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Journal Description

JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a sister journal of JMIR, the leading eHealth journal. JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, Scopus, and Science Citation Index Expanded (SCIE), and in June 2018 received an Impact Factor of 4.541, which ranks the journal #2 (behind JMIR) out of 25 journals in the medical informatics category indexed by the Science Citation Index Expanded (SCIE) by Thomson Reuters/Clarivate

The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.

JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research

JMIR mHealth and uHealth features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.

JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics.


Recent Articles:

  • Source: Rawpixel; Copyright: Jira; URL:; License: Public Domain (CC0).

    Toward an Ethically Founded Framework for the Use of Mobile Phone Call Detail Records in Health Research


    Data derived from the plethora of networked digital devices hold great potential for public benefit. Among these, mobile phone call detail records (CDRs) present novel opportunities for research and are being used in a variety of health geography studies. Research suggests that the public is amenable to the use of anonymized CDRs for research; however, further work is needed to show that such data can be used appropriately. This study works toward an ethically founded data governance framework with social acceptability. Using a multifaceted approach, this study draws upon data governance arrangements in published health research using CDRs, with a consideration of public views and the public’s information expectations from mobile network operators, and data use scenarios of CDRs in health research. The findings were considered against a backdrop of legislative and regulatory requirements. CDRs can be used at various levels of data and geographic granularity and may be integrated with additional, publicly available or restricted datasets. As such, there may be a significant risk of identity disclosure, which must be mitigated with proportionate control measures. An indicative relative risk of the disclosure model is proposed to aid this process. Subsequently, a set of recommendations is presented, including the need for greater transparency, accountability, and incorporation of public views for social acceptability. This study addresses the need for greater clarity and consistency in data governance for CDRs in health research. While recognizing the need to protect commercial interests, we propose that these recommendations be used to contribute toward an ethically founded practical framework to promote the safe, socially acceptable use of CDR data for public benefit. This pattern needs to be repeated for the appropriate use of new and emerging data types from other networking devices and the wider internet of things.

  • Source: Freepik; Copyright: katemangostar; URL:; License: Licensed by JMIR.

    Theory-Based Predictors of Mindfulness Meditation Mobile App Usage: A Survey and Cohort Study


    Background: Mindfulness meditation has become increasingly popular over the last few years, due in part to the increase in mobile apps incorporating the practice. Although studies have demonstrated the potential of mindfulness meditation to positively impact health, little has been uncovered about what predicts engagement in mindfulness meditation. Understanding the predictors of mindfulness meditation may help practitioners and phone app developers improve intervention strategies and app experience. Objective: The purpose of this study was to use the Theory of Planned Behavior and Temporal Self-Regulation Theory to determine factors predicting mindfulness meditation mobile app use. Methods: The sample consisted of 85 undergraduate students with no prior mindfulness meditation experience. During their first laboratory visit, participants completed tasks to measure their executive functioning and a survey to measure Theory of Planned Behavior constructs about mindfulness meditation. Over the following 2 weeks, participants logged the days and minutes that they practiced mindfulness meditation using a phone app. Hierarchical regression modeling was used to analyze the data. Results: After controlling for demographic factors, participant subjective norms (beta=14.51, P=.001) and intentions (beta=36.12, P=.001) were predictive of the number of minutes practicing mindfulness. Participant executive functioning did not predict mindfulness meditation practice, nor did it moderate the link between intentions and mindfulness meditation practice. Participant attitudes (beta=0.44, P<.001) and perceived control (beta=0.42, P=.002) were positively associated with intentions to practice mindfulness. Conclusions: These results suggest that among college student populations, the Theory of Planned Behavior may be useful in predicting the use of mindfulness meditation phone apps. However, participant executive functioning was not a predictor or moderator of mindfulness practice, and Temporal Self-Regulation Theory may be less useful for explaining mindfulness meditation behaviors using phone apps over a short period of time among college students. The results have implications for public health professionals, suggesting that a focus on subjective norms and intentions may promote mindfulness meditation practice using phone apps.

  • The ImagineCare remote care application being used to monitor blood pressure with the use of a Bluetooth enabled blood pressure cuff. Source: Image created by the Authors; Copyright: ImagineCare; URL:; License: Creative Commons Attribution (CC-BY).

