JMIR Publications

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  mHealth for Telemedicine and Homecare

  Mar 20, 2019

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

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  Authors List:

  • Curtis L Petersen  
  • William B Weeks  
  • Olof Norin  
  • James N Weinstein  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e11082

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  Security and Privacy of mHealth and...

  Mar 20, 2019

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

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  Authors List:

  • Leonardo Horn Iwaya  
  • Simone Fischer-Hübner  
  • Rose-Mharie Åhlfeldt  
  • Leonardo A Martucci  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e11642

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  mHealth for Symptom and Disease...

  Mar 19, 2019

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

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  Authors List:

  • Victoria Mazoteras-Pardo  
  • Ricardo Becerro-De-Bengoa-Vallejo  
  • Marta Elena Losa-Iglesias  
  • Daniel López-López  
  • Patricia Palomo-López  
  • David Rodríguez-Sanz  
  • César Calvo-Lobo  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e13137

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  Reviews

  Mar 19, 2019

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

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  Authors List:

  • Laura Vergani  
  • Giulia Marton  
  • Silvia Francesca Maria Pizzoli  
  • Dario Monzani  
  • Ketti Mazzocco  
  • Gabriella Pravettoni  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e10855

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  mHealth in a Clinical Setting

  Mar 19, 2019

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

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  Authors List:

  • Maarten Falter  
  • Werner Budts  
  • Kaatje Goetschalckx  
  • Véronique Cornelissen  
  • Roselien Buys  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e11889

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  mHealth for Data Collection and Research

  Mar 19, 2019

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

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  Authors List:

  • Alex W DaSilva  
  • Jeremy F Huckins  
  • Rui Wang  
  • Weichen Wang  
  • Dylan D Wagner  
  • Andrew T Campbell  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e12084

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  Design and Formative Evaluation of...

  Mar 18, 2019

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

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  Authors List:

  • Elizabeth Donovan  
  • Sarah R Martin  
  • Laura C Seidman  
  • Lonnie K Zeltzer  
  • Tara M Cousineau  
  • Laura A Payne  
  • Meredith Trant  
  • Marjorie Weiman  
  • Marla Knoll  
  • Noah C Federman  

  Abstract:

  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: ClinicalTrials.gov NCT03130751; https://clinicaltrials.gov/ct2/show/NCT03130751

  • JMIR Mhealth Uhealth 2019;7(3):e10921

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  mHealth for Wellness, Behavior Change...

  Mar 14, 2019

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

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  Authors List:

  • Kathleen P Tebb  
  • Sang Leng Trieu  
  • Rosario Rico  
  • Robert Renteria  
  • Felicia Rodriguez  
  • Maryjane Puffer  

  Abstract:

  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: ClinicalTrials.gov NCT02847858; https://clinicaltrials.gov/ct2/show/NCT02847858 (Archived by WebCite at http://www.webcitation.org/761yVIRTp).

  • JMIR Mhealth Uhealth 2019;7(3):e11163

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  mHealth for Treatment Adherence

  Mar 14, 2019

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

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  Authors List:

  • Victoria Haldane  
  • Yao Guo Tan  
  • Krichelle Wei Qi Teo  
  • Joel Jun Kai Koh  
  • Aastha Srivastava  
  • Rui Xiang Cheng  
  • Yi Cheng Yap  
  • Pei-Shi Ong  
  • Rob M van Dam  
  • Jie Min Foo  
  • Falk Müller-Riemenschneider  
  • Gerald Choon-Huat Koh  
  • Pablo Perel  
  • Helena Legido-Quigley  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e11108

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  mHealth for Wellness, Behavior Change...

  Mar 12, 2019

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

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  Authors List:

  • Brianna S Fjeldsoe  
  • Ana D Goode  
  • Philayrath Phongsavan  
  • Adrian Bauman  
  • Genevieve Maher  
  • Elisabeth Winkler  
  • Jennifer Job  
  • Elizabeth G Eakin  

  Abstract:

  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; https://www.anzctr.org. au/Trial/Registration/TrialReview.aspx?id=364821&isReview=true

  • JMIR Mhealth Uhealth 2019;7(3):e11070

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  Fitness Trackers and Smart...

  Mar 12, 2019

  Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data

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  Authors List:

  • Louis Faust  
  • Cheng Wang  
  • David Hachen  
  • Omar Lizardo  
  • Nitesh V Chawla  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e11075

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  Wearable Devices and Sensors

  Mar 11, 2019

  Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study

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  Authors List:

  • Benjamin W Nelson  
  • Nicholas B Allen  

  Abstract:

  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.

  • JMIR Mhealth Uhealth 2019;7(3):e10828

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