Antidepressant nonadherence is pervasive, driving relapse, hospitalization, suicide risk, and billions in avoidable costs. Clinicians need tools that detect adherence lapses promptly, yet current methods are either invasive (serum assays, neuroimaging) or proxy-based and inaccurate (pill counts, pharmacy refills). We present the first noninvasive biomarker that detects antidepressant intake from a single night of sleep. A transformer-based model analyzes sleep data from a consumer wearable or contactless wireless sensor to infer antidepressant intake, enabling remote, effortless, daily adherence assessment at home. Across six datasets comprising 62,000 nights from >20,000 participants (1,800 antidepressant users), the biomarker achieved AUROC = 0.84, generalized across drug classes, scaled with dose, and remained robust to concomitant psychotropics. Longitudinal monitoring captured real-world initiation, tapering, and lapses. This approach offers objective, scalable adherence surveillance with potential to improve depression care and outcomes.
@article{arxiv.2510.10364,
title = {Transformer Model Detects Antidepressant Use From a Single Night of Sleep, Unlocking an Adherence Biomarker},
author = {Ali Mirzazadeh and Simon Cadavid and Kaiwen Zha and Chao Li and Sultan Alzahrani and Manar Alawajy and Joshua Korzenik and Kreshnik Hoti and Charles Reynolds and David Mischoulon and John Winkelman and Maurizio Fava and Dina Katabi},
journal= {arXiv preprint arXiv:2510.10364},
year = {2025}
}