    Development and Implementation of a Person-Centered, Technology-Enhanced Care Model For Managing Chronic Conditions: Cohort Study


    Background: Caring for individuals with chronic conditions is labor intensive, requiring ongoing appointments, treatments, and support. The growing number of individuals with chronic conditions makes this support model unsustainably burdensome on health care systems globally. Mobile health technologies are increasingly being used throughout health care to facilitate communication, track disease, and provide educational support to patients. Such technologies show promise, yet they are not being used to their full extent within US health care systems. Objective: The purpose of this study was to examine the use of staff and costs of a remote monitoring care model in persons with and without a chronic condition. Methods: At Dartmouth-Hitchcock Health, 2894 employees volunteered to monitor their health, transmit data for analysis, and communicate digitally with a care team. Volunteers received Bluetooth-connected consumer-grade devices that were paired to a mobile phone app that facilitated digital communication with nursing and health behavior change staff. Health data were collected and automatically analyzed, and behavioral support communications were generated based on those analyses. Care support staff were automatically alerted according to purpose-developed algorithms. In a subgroup of participants and matched controls, we used difference-in-difference techniques to examine changes in per capita expenditures. Results: Participants averaged 41 years of age; 72.70% (2104/2894) were female and 12.99% (376/2894) had at least one chronic condition. On average each month, participants submitted 23 vital sign measurements, engaged in 1.96 conversations, and received 0.25 automated messages. Persons with chronic conditions accounted for 39.74% (8587/21,607) of all staff conversations, with higher per capita conversation rates for all shifts compared to those without chronic conditions (P<.001). Additionally, persons with chronic conditions engaged nursing staff more than those without chronic conditions (1.40 and 0.19 per capita conversations, respectively, P<.001). When compared to the same period in the prior year, per capita health care expenditures for persons with chronic conditions dropped by 15% (P=.06) more than did those for matched controls. Conclusions: The technology-based chronic condition management care model was frequently used and demonstrated potential for cost savings among participants with chronic conditions. While further studies are necessary, this model appears to be a promising solution to efficiently provide patients with personalized care, when and where they need it.

  • Source: Flickr; Copyright: Pan American Health Organization; URL:; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Mobile Health Systems for Community-Based Primary Care: Identifying Controls and Mitigating Privacy Threats


    Background: Community-based primary care focuses on health promotion, awareness raising, and illnesses treatment and prevention in individuals, groups, and communities. Community Health Workers (CHWs) are the leading actors in such programs, helping to bridge the gap between the population and the health system. Many mobile health (mHealth) initiatives have been undertaken to empower CHWs and improve the data collection process in the primary care, replacing archaic paper-based approaches. A special category of mHealth apps, known as mHealth Data Collection Systems (MDCSs), is often used for such tasks. These systems process highly sensitive personal health data of entire communities so that a careful consideration about privacy is paramount for any successful deployment. However, the mHealth literature still lacks methodologically rigorous analyses for privacy and data protection. Objective: In this paper, a Privacy Impact Assessment (PIA) for MDCSs is presented, providing a systematic identification and evaluation of potential privacy risks, particularly emphasizing controls and mitigation strategies to handle negative privacy impacts. Methods: The privacy analysis follows a systematic methodology for PIAs. As a case study, we adopt the GeoHealth system, a large-scale MDCS used by CHWs in the Family Health Strategy, the Brazilian program for delivering community-based primary care. All the PIA steps were taken on the basis of discussions among the researchers (privacy and security experts). The identification of threats and controls was decided particularly on the basis of literature reviews and working group meetings among the group. Moreover, we also received feedback from specialists in primary care and software developers of other similar MDCSs in Brazil. Results: The GeoHealth PIA is based on 8 Privacy Principles and 26 Privacy Targets derived from the European General Data Protection Regulation. Associated with that, 22 threat groups with a total of 97 subthreats and 41 recommended controls were identified. Among the main findings, we observed that privacy principles can be enhanced on existing MDCSs with controls for managing consent, transparency, intervenability, and data minimization. Conclusions: Although there has been significant research that deals with data security issues, attention to privacy in its multiple dimensions is still lacking for MDCSs in general. New systems have the opportunity to incorporate privacy and data protection by design. Existing systems will have to address their privacy issues to comply with new and upcoming data protection regulations. However, further research is still needed to identify feasible and cost-effective solutions.

  • Source: Pixabay; Copyright: Gerald Oswald; URL:; License: Licensed by JMIR.

    Validation in the General Population of the iHealth Track Blood Pressure Monitor for Self-Measurement According to the European Society of Hypertension...


