Samartphone sebagai Media Edukasi pada Pasien Diabetes Mellitus: A Systematic Review

Background: Type 2 diabetes (T2D) is associated with various health complications and health service visits, resulting in high costs for patients and the community. As a result, worldwide exponential growth of T2D has become a major issue. One strategy is to provide electronic (e-) health interventions. This intervention can reach many individuals in cost-effective and effective way to change behavior.Objective: To find out the development of smartphones as an educational medium in patients with type 2 diabetes mellitus. Method: This Systematic Review was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA). The database used in this study was Scopus, Proquest and Pubmed were limited to the last 5 years of publication from 2016 to 2020, full text article and in English. The keywords used were "diabetes mellitus type 2" AND "education" AND "smartphone". This systematic review used 10 articles that fit the inclusion criteria. Results: Increased used of technology in the treatment of diabetes facilitates increased communication between nurses and patients. Social networking technology was developing at an impressive pace. Recent advances in mobile health (mHealth) have created new opportunities to improve DMT2 self-management through tools to facilitate healthy eating, exercise, and access to health services. One such innovative model involves an integrated system that connects patients via smartphone phones with their support network. Recommendation: The recommendation for further research is to see the effectiveness and benefits of smartphone development, especially those from Indonesi

Using Smartphone Technology to Enhance Self-Management Support in Adults with Type 2 Diabetes in Primary Care

Background: Self-care is a crucial component in the management of Type 2 Diabetes Mellitus (T2DM). The literature shows that frequent feedback on blood sugar recordings together with reminders can lead to improvements in patient’s glucose control and diabetic self-care. The Chronic Care Model developed by Wagner emphasizes the importance of patient participation in the management of their chronic diseases. Purpose: To explore the possibility of using smartphone technology to help adults with T2DM better manage this chronic disease in a primary care clinic in New England. Methods: A smartphone application (App) OnTrack Diabetes (OnTrack) and text messaging were used to enhance communication between the patient and the provider. After receiving facility and IRB waivers, participants were recruited and trained on the downloaded App. Data was generated through text messages, the App and DNP student journaling, which was evaluated for patient and provider satisfaction with the App. Results: This project lasted about five months with five out of the seven participants recruited actually completing the project. Many (60%) of the participants found the App useful, 80% followed through with the instructions in the text messages and 60% intend to continue to use the App. Three out of five providers liked the App and intend to continue to use it. Most (80%) of the participants noticed a decrease in blood sugar, hemoglobin A1C and weight. Conclusion: This project confirms that an App and text messaging may be a useful tool for primary care providers to enhance self management in patients with T2DM and that frequent communication with the patient in between face to face office visits keeps them engaged and more compliant with diabetes management

Integrating Technology to Support and Maintain Glycemic Control in People With Diabetes

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCEType II diabetes is a chronic disease state that leads to increased morbidity and mortality and impacts the lives of millions of Americans. This quality improvement project explored the use of a free smartphone application, Glucose Buddy, in aiding people with Type II diabetes to achieve and maintain glycemic control. The project was conducted through the involvement of patients at the Creekside Family Health Clinic in Ketchikan, Alaska over a three month time period. Pre-intervention hemoglobin A1c (HA1c) was compared with post-intervention HA1c. The project, due to the small sample size and high withdraw rate, was not statistically significant. However, there was clinical significance as it showed a decrease in HA1c levels in 60% of the participants.Abstract / Introduction / Literature Review and Synthesis / Problem Statement / Research Question / Methodology / Results / Limitations / Conclusions / Outcomes / Impact on Practice / Dissemination / References / Appendix A / Appendix B / Appendix C / Appendix

