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Attrition in Digital Self-Management Interventions for Patients With Metabolic Dysfunction Associated Steatotic Liver
Rui Pang1, Yihong Xu1, Xiaoxiao Yu1
1Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China, 86 13857188922.
Journal of Medical Internet Research
|August 10, 2026
Summary
Digital self-management for metabolic dysfunction-associated steatotic liver disease (MASLD) shows 80% retention, but long-term adherence depends on support and feedback, not just platform type. Understanding attrition is key for effective MASLD care.
Area of Science:
- Digital health interventions
- Liver disease management
- Patient self-management
Background:
- Digital self-management is crucial for metabolic dysfunction-associated steatotic liver disease (MASLD) care.
- Long-term engagement with digital interventions is essential for clinical benefit.
- Attrition, encompassing retention and adherence, is a critical but understudied aspect of digital MASLD interventions.
Purpose of the Study:
- To integrate quantitative retention metrics with qualitative adherence insights.
- To characterize the determinants of attrition in digital MASLD self-management interventions.
Main Methods:
- Systematic review following PRISMA-S and PRISMA 2020 guidelines.
- Comprehensive search of five databases; updated search in April 2026.
- Convergent segregated design: meta-analysis for retention, framework synthesis for adherence.
Main Results:
- Pooled retention proportion of 80% (95% CI 72%-87%) with significant heterogeneity.
- Adherence varied widely; key domains included platform design, human support, motivational strategies, and patient factors.
- Factors attenuating engagement included access friction, gated coaching, lack of biological feedback, and psychological comorbidity.
Conclusions:
- This review provides a comprehensive framework for MASLD attrition by integrating retention and adherence.
- Long-term adherence is influenced by human support, relevant feedback, and psychological screening, more than platform type.
- Future digital MASLD interventions require standardized reporting to differentiate retention and adherence for accurate evaluation.
