{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T05:56:20Z","timestamp":1792130180014,"version":"4.4.0"},"reference-count":70,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Diabetes"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Digital health interventions, including artificial intelligence (AI)-driven solutions, offer promise for type 2 diabetes mellitus (T2DM) and prediabetes management through enhanced self-management, adherence, and personalization. However, engagement challenges and barriers, particularly among young adults and diverse populations, persist. Existing reviews emphasize clinical outcomes while neglecting engagement factors crucial to intervention success. This review highlights engagement barriers and facilitators, offering insights into improving digital health solutions for diabetes management.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The objective of this systematic literature review is to explore the barriers, facilitators, and outcomes of digital health interventions, focusing on the current state of AI applications while including partial AI and non-AI interventions, for managing and preventing T2DM and prediabetes, to inform the development of user-centered, inclusive digital health interventions for diabetes care. Unlike prior reviews, this review aims to inform the development of user-centered, inclusive digital health interventions for diabetes care, with a focus on engagement across various AI interventions and diverse populations.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A systematic search of PubMed, Scopus, CINAHL, and additional sources was conducted for studies published between January 2016 and October 2025. Eligibility criteria included English-language, peer-reviewed studies focused on digital health interventions for adults with T2DM or prediabetes, reporting engagement, barriers, facilitators, or outcomes. Data were synthesized narratively using thematic analysis, guided by self-determination theory and user-centered design. Quality appraisal was conducted using Critical Appraisal Skills Program, Mixed Methods Appraisal Tool, and AMSTAR-2 tools.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n From the 37 studies (14 quantitative, 3 qualitative, 7 mixed-methods, and 13 reviews), interventions comprised 19 AI-driven (eg, chatbots, ML models, and conversational agent or hybrid), 3 partially AI-driven, and 15 non-AI solutions (eg, apps and lifestyle programs), mostly from the USA (n=15). Key barriers to engagement included inadequate personalization (15\/37, 41%), environmental constraints (11\/37, 11%), cultural and language mismatches (14\/37, 38%), and AI-specific concerns (eg, bias and privacy). Facilitators included personalized feedback (19\/37, 51%), cultural tailoring (17\/37, 46%), user-friendly design, and peer support. AI-driven interventions demonstrated moderate improvements in clinical outcomes (eg, lowering HbA\n 1c<\/jats:sub>\n , weight loss, and normoglycemia conversion). However, these tools often struggled with keeping users involved and building trust. Non-AI solutions performed similarly but lacked adaptive features.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This review offers novel insights by synthesizing engagement barriers and facilitators across AI and non-AI intervention domains, often neglected in previous studies. It highlights the necessity for testing adaptive, culturally tailored, and user-centered AI interventions to address engagement challenges in T2DM and prediabetes management. Integrating personalization, precision, and value-based care can improve outcomes and scalability. The findings guide the creation of inclusive, AI-driven solutions aligned with self-determination theory and user-centered design principles.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/80582","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:30:08Z","timestamp":1766158208000},"page":"e80582-e80582","source":"Crossref","is-referenced-by-count":8,"title":["Digital Health Solutions for Type 2 Diabetes and Prediabetes: Systematic Review of Engagement Barriers, Facilitators, and Outcomes"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0518-2849","authenticated-orcid":false,"given":"Ayesha","family":"Thanthrige","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1314-8843","authenticated-orcid":false,"given":"Nilmini","family":"Wickramasinghe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"R1","unstructured":"Diabetes facts and figures. International Diabetes Federation. URL: https:\/\/www.idf.org [Accessed 27-01-2026]"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045","volume":"183","author":"Sun","journal-title":"Diabetes Res Clin Pract"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.00108","article-title":"Review of ongoing activities and challenges to improve the care of patients with type 2 diabetes across africa and