App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison

[EN] Background: Research in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored. Objective: To study the gap between literature and available apps for type 1 diabetes self-management and patient empowerment and to discover the features that an ideal app should provide to people with diabetes. Methods: The methodology comprises systematic reviews in the scientific literature and app marketplaces. We included articles describing interventions that demonstrated an effect on diabetes management with particular clinical endpoints through the use of mobile technologies. The features of these apps were gathered in a taxonomy of what an ideal app should look like to then assess which of these features are available in the market. Results: The literature search resulted in 231 matches. Of these, 55 met the inclusion criteria. A taxonomy featuring 3 levels of characteristics was designed based on 5 papers which were selected for the synthesis. Level 1 includes 10 general features (Personalization, Family support, Agenda, Data record, Insulin bolus calculator, Data management, Interaction, Tips and support, Reminders, and Rewards) Level 2 and Level 3 included features providing a descriptive detail of Level 1 features. Eighty apps matching the inclusion criteria were analyzed. None of the assessed apps fulfilled the features of the taxonomy of an ideal app. Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Agenda (5/80, 6.3%) and Rewards (3/80, 3.8%) where the less predominant. The operating system was not associated with the number of features (P=.42, F=.81) nor the type of feature (P=.20, ¿2=11.7). Apps were classified according to the number of level 1 features and sorted into quartiles. First quartile apps had a regular distribution of the ten features in the taxonomy whereas the other 3 quartiles had an irregular distribution. Conclusions: There are significant gaps between research and the market in mobile health for type 1 diabetes management. While the literature focuses on aspects related to gamification, rewarding, and social communities, the available apps are focused on disease management aspects such as data record and appointments. Personalized and tailored empowerment features should be included in commercial apps for large-scale assessment of potential in the self-management of the diseaseMartinez-Millana, A.; Jarones, E.; Fernández Llatas, C.; Hartvigsen, G.; Traver Salcedo, V. (2018). App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison. JMIR mHealth and uHealth. 6(11). https://doi.org/10.2196/12237S611(2016). 2. Classification and Diagnosis of Diabetes. Diabetes Care, 40(Supplement 1), S11-S24. doi:10.2337/dc17-s005Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Practice, 103(2), 137-149. doi:10.1016/j.diabres.2013.11.002Modern-Day Clinical Course of Type 1 Diabetes Mellitus After 30 Years’ Duration. (2009). Archives of Internal Medicine, 169(14), 1307. doi:10.1001/archinternmed.2009.193Martinez-Millana, A., Fico, G., Fernández-Llatas, C., & Traver, V. (2015). Performance assessment of a closed-loop system for diabetes management. Medical & Biological Engineering & Computing, 53(12), 1295-1303. doi:10.1007/s11517-015-1245-3Lim, S., Kang, S. M., Shin, H., Lee, H. J., Won Yoon, J., Yu, S. H., … Jang, H. C. (2011). Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System. Diabetes Care, 34(2), 308-313. doi:10.2337/dc10-1447Wang, J., Wang, Y., Wei, C., Yao, N. (Aaron), Yuan, A., Shan, Y., & Yuan, C. (2014). Smartphone Interventions for Long-Term Health Management of Chronic Diseases: An Integrative Review. Telemedicine and e-Health, 20(6), 570-583. doi:10.1089/tmj.2013.0243Ashurst, E. J., Jones, R. B., Abraham, C., Jenner, M., Boddy, K., Besser, R. E., & Hammersley, S. (2014). The Diabetes App Challenge: User-Led Development and Piloting of Internet Applications Enabling Young People With Diabetes to Set the Focus for Their Diabetes Consultations. Medicine 2.0, 3(2), e5. doi:10.2196/med20.3032Chomutare, T., Fernandez-Luque, L., Årsand, E., & Hartvigsen, G. (2011). Features of Mobile Diabetes Applications: Review of the Literature and Analysis of Current Applications Compared Against Evidence-Based Guidelines. Journal of Medical Internet Research, 13(3), e65. doi:10.2196/jmir.1874Chavez, S., Fedele, D., Guo, Y., Bernier, A., Smith, M., Warnick, J., & Modave, F. (2017). Mobile Apps for the Management of Diabetes. Diabetes Care, 40(10), e145-e146. doi:10.2337/dc17-0853Castensøe-Seidenfaden, P., Reventlov