TY - JOUR AU - Nebeker, Camille AU - Murray, Kate AU - Holub, Christina AU - Haughton, Jessica AU - Arredondo, M. Elva PY - 2017/06/28 TI - Acceptance of Mobile Health in Communities Underrepresented in Biomedical Research: Barriers and Ethical Considerations for Scientists JO - JMIR Mhealth Uhealth SP - e87 VL - 5 IS - 6 KW - telemedicine KW - cultural diversity KW - ethics, research KW - ethics committees KW - research KW - privacy KW - informed consent N2 - Background: The rapid expansion of direct-to-consumer wearable fitness products (eg, Flex 2, Fitbit) and research-grade sensors (eg, SenseCam, Microsoft Research; activPAL, PAL Technologies) coincides with new opportunities for biomedical and behavioral researchers. Underserved communities report among the highest rates of chronic disease and could benefit from mobile technologies designed to facilitate awareness of health behaviors. However, new and nuanced ethical issues are introduced with new technologies, which are challenging both institutional review boards (IRBs) and researchers alike. Given the potential benefits of such technologies, ethical and regulatory concerns must be carefully considered. Objective: Our aim was to understand potential barriers to using wearable sensors among members of Latino, Somali and Native Hawaiian Pacific Islander (NHPI) communities. These ethnic groups report high rates of disparate health conditions and could benefit from wearable technologies that translate the connection between physical activity and desired health outcomes. Moreover, these groups are traditionally under-represented in biomedical research. Methods: We independently conducted formative research with individuals from southern California, who identified as Latino, Somali, or Native Hawaiian Pacific Islander (NHPI). Data collection methods included survey (NHPI), interview (Latino), and focus group (Somali) with analysis focusing on cross-cutting themes. Results: The results pointed to gaps in informed consent, challenges to data management (ie, participant privacy, data confidentiality, and data sharing conventions), social implications (ie, unwanted attention), and legal risks (ie, potential deportation). Conclusions: Results shed light on concerns that may escalate the digital divide. Recommendations include suggestions for researchers and IRBs to collaborate with a goal of developing meaningful and ethical practices that are responsive to diverse research participants who can benefit from technology-enabled research methods. Trial Registration: ClinicalTrials.gov NCT02505165; https://clinicaltrials.gov/ct2/show/NCT02505165 (Archived by WebCite at http://www.Webcitation.org/6r9ZSUgoT) UR - http://mhealth.jmir.org/2017/6/e87/ UR - http://dx.doi.org/10.2196/mhealth.6494 UR - http://www.ncbi.nlm.nih.gov/pubmed/28659258 ID - info:doi/10.2196/mhealth.6494 ER - TY - JOUR AU - Kessel, Anne Kerstin AU - Vogel, ME Marco AU - Kessel, Carmen AU - Bier, Henning AU - Biedermann, Tilo AU - Friess, Helmut AU - Herschbach, Peter AU - von Eisenhart-Rothe, Rüdiger AU - Meyer, Bernhard AU - Kiechle, Marion AU - Keller, Ulrich AU - Peschel, Christian AU - Schmid, M. Roland AU - Combs, E. Stephanie PY - 2017/06/14 TI - Mobile Health in Oncology: A Patient Survey About App-Assisted Cancer Care JO - JMIR Mhealth Uhealth SP - e81 VL - 5 IS - 6 KW - clinical oncology KW - surveys and questionnaires KW - mobile apps KW - mHealth KW - eHealth N2 - Background: In the last decade, the health care sector has been enriched by numerous innovations such as apps and connected devices that assist users in weight reduction and diabetes management. However, only a few native apps in the oncological context exist, which support patients during treatment and aftercare. Objective: The objective of this study was to analyze patients? acceptance regarding app use and to investigate the functions of an oncological app that are most required, and the primary reasons for patients to refuse app-assisted cancer care. Methods: We designed and conducted a survey with 23 questions, inquiring patients about their technical knowledge and equipment, as well as the possible advantages and disadvantages, data transfer, and general functionality of an app. Results: A total of 375 patients participated; the participation rate was 60.7% (375/618). Gender distribution was about 3:4 (female:male) with a median age of 59 years (range 18-92 years). Whereas 69.6% (261/375) of patients used mobile devices, 16.3% (61/375) did not own one, and 9.1% (34/375) only used a personal computer (PC). About half of the patients rated their usability skills as very good and good (18.9% 71/375; 35.2% 132/375), 23.5% (88/375) described their skills as intermediate, and 14.4% (54/375) as bad. Of all patients, 182 (48.5%, 182/375) were willing to send data to their treating clinic via an app, that is, to a server (61.0% 111/182) or as email (33.5%, 61/182). About two-thirds (68.7%, 125/182) believed that additional and regularly sent data would be an ideal complement to the standard follow-up procedure. Additionally, 86.8% (158/182) wished to be contacted by a physician when entered data showed irregularities. Because of lack of skills (34.4%, 56/163), concerns about the use of data (35.0%, 57/163), lack of capable devices (25.8%, 42/163), and the wish for personal contact with the treating physician (47.2%, 77/163), a total of 163 (43.5%, 163/375) patients refused to use an app. Pearson correlation showed a significant but mild relationship between age and app use (P=.03, r=?.12), favoring younger age; male gender correlated as well (P=.04; r=?.11). Conclusions: The results show that the introduction of mobile apps needs to follow different strategies depending on the patients? attitude. Age and gender seem to be the strongest predictive factors. For oncology patients, our survey showed that about half of the patients were willing to send data via an app supporting their treatment. In the future, clinical data such as quality of life and treatment satisfaction recorded by mobile health (mHealth) devices could be used to evaluate and improve therapy workflow. Furthermore, apps could support classical visits, document adverse effects, and remind patients of treatment dates or drug intake. UR - http://mhealth.jmir.org/2017/6/e81/ UR - http://dx.doi.org/10.2196/mhealth.7689 UR - http://www.ncbi.nlm.nih.gov/pubmed/28615159 ID - info:doi/10.2196/mhealth.7689 ER - TY - JOUR UR - ID - ref1 ER - TY - JOUR AU - Mao, Yuqing Alice AU - Chen, Connie AU - Magana, Candy AU - Caballero Barajas, Karla AU - Olayiwola, Nwando J. PY - 2017/06/08 TI - A Mobile Phone-Based Health Coaching Intervention for Weight Loss and Blood Pressure Reduction in a National Payer Population: A Retrospective Study JO - JMIR Mhealth Uhealth SP - e80 VL - 5 IS - 6 KW - digital health coaching KW - overweight KW - obesity KW - mobile health KW - weight KW - blood pressure N2 - Background: The prevalence of obesity and associated metabolic conditions continue to be challenging and costly to address for health care systems; 71% of American adults were overweight, with 35% of men and 40% of women diagnosed with obesity in 2014. Digital health coaching is an innovative approach to decreasing the barriers of cost and accessibility of receiving health coaching for the prevention and management of chronic disease in overweight or obese individuals. Objective: To evaluate the early impact of a mobile phone-based health coaching service on weight loss and blood pressure management in a commercially insured population. Methods: This was a retrospective study using existing registry data from a pilot commercial collaboration between Vida Health and a large national insurance provider, which enrolled adult members who were overweight (body mass index >25 kg/m2) and able to engage in a mobile phone-based coaching intervention. Participants received 4 months of intensive health coaching via live video, phone, and text message through the Vida Health app. Participants were also provided with a wireless scale, pedometer, and blood pressure cuff. Of the 1012 enrolled, 763 (75.40%) participants had an initial weight upon enrollment and final weight between 3 and 5 months from enrollment; they served as our intervention group. There were 73 participants out of the 1012 (7.21%) who had weight data 4 months prior to and after Vida coaching, who served as the matched-pair control group. Results: Participants in the intervention group lost an average of 3.23% total body weight (TBW) at 4 months of coaching and 28.6% (218/763) intervention participants achieved a clinically significant weight loss of 5% or more of TBW, with an average of 9.46% weight loss in this cohort. In the matched-pair control group, participants gained on average 1.81% TBW in 4 months without Vida coaching and lost, on average, 2.47% TBW after 4 months of Vida coaching, demonstrating a statistically significant difference of 4.28% in mean percentage weight change (P<.001). Among 151 intervention participants with blood pressure data, 112 (74.2%) had a baseline