@Article{info:doi/10.2196/27109, author="Stanger, Catherine and Kowatsch, Tobias and Xie, Haiyi and Nahum-Shani, Inbal and Lim-Liberty, Frances and Anderson, Molly and Santhanam, Prabhakaran and Kaden, Sarah and Rosenberg, Briana", title="A Digital Health Intervention (SweetGoals) for Young Adults With Type 1 Diabetes: Protocol for a Factorial Randomized Trial", journal="JMIR Res Protoc", year="2021", month="Feb", day="23", volume="10", number="2", pages="e27109", keywords="type 1 diabetes", keywords="mhealth", keywords="incentives", keywords="health coaching", keywords="young adults", abstract="Background: Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve target range glycemic control. Few interventions, however, have been developed specifically for this age group. Objective: In this randomized trial, we will provide a mobile app (SweetGoals) to all participants as a ``core'' intervention. The app prompts participants to upload data from their diabetes devices weekly to a device-agnostic uploader (Glooko), automatically retrieves uploaded data, assesses daily and weekly self-management goals, and generates feedback messages about goal attainment. Further, the trial will test two unique intervention components: (1) incentives to promote consistent daily adherence to goals, and (2) web health coaching to teach effective problem solving focused on personalized barriers to self-management. We will use a novel digital direct-to-patient recruitment method and intervention delivery model that transcends the clinic. Methods: A 2x2 factorial randomized trial will be conducted with 300 young adults ages 19-25 with type 1 diabetes and (Hb)A1c ? 8.0\%. All participants will receive the SweetGoals app that tracks and provides feedback about two adherence targets: (a) daily glucose monitoring; and (b) mealtime behaviors. Participants will be randomized to the factorial combination of incentives and health coaching. The intervention will last 6 months. The primary outcome will be reduction in A1c. Secondary outcomes include self-regulation mechanisms in longitudinal mediation models and engagement metrics as a predictor of outcomes. Participants will complete 6- and 12-month follow-up assessments. We hypothesize greater sustained A1c improvements in participants who receive coaching and who receive incentives compared to those who do not receive those components. Results: Data collection is expected to be complete by February 2025. Analyses of primary and secondary outcomes are expected by December 2025. Conclusions: Successful completion of these aims will support dissemination and effectiveness studies of this intervention that seeks to improve glycemic control in this high-risk and understudied population of young adults with T1D. Trial Registration: ClinicalTrials.gov NCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473 International Registered Report Identifier (IRRID): PRR1-10.2196/27109 ", doi="10.2196/27109", url="https://www.researchprotocols.org/2021/2/e27109", url="http://www.ncbi.nlm.nih.gov/pubmed/33620330" } @Article{info:doi/10.2196/24030, author="Fundoiano-Hershcovitz, Yifat and Hirsch, Abigail and Dar, Sharon and Feniger, Eitan and Goldstein, Pavel", title="Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study", journal="JMIR Diabetes", year="2021", month="Feb", day="18", volume="6", number="1", pages="e24030", keywords="blood glucose", keywords="mHealth", keywords="diabetes", keywords="self-management", keywords="digital engagement", abstract="Background: The use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes. Objective: This study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels. Methods: This retrospective real-world?analysis followed?998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases.?Subjects included ``nontaggers'' (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and ``taggers'' (users who used these features, n=413) representing increased digital engagement.?Within- and between-person variabilities in tagging behavior were disaggregated?to reveal the association between tagging behavior and blood glucose levels. The associations between an individual's tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout. Results: Analysis revealed significant?improvement in?the monthly average?blood glucose?level during the first 6 months (t=?10.01, P<.001), which was maintained during?the following 6 months (t=?1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to?nontaggers (t=2.15, P=.03). Additional findings included a within-user?quasicausal nonlinear link between tagging behavior and glucose control improvement?with?a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43\% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=?11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20). Conclusions: This study sheds light on the source of the?association between user?engagement?with a diabetes tracking?app?and?the clinical condition, highlighting the importance of within-person changes?versus between-person differences.?Our findings underscore the need for and provide a basis for a personalized approach to digital health. ", doi="10.2196/24030", url="http://diabetes.jmir.org/2021/1/e24030/", url="http://www.ncbi.nlm.nih.gov/pubmed/33599618" } @Article{info:doi/10.2196/23252, author="Eberle, Claudia and Stichling, Stefanie", title="Effect of Telemetric Interventions on Glycated Hemoglobin A1c and Management of Type 2 Diabetes Mellitus: Systematic Meta-Review", journal="J Med Internet Res", year="2021", month="Feb", day="17", volume="23", number="2", pages="e23252", keywords="telemedicine", keywords="telemetry", keywords="diabetes", abstract="Background: Diabetes mellitus is a chronic burden, with a prevalence that is increasing worldwide. Telemetric interventions have attracted great interest and may provide effective new therapeutic approaches for improving type 2 diabetes mellitus (T2DM) care. Objective: The objective of this study was to analyze the clinical effectiveness of telemetric interventions on glycated hemoglobin A1c (HbA1c) specifically and T2DM management generally in a systematic meta-review. Methods: A systematic literature search was performed in PubMed, CINAHL, Cochrane Library, Web of Science Core Collection, and EMBASE databases from January 2008 to April 2020. Studies that addressed HbA1c, blood pressure, fasting blood glucose, BMI, diabetes-related and health-related quality of life, cost-effectiveness, time savings, and the clinical effectiveness of telemetric interventions were analyzed. In total, 73 randomized controlled trials (RCTs), 10 systematic reviews/meta-analyses, 9 qualitative studies, 2 cohort studies, 2 nonrandomized controlled studies, 2 observational studies, and 1 noncontrolled intervention study were analyzed. Results: Overall, 1647 citations were identified. After careful screening, 99 studies (n=15,939 patients; n=82,436 patient cases) were selected by two independent reviewers for inclusion in the review. Telemetric interventions were categorized according to communication channels to health care providers: (1) ``real-time video'' interventions, (2) ``real-time audio'' interventions, (3) ``asynchronous'' interventions, and (4) ``combined'' interventions. To analyze changes in HbA1c, suitable RCTs were pooled and the average was determined. An HbA1c decrease of --1.15\% (95\% CI --1.84\% to --0.45\%), yielding an HbA1c value of 6.95\% (SD 0.495), was shown in studies using 6-month ``real-time video'' interventions. Conclusions: Telemetric interventions clearly improve HbA1c values in both the short term and the long term and contribute to the effective management of T2DM. More studies need to be done in greater detail. ", doi="10.2196/23252", url="http://www.jmir.org/2021/2/e23252/", url="http://www.ncbi.nlm.nih.gov/pubmed/33595447" } @Article{info:doi/10.2196/14760, author="Jung, Hyunggu and Demiris, George and Tarczy-Hornoch, Peter and Zachry, Mark", title="A Novel Food Record App for Dietary Assessments Among Older Adults With Type 2 Diabetes: Development and Usability Study", journal="JMIR Form Res", year="2021", month="Feb", day="17", volume="5", number="2", pages="e14760", keywords="mobile health", keywords="older adults", keywords="diabetes", keywords="dietary assessment", keywords="smartphone app", keywords="usability test", abstract="Background: More than 1 in 4 people in the United States aged 65 years and older have type 2 diabetes. For diabetes care, medical nutrition therapy is recommended as a clinically effective intervention. Previous researchers have developed and validated dietary assessment methods using images of food items to improve the accuracy of self-reporting over traditional methods. Nevertheless, little is known about the usability of image-assisted dietary