable technology for improving activity and participation in adult stroke survivors. Methods: Randomized controlled trials (RCTs) of lower limb wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs. Results: In the review, we included 11 RCTs with collectively 550 participants at baseline and 474 participants at final follow-up including control groups and participants post stroke. Participants' stroke type and severity varied. Only one study found significant between-group differences for systems functioning and activity. Across the included RCTs, the lowest number of participants was 12 and the highest was 151 with a mean of 49 participants. The lowest number of participants to drop out of an RCT was zero in two of the studies and 19 in one study. Significant between-group differences were found across three of the 11 included trials. Out of the activity and participation measures alone, P values ranged from P=.87 to P ≤.001. Conclusions: This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes, appropriateness of the RCT methodology for complex interventions, a lack of appropriate analysis of outcome data, and participant stroke severity. %M 27717920 %R 10.2196/jmir.5891 %U http://www.jmir.org/2016/10/e259/ %U https://doi.org/10.2196/jmir.5891 %U http://www.ncbi.nlm.nih.gov/pubmed/27717920 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 10 %P e268 %T Influence of Pedometer Position on Pedometer Accuracy at Various Walking Speeds: A Comparative Study %A Ehrler,Frederic %A Weber,Chloé %A Lovis,Christian %+ Division of Medical Information Sciences, University Hospitals of Geneva, Gabrielle-Perret-Gentil, 4, Geneva, 1205, Switzerland, 41 22 37 28697, frederic.ehrler@hcuge.ch %K frail elderly %K mHealth %K walking %K motor activity %D 2016 %7 06.10.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Demographic growth in conjunction with the rise of chronic diseases is increasing the pressure on health care systems in most OECD countries. Physical activity is known to be an essential factor in improving or maintaining good health. Walking is especially recommended, as it is an activity that can easily be performed by most people without constraints. Pedometers have been extensively used as an incentive to motivate people to become more active. However, a recognized problem with these devices is their diminishing accuracy associated with decreased walking speed. The arrival on the consumer market of new devices, worn indifferently either at the waist, wrist, or as a necklace, gives rise to new questions regarding their accuracy at these different positions. Objective: Our objective was to assess the performance of 4 pedometers (iHealth activity monitor, Withings Pulse O2, Misfit Shine, and Garmin vívofit) and compare their accuracy according to their position worn, and at various walking speeds. Methods: We conducted this study in a controlled environment with 21 healthy adults required to walk 100 m at 3 different paces (0.4 m/s, 0.6 m/s, and 0.8 m/s) regulated by means of a string attached between their legs at the level of their ankles and a metronome ticking the cadence. To obtain baseline values, we asked the participants to walk 200 m at their own pace. Results: A decrease of accuracy was positively correlated with reduced speed for all pedometers (12% mean error at self-selected pace, 27% mean error at 0.8 m/s, 52% mean error at 0.6 m/s, and 76% mean error at 0.4 m/s). Although the position of the pedometer on the person did not significantly influence its accuracy, some interesting tendencies can be highlighted in 2 settings: (1) positioning the pedometer at the waist at a speed greater than 0.8 m/s or as a necklace at preferred speed tended to produce lower mean errors than at the wrist position; and (2) at a slow speed (0.4 m/s), pedometers worn at the wrist tended to produce a lower mean error than in the other positions. Conclusions: At all positions, all tested pedometers generated significant errors at slow speeds and therefore cannot be used reliably to evaluate the amount of physical activity for people walking slower than 0.6 m/s (2.16 km/h, or 1.24 mph). At slow speeds, the better accuracy observed with pedometers worn at the wrist could constitute a valuable line of inquiry for the future development of devices adapted to elderly people. %M 27713114 %R 10.2196/jmir.5916 %U http://www.jmir.org/2016/10/e268/ %U https://doi.org/10.2196/jmir.5916 %U http://www.ncbi.nlm.nih.gov/pubmed/27713114 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 3 %P e110 %T Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions %A Dominick,Gregory M %A Winfree,Kyle N %A Pohlig,Ryan T %A Papas,Mia A %+ University of Delaware, Department of Behavioral Health and Nutrition, 023 Carpenter Sports Building, 26 North College Avenue, Newark, DE, 19716, United States, 1 302 831 3672, gdominic@udel.edu %K Fitbit %K activity tracker %K actigraphy %K physical activity %K aerobic exercise %K validity %D 2016 %7 19.09.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity monitors such as Fitbit enable users to track various attributes of their physical activity (PA) over time and have the potential to be used in research to promote and measure PA behavior. However, the measurement accuracy of Fitbit in absolute free-living conditions is largely unknown. Objective: To examine the measurement congruence between Fitbit Flex and ActiGraph GT3X for quantifying steps, metabolic equivalent tasks (METs), and proportion of time in sedentary activity and light-, moderate-, and vigorous-intensity PA in healthy adults in free-living conditions. Methods: A convenience sample of 19 participants (4 men and 15 women), aged 18-37 years, concurrently wore the Fitbit Flex (wrist) and ActiGraph GT3X (waist) for 1- or 2-week observation periods (n=3 and n=16, respectively) that included self-reported bouts of daily exercise. Data were examined for daily activity, averaged over 14 days and for minutes of reported exercise. Average day-level data included steps, METs, and proportion of time in different intensity levels. Minute-level data included steps, METs, and mean intensity score (0 = sedentary, 3 = vigorous) for overall reported exercise bouts (N=120) and by exercise type (walking, n=16; run or sports, n=44; cardio machine, n=20). Results: Measures of steps were similar between devices for average day- and minute-level observations (all P values > .05). Fitbit significantly overestimated METs for average daily activity, for overall minutes of reported exercise bouts, and for walking and run or sports exercises (mean difference 0.70, 1.80, 3.16, and 2.00 METs, respectively; all P values < .001). For average daily activity, Fitbit significantly underestimated the proportion of time in sedentary and light intensity by 20% and 34%, respectively, and overestimated time by 3% in both moderate and vigorous intensity (all P values < .001). Mean intensity scores were not different for overall minutes of exercise or for run or sports and cardio-machine exercises (all P values > .05). Conclusions: Fitbit Flex provides accurate measures of steps for daily activity and minutes of reported exercise, regardless of exercise type. Although the proportion of time in different intensity levels varied between devices, examining the mean intensity score for minute-level bouts across different exercise types enabled interdevice comparisons that revealed similar measures of exercise intensity. Fitbit Flex is shown to have measurement limitations that may affect its potential utility and validity for measuring PA attributes in free-living conditions. %M 27644334 %R 10.2196/mhealth.6281 %U http://mhealth.jmir.org/2016/3/e110/ %U https://doi.org/10.2196/mhealth.6281 %U http://www.ncbi.nlm.nih.gov/pubmed/27644334 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 9 %P e239 %T Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry %A Gomersall,Sjaan R %A Ng,Norman %A Burton,Nicola W %A Pavey,Toby G %A Gilson,Nicholas D %A Brown,Wendy J %+ Centre for Research on Exercise, Physical Activity and Health, School of Human Movement and Nutrition Sciences, The University of Queensland, Building 26B, Blair Drive, Brisbane, 4072, Australia, 61 733653115, s.gomersall1@uq.edu.au %K activity tracker %K physical activity %K sedentary behavior %K accelerometry %K Fitbit %K Jawbone %D 2016 %7 07.09.