    Background: High blood pressure is one of the most common reasons why patients seek assistance in daily clinical practice. Screening for hypertension is fundamental and, because hypertension is identified only when blood pressure is measured, accurate measurements are key to the diagnosis and management of this disease. The European Society of Hypertension International Protocol revision 2010 (ESH-IP2) was developed to assess the validity of automatic blood pressure measuring devices that are increasingly being used to replace mercury sphygmomanometers. Objective: We sought to determine whether the iHealth Track blood pressure monitor meets ESH-IP2 requirements for self-measurement of blood pressure and heart rate at the brachial level and is appropriate for use in the general population. Methods: This study was a descriptive investigation. ESH-IP2 requires a total number of 33 participants. For each measure, the difference between observer and device blood pressure and heart rate values is calculated. In all, 99 pairs of blood pressure differences are classified into 3 categories (≤5, ≤10, and ≤15 mm Hg), and 99 pairs of heart rate differences are classified into 3 categories (≤3, ≤5, and ≤8 beats/min). We followed these protocol procedures in a convenience sample of 33 participants. Results: iHealth Track fulfilled ESH-IP2 requirements and passed the validation process successfully. We observed an absolute difference within 5 mm Hg in 75 of 99 comparisons for systolic blood pressure, 78 of 99 comparisons for diastolic blood pressure, and 89 of 99 comparisons for heart rate. The mean differences between the test and standard readings were 4.19 (SD 4.48) mm Hg for systolic blood pressure, 3.74 (SD 4.55) mm Hg for diastolic blood pressure, and 1.95 (SD 3.27) beats/min for heart rate. With regard to part 2 of ESH-IP2, we observed a minimum of 2 of 3 measurements within a 5-mm Hg difference in 29 of 33 participants for systolic blood pressure and 26 of 33 for diastolic blood pressure, and a minimum of 2 of 3 measurements within a 3-beat/min difference in 30 of 33 participants for heart rate. Conclusions: iHealth Track readings differed from the standard by less than 5, 10, and 15 mm Hg, fulfilling ESH-IP2 requirements. Consequently, this device is suitable for use in the general population.

  • Source: pxhere; Copyright: pxhere; URL:; License: Public Domain (CC0).

    Training Cognitive Functions Using Mobile Apps in Breast Cancer Patients: Systematic Review


    Background: Breast cancer is an invalidating disease and its treatment can bring serious side effects that have a physical and psychological impact. Specifically, cancer treatment generally has a strong impact on cognitive function. In recent years, new technologies and eHealth have had a growing influence on health care and innovative mobile apps can be useful tools to deliver cognitive exercise in the patient’s home. Objective: This systematic review gives an overview of the state-of-the-art mobile apps aimed at training cognitive functions to better understand whether these apps could be useful tools to counteract cognitive impairment in breast cancer patients. Methods: We searched in a systematic way all the full-text articles from the PubMed and Embase databases. Results: We found eleven studies using mobile apps to deliver cognitive training. They included a total of 819 participants. App and study characteristics are presented and discussed, including cognitive domains trained (attention, problem solving, memory, cognitive control, executive function, visuospatial function, and language). None of the apps were specifically developed for breast cancer patients. They were generally developed for a specific clinical population. Only 2 apps deal with more than 1 cognitive domain, and only 3 studies focus on the efficacy of the app training intervention. Conclusions: These results highlight the lack of empirical evidence on the efficacy of currently available apps to train cognitive function. Cognitive domains are not well defined across studies. It is noteworthy that no apps are specifically developed for cancer patients, and their applicability to breast cancer should not be taken for granted. Future studies should test the feasibility, usability, and effectiveness of available cognitive training apps in women with breast cancer. Due to the complexity and multidimensionality of cognitive difficulties in this cancer population, it may be useful to design, develop, and implement an ad hoc app targeting cognitive impairment in breast cancer patients.

  • Source: Pexels; Copyright: Ingo Joseph; URL:; License: Licensed by JMIR.

    Accuracy of Apple Watch Measurements for Heart Rate and Energy Expenditure in Patients With Cardiovascular Disease: Cross-Sectional Study