assessment methods for older adults with diabetes. Objective: The aims of this study were (1) to create a food record app for dietary assessments (FRADA) that would support image-assisted dietary assessments, and (2) to evaluate the usability of FRADA for older adults with diabetes. Methods: For the development of FRADA, we identified design principles that address the needs of older adults and implemented three fundamental tasks required for image-assisted dietary assessments: capturing, viewing, and transmitting images of food based on the design principles. For the usability assessment of FRADA, older adults aged 65 to 80 years (11 females and 3 males) were assigned to interact with FRADA in a lab-based setting. Participants' opinions of FRADA and its usability were determined by a follow-up survey and interview. As an evaluation indicator of usability, the responses to the survey, including an after-scenario questionnaire, were analyzed. Qualitative data from the interviews confirmed the responses to the survey. Results: We developed a smartphone app that enables older adults with diabetes to capture, view, and transmit images of food items they consumed. The findings of this study showed that FRADA and its instructions for capturing, viewing, and transmitting images of food items were usable for older adults with diabetes. The survey showed that participants found FRADA easy to use and would consider using FRADA daily. The analysis of the qualitative data from interviews revealed multiple categories, such as the usability of FRADA, potential benefits of using FRADA, potential features to be added to FRADA, and concerns of older adults with diabetes regarding interactions with FRADA. Conclusions: This study demonstrates in a lab-based setting not only the usability of FRADA by older adults with diabetes but also potential opportunities using FRADA in real-world settings. The findings suggest implications for creating a smartphone app for an image-assisted dietary assessment. Future work still remains to evaluate the feasibility and validity of FRADA with multiple stakeholders, including older adults with diabetes and dietitians. ", doi="10.2196/14760", url="http://formative.jmir.org/2021/2/e14760/", url="http://www.ncbi.nlm.nih.gov/pubmed/33493129" } @Article{info:doi/10.2196/23687, author="Forsyth, R. Jessica and Chase, Hannah and Roberts, W. Nia and Armitage, C. Laura and Farmer, J. Andrew", title="Application of the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies in Assessing Mobile-Delivered Technologies for the Self-Management of Type 2 Diabetes Mellitus: Scoping Review", journal="JMIR Diabetes", year="2021", month="Feb", day="16", volume="6", number="1", pages="e23687", keywords="type 2 diabetes", keywords="health technology", keywords="self-management", keywords="mobile health", keywords="mobile applications", keywords="guidelines", abstract="Background: There is a growing role of digital health technologies (DHTs) in the management of chronic health conditions, specifically type 2 diabetes. It is increasingly important that health technologies meet the evidence standards for health care settings. In 2019, the National Institute for Health and Care Excellence (NICE) published the NICE Evidence Standards Framework for DHTs. This provides guidance for evaluating the effectiveness and economic value of DHTs in health care settings in the United Kingdom. Objective: The aim of this study is to assess whether scientific articles on DHTs for the self-management of type 2 diabetes mellitus report the evidence suggested for implementation in clinical practice, as described in the NICE Evidence Standards Framework for DHTs. Methods: We performed a scoping review of published articles and searched 5 databases to identify systematic reviews and primary studies of mobile device--delivered DHTs that provide self-management support for adults with type 2 diabetes mellitus. The evidence reported within articles was assessed against standards described in the NICE framework. Results: The database search yielded 715 systematic reviews, of which, 45 were relevant and together included 59 eligible primary studies. Within these, there were 39 unique technologies. Using the NICE framework, 13 technologies met best practice standards, 3 met minimum standards only, and 23 technologies did not meet minimum standards. Conclusions: On the assessment of peer-reviewed publications, over half of the identified DHTs did not appear to meet the minimum evidence standards recommended by the NICE framework. The most common reasons for studies of DHTs not meeting these evidence standards included the absence of a comparator group, no previous justification of sample size, no measurable improvement in condition-related outcomes, and a lack of statistical data analysis. This report provides information that will enable researchers and digital health developers to address these limitations when designing, delivering, and reporting digital health technology research in the future. ", doi="10.2196/23687", url="http://diabetes.jmir.org/2021/1/e23687/", url="http://www.ncbi.nlm.nih.gov/pubmed/33591278" } @Article{info:doi/10.2196/23477, author="Eberle, Claudia and L{\"o}hnert, Maxine and Stichling, Stefanie", title="Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review", journal="JMIR Mhealth Uhealth", year="2021", month="Feb", day="15", volume="9", number="2", pages="e23477", keywords="diabetes mellitus", keywords="mobile apps", keywords="mHealth apps", keywords="medical apps", abstract="Background: According to the World Health Organization, the worldwide prevalence of diabetes mellitus (DM) is increasing dramatically and DM comprises a large part of the global burden of disease. At the same time, the ongoing digitalization that is occurring in society today offers novel possibilities to deal with this challenge, such as the creation of mobile health (mHealth) apps. However, while a great variety of DM-specific mHealth apps exist, the evidence in terms of their clinical effectiveness is still limited. Objective: The objective of this review was to evaluate the clinical effectiveness of mHealth apps in DM management by analyzing health-related outcomes in patients diagnosed with type 1 DM (T1DM), type 2 DM (T2DM), and gestational DM. Methods: A scoping review was performed. A systematic literature search was conducted in MEDLINE (PubMed), Cochrane Library, EMBASE, CINAHL, and Web of Science Core Collection databases for studies published between January 2008 and October 2020. The studies were categorized by outcomes and type of DM. In addition, we carried out a meta-analysis to determine the impact of DM-specific mHealth apps on the management of glycated hemoglobin (HbA1c). Results: In total, 27 studies comprising 2887 patients were included. We analyzed 19 randomized controlled trials, 1 randomized crossover trial, 1 exploratory study, 1 observational study, and 5 pre-post design studies. Overall, there was a clear improvement in HbA1c values in patients diagnosed with T1DM and T2DM. In addition, positive tendencies toward improved self-care and self-efficacy as a result of mHealth app use were found. The meta-analysis revealed an effect size, compared with usual care, of a mean difference of --0.54\% (95\% CI --0.8 to --0.28) for T2DM and --0.63\% (95\% CI --0.93 to --0.32) for T1DM. Conclusions: DM-specific mHealth apps improved the glycemic control by significantly reducing HbA1c values in patients with T1DM and T2DM patients. In general, mHealth apps effectively enhanced DM management. However, further research in terms of clinical effectiveness needs to be done in greater detail. ", doi="10.2196/23477", url="http://mhealth.jmir.org/2021/2/e23477/", url="http://www.ncbi.nlm.nih.gov/pubmed/33587045" } @Article{info:doi/10.2196/23338, author="Li, Jing and Wei, Dong and Liu, Shuyi and Li, Mingxia and Chen, Xi and Chen, Li and Wu, Yuelei and Zhou, Wen and Ouyang, Lingyun and Tan, Cuixia and Meng, Hongdao and Tong, Nanwei", title="Efficiency of an mHealth App and Chest-Wearable Remote Exercise Monitoring Intervention in Patients With Type 2 Diabetes: A Prospective, Multicenter Randomized Controlled Trial", journal="JMIR Mhealth Uhealth", year="2021", month="Feb", day="9", volume="9", number="2", pages="e23338", keywords="type 2 diabetes", keywords="fitness app", keywords="heart rate band", keywords="exercise monitoring", keywords="randomized controlled trial", keywords="mobile phone", abstract="Background: Exercise has been recommended as a cornerstone for diabetes management. Supervised exercise is more efficient than unsupervised exercise but is less convenient and accessible. Objective: We aimed to determine the efficiency of exercise using a fitness app and heart rate band to remotely monitor