Human-computer interactions predict mental health

TL;DR

This paper introduces MAILA, a machine-learning framework that uses everyday human-computer interactions to accurately infer mental health states, enabling scalable digital phenotyping and AI applications.

Contribution

MAILA is a novel framework that predicts mental health from digital activity data with high accuracy, capturing dynamic states and improving AI inference capabilities.

Findings

MAILA achieves near-ceiling accuracy at the group level.

It tracks mental states along 13 clinically relevant dimensions.

It enhances large language models' ability to infer 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 mental health with biomarker accuracy. 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 tracks dynamic mental states along 13 clinically relevant dimensions, resolves circadian fluctuations and experimental manipulations of arousal and valence, achieves near-ceiling accuracy at the group level, captures information that is only partially reflected in verbal self-report, and improves the ability of large language models to infer user mental health. By extracting signatures of psychological function that have so far remained untapped, MAILA establishes human-computer interactions as a new modality for scalable digital phenotyping and a foundation for context-aware artificial intelligence.