    Background: Wrist-worn tracking devices such as the Apple Watch are becoming more integrated in health care. However, validation studies of these consumer devices remain scarce. Objectives: This study aimed to assess if mobile health technology can be used for monitoring home-based exercise in future cardiac rehabilitation programs. The purpose was to determine the accuracy of the Apple Watch in measuring heart rate (HR) and estimating energy expenditure (EE) during a cardiopulmonary exercise test (CPET) in patients with cardiovascular disease. Methods: Forty patients (mean age 61.9 [SD 15.2] yrs, 80% male) with cardiovascular disease (70% ischemic, 22.5% valvular, 7.5% other) completed a graded maximal CPET on a cycle ergometer while wearing an Apple Watch. A 12-lead electrocardiogram (ECG) was used to measure HR; indirect calorimetry was used for EE. HR was analyzed at three levels of intensity (seated rest, HR1; moderate intensity, HR2; maximal performance, HR3) for 30 seconds. The EE of the entire test was used. Bias or mean difference (MD), standard deviation of difference (SDD), limits of agreement (LoA), mean absolute error (MAE), mean absolute percentage error (MAPE), and intraclass correlation coefficients (ICCs) were calculated. Bland-Altman plots and scatterplots were constructed. Results: SDD for HR1, HR2, and HR3 was 12.4, 16.2, and 12.0 bpm, respectively. Bias and LoA (lower, upper LoA) were 3.61 (–20.74, 27.96) for HR1, 0.91 (–30.82, 32.63) for HR2, and –1.82 (–25.27, 21.63) for HR3. MAE was 6.34 for HR1, 7.55 for HR2, and 6.90 for HR3. MAPE was 10.69% for HR1, 9.20% for HR2, and 6.33% for HR3. ICC was 0.729 (P<.001) for HR1, 0.828 (P<.001) for HR2, and 0.958 (P<.001) for HR3. Bland-Altman plots and scatterplots showed good correlation without systematic error when comparing Apple Watch with ECG measurements. SDD for EE was 17.5 kcal. Bias and LoA were 30.47 (–3.80, 64.74). MAE was 30.77; MAPE was 114.72%. ICC for EE was 0.797 (P<.001). The Bland-Altman plot and a scatterplot directly comparing Apple Watch and indirect calorimetry showed systematic bias with an overestimation of EE by the Apple Watch. Conclusions: In patients with cardiovascular disease, the Apple Watch measures HR with clinically acceptable accuracy during exercise. If confirmed, it might be considered safe to incorporate the Apple Watch in HR-guided training programs in the setting of cardiac rehabilitation. At this moment, however, it is too early to recommend the Apple Watch for cardiac rehabilitation. Also, the Apple Watch systematically overestimates EE in this group of patients. Caution might therefore be warranted when using the Apple Watch for measuring EE.

  • Source: Flickr; Copyright: UBC Learning Commons; URL:; License: Creative Commons Attribution (CC-BY).

    Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data


    Background: Stress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all-time high. Traditionally, the dominant way to assess stress levels has been through pen-and-paper surveys. Objective: The aim of this study is to use passive sensing data collected via mobile phones to obtain a rich and potentially less-biased source of data that can be used to help better understand stressors in the college experience. Methods: We used a mobile sensing app, StudentLife, in tandem with a pictorial mobile phone–based measure of stress, the Mobile Photographic Stress Meter, to investigate the situations and contexts that are more likely to precipitate stress. Results: Using recently developed methods for handling high-dimensional longitudinal data, penalized generalized estimating equations, we identified a set of mobile sensing features (absolute values of beta >0.001 and robust z>1.96) across the domains of social activity, movement, location, and ambient noise that were predictive of student stress levels. Conclusions: By combining recent statistical methods and mobile phone sensing, we have been able to study stressors in the college experience in a way that is more objective, detailed, and less intrusive than past research. Future work can leverage information gained from passive sensing and use that to develop real-time, targeted interventions for students experiencing a stressful time.

  • Source: The Authors / Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    A Mobile-Based Mindfulness and Social Support Program for Adolescents and Young Adults With Sarcoma: Development and Pilot Testing


    Background: Approximately 70,000 adolescents and young adults (AYA) are diagnosed with cancer each year in the United States. Sarcomas carry a particularly high symptom burden and are some of the most common cancers among AYA. Recent work has documented significant levels of unmet needs among AYA with cancer, particularly the need for psychosocial support. Mobile technology may be a cost-effective and efficient way to deliver a psychosocial intervention to AYA with cancer and cancer survivors. Objective: The two aims of this study were to (1) develop a pilot version of a mobile-based mindfulness and social support program and (2) evaluate program usage and acceptability. An exploratory aim was to examine change in psychosocial outcomes. Methods: Thirty-seven AYA with sarcoma or sarcoma survivors, parents, and health care providers participated in the study. Semistructured interviews were conducted with 10 AYA, parents of five of the adolescents, and six health care providers. Themes from the interviews helped to inform the development of a mobile-based mindfulness pilot program and a companion Facebook-based social support group. Twenty AYA consented to participate in a single-arm pre-post evaluation of the program; 17 downloaded the app and joined the Facebook group. Seven of these participants had participated in the semistructured interviews. Six additional health care providers consented to participate in the evaluation stage. Results: On average, participants completed 16.9 of the 28 unique sessions and used the mindfulness app for a mean 10.2 (SD 8.2) days during the 28-day evaluation period. The majority of participants (16/17) engaged in the social group and posted at least one reply to the moderator’s prompts. The mean number of responses per person to the moderator of the social group was 15.2 of 31 (49%, range 0%-97%). Both AYA and health care providers responded positively to the Mindfulness for Resilience in Illness program and offered useful recommendations for improvements. Exploratory psychosocial analyses indicated there were no significant differences from pretest to posttest on measures of perceived social support, mindfulness, body image, or psychological functioning. Conclusions: This study offers preliminary support for the feasibility and acceptability of a mobile-based mindfulness and Facebook-based social support program for AYA with sarcoma. The feedback from AYA and health care providers will assist in creating a fully developed intervention. Trial Registration: NCT03130751;

  • Source: Pixabay; Copyright: younizealous; URL:; License: Licensed by JMIR.