Husted, G., Teilmann, G., Hommel, E., Olsen, B. S., & Kensing, F. (2017). Designing a Self-Management App for Young People With Type 1 Diabetes: Methodological Challenges, Experiences, and Recommendations. JMIR mHealth and uHealth, 5(10), e124. doi:10.2196/mhealth.8137HigginsJCochrane Handbook for Systematic Reviews of Interventions Version 520112018-10-29The Cochrane Collaborationhttps://training.cochrane.org/handbookCastensøe-Seidenfaden, P., Husted, G. R., Jensen, A. K., Hommel, E., Olsen, B., Pedersen-Bjergaard, U., … Teilmann, G. (2018). Testing a Smartphone App (Young with Diabetes) to Improve Self-Management of Diabetes Over 12 Months: Randomized Controlled Trial. JMIR mHealth and uHealth, 6(6), e141. doi:10.2196/mhealth.9487Cafazzo, J. A., Casselman, M., Hamming, N., Katzman, D. K., & Palmert, M. R. (2012). Design of an mHealth App for the Self-management of Adolescent Type 1 Diabetes: A Pilot Study. Journal of Medical Internet Research, 14(3), e70. doi:10.2196/jmir.2058Goyal, S., Nunn, C. A., Rotondi, M., Couperthwaite, A. B., Reiser, S., Simone, A., … Palmert, M. R. (2017). A Mobile App for the Self-Management of Type 1 Diabetes Among Adolescents: A Randomized Controlled Trial. JMIR mHealth and uHealth, 5(6), e82. doi:10.2196/mhealth.7336Kirwan, M., Vandelanotte, C., Fenning, A., & Duncan, M. J. (2013). Diabetes Self-Management Smartphone Application for Adults With Type 1 Diabetes: Randomized Controlled Trial. Journal of Medical Internet Research, 15(11), e235. doi:10.2196/jmir.2588Clements, M. A., & Staggs, V. S. (2017). A Mobile App for Synchronizing Glucometer Data: Impact on Adherence and Glycemic Control Among Youths With Type 1 Diabetes in Routine Care. Journal of Diabetes Science and Technology, 11(3), 461-467. doi:10.1177/1932296817691302Ryan, E. A., Holland, J., Stroulia, E., Bazelli, B., Babwik, S. A., Li, H., … Greiner, R. (2017). Improved A1C Levels in Type 1 Diabetes with Smartphone App Use. Canadian Journal of Diabetes, 41(1), 33-40. doi:10.1016/j.jcjd.2016.06.001Sun, C., Malcolm, J. C., Wong, B., Shorr, R., & Doyle, M.-A. (2019). Improving Glycemic Control in Adults and Children With Type 1 Diabetes With the Use of Smartphone-Based Mobile Applications: A Systematic Review. Canadian Journal of Diabetes, 43(1), 51-58.e3. doi:10.1016/j.jcjd.2018.03.010Chen, L., Chuang, L.-M., Chang, C.-H., Wang, C.-S., Wang, I.-C., Chung, Y., … Lai, F. (2013). Evaluating Self-Management Behaviors of Diabetic Patients in a Telehealthcare Program: Longitudinal Study Over 18 Months. Journal of Medical Internet Research, 15(12), e266. doi:10.2196/jmir.2699Tomky, D., Tomky, D., Cypress, M., Dang, D., Maryniuk, M., Peyrot, M., & Mensing, C. (2008). Aade Position Statement. The Diabetes Educator, 34(3), 445-449. doi:10.1177/0145721708316625Ye, Q., Khan, U., Boren, S. A., Simoes, E. J., & Kim, M. S. (2018). An Analysis of Diabetes Mobile Applications Features Compared to AADE7™: Addressing Self-Management Behaviors in People With Diabetes. Journal of Diabetes Science and Technology, 12(4), 808-816. doi:10.1177/1932296818754907Holtz, B. E., Murray, K. M., Hershey, D. D., Dunneback, J. K., Cotten, S. R., Holmstrom, A. J., … Wood, M. A. (2017). Developing a Patient-Centered mHealth App: A Tool for Adolescents With Type 1 Diabetes and Their Parents. JMIR mHealth and uHealth, 5(4), e53. doi:10.2196/mhealth.6654Gabarron, E., Årsand, E., & Wynn, R. (2018). Social Media Use in Interventions for Diabetes: Rapid Evidence-Based Review. Journal of Medical Internet Research, 20(8), e10303. doi:10.2196/10303Sannino, G., Forastiere, M., & De Pietro, G. (2017). A Wellness Mobile Application for Smart Health: Pilot Study Design and Results. Sensors, 17(3), 611. doi:10.3390/s17030611Adams, R. (2010). Improving health outcomes with better patient understanding and education. Risk Management and Healthcare Policy, 61. doi:10.2147/rmhp.s7500Giunti, G. (2018). 3MD for Chronic Conditions, a Model for Motivational mHealth Design: Embedded Case Study. JMIR Serious Games, 6(3), e11631. doi:10.2196/11631Dagliati, A., Sacchi, L., Tibollo, V., Cogni, G., Teliti, M., Martinez-Millana, A., … Bellazzi, R. (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association, 25(5), 538-547. doi:10.1093/jamia/ocx159Martinez-Millana, A., Bayo-Monton, J.-L., Argente-Pla, M., Fernandez-Llatas, C., Merino-Torres, J., & Traver-Salcedo, V. (2017). 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Patient characteristics associated with objective measures of digital health tool use in the United States: A literature review.

