Mobile and App‐Based Digital Interventions in Breast Cancer Care: An Umbrella Review of Systematic Reviews and Meta‐Analyses

Akbota Sabyr, Amin Tamadon, Gulmira M. Zharmakhanova, Nadiar Maratovich Mussin, Kulyash R. Zhilisbayeva, Ramazon Safarzoda Sharoffidin

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1155/ijta/5282386
Data confidence 70 medium
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Abstract

Background Mobile and app‐based digital health interventions have rapidly emerged as transformative tools for breast cancer prevention, diagnosis, treatment adherence, and survivorship support. However, the expanding evidence base remains fragmented across multiple systematic reviews and meta‐analyses of varying quality. Objective This umbrella review was aimed at synthesizing and critically appraising the evidence from systematic reviews and meta‐analyses evaluating mobile and app‐based digital health interventions across the breast cancer care continuum, including prevention/early detection support, treatment adherence, symptom self‐management, psychosocial outcomes, communication, and survivorship. Methods A comprehensive search of PubMed, Scopus, and Web of Science was conducted from January 2000 to October 2025, following PRISMA 2020 and Joanna Briggs Institute (JBI) umbrella review guidelines. Eligible studies included systematic reviews or meta‐analyses examining mobile or app‐based interventions across prevention, early detection support, treatment, survivorship, adherence, symptom management, psychosocial support, and care coordination. Methodological quality was assessed using AMSTAR‐2, and overlap was evaluated through the corrected covered area (CCA). Both qualitative syntheses and quantitative meta‐analytic comparisons were performed, with subgroup analyses by app type (AI‐based vs. rule‐based) and implementation setting (clinical vs. community). Results Twelve systematic reviews and meta‐analyses published between 2016 and 2025 met the inclusion criteria. Most reviews demonstrated high methodological quality (67% rated “high” on AMSTAR‐2) and reported consistent short‐term benefits in patient engagement, symptom control, treatment adherence, and quality of life. Pooled analyses of comparable outcome measures within domains showed similar effectiveness across app types (AI − based pooled effect = 0.42; rule − based pooled effect = 0.36) and settings (clinical pooled effect = 0.38; community pooled effect = 0.33) with negligible heterogeneity ( I 2 = 0 % ). Visual inspection of funnel and bubble plots did not suggest marked asymmetry; however, interpretation should be cautious given that synthesis was conducted at the review level. Recent reviews indicated a shift toward AI‐driven, interoperable, and validated platforms with improved methodological rigor under PRISMA 2020 standards. Conclusions Evidence is strongest for short‐term improvements in engagement, adherence, symptom management, and psychosocial outcomes. Evidence for validated diagnostic performance and long‐term clinical endpoints remains limited and heterogeneous.