    A Mobile Health Contraception Decision Support Intervention for Latina Adolescents: Implementation Evaluation for Use in School-Based Health Centers


    Background: Health care providers are a trusted and accurate source of sexual health information for most adolescents, and clinical guidelines recommend that all youth receive comprehensive, confidential sexual health information and services. However, these guidelines are followed inconsistently. Providers often lack the time, comfort, and skills to provide patient-centered comprehensive contraceptive counseling and services. There are significant disparities in the provision of sexual health services for Latino adolescents, which contribute to disproportionately higher rates of teenage pregnancy. To address this, we developed Health-E You or Salud iTu in Spanish, an evidence-informed mobile health (mHealth) app, to provide interactive, individually tailored sexual health information and contraception decision support for English and Spanish speakers. It is designed to be used in conjunction with a clinical encounter to increase access to patient-centered contraceptive information and services for adolescents at risk of pregnancy. Based on user input, the app provides tailored contraceptive recommendations and asks the youth to indicate what methods they are most interested in. This information is shared with the provider before the in-person visit. The app is designed to prepare youth for the visit and acts as a clinician extender to support the delivery of health education and enhance the quality of patient-centered sexual health care. Despite the promise of this app, there is limited research on the integration of such interventions into clinical practice. Objective: This study described efforts used to support the successful adoption and implementation of the Health-E You app in clinical settings and described facilitators and barriers encountered to inform future efforts aimed at integrating mHealth interventions into clinical settings. Methods: This study was part of a larger, cluster randomized control trial to evaluate the effectiveness of Health-E You on its ability to reduce health disparities in contraceptive knowledge, access to contraceptive services, and unintended pregnancies among sexually active Latina adolescents at 18 school-based health centers (SBHCs) across Los Angeles County, California. App development and implementation were informed by the theory of diffusion of innovation, the Patient-Centered Outcomes Research Institute’s principles of engagement, and iterative pilot testing with adolescents and clinicians. Implementation facilitators and barriers were identified through monthly conference calls, site visits, and quarterly in-person collaborative meetings. Results: Implementation approaches enhanced the development, adoption, and integration of Health-E You into SBHCs. Implementation challenges were also identified to improve the integration of mHealth interventions into clinical settings. Conclusions: This study provides important insights that can inform and improve the implementation efforts for future mHealth interventions. In particular, an implementation approach founded in a strong theoretical framework and active engagement with patient and community partners can enhance the development, adoption, and integration of mHealth technologies into clinical practice. Trial Registration: NCT02847858; (Archived by WebCite at

  • Source: Freepik; Copyright: Freepik; URL:; License: Licensed by JMIR.

    Perspectives on Acceptance and Use of a Mobile Health Intervention for the Prevention of Atherosclerotic Cardiovascular Disease in Singapore: Mixed-Methods...


    Background: Cardiovascular disease, including atherosclerotic cardiovascular disease (ASCVD), is a growing public health threat globally and many individuals remain undiagnosed, untreated, and uncontrolled. Simultaneously, mobile health (mHealth) interventions using short messaging service (SMS) have gained popularity globally. There is an opportunity for innovative approaches such as mHealth to encourage and enable adherence to medications for ASCVD and its risk factors. Objective: This study aimed to understand mobile technology acceptance, use, and facilitating conditions among the study population ahead of the design of an mHealth intervention. Methods: Using data from a mixed-methods study conducted in Singapore, we conducted a cross-sectional survey with 100 participants and in-depth, semistructured interviews with 20 patients. All participants were over the age of 40 years with ASCVD or its risk factors. Interviews were conducted in English and Mandarin and if needed translated to English. Nvivo 11 (QSR International) was used for analyses. Results: Participants reported their perspectives on technology use and preferences, including low or sporadic mobile phone use and usability concerns including small screen and text size, among others; the benefit of previous mHealth use in creating a favorable opinion of SMS for health information; trust in both the source of mHealth SMS, as well as in treatment; the formation of habits; and fear of sequelae or death for facilitating intention to use an mHealth intervention and adhere to medication. We also highlighted a case that underscored the importance of the period after diagnosis in habit forming as an opportunity for an mHealth intervention. Conclusions: We explored both technology- and adherence-related factors that influence a patient’s intention to use an mHealth intervention for adherence to ASCVD medication in Singapore. We highlighted the importance of identifying the right opportunity to engage with patients and promote an mHealth intervention for adherence, such as immediately following diagnosis when patients are establishing medication-taking habits.

