Human-computer interactions predict mental health
This work addresses the critical roadblock of scalable and equitable mental health care by providing a novel digital phenotyping tool.
The paper tackles the problem of scalable mental health assessments by showing that everyday human-computer interactions can predict mental health with state-of-the-art biomarker precision, achieving near-ceiling accuracy for group-level predictions using a dataset of 20,000 recordings from 9,000 participants.
Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode mental health with state-of-the-art biomarker precision. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA on 20,000 cursor and touchscreen recordings labelled with 1.3 million mental-health self-reports collected from 9,000 participants. The dataset includes 2,000 individuals assessed longitudinally, 1,500 diagnosed with depression, and 500 with obsessive-compulsive disorder. MAILA tracks dynamic mental states along three orthogonal dimensions, identifies individuals living with mental illness, and achieves near-ceiling accuracy when predicting group-level mental health. By extracting non-verbal signatures of psychological function that have so far remained untapped, MAILA represents a key step toward scalable digital phenotyping and foundation models for mental health.