blood pressure that was above the goal (systolic blood pressure >120 mmHg); 55 out of 112 (49.1%) participants improved their blood pressure at 4 months by an entire hypertensive stage?as defined by the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Conclusions: Mobile phone app-based health coaching interventions can be an acceptable and effective means to promote weight loss and improve blood pressure management in overweight or obese individuals. Given the ubiquity of mobile phones, digital health coaching may be an innovative solution to decreasing barriers of access to much-needed weight management interventions for obesity. UR - http://mhealth.jmir.org/2017/6/e80/ UR - http://dx.doi.org/10.2196/mhealth.7591 UR - http://www.ncbi.nlm.nih.gov/pubmed/28596147 ID - info:doi/10.2196/mhealth.7591 ER - TY - JOUR AU - Wright, C. Cassandra J. AU - Dietze, M. Paul AU - Lim, C. Megan S. PY - 2017/06/20 TI - Beyond Basic Feedback in Mobile Brief Interventions: Designing SMS Message Content for Delivery to Young Adults During Risky Drinking Events JO - JMIR Mhealth Uhealth SP - e79 VL - 5 IS - 6 KW - alcohol drinking KW - young adult KW - mHealth KW - text messaging KW - motivational interviewing KW - community-based participatory research N2 - Background: Brief interventions can reduce alcohol consumption in young people through screening and delivery of personally relevant feedback. Recently, Web and mobile platforms have been harnessed to increase the reach of brief interventions. Existing literature on mobile-based alcohol brief interventions indicates mixed use of theory in developing interventions. There is no research available to guide the development of SMS text messaging (short message service, SMS) interventions delivered during risky drinking events. Objective: The aim of this study was to develop and pilot an alcohol-related risk-reduction brief intervention delivered by SMS to Australian young adults during drinking events. This paper describes the development of intervention message content, with specific focus on the context of delivery during drinking events. Methods: A sample of 42 young adults attended 4 workshops; these comprised focus-group style discussion on drinking habits and motivations, discussion of intervention design, analysis of existing alcohol media campaigns, and participant development of message content. Data were analyzed thematically. Results: Participants described a focus on having fun and blocking out any incongruent negative influences during drinking episodes. For content to be acceptable, nonjudgmental and non-authoritative language was deemed essential. A preference for short, actionable messages was observed, including suggestions for reminders around drinking water, organizing transport home, checking on friends, and plans the next day. Participants were excited about the potential for messages to be tailored to individuals, as previous alcohol-related campaigns were deemed too generic and often irrelevant. Normative-based messages were also perceived as largely irrelevant as participants felt that they understood the drinking-related norms of their immediate peers already. Conclusions: Findings from this study offer insights into young adults? drinking events and practical advice for designing alcohol-related brief interventions. During our formative development process, we demonstrated a neat correspondence between young people?s preferences for alcohol harm reduction interventions and the theoretical principles of brief interventions, including acceptable topics and message style. UR - http://mhealth.jmir.org/2017/6/e79/ UR - http://dx.doi.org/10.2196/mhealth.6497 UR - http://www.ncbi.nlm.nih.gov/pubmed/28634153 ID - info:doi/10.2196/mhealth.6497 ER - TY - JOUR AU - Rodin, Alexander AU - Shachak, Aviv AU - Miller, Aaron AU - Akopyan, Vladimir AU - Semenova, Nataliya PY - 2017/06/14 TI - Mobile Apps for Eye Care in Canada: An Analysis of the iTunes Store JO - JMIR Mhealth Uhealth SP - e84 VL - 5 IS - 6 KW - mobile applications KW - mobile phone KW - ophthalmology KW - optometry N2 - Background: Mobile phone screens can facilitate stimulation to various components of the visual system and many mobile apps are accepted as a means of providing clinical assessments for the oculo-visual system. Although many of these apps are intended for use in clinical settings, there is a growing number of apps in eye care developed for self-tests and eye exercises for lay people. These and other features, however, have not yet been well described. Objective: Our objective was to identify, describe, and categorize mobile apps related to eye care that are available to users in the Canadian iTunes market. Methods: We conducted an extensive search of the Apple iTunes Store for apps related to eye care. We used the terms ?eye,? ?eye care,? ?vision,? and ?eye test? and included apps that are targeted at both lay people and medical professionals. We excluded apps whose primary function is not related to eye care. Eligible apps were categorized by primary purpose, based on how they were described by their developers in the iTunes Store. Results: Our search yielded 10,657 apps, of which 427 met our inclusion criteria. After removing duplicates, 355 unique apps were subject to further review. We assigned the eligible apps to three distinct categories: 39/355 apps (11.0%) were intended for use by medical professionals, 236 apps (66.5%, 236/355) were intended for use by lay people, and 80 apps (22.5%, 80/355) were intended for marketing eye care and eye-care products. We identified 9 subcategories of apps based on the descriptions of their primary functions. Apps for medical professionals fell into three subcategories: clinical calculators (n=6), clinical diagnostic tools (n=18), and education and networking apps for professionals (n=15). Apps for lay people fell into four subcategories: self-testing (n=153), eye exercises (n=30), patient tools and low vision aids (n=35), and apps for patient education (n=18). Mixed-use apps (n=80) were placed into two subcategories: marketing of individual practitioners or eye-care products (n=72) and marketing of multiple eye-care products or professional services. Conclusions: The most extensive subcategory pertaining to eye care consisted of apps for use by lay people, especially for conducting self-tests (n=236). This study revealed a previously uncharacterized category of apps intended for use by doctors and patients, of which the primary goal is marketing of eye-care services and products (n=80). UR - http://mhealth.jmir.org/2017/6/e84/ UR - http://dx.doi.org/10.2196/mhealth.7055 UR - http://www.ncbi.nlm.nih.gov/pubmed/28615154 ID - info:doi/10.2196/mhealth.7055 ER - TY - JOUR AU - Huang, Hsiao-Ying AU - Bashir, Masooda PY - 2017/06/28 TI - Users? Adoption of Mental Health Apps: Examining the Impact of Information Cues JO - JMIR Mhealth Uhealth SP - e83 VL - 5 IS - 6 KW - user interaction design KW - recommendation system KW - mobile app search KW - mental health KW - anxiety N2 - Background: Numerous mental health apps have been developed and made available to users on the current app market. Users may find it difficult and overwhelming to select apps from the hundreds of choices that are available in the app marketplace. Clarifying what information cues may impact a user?s selection and adoption of mental health apps is now a critical and pressing issue. Objective: The aim of this study was to investigate the impact of information cues on users? adoption of anxiety apps using observational data from the Android app market. Methods: A systematic search of anxiety apps was conducted on the Android app store by using keywords search. The title and metadata information of a total of 274 apps that met our criteria were collected and analyzed. Three trained researchers recorded the app rankings from the search results page on different dates and Web browsers. Results: Our results show that ratings (r=.56, P<.001) and reviews (r=.39, P<.001) have significant positive correlations with the number of installs, and app prices have significant negative correlations with installs (r=?.36). The results also reveal that lower-priced apps have higher ratings (r=?.23, P<.001) and a greater number of app permission requests (r=.18, P=.002) from the device. For app titles, we found that apps with titles related to symptoms have significantly lower installs than apps with titles that are not related to symptoms (P<.001). Conclusions: This study revealed a relationship between information cues and users? adoption of mental health apps by analyzing observational data. As the first of its kind, we found impactful indicators for mental health app adoptions. We also discovered a labeling effect of app titles that could hinder mental health app adoptions and which may provide insight for future designs of mental health apps and their search mechanisms. UR - http://mhealth.jmir.org/2017/6/e83/ UR - http://dx.doi.org/10.2196/mhealth.6827 UR - http://www.ncbi.nlm.nih.gov/pubmed/28659256 ID - info:doi/10.2196/mhealth.6827 