ArticleJMIR mHealth and uHealth2026
Immediate and Sustained Improvements in Mood and Stress Associated With Yuna, an AI-Powered Digital Mental Health Intervention: Real-World Retrospective Study.
Article in JMIR mHealth and uHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 ( Conclusions: Use of Yuna, an AI-powered DMHI, was associated with perceived within-session improvements in mood and stress, with preliminary evidence of gradual improvements in mood and stress across repeated sessions. However, the absence of a control group and potential selection bias preclude causal conclusions. These findings offer promising, real-world evidence for AI-powered DMHIs as accessible, on-demand support tools. Prospective, controlled designs are needed to establish causal effects and evaluate the sustainability of observed improvements.
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Registered trials
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