patients with type 2 diabetes in comparison with that of traditional exercise. Methods: Patients with type 2 diabetes without severe complications or comorbidities were recruited to participate in this multicenter randomized controlled trial and were allocated to either the intervention or control group (1:1 ratio). Participants in both groups were asked to engage in moderate to vigorous physical activity for at least 150 minutes per week; each participant was prescribed individualized exercises. Participants in the intervention group were asked to follow exercise videos on the app and to wear a chest band; heart rate, exercise duration, and exercise intensity were recorded by the app. Participants in the control group self-reported exercise intensity and duration. Cardiopulmonary endurance, body composition, blood glucose level, and insulin level were assessed before and after a 3-month exercise program. Results: Of the 101 participants who were enrolled, the majority of them (85/101, 84.2\%) completed the study. Both groups had similar baseline characteristics, with the exception that participants in the intervention group were slightly younger and less likely to have hypertension. Self-reported exercise duration was longer than app-recorded exercise duration (control: mean 214 minutes/week; intervention: mean 193 minutes/week); in addition, a higher proportion of participants in the control group (29/41, 71\%) than in the intervention group (18/44, 41\%) met the 150-minute target for moderate to vigorous physical activity. However, compared with the control group, the intervention group had a larger increase in cardiopulmonary endurance (mean difference --2.0 bpm [beats per minute] vs 1.0 bpm; P=.02) and a larger decrease in body fat percentage (mean difference --1.8\% vs --0.8\%; P=.01). There was no difference in hemoglobin A1c level reduction between the two groups, yet more participants in the intervention group stopped taking their antidiabetic drugs or had their dosages lowered by an endocrinologist, compared with those in the control group. There were no serious adverse events in either group. Conclusions: This was the first randomized controlled trial in China, to our knowledge, to test the efficiency of exercise using a fitness app and heart rate band to remotely monitor prescribed exercise in patients with type 2 diabetes. The findings of our study suggest that exercise programs may be more efficient if participants are remotely monitored with an app and heart rate band than if participants are not monitored. Trial Registration: Chinese Clinical Trial Register ChiCTR1800015963; http://www.chictr.org.cn/showprojen.aspx?proj=27080 ", doi="10.2196/23338", url="https://mhealth.jmir.org/2021/2/e23338", url="http://www.ncbi.nlm.nih.gov/pubmed/33560244" } @Article{info:doi/10.2196/17537, author="Batch, C. Bryan and Spratt, E. Susan and Blalock, V. Dan and Benditz, Chad and Weiss, Andi and Dolor, J. Rowena and Cho, H. Alex", title="General Behavioral Engagement and Changes in Clinical and Cognitive Outcomes of Patients with Type 2 Diabetes Using the Time2Focus Mobile App for Diabetes Education: Pilot Evaluation", journal="J Med Internet Res", year="2021", month="Jan", day="20", volume="23", number="1", pages="e17537", keywords="mobile technology", keywords="diabetes", keywords="self management support", keywords="self efficacy", keywords="illness perception", abstract="Background: Type 2 diabetes affects 30 million Americans, representing a significant cause of morbidity and mortality. Self-management support is an important component of chronic illness care and is a key pillar of the chronic care model. Face-to-face teaching and patient education materials suffer from being static or incompatible with mobile lifestyles. Digital apps provide a self-management support alternative that is convenient and scalable. Objective: This pilot study tested the real-world deployment of a self-guided mobile app for diabetes education (Time2Focus app; MicroMass Communications Inc, Cary, NC), which utilizes evidence-based content and gamification to deliver an interactive learning experience. Methods: Primary care providers were approached for permission to invite their patients to participate. Eligible patients were 18 to 89 years of age, had a diagnosis of type 2 diabetes, hemoglobin A1c (HbA1c) ?8\% and <12\% in the past 3 months, an active online patient portal account (tied to the electronic health record), and access to an iOS or Android smartphone. Interested patients were emailed a baseline survey, and once this was completed, were sent instructions for downloading the Time2Focus app. After completing all 12 levels, participants were sent a follow-up survey. The primary outcome was the change in HbA1c. Secondary outcomes included medication adherence, self-care activities, self-reporting of physical activities, diabetes self-efficacy, illness perceptions, diabetes distress scale, and users' engagement with and rating of the app. Results: Of 1355 potentially eligible patients screened, 201 were consented. Of these 201 patients, 101 (50.2\%) did not download the app. Of the 100 participants (49.8\%) who downloaded the app, 16 (16.0\%) completed 0 levels, 26 (26.0\%) completed 1 to 4 levels, 10 (10.0\%) completed 5 to 11 levels, and 48 (48.0\%) completed all 12 levels of the app and the follow-up survey. Those completing one or more levels had a mean pre/post-HbA1c change of --0.41\% (compared to --0.32\% among those who completed zero levels); however, the unadjusted two-tailed t test indicated no significant difference between the two groups (P=.73). Diabetes self-efficacy showed a large and significant increase during app usage for completers (mean change 1.28, P<.001, d=.83). Severity of illness perceptions showed a small but significant decrease during app usage for completers (mean change --0.51, P=.004, d=.43). Diabetes distress showed a small but significant decrease during app usage for completers (mean change --0.45, P=.006, d=.41). The net promoter score was 62.5, indicating that those who completed all levels of the app rated it highly and would recommend it to others. Conclusions: Participants who engaged in all 12 levels of the Time2Focus mobile app showed an improvement in diabetes self-efficacy and a decrease in severity of illness perceptions. The decrease in HbA1c observed in app users relative to nonusers during this limited pilot study was not statistically significant. However, uptake and application of lessons learned from self-management support may be delayed. Further research is needed to address how to increase engagement through self-management support and to investigate if follow up over a longer period demonstrates a significant change in outcomes such as HbA1c. ", doi="10.2196/17537", url="http://www.jmir.org/2021/1/e17537/", url="http://www.ncbi.nlm.nih.gov/pubmed/33470947" } @Article{info:doi/10.2196/16146, author="Flors-Sidro, Javier Jos{\'e} and Househ, Mowafa and Abd-Alrazaq, Alaa and Vidal-Alaball, Josep and Fernandez-Luque, Luis and Sanchez-Bocanegra, Luis Carlos", title="Analysis of Diabetes Apps to Assess Privacy-Related Permissions: Systematic Search of Apps", journal="JMIR Diabetes", year="2021", month="Jan", day="13", volume="6", number="1", pages="e16146", keywords="diabetes mellitus", keywords="privacy", keywords="mobile apps", keywords="dangerous permissions", abstract="Background: Mobile health has become a major vehicle of support for people living with diabetes. Accordingly, the availability of mobile apps for diabetes has been steadily increasing. Most of the previous reviews of diabetes apps have focused on the apps' features and their alignment with clinical guidelines. However, there is a lack of knowledge on the actual compliance of diabetes apps with privacy and data security guidelines. Objective: The aim of this study was to assess the levels of privacy of mobile apps for diabetes to contribute to the raising of awareness of privacy issues for app users, developers, and governmental data protection regulators. Methods: We developed a semiautomatic app search module capable of retrieving Android apps' privacy-related information, particularly the dangerous permissions required by apps, with the aim of analyzing privacy aspects related to diabetes apps. Following the research selection criteria, the original 882 apps were narrowed down to 497 apps that were included in the analysis. Results: Approximately 60\% of the analyzed diabetes apps requested potentially dangerous permissions, which pose a significant risk to users' data privacy. In addition, 28.4\% (141/497) of the apps did not provide a website for their privacy policy. Moreover, it was found that 40.0\% (199/497) of the apps contained advertising, and some apps that claimed not