Clinical Trials for Digital Health Interventions: An Umbrella Review of Study Independence and the Developer Effect

Digital health interventions (DHIs) using apps, web-based platforms, or other digital tools are increasingly deployed to support prevention, health promotion, and disease management. Unlike mandates and reporting guidelines established for traditional pharmaceutical trials, it remains unclear to what extent DHI trials vary in the degree of developer involvement in trial conduct or sponsorship, and whether such involvement should raise concerns about risk of bias in estimated trial effectiveness. This umbrella review aimed to narratively map and summarize published systematic reviews of randomized controlled trials (RCTs) evaluating DHIs, with a focus on quantifying developer involvement and its association with significance of reported trial outcomes. Systematic reviews published between 2013 and 2025 were identified from four databases, targeting interventions in nutrition, maternal health, mental health, and sleep. Data were extracted on trial characteristics, developer involvement, preregistration, participant population, DHI cost and public availability, and outcome significance. Across 229 trials from 29 systematic reviews, 73% of trials were conducted with direct developer involvement, whereas 27% were independent. Developer-involved trials were more likely to be preregistered than independent trials (OR = 2.47, p = 0.004). When weighted by sample size, developer-involved trials had higher odds of reporting statistically significant results than independent trials within each category (OR=1.23, 95% CI 1.16–1.31, p < 0.001). This review highlights the prevalence of developer involvement in DHI trials and its potential influence on reported outcomes, underscoring the need for greater transparency in publications, independent efficacy trials, and regulatory oversight for digital health technologies. Future research should examine how varying forms of developer engagement affect trial design, reporting, and effect sizes, to ensure credible, safe, and effective evidence generation in digital health.