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Activity trackers are increasingly popular with both consumers and researchers for monitoring activity and for promoting positive behavior change. However, there is a lack of research investigating the performance of these devices in free-living contexts, for which findings are likely to vary from studies conducted in well-controlled laboratory settings. Objective: The aim was to compare Fitbit One and Jawbone UP estimates of steps, moderate-to-vigorous physical activity (MVPA), and sedentary behavior with data from the ActiGraph GT3X+ accelerometer in a free-living context. Methods: Thirty-two participants were recruited using convenience sampling; 29 provided valid data for this study (female: 90%, 26/29; age: mean 39.6, SD 11.0 years). On two occasions for 7 days each, participants wore an ActiGraph GT3X+ accelerometer on their right hip and either a hip-worn Fitbit One (n=14) or wrist-worn Jawbone UP (n=15) activity tracker. Daily estimates of steps and very active minutes were derived from the Fitbit One (n=135 days) and steps, active time, and longest idle time from the Jawbone UP (n=154 days). Daily estimates of steps, MVPA, and longest sedentary bout were derived from the corresponding days of ActiGraph data. Correlation coefficients and Bland-Altman plots with examination of systematic bias were used to assess convergent validity and agreement between the devices and the ActiGraph. Cohen’s kappa was used to assess the agreement between each device and the ActiGraph for classification of active versus inactive (≥10,000 steps per day and ≥30 min/day of MVPA) comparable with public health guidelines. Results: Correlations with ActiGraph estimates of steps and MVPA ranged between .72 and .90 for Fitbit One and .56 and .75 for Jawbone UP. Compared with ActiGraph estimates, both devices overestimated daily steps by 8% (Fitbit One) and 14% (Jawbone UP). However, mean differences were larger for daily MVPA (Fitbit One: underestimated by 46%; Jawbone UP: overestimated by 50%). There was systematic bias across all outcomes for both devices. Correlations with ActiGraph data for longest idle time (Jawbone UP) ranged from .08 to .19. Agreement for classifying days as active or inactive using the ≥10,000 steps/day criterion was substantial (Fitbit One: κ=.68; Jawbone UP: κ=.52) and slight-fair using the criterion of ≥30 min/day of MVPA (Fitbit One: κ=.40; Jawbone UP: κ=.14). Conclusions: There was moderate-strong agreement between the ActiGraph and both Fitbit One and Jawbone UP for the estimation of daily steps. However, due to modest accuracy and systematic bias, they are better suited for consumer-based self-monitoring (eg, for the public consumer or in behavior change interventions) rather than to evaluate research outcomes. The outcomes that relate to health-enhancing MVPA (eg, “very active minutes” for Fitbit One or “active time” for Jawbone UP) and sedentary behavior (“idle time” for Jawbone UP) should be used with caution by consumers and researchers alike. %M 27604226 %R 10.2196/jmir.5531 %U http://www.jmir.org/2016/9/e239/ %U https://doi.org/10.2196/jmir.5531 %U http://www.ncbi.nlm.nih.gov/pubmed/27604226 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 3 %P e101 %T Retrofit Weight-Loss Outcomes at 6, 12, and 24 Months and Characteristics of 12-Month High Performers: A Retrospective Analysis %A Painter,Stefanie %A Ditsch,Gary %A Ahmed,Rezwan %A Hanson,Nicholas Buck %A Kachin,Kevin %A Berger,Jan %+ Retrofit, Inc, 123 N Wacker Drive, Suit 1250, Chicago, IL,, United States, 1 304 546 9968, stefanie@retrofitme.com %K behavior %K body mass index %K BMI %K engagement %K fitness %K self-monitoring %K obesity %K overweight %K weight loss %D 2016 %7 22.08.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Obesity is the leading cause of preventable death costing the health care system billions of dollars. Combining self-monitoring technology with personalized behavior change strategies results in clinically significant weight loss. However, there is a lack of real-world outcomes in commercial weight-loss program research. Objective: Retrofit is a personalized weight management and disease-prevention solution. This study aimed to report Retrofit’s weight-loss outcomes at 6, 12, and 24 months and characterize behaviors, age, and sex of high-performing participants who achieved weight loss of 10% or greater at 12 months. Methods: A retrospective analysis was performed from 2011 to 2014 using 2720 participants enrolled in a Retrofit weight-loss program. Participants had a starting body mass index (BMI) of >25 kg/m² and were at least 18 years of age. Weight measurements were assessed at 6, 12, and 24 months in the program to evaluate change in body weight, BMI, and percentage of participants who achieved 5% or greater weight loss. A secondary analysis characterized high-performing participants who lost ≥10% of their starting weight (n=238). Characterized behaviors were evaluated, including self-monitoring through weigh-ins, number of days wearing an activity tracker, daily step count average, and engagement through coaching conversations via Web-based messages, and number of coaching sessions attended. Results: Average weight loss at 6 months was −5.55% for male and −4.86% for female participants. Male and female participants had an average weight loss of −6.28% and −5.37% at 12 months, respectively. Average weight loss at 24 months was −5.03% and −3.15% for males and females, respectively. Behaviors of high-performing participants were assessed at 12 months. Number of weigh-ins were greater in high-performing male (197.3 times vs 165.4 times, P=.001) and female participants (222 times vs 167 times, P<.001) compared with remaining participants. Total activity tracker days and average steps per day were greater in high-performing females (304.7 vs 266.6 days, P<.001; 8380.9 vs 7059.7 steps, P<.001, respectively) and males (297.1 vs 255.3 days, P<.001; 9099.3 vs 8251.4 steps, P=.008, respectively). High-performing female participants had significantly more coaching conversations via Web-based messages than remaining female participants (341.4 vs 301.1, P=.03), as well as more days with at least one such electronic message (118 vs 108 days, P=.03). High-performing male participants displayed similar behavior. Conclusions: Participants on the Retrofit program lost an average of −5.21% at 6 months, −5.83% at 12 months, and −4.09% at 24 months. High-performing participants show greater adherence to self-monitoring behaviors of weighing in, number of days wearing an activity tracker, and average number of steps per day. Female high performers have higher coaching engagement through conversation days and total number of coaching conversations. %M 27549134 %R 10.2196/mhealth.5873 %U http://mhealth.jmir.org/2016/3/e101/ %U https://doi.org/10.2196/mhealth.5873 %U http://www.ncbi.nlm.nih.gov/pubmed/27549134 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 3 %P e164 %T Incorporating Novel Mobile Health Technologies Into Management of Knee Osteoarthritis in Patients Treated With Intra-Articular Hyaluronic Acid: Rationale and Protocol of a Randomized Controlled Trial %A Jones,Donald %A Skrepnik,Nebojsa %A Toselli,Richard M %A Leroy,Bruno %+ Scripps Translational Science Institute, 3344 North Torrey Pines Ct, Suite 300, La Jolla, CA,, United States, 1 858 554 5710, donaldj@cardiffoceangroup.com %K mHealth %K osteoarthritis %K pain %K physical therapy %D 2016 %7 09.08.2016 %9 Protocol %J JMIR Res Protoc %G English %X Background: Osteoarthritis (OA) of the knee is one of the leading causes of disability in the United States. One relatively new strategy that could be helpful in the management of OA is the use of mHealth technologies, as they can be used to increase physical activity and promote exercise, which are key components of knee OA management. Objective: Currently, no published data on the use of a mHealth approach to comprehensively monitor physical activity in patients with OA are available, and similarly, no data on whether mHealth technologies can impact outcomes are available. Our objective is to evaluate the effectiveness of mHealth technology as part of a tailored, comprehensive management strategy for patients with knee OA. Methods: The study will assess the impact of a smartphone app that integrates data from a wearable activity monitor (thereby both encouraging changes in mobility as well as tracking them) combined with education about the benefits of walking on patient mobility. The results from the intervention group will be compared with data from a control group of individuals who are given the same Arthritis Foundation literature regarding the benefits of walking and wearable activity monitors but who do not have access to the data from those monitors. Activity monitors will capture step count estimates and will compare those with patients’ step goals, calories burned, and distance walked. Patients using the novel smartphone app will be able to enter information on their daily pain, mood, and sleep quality. The relationships among activity and pain, activity and mood, and sleep will be assessed, as will patient satisfaction with and adherence to the mobile app. Results: We present information on an upcoming trial that will prospectively assess the ability of a mobile app to improve mobility for knee OA patients who are treated with intra-articular hyaluronic acid. Conclusions: We anticipate the results of this study will support the concept that mHealth technologies provide continuous, real-time feedback to patients with OA on their overall level of activity for a more proactive, personalized approach to treatment that may help modify behavior and assist with self-management through treatment support in the form of motivational messages and reminders. %M 27506148 %R 10.2196/resprot.5940 %U http://www.researchprotocols.org/2016/3/e164/ %U https://doi.org/10.2196/resprot.5940 %U http://www.ncbi.nlm.nih.gov/pubmed/27506148 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 8 %P e215 %T Long-Term Effects of an Internet-Mediated Pedometer-Based Walking Program for Chronic Obstructive Pulmonary Disease: Randomized Controlled Trial %A Moy,Marilyn L %A Martinez,Carlos H %A Kadri,Reema %A Roman,Pia %A Holleman,Robert G %A Kim,Hyungjin Myra %A Nguyen,Huong Q %A Cohen,Miriam D %A Goodrich,David E %A Giardino,Nicholas D %A Richardson,Caroline R %+ Department of Family Medicine, University of Michigan, 1018 Fuller St., Ann Arbor, MI, 48104, United States, 1 734 998 7120 ext 316, caroli@umich.edu %K bronchitis, chronic %K emphysema %K pulmonary disease, chronic obstructive %K quality of life %K exercise %K motor activity %K Internet %D 2016 %7 08.08.