to contain advertisements actually did. Ninety-five percent of the apps were free, and those belonging to the ``medical'' and ``health and fitness'' categories were the most popular. However, app users do not always realize that the free apps' business model is largely based on advertising and, consequently, on sharing or selling their private data, either directly or indirectly, to unknown third parties. Conclusions: The aforementioned findings confirm the necessity of educating patients and health care providers and raising their awareness regarding the privacy aspects of diabetes apps. Therefore, this research recommends properly and comprehensively training users, ensuring that governments and regulatory bodies enforce strict data protection laws, devising much tougher security policies and protocols in Android and in the Google Play Store, and implicating and supervising all stakeholders in the apps' development process. ", doi="10.2196/16146", url="http://diabetes.jmir.org/2021/1/e16146/", url="http://www.ncbi.nlm.nih.gov/pubmed/33439129" } @Article{info:doi/10.2196/21727, author="Mueller, Christian and Schauerte, Isabel and Martin, Stephan", title="Evaluation of Self-Care Activities and Quality of Life in Patients With Type 2 Diabetes Mellitus Treated With Metformin Using the 2D Matrix Code of Outer Drug Packages as Patient Identifier: Protocol for the DePRO Proof-of-Concept Observational Study", journal="JMIR Res Protoc", year="2021", month="Jan", day="11", volume="10", number="1", pages="e21727", keywords="self-care activities", keywords="quality of life", keywords="type 2 diabetes mellitus", keywords="patient-reported outcome measures", keywords="digital observational study", keywords="bring your own device", abstract="Background: Diabetes mellitus (DM) is one of the most common noncommunicable diseases. DM has a substantial negative impact on patients' quality of life, which is measured using a variety of diabetes-specific measures covering multiple aspects of patients' psychological state, behavior, and treatment satisfaction. A fully digital data collection system, including patient identification, would represent a substantial advance in how these patient-reported outcome (PRO) data are measured. Within the European Union, one way to identify patients without the involvement of health care professionals is to use the unique 2D matrix codes on the packaging of prescription medication---for example, metformin, the recommended initial treatment for patients with type 2 DM (T2DM). Objective: In the DePRO study we aim to (1) describe the self-care activities of patients with T2DM using metformin-containing medication; (2) describe the self-reported health status (eg, presence of diabetes complications and quality of life) of these patients; (3) describe associations between self-care activities and demographics and disease characteristics; and (4) assess the usability of the my ePRO app. Methods: DePRO is an observational, multicenter, cross-sectional, digital, patient-driven study conducted in Germany. Patients with a prescription for a metformin-containing medication will be given a postcard by their pharmacist, which will include a download link for the my ePRO app. In total, 12 diabetes-focused pharmacies, selected to represent urban and rural areas, will be recruited. Participants will use their own mobile device (bring your own device) to download the my ePRO app and access the DePRO study, for which they can register using the 2D matrix code on their medication. An electronic informed consent form will be displayed to the patients and only after giving consent will patients be able to complete the study questionnaires. The PRO instruments used in the study are the Summary of Diabetes Self-Care Activities Scale, the Diabetes Treatment Satisfaction Questionnaire, and the 5 level, 5-dimension EuroQol Questionnaire. Patients will also be asked to complete a questionnaire with items addressing demographics, patient characteristics, disease history, complications, and concomitant medications. Data will be transferred to the study database by the app upon completion of each questionnaire. Statistical analyses of primary and secondary endpoints will be exploratory and descriptive. Results: Enrollment began in June 2020. The estimated study completion date is December 31, 2020, and the planned sample size is 300 patients. Conclusions: The DePRO study uses completely digital data collection, including authentication of eligible patients and completion of the study questionnaires. Therefore, the design of the DePRO study represents a substantial advance in the evaluation of the digital capturing of PRO data. Trial Registration: ClinicalTrials.gov NCT04383041; https://clinicaltrials.gov/ct2/show/NCT04383041 International Registered Report Identifier (IRRID): PRR1-10.2196/21727 ", doi="10.2196/21727", url="http://www.researchprotocols.org/2021/1/e21727/", url="http://www.ncbi.nlm.nih.gov/pubmed/33427685" } @Article{info:doi/10.2196/19650, author="Alhodaib, Ibrahim Hala and Antza, Christina and Chandan, Singh Joht and Hanif, Wasim and Sankaranarayanan, Sailesh and Paul, Sunjay and Sutcliffe, Paul and Nirantharakumar, Krishnarajah", title="Mobile Clinical Decision Support System for the Management of Diabetic Patients With Kidney Complications in UK Primary Care Settings: Mixed Methods Feasibility Study", journal="JMIR Diabetes", year="2020", month="Nov", day="18", volume="5", number="4", pages="e19650", keywords="eHealth", keywords="clinical decision support application", keywords="diabetes mellitus", keywords="chronic kidney disease", keywords="feasibility study", abstract="Background: Attempts to utilize eHealth in diabetes mellitus (DM) management have shown promising outcomes, mostly targeted at patients; however, few solutions have been designed for health care providers. Objective: The purpose of this study was to conduct a feasibility project developing and evaluating a mobile clinical decision support system (CDSS) tool exclusively for health care providers to manage chronic kidney disease (CKD) in patients with DM. Methods: The design process was based on the 3 key stages of the user-centered design framework. First, an exploratory qualitative study collected the experiences and views of DM specialist nurses regarding the use of mobile apps in clinical practice. Second, a CDSS tool was developed for the management of patients with DM and CKD. Finally, a randomized controlled trial examined the acceptability and impact of the tool. Results: We interviewed 15 DM specialist nurses. DM specialist nurses were not currently using eHealth solutions in their clinical practice, while most nurses were not even aware of existing medical apps. However, they appreciated the potential benefits that apps may bring to their clinical practice. Taking into consideration the needs and preferences of end users, a new mobile CDSS app, ``Diabetes \& CKD,'' was developed based on guidelines. We recruited 39 junior foundation year 1 doctors (44\% male) to evaluate the app. Of them, 44\% (17/39) were allocated to the intervention group, and 56\% (22/39) were allocated to the control group. There was no significant difference in scores (maximum score=13) assessing the management decisions between the app and paper-based version of the app's algorithm (intervention group: mean 7.24 points, SD 2.46 points; control group: mean 7.39, SD 2.56; t37=--0.19, P=.85). However, 82\% (14/17) of the participants were satisfied with using the app. Conclusions: The findings will guide the design of future CDSS apps for the management of DM, aiming to help health care providers with a personalized approach depending on patients' comorbidities, specifically CKD, in accordance with guidelines. ", doi="10.2196/19650", url="https://diabetes.jmir.org/2020/4/e19650", url="http://www.ncbi.nlm.nih.gov/pubmed/33206055" } @Article{info:doi/10.2196/22212, author="B{\"o}hm, Anna-Katharina and Jensen, Lind Morten and S{\o}rensen, Reinholdt Mads and Stargardt, Tom", title="Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study", journal="JMIR Mhealth Uhealth", year="2020", month="Nov", day="6", volume="8", number="11", pages="e22212", keywords="user engagement", keywords="user activity", keywords="mHealth", keywords="diabetes mellitus", keywords="diabetes apps", abstract="Background: Patient support apps have risen in popularity and provide novel opportunities for self-management of diabetes. Such apps offer patients to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth). Objective: This study aims to analyze the extent to which users engage with mHealth for diabetes and identify patient characteristics that are associated with engagement. Methods: The analysis is based on real-world data obtained by Novo Nordisk's