%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e64400 %T Effects of Digital Intelligent Interventions on Self-Management of Patients With Diabetic Foot: Systematic Review %A Zhou,Jinyan %A Ding,Shanni %A Xu,Yihong %A Pan,Hongying %+ Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun East Road, Hangzhou, 310016, China, 86 13857188922, 3191016@zju.edu.cn %K diabetic foot %K self-management %K digital intelligent intervention %K systematic review %K mobile phone %D 2025 %7 25.3.2025 %9 Review %J J Med Internet Res %G English %X Background: Diabetic foot (DF) is one of the most common and serious complications of diabetes. Effective self-management by patients can delay disease progression and improve quality of life. Digital intelligent technologies have emerged as advantageous in assisting patients with chronic diseases in self-management. However, the impact of digital intelligent technologies on self-management of patients with DF remains unclear. Objective: This systematic review aimed to determine the effects of digital intelligent interventions on self-management in patients with DF. Methods: A systematic literature search was conducted across PubMed, Web of Science, Embase, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Cochrane Central Register of Controlled Trial, ProQuest, China National Knowledge Internet, WanFang, China Science and Technology Journal Database, and SinoMed up to February 6, 2025, to identify eligible articles. Randomized controlled trials (RCTs) that assessed the effects of digital intelligent interventions on self-management of patients with DF were included. In total, 2 researchers independently conducted literature screening, quality assessment, and data extraction. The Cochrane Risk of Bias 2.0 tool (revised version 2019) for RCTs was used to assess the quality of the studies. A qualitative synthesis was conducted on the extracted data. Results: In total, 1079 articles were retrieved, and 18 RCTs were included. All studies were rated as having a high risk of bias. The digital intelligent interventions in the included studies varied in forms, components, and durations. The intervention forms included WeChat (Tencent Holdings Limited; 7/18, 39%), apps (4/18, 22%), electronic platforms (3/18, 17%), mixed interventions (3/18, 17%), and smartphone thermography (1/18, 6%). The intervention components included self-management education (17/18, 94%), blood glucose and foot condition monitoring (8/18, 44%), self-management supervision and follow-up (6/18, 33%), and other components like foot risk assessment, foot care reminders, visit reminders, and remote consultations. Intervention durations ranged from 5 weeks to 12 months, with the majority (10/18, 56%) lasting 6 months. Among the 18 included studies, 17 studies (17/18, 94%) indicated that, compared with routine care, digital intelligent interventions significantly improved the self-management behaviors of patients with DF, including diabetes control, foot care behaviors, and blood glucose monitoring. Only 1 study (1/18, 6%) showed that the effects of digital intelligent interventions were not significantly different from those of routine care. Conclusions: In this systematic review, evidence suggests that digital intelligent interventions can improve self-management behaviors and capabilities in patients with DF. However, due to the overall low quality of the included studies, current evidence should be interpreted and applied with caution. This field is still in the exploratory stage, with significant heterogeneity among different studies and a lack of consensus on intervention strategies, necessitating further exploration tailored to different populations. Future RCTs with large sample sizes and rigorous design are needed to develop high-quality evidence. Trial Registration: PROSPERO CRD42024524473; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024524473 %M 40132178 %R 10.2196/64400 %U https://www.jmir.org/2025/1/e64400 %U https://doi.org/10.2196/64400 %U http://www.ncbi.nlm.nih.gov/pubmed/40132178 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 9 %N %P e58327 %T Personalized and Culturally Tailored Features of Mobile Apps for Gestational Diabetes Mellitus and Their Impact on Patient Self-Management: Scoping Review %A Jones,Catherine %A Cui,Yi %A Jeminiwa,Ruth %A Bajracharya,Elina %A Chang,Katie %A Ma,Tony %K gestational diabetes %K pregnancy %K cultural tailoring %K cultural adaptation %K personalization %K maternal health %K mobile health %K mHealth %K smartphone %K mobile app %K mobile phone %K self management; social determinants of health %D 2024 %7 12.12.2024 %9 %J JMIR Diabetes %G English %X Background: Gestational diabetes mellitus (GDM) is an increasingly common high-risk pregnancy condition requiring intensive daily self-management, placing the burden of care directly on the patient. Understanding personal and cultural differences among patients is critical for delivering optimal support for GDM self-management, particularly in high-risk populations. Although mobile apps for GDM self-management are being used, limited research has been done on the personalized and culturally tailored features of these apps and their impact on patient self-management. Objective: This scoping review aims to explore the extent to which published studies report the integration and effectiveness of personalized and culturally tailored features in GDM mobile apps for patient self-management support. Methods: We examined English-language peer-reviewed articles published between October 2016 and May 2023 from PubMed, CINAHL, PsycINFO, ClinicalTrials.gov, Proquest Research Library, and Google Scholar using search terms related to digital tools, diabetes, pregnancy, and cultural tailoring. We reviewed eligible articles and extracted data using the Arskey and O’Malley methodological framework. Results: Our search yielded a total of 1772 articles after the removal of duplicates and 158 articles for full-text review. A total of 21 articles that researched 15 GDM mobile apps were selected for data extraction. Our results demonstrated the stark contrast between the number of GDM mobile apps with personalized features for the individual user (all 15 mobile apps) and those culturally tailored for a specific population (only 3 of the 15 mobile apps). Our findings showed that GDM mobile apps with personalized and culturally tailored features were perceived to be useful to patients and had the potential to improve patients’ adherence to glycemic control and nutrition plans. Conclusions: There is a strong need for increased research and development to foster the implementation of personalized and culturally tailored features in GDM mobile apps for self-management that cater to patients from diverse backgrounds and ethnicities. Personalized and culturally tailored features have the potential to serve the unique needs of patients more efficiently and effectively than generic features alone; however, the impacts of such features still need to be adequately studied. Recommendations for future research include examining the cultural needs of different ethnicities within the increasingly diverse US population in the context of GDM self-management, conducting participatory-based research with these groups, and designing human-centered mobile health solutions for both patients and providers. %R 10.2196/58327 %U https://diabetes.jmir.org/2024/1/e58327 %U https://doi.org/10.2196/58327 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e54826 %T The Role of Smartwatch Technology in the Provision of Care for Type 1 or 2 Diabetes Mellitus or Gestational Diabetes: Systematic Review %A Diez Alvarez,Sergio %A Fellas,Antoni %A Wynne,Katie %A Santos,Derek %A Sculley,Dean %A Acharya,Shamasunder %A Navathe,Pooshan %A Gironès,Xavier %A Coda,Andrea %K diabetes mellitus %K flash glucose monitoring %K digital health %K smartwatch %K smartphones %K mHealth %K mobile health %K glucose monitoring %K diabetes %K gestational diabetes %K systematic review %K smartwatch technology %K blood glucose %K medication adherence %K self-monitoring %K usability %K feasibility %K mobile phone %D 2024 %7 3.12.2024 %9 %J JMIR Mhealth Uhealth %G English %X Background: The use of smart technology