A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial

Abstract Ecological Momentary Interventions (EMIs) using machine learning (ML)-based assignment algorithms may improve mental health outcomes by delivering more person-tailored content, but evidence is pending. The study aimed to determine whether ML–based assignment of EMI components augments effects on momentary mental health outcomes when compared to random assignment in youth from the general population and psychological counselling services. A within-subject micro-randomized trial was conducted. Participants were randomly assigned up to seven times daily (1:1 ratio; up to 210 decision points) to either an ML-based (experimental condition) or a random (active control condition) assignment of EMI components. Proximal outcomes were time-lagged changes in positive affect, momentary resilience, and negative affect at t n+1 . Feasibility and safety were assessed. Distal outcomes included psychological distress, resilience, and emotion regulation. A total of 49 youths (mean age 20.6; 78% female) were included. At baseline, participants reported mild-to-moderate psychological distress on average (K10 mean = 23.8, SD = 7.1), with more than one third reporting moderate or severe distress. An initial, outcome-specific signal favoring ML-based over random assignment was observed for momentary resilience ( B = 0.147, 95% confidence interval (CI), 0.004 – 0.290, p = 0.044), whereas there was no evidence of beneficial effects on positive or negative affect. Feasibility indicators supported delivery of the AI4U training, with favorable ratings of satisfaction, acceptability, and usability; no serious adverse events were reported. Uncontrolled pre-post comparisons suggested a small reduction in psychological distress (d = −0.23) and improvements in resilience (d = 0.55) and adaptive emotion regulation (d = 0.53). Taken together, this study demonstrates the feasibility and preliminary safety of a ML-based adaptive EMI in youth and provides an initial, outcome-specific signal that ML-based assignment may improve momentary resilience relative to random assignment, whilst underscoring the need for larger, adequately powered MRTs and formal validation of whether forecasting performance translates into policy value.