Cornerstones4Care Powered by Glooko diabetes support app. User engagement was assessed as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app. Results: A total of 9051 individuals initiated use after registration and could be observed for 180 days. Among these, 55.39\% (5013/9051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2075/3649, 56.86\%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively. Conclusions: Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should take into account privacy concerns of the users at the same time. Users' engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self-management of their disease. ", doi="10.2196/22212", url="http://mhealth.jmir.org/2020/11/e22212/", url="http://www.ncbi.nlm.nih.gov/pubmed/32975198" } @Article{info:doi/10.2196/19869, author="Xu, Zidu and Geng, Ji and Zhang, Shuai and Zhang, Kexin and Yang, Lin and Li, Jing and Li, Jiao", title="A Mobile-Based Intervention for Dietary Behavior and Physical Activity Change in Individuals at High Risk for Type 2 Diabetes Mellitus: Randomized Controlled Trial", journal="JMIR Mhealth Uhealth", year="2020", month="Nov", day="3", volume="8", number="11", pages="e19869", keywords="transtheoretical model", keywords="type 2 diabetes mellitus", keywords="high risk", keywords="social media", keywords="dietary behavior", keywords="physical activity", abstract="Background: Intensive lifestyle modifications have proved effective in preventing type 2 diabetes mellitus (T2DM), yet the efficiency and effectiveness of these modifications need to be improved. Emerging social media interventions are considered useful in promoting these lifestyles; nevertheless, few studies have investigated the effectiveness of combining them with behavior theory. Objective: This study aims to examine the effectiveness of a 6-month mobile-based intervention (DHealthBar, a WeChat applet) combined with behavioral theory compared with a printed intervention in improving dietary behaviors, physical activity, and intention to change these behaviors among populations at high risk for T2DM. Methods: Participants aged 23 to 67 years were recruited offline in Beijing, China, and were randomized into the intervention group or the control group, which received educational content via DHealthBar or a printed handbook, respectively. Educational materials were culturally tailored recommendations on improving dietary behaviors, physical activity, and intention to change based on the transtheoretical model. Participants in the intervention arm received push notifications twice per week on WeChat and had access to the educational content for the 6-month study period. Participants in the control arm received the same intervention content through printed materials. The outcomes of participants' behavior change, intention to change behavior, and anthropometric characteristics were collected via online measuring tools at baseline, 3 months, and 6 months. Results: In this study, 79 enrolled individuals completed baseline information collection (control: n=38 vs intervention: n=41), and 96\% (76/79) completed the 6-month follow-up visit. Attrition rates did not differ significantly between the 2 groups ($\chi$21=0.0, P=.61). Baseline equivalence was found. Participants in both groups reported a statistically significant decrease in energy intake at the 2 follow-up assessments compared with baseline (3 months, control: exp[$\beta$]=0.83, 95\% CI 0.74-0.92 vs intervention: exp[$\beta$]=0.76, 95\% CI 0.68-0.85; 6 months, control: exp[$\beta$]=0.87, 95\% CI 0.78-0.96 vs intervention: exp[$\beta$]=0.57, 95\% CI 0.51-0.64). At 6 months, a significantly larger decrease was observed in the intervention group in energy, fat, and carbohydrate intake, accompanied with a significantly larger increase in moderate-intensity physical activity compared with the control group (energy: exp[$\beta$]=0.66, 95\% CI 0.56-0.77; fat: exp[$\beta$]=0.71, 95\% CI 0.54-0.95; carbohydrates: exp[$\beta$]=0.83, 95\% CI 0.66-1.03; moderate-intensity physical activity: exp[$\beta$]=2.05, 95\% CI 1.23-3.44). After 6 months of the intervention, participants in the intervention group were more likely to be at higher stages of dietary behaviors (exp[$\beta$]=26.80, 95\% CI 3.51-204.91) and physical activity (exp[$\beta$]=15.60, 95\% CI 2.67-91.04) than the control group. Conclusions: DHealthBar was initially effective in improving dietary behavior, physical activity, and intention to change these behaviors among populations who were at high risk of developing T2DM, with significant differences in the changes of outcomes over the 6-month intervention period. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000032323; https://tinyurl.com/y4h8q4uf ", doi="10.2196/19869", url="https://mhealth.jmir.org/2020/11/e19869", url="http://www.ncbi.nlm.nih.gov/pubmed/33141092" } @Article{info:doi/10.2196/18922, author="Ramallo-Fari{\~n}a, Yolanda and Garc{\'i}a-Bello, Angel Miguel and Garc{\'i}a-P{\'e}rez, Lidia and Boronat, Mauro and W{\"a}gner, M. Ana and Rodr{\'i}guez-Rodr{\'i}guez, Leticia and de Pablos-Velasco, Pedro and Llorente G{\'o}mez de Segura, Ignacio and Gonz{\'a}lez- Pacheco, Himar and Carmona Rodr{\'i}guez, Montserrat and Serrano-Aguilar, Pedro and ", title="Effectiveness of Internet-Based Multicomponent Interventions for Patients and Health Care Professionals to Improve Clinical Outcomes in Type 2 Diabetes Evaluated Through the INDICA Study: Multiarm Cluster Randomized Controlled Trial", journal="JMIR Mhealth Uhealth", year="2020", month="Nov", day="2", volume="8", number="11", pages="e18922", keywords="behavior modification", keywords="primary care", keywords="type 2 diabetes mellitus", keywords="patients adherence", keywords="eHealth", abstract="Background: Type 2 diabetes mellitus (T2DM) is a chronic disease in which health outcomes are related to decision making by patients and health care professionals. Objective: This study aims to assess the effectiveness of internet-based multicomponent interventions to support decision making of all actors involved in the care of patients with T2DM in primary care. Methods: The INDICA study is an open, community-based, multicenter trial with random allocation to usual care or the intervention for patients, the intervention for health care professionals in primary care, or the combined intervention for both. In the intervention for patients, participants received an educational group program and were monitored and supported by logs, a web-based platform, and automated SMS. Those in the intervention for professionals also received an educational program, a decision support tool embedded in the electronic clinical record, and periodic feedback about patients' results. A total of 2334 people with T2DM, regardless of glycated hemoglobin (HbA1c) levels and without diabetes-related complications, were included. The primary end point was change in HbA1c level. The main analysis was performed using multilevel mixed models. Results: For the overall sample, the intervention for patients attained a significant mean reduction in HbA1c levels of ?0.27 (95\% CI ?0.45 to ?0.10) at month 3 and ?0.26 (95\% CI ?0.44 to ?0.08) at month 6 compared with usual care, which remained marginally significant at month 12. A clinically relevant reduction in HbA1c level was observed in 35.6\% (191/537) of patients in the intervention for patients and 26.0\% (152/586) of those in usual care at month 12 (P=.006). In the combined intervention, HbA1c reduction was significant until month 18 (181/557, 32.6\% vs 140/586, 23.9\%; P=.009). Considering the subgroup of patients uncontrolled at baseline, all interventions produced significant reductions in HbA1c levels across the entire study period: ?0.49 (95\% CI ?0.70 to ?0.27) for the intervention for patients, ?0.35 (95\% CI ?0.59 to ?0.14) for the intervention for professionals, and ?0.35 (95\% CI ?0.57 to ?0.13) for the combined intervention. Differences in HbA1c for the area under the curve considering the entire period were significant for the intervention for patients and the combined intervention compared with usual care (P=.03 for both). Compared with usual care, the intervention for professionals and the combined intervention had significant longer-term reductions in systolic and diastolic blood pressure. Conclusions: In uncontrolled patients, the intervention for patients at baseline provided clinically relevant and significant longer-term reductions of HbA1c levels. The intervention for professionals and combined intervention also improved the cardiovascular risk profile of patients. Trial Registration: ClinicalTrials.gov NCT01657227; https://clinicaltrials.gov/ct2/show/NCT01657227 ", doi="10.2196/18922", url="https://mhealth.jmir.org/2020/11/e18922", url="http://www.ncbi.nlm.nih.gov/pubmed/33136059" } @Article{info:doi/10.2196/20353, author="Khowaja, Kamran