in the management of all forms of diabetes mellitus has grown significantly in the past 10 years. Technologies such as the smartwatch have been proposed as a method of assisting in the monitoring of blood glucose levels as well as other alert prompts such as medication adherence and daily physical activity targets. These important outcomes reach across all forms of diabetes and have the potential to increase compliance of self-monitoring with the aim of improving long-term outcomes such as hemoglobin A1c (HbA1c). Objective: This systematic review aims to explore the literature for evidence of smartwatch technology in type 1, 2, and gestational diabetes. Methods: A systematic review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to August 2023), Embase (January 1980 to August 2023), Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, ACM Digital Libraries, and Web of Science databases. Type 1, type 2, and gestational diabetes were eligible for inclusion. Quantitative studies such as prospective cohort or randomized clinical trials that explored the feasibility, usability, or effect of smartwatch technology in people with diabetes were eligible. Outcomes of interest were changes in blood glucose or HbA1c, physical activity levels, medication adherence, and feasibility or usability scores. Results: Of the 8558 titles and abstracts screened, 5 studies were included for qualitative synthesis in this review. A total of 322 participants with either type 1 or type 2 diabetes mellitus were included in the review. A total of 4 studies focused on the feasibility and usability of smartwatch technology in diabetes management. One study conducted a proof-of-concept randomized clinical trial including smartwatch technology for exercise time prescriptions for participants with type 2 diabetes mellitus. Adherence of participants to smartwatch technology varied between included studies, with one reporting input submissions of 58% and another reporting that participants logged 50% more entries than they were required to. One study reported significantly improved glycemic control with integrated smartwatch technology, with increased exercise prescriptions; however, this study was not powered and required a longer observational period. Conclusions: This systematic review has highlighted the lack of robust randomized clinical trials that explore the efficacy of smartwatch technology in the management of patients with type 1, type 2, and gestational diabetes. Further research is required to establish the role of integrated smartwatch technology in important outcomes such as glycemic control, exercise participation, drug adherence, and diet monitoring in people with all forms of diabetes mellitus. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019136825; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136825 %R 10.2196/54826 %U https://mhealth.jmir.org/2024/1/e54826 %U https://doi.org/10.2196/54826 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e51802 %T Understanding Gaps in the Hypertension and Diabetes Care Cascade: Systematic Scoping Review %A Wang,Jie %A Tan,Fangqin %A Wang,Zhenzhong %A Yu,Yiwen %A Yang,Jingsong %A Wang,Yueqing %A Shao,Ruitai %A Yin,Xuejun %+ School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, 31 Beijige San Tiao, Dongcheng District, Beijing, 100005, China, 86 18600988138, yinxuejun@cams.cn %K care cascade %K hypertension %K diabetes %K scoping review %K hypertension and diabetes care %K review %D 2024 %7 16.2.2024 %9 Review %J JMIR Public Health Surveill %G English %X Background: Hypertension and diabetes are global health challenges requiring effective management to mitigate their considerable burden. The successful management of hypertension and diabetes requires the completion of a sequence of stages, which are collectively termed the care cascade. Objective: This scoping review aimed to describe the characteristics of studies on the hypertension and diabetes care cascade and identify potential interventions as well as factors that impact each stage of the care cascade. Methods: The method of this scoping review has been guided by the framework by Arksey and O’Malley. We systematically searched MEDLINE, Embase, and Web of Science using terms pertinent to hypertension, diabetes, and specific stages of the care cascade. Articles published after 2011 were considered, and we included all studies that described the completion of at least one stage of the care cascade of hypertension and diabetes. Study selection was independently performed by 2 paired authors. Descriptive statistics were used to elucidate key patterns and trends. Inductive content analysis was performed to generate themes regarding the barriers and facilitators for improving the care cascade in hypertension and diabetes management. Results: A total of 128 studies were included, with 42.2% (54/128) conducted in high-income countries. Of them, 47 (36.7%) focused on hypertension care, 63 (49.2%) focused on diabetes care, and only 18 (14.1%) reported on the care of both diseases. The majority (96/128, 75.0%) were observational in design. Cascade stages documented in the literature were awareness, screening, diagnosis, linkage to care, treatment, adherence to medication, and control. Most studies focused on the stages of treatment and control, while a relative paucity of studies examined the stages before treatment initiation (76/128, 59.4% vs 52/128, 40.6%). There was a wide spectrum of interventions aimed at enhancing the hypertension and diabetes care cascade. The analysis unveiled a multitude of individual-level and system-level factors influencing the successful completion of cascade sequences in both high-income and low- and middle-income settings. Conclusions: This review offers a comprehensive understanding of hypertension and diabetes management, emphasizing the pivotal factors that impact each stage of care. Future research should focus on upstream cascade stages and context-specific interventions to optimize patient retention and care outcomes. %M 38149840 %R 10.2196/51802 %U https://publichealth.jmir.org/2024/1/e51802 %U https://doi.org/10.2196/51802 %U http://www.ncbi.nlm.nih.gov/pubmed/38149840 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 8 %N %P e44652 %T Technology-Supported Integrated Care Innovations to Support Diabetes and Mental Health Care: Scoping Review %A Racey,Megan %A Whitmore,Carly %A Alliston,Paige %A Cafazzo,Joseph A %A Crawford,Allison %A Castle,David %A Dragonetti,Rosa %A Fitzpatrick-Lewis,Donna %A Jovkovic,Milos %A Melamed,Osnat C %A Naeem,Farooq %A Senior,Peter %A Strudwick,Gillian %A Ramdass,Seeta %A Vien,Victor %A Selby,Peter %A Sherifali,Diana %+ McMaster Evidence Review and Synthesis Team, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4L8, Canada, 1 905 525 9140 ext 21435, dsherif@mcmaster.ca %K technology %K mental health %K type 2 diabetes %K type 1 diabetes %K virtual care %K integrated care %K scoping review %K health information technology %K digital health %K support %K psychosocial %K education %K application %K distress %K clinical integration %K intervention %D 2023 %7 9.5.2023 %9 Review %J JMIR Diabetes %G English %X Background: For individuals living with diabetes and its psychosocial comorbidities (eg, depression, anxiety, and distress), there remains limited access to interprofessional, integrated care that includes mental health support, education, and follow-up. Health technology, broadly defined as the application of organized knowledge or skill as software, devices, and systems to solve health problems and improve quality of life, is emerging as a means of addressing these gaps. There is thus a need to understand how such technologies are being used to support, educate, and help individuals living with co-occurring diabetes and mental health distress or disorder. Objective: The purpose of this scoping review was to (1) describe the literature on technology-enabled integrated interventions for diabetes and mental health; (2) apply frameworks from the Mental Health Commission of Canada and World Health Organization to elucidate the components, type, processes, and users of technology-enabled integrated interventions for diabetes and mental health; and (3) map the