The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States.We conducted a literature review of studies of patient-facing digital health tools that objectively evaluated use (eg, system/platform data representing frequency of use) by patient characteristics (eg, age, race or ethnicity, income, digital literacy). We included any type of patient-facing digital health tool except patient portals. We reran results using the subset of studies identified as having robust methodology to detect differences in patient characteristics.We included 29 studies; 13 had robust methodology. Most studies examined smartphone apps and text messaging programs for chronic disease management and evaluated only 1-3 patient characteristics, primarily age and gender. Overall, the majority of studies found no association between patient characteristics and use. Among the subset with robust methodology, white race and poor health status appeared to be associated with higher use.Given the substantial investment in digital health tools, it is surprising how little is known about the types of patients who use them. Strategies that engage diverse populations in digital health tool use appear to be needed.Few studies evaluate objective measures of digital health tool use by patient characteristics, and those that do include a narrow range of characteristics. Evidence suggests that resources and need drive use

Increasing the Capacity of Primary Care Through Enabling Technology.

Primary care is the foundation of effective and high-quality health care. The role of primary care clinicians has expanded to encompass coordination of care across multiple providers and management of more patients with complex conditions. Enabling technology has the potential to expand the capacity for primary care clinicians to provide integrated, accessible care that channels expertise to the patient and brings specialty consultations into the primary care clinic. Furthermore, technology offers opportunities to engage patients in advancing their health through improved communication and enhanced self-management of chronic conditions. This paper describes enabling technologies in four domains (the body, the home, the community, and the primary care clinic) that can support the critical role primary care clinicians play in the health care system. It also identifies challenges to incorporating these technologies into primary care clinics, care processes, and workflow

Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review

Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps

Prescribable mHealth apps identified from an overview of systematic reviews

AbstractMobile health apps aimed towards patients are an emerging field of mHealth. Their potential for improving self-management of chronic conditions is significant. Here, we propose a concept of “prescribable” mHealth apps, defined as apps that are currently available, proven effective, and preferably stand-alone, i.e., that do not require dedicated central servers and continuous monitoring by medical professionals. Our objectives were to conduct an overview of systematic reviews to identify such apps, assess the evidence of their effectiveness, and to determine the gaps and limitations in mHealth app research. We searched four databases from 2008 onwards and the Journal of Medical Internet Research for systematic reviews of randomized controlled trials (RCTs) of stand-alone health apps. We identified 6 systematic reviews including 23 RCTs evaluating 22 available apps that mostly addressed diabetes, mental health and obesity. Most trials were pilots with small sample size and of short duration. Risk of bias of the included reviews and trials was high. Eleven of the 23 trials showed a meaningful effect on health or surrogate outcomes attributable to apps. In conclusion, we identified only a small number of currently available stand-alone apps that have been evaluated in RCTs. The overall low quality of the evidence of effectiveness greatly limits the prescribability of health apps. mHealth apps need to be evaluated by more robust RCTs that report between-group differences before becoming prescribable. Systematic reviews should incorporate sensitivity analysis of trials with high risk of bias to better summarize the evidence, and should adhere to the relevant reporting guideline.</jats:p