  • The "Get Healthy Stay Healthy" extended contact text message intervention. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Get Healthy, Stay Healthy: Evaluation of the Maintenance of Lifestyle Changes Six Months After an Extended Contact Intervention


    Background: Extended intervention contact after an initial, intensive intervention is becoming accepted as best practice in behavioral weight control interventions. Whether extended contact mitigates weight regain in the longer term or it simply delays weight regain until after the extended intervention contact ceases is not clear. Objective: This study aimed to evaluate, in multiple ways, maintenance of weight, diet, and physical activity outcomes following Get Healthy, Stay Healthy (GHSH), a text message–delivered extended contact intervention. Methods: Clients completing the Get Healthy Service (GHS) lifestyle telephone coaching program were randomized to receive GHSH (n=114) or standard care (no additional contact, n=114) and were assessed at baseline (following completion of GHS), 6 months (following completion of GHSH), and 12 months (noncontact maintenance follow-up). At all 3 assessments, participants self-reported their body weight, waist circumference, physical activity (walking and moderate and vigorous sessions/week), and dietary behaviors (fruit and vegetable serves/day, cups of sweetened drinks per day, takeaway meals per week; fat, fiber, and total indices from the Fat and Fiber Behavior Questionnaire). Moderate-to-vigorous physical activity (MVPA) was also assessed via accelerometry. Maintenance was examined multiple ways: (1) using traditional methods to assess and compare group averages after some period of noncontact (ie, at 12 months), (2) using a novel approach to assess and compare group average changes over the first 6 months of noncontact, and (3) exploring individual participant changes (increase/decrease/no change) over the first 6 months of noncontact. Results: Retention over the 12-month trial was high (92.5%, 211/228). Participants had a mean (SD) age of 53.4 (SD 12.3) years and a baseline body mass index of 29.2 (SD 5.9) kg/m2. The between-group differences detected at 6 months were still present and statistically significant at 12 months for bodyweight (−1.33 kg [−2.61 to −0.05]) and accelerometer-assessed MVPA (24.9 min/week [5.8-44.0]). None of the other outcomes were significantly favored compared with the control group at 12 months. Changes over their first 6 months of noncontact for the GHSH group were significantly better than the control group in terms of accelerometer-measured MVPA and self-reported moderate activity (other differences between the groups were all nonsignificant). In addition to the maintenance seen in the group averages, most intervention participants had maintained their behavioral outcomes during the first 6 months of noncontact. Conclusions: The GHSH participants were better off relative to where they were initially, and relative to their counterparts, not receiving extended contact in terms of MVPA. However, based on the between-group difference in bodyweight over the first 6 months of noncontact, GHSH does appear to simply delay the inevitable weight regain. However, this delay in weight regain, coupled with sustained improvements in MVPA, has public health benefits. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12613000949785; au/Trial/Registration/TrialReview.aspx?id=364821&isReview=true

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  • Combination of Indoor Localization and Wearable Sensor-Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute rehabilitation: Baseline Study Results

    Date Submitted: Mar 21, 2019

    Open Peer Review Period: Mar 22, 2019 - Mar 28, 2019

    Background: Healthcare, 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 en...

    Background: Healthcare, 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 to harness them in a wide range of healthcare 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, standardize the concept of frailty, well-being/independence and many other applications. Most wearable devices such as activity trackers and smartwatches are equipped with low cost embedded sensors (e.g., accelerometer, gyroscope, barometer, heart rate monitor) 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 [1]-[4], we investigated and introduced a generic framework (Sensing At-Risk Population - SARP) that draws upon classification of human movements using a 3-axial accelerometer and extracting indoor localization using BLE beacons, in concert. Objective: To examine combination of physical activities and indoor locations of patients extracted at baseline on a cohort of 154 rehabilitation-dwelling patients. We aimed to explore the capability of significant physical activity and indoor localization features to discriminate between subacute care patients that are readmitted 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 respect to indoor localization. χ^2and 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 five most statistically significant features in distinguishing outcome groups at baseline. Energy intensity of resident room (P < .001, d=1.25) was achieved by capturing indoor localization information. Random forests revealed that energy intensity of resident room, as a standalone attribute, is the most sensitive parameters in identification of outcome groups (AUC=0.84). Conclusions: This study demonstrates that 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 re-hospitalized.

  • Development of a mobile app for a resource limited setting to guide the grading and management of drug-induced hearing loss for patients with DR-TB

    Date Submitted: Mar 16, 2019

    Open Peer Review Period: Mar 19, 2019 - May 14, 2019

    Background: Tuberculosis, affecting millions of people worldwide, is treated with medication including aminoglycosides and polypeptides. Generally, individuals respond differently to medication as a r...