the implications for the future","volume":"11","author":"Godman","journal-title":"Front Pharmacol"},{"issue":"2","key":"R4","first-page":"65","volume":"11","author":"Sami","journal-title":"Int J Health Sci (Qassim)"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.2147\/DMSO.S319895","article-title":"A review of current trends with type 2 diabetes epidemiology, aetiology, pathogenesis, treatments and future perspectives","volume":"14","author":"Reed","journal-title":"Diabetes Metab Syndr Obes"},{"issue":"7","key":"R6","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2024-093768","article-title":"Systematic review protocol for effectiveness and cost-effectiveness of non-surgical interventions to prevent diabetes progression in adults with prediabetes","volume":"15","author":"Sui","journal-title":"BMJ Open"},{"issue":"3","key":"R7","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s13300-025-01692-0","article-title":"Digital health technology in diabetes management in the Asia-Pacific region: a narrative review of the current scenario and future outlook","volume":"16","author":"Gardner","journal-title":"Diabetes Ther"},{"issue":"12","key":"R8","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.40232","article-title":"Use of voice-based conversational artificial intelligence for basal insulin prescription management among patients with type 2 diabetes: a randomized clinical trial","volume":"6","author":"Nayak","journal-title":"JAMA Netw Open"},{"issue":"10397","key":"R9","doi-asserted-by":"publisher","DOI":"10.2196\/60380","article-title":"Application of chatbots to help patients self-manage diabetes: systematic review and meta-analysis","volume":"26","author":"Wu","journal-title":"J Med Internet Res"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.2196\/40789","article-title":"Artificial intelligence-based chatbots for promoting health behavioral changes: systematic review","volume":"25","author":"Aggarwal","journal-title":"J Med Internet Res"},{"issue":"9","key":"R11","doi-asserted-by":"publisher","DOI":"10.2196\/20283","article-title":"Rates of attrition and dropout in app-based interventions for chronic disease: systematic review and meta-analysis","volume":"22","author":"Meyerowitz-Katz","journal-title":"J Med Internet Res"},{"issue":"2","key":"R12","doi-asserted-by":"publisher","first-page":"3223","DOI":"10.55248\/gengpi.6.0225.0952","article-title":"AI-enhanced telemedicine: a common-sense approach to chronic disease management and a tool to bridging the gap in healthcare disparities","volume":"6","author":"Mbanugo","journal-title":"Int J Res Publ Rev"},{"issue":"1630588","key":"R13","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2025.1630588","article-title":"The effectiveness of digital health intervention on glycemic control and physical activity in patients with type 2 diabetes: a systematic review and meta-analysis","volume":"7","author":"Xue","journal-title":"Front Digit Health"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","DOI":"10.1186\/s12966-021-01224-6","article-title":"A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss","volume":"18","author":"Oh","journal-title":"Int J Behav Nutr Phys Act"},{"issue":"1","key":"R15","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1037\/0003-066X.55.1.68","article-title":"Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being","volume":"55","author":"Ryan","journal-title":"American Psychologist"},{"key":"R16","doi-asserted-by":"crossref","unstructured":"Norman DA Draper SW . User Centered System Design: New Perspectives on Human-Computer Interaction. Taylor & Francis group; 1986. [doi: 10.1201\/b15703] ISBN: 9780367807320","DOI":"10.1201\/b15703"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcte.2024.100364","article-title":"Employing user-centered design and education sciences to inform training of diabetes survival skills","volume":"37","author":"Prince","journal-title":"J Clin Transl Endocrinol"},{"issue":"1","key":"R18","doi-asserted-by":"publisher","DOI":"10.1186\/s13643-023-02308-z","article-title":"Self-determination theory interventions versus usual care in people with diabetes: a systematic review with meta-analysis and trial sequential analysis","volume":"12","author":"Mathiesen","journal-title":"Syst Rev"},{"key":"R19","doi-asserted-by":"crossref","unstructured":"Cochrane Handbook for Systematic Reviews of Interventions. Second ed. Wiley; 2019. [doi: 10.1002\/9781119536604]","DOI":"10.1002\/9781119536604"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","journal-title":"BMJ"},{"key":"R21","unstructured":"Optimizing engagement in digital health interventions for diabetes and prediabetes: a systematic review. OSF. URL: https:\/\/doi.org\/10.17605\/OSF.IO\/FC752 [Accessed 27-01-2026]"},{"key":"R22","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2021.105159","article-title":"Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling","volume":"144","author":"Razavi","journal-title":"Environ Model Softw"},{"key":"R23","doi-asserted-by":"publisher","DOI":"10.2196\/67435","article-title":"Evolving digital health technologies: aligning with and enhancing the national institute for health and care excellence evidence standards framework","volume":"13","author":"Bahadori","journal-title":"JMIR Mhealth Uhealth"},{"issue":"1","key":"R24","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.ijnss.2018.12.002","article-title":"Interventions for self-management of type 2 diabetes: an integrative review","volume":"6","author":"Carpenter","journal-title":"Int J Nurs Sci"},{"issue":"12","key":"R25","doi-asserted-by":"publisher","DOI":"10.2196\/39483","article-title":"Changes in a digital type 2 diabetes self-management intervention during national rollout: mixed methods study of fidelity","volume":"24","author":"Benton","journal-title":"J Med Internet Res"},{"issue":"5","key":"R26","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.pcd.2018.07.003","article-title":"Effects of lifestyle changes on adults with prediabetes: a systematic review and meta-analysis","volume":"12","author":"Glechner","journal-title":"Prim Care Diabetes"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.2196\/63975","article-title":"Effectiveness of different intervention modes in lifestyle intervention for the prevention of type 2 diabetes and the reversion to normoglycemia in adults with prediabetes: systematic review and meta-analysis of randomized controlled trials","volume":"27","author":"Wang","journal-title":"J Med Internet Res"},{"issue":"oct18 2","key":"R28","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.d5928","article-title":"The cochrane collaboration\u2019s tool for assessing risk of bias in randomised trials","volume":"343","author":"Higgins","journal-title":"BMJ"},{"issue":"1","key":"R29","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1177\/2632084320947559","article-title":"Optimising the value of the critical appraisal skills programme (CASP) tool for quality appraisal in qualitative evidence synthesis","volume":"1","author":"Long","journal-title":"Research Methods in Medicine & Health Sciences"},{"key":"R30","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.j4008","article-title":"AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both","volume":"358","author":"Shea","journal-title":"BMJ"},{"issue":"3","key":"R31","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1111\/jep.12884","article-title":"Improving the usefulness of a tool for appraising the quality of qualitative, quantitative and mixed methods studies, the Mixed Methods Appraisal Tool (MMAT)","volume":"24","author":"Hong","journal-title":"J Eval Clin Pract"},{"issue":"4","key":"R32","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.ijnurstu.2009.01.009","article-title":"A scoring system for appraising mixed methods research, and concomitantly appraising qualitative, quantitative and mixed methods primary studies in mixed studies reviews","volume":"46","author":"Pluye","journal-title":"Int J Nurs Stud"},{"issue":"1","key":"R33","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1177\/1356389008097871","article-title":"Testing methodological guidance on the conduct of narrative synthesis in systematic reviews: effectiveness of interventions to promote smoke alarm ownership and function","volume":"15","author":"Rodgers","journal-title":"Evaluation (Lond)"},{"key":"R34","unstructured":"Hussain W Grundy J . Advice for diabetes self-management by chatgpt models: challenges and recommendations. Arxiv. Preprint posted online on 2025. [doi: 10.48550\/arxiv.2501.07931] [Medline: 41333165]"},{"issue":"2","key":"R35","doi-asserted-by":"publisher","DOI":"10.2196\/51300","article-title":"An AI dietitian for type 2 diabetes mellitus management based on large language and image recognition models: preclinical concept validation study","volume":"25","author":"Sun","journal-title":"J Med Internet Res"},{"issue":"6","key":"R36","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1016\/j.pcad.2020.06.012","article-title":"Physical fitness and activity changes after a 24-week soccer-based adaptation of the U.S diabetes prevention program intervention in Hispanic men","volume":"63","author":"Frediani","journal-title":"Prog Cardiovasc Dis"},{"key":"R37","doi-asserted-by":"publisher","DOI":"10.2196\/58319","article-title":"Efficacy of a mobile health-based behavioral treatment for lifestyle modification in type 2 diabetes self-management: greenhabit randomized controlled trial","volume":"27","author":"Ruiz-Leon","journal-title":"J Med Internet Res"},{"issue":"1","key":"R38","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01034-7","article-title":"AI-based diabetes care: risk prediction models and implementation concerns","volume":"7","author":"Wang","journal-title":"NPJ Digit