and Al-Thani, Dena", title="New Checklist for the Heuristic Evaluation of mHealth Apps (HE4EH): Development and Usability Study", journal="JMIR Mhealth Uhealth", year="2020", month="Oct", day="28", volume="8", number="10", pages="e20353", keywords="mHealth", keywords="eHealth", keywords="heuristic evaluation", keywords="expert evaluation", keywords="self-monitoring", keywords="behavior change", keywords="design guidelines", keywords="framework", abstract="Background: Diabetes is one of the leading causes of death in developing countries. Existing mobile health (mHealth) app design guidelines lack a description of the support of continuous self-monitoring of health status, behavior change to improve and adopt a healthy lifestyle, and communication with health educators and health care professionals in case of any need. Objective: This paper presents the development of a specialized set of heuristics called heuristic evaluation for mHealth apps (HE4EH) as an all-in-one tool and its applicability by performing a heuristic evaluation of an mHealth app. Methods: An extensive review of heuristics and checklists was used to develop the HE4EH. The HE4EH was evaluated by domain experts for heuristics, checklist items, severity ratings, and overall satisfaction. The OneTouch app, which helps individuals with diabetes manage their blood glucose levels, was evaluated using HE4EH to identify usability problems that need to be fixed in the app. Results: The expert evaluation of HE4EH revealed that the heuristics were important, relevant, and clear. The checklist items across the heuristics were clear, relevant, and acceptably grouped. In terms of evaluating the OneTouch app using the HE4EH, the most frequently violated heuristics included Content, Visibility, Match, and Self-monitoring. Most of the usability problems found were minor. The system usability scale score indicated that the OneTouch app is marginally acceptable. Conclusions: This heuristic evaluation using the OneTouch app shows that the HE4EH can play a vital role for designers, researchers, and practitioners to use HE4EH heuristics and checklist items as a tool to design a new or evaluate and improve an existing mHealth app. ", doi="10.2196/20353", url="http://mhealth.jmir.org/2020/10/e20353/", url="http://www.ncbi.nlm.nih.gov/pubmed/33112252" } @Article{info:doi/10.2196/22074, author="Alfonsi, E. Jeffrey and Choi, Y. Elizabeth E. and Arshad, Taha and Sammott, S. Stacie-Ann and Pais, Vanita and Nguyen, Cynthia and Maguire, R. Bryan and Stinson, N. Jennifer and Palmert, R. Mark", title="Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial", journal="JMIR Mhealth Uhealth", year="2020", month="Oct", day="28", volume="8", number="10", pages="e22074", keywords="carbohydrate counting", keywords="type 1 diabetes", keywords="image recognition", keywords="youth", keywords="digital health applications (apps)", keywords="mHealth", abstract="Background: Carbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates. Objective: Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy. Methods: Iterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA1c level was also assessed. Results: Use of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA1c levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43\% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study. Conclusions: Our results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management. Trial Registration: ClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142 ", doi="10.2196/22074", url="http://mhealth.jmir.org/2020/10/e22074/", url="http://www.ncbi.nlm.nih.gov/pubmed/33112249" } @Article{info:doi/10.2196/17135, author="Schoenthaler, Antoinette and Leon, Michelle and Butler, Mark and Steinhaeuser, Karsten and Wardzinski, William", title="Development and Evaluation of a Tailored Mobile Health Intervention to Improve Medication Adherence in Black Patients With Uncontrolled Hypertension and Type 2 Diabetes: Pilot Randomized Feasibility Trial", journal="JMIR Mhealth Uhealth", year="2020", month="Sep", day="23", volume="8", number="9", pages="e17135", keywords="mHealth", keywords="medication adherence", keywords="hypertension", keywords="type 2 diabetes", keywords="African Americans", abstract="Background: Research has underscored the need to develop socioculturally tailored interventions to improve adherence behaviors in minority patients with hypertension (HTN) and type 2 diabetes (T2D). Novel mobile health (mHealth) approaches are potential methods for delivering tailored interventions to minority patients with increased cardiovascular risk. Objective: This study aims to develop and evaluate the acceptability and preliminary efficacy of a tailored mHealth adherence intervention versus attention control (AC) on medication adherence, systolic blood pressure (SBP), diastolic blood pressure (DBP), and hemoglobin A1c (HbA1c) at 3 months in 42 Black patients with uncontrolled HTN and/or T2D who were initially nonadherent to their medications. Methods: This was a two-phase pilot study consisting of a formative phase and a clinical efficacy phase. The formative phase consisted of qualitative interviews with 10 members of the target patient population (7/10, 70\% female; mean age 65.8 years, SD 5.6) to tailor the intervention based on the Information-Motivation-Behavioral skills model of adherence. The clinical efficacy phase consisted of a 3-month pilot randomized controlled trial to evaluate the tailored mHealth intervention versus an AC. The tablet-delivered intervention included a tailoring survey, an individualized adherence profile, and a personalized list of interactive adherence-promoting modules, whereas AC included the tailoring survey and health education videos delivered on the tablet. Acceptability was assessed through semistructured exit interviews. Medication adherence was assessed using the 8-item Morisky Medication Adherence Scale, whereas blood pressure and HbA1c were assessed using automated devices. Results: In phase 1, thematic analysis of the semistructured interviews revealed the following 5 major barriers to adherence: disruptions in daily routine, forgetfulness, concerns about adverse effects, preference for natural remedies, and burdens of medication taking. Patients recommended the inclusion of modules that address improving patient-provider communication, peer vignettes, and stress reduction strategies to facilitate adherence. A total of 42 Black patients (23/42, 55\% male; mean age 57.6 years, SD 11.1) participated in the clinical efficacy pilot trial. At 3 months, both groups showed significant improvements in adherence (mean 1.35, SD 1.60; P<.001) and SBP (?4.76 mm Hg; P=.04) with no between-group differences (P=.50 and P=.10). The decreases in DBP and HbA1c over time were nonsignificant (?1.97 mm Hg; P=.20; and ?0.2\%; P=.45, respectively). Patients reported high acceptability of the intervention for improving their adherence. Conclusions: This pilot study demonstrated preliminary evidence on the acceptability of a tailored mHealth adherence intervention among a sample of Black patients with uncontrolled HTN and T2D who were initially nonadherent to their medications. Future research should explore whether repeated opportunities to use the mHealth intervention would result in improvements in behavioral and clinical outcomes over time. Modifications to the intervention as a result of the pilot study should guide future efforts. Trial Registration: ClinicalTrials.gov NCT01643473; http://clinicaltrials.gov/ct2/show/ NCT01643473 ", doi="10.2196/17135", url="http://mhealth.jmir.org/2020/9/e17135/", url="http://www.ncbi.nlm.nih.gov/pubmed/32965230" } @Article{info:doi/10.2196/18660, author="Kriventsov, Stan and Lindsey, Alexander and Hayeri, Amir", title="The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study", journal="JMIR Diabetes", year="2020", month="Sep", day="22", volume="5", number="3", pages="e18660", keywords="blood glucose predictions", keywords="type 1 diabetes", keywords="artificial intelligence", keywords="machine learning", keywords="digital health", keywords="mobile phone", abstract="Background: Diabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes must monitor their glycemic levels to keep them in a healthy range. This task is made easier by using continuous glucose monitoring (CGM) devices and relaying their output to smartphone apps, thus providing users with real-time information on their glycemic fluctuations and possibly predicting future trends. Objective: This study aims to discuss various challenges of predictive monitoring of glycemia and examines the accuracy and blood glucose control effects of Diabits, a smartphone app that helps patients with diabetes