Attrition in Digital Self-Management Interventions for Patients With Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD): Mixed Methods Systematic Review.

Background: Lifestyle modification delivered through digital self-management is central to metabolic dysfunction-associated steatotic liver disease (MASLD) care, yet long-term engagement remains the threshold beyond which clinical benefit is realized. Understanding attrition requires examining both retention (dropout) and adherence (usage quality), which are often evaluated in isolation. Existing systematic reviews of digital interventions for MASLD have focused predominantly on clinical effectiveness, leaving less attention on attrition. Objective: This study aimed to integrate quantitative retention metrics with qualitative adherence insights and characterize the determinants of attrition in digital MASLD self-management interventions. Methods: Following PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, a comprehensive search of five databases (PubMed, Web of Science, Embase, Cochrane Library, and CINAHL) was conducted. The initial search was conducted in June 2025, and a subsequent update was made on April 17, 2026. Eligible studies enrolled adults with MASLD or nonalcoholic fatty liver disease in structured digital self-management interventions reporting retention or adherence data. Methodological quality was assessed using the Mixed Methods Appraisal Tool. A convergent segregated design was adopted. Retention proportions were pooled using random-effects meta-analysis with logit transformation, restricted maximum likelihood estimation, and Hartung-Knapp-Sidik-Jonkman adjustment. Adherence data were synthesized through inductive framework synthesis. Findings were subsequently integrated narratively. Results: In total, 21 studies met the eligibility criteria, of which 15 (n=1,032) contributed to the quantitative synthesis. The pooled retention proportion was 80% (95% CI 72%-87%) with substantial between-study heterogeneity (I²=73.6%). App-based platforms showed the highest point estimate and the lowest within-group heterogeneity, although no subgroup difference reached statistical significance. Adherence varied widely and was not amenable to meta-analytic pooling. Thematic synthesis identified 4 interacting domains shaping adherence, namely platform and design, human support and professional integration, motivational and behavioral strategies, and patient-level characteristics. Access friction at entry, gated coaching architecture, the absence of proximal biological feedback, and psychological comorbidity recurred as attenuators of long-term engagement. Conclusions: This review innovatively integrates retention and adherence to provide a comprehensive framework of attrition dynamics specific to MASLD. While retention compared favorably with adjacent fields, long-term adherence depended less on platform type than on accessible human support, alignment of feedback with the disease's silent course, and psychological screening. Although evidence certainty was rated very low under Grading of Recommendations Assessment, Development and Evaluation, reflecting blinding constraints intrinsic to digital interventions and a predominance of pilot or feasibility designs, these findings carry clear real-world implications. Future interventions would benefit from establishing standardized, component-level reporting that distinguishes retention from adherence, to reliably evaluate the true therapeutic potential of digital MASLD interventions.