Med"},{"issue":"5","key":"R39","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1177\/19322968241253568","article-title":"Building trustworthy generative artificial intelligence for diabetes care and limb preservation: a medical knowledge extraction case","volume":"19","author":"Mashatian","journal-title":"J Diabetes Sci Technol"},{"issue":"1","key":"R40","doi-asserted-by":"publisher","DOI":"10.1007\/s44163-021-00005-1","article-title":"Diabetes and conversational agents: the AIDA project case study","volume":"1","author":"Alloatti","journal-title":"Discov Artif Intell"},{"issue":"6","key":"R41","doi-asserted-by":"publisher","DOI":"10.2196\/18167","article-title":"Mobile-Based and cloud-based system for self-management of people with type 2 diabetes: development and usability evaluation","volume":"23","author":"Salari","journal-title":"J Med Internet Res"},{"issue":"4","key":"R42","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1177\/19322968211007124","article-title":"Design and development of a mobile-based self-care application for patients with type 2 diabetes","volume":"16","author":"Mehraeen","journal-title":"J Diabetes Sci Technol"},{"key":"R43","doi-asserted-by":"publisher","DOI":"10.2196\/63393","article-title":"A culturally sensitive mobile app (DiaFriend) to improve self-care in patients with type 2 diabetes: development study","volume":"9","author":"LeSeure","journal-title":"JMIR Diabetes"},{"issue":"6","key":"R44","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1001\/jamainternmed.2023.1838","article-title":"Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum","volume":"183","author":"Ayers","journal-title":"JAMA Intern Med"},{"key":"R45","unstructured":"Nguyen TT Sim K Anthony To Yiu K O\u2019Donnell RR Lim ST Wang W et al. . arXiv. Designing AI-Based Conversational Agent for Diabetes Care in a Multilingual Context. Preprint posted online on May 20, 2021. [doi: 10.48550\/arXiv.2105.09490]"},{"issue":"1","key":"R46","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02824-5","article-title":"Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions","volume":"24","author":"Shiferaw","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"R47","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-023-09879-6","article-title":"The effectiveness of mobile application for monitoring diabetes mellitus and hypertension in the adult and elderly population: systematic review and meta-analysis","volume":"23","author":"de Souza Ferreira","journal-title":"BMC Health Serv Res"},{"key":"R48","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1199746","article-title":"Evaluation of feasibility and acceptability of a web-based diabetes prevention program (DPP) for diabetes risk reduction in Chinese Americans in New York City","volume":"11","author":"Yeh","journal-title":"Front Public Health"},{"issue":"9","key":"R49","doi-asserted-by":"publisher","DOI":"10.2196\/22845","article-title":"Artificial intelligence chatbot behavior change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet: viewpoint","volume":"22","author":"Zhang","journal-title":"J Med Internet Res"},{"issue":"23","key":"R50","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1001\/jama.2025.19563","article-title":"An AI-powered lifestyle intervention vs human coaching in the diabetes prevention program","volume":"334","author":"Mathioudakis","journal-title":"JAMA"},{"key":"R51","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2020.108388","article-title":"Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST - IT Ramadan study)","volume":"169","author":"Elhadd","journal-title":"Diabetes Res Clin Pract"},{"issue":"1","key":"R52","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-76584-7","article-title":"One-year outcomes of a digital twin intervention for type 2 diabetes: a retrospective real-world study","volume":"14","author":"Shamanna","journal-title":"Sci Rep"},{"issue":"5","key":"R53","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1016\/j.pec.2020.10.017","article-title":"Communicating personalized risk of diabetes and offering weight reduction program choice: recruitment, participation, and outcomes","volume":"104","author":"Bailey-Davis","journal-title":"Patient Educ Couns"},{"issue":"9","key":"R54","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0564-8","article-title":"Mobile applications for control and self management of diabetes: a systematic review","volume":"40","author":"Brzan","journal-title":"J Med Syst"},{"issue":"3","key":"R55","doi-asserted-by":"publisher","DOI":"10.2196\/35039","article-title":"Association between mobile health app engagement and weight loss and glycemic control in adults with type 2 diabetes and prediabetes (D\u2019LITE Study): prospective cohort study","volume":"7","author":"Lim","journal-title":"JMIR Diabetes"},{"key":"R56","doi-asserted-by":"publisher","DOI":"10.2196\/63209","article-title":"Assessing the effectiveness of digital health