Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Problem Framing for Digital Diabetes Solutions: A Systematic Literature Review of Engagement Barriers, Facilitators, and Outcomes
ABSTRACT
Background:
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 personalisation. 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.
Objective:
This systematic literature review (SLR) sought to explore the barriers, facilitators and outcomes of digital health interventions, focusing on the current state of Artificial AI applications while including non-AI interventions, for managing and preventing T2DM and prediabetes, to inform the development of user-centered, inclusive digital health interventions for diabetes care.
Methods:
A systematic search of PubMed, Scopus, CINAHL, and additional sources was conducted for studies published between January 2016 and December 2024. 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 (SDT) and User-Centered Design (UCD). Quality appraisal was conducted using CASP, MMAT, and AMSTAR-2 tools.
Results:
From the filtered 32 studies (12 quantitative, 5 qualitative, 5 mixed-methods, 10 reviews/other), interventions comprised 17 AI-driven, 3 partially AI-driven, and 12 non-AI solutions, mostly originated from the USA. populations and settings. Barriers to engagement included high dropout rates, poor personalisation, low-risk perception, cultural and language mismatches, and AI-specific concerns (e.g., bias, privacy). Facilitators included personalized feedback, cultural tailoring, user-friendly design, and peer support. AI-driven interventions demonstrated moderate improvements in clinical outcomes (HbA1c reductions of 0.3%–0.39%; weight loss 7.3%–10.6%) but faced notable engagement and trust barriers. Non-AI solutions contributed similarly but lacked adaptive features.
Conclusions:
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 adaptive, culturally tailored, and user-centered AI interventions to address engagement challenges in T2DM and prediabetes management. Integrating personalisation, precision, and value-based care can improve outcomes and scalability. The findings guide the creation of inclusive, AI-driven solutions aligned with SDT and UCD principles. This study marks Phase 1 (problem identification and motivation) of a Design Science Research Methodology (DSRM) PhD project aimed at advancing equitable digital health solutions for diabetes care.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.