Immediate and Sustained Improvements in Mood and Stress Associated With Yuna, an AI-Powered Digital Mental Health Intervention: Real-World Retrospective Study

Vollständiger Abstract

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Abstract Background AI-powered digital mental health interventions (DMHIs) are a promising approach to address barriers to traditional mental health care. However, real-world evidence of their immediate and sustained benefits remains limited. Objective The purpose of this real-world, retrospective study is to explore patterns of perceived mood and stress change associated with the use of Yuna, an AI-powered DMHI. We aimed to (1) describe user demographics and session characteristics, (2) quantify the magnitude of mood and stress change within sessions and over time with continued Yuna use, and (3) identify session-level factors associated with changes in mood and stress. Methods Adult Yuna users (aged ≥18 y) who initiated at least one session with the Yuna app were included in this study. Users self-reported mood and stress on Visual Analog Scales (VASs; range 0‐1) before and after sessions. Linear mixed effects models were used to explore the immediate, within-session improvements in mood and stress, and the sustained, between-session changes in symptoms of mood and stress. Linear mixed effects models were also used to examine session-level predictors of within-session improvements, including baseline symptom severity, session duration, total number of unique therapeutic approaches used, safety guardrail activation, and gender. Results A total of 5549 real-world users were included (2901/5549, 52.3% female; mean sessions 3.44, SD 9.83). Users demonstrated significant, immediate within-session improvements in both mood ( d =0.55) and stress ( d =0.56), with sensitivity analyses yielding consistent results. Between-session analyses revealed gradual improvements in baseline mood ( d =−0.009) and stress (d=−0.011). Baseline symptom severity was the strongest predictor of immediate, within-session change (mood: β=.118, SE 0.003; P