Problem Framing for Digital Diabetes Solutions: A Systematic Literature Review of Engagement Barriers, Facilitators, and Outcomes (Preprint)

<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> 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. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> 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. </sec> <sec> <title>CONCLUSIONS</title> 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. </sec>