Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Oct 16, 2025
Date Accepted: Jun 4, 2026
Alleviating nurse burnout with an AI-selected mobile CBT-based intervention: A mixed-methods randomized controlled trial
ABSTRACT
Background:
Nurse burnout is a pervasive global problem driven by heavy workloads, emotional demands, and chronic occupational stress. Cognitive behavioral therapy (CBT) has been shown to effectively reduce burnout; however, most digital CBT programs use standardized approaches that overlook individual differences in burnout profiles. With advances in artificial intelligence (AI), algorithm-based recommendation systems now enable personalized intervention delivery by matching specific CBT modules to users’ needs.
Objective:
This study aimed to test the effects of AI-selected CBT on nurse burnout and to describe participants’ experiences with AI selecting the CBTs for the program.
Methods:
This randomized controlled trial included 125 nurse participants (experimental group = 62, control group = 63) and was conducted between October 2024 and December 2024. Burnout (client-related, personal, and work-related subdomains), coping strategies, job stress, and stress response were assessed. Data were analyzed using repeated measures analysis of variance and independent t-tests, with job stress and stress response as covariates. Open-ended questions from the survey and interview data from five participants in the experimental group were analyzed using thematic content analysis.
Results:
The experimental group showed significantly reduced client-related (F = 7.548, P = .007), personal (F = 6.533, P = .012), and work-related burnout (F = 38.194, P < .0001). Qualitative findings indicated that the participants had confidence in the AI algorithm’s selection of CBTs.
Conclusions:
This study suggests that AI-selected mobile CBT can alleviate nurse burnout and that participants trusted the AI-driven selection of interventions. Future research should explore the sustainability of these effects and optimize the intervention duration to enhance engagement and impact. Clinical Trial: Clinical Research Information Service (CRIS) KCT0009853; https://cris.nih.go.kr/
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