Digital health interventions reduce occupational burnout in nurses with moderate effect sizesDigital Health Tools Show Promise for Reducing Nurse Burnout

ObjectiveTo systematically evaluate and meta-analyze the effectiveness of digital health interventions (DHIs) in reducing occupational burnout among nurses and nursing staff compared with usual care, waitlist control, or non-digital interventions.MethodsFollowing PRISMA 2020 guidelines, six electronic databases (PubMed/MEDLINE, CINAHL, Embase, Web of Science, PsycINFO, and Scopus) were searched from January 2015 to March 2025 for randomized controlled trials and quasi-experimental studies. Risk of bias was assessed using Cochrane RoB 2 and JBI checklists. Random-effects meta-analysis using the DerSimonian–Laird method, pre-specified subgroup and sensitivity analyses, publication-bias assessment, and GRADE certainty assessment were performed.ResultsThirty-seven studies encompassing approximately 8,450 nurses and nursing staff across 14 countries were included, of which 28 provided data for quantitative synthesis. The pooled standardized mean difference indicated a statistically significant moderate reduction in burnout (SMD = −0.47; 95% CI: −0.65 to −0.29; p < 0.001; I2 = 72%). Web-based cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT) programs showed the largest pooled effect (k = 10; SMD = −0.72; 95% CI: −1.05 to −0.39), followed by AI-tailored mobile interventions (k = 3; SMD = −0.61; 95% CI: −0.93 to −0.29; I2 = 41%). Emotional exhaustion (EE) was the most responsive burnout dimension (SMD = −0.53), whereas personal accomplishment (PA) showed the weakest improvement (SMD = +0.24). Guided interventions produced larger effects than self-guided programs (p = 0.04), and longer-duration interventions showed larger pooled effects than brief programs.ConclusionDHIs, particularly structured and guided web-based CBT/ACT programs, were associated with moderate reductions in occupational burnout among nurses and nursing staff. Early evidence for AI-tailored interventions is promising but requires independent replication. The findings support the integration of evidence-based DHIs into broader workforce well-being strategies that combine individual support with organizational action on the structural determinants of burnout.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261365184.