level of integration of interventions for diabetes and mental health. Methods: We searched 6 databases from inception to February 2022 for English-language, peer-reviewed studies of any design or type that used technology to actively support both diabetes and any mental health distress or disorder in succession or concurrently among people with diabetes (type 1 diabetes, type 2 diabetes, and gestational diabetes). Reviewers screened citations and extracted data including study characteristics and details about the technology and integration used. Results: We included 24 studies described in 38 publications. These studies were conducted in a range of settings and sites of care including both web-based and in-person settings. Studies were mostly website-based (n=13) and used technology for wellness and prevention (n=16) and intervention and treatment (n=15). The primary users of these technologies were clients and health care providers. All the included intervention studies (n=20) used technology for clinical integration, but only 7 studies also used the technology for professional integration. Conclusions: The findings of this scoping review suggest that there is a growing body of literature on integrated care for diabetes and mental health enabled by technology. However, gaps still exist with how to best equip health care professionals with the knowledge and skills to offer integrated care. Future research is needed to continue to explore the purpose, level, and breadth of technology-enabled integration to facilitate an approach to overcome or address care fragmentation for diabetes and mental health and to understand how health technology can further drive the scale-up of innovative integrated interventions. %M 37159256 %R 10.2196/44652 %U https://diabetes.jmir.org/2023/1/e44652 %U https://doi.org/10.2196/44652 %U http://www.ncbi.nlm.nih.gov/pubmed/37159256 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 7 %N 4 %P e38910 %T Smartphone Apps for Surveillance of Gestational Diabetes: Scoping Review %A Smyth,Suzanne %A Curtin,Eimear %A Tully,Elizabeth %A Molphy,Zara %A Breathnach,Fionnuala %+ Rotunda Hospital, Parnell Square, Dublin, D01P5W9, Ireland, 353 1 402 2540 ext 2540, suzannejksmyth@gmail.com %K gestational diabetes %K digital health %K mHealth %K telemedicine %K diabetes %K apps %K smartphone %K remote feedback %D 2022 %7 21.11.2022 %9 Review %J JMIR Diabetes %G English %X Background: Developments and evolutions in the information and communication technology sector have provided a solid foundation for the emergence of mobile health (mHealth) in recent years. The cornerstone to management of gestational diabetes mellitus (GDM) is the self-management of glycemic indices, dietary intake, and lifestyle adaptations. Given this, it is readily adaptable to incorporation of remote monitoring strategies involving mHealth solutions. Objective: We sought to examine and assess the available smartphone apps which enable self-monitoring and remote surveillance of GDM with a particular emphasis on the generation of individualized patient feedback. Methods: Five databases were searched systematically for any studies evaluating mHealth-supported smartphone solutions for GDM management from study inception until January 2022. The studies were screened and assessed for eligibility of inclusion by 2 independent reviewers. Ultimately, 17 studies were included involving 1871 patients across 11 different countries. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) conceptual framework was adhered to for data extraction and categorization purposes. Results: All studies analyzed as part of this review facilitated direct uploading of data from the handheld glucometer to the downloaded patient-facing smartphone app. Glycemic data were captured by all studies and were reassuringly found to be either improved or noninferior to extant models of hospital-based care. Feedback was delivered in either an automated fashion through in-app communication from the health care team or facilitated through bidirectional communication with the app and hospital portal. Although resource utilization and cost-effective analyses were reported in some studies, the results were disparate and require more robust analysis. Where patient and staff satisfaction levels were evaluated, the response was overwhelmingly positive for mHealth smartphone–delivered care strategies. Emergency cesarean section rates were reduced; however, elective cesarean sections were comparatively increased among studies where the mode of delivery was assessed. Most reviewed studies did not identify any differences in maternal, perinatal, or neonatal health when app-based care was compared with usual in-person review. Conclusions: This comprehensive scoping review highlights the feasibility, reliability, and acceptability of app-assisted health care for the management of GDM. Although further exploration of the economic benefit is required prior to implementation in a real-world clinical setting, the prospect of smartphone-assisted health care for GDM is hugely promising %M 36409549 %R 10.2196/38910 %U https://diabetes.jmir.org/2022/4/e38910 %U https://doi.org/10.2196/38910 %U http://www.ncbi.nlm.nih.gov/pubmed/36409549 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 7 %N 3 %P e34699 %T Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review %A Tsichlaki,Stella %A Koumakis,Lefteris %A Tsiknakis,Manolis %+ Department of Electrical & Computer Engineering, Hellenic Mediterranean University, Gianni Kornarou, Estavromenos 1, Heraklion, 71004, Greece, 30 6945231917, stsichlaki@gmail.com %K type 1 diabetes %K hypoglycemia %K predictive models %K continuous glucose monitoring %K heart rate variability %K artificial intelligence %D 2022 %7 21.7.2022 %9 Review %J JMIR Diabetes %G English %X Background: Diabetes is a chronic condition that necessitates regular monitoring and self-management of the patient’s blood glucose levels. People with type 1 diabetes (T1D) can live a productive life if they receive proper diabetes care. Nonetheless, a loose glycemic control might increase the risk of developing hypoglycemia. This incident can occur because of a variety of causes, such as taking additional doses of insulin, skipping meals, or overexercising. Mainly, the symptoms of hypoglycemia range from mild dysphoria to more severe conditions, if not detected in a timely manner. Objective: In this review, we aimed to report on innovative detection techniques and tactics for identifying and preventing hypoglycemic episodes, focusing on T1D. Methods: A systematic literature search following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was performed focusing on the PubMed, GoogleScholar, IEEEXplore, and ACM Digital Library to find articles on technologies related to hypoglycemia detection in patients with T1D. Results: The presented approaches have been used or devised to enhance blood glucose monitoring and boost its efficacy in forecasting future glucose levels, which could aid the prediction of future episodes of hypoglycemia. We detected 19 predictive models for hypoglycemia, specifically on T1D, using a wide range of algorithmic methodologies, spanning from statistics (1.9/19, 10%) to machine learning (9.88/19, 52%) and deep learning (7.22/19, 38%). The algorithms used most were the Kalman filtering and classification models (support vector machine, k-nearest neighbors, and random forests). The performance of the predictive models was found to be satisfactory overall, reaching accuracies between 70% and 99%, which proves that such technologies are capable of facilitating the prediction of T1D hypoglycemia. Conclusions: It is evident that continuous glucose monitoring can improve glucose control in diabetes; however, predictive models for hypo- and hyperglycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mobile health in T1D. Prospective studies are required to demonstrate the value of such models in real-life mobile health interventions. %M 35862181 %R 10.2196/34699 %U https://diabetes.jmir.org/2022/3/e34699 %U https://doi.org/10.2196/34699 %U http://www.ncbi.nlm.nih.gov/pubmed/35862181 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 7 %N 2 %P e33264 %T Smartphone Apps for Diabetes