ER - TY - JOUR AU - Thompson, David AU - Mackay, Teresa AU - Matthews, Maria AU - Edwards, Judith AU - Peters, S. Nicholas AU - Connolly, B. Susan PY - 2017/06/12 TI - Direct Adherence Measurement Using an Ingestible Sensor Compared With Self-Reporting in High-Risk Cardiovascular Disease Patients Who Knew They Were Being Measured: A Prospective Intervention JO - JMIR Mhealth Uhealth SP - e76 VL - 5 IS - 6 KW - cardiac prevention and rehabilitation KW - adherence KW - mHealth KW - remote monitoring KW - cardiovascular diseases KW - primary prevention KW - medication adherence KW - telemedicine N2 - Background: Use of appropriate cardioprotective medication is a cornerstone of cardiovascular disease prevention, but less-than-optimal patient adherence is common. Thus, strategies for improving adherence are recommended to adopt a multifaceted approach. Objective: The objective of our study was to test a system comprising a biodegradable, ingestible sensor for direct measurement of medication ingestion in a group of patients at elevated cardiovascular risk attending a cardiac prevention and rehabilitation program. Methods: In this prospective intervention trial in a single group of 21 patients running from April 2014 to June 2015, we measured adherence by self-report and adherence determined objectively by the system. The sensor emits a signal when it encounters the acidic environment of the stomach, detectable by an externally worn patch and linked software app. Longitudinal adherence data in the form of daily progress charts for sensed dosing events as compared with scheduled dosing are visible to patients on their tablet computer?s medication dosing app, thus providing patients with continuous medication adherence feedback. We sought feedback on patient acceptability by questionnaire assessment. Participants used the system for the 12-week period of their cardiac prevention and rehabilitation program. Results: Only 1 patient at initial assessment and 1 patient at end-of-program assessment reported often missing medication. The remaining patients reported never missing medication or had missing data. Only 12 (57%) of patients overall achieved system-determined adherence of 80% or more, and 3 patients had scores below 40%. Participants reported high levels of acceptability. Conclusions: This integrated system was well tolerated in a group of 21 patients over an appreciable time frame. Its ability to measure adherence reveals the sizeable disconnect between patient self-reported adherence and actual medication taking and has promising potential for clinical use as a tool to encourage better medication-taking behavior due to its ability to provide continuous patient-level feedback. UR - http://mhealth.jmir.org/2017/6/e76/ UR - http://dx.doi.org/10.2196/mhealth.6998 UR - http://www.ncbi.nlm.nih.gov/pubmed/28606895 ID - info:doi/10.2196/mhealth.6998 ER - TY - JOUR AU - Shen, Chen AU - Wang, Ping Man AU - Chu, TW Joanna AU - Wan, Alice AU - Viswanath, Kasisomayajula AU - Chan, Chee Sophia Siu AU - Lam, Hing Tai PY - 2017/06/05 TI - Health App Possession Among Smartphone or Tablet Owners in Hong Kong: Population-Based Survey JO - JMIR Mhealth Uhealth SP - e77 VL - 5 IS - 6 KW - apps KW - smartphone KW - Chinese N2 - Background: Health apps are increasingly used with important implications for health. Hong Kong is one of the most technologically advanced and connected cities?smartphone ownership and Internet access rates are among the highest in the world. Objective: We investigated the prevalence of health app possession and related sociodemographic factors and health behaviors among smartphone or tablet owners in Hong Kong. Methods: A territory-wide population-based dual (landline and mobile) telephone survey was conducted in 2016. Respondents were asked whether they had health-related apps on their smartphones or tablets and what functions were available on the apps (eg, tracking physical activity and logging health records). Logistic regression was used to calculate the adjusted odds ratio (aOR) and 95% CI of health app possession for different demographic characteristics, socioeconomic position (education, employment, and income), health behaviors (smoking, alcohol, and physical activity) and health (body mass index and chronic diseases). Results: Of the 4129 smartphone or tablet owners (81.28%, 4129/5080 respondents), 995 (24.10%) had a health app. Tracking physical activity (67.0% of 995) and logging health records (43.0% of 995) were the most common functions of the health