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Regular physical activity (PA) is recommended for persons with chronic obstructive pulmonary disease (COPD). Interventions that promote PA and sustain long-term adherence to PA are needed. Objective: We examined the effects of an Internet-mediated, pedometer-based walking intervention, called Taking Healthy Steps, at 12 months. Methods: Veterans with COPD (N=239) were randomized in a 2:1 ratio to the intervention or wait-list control. During the first 4 months, participants in the intervention group were instructed to wear the pedometer every day, upload daily step counts at least once a week, and were provided access to a website with four key components: individualized goal setting, iterative feedback, educational and motivational content, and an online community forum. The subsequent 8-month maintenance phase was the same except that participants no longer received new educational content. Participants randomized to the wait-list control group were instructed to wear the pedometer, but they did not receive step-count goals or instructions to increase PA. The primary outcome was health-related quality of life (HRQL) assessed by the St George’s Respiratory Questionnaire Total Score (SGRQ-TS); the secondary outcome was daily step count. Linear mixed-effect models assessed the effect of intervention over time. One participant was excluded from the analysis because he was an outlier. Within the intervention group, we assessed pedometer adherence and website engagement by examining percent of days with valid step-count data, number of log-ins to the website each month, use of the online community forum, and responses to a structured survey. Results: Participants were 93.7% male (223/238) with a mean age of 67 (SD 9) years. At 12 months, there were no significant between-group differences in SGRQ-TS or daily step count. Between-group difference in daily step count was maximal and statistically significant at month 4 (P<.001), but approached zero in months 8-12. Within the intervention group, mean 76.7% (SD 29.5) of 366 days had valid step-count data, which decreased over the months of study (P<.001). Mean number of log-ins to the website each month also significantly decreased over the months of study (P<.001). The online community forum was used at least once during the study by 83.8% (129/154) of participants. Responses to questions assessing participants’ goal commitment and intervention engagement were not significantly different at 12 months compared to 4 months. Conclusions: An Internet-mediated, pedometer-based PA intervention, although efficacious at 4 months, does not maintain improvements in HRQL and daily step counts at 12 months. Waning pedometer adherence and website engagement by the intervention group were observed. Future efforts should focus on improving features of PA interventions to promote long-term behavior change and sustain engagement in PA. ClinicalTrial: Clinicaltrials.gov NCT01102777; https://clinicaltrials.gov/ct2/show/NCT01102777 (Archived by WebCite at http://www.webcitation.org/6iyNP9KUC) %M 27502583 %R 10.2196/jmir.5622 %U http://www.jmir.org/2016/8/e215/ %U https://doi.org/10.2196/jmir.5622 %U http://www.ncbi.nlm.nih.gov/pubmed/27502583 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 7 %P e209 %T A Mobile Ecological Momentary Assessment Tool (devilSPARC) for Nutrition and Physical Activity Behaviors in College Students: A Validation Study %A Bruening,Meg %A van Woerden,Irene %A Todd,Michael %A Brennhofer,Stephanie %A Laska,Melissa N %A Dunton,Genevieve %+ Arizona State University, 550 N 5th Street, Phoenix, AZ, 85004, United States, 1 602 827 2266, meg.bruening@asu.edu %K validation study %K ecological momentary assessment %K nutritional status %K physical activity %K sedentary activity %K emerging adults %D 2016 %7 27.07.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: The majority of nutrition and physical activity assessments methods commonly used in scientific research are subject to recall and social desirability biases, which result in over- or under-reporting of behaviors. Real-time mobile-based ecological momentary assessments (mEMAs) may result in decreased measurement biases and minimize participant burden. Objective: The aim was to examine the validity of a mEMA methodology to assess dietary and physical activity levels compared to 24-hour dietary recalls and accelerometers. Methods: This study was a pilot test of the SPARC (Social impact of Physical Activity and nutRition in College) study, which aimed to determine the mechanism by which friendship networks impact weight-related behaviors among young people. An mEMA app, devilSPARC, was developed to assess weight-related behaviors in real time. A diverse sample of 109 freshmen and community mentors attending a large southwestern university downloaded the devilSPARC mEMA app onto their personal mobile phones. Participants were prompted randomly eight times per day over the course of 4 days to complete mEMAs. During the same 4-day period, participants completed up to three 24-hour dietary recalls and/or 4 days of accelerometry. Self-reported mEMA responses were compared to 24-hour dietary recalls and accelerometry measures using comparison statistics, such as match rate, sensitivity and specificity, and mixed model odds ratios, adjusted for within-person correlation among repeated measurements. Results: At the day level, total dietary intake data reported through the mEMA app reflected eating choices also captured by the 24-hour recall. Entrées had the lowest match rate, and fruits and vegetables had the highest match rate. Widening the window of aggregation of 24-hour dietary recall data on either side of the mEMA response resulted in increased specificity and decreased sensitivity. For physical activity behaviors, levels of activity reported through mEMA differed for sedentary versus non-sedentary activity at the day level as measured by accelerometers. Conclusions: The devilSPARC mEMA app is valid for assessing eating behaviors and the presence of sedentary activity at the day level. This mEMA may be useful in studies examining real-time weight-related behaviors. %M 27465701 %R 10.2196/jmir.5969 %U http://www.jmir.org/2016/7/e209/ %U https://doi.org/10.2196/jmir.5969 %U http://www.ncbi.nlm.nih.gov/pubmed/27465701 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 5 %N 3 %P e153 %T ActiviTeen: A Protocol for Deployment of a Consumer Wearable Device in an Academic Setting %A Ortiz,Alexa M %A Tueller,Stephen J %A Cook,Sarah L %A Furberg,Robert D %+ RTI International, 3040 E. Cornwallis Road, Research Triangle Park, NC, 27709, United States, 1 9193163344, amortiz@rti.org %K mHealth %K clinical research protocol %K Fitbit %K physical activity tracker %K survival analaysis %K technology deployment %K education %D 2016 %7 25.07.2016 %9 Protocol %J JMIR Res Protoc %G English %X Background: Regular physical activity (PA) can be an important indicator of health across an individual’s life span. Consumer wearables, such as Fitbit or Jawbone, are becoming increasingly popular to track PA. With the increased adoption of activity trackers comes the increased generation of valuable individual-based data. Generated data has the potential to provide detailed insights into the user’s behavior and lifestyle. Objective: The primary objective of the described study is to evaluate the feasibility of individual data collection from the selected consumer wearable device (the Fitbit Zip). The rate of user attrition and barriers preventing the use of consumer wearable devices will also be evaluated as secondary objectives. Methods: The pilot study will occur in two stages and employs a long-term review and analysis with a convenience sample of 30 students attending Research Triangle High School. For the first stage, students will initially be asked to wear the Fitbit Zip over the course of 4 weeks. During which time, their activity data and step count will be collected. Students will also be asked to complete a self-administered survey at the beginning and conclusion of the first stage. The second stage will continue to collect students’ activity data and step count over an additional 3-month period. Results: We are anticipating results for this study by the end of 2016. Conclusion: This study will provide insight into the data collection procedures surrounding consumer wearable devices and could serve as the future foundation for other studies deploying consumer wearable devices in educational settings. %M 27457824 %R 10.2196/resprot.5934 %U http://www.researchprotocols.org/2016/3/e153/ %U https://doi.org/10.2196/resprot.5934 %U http://www.ncbi.nlm.nih.gov/pubmed/27457824 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 3 %N 2 %P e7 %T Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy %A Pande,Amit %A Mohapatra,Prasant %A Nicorici,Alina %A Han,Jay J %+ Department of Computer Science, University of California Davis, One Shields Ave, Davis, CA,, United States, 1 530 554 1554, pande@ucdavis.edu %K accelerometry %K physical activity %K heart rate %K neuromuscular disease %K children %D 2016 %7 19.07.2016 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Children with physical impairments are at a greater risk for obesity and decreased physical activity. A better understanding of physical activity pattern and energy expenditure (EE) would lead to a more targeted approach to intervention. Objective: This study focuses on studying the use of machine-learning algorithms for EE estimation in children with disabilities. A pilot study was conducted on children with Duchenne muscular dystrophy (DMD) to identify important factors for determining EE and develop a novel algorithm to accurately estimate EE from wearable sensor-collected data. Methods: There were 7 boys with DMD, 6 healthy control boys, and 22 control adults recruited. Data were collected using smartphone