monitor and manage their blood glucose levels in real time. Methods: Using data from CGM devices and user input, Diabits applies machine learning techniques to create personalized patient models and predict blood glucose fluctuations up to 60 min in advance. These predictions give patients an opportunity to take pre-emptive action to maintain their blood glucose values within the reference range. In this retrospective observational cohort study, the predictive accuracy of Diabits and the correlation between daily use of the app and blood glucose control metrics were examined based on real app users' data. Moreover, the accuracy of predictions on the 2018 Ohio T1DM (type 1 diabetes mellitus) data set was calculated and compared against other published results. Results: On the basis of more than 6.8 million data points, 30-min Diabits predictions evaluated using Parkes Error Grid were found to be 86.89\% (5,963,930/6,864,130) clinically accurate (zone A) and 99.56\% (6,833,625/6,864,130) clinically acceptable (zones A and B), whereas 60-min predictions were 70.56\% (4,843,605/6,864,130) clinically accurate and 97.49\% (6,692,165/6,864,130) clinically acceptable. By analyzing daily use statistics and CGM data for the 280 most long-standing users of Diabits, it was established that under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not interact with the app was 154.0 (SD 47.2) mg/dL, with 67.52\% of the time spent in the healthy 70 to 180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 (SD 42.0) mg/dL (P<.001), whereas the time in euglycemic range increased to 74.28\% (P<.001). On the Ohio T1DM data set of 6 patients with type 1 diabetes, 30-min predictions of the base Diabits model had an average root mean square error of 18.68 (SD 2.19) mg/dL, which is an improvement over the published state-of-the-art results for this data set. Conclusions: Diabits accurately predicts future glycemic fluctuations, potentially making it easier for patients with diabetes to maintain their blood glucose in the reference range. Furthermore, an improvement in glucose control was observed on days with more frequent Diabits use. ", doi="10.2196/18660", url="http://diabetes.jmir.org/2020/3/e18660/", url="http://www.ncbi.nlm.nih.gov/pubmed/32960180" } @Article{info:doi/10.2196/16745, author="Osborn, Y. Chandra and Hirsch, Ashley and Sears, E. Lindsay and Heyman, Mark and Raymond, Jennifer and Huddleston, Brian and Dachis, Jeff", title="One Drop App With an Activity Tracker for Adults With Type 1 Diabetes: Randomized Controlled Trial", journal="JMIR Mhealth Uhealth", year="2020", month="Sep", day="17", volume="8", number="9", pages="e16745", keywords="diabetes", keywords="type 1 diabetes", keywords="digital therapy", keywords="mobile app", keywords="coaching", keywords="glucometer", keywords="activity tracker", abstract="Background: In 2017, mobile app support for managing diabetes was available to 64\% of the global population of adults with diabetes. One Drop's digital therapeutics solution includes an evidence-based mobile app with global reach, a Bluetooth-connected glucometer, and in-app coaching from Certified Diabetes Educators. Among people with type 1 diabetes and an estimated hemoglobin A1c level?7.5\%, using One Drop for 3 months has been associated with an improved estimated hemoglobin A1c level of 22.2 mg/dL (--0.80\%). However, the added value of integrated activity trackers is unknown. Objective: We conducted a pragmatic, remotely administered randomized controlled trial to evaluate One Drop with a new-to-market activity tracker against One Drop only on the 3-month hemoglobin A1c level of adults with type 1 diabetes. Methods: Social media advertisements and online newsletters were used to recruit adults (?18 years old) diagnosed (?1 year) with T1D, na{\"i}ve to One Drop's full solution and the activity tracker, with a laboratory hemoglobin A1c level?7\%. Participants (N=99) were randomized to receive One Drop and the activity tracker or One Drop only at the start of the study. The One Drop only group received the activity tracker at the end of the study. Multiple imputation, performed separately by group, was used to correct for missing data. Analysis of covariance models, controlling for baseline hemoglobin A1c, were used to evaluate 3-month hemoglobin A1c differences in intent-to-treat (ITT) and per protocol (PP) analyses. Results: The enrolled sample (N=95) had a mean age of 41 (SD 11) years, was 73\% female, 88\% White, diagnosed for a mean of 20 (SD 11) years, and had a mean hemoglobin A1c level of 8.4\% (SD 1.2\%); 11\% of the participants did not complete follow up. Analysis of covariance assumptions were met for the ITT and PP models. In ITT analysis, participants in the One Drop and activity tracker condition had a significantly lower 3-month hemoglobin A1c level (mean 7.9\%, SD 0.60\%, 95\% CI 7.8-8.2) than that of the participants in the One Drop only condition (mean 8.4\%, SD 0.62\%, 95\% CI 8.2-8.5). In PP analysis, participants in the One Drop and activity tracker condition also had a significantly lower 3-month hemoglobin A1c level (mean 7.9\%, SD 0.59\%, 95\% CI 7.7-8.1) than that of participants in the One Drop only condition (mean 8.2\%, SD 0.58\%, 95\% CI 8.0-8.4). Conclusions: Participants exposed to One Drop and the activity tracker for the 3-month study period had a significantly lower 3-month hemoglobin A1c level compared to that of participants exposed to One Drop only during the same timeframe. One Drop and a tracker may work better together than alone in helping people with type 1 diabetes. Trial Registration: ClinicalTrials.gov NCT03459573; https://clinicaltrials.gov/ct2/show/NCT03459573. ", doi="10.2196/16745", url="http://mhealth.jmir.org/2020/9/e16745/", url="http://www.ncbi.nlm.nih.gov/pubmed/32540842" } @Article{info:doi/10.2196/16053, author="Tsuji, Shintaro and Ishikawa, Tomoki and Morii, Yasuhiro and Zhang, Hongjian and Suzuki, Teppei and Tanikawa, Takumi and Nakaya, Jun and Ogasawara, Katsuhiko", title="Cost-Effectiveness of a Continuous Glucose Monitoring Mobile App for Patients With Type 2 Diabetes Mellitus: Analysis Simulation", journal="J Med Internet Res", year="2020", month="Sep", day="17", volume="22", number="9", pages="e16053", keywords="Markov model", keywords="telehealth", keywords="continuous glucose monitoring (CGM)", keywords="type 2 diabetes mellitus", keywords="cost-effectiveness", keywords="incremental cost and effective ratio (ICER)", abstract="Background: Apps for real-time continuous glucose monitoring (CGM) on smartphones and other devices linked to CGM systems have recently been developed, and such CGM apps are also coming into use in Japan. In comparison with conventional retrospective CGM, the use of CGM apps improves patients' own blood glucose control, which is expected to help slow the progression of type 2 diabetes mellitus (DM) and prevent complications, but the effect of their introduction on medical costs remains unknown. Objective: Our objective in this study was to perform an economic appraisal of CGM apps from the viewpoint of assessing public medical costs associated with type 2 DM, using the probability of developing type 2 DM--associated complications, and data on medical costs and utility value to carry out a medical cost simulation using a Markov model in order to ascertain the cost-effectiveness of the apps. Methods: We developed a Markov model with the transition states of insulin therapy, nephrosis, dialysis, and cardiovascular disease, all of which have a major effect on medical costs, to identify changes in medical costs and utility values resulting from the introduction of a CGM app and calculated the incremental cost-effectiveness ratio (ICER). Results: The ICER for CGM app use was US \$33,039/quality-adjusted life year (QALY). Conclusions: Sensitivity analyses showed that, with the exception of conditions where the transition probability of insulin therapy, utility value, or increased medical costs increases, the ICER for the introduction of CGM apps was below the threshold of US \$43,478/QALY used by the Central Social Insurance Medical Council. Our results provide basic data on the cost-effectiveness of introducing CGM apps, which are currently starting to come into use. ", doi="10.2196/16053", url="https://www.jmir.org/2020/9/e16053", url="http://www.ncbi.nlm.nih.gov/pubmed/32940613" } @Article{info:doi/10.2196/17083, author="Alenazi, A. Hanan and Jamal, Amr and Batais, A. Mohammed", title="Identification of Type 2 Diabetes Management Mobile App Features and Engagement Strategies: Modified Delphi Approach", journal="JMIR Mhealth Uhealth", year="2020", month="Sep", day="11", volume="8", number="9", pages="e17083", keywords="diabetes", keywords="mobile features", keywords="engagement strategies", keywords="mobile