Medication Adherence: Systematic Review %A Islam,Sheikh Mohammed Shariful %A Mishra,Vinaytosh %A Siddiqui,Muhammad Umer %A Moses,Jeban Chandir %A Adibi,Sasan %A Nguyen,Lemai %A Wickramasinghe,Nilmini %+ Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, 221 Burwood Highway, Burwood, Melbourne, 3125, Australia, 61 0392468393, shariful@deakin.edu.au %K smartphones %K digital health %K diabetes %K medication adherence %K applications %K apps %K mHealth %K mobile health %K task-technology fit %D 2022 %7 21.6.2022 %9 Review %J JMIR Diabetes %G English %X Background: Diabetes is one of the leading noncommunicable chronic diseases globally. In people with diabetes, blood glucose levels need to be monitored regularly and managed adequately through healthy lifestyles and medications. However, various factors contribute to poor medication adherence. Smartphone apps can improve medication adherence in people with diabetes, but it is not clear which app features are most beneficial. Objective: This study aims to systematically review and evaluate high-quality apps for diabetes medication adherence, which are freely available to the public in Android and Apple app stores and present the technical features of the apps. Methods: We systematically searched Apple App Store and Google Play for apps that assist in diabetes medication adherence, using predefined selection criteria. We assessed apps using the Mobile App Rating Scale (MARS) and calculated the mean app-specific score (MASS) by taking the average of app-specific scores on 6 dimensions, namely, awareness, knowledge, attitudes, intention to change, help-seeking, and behavior change rated on a 5-point scale (1=strongly disagree and 5=strongly agree). We used the mean of the app’s performance on these 6 dimensions to calculate the MASS. Apps that achieved a total MASS mean quality score greater than 4 out of 5 were considered to be of high quality in our study. We formulated a task-technology fit matrix to evaluate the apps for diabetes medication adherence. Results: We identified 8 high-quality apps (MASS score≥4) and presented the findings under 3 main categories: characteristics of the included apps, app features, and diabetes medication adherence. Our framework to evaluate smartphone apps in promoting diabetes medication adherence considered physiological factors influencing diabetes and app features. On evaluation, we observed that 25% of the apps promoted high adherence and another 25% of the apps promoted moderate adherence. Finally, we found that 50% of the apps provided low adherence to diabetes medication. Conclusions: Our findings show that almost half of the high-quality apps publicly available for free did not achieve high to moderate medication adherence. Our framework could have positive implications for the future design and development of apps for patients with diabetes. Additionally, apps need to be evaluated using a standardized framework, and only those promoting higher medication adherence should be prescribed for better health outcomes. %M 35727613 %R 10.2196/33264 %U https://diabetes.jmir.org/2022/2/e33264 %U https://doi.org/10.2196/33264 %U http://www.ncbi.nlm.nih.gov/pubmed/35727613 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 7 %N 2 %P e34681 %T Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review %A Zheng,Yaguang %A Dickson,Victoria Vaughan %A Blecker,Saul %A Ng,Jason M %A Rice,Brynne Campbell %A Melkus,Gail D’Eramo %A Shenkar,Liat %A Mortejo,Marie Claire R %A Johnson,Stephen B %+ Rory Meyers College of Nursing, New York University, 433 1st Avenue, New York, NY, 10010, United States, 1 212 998 5170, yaguang.zheng@nyu.edu %K hypoglycemia %K natural language processing %K electronic health records %K diabetes %D 2022 %7 16.5.2022 %9 Review %J JMIR Diabetes %G English %X Background: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. Objective: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. Methods: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. Results: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. Conclusions: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing. %M 35576579 %R 10.2196/34681 %U https://diabetes.jmir.org/2022/2/e34681 %U https://doi.org/10.2196/34681 %U http://www.ncbi.nlm.nih.gov/pubmed/35576579 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 7 %N 1 %P e28861 %T Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review %A Hettiarachchi,Chirath %A Daskalaki,Elena %A Desborough,Jane %A Nolan,Christopher J %A O’Neal,David %A Suominen,Hanna %+ School of Computing, College of Engineering and Computer Science, The Australian National University, Hanna Neumann Building, 145 Science Road, Acton, Canberra, 2600, Australia, 61 414066695, chirath.hettiarachchi@anu.edu.au %K diabetes mellitus, type 1 %K pancreas, artificial %K algorithms %K multivariate analysis %K insulin infusion systems %K control systems %D 2022 %7 24.2.2022 %9 Review %J JMIR Diabetes %G English %X Background: Type 1 diabetes (T1D) is a chronic autoimmune disease in which a deficiency in insulin production impairs the glucose homeostasis of the body. Continuous subcutaneous infusion of insulin is a commonly used treatment method. Artificial pancreas systems (APS) use continuous glucose level monitoring and continuous subcutaneous infusion of insulin in a closed-loop mode incorporating a controller (or control algorithm). However, the operation of APS is challenging because of complexities arising during meals, exercise, stress, sleep, illnesses, glucose sensing and insulin action delays, and the cognitive burden. To overcome these challenges, options to augment APS through integration of additional inputs, creating multi-input APS (MAPS), are being investigated. Objective: The aim of this survey is to identify and analyze input data, control architectures, and validation methods of MAPS to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D. Methods: A literature survey was conducted using the Scopus, PubMed, and IEEE Xplore databases for the period January 1, 2005, to February 10, 2020. On the basis of the search criteria, 1092 articles were initially shortlisted, of which 11 (1.01%) were selected for an in-depth narrative analysis. In addition, 6 clinical studies associated with the selected studies were also analyzed. Results: Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller through activity detection, directly incorporated for decision-making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through the use of simulators based on different physiological models and clinical trials. Conclusions: The integration of additional physiological signals with continuous glucose level monitoring has the potential to optimize glucose control in people with T1D through addressing the identified limitations of APS. Most of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator regarding MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life. %M 35200143 %R 10.2196/28861 %U https://diabetes.jmir.org/2022/1/e28861 %U https://doi.org/10.2196/28861 %U http://www.ncbi.nlm.nih.gov/pubmed/35200143 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e33348 %T Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis %A Keller,Roman %A Hartmann,Sven %A Teepe,Gisbert Wilhelm %A Lohse,Kim-Morgaine %A Alattas,Aishah %A Tudor Car,Lorainne %A Müller-Riemenschneider,Falk %A von Wangenheim,Florian %A Mair,Jacqueline Louise %A Kowatsch,Tobias %+ Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore, 65 82645302, roman.keller@sec.ethz.ch %K digital health companies %K health care %K type 2 diabetes %K prevention %K management %K conversational agent %K digital behavior change intervention %K investment %K just-in-time adaptive intervention %K digital health %K diabetes %K agent %K behavior %D 2022 %7 7.1.2022 %9 Review %J J Med Internet Res %G English %X Background: Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which these interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches. Objective: Our objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms. Methods: A systematic search was conducted using 2 venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs’ websites, and data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs’ publications, websites, and mobile apps were reviewed with regard to the intervention characteristics. Results: The 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A1c (HbA1c) outcomes between the intervention and control groups. However, all the studies reported HbA1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs. Conclusions: Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions. %M 34994693 %R 10.2196/33348 %U https://www.jmir.org/2022/1/e33348 %U https://doi.org/10.2196/33348 %U http://www.ncbi.nlm.nih.gov/pubmed/34994693 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 10 %P e29727 %T Patient-Reported Benefits and Limitations of Mobile Health Technologies for Diabetes in Pregnancy: Protocol for a Scoping Review %A Sushko,Katelyn %A Wang,Qi Rui %A Tschirhart Menezes,Holly %A Fitzpatrick-Lewis,Donna %A Sherifali,Diana %+ School of Nursing, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8N 375, Canada, 1 905 979 5192, sushkokj@mcmaster.ca %K diabetes %K pregnancy %K type 1 diabetes %K type 2 diabetes %K gestational diabetes mellitus %K mobile health %K mHealth %K virtual care %K scoping review %D 2021 %7 29.10.