accelerometer and chest-worn heart rate sensors. The gold standard EE values were obtained from the COSMED K4b2 portable cardiopulmonary metabolic unit worn by boys (aged 6-10 years) with DMD and controls. Data from this sensor setup were collected simultaneously during a series of concurrent activities. Linear regression and nonlinear machine-learning–based approaches were used to analyze the relationship between accelerometer and heart rate readings and COSMED values. Results: Existing calorimetry equations using linear regression and nonlinear machine-learning–based models, developed for healthy adults and young children, give low correlation to actual EE values in children with disabilities (14%-40%). The proposed model for boys with DMD uses ensemble machine learning techniques and gives a 91% correlation with actual measured EE values (root mean square error of 0.017). Conclusions: Our results confirm that the methods developed to determine EE using accelerometer and heart rate sensor values in normal adults are not appropriate for children with disabilities and should not be used. A much more accurate model is obtained using machine-learning–based nonlinear regression specifically developed for this target population. %M 28582264 %R 10.2196/rehab.4340 %U http://rehab.jmir.org/2016/2/e7/ %U https://doi.org/10.2196/rehab.4340 %U http://www.ncbi.nlm.nih.gov/pubmed/28582264 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 3 %P e84 %T Reciprocal Reinforcement Between Wearable Activity Trackers and Social Network Services in Influencing Physical Activity Behaviors %A Chang,Rebecca Cherng-Shiow %A Lu,Hsi-Peng %A Yang,Peishan %A Luarn,Pin %+ School of Management, National Taiwan University of Science and Technology, No.43,, Sec. 4, Keelung Rd., Da'an Dist.,, Taipei City, 10607, Taiwan, 886 935150088, rkuei06@gmail.com %K Wearable activity trackers %K wearables %K physical activity %K social support %K social network services %K behavior change techniques %D 2016 %7 05.07.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Wearable activity trackers (WATs) are emerging consumer electronic devices designed to support physical activities (PAs), which are based on successful behavior change techniques focusing on goal-setting and frequent behavioral feedbacks. Despite their utility, data from both recent academic and market research have indicated high attrition rates of WAT users. Concurrently, evidence shows that social support (SS), delivered/obtained via social network services or sites (SNS), could increase adherence and engagement of PA intervention programs. To date, relatively few studies have looked at how WATs and SS may interact and affect PAs. Objective: The purpose of this study was to explore how these two Internet and mobile technologies, WATs and SNS, could work together to foster sustainable PA behavior changes and habits among middle-aged adults (40-60 years old) in Taiwan. Methods: We used purposive sampling of Executive MBA Students from National Taiwan University of Science and Technology to participate in our qualitative research. In-depth interviews and focus groups were conducted with a total of 15 participants, including 9 WAT users and 6 nonusers. Analysis of the collected materials was done inductively using the thematic approach with no preset categories. Two authors from different professional backgrounds independently annotated and coded the transcripts, and then discussed and debated until consensus was reached on the final themes. Results: The thematic analysis revealed six themes: (1) WATs provided more awareness than motivation in PA with goal-setting and progress monitoring, (2) SS, delivered/obtained via SNS, increased users’ adherence and engagement with WATs and vice versa, (3) a broad spectrum of configurations would be needed to deliver WATs with appropriately integrated SS functions, (4) WAT design, style, and appearance mattered even more than those of smartphones, as they are body-worn devices, (5) the user interfaces of WATs left a great deal to be desired, and (6) privacy concerns must be addressed before more mainstream consumers would consider adopting WATs. Conclusions: Participants perceived WATs as an awareness tool to understand one’s PA level. It is evident from our study that SS, derived from SNS and other pertinent vehicles such as the LINE social messaging application (similar to WhatsApp and WeChat), will increase the engagement and adherence of WAT usage. Combining WATs and SNS enables cost-effective, scalable PA intervention programs with end-to-end services and data analytics capabilities, to elevate WATs from one-size-fits-all consumer electronics to personalized PA assistants. %M 27380798 %R 10.2196/mhealth.5637 %U http://mhealth.jmir.org/2016/3/e84/ %U https://doi.org/10.2196/mhealth.5637 %U http://www.ncbi.nlm.nih.gov/pubmed/27380798 %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 %@ 1438-8871 %I JMIR Publications %V 18 %N 6 %P e106 %T Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity %A Dunton,Genevieve Fridlund %A Dzubur,Eldin %A Intille,Stephen %+ Department of Preventive Medicine, University of Southern California, 2001 N Soto St., Los Angeles, CA,, United States, 1 3238650805, dunton@usc.edu %K mobile phones %K ecological momentary assessment %K accelerometer %K physical activity %D 2016 %7 01.06.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Objective physical activity monitors (eg, accelerometers) have high rates of nonwear and do not provide contextual information about behavior. Objective: This study tested performance and value of a mobile phone app that combined objective and real-time self-report methods to measure physical activity using sensor-informed context-sensitive ecological momentary assessment (CS-EMA). Methods: The app was programmed to prompt CS-EMA surveys immediately after 3 types of events detected by the mobile phone’s built-in motion sensor: (1) Activity (ie, mobile phone movement), (2) No-Activity (ie, mobile phone nonmovement), and (3) No-Data (ie, mobile phone or app powered off). In addition, the app triggered random (ie, signal-contingent) ecological momentary assessment (R-EMA) prompts (up to 7 per day). A sample of 39 ethnically diverse high school students in the United States (aged 14-18, 54% female) tested the app over 14 continuous days during nonschool time. Both CS-EMA and R-EMA prompts assessed activity type (eg, reading or doing homework, eating or drinking, sports or exercising) and contextual characteristics of the activity (eg, location, social company, purpose). Activity was also measured with a waist-worn Actigraph accelerometer. Results: The average CS-EMA + R-EMA prompt compliance and survey completion rates were 80.5% and 98.5%, respectively. More moderate-to-vigorous intensity physical activity was recorded by the waist-worn accelerometer in the 30 minutes before CS-EMA activity prompts (M=5.84 minutes) than CS-EMA No-Activity (M=1.11 minutes) and CS-EMA No-Data (M=0.76 minute) prompts (P’s<.001). Participants were almost 5 times as likely to report going somewhere (ie, active or motorized transit) in the 30 minutes before CS-EMA Activity than R-EMA prompts (odds ratio=4.91, 95% confidence interval=2.16-11.12). Conclusions: Mobile phone apps using motion sensor–informed CS-EMA are acceptable among high school students and may be used to augment objective physical activity data collected from traditional waist-worn accelerometers. %M 27251313 %R 10.2196/jmir.5398 %U http://www.jmir.org/2016/6/e106/ %U https://doi.org/10.2196/jmir.5398 %U http://www.ncbi.nlm.nih.gov/pubmed/27251313 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 5 %N 2 %P e73 %T Organizational-Level Strategies With or Without an Activity Tracker to Reduce Office Workers’ Sitting Time: Rationale and Study Design of a Pilot Cluster-Randomized Trial %A Brakenridge,Charlotte L %A Fjeldsoe,Brianna S %A Young,Duncan C %A Winkler,Elisabeth A H %A Dunstan,David W %A Straker,Leon M %A Brakenridge,Christian J %A Healy,Genevieve N %+ The University of Queensland, School of Public Health, Level 4, Public Health Building, Herston Rd, Brisbane, 4006, Australia, 61 0733655163, c.brakenridge@uq.edu.au %K wearable device %K self-monitoring %K sedentary lifestyle %K office workers %K light intensity activity %K ecological model %K workplace %K trial %K objective %K activity monitor %D 2016 %7 25.05.2016 %9 Original Paper %J JMIR Res Protoc %G English %X Background: The office workplace is a key setting in which to address excessive sitting time and inadequate physical activity. One major influence on workplace sitting is the organizational environment. However, the impact of organizational-level strategies on individual level activity change is unknown. Further, the emergence of sophisticated, consumer-targeted wearable activity trackers that facilitate real-time self-monitoring of activity, may be a useful adjunct to support organizational-level strategies, but to date have received little evaluation in this workplace setting. Objective: The aim of this study is to evaluate the feasibility, acceptability, and effectiveness of organizational-level strategies with or without an activity tracker on sitting, standing, and stepping in office workers in the short (3 months, primary aim) and long-term (12 months, secondary aim). Methods: This study is a pilot, cluster-randomized trial (with work teams as the unit of clustering) of two interventions in office workers: organizational-level support strategies (eg, visible management support, emails) or organizational-level strategies plus the use of a waist-worn activity tracker (the LUMOback) that enables self-monitoring of sitting, standing, and stepping time and enables users to set sitting and posture alerts. The key intervention message is to ‘Stand Up, Sit Less, and Move More.’ Intervention elements will be implemented from within the organization by the Head of Workplace Wellbeing. Participants will be recruited via email and enrolled face-to-face. Assessments will occur at baseline, 3, and 12 months. Time spent sitting, sitting in prolonged (≥30 minute) bouts, standing, and stepping during work hours and across the day will be measured with activPAL3 activity monitors (7 days, 24 hours/day protocol), with total sitting time and sitting time during work hours the primary outcomes. Web-based questionnaires, LUMOback recorded data, telephone interviews, and focus groups will measure the feasibility and acceptability of both interventions and potential predictors of behavior change. Results: Baseline and follow-up data collection has finished. Results are expected in 2016. Conclusions: This pilot, cluster-randomized trial will evaluate the feasibility, acceptability, and effectiveness of two interventions targeting reductions in sitting and increases in standing and stepping in office workers. Few studies have evaluated these intervention strategies and this study has the potential to contribute both short and long-term findings. %M 27226457 %R 10.2196/resprot.5438 %U http://www.researchprotocols.org/2016/2/e73/ %U https://doi.org/10.2196/resprot.5438 %U http://www.ncbi.nlm.nih.gov/pubmed/27226457 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 18 %N 5 %P e99 %T The Emergence of Personalized Health Technology %A Allen,Luke Nelson %A Christie,Gillian Pepall %+ Harvard T.H. Chan School of Public Health, Department of Global Health and Population, 677 Huntington Av, Boston, MA, Ma 02115, United States, 1 1865289471, luke.allen@mail.harvard.edu %K personalized health technology %K population health %K frugal innovation %K ethics %K socioeconomic factors, inequalities %K technology, health %D 2016 %7 10.05.2016 %9 Viewpoint %J J Med Internet Res %G English %X Personalized health technology is a noisy new entrant to the health space, yet to make a significant impact on population health but seemingly teeming with potential. Devices including wearable fitness trackers and healthy-living apps are designed to help users quantify and improve their health behaviors. Although the ethical issues surrounding data privacy have received much attention, little is being said about the impact on socioeconomic health inequalities. Populations who stand to benefit the most from these technologies are unable to afford, access, or use them. This paper outlines the negative impact that these technologies will have on inequalities unless their user base can be radically extended to include vulnerable populations. Frugal innovation and public–private partnership are discussed as the major means for reaching this end. %M 27165944 %R 10.2196/jmir.5357 %U http://www.jmir.org/2016/5/e99/ %U https://doi.org/10.2196/jmir.5357 %U http://www.ncbi.nlm.nih.gov/pubmed/27165944 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 18 %N 5 %P e90 %T Devices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review %A Sanders,James P %A Loveday,Adam %A Pearson,Natalie %A Edwardson,Charlotte %A Yates,Thomas %A Biddle,Stuart JH %A Esliger,Dale W %+ Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit, Loughborough University, Sir John Beckwith Building, Loughborough, LE11 3TU, United Kingdom, 44 7538330734, J.Sanders2@lboro.ac.uk %K sitting time %K physical activity %K measurement %K feedback %K activity monitor %K scoping review %D 2016 %7 04.05.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: It is well documented that meeting the guideline levels (150 minutes per week) of moderate-to-vigorous physical activity (PA) is protective against chronic disease. Conversely, emerging evidence indicates the deleterious effects of prolonged sitting. Therefore, there is a need to change both behaviors. Self-monitoring of behavior is one of the most robust behavior-change techniques available. The growing number of technologies in the consumer electronics sector provides a unique opportunity for individuals to self-monitor their behavior. Objective: The aim of this study is to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary time and/or PA. Methods: To identify technologies, four scientific databases were systematically searched using key terms related to behavior, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1, 2015. Results: The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored PA, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring PA and/or sedentary behavior; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on PA (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary time (activPAL VT, the Lumo Back, and Darma). Conclusions: There are a large number of devices that self-monitor PA; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behavior change interventions, although the growing field of wearable technology may facilitate this to change. %M 27145905 %R 10.2196/jmir.5373 %U http://www.jmir.org/2016/5/e90/ %U https://doi.org/10.2196/jmir.5373 %U http://www.ncbi.nlm.nih.gov/pubmed/27145905 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 2 %P e40 %T Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis %A Mercer,Kathryn %A Li,Melissa %A Giangregorio,Lora %A Burns,Catherine %A Grindrod,Kelly %+ School of Pharmacy, University of Waterloo, 200 University Ave West, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 21358, kelly.grindrod@uwaterloo.ca %K older adults %K physical activity %K wearables %K mobile health %K chronic disease management %D 2016 %7 27.04.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Wearable activity trackers are promising as interventions that offer guidance and support for increasing physical activity and health-focused tracking. Most adults do not meet their recommended daily activity guidelines, and wearable fitness trackers are increasingly cited as having great potential to improve the physical activity levels of adults. Objective: The objective of this study was to use the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy to examine if the design of wearable activity trackers incorporates behavior change techniques (BCTs). A secondary objective was to critically analyze whether the BCTs present relate to known drivers of behavior change, such as self-efficacy, with the intention of extending applicability to older adults in addition to the overall population. Methods: Wearing each device for a period of 1 week, two independent raters used CALO-RE taxonomy to code the BCTs of the seven wearable activity trackers available in Canada as of March 2014. These included Fitbit Flex, Misfit Shine, Withings Pulse, Jawbone UP24, Spark Activity Tracker by SparkPeople, Nike+ FuelBand SE, and Polar Loop. We calculated interrater reliability using Cohen's kappa. Results: The average number of BCTs identified was 16.3/40. Withings Pulse had the highest number of BCTs and Misfit Shine had the lowest. Most techniques centered around self-monitoring and self-regulation, all of which have been associated with improved physical activity in older adults. Techniques related to planning and providing instructions were scarce. Conclusions: Overall, wearable activity trackers contain several BCTs that have been shown to increase physical activity in older adults. Although more research and development must be done to fully understand the potential of wearables as health interventions, the current wearable trackers offer significant potential with regard to BCTs relevant to uptake by all populations, including older adults. %M 27122452 %R 10.2196/mhealth.4461 %U http://mhealth.jmir.org/2016/2/e40/ %U https://doi.org/10.2196/mhealth.4461 %U http://www.ncbi.nlm.nih.gov/pubmed/27122452 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 2 %P e35 %T Older Adults’ Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity %A McMahon,Siobhan K %A Lewis,Beth %A Oakes,Michael %A Guan,Weihua %A Wyman,Jean F %A Rothman,Alexander J %+ University of Minnesota, School of Nursing, 308 Harvard Street SE, Minneapolis, MN, 55455, United States, 1 2182903422, skmcmaho@umn.edu %K Aged %K Mobile Health %K Self-Appraisal %K Physical Activity %K Motivation %K Monitoring %K Ambulatory %K Wearables %D 2016 %7 13.04.