    Background: Tuberculosis, affecting millions of people worldwide, is treated with medication including aminoglycosides and polypeptides. Generally, individuals respond differently to medication as a result of their genetic inheritance. These differences in genetic inheritance can result in the under- or over-dosing of medication, which may affect the efficacy, or in the case of aminoglycosides and polypeptides used in the treatment of all forms of tuberculosis (TB), result in ototoxicity. When ototoxicity is detected physicians should adjust dosages to minimize further ototoxicity and hearing loss, yet there are no suitable grading systems to define ‘significant’ hearing loss. Objective: The aim was to develop a standardized grading system by making use of an eHealth system to ensure a user-friendly method to interpret hearing test results, calculate significant hearing loss and provide recommendations with regards to dosage adjustments and management. It further aimed to establish the sensitivity of newly developed grading scale. Methods: This grading system was developed in South Africa based on data that was obtained from an audiology and pharmacokinetic study in Drug-Resistant Tuberculosis (DR-TB) patients at two DR-TB units at state run hospitals. This feasibility study employed a prospective, cross-sectional, exploratory, descriptive and case series research design, with a total of 22 participants. Participants underwent audiological and pharmacological assessments at baseline and every two weeks for the first three months of treatment. Various professionals (8 in total) were subsequently involved in the development of the eHealth system, including a software engineer, four audiologists, a pharmacist, medical doctor and a nurse. The application underwent 14 modifications which involved data storage, ease of usability, grades and the risk factor checklist. Results: An ototoxicity grading system within a mobile application for use by doctors, nurses and audiologists was developed for patients with DR-TB. The purpose of this user-friendly ototoxicity calculator, ‘OtoCalc™’ is to: i) assist health professionals in assessing patients for ototoxicity; ii) establish the clinical significance of ototoxicity, by calculating the grade of hearing loss; iii) monitor the progression of hearing loss; and iv) enable systematic referral and management of patients according to their needs. Conclusions: This newly developed system is more sensitive for ototoxicity in DR-TB patients when compared to the existing grading methods. This application needs to be trialed in a larger sample to establish the data security, user-ease and suitability within this population.

  • Preferences for user experiences in digital smartphone mental health applications: implications for design in adolescents.

    Date Submitted: Mar 13, 2019

    Open Peer Review Period: Mar 18, 2019 - May 13, 2019

    Background: Mental health digital applications hold promise to provide scalable solutions to individual self-care, education, and illness prevention. However, a common problem with mental health apps...

    Background: Mental health digital applications hold promise to provide scalable solutions to individual self-care, education, and illness prevention. However, a common problem with mental health apps is that they lack engaging user interfaces and experiences, often resulting in high attrition. While guidelines for new digital interventions in adults have begun to examine engagement, there is a paucity of evidence in how to best address digital interventions in adolescents. As adolescence is a period of great transition and can lead to a vulnerability to mental illness in later life, understanding how best to engage this population is crucial. Objective: In this study we sought to gather qualitative evidence in a sample of adolescents in the UK about their experience of a mental health app. Methods: We used the COREQ guidelines for reporting qualitative data. Adolescents were asked to use the app for a week and were then interviewed using a semi-structured questionnaire. Results: Authors developed five themes from the data that were important in whether the app was perceived to be useful and effective: User Experience/User Interface such as loading screens and portrait mode, specific Location and Frequency of app access, Effectiveness of the app for mental health goals, Content, e.g. games and meditation, and the Reasons for Use , i.e. what participants’ goals were before using the app. Conclusions: In convergence with previous research, we suggest these aspects of app development should be considered crucial in future apps aimed at preventing and supporting mental illness. Clinical Trial: TCYP171110

  • Use of a Smartphone Application for Weight Loss versus a Paper-Based Dietary Diary in Overweight Adults: A Randomized Trial

    Date Submitted: Mar 15, 2019

    Open Peer Review Period: Mar 18, 2019 - May 13, 2019

    Background: There is increasing evidence that mobile health (mHealth) tools have value in dietary monitoring and assessment. Objective: This study aims to evaluate the effectiveness of a mobile dietar...