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: For women with pre-existing and gestational diabetes mellitus, pregnancy involves specialized and intensive medical care to improve maternal and infant outcomes. Medical management for patients with diabetes in pregnancy typically occurs via frequent face-to-face outpatient appointments. Barriers to face-to-face care during the COVID-19 pandemic have signaled the need for high-quality, patient-centered virtual health care modalities, such as mobile health (mHealth). Objective: The objective of the proposed scoping review is to identify the patient-reported benefits and limitations of mHealth technology among women with diabetes in pregnancy. We also aim to determine how the women’s experiences align with the best practice standards for patient-centered communication. Methods: Arksey and O’Malley’s framework for conducting scoping reviews with refinements by Levac et al will be used to guide the conduct of this scoping review. Relevant studies will be identified through comprehensive database searches of MEDLINE, Embase, Emcare, and PsycINFO. Following database searches, studies will be screened for eligibility at the title, abstract, and full-text level by two independent reviewers, with the inclusion of a third reviewer if required to reach consensus. Data charting of included studies will be conducted by one reviewer using a standardized data extraction form and verified independently by a second reviewer. Synthesis of results will be guided by Thomas and Harden’s “Methods for the Thematic Synthesis of Qualitative Research in Systematic Reviews.” Results: As of August 2020, we have carried out the qualitative searches in the electronic databases MEDLINE, Embase, Emcare, and PsycINFO (Ovid interface) for a combined total of 8207 articles. Next, we plan to conduct the quantitative searches in the electronic databases MEDLINE, Embase, and Emcare (Ovid interface). We also plan to review the reference lists of relevant studies to identify additional eligible studies. Conclusions: With the results of this review, we hope to describe the patient-reported benefits and limitations of mHealth technology for women with diabetes in pregnancy. Furthermore, we aim to determine how women’s experiences align with the best practice standards for patient-centered communication. Ultimately, our review can provide valuable information for guideline developers, policy makers, and clinicians related to mobile technologies to support virtual care delivery for women with diabetes in pregnancy. International Registered Report Identifier (IRRID): PRR1-10.2196/29727 %M 34714251 %R 10.2196/29727 %U https://www.researchprotocols.org/2021/10/e29727 %U https://doi.org/10.2196/29727 %U http://www.ncbi.nlm.nih.gov/pubmed/34714251 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e24982 %T Clinical Effectiveness of Different Technologies for Diabetes in Pregnancy: Systematic Literature Review %A Eberle,Claudia %A Loehnert,Maxine %A Stichling,Stefanie %+ Medicine with Specialization in Internal Medicine and General Medicine, Hochschule Fulda - University of Applied Sciences, Leipziger Street 123, Fulda, 36037, Germany, 49 661 9640 ext 6328, claudia.eberle@hs-fulda.de %K diabetes technologies %K diabetes management %K pregnancy %K digital health %K eHealth %K systematic review %D 2021 %7 28.4.2021 %9 Review %J J Med Internet Res %G English %X Background: Hyperglycemia in pregnancy occurs worldwide and is closely associated with health issues in women and their offspring, such as pregnancy and birth complications, respectively, as well as comorbidities, such as metabolic and cardiovascular diseases. To optimize the management of diabetic pregnancies, sustainable strategies are urgently needed. Investigation of constantly evolving technologies for diabetes that help to manage pregnancy and health is required. Objective: We aimed to conduct a systematic review to assess the clinical effectiveness of technologies for diabetes in pregnancy. Methods: Relevant databases including MEDLINE (PubMed), Cochrane Library, Embase, CINAHL, and Web of Science Core Collection were searched in September 2020 for clinical studies (2008-2020). Findings were organized by type of diabetes, type of technology, and outcomes (glycemic control, pregnancy- and birth-related outcomes, and neonatal outcomes). Study quality was assessed using Effective Public Health Practice Project criteria. Results: We identified 15 randomized controlled trials, 3 randomized crossover trials, 2 cohort studies, and 2 controlled clinical trials. Overall, 9 studies focused on type 1 diabetes, 0 studies focused on gestational diabetes, and 3 studies focused on both type 1 diabetes and type 2 diabetes. We found that 9 studies were strong quality, 11 were moderate quality, and 2 were weak quality. Technologies for diabetes seemed to have particularly positive effects on glycemic control in all types of diabetes, shown by some strong and moderate quality studies. Positive trends in pregnancy-related, birth-related, and neonatal outcomes were observed. Conclusions: Technologies have the potential to effectively improve the management of diabetes during pregnancy. Further research on the clinical effectiveness of these technologies is needed, especially in pregnant women with type 2 diabetes. %M 33908894 %R 10.2196/24982 %U https://www.jmir.org/2021/4/e24982 %U https://doi.org/10.2196/24982 %U http://www.ncbi.nlm.nih.gov/pubmed/33908894 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e23244 %T Clinical Improvements by Telemedicine Interventions Managing Type 1 and Type 2 Diabetes: Systematic Meta-review %A Eberle,Claudia %A Stichling,Stefanie %+ Medicine with specialization in Internal Medicine and General Medicine, Hochschule Fulda - University of Applied Sciences, Leipziger Strasse 123, Fulda, 36037, Germany, 49 661 9640 ext 6328, claudia.eberle@hs-fulda.de %K type 1 diabetes %K type 2 diabetes %K eHealth %K telemedicine %K disease management %K systematic review %K mobile phone %D 2021 %7 19.2.2021 %9 Review %J J Med Internet Res %G English %X Background: Diabetes mellitus (DM) is one of the world’s greatest health threats with rising prevalence. Global digitalization leads to new digital approaches in diabetes management, such as telemedical interventions. Telemedicine, which is the use of information and communication technologies, may provide medical services over spatial distances to improve clinical patient outcomes by increasing access to diabetes care and medical information. Objective: This study aims to examine whether telemedical interventions effectively improve diabetes control using studies that pooled patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), and whether the benefits are greater in patients diagnosed with T2DM than in those diagnosed with T1DM. We analyzed the primary outcome glycated hemoglobin A1c (HbA1c) and the secondary outcomes fasting blood glucose (FBG), blood pressure (BP), body weight, BMI, quality of life (QoL), cost, and time saving. Methods: Publications were systematically