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Physical activity contributes to older adults’ autonomy, mobility, and quality of life as they age, yet fewer than 1 in 5 engage in activities as recommended. Many older adults track their exercise using pencil and paper, or their memory. Commercially available physical activity monitors (PAM) have the potential to facilitate these tracking practices and, in turn, physical activity. An assessment of older adults’ long-term experiences with PAM is needed to understand this potential. Objective: To assess short and long-term experiences of adults >70 years old using a PAM (Fitbit One) in terms of acceptance, ease-of-use, and usefulness: domains in the technology acceptance model. Methods: This prospective study included 95 community-dwelling older adults, all of whom received a PAM as part of randomized controlled trial piloting a fall-reducing physical activity promotion intervention. Ten-item surveys were administered 10 weeks and 8 months after the study started. Survey ratings are described and analyzed over time, and compared by sex, education, and age. Results: Participants were mostly women (71/95, 75%), 70 to 96 years old, and had some college education (68/95, 72%). Most participants (86/95, 91%) agreed or strongly agreed that the PAM was easy to use, useful, and acceptable both 10 weeks and 8 months after enrolling in the study. Ratings dropped between these time points in all survey domains: ease-of-use (median difference 0.66 points, P=.001); usefulness (median difference 0.16 points, P=.193); and acceptance (median difference 0.17 points, P=.032). Differences in ratings by sex or educational attainment were not statistically significant at either time point. Most participants 80+ years of age (28/37, 76%) agreed or strongly agreed with survey items at long-term follow-up, however their ratings were significantly lower than participants in younger age groups at both time points. Conclusions: Study results indicate it is feasible for older adults (70-90+ years of age) to use PAMs when self-tracking their physical activity, and provide a basis for developing recommendations to integrate PAMs into promotional efforts. Trial Registration: Clinicaltrials.gov NCT02433249; https://clinicaltrials.gov/ct2/show/NCT02433249 (Archived by WebCite at http://www.webcitation.org/6gED6eh0I) %M 27076486 %R 10.2196/mhealth.5120 %U http://mhealth.jmir.org/2016/2/e35/ %U https://doi.org/10.2196/mhealth.5120 %U http://www.ncbi.nlm.nih.gov/pubmed/27076486 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 18 %N 4 %P e69 %T Cardiac Patients’ Walking Activity Determined by a Step Counter in Cardiac Telerehabilitation: Data From the Intervention Arm of a Randomized Controlled Trial %A Thorup,Charlotte %A Hansen,John %A Grønkjær,Mette %A Andreasen,Jan Jesper %A Nielsen,Gitte %A Sørensen,Erik Elgaard %A Dinesen,Birthe Irene %+ Department of Cardiothoracic Surgery, Aalborg University Hospital, Søndre Skovvej 5, 313, Aalborg, 9000, Denmark, 45 20729950, cbt@rn.dk %K heart disease %K rehabilitation %K step counters %K physical activity %K telerehabilitation %D 2016 %7 04.04.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Walking represents a large part of daily physical activity. It reduces both overall and cardiovascular diseases and mortality and is suitable for cardiac patients. A step counter measures walking activity and might be a motivational tool to increase and maintain physical activity. There is a lack of knowledge about both cardiac patients’ adherence to step counter use in a cardiac telerehabilitation program and how many steps cardiac patients walk up to 1 year after a cardiac event. Objective: The purpose of this substudy was to explore cardiac patients’ walking activity. The walking activity was analyzed in relation to duration of pedometer use to determine correlations between walking activity, demographics, and medical and rehabilitation data. Methods: A total of 64 patients from a randomized controlled telerehabilitation trial (Teledi@log) from Aalborg University Hospital and Hjoerring Hospital, Denmark, from December 2012 to March 2014 were included in this study. Inclusion criteria were patients hospitalized with acute coronary syndrome, heart failure, and coronary artery bypass grafting or valve surgery. In Teledi@log, the patients received telerehabilitation technology and selected one of three telerehabilitation settings: a call center, a community health care center, or a hospital. Monitoring of steps continued for 12 months and a step counter (Fitbit Zip) was used to monitor daily steps. Results: Cardiac patients walked a mean 5899 (SD 3274) steps per day, increasing from mean 5191 (SD 3198) steps per day in the first week to mean 7890 (SD 2629) steps per day after 1 year. Adherence to step counter use lasted for a mean 160 (SD 100) days. The patients who walked significantly more were younger (P=.01) and continued to use the pedometer for a longer period (P=.04). Furthermore, less physically active patients weighed more. There were no significant differences in mean steps per day for patients in the three rehabilitation settings or in the disease groups. Conclusions: This study indicates that cardiac telerehabilitation at a call center can support walking activity just as effectively as telerehabilitation at either a hospital or a health care center. In this study, the patients tended to walk fewer steps per day than cardiac patients in comparable studies, but our study may represent a more realistic picture of walking activity due to the continuation of step counter use. Qualitative studies on patients’ behavior and motivation regarding step counter use are needed to shed light on adherence to and motivation to use step counters. Trial Registration: ClinicalTrails.gov NCT01752192; https://clinicaltrials.gov/ct2/show/NCT01752192 (Archived by WebCite at http://www.webcitation.org/6fgigfUyV) %M 27044310 %R 10.2196/jmir.5191 %U http://www.jmir.org/2016/4/e69/ %U https://doi.org/10.2196/jmir.5191 %U http://www.ncbi.nlm.nih.gov/pubmed/27044310 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 4 %N 1 %P e7 %T Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation %A Mercer,Kathryn %A Giangregorio,Lora %A Schneider,Eric %A Chilana,Parmit %A Li,Melissa %A Grindrod,Kelly %+ School of Pharmacy, Faculty of Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567 ext 21358, kgrindrod@uwaterloo.ca %K chronic disease %K physical activity %K sedentary lifestyle %K wearables %D 2016 %7 27.01.2016 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Physical inactivity and sedentary behavior increase the risk of chronic illness and death. The newest generation of “wearable” activity trackers offers potential as a multifaceted intervention to help people become more active. Objective: To examine the usability and usefulness of wearable activity trackers for older adults living with chronic illness. Methods: We recruited a purposive sample of 32 participants over the age of 50, who had been previously diagnosed with a chronic illness, including vascular disease, diabetes, arthritis, and osteoporosis. Participants were between 52 and 84 years of age (mean 64); among the study participants, 23 (72%) were women and the mean body mass index was 31 kg/m2. Participants tested 5 trackers, including a simple pedometer (Sportline or Mio) followed by 4 wearable activity trackers (Fitbit Zip, Misfit Shine, Jawbone Up 24, and Withings Pulse) in random order. Selected devices represented the range of wearable products and features available on the Canadian market in 2014. Participants wore each device for at least 3 days and evaluated it using a questionnaire developed from the Technology Acceptance Model. We used focus groups to explore participant experiences and a thematic analysis approach to data collection and analysis. Results: Our study resulted in 4 themes: (1) adoption within a comfort zone; (2) self-awareness and goal setting; (3) purposes of data tracking; and (4) future of wearable activity trackers as health care devices. Prior to enrolling, few participants were aware of wearable activity trackers. Most also had been asked by a physician to exercise more and cited this as a motivation for testing the devices. None of the participants planned to purchase the simple pedometer after the study, citing poor accuracy and data loss, whereas 73% (N=32) planned to purchase a wearable activity tracker. Preferences varied but 50% felt they would buy a Fitbit and 42% felt they would buy a Misfit, Jawbone, or Withings. The simple pedometer had a mean acceptance score of 56/95 compared with 63 for the Withings, 65 for the Misfit and Jawbone, and 68 for the Fitbit. To improve usability, older users may benefit from devices that have better compatibility with personal computers or less-expensive Android mobile phones and tablets, and have comprehensive paper-based user manuals and apps that interpret user data. Conclusions: For older adults living with chronic illness, wearable activity trackers are perceived as useful and acceptable. New users may need support to both set up the device and learn how to interpret their data. %M 26818775 %R 10.2196/mhealth.4225 %U http://mhealth.jmir.org/2016/1/e7/ %U https://doi.org/10.2196/mhealth.4225 %U http://www.ncbi.nlm.nih.gov/pubmed/26818775 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 4 %P e96 %T Use of the Fitbit to Measure Adherence to a Physical Activity Intervention Among Overweight or Obese, Postmenopausal Women: Self-Monitoring Trajectory During 16 Weeks %A Cadmus-Bertram,Lisa %A Marcus,Bess H %A Patterson,Ruth E %A Parker,Barbara A %A Morey,Brittany L %+ Department of Kinesiology, University of Wisconsin - Madison, 2000 Observatory Drive, Madison, WI, 53706, United States, 1 608 265 5946, cadmusbertra@wisc.edu %K exercise %K health behavior %K health promotion %K Internet %K mHealth %K motor activity %K physical activity %K technology %K women %D 2015 %7 19.11.2015 %9 Original Paper %J JMIR mHealth uHealth %G English %X Background: Direct-to-consumer trackers and devices have potential to enhance theory-based physical activity interventions by offering a simple and pleasant way to help participants self-monitor their behavior. A secondary benefit of these devices is the opportunity for investigators to objectively track adherence to physical activity goals across weeks or even months, rather than relying on self-report or a small number of accelerometry wear periods. The use of consumer trackers for continuous monitoring of adherence has considerable potential to enhance physical activity research, but few studies have been published in this rapidly developing area. Objective: The objective of the study was to assess the trajectory of physical activity adherence across a 16-week self-monitoring intervention, as measured by the Fitbit tracker. Methods: Participants were 25 overweight or