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.