{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:59:40Z","timestamp":1790791180562,"version":"4.1.0"},"reference-count":167,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Interact J Med Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Intervention studies frequently report that fewer than half of adolescent participants complete digital health interventions as intended, yet a systematic synthesis of the factors driving this low adherence across lifestyle domains is lacking. Understanding these factors is essential for informing the design of interventions that can sustain adherence and achieve meaningful behavioral outcomes.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This systematic review identified and analyzed factors influencing adherence to digital health interventions targeting key adolescent lifestyle domains: physical activity, dietary habits, obesity management, alcohol consumption, and tobacco use.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We systematically searched PubMed, PsycINFO, Embase, and CINAHL in March 2024, with the search updated in June 2026, for studies of digital interventions targeting adolescents (10\u201019 years). Studies were included if they reported adherence outcomes. Data on intervention characteristics, user-related factors, and adherence outcomes were extracted and narratively synthesized using a framework distinguishing intervention-related and user-related factors across 3 evidence types (statistical, descriptive, and qualitative). Study-level attrition was additionally pooled in a random-effects meta-analysis.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 116 studies, comprising 46,029 participants, were included. The average completion rate (binary measure) was 66.9% (SD 26%), while average adherence to individual intervention components was 55.2% (SD 25.5%). A meta-analysis of attrition across 108 comparisons yielded a pooled dropout of 16.9% (95% CI 13.6\u201020.9), with very high between-study heterogeneity and no significant moderators. Consistently reported facilitators included personalization, user-friendly design, reminders, goal setting, gamification, and human support (particularly health coaching and parental involvement). The most common barriers were technical difficulties and poor system usability. Evidence regarding the effects of intervention duration and social components on adherence was inconsistent.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Given that adolescents frequently underuse digital health interventions, completing only about half of intervention components on average, the effectiveness of these interventions is fundamentally constrained by adherence challenges. Adherence is primarily driven by user-centered design features and robust technical performance, while the impact of social and user-related factors varies by health domain and study context. Future research should adopt standardized adherence frameworks and reporting standards to enable meaningful cross-study comparisons and the development of evidence-based engagement strategies.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/84822","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T14:45:05Z","timestamp":1786027505000},"page":"e84822-e84822","source":"Crossref","is-referenced-by-count":0,"title":["Factors Influencing Adherence to Digital Lifestyle Interventions for Adolescents: Systematic Review and Meta-Analysis of 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