obese, postmenopausal women enrolled in the intervention arm of a randomized controlled physical activity intervention trial. Each participant received a 16-week technology-based intervention that used the Fitbit physical activity tracker and website. The overall study goal was 150 minutes/week of moderate to vigorous intensity physical activity (MVPA) and 10,000 steps/day; however, goals were set individually for each participant and updated at Week 4 based on progress. Adherence data were collected by the Fitbit and aggregated by Fitabase. Participants also wore an ActiGraph GT3X+ accelerometer for 7 days prior to the intervention and again during Week 16. Results: The median participant logged 10 hours or more/day of Fitbit wear on 95% of the 112 intervention days, with no significant decline in wear over the study period. Participants averaged 7540 (SD 2373) steps/day and 82 minutes/week (SD 43) of accumulated “fairly active” and “very active” minutes during the intervention. At Week 4, 80% (20/25) of women chose to maintain/increase their individual MVPA goal and 72% (18/25) of participants chose to maintain/increase their step goal. Physical activity levels were relatively stable after peaking at 3 weeks, with only small declines of 8% for steps (P=.06) and 14% for MVPA (P=.05) by 16 weeks. Conclusions: These data indicate that a sophisticated, direct-to-consumer activity tracker encouraged high levels of self-monitoring that were sustained over 16 weeks. Further study is needed to determine how to motivate additional gains in physical activity and evaluate the long-term utility of the Fitbit tracker as part of a strategy for chronic disease prevention. Trial Registration: Clinicaltrials.gov NCT01837147; http://clinicaltrials.gov/ct2/show/NCT01837147 (Archived by WebCite at http://www.webcitation.org/6d0VeQpvB) %M 26586418 %R 10.2196/mhealth.4229 %U http://mhealth.jmir.org/2015/4/e96/ %U https://doi.org/10.2196/mhealth.4229 %U http://www.ncbi.nlm.nih.gov/pubmed/26586418 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 17 %N 11 %P e260 %T Bringing Health and Fitness Data Together for Connected Health Care: Mobile Apps as Enablers of Interoperability %A Gay,Valerie %A Leijdekkers,Peter %+ Faculty of Engineering and Information Technology, University of Technology Sydney, PO box 123, Broadway NSW, 2007, Australia, 61 2 9514 4645, Valerie.Gay@uts.edu.au %K health informatics %K connected health %K pervasive and mobile computing %K ubiquitous and mobile devices %D 2015 %7 18.11.2015 %9 Viewpoint %J J Med Internet Res %G English %X Background: A transformation is underway regarding how we deal with our health. Mobile devices make it possible to have continuous access to personal health information. Wearable devices, such as Fitbit and Apple’s smartwatch, can collect data continuously and provide insights into our health and fitness. However, lack of interoperability and the presence of data silos prevent users and health professionals from getting an integrated view of health and fitness data. To provide better health outcomes, a complete picture is needed which combines informal health and fitness data collected by the user together with official health records collected by health professionals. Mobile apps are well positioned to play an important role in the aggregation since they can tap into these official and informal health and data silos. Objective: The objective of this paper is to demonstrate that a mobile app can be used to aggregate health and fitness data and can enable interoperability. It discusses various technical interoperability challenges encountered while integrating data into one place. Methods: For 8 years, we have worked with third-party partners, including wearable device manufacturers, electronic health record providers, and app developers, to connect an Android app to their (wearable) devices, back-end servers, and systems. Results: The result of this research is a health and fitness app called myFitnessCompanion, which enables users to aggregate their data in one place. Over 6000 users use the app worldwide to aggregate their health and fitness data. It demonstrates that mobile apps can be used to enable interoperability. Challenges encountered in the research process included the different wireless protocols and standards used to communicate with wireless devices, the diversity of security and authorization protocols used to be able to exchange data with servers, and lack of standards usage, such as Health Level Seven, for medical information exchange. Conclusions: By limiting the negative effects of health data silos, mobile apps can offer a better holistic view of health and fitness data. Data can then be analyzed to offer better and more personalized advice and care. %M 26581920 %R 10.2196/jmir.5094 %U http://www.jmir.org/2015/11/e260/ %U https://doi.org/10.2196/jmir.5094 %U http://www.ncbi.nlm.nih.gov/pubmed/26581920 %0 Journal Article %@ 1929-0748 %I JMIR Publications Inc. %V 4 %N 3 %P e108 %T The Walking Interventions Through Texting (WalkIT) Trial: Rationale, Design, and Protocol for a Factorial Randomized Controlled Trial of Adaptive Interventions for Overweight and Obese, Inactive Adults %A Hurley,Jane C %A Hollingshead,Kevin E %A Todd,Michael %A Jarrett,Catherine L %A Tucker,Wesley J %A Angadi,Siddhartha S %A Adams,Marc A %+ Exercise Science and Health Promotion, School of Nutrition and Health Promotion, Arizona State University, NHI-2 Bldg, 550 North 3rd Street, Phoenix, AZ, , United States, 1 602 827 2470, marc.adams@asu.edu %K just in time adaptive interventions %K Fitbit %K exercise %K overweight %K inactive %K text messaging %K SMS %K percentile schedule of reinforcement %K mHealth %D 2015 %7 11.09.2015 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Walking is a widely accepted and frequently targeted health promotion approach to increase physical activity (PA). Interventions to increase PA have produced only small improvements. Stronger and more potent behavioral intervention components are needed to increase time spent in PA, improve cardiometabolic risk markers, and optimize health. Objective: Our aim is to present the rationale and methods from the WalkIT Trial, a 4-month factorial randomized controlled trial (RCT) in inactive, overweight/obese adults. The main purpose of the study was to evaluate whether intensive adaptive components result in greater improvements to adults’ PA compared to the static intervention components. Methods: Participants enrolled in a 2x2 factorial RCT and were assigned to one of four semi-automated, text message–based walking interventions. Experimental components included adaptive versus static steps/day goals, and immediate versus delayed reinforcement. Principles of percentile shaping and behavioral economics were used to operationalize experimental components. A Fitbit Zip measured the main outcome: participants’ daily physical activity (steps and cadence) over the 4-month duration of the study. Secondary outcomes included self-reported PA, psychosocial outcomes, aerobic fitness, and cardiorespiratory risk factors assessed pre/post in a laboratory setting. Participants were recruited through email listservs and websites affiliated with the university campus, community businesses and local government, social groups, and social media advertising. Results: This study has completed data collection as of December 2014, but data cleaning and preliminary analyses are still in progress. We expect to complete analysis of the main outcomes in late 2015 to early 2016. Conclusions: The Walking Interventions through Texting (WalkIT) Trial will further the understanding of theory-based intervention components to increase the PA of men and women who are healthy, insufficiently active and are overweight or obese. WalkIT is one of the first studies focusing on the individual components of combined goal setting and reward structures in a factorial design to increase walking. The trial is expected to produce results useful to future research interventions and perhaps industry initiatives, primarily focused on mHealth, goal setting, and those looking to promote behavior change through performance-based incentives. Trial Registration: ClinicalTrials.gov NCT02053259; https://clinicaltrials.gov/ct2/show/NCT02053259 (Archived by WebCite at http://www.webcitation.org/6b65xLvmg). %M 26362511 %R 10.2196/resprot.4856 %U http://www.researchprotocols.org/2015/3/e108/ %U https://doi.org/10.2196/resprot.4856 %U http://www.ncbi.nlm.nih.gov/pubmed/26362511 %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 %@ 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 %@ 1929-073X %I JMIR Publications Inc. %V 3 %N 4 %P e14 %T Validity and Usability of Low-Cost Accelerometers for Internet-Based Self-Monitoring of Physical Activity in Patients With Chronic Obstructive Pulmonary Disease %A Vooijs,Martijn %A Alpay,Laurence L %A Snoeck-Stroband,Jiska B %A Beerthuizen,Thijs %A Siemonsma,Petra C %A Abbink,Jannie J %A Sont,Jacob K %A Rövekamp,Ton A %+ Rijnlands Rehabilitation Center, Department of Cardiac and Pulmonary Rehabilitation, Wassenaarseweg 501, Leiden, 2333AL, Netherlands, 31 715195300, m.vooijs@rrc.nl %K accelerometers %K activity monitoring %K chronic obstructive pulmonary disease %K validity %K usability %D 2014 %7 27.10.2014 %9 Original Paper %J Interact J Med Res %G English %X Background: The importance of regular physical activity for patients with chronic obstructive pulmonary disease (COPD) is well-established. However, many patients do not meet the recommended daily amount. Accelerometers might provide patients with the information needed to increase physical activity in daily life. Objective: Our objective was to assess the validity and usability of low-cost Internet-connected accelerometers. Furthermore we explored patients’ preferences with regards to the presentation of and feedback on monitored physical activity. Methods: To assess concurrent validity we conducted a field validation study with patients who wore two low-cost accelerometers, Fitbit and Physical Activity Monitor (PAM), at the same time along with a sophisticated multisensor accelerometer (SenseWear Armband) for 48 hours. Data on energy expenditure assessed from registrations from the two low-cost accelerometers were compared to the well validated SenseWear Armband which served as a reference criterion. Usability was examined in a cross-over study with patients who, in succession, wore the Fitbit and the PAM for 7 consecutive days and filled out a 16 item questionnaire with regards to the use of the corresponding device Results: The agreement between energy expenditure (METs) from the SenseWear Armband with METs estimated by the Fitbit and PAM was good (r=.77) and moderate (r=.41), respectively. The regression model that was developed for the Fitbit explained 92% whereas the PAM-model could explain 89% of total variance in METs measured by the SenseWear. With regards to the usability, both the Fitbit and PAM were well rated on all items. There were no significant differences between the two devices. Conclusions: The low-cost Fitbit and PAM are valid and usable devices to measure physical activity in patients with COPD. These devices may be useful in long-term interventions aiming at increasing physical activity levels in these patients. %M 25347989 %R 10.2196/ijmr.3056 %U http://www.i-jmr.org/2014/4/e14/ %U https://doi.org/10.2196/ijmr.3056 %U http://www.ncbi.nlm.nih.gov/pubmed/25347989 %0 Journal Article %@ 1438-8871 %I JMIR Publications Inc. %V 16 %N 8 %P e192 %T Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis %A Lyons,Elizabeth J %A Lewis,Zakkoyya H %A Mayrsohn,Brian G %A Rowland,Jennifer L %+ The University of Texas Medical Branch, Institute for Translational Sciences, 301 University Blvd, Galveston, TX, 77555-0342, United States, 1 409 772 1917, ellyons@utmb.edu %K electronic activity monitor %K mobile %K mhealth %K physical activity %K behavior change technique %D 2014 %7 15.08.2014 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another. Objective: The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors. Methods: Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature. Results: All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory. Conclusions: Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings. %M 25131661 %R 10.2196/jmir.3469 %U http://www.jmir.org/2014/8/e192/ %U https://doi.org/10.2196/jmir.3469 %U http://www.ncbi.nlm.nih.gov/pubmed/25131661 %0 Journal Article %@ 14388871 %I JMIR Publications Inc. %V 15 %N 8 %P e181 %T Pedometer-Based Internet-Mediated Intervention For Adults With Chronic Low Back Pain: Randomized Controlled Trial %A Krein,Sarah L %A Kadri,Reema %A Hughes,Maria %A Kerr,Eve A %A Piette,John D %A Holleman,Rob %A Kim,Hyungjin Myra %A Richardson,Caroline R %+ VA Ann Arbor Center for Clinical Management Research, VA Ann Arbor Healthcare System, HSR&D (152), PO Box 130170, Ann Arbor, MI, 48113, United States, 1 734 845 3621, skrein@umich.edu %K chronic pain %K Internet %K randomized controlled trial %K exercise therapy %D 2013 %7 22.08.2013 %9 Original Paper %J J Med Internet Res %G English %X Background: Chronic pain, especially back pain, is a prevalent condition that is associated with disability, poor health status, anxiety and depression, decreased quality of life, and increased health services use and costs. Current evidence suggests that exercise is an effective strategy for managing chronic pain. However, there are few clinical programs that use generally available tools and a relatively low-cost approach to help patients with chronic back pain initiate and maintain an exercise program. Objective: The objective of the study was to determine whether a pedometer-based, Internet-mediated intervention can reduce chronic back pain-related disability. Methods: A parallel group randomized controlled trial was conducted with 1:1 allocation to the intervention or usual care group. 229 veterans with nonspecific chronic back pain were recruited from one Department of Veterans Affairs (VA) health care system. Participants randomized to the intervention received an uploading pedometer and had access to a website that provided automated walking goals, feedback, motivational messages, and social support through an e-community (n=111). Usual care participants (n=118) also received the uploading pedometer but did not receive the automated feedback or have access to the website. The primary outcome was measured using the Roland Morris Disability Questionnaire (RDQ) at 6 months (secondary) and 12 months (primary) with a difference in mean scores of at least 2 considered clinically meaningful. Both a complete case and all case analysis, using linear mixed effects models, were conducted to assess differences between study groups at both time points. Results: Baseline mean RDQ scores were greater than 9 in both groups. Primary outcome data were provided by approximately 90% of intervention and usual care participants at both 6 and 12 months. At 6 months, average RDQ scores were 7.2 for intervention participants compared to 9.2 for usual care, an adjusted difference of 1.6 (95% CI 0.3-2.8, P=.02) for the complete case analysis and 1.2 (95% CI -0.09 to 2.5, P=.07) for the all case analysis. A post hoc analysis of patients with baseline RDQ scores ≥4 revealed even larger adjusted differences between groups at 6 months but at 12 months the differences were no longer statistically significant. Conclusions: Intervention participants, compared with those receiving usual care, reported a greater decrease in back pain-related disability in the 6 months following study enrollment. Between-group differences were especially prominent for patients reporting greater baseline levels of disability but did not persist over 12 months. Primarily, automated interventions may be an efficient way to assist patients with managing chronic back pain; additional support may be needed to ensure continuing improvements. Trial Registration: ClinicalTrials.gov NCT00694018; http://clinicaltrials.gov/ct2/show/NCT00694018 (Archived by WebCite at http://www.webcitation.org/6IsG4Y90E). %M 23969029 %R 10.2196/jmir.2605 %U http://www.jmir.org/2013/8/e181/ %U https://doi.org/10.2196/jmir.2605 %U http://www.ncbi.nlm.nih.gov/pubmed/23969029 %0 Journal Article %@ 1438-8871 %I Gunther Eysenbach %V 14 %N 5 %P e130 %T Classification Accuracies of Physical Activities Using Smartphone Motion Sensors %A Wu,Wanmin %A Dasgupta,Sanjoy %A Ramirez,Ernesto E %A Peterson,Carlyn %A Norman,Gregory J %+ Center For Wireless & Population Health Systems, Department of Family & Preventive Medicine, University of California, San Diego, 9500 Gilman Drive, Dept. 0811, La Jolla, CA, 92093-0811, United States, 1 (858)534 9302, gnorman@ucsd.edu %K Activity classification %K machine learning %K accelerometer %K gyroscope %K smartphone %D 2012 %7 05.10.2012 %9 Original Paper %J J Med Internet Res %G English %X Background: Over the past few years, the world has witnessed an unprecedented growth in smartphone use. With sensors such as accelerometers and gyroscopes on board, smartphones have the potential to enhance our understanding of health behavior, in particular physical activity or the lack thereof. However, reliable and valid activity measurement using only a smartphone in situ has not been realized. Objective: To examine the validity of the iPod Touch (Apple, Inc.) and particularly to understand the value of using gyroscopes for classifying types of physical activity, with the goal of creating a measurement and feedback system that easily integrates into individuals’ daily living. Methods: We collected accelerometer and gyroscope data for 16 participants on 13 activities with an iPod Touch, a device that has essentially the same sensors and computing platform as an iPhone. The 13 activities were sitting, walking, jogging, and going upstairs and downstairs at different paces. We extracted time and frequency features, including mean and variance of acceleration and gyroscope on each axis, vector magnitude of acceleration, and fast Fourier transform magnitude for each axis of acceleration. Different classifiers were compared using the Waikato Environment for Knowledge Analysis (WEKA) toolkit, including C4.5 (J48) decision tree, multilayer perception, naive Bayes, logistic, k-nearest neighbor (kNN), and meta-algorithms such as boosting and bagging. The 10-fold cross-validation protocol was used. Results: Overall, the kNN classifier achieved the best accuracies: 52.3%–79.4% for up and down stair walking, 91.7% for jogging, 90.1%–94.1% for walking on a level ground, and 100% for sitting. A 2-second sliding window size with a 1-second overlap worked the best. Adding gyroscope measurements proved to be more beneficial than relying solely on accelerometer readings for all activities (with improvement ranging from 3.1% to 13.4%). Conclusions: Common categories of physical activity and sedentary behavior (walking, jogging, and sitting) can be recognized with high accuracies using both the accelerometer and gyroscope onboard the iPod touch or iPhone. This suggests the potential of developing just-in-time classification and feedback tools on smartphones. %M 23041431 %R 10.2196/jmir.2208 %U http://www.jmir.org/2012/5/e130/ %U https://doi.org/10.2196/jmir.2208 %U http://www.ncbi.nlm.nih.gov/pubmed/23041431