A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial.
Transl PsychiatrySep 26, 2026 (epub)
Christian Rauschenberg, Frederike Schirmbeck, Janik Fechtelpeter et al.
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In 30 seconds
This micro-randomized trial investigated the effects of machine learning (ML)-based ecological momentary interventions (EMIs) on mental health outcomes in 49 youth participants. The study found that ML-based assignment improved momentary resilience compared to random assignment, with a significant effect size (B = 0.147, p = 0.044).
Key findings
- ML-based assignment showed a significant improvement in momentary resilience (B = 0.147, p = 0.044) compared to random assignment.
- Uncontrolled pre-post comparisons indicated a small reduction in psychological distress (d = -0.23).
- Participants reported improvements in resilience (d = 0.55) and adaptive emotion regulation (d = 0.53).
- Feasibility indicators showed high satisfaction, acceptability, and usability of the intervention.
Why it matters
The study highlights the potential of machine learning to enhance the personalization of mental health interventions, which may lead to better outcomes for youth experiencing psychological distress.