behavior strategies on type 2 diabetes management: systematic review and network meta-analysis","volume":"27","author":"Li","journal-title":"J Med Internet Res"},{"issue":"3","key":"R57","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1007\/s00125-019-05061-y","article-title":"A pragmatic and scalable strategy using mobile technology to promote sustained lifestyle changes to prevent type 2 diabetes in India and the UK: a randomised controlled trial","volume":"63","author":"Nanditha","journal-title":"Diabetologia"},{"issue":"8","key":"R58","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.2337\/dci24-0001","article-title":"The final frontier in diabetes care: implementing research in real-world practice","volume":"47","author":"Green","journal-title":"Diabetes Care"},{"issue":"10","key":"R59","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.1016\/j.cjca.2024.07.028","article-title":"Artificial intelligence-based digital biomarkers for type 2 diabetes: a review","volume":"40","author":"Jabara","journal-title":"Can J Cardiol"},{"issue":"5","key":"R60","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000492","article-title":"Achieving health equity through conversational AI: a roadmap for design and implementation of inclusive chatbots in healthcare","volume":"3","author":"Nadarzynski","journal-title":"PLOS Digit Health"},{"issue":"10","key":"R61","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1111\/jdi.13531","article-title":"Efficacy of lifestyle intervention program for Arab women with prediabetes using social media as an alternative platform of delivery","volume":"12","author":"Al-Hamdan","journal-title":"J Diabetes Investig"},{"issue":"10176","key":"R62","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1016\/S0140-6736(19)30368-X","article-title":"Automated insulin dosing guidance to optimise insulin management in patients with type 2 diabetes: a multicentre, randomised controlled trial","volume":"393","author":"Bergenstal","journal-title":"Lancet"},{"key":"R63","doi-asserted-by":"publisher","DOI":"10.2196\/66441","article-title":"Effectiveness of digital diabetes management technology on blood glucose in patients with type 2 diabetes at home: systematic review and meta-analysis","volume":"27","author":"Xiao","journal-title":"J Med Internet Res"},{"issue":"7","key":"R64","doi-asserted-by":"publisher","DOI":"10.3390\/nu12072022","article-title":"Prediabetes conversion to normoglycemia Is superior adding a low-carbohydrate and energy deficit formula diet to lifestyle intervention-a 12-month subanalysis of the ACOORH trial","volume":"12","author":"R\u00f6hling","journal-title":"Nutrients"},{"issue":"12","key":"R65","doi-asserted-by":"publisher","DOI":"10.1007\/s11892-021-01423-2","article-title":"Artificial intelligence in current diabetes management and prediction","volume":"21","author":"Nomura","journal-title":"Curr Diab Rep"},{"key":"R66","doi-asserted-by":"publisher","DOI":"10.21037\/mhealth-24-57","article-title":"From diabetes care to prevention: review of prediabetes apps in the DACH region","volume":"11","author":"Lim","journal-title":"Mhealth"},{"issue":"1","key":"R67","doi-asserted-by":"publisher","DOI":"10.1186\/s13063-024-08177-8","article-title":"Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial","volume":"25","author":"Abusamaan","journal-title":"Trials"},{"issue":"10","key":"R68","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1111\/jdi.13531","article-title":"Efficacy of lifestyle intervention program for Arab women with prediabetes using social media as an alternative platform of delivery","volume":"12","author":"Al\u2010Hamdan","journal-title":"J of Diabetes Invest"},{"issue":"7","key":"R69","doi-asserted-by":"publisher","DOI":"10.3390\/nu12072022","article-title":"Prediabetes conversion to normoglycemia Is superior adding a low-carbohydrate and energy deficit formula diet to lifestyle intervention-A 12-month subanalysis of the ACOORH trial","volume":"12","author":"R\u00f6hling","journal-title":"Nutrients"},{"issue":"1","key":"R70","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-023-16066-z","article-title":"Effect of artificial intelligence-based health education accurately linking system (AI-HEALS) for type 2 diabetes self-management: protocol for a mixed-methods study","volume":"23","author":"Wu","journal-title":"BMC Public Health"}],"container-title":["JMIR Diabetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/diabetes.jmir.org\/2026\/1\/e80582","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:14:08Z","timestamp":1782843248000},"score":1,"resource":{"primary":{"URL":"https:\/\/diabetes.jmir.org\/2026\/1\/e80582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":70,"URL":"https:\/\/doi.org\/10.2196\/80582","relation":{},"ISSN":["2371-4379"],"issn-type":[{"value":"2371-4379","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,12]]},"article-number":"v11i2e80582"}}