identified by searching Cochrane Library, MEDLINE via PubMed, Web of Science Core Collection, Embase, and CINAHL databases for studies published between January 2008 and April 2020, considering systematic reviews (SRs), meta-analyses (MAs), randomized controlled trials (RCTs), and clinical trials (CTs). Study quality was assessed using the A Measurement Tool to Assess Systematic Reviews, Effective Public Health Practice Project, and National Institute for Health and Care Excellence qualitative checklist. We organized the trials by communication technologies in real-time video or audio interventions, asynchronous interventions, and combined interventions (synchronous and asynchronous communication). Results: From 1116 unique citations, we identified 31 eligible studies (n=15 high, n=14 moderate, n=1 weak, and n=1 critically low quality). We selected 21 SRs and MAs, 8 RCTs, 1 non-RCT, and 1 qualitative study. Of the 10 trials, 3 were categorized as real-time video, 1 as real-time video and audio, 4 as asynchronous, and 2 as combined intervention. Significant decline in HbA1c levels based on pooled T1DM and T2DM patients data ranged from −0.22% weighted mean difference (WMD; 95% CI −0.28 to −0.15; P<.001) to −0.64% mean difference (95% CI −1.01 to −0.26; P<.001). The intervention effect on lowering HbA1c values might be significantly smaller for patients with T1DM than for patients with T2DM. Evidence on the impact on BP, body weight, FBG, cost effectiveness, and time saving was smaller compared with HbA1c but indicated potential in some publications. Conclusions: Telemedical interventions might be clinically effective in improving diabetes control overall, and they might significantly improve HbA1c concentrations. Patients with T2DM could benefit more than patients with T1DM regarding lowering HbA1c levels. Further studies with longer duration and larger cohorts are necessary. %M 33605889 %R 10.2196/23244 %U http://www.jmir.org/2021/2/e23244/ %U https://doi.org/10.2196/23244 %U http://www.ncbi.nlm.nih.gov/pubmed/33605889 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 6 %N 1 %P e23687 %T 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 %A Forsyth,Jessica R %A Chase,Hannah %A Roberts,Nia W %A Armitage,Laura C %A Farmer,Andrew J %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 1865 617942, laura.armitage@phc.ox.ac.uk %K type 2 diabetes %K health technology %K self-management %K mobile health %K mobile applications %K guidelines %D 2021 %7 16.2.2021 %9 Review %J JMIR Diabetes %G English %X 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. %M 33591278 %R 10.2196/23687 %U http://diabetes.jmir.org/2021/1/e23687/ %U https://doi.org/10.2196/23687 %U http://www.ncbi.nlm.nih.gov/pubmed/33591278 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 1 %P e17740 %T Assessment of Psychological Distress in Adults With Type 2 Diabetes Mellitus Through Technologies: Literature Review %A Bassi,Giulia %A Gabrielli,Silvia %A Donisi,Valeria %A Carbone,Sara %A Forti,Stefano %A Salcuni,Silvia %+ Department of Developmental Psychology and Socialization, University of Padova, Via Venezia 12, Padova, 35131, Italy, 39 393477334405, giulia.bassi@phd.unipd.it %K type 2 diabetes mellitus %K technology assessment %K psychological distress %K technology %K review %K mobile phone %D 2021 %7 7.1.2021 %9 Review %J J Med Internet Res %G English %X Background: The use of technological devices can support the self-management of individuals with type 2 diabetes mellitus (T2DM), particularly in addressing psychological distress. However, there is poor consistency in the literature regarding the use of psychological instruments for the web-based screening of patients’ psychological distress and subsequent monitoring of their psychological condition during digital interventions. Objective: This study aims to review previous literature on the types of psychological instruments delivered in digital interventions for assessing depression, anxiety, and stress in patients with T2DM. Methods: The literature review was conducted using the PsycINFO, CINAHL and PubMed databases, in which the following terms were considered: diabetes mellitus, measure, assessment, self-care, self-management, depression, anxiety, stress, technology, eHealth, mobile health, mobile phone, device, and smartphone. Results: In most studies, psychological assessments were administered on paper. A few studies deployed self-reporting techniques employing automated telephonic assessment, a call system for screening and monitoring patients’ conditions and preferences, or through telephone interviews via interactive voice response calls, a self-management support program leveraging tailored messages and structured emails. Other studies used simple telephone interviews and included the use of apps for tablets and smartphones to assess the psychological well-being of patients. Finally, some studies deployed mood rating scales delivered through tailored text message–based support systems. Conclusions: The deployment of appropriate psychological tools in digital interventions allows researchers and clinicians to make the screening of anxiety, stress, and depression symptoms faster and easier in patients with T2DM. Data from this literature review suggest that mobile health solutions may be preferred tools to use in such digital interventions. %M 33410762 %R 10.2196/17740 %U https://www.jmir.org/2021/1/e17740 %U https://doi.org/10.2196/17740 %U http://www.ncbi.nlm.nih.gov/pubmed/33410762 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 4 %N 2 %P e11526 %T Diabetes, Care Homes, and the Influence of Technology on Practice and Care Delivery in Care Homes: Systematic Review and Qualitative Synthesis %A Mathews,Rebecca %A O'Malley,Chris %A Hall,Jenny M %A Macaden,Leah %A MacRury,Sandra %+ Division of Rural Health and Wellbeing, Centre for Health Science, University of the Highlands and Islands, Old Perth Road, Inverness, IV2 3JH, United Kingdom, 44 1463279583, sandra.macrury@uhi.ac.uk %K diabetes mellitus %K technology %K residential facilities %K nursing homes %D 2019 %7 22.04.2019 %9 Review %J JMIR Diabetes %G English %X Background: Diabetes is increasing in prevalence and complexity in the care home setting, affecting up to a quarter of care home residents. Health outcomes for these residents are impacted by management of the disease, health care professionals (HCPs)’ decision-making skills within the care home setting, and access to specialist services. The use of technology has the potential to recognize opportunities for early intervention that enables efficient responsive care, taking a fundamental role in linking the care home community to wider multidisciplinary teams for support. Objective: The aim of this paper was to identify evidence that explores factors relevant to the use of technology in and around the care home setting to aid in the management of diabetes. Methods: Databases searched using a structured prespecified approach included: PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), OVID Nursing database, Scopus, MEDLINE, the Cochrane Library, and the King’s Fund from 2012 to 2017: handsearching was undertaken additionally for any gray literature. Preferred Reporting Items for Systematic review and Meta-Analysis Protocol was used as protocol with Risk of Bias in Systematic reviews a tool to assess the risk of bias across studies. Studies had to include interventions that combined technology to or from the care home setting to support residents living with diabetes. Results: The combined search strategy identified a total of 493 electronic records. Of these, 171 papers were screened for eligibility, 66 full papers were accessed, and 13 have been included in this study. Qualitative synthesis has identified different strands of research evidence in what and how technology is currently being used in and around care homes to enhance diabetes management. New initiatives and implementations of technology and emerging models of care that included the use of technology have also been included. Conclusions: By triangulating the perspectives of HCPs, practitioners, specialists, and members of the care home community, the authors anticipate that this review will represent an