app", keywords="Delphi consensus", abstract="Background: Diabetes is a significant public health issue. Saudi Arabia has the highest prevalence of type 2 diabetes mellitus (T2DM) in the Arab world. Currently, it affects 31.6\% of the general population, and the prevalence of T2DM is predicted to rise to 45.36\% by 2030. Mobile health (mHealth) offers improved and cost-effective care to people with T2DM. However, the efficiency of engagement strategies and features of this technology need to be reviewed and standardized according to stakeholder and expert perspectives. Objective: The main objective of this study was to identify the most agreed-upon features for T2DM self-management mobile apps; the secondary objective was to identify the most agreed-upon strategies that prompt users to use these apps. Methods: In this study, a 4-round modified Delphi method was applied by experts in the domain of diabetes care. Results: In total, 11 experts with a mean age of 47.09 years (SD 11.70) consented to participate in the study. Overall, 36 app features were generated. The group of experts displayed weak agreement in their ranking of intervention components (Kendall W=0.275; P<.001). The top 5 features included insulin dose adjustment according to carbohydrate counting and blood glucose readings (5.36), alerting a caregiver of abnormal or critical readings (6.09), nutrition education (12.45), contacts for guidance if required (12.64), and offering patient-specific education tailored to the user's goals, needs, and blood glucose readings (12.90). In total, 21 engagement strategies were generated. Overall, the experts showed a moderate degree of consensus in their strategy rankings (Kendall W=0.454; P<.001). The top 5 engagement strategies included a user-friendly design (educational and age-appropriate design; 2.82), a free app (3.73), allowing the user to communicate or send information/data to a health care provider (HCP; 5.36), HCPs prescribing the mobile app in the clinic and asking about patients' app use compliance during clinical visits (6.91), and flexibility and customization (7.91). Conclusions: This is the first study in the region consisting of a local panel of experts from the diabetes field gathering together. We used an iterative process to combine the experts' opinions into a group consensus. The results of this study could thus be useful for health app developers and HCPs and inform future decision making on the topic. ", doi="10.2196/17083", url="http://mhealth.jmir.org/2020/9/e17083/", url="http://www.ncbi.nlm.nih.gov/pubmed/32678798" } @Article{info:doi/10.2196/17709, author="Su, Jingyuan and Dugas, Michelle and Guo, Xitong and Gao, (Gordon) Guodong", title="Influence of Personality on mHealth Use in Patients with Diabetes: Prospective Pilot Study", journal="JMIR Mhealth Uhealth", year="2020", month="Aug", day="10", volume="8", number="8", pages="e17709", keywords="mHealth", keywords="diabetes", keywords="adoption", keywords="active utilization", keywords="personality traits", keywords="app", abstract="Background: Mobile technology for health (mHealth) interventions are increasingly being used to help improve self-management among patients with diabetes; however, these interventions have not been adopted by a large number of patients and often have high dropout rates. Patient personality characteristics may play a critical role in app adoption and active utilization, but few studies have focused on addressing this question. Objective: This study aims to address a gap in understanding of the relationship between personality traits and mHealth treatment for patients with diabetes. We tested the role of the five-factor model of personality traits (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) in mHealth adoption preference and active utilization. Methods: We developed an mHealth app (DiaSocial) aimed to encourage diabetes self-management. We recruited 98 patients with diabetes---each patient freely chose whether to receive the standard care or the mHealth app intervention. Patient demographic information and patient personality characteristics were assessed at baseline. App usage data were collected to measure user utilization of the app. Patient health outcomes were assessed with lab measures of glycated hemoglobin (HbA1c level). Logistic regression models and linear regression were employed to explore factors predicting the relationship between mHealth use (adoption and active utilization) and changes in health outcome. Results: Of 98 study participants, 46 (47\%) downloaded and used the app. Relatively younger patients with diabetes were 9\% more likely to try and use the app (P=.02, odds ratio [OR] 0.91, 95\% CI 0.85-0.98) than older patients with diabetes were. Extraversion was negatively associated with adoption of the mHealth app (P=.04, OR 0.71, 95\% CI 0.51-0.98), and openness to experience was positively associated with adoption of the app (P=.03, OR 1.73, 95\% CI 1.07-2.80). Gender (P=.43, OR 0.66, 95\% CI 0.23-1.88), education (senior: P=.99, OR 1.00, 95\% CI 0.32-3.11; higher: P=.21, OR 2.51, 95\% CI 0.59-10.66), and baseline HbA1c level (P=.36, OR 0.79, 95\% CI 0.47-1.31) were not associated with app adoption. Among those who adopted the app, a low education level (senior versus primary P=.003; higher versus primary P=.03) and a high level of openness to experience (P=.048, OR 2.01, 95\% CI 1.01-4.00) were associated with active app utilization. Active users showed a significantly greater decrease in HbA1c level than other users ($\Delta$HbA1c=?0.64, P=.05). Conclusions: This is one of the first studies to investigate how different personality traits influence the adoption and active utilization of an mHealth app among patients with diabetes. The research findings suggest that personality is a factor that should be considered when trying to identify patients who would benefit the most from apps for diabetes management. ", doi="10.2196/17709", url="https://mhealth.jmir.org/2020/8/e17709", url="http://www.ncbi.nlm.nih.gov/pubmed/32773382" } @Article{info:doi/10.2196/17534, author="Nelson, A. Lyndsay and Spieker, Andrew and Greevy, Robert and LeStourgeon, M. Lauren and Wallston, A. Kenneth and Mayberry, S. Lindsay", title="User Engagement Among Diverse Adults in a 12-Month Text Message--Delivered Diabetes Support Intervention: Results from a Randomized Controlled Trial", journal="JMIR Mhealth Uhealth", year="2020", month="Jul", day="21", volume="8", number="7", pages="e17534", keywords="engagement", keywords="text messaging", keywords="mobile health", keywords="mHealth", keywords="mobile phone", keywords="technology", keywords="diabetes mellitus, type 2", keywords="self-management", keywords="self-care", keywords="medication adherence", abstract="Background: Text message--delivered interventions are a feasible and scalable approach for improving chronic disease self-care and reducing health disparities; however, information on long-term user engagement with these interventions is limited. Objective: The aim of this study is to examine user engagement in a 12-month text message--delivered intervention supporting diabetes self-care, called REACH (Rapid Education/Encouragement And Communications for Health), among racially and socioeconomically diverse patients with type 2 diabetes (T2D). We explored time trends in engagement, associations between patient characteristics and engagement, and whether the addition of a human component or allowing patients to change their text frequency affected engagement. Qualitative data informed patients' subjective experience of their engagement. Methods: We recruited patients with T2D for a randomized trial evaluating mobile phone support relative to enhanced treatment as usual. This analysis was limited to participants assigned to the intervention. Participants completed a survey and hemoglobin A1c (HbA1c) test and received REACH text messages, including self-care promotion texts, interactive texts asking about medication adherence, and adherence feedback texts. For the first 6 months, texts were sent daily, and half of the participants also received monthly phone coaching. After 6 months, coaching stopped, and participants had the option to receive fewer texts for the subsequent 6 months. We defined engagement via responses to the interactive texts and responses to a follow-up interview. We used regression models to analyze associations with response rate and thematic and structural analysis to understand participants' reasons for responding to the texts and their preferred text frequency. Results: The participants were, on average, aged 55.8 (SD 9.8) years, 55.2\% (137/248) female, and 52.0\% (129/248) non-White; 40.7\% (101/248) had ? a high school education, and 40.7\% (101/248) had an annual household income