Human-computer interactions predict mental health

Abstract

Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode high-dimensional information about self-reported psychological distress and wellbeing. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA on 18,200 cursor and touchscreen recordings labeled with 1.3 million mental-health self-reports collected from 9,500 participants. MAILA predicts dynamic mental states along 13 dimensions of distress and wellbeing, detects within-person changes over time, and resolves experimentally induced fluctuations in experienced arousal and valence. At the group level, MAILA recovers demographic and time-of-day patterns in self-reported mental health with high fidelity. MAILA also captures information only partially reflected in verbal self-report and, in a synthetic proof of concept, improves the ability of a frontier large language model to infer user mental health. By extracting signatures of psychological function that have so far remained untapped, MAILA provides the first large-scale, systematic proof of principle that human-computer interactions encode multidimensional and transferable information about mental health.