apps. Overall, younger age, higher education, and household income were associated with having health apps (all P<.001). Compared with physical inactivity, engaging in moderate physical activity ?1 day/week was associated with having health apps (aOR 1.45 [95% CI 1.20-1.75] for 1-3 days/week, and aOR 1.32 [95% CI 1.07-1.62] for ?4 days/week). Having a history of chronic diseases was associated with having health apps (aOR 1.36 [95% CI 1.11-1.68]). Conclusions: We have shown a lower prevalence of use of information and communication technologies (ICTs) in respondents with lower education and income in the most developed Chinese city. This could be seen as a confirmation of the ?Inverse information law,? which suggests that those most in need have less use of services and hence receive less benefits from advancements in medicine and health related ICTs. UR - http://mhealth.jmir.org/2017/6/e77/ UR - http://dx.doi.org/10.2196/mhealth.7628 UR - http://www.ncbi.nlm.nih.gov/pubmed/28583905 ID - info:doi/10.2196/mhealth.7628 ER - TY - JOUR AU - Boyle, Leah AU - Grainger, Rebecca AU - Hall, M. Rosemary AU - Krebs, D. Jeremy PY - 2017/06/30 TI - Use of and Beliefs About Mobile Phone Apps for Diabetes Self-Management: Surveys of People in a Hospital Diabetes Clinic and Diabetes Health Professionals in New Zealand JO - JMIR Mhealth Uhealth SP - e85 VL - 5 IS - 6 KW - mHealth, mobile applications KW - telemedicine KW - diabetes mellitus N2 - Background: People with diabetes mellitus (DM) are using mobile phone apps to support self-management. The numerous apps available to assist with diabetes management have a variety of functions. Some functions, like insulin dose calculators, have significant potential for harm. Objectives: The study aimed to establish (1) whether people with DM in Wellington, New Zealand, use apps for DM self-management and evaluate desirable features of apps and (2) whether health professionals (HPs) in New Zealand treating people with DM recommend apps to patients, the features HPs regard as important, and their confidence with recommending apps. Methods: A survey of patients seen at a hospital diabetes clinic over 12 months (N=539) assessed current app use and desirable features. A second survey of HPs attending a diabetes conference (n=286) assessed their confidence with app recommendations and perceived usefulness. Results: Of the 189 responders (35.0% response rate) to the patient survey, 19.6% (37/189) had used a diabetes app. App users were younger and in comparison to other forms of diabetes mellitus, users prominently had type 1 DM. The most favored feature of the app users was a glucose diary (87%, 32/37), and an insulin calculator was the most desirable function for a future app (46%, 17/37). In non-app users, the most desirable feature for a future app was a glucose diary (64.4%, 98/152). Of the 115 responders (40.2% response rate) to the HPs survey, 60.1% (68/113) had recommended a diabetes app. Diaries for blood glucose levels and carbohydrate counting were considered the most useful app features and the features HPs felt most confident to recommend. HPs were least confident in recommending insulin calculation apps. Conclusions: The use of apps to record blood glucose was the most favored function in apps used by people with diabetes, with interest in insulin dose calculating function. HPs do not feel confident in recommending insulin dose calculators. There is an urgent need for an app assessment process to give confidence in the quality and safety of diabetes management apps to people with diabetes (potential app users) and HPs (potential app prescribers). UR - http://mhealth.jmir.org/2017/6/e85/ UR - http://dx.doi.org/10.2196/mhealth.7263 UR - http://www.ncbi.nlm.nih.gov/pubmed/28666975 ID - info:doi/10.2196/mhealth.7263 ER - TY - JOUR AU - Taki, Sarah AU - Lymer, Sharyn AU - Russell, Georgina Catherine AU - Campbell, Karen AU - Laws, Rachel AU - Ong, Kok-Leong AU - Elliott, Rosalind AU - Denney-Wilson, Elizabeth PY - 2017/06/29 TI - Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index JO - JMIR Mhealth Uhealth SP - e89 VL - 5 IS - 6 KW - mHealth KW - social medium KW - infant obesity KW - infant development KW - children KW - infants KW - practitioners KW - primary healthcare N2 - Background: Childhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics. Objective: The aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement. Methods: The Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ?3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant?s date of birth. Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models. Results: The overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including ?app only? users or ?both? app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005). Conclusions: The EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions. UR - http://mhealth.jmir.org/2017/6/e89/ UR - http://dx.doi.org/10.2196/mhealth.7236 UR - http://www.ncbi.nlm.nih.gov/pubmed/28663164 ID - info:doi/10.2196/mhealth.7236 ER - TY - JOUR AU - van Kasteren, Yasmin AU - Bradford, Dana AU - Zhang, Qing AU - Karunanithi, Mohan AU - Ding, Hang PY - 2017/06/13 TI - Understanding Smart Home Sensor Data for Ageing in Place Through Everyday Household Routines: A Mixed Method Case Study JO - JMIR Mhealth Uhealth SP - e52 VL - 5 IS - 6 KW - activities of daily living KW - aged KW - remote sensing technology N2 - Background: An ongoing challenge for smart homes research for aging-in-place is how to make sense of the large amounts of data from in-home sensors to facilitate real-time monitoring and develop reliable alerts. Objective: The objective of our study was to explore the usefulness of a routine-based approach for making sense of smart home data for the elderly. Methods: Maximum variation sampling was used to select three cases for an in-depth mixed methods exploration of the daily routines of three elderly participants in a smart home trial using 180 days of power use and motion sensor data and longitudinal interview data. Results: Sensor data accurately matched self-reported routines. By comparing daily movement data with personal routines, it was possible to identify changes in routine that signaled illness, recovery from bereavement, and gradual deterioration of sleep quality and daily movement. Interview and sensor data also identified changes in routine with variations in temperature and daylight hours. Conclusions: The findings demonstrated that a routine-based approach makes interpreting sensor data easy, intuitive, and transparent. They highlighted the importance of understanding and accounting for individual differences in preferences for routinization and the influence of the cyclical nature of daily routines, social or cultural rhythms, and seasonal changes in temperature and daylight hours when interpreting information based on sensor data. This research has demonstrated the usefulness of a routine-based approach for making sense of smart home data, which has furthered the understanding of the challenges that need to be addressed in order to make real-time monitoring and effective alerts a reality. UR - http://mhealth.jmir.org/2017/6/e52/ UR - http://dx.doi.org/10.2196/mhealth.5773 UR - http://www.ncbi.nlm.nih.gov/pubmed/28611014 ID - info:doi/10.2196/mhealth.5773 ER - TY - JOUR AU - Li, C. Linda AU - Sayre, C. Eric AU - Xie, Hui AU - Clayton, Cam AU - Feehan, M. Lynne PY - 2017/06/26 TI - A Community-Based Physical Activity Counselling Program for People With Knee Osteoarthritis: Feasibility and Preliminary Efficacy of the Track-OA Study JO - JMIR Mhealth Uhealth SP - e86 VL - 5 IS - 6 KW - osteoarthritis KW - physical activity KW - sedentary behavior KW - sedentary lifestyle KW - wearables KW - digital technology KW - fitness trackers KW - exercise N2 - 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) UR - http://mhealth.jmir.org/2017/6/e86/ UR - http://dx.doi.org/10.2196/mhealth.7863 UR - http://www.ncbi.nlm.nih.gov/pubmed/28652228 ID - info:doi/10.2196/mhealth.7863 ER - TY - JOUR AU - Modave, François AU - Guo, Yi AU - Bian, Jiang AU - Gurka, J. Matthew AU - Parish, Alice AU - Smith, D. Megan AU - Lee, M. Alexandra AU - Buford, W. Thomas PY - 2017/06/28 TI - Mobile Device Accuracy for Step Counting Across Age Groups JO - JMIR Mhealth Uhealth SP - e88 VL - 5 IS - 6 KW - mobile KW - devices KW - physical activity KW - weight reduction KW - adults N2 - 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. UR - https://mhealth.jmir.org/2017/6/e88/ UR - http://dx.doi.org/10.2196/mhealth.7870 UR - http://www.ncbi.nlm.nih.gov/pubmed/28659255 ID - info:doi/10.2196/mhealth.7870 ER - TY - JOUR AU - Baptista, Shaira AU - Oldenburg, Brian AU - O'Neil, Adrienne PY - 2017/06/09 TI - Response to ?Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS)? JO - JMIR Mhealth Uhealth SP - e16 VL - 5 IS - 6 KW - mobile apps KW - mhealth KW - app quality UR - http://mhealth.jmir.org/2017/6/e16/ UR - http://dx.doi.org/10.2196/mhealth.6419 UR - http://www.ncbi.nlm.nih.gov/pubmed/28600277 ID - info:doi/10.2196/mhealth.6419 ER -