    Background: There is increasing evidence that mobile health (mHealth) tools have value in dietary monitoring and assessment. Objective: This study aims to evaluate the effectiveness of a mobile dietary self-monitoring application (app) for weight loss versus a paper-based diary among adults with a body mass index (BMI) of 23 kg/m2 or above. Methods: A total of 33 men and 17 women aged 18-39 years participated in a six-week randomized trial. We randomly assigned participants to one of two groups: (A) Smartphone app group (n=25) or (B) paper-based diary group (n=25). The smartphone app group recorded foods and dietary supplements that they consumed and received immediate dietary feedback using ‘Well-D’, a fully-automated dietary self-monitoring app developed by our team. The paper-diary group was instructed to record foods or supplements that they consumed using the self-recorded diary. The primary outcomes were weight, BMI, waist circumference, body fat mass and skeletal muscle mass. We also examined changes in nutrient intakes including energy, carbohydrate, protein, fat, dietary fiber, vitamins, and minerals using 3-day 24-hour recalls (24HR) across time at pre- and post-intervention. Differences in changes between the two groups were analyzed using the independent t-test or Wilcoxon Mann-Whitney test. All of the data were analyzed by the intent to treat analysis. Results: The numbers of days recorded (mean ± SD) was 18.5 ± 14.1 for the app group and 15.5 ± 10.1 for the paper-based diary group. The differences in changes of weight, BMI and waist circumference were not significantly different between the app group and paper-based diary group (p=0.33, 0.34, and 0.70, respectively). Similarly, changes in body fat mass or skeletal muscle mass did not differ between the two groups (p=0.71 and 0.054, respectively). Although energy intake was reduced in both groups, there was no statistical difference in change of energy intake between the two groups (p=0.98). Conclusions: There were no differences in changes of anthropometric measures and nutrient intakes between the app group and the paper-based diary group. Both mobile dietary self-monitoring app and paper-based diary may be useful for improving anthropometric measures. Clinical Trial: KCT, KCT0003170;

  • A three-arm quasi-experimental evaluation comparing social and communication apps, telephone, and usual care for improving diabetes self-management

    Date Submitted: Mar 15, 2019

    Open Peer Review Period: Mar 18, 2019 - May 13, 2019

    Background: There are many technology-assisted innovations used to address disease management. However, most of them are not broadly used by older adults due to their cost. Besides, disease management...

    Background: There are many technology-assisted innovations used to address disease management. However, most of them are not broadly used by older adults due to their cost. Besides, disease management through the technology-assisted innovations does not compared with other interventions. Objective: This study tested the employment of a widely and freely used social and communication app for helping older diabetes patients manage their distress and glycemic control. It also compared the effectiveness with two other methods, including telephone and conventional health education, and to determine which sub-group experiences the most effects within each intervention. Methods: Type 2 diabetes patients aged ≥50 were recruited from Southern Taiwan (N=231) and were allocated to different 3-month interventions. Results: Participants in the mobile-based group had a significant reduction in hemoglobin A1C as compared with the telephone-based and usual care groups (mean change=-0.4, 0.1, 0.03, respectively, P=0.02). Diabetes-specific distress reduced to a greater extent in the mobile-based group as compared to the other two groups (mean change=-5.16, -3.49, and -2.44, respectively, P=0.02). Subgroup analyses further revealed that the effects on reducing blood glucose level and diabetes-related distress in the social and communication app group was especially evident in those with lower distress scores and those who aged less than 60 years or with higher educational levels, respectively. Conclusions: Findings from this study inform a more flexible use of social and communication apps in patient education and counselling. Clinical Trial: This study was approved by the Institution Review Board (IRB) of National Cheng Kung University Hospital in Taiwan (No. A-ER- 102-425). This study is identical with what the IRB assessed when providing approval before the trial started.

  • The functional pattern of in-store mobile apps of self-management in diabetes: a cross-sectional survey in China and the US

    Date Submitted: Mar 11, 2019

    Open Peer Review Period: Mar 14, 2019 - May 9, 2019

    Background: The mobile health intervention was widely used for the self-management of diabetes which is one of the most burdensome noncommunicable chronic diseases in the world. However, little is kno...

    Background: The mobile health intervention was widely used for the self-management of diabetes which is one of the most burdensome noncommunicable chronic diseases in the world. However, little is known about the patterns of the in-store mobile applications (apps) for diabetes. Objective: Our study aims to investigate the functional pattern of the in-store mobile apps of self-management for diabetes in the US and China using a predefined functional taxonomy. Methods: We screened the apps by searching “diabetes” in English or Chinese from Apple iTunes Store and Android Markets (both in the US and China) and included apps of self-management for diabetes. We examined the validity and reliability of the predefined functional taxonomy. We then classified all functions in the included apps according to the taxonomy and compared the difference of the pattern of the apps between the US and China. Results: We included 171 mobile diabetes apps, with 133 from the US and 38 from China. The apps from both countries faced the challenges of evidence-based information, proper risk assessment and declaration, especially Chinese apps. Chinese apps provide more inter-human communication (general communication: Chinese vs US apps, 39.5% vs 18.0%, P=.006 and patient-clinician communication: Chinese vs US apps, 68.4% vs 6.0%, P<.001), while the US apps included more decision-making modules (Chinese vs US apps, 0% vs 23.3%, P=.001), which is a high-risk module. Both complication prevention (Chinese vs US apps, 7.9% vs 3.8%, p=.05) and psychological care (Chinese vs US apps, 0% vs 0.8%, p>.99) are neglected by the two countries. Conclusions: The functional patterns of the in-store mobile apps of self-management for diabetes were different between China and the US. The design of the functions needs to be optimized and had better be under surveillance.