up-to-date, evidence-based overview of the potential for using technology within the care home setting for diabetes management as well as stimulate research in this area. %M 31008705 %R 10.2196/11526 %U http://diabetes.jmir.org/2019/2/e11526/ %U https://doi.org/10.2196/11526 %U http://www.ncbi.nlm.nih.gov/pubmed/31008705 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 3 %N 3 %P e13 %T Web-Based Interventions for Depression in Individuals with Diabetes: Review and Discussion %A Franco,Pamela %A Gallardo,Ana María %A Urtubey,Xavier %+ Psychology Department, Universidad del Desarrollo, Av Plaza 680, Santiago, 7610658, Chile, 56 223279110, p.franco@udd.cl %K Web-based intervention %K internet %K depression %K diabetes %K cognitive behavioral therapy %D 2018 %7 14.09.2018 %9 Review %J JMIR Diabetes %G English %X Background: Depression is twice as common in people with diabetes, and this comorbidity worsens the course of both pathologies. In clinical practice guidelines, screening and treatment of depression in patients with diabetes are highly recommended. However, depression is still both underrecognized and undertreated. To find ways to enhance their reach, psychological treatments have taken advantage of benefits of internet and technological devices as delivery formats, providing interventions that require considerably less (or even no) interaction time with therapists. Web-based treatments hold promise for effective interventions at low cost with positive results. Objective: The objectives of this review were to describe Web-based interventions for depression in individuals with diabetes and to discuss these studies’ procedures and findings in light of evidence from a wider range of interventions for depression and diabetes. Methods: A comprehensive literature search was conducted in PsycINFO and MEDLINE electronic databases. Studies were included when they met the following selection criteria: the study was available in a peer-reviewed journal mainly publishing studies written in either English or Spanish; the studied sample comprised individuals with diabetes; the intervention targeted depression symptomatology; the intervention was accessible via the internet; and the intervention was accessible via the internet with little or no clinician support. Results: Overall, 5 research studies were identified in the review. All studies were randomized controlled trials, and most used a wait list as a control; 4 studies reported treatment dropout, rates of which varied from 13% to 42%. Studies supported the notion that the Web-based format is a suitable psychology service delivery option for diabetic individuals with depression (effect size range for completers 0.7-0.89). Interventions varied in their characteristics but most were clinical-assisted, had a cognitive behavioral therapy approach, used diabetes-specific topics, had a weekly modular display, used homework assignments, and had some adherence management strategy. These characteristics are consistent with the intervention features associated with positive results in the literature. Conclusions: The analyzed studies’ findings and procedures are discussed in light of evidence drawn from a wider range of reviews on Web-based interventions for depression and diabetes. Consistent with previous research on depression treatment, Web-based interventions for depression among individuals with diabetes have shown positive results. Future research should contribute new evidence as to why these interventions are effective, for whom, and which particular aspects can increase patients’ adherence. %M 30291082 %R 10.2196/diabetes.9694 %U http://diabetes.jmir.org/2018/3/e13/ %U https://doi.org/10.2196/diabetes.9694 %U http://www.ncbi.nlm.nih.gov/pubmed/30291082 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 5 %P e10775 %T Artificial Intelligence for Diabetes Management and Decision Support: Literature Review %A Contreras,Ivan %A Vehi,Josep %+ Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Campus Montilivi. Edifici P4, Girona, 17003, Spain, 34 620131826, josep.vehi@udg.edu %K diabetes management %K artificial intelligence %K machine learning %K mobile computing %K blood glucose %D 2018 %7 30.05.2018 %9 Review %J J Med Internet Res %G English %X Background: Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective: The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods: A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results: We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life. %R 10.2196/10775 %U http://www.jmir.org/2018/5/e10775/ %U https://doi.org/10.2196/10775 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 5 %P e172 %T Effectiveness of Internet-Based Interventions on Glycemic Control in Patients With Type 2 Diabetes: Meta-Analysis of Randomized Controlled Trials %A Shen,Ying %A Wang,Fengbin %A Zhang,Xing %A Zhu,Xiaorou %A Sun,Qiudan %A Fisher,Edwin %A Sun,Xinying %+ School of Public Health, Peking University Health Science Center, 38, Xueyuan Road, Haidian District, Beijing, China, Beijing, 100191, China, 86 86 13691212050, xysun@bjmu.edu.cn %K internet %K type 2 diabetes mellitus %K HbA1c %K randomized controlled trial %K meta-analysis %D 2018 %7 07.05.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: The popularity of internet as an area of research has grown manifold over the years. Given its rapid development and increasing coverage worldwide, internet-based interventions seem to offer a promising option to ameliorate huge burdens brought by type 2 diabetes mellitus. However, studies conducted by different researchers have provided contradictory results on the effect of internet-based interventions in glycemic control. Objective: This meta-analysis aims to summarize currently available evidence and evaluate the overall impact of internet-based interventions on glycemic management of type 2 diabetic patients. Methods: A systematic literature search was performed in PubMed, ScienceDirect, and Web of Science. Randomized controlled trials that used glycosylated hemoglobin values as the outcome measure of glycemic control were considered. Risk of bias and publication bias were evaluated. Results: Of the 492 studies, 35 were included in meta-analysis, and results indicated that the weighted mean difference (WMD) between usual care and internet-based interventions at endpoint was –0.426% (95% CI –0.540 to –0.312; P<.001). Subgroup analyses revealed that intervention duration ≤3 months yielded optimal performance (WMD –0.51%; 95% CI –0.71 to –0.31; P<.001). Combined mobile and website interventions were substantially superior to solely Web-based and mobile-based interventions in glycemic control (combined WMD –0.77%, 95% CI –1.07 to –0.47; P<.001; Web only: WMD –0.48%; 95% CI –0.71 to –0.24, P<.001; mobile only WMD –0.31%, 95% CI –0.49 to –0.14; P<.001). Furthermore, the effect of interventions with automated feedbacks was similar to those with manual feedbacks, and studies with internet-based educational contents were more effective in glycemic control. The assessment revealed a low risk of bias. Conclusions: In conclusion, utilization of internet-based intervention is beneficial for patients with type 2 diabetes mellitus, and taking full advantage of this type of intervention may substantially reduce the incidence of complications and improve quality of life. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO): CRD42017058032; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=58032 (Archived by WebCite at http://www.webcitation.org/6yY7eQNHr) %M 29735475 %R 10.2196/jmir.9133 %U http://www.jmir.org/2018/5/e172/ %U https://doi.org/10.2196/jmir.9133 %U http://www.ncbi.nlm.nih.gov/pubmed/29735475 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 2 %N 2 %P e20 %T Digital Health for Medication Adherence in Adult Diabetes or Hypertension: An Integrative Review %A Conway,Cheryl Moseley %A Kelechi,Teresa J %+ School of Nursing, Western Carolina University, 1 University Way, Cullowhee, NC,, United States, 1 828 778 6019, conway@musc.edu %K digital health %K medication adherence %K Chronic Care Model %K diabetes %K hypertension %D 2017 %7 16.08.2017 %9 Review %J JMIR Diabetes %G English %X Background: Optimal management of chronic diseases, such as type 2 diabetes and hypertension, often include prescription medications. Medication adherence (MA) is one component of self-management. Optimization through digital health—eHealth and mHealth—could enhance patient awareness and/or communication between the patient and provider. Objective: Medication adherence is a major issue that affects 50%-60% of chronically ill adults. Digital health refers to eHealth and mHealth, collectively, and as these technologies become more accessible, remote health delivery is increasingly available as an adjunct to improve medication adherence; communicate with patients and providers; and provide education to patients, families, and communities. The objective of this integrative review was to examine the types of digital health technologies that targeted medication adherence in the adult population with diabetes or hypertension. Methods: An integrative review was conducted using databases within EBSCOhost, PubMed, and Scopus. Eligible studies available as of September 2016 had to be written in English, had to contain digital health interventions to improve medication adherence to prescription medications in adults 18 years or older, and had to focus on diabetes or hypertension. Results: Of the 337 located studies, 13 (3.9%) used a digital health intervention for medication adherence to prescribed medications for diabetes or hypertension and were assessed according to the Chronic Care Model. Conclusions: The 13 studies included in this review found no conclusive evidence of improved medication adherence using digital health interventions such as interactive voice response (IVR), short message service (SMS) text messaging, telemonitoring, and interactive software technology. Among the 13 studies were digital health interventions that foster medication adherence via one-way communication to the patient or two-way communication between the patient and health care provider for adjunct medication adherence strategies. More research is needed to determine which digital health interventions are most beneficial for individuals with diabetes or hypertension. %M 30291093 %R 10.2196/diabetes.8030 %U http://diabetes.jmir.org/2017/2/e20/ %U https://doi.org/10.2196/diabetes.8030 %U http://www.ncbi.nlm.nih.gov/pubmed/30291093 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 11 %P e290 %T Telemedicine Technologies for Diabetes in Pregnancy: A Systematic Review and Meta-Analysis %A Ming,Wai-Kit %A Mackillop,Lucy H %A Farmer,Andrew J %A Loerup,Lise %A Bartlett,Katy %A Levy,Jonathan C %A Tarassenko,Lionel %A Velardo,Carmelo %A Kenworthy,Yvonne %A Hirst,Jane E %+ Nuffield Department of Obstetrics & Gynaecology, John Radcliffe Hospital, Level 3, Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom, 44 01865 221019, jane.hirst@obs-gyn.ox.ac.uk %K pregnancy %K diabetes mellitus %K telemedicine %K review %K meta-analysis %K pregnancy in diabetics %D 2016 %7 09.11.2016 %9 Review %J J Med Internet Res %G English %X Background: Diabetes in pregnancy is a global problem. Technological innovations present exciting opportunities for novel approaches to improve clinical care delivery for gestational and other forms of diabetes in pregnancy. Objective: To perform an updated and comprehensive systematic review and meta-analysis of the literature to determine whether telemedicine solutions offer any advantages compared with the standard care for women with diabetes in pregnancy. Methods: The review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Randomized controlled trials (RCT) in women with diabetes in pregnancy that compared telemedicine blood glucose monitoring with the standard care were identified. Searches were performed in SCOPUS and PubMed, limited to English language publications between January 2000 and January 2016. Trials that met the eligibility criteria were scored for risk of bias using the Cochrane Collaborations Risk of Bias Tool. A meta-analysis was performed using Review Manager software version 5.3 (Nordic Cochrane Centre, Cochrane Collaboration). Results: A total of 7 trials were identified. Meta-analysis demonstrated a modest but statistically significant improvement in HbA1c associated with the use of a telemedicine technology. The mean HbA1c of women using telemedicine was 5.33% (SD 0.70) compared with 5.45% (SD 0.58) in the standard care group, representing a mean difference of −0.12% (95% CI −0.23% to −0.02%). When this comparison was limited to women with gestational diabetes mellitus (GDM) only, the mean HbA1c of women using telemedicine was 5.22% (SD 0.70) compared with 5.37% (SD 0.61) in the standard care group, mean difference −0.14% (95% CI −0.25% to −0.04%). There were no differences in other maternal and neonatal outcomes reported. Conclusions: There is currently insufficient evidence that telemedicine technology is superior to standard care for women with diabetes in pregnancy; however, there was no evidence of harm. No trials were identified that assessed patient satisfaction or cost of care delivery, and it may be in these areas where these technologies may be found most valuable. %M 27829574 %R 10.2196/jmir.6556 %U http://www.jmir.org/2016/11/e290/ %U https://doi.org/10.2196/jmir.6556 %U http://www.ncbi.nlm.nih.gov/pubmed/27829574 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 11 %P e291 %T Preconception Care Education for Women With Diabetes: A Systematic Review of Conventional and Digital Health Interventions %A Nwolise,Chidiebere Hope %A Carey,Nicola %A Shawe,Jill %+ School of Health Sciences, Faculty of Health & Medical Sciences, University of Surrey, Fifth Floor, Duke of Kent Building, Guildford, GU2 7XH, United Kingdom, 44 1483686717, c.nwolise@surrey.ac.uk %K preconception care %K education %K diabetes mellitus %K women %K review %K smartphone %K mobile applications %K technology %D 2016 %7 08.11.2016 %9 Review %J J Med Internet Res %G English %X Background: Worldwide, 199.5 million women have diabetes mellitus (DM). Preconception care (PCC) education starting from adolescence has been recommended as an effective strategy for safeguarding maternal and child health. However, traditional preconception care advice provided by health care professionals (HCPs) within clinic settings is hindered by inadequate resources, suboptimal coverage, and busy clinics. Electronic health (eHealth), which is instrumental in solving problems around scarce health resources, could be of value in overcoming these limitations and be used to improve preconception care and pregnancy outcomes for women with DM. Objective: The objectives were to: (1) identify, summarize, and critically appraise the current methods of providing PCC education; (2) examine the relationship between PCC educational interventions (including use of technology as an intervention medium) on patient and behavioral outcomes; and (3) highlight limitations of current interventions and make recommendations for development of eHealth in this field. Methods: Electronic databases were searched using predefined search terms for PCC education in women with type 1 or 2 DM for quantitative studies from 2003 until June 2016. Of the 1969 titles identified, 20 full papers were retrieved and 12 papers were included in this review. Results: The reviewed studies consistently reported that women receiving educational interventions via health care professionals and eHealth had significantly improved levels of glycosylated hemoglobin (P<.001) with fewer preterm deliveries (P=.02) and adverse fetal outcomes (P=.03). Significant improvements in knowledge (P<.001) and attitudes toward seeking PCC (P=.003) were reported along with reduced barriers (P<.001). Conclusions: PCC has a positive effect on pregnancy outcomes for women with DM. However, uptake of PCC is low and the use of eHealth applications for PCC of women with DM is still in its infancy. Initial results are promising; however, future research incorporating mobile phones and apps is needed. Clearly, there is much to be done if the full potential of eHealth PCC to improve obstetric outcomes for women with DM is to be realized. %M 27826131 %R 10.2196/jmir.5615 %U http://www.jmir.org/2016/11/e291/ %U https://doi.org/10.2196/jmir.5615 %U http://www.ncbi.nlm.nih.gov/pubmed/27826131