Effects of Stand-Alone Digital Lifestyle Interventions on Weight-Related Outcomes in Adults With Overweight or Obesity: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Obesity is a major global health concern, and scalable digital solutions are urgently needed. While digital lifestyle interventions (DLSIs) have shown promise, prior meta-analyses often included hybrid formats with human support, limiting insights into the effectiveness of fully digital interventions. Objective: This study aimed to evaluate the independent effects of standalone DLSIs-defined as interventions delivered exclusively via digital platforms without in-person or adjunctive support-on anthropometric and dietary outcomes in adults with overweight or obesity. Methods: We searched MEDLINE, Embase, PsycINFO, Web of Science, and the Cochrane Library from inception through March 4, 2026. Eligible studies were randomized controlled trials (RCTs) evaluating stand-alone DLSIs in adults with overweight or obesity. Interventions were included if they targeted diet or physical activity exclusively through digital platforms. We included fully automated, asynchronous, or one-to-many synchronous systems without individualized support. Studies involving hybrid interventions, including one-to-one synchronous human interaction, nonadult populations, or non-RCT designs, were excluded. Two independent reviewers performed study selection and data extraction. Risk of bias was assessed using the Cochrane Risk of Bias 2.0 tool (Cochrane Bias Methods Group). Meta-analysis used a random-effects model with the Hartung-Knapp-Sidik-Jonkman method, and heterogeneity was assessed using I2 statistics. The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: A total of 19 RCTs involving 3556 participants were included. Stand-alone DLSIs significantly improved anthropometric outcomes compared to controls (standardized mean difference 0.26, 95% CI 0.14-0.38; 95% prediction interval [PI] -0.16 to 0.68; P