Understanding Adherence to Digital Health Technologies: Systematic Review of Predictive Factors.

Background: Digital health technologies (DHTs) are transformative solutions for health care challenges; however, sustaining long-term adherence remains a significant barrier, limiting their effectiveness. Objective: This systematic review aims to identify and categorize factors influencing adherence to DHTs and to identify theoretical foundations used to predict it. Methods: This review was conducted according to the PICO (population, intervention, comparison, outcome) strategy and followed the Cochrane Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. The protocol was prospectively registered on PROSPERO (CRD42024628168). Literature searches were performed in December 2024 in PubMed, PsycINFO, Scopus, and IEEE Xplore for studies published between 2019 and 2024 in English, Portuguese, or Spanish. Studies were eligible if they investigated factors influencing adherence to DHTs or theoretical foundations and tools predicting adherence. Nonpeer-reviewed studies, study protocols, and studies that did not explicitly report adherence outcomes were excluded. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tools. Data were synthesized narratively through inductive thematic analysis, with factors influencing adherence extracted and categorized. Results: In total, 61 studies were included, mostly quantitative and conducted in Europe and North America. The populations were mainly patients with medical conditions, and most studies focused on mobile health apps. Study quality was moderate to high. The findings highlight a complex and multifaceted range of factors influencing adherence, which were categorized into four key domains: (1) personal factors (sociodemographic characteristics, health status, user characteristics, and personal beliefs and perceptions), (2) technology and intervention content factors (infrastructure and accessibility, user experience and performance, and content and features of the intervention), (3) social and support system factors (family and informal support and health care professional support), and (4) contextual factors. Among the theoretical foundations identified, the Unified Theory of Acceptance and Use of Technology (UTAUT) emerged as the most frequently applied. Conclusions: The findings highlight the need for integrative, health-specific models that combine behavioral, technological, and clinical aspects. Future research should focus on developing standardized adherence metrics and exploring the interactions between these factors to improve predictive models. However, the evidence base is limited by heterogeneity in study designs and adherence definitions, potential publication, and language bias.