Accurately Predicting Mood Episodes in Mood Disorder Patients Using Wearable Sleep and Circadian Rhythm Features

초록

Introduction: Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in mood disorder patients. However, current models often require various data types, limiting real-world application. Materials and methods: Here, we develop models that predict future episodes using only sleep-wake data, easily gathered through smartphones and wearables when trained on an individual’s sleep-wake history and past mood episodes. Using mathematical modeling to longitudinal data from 168 patients (587 days average clinical follow-up, 267 days wearable data), we derived 36 sleep and circadian rhythm features. Results: These features enabled accurate next-day predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, 0.95). Notably, daily circadian phase shifts were the most significant predictors: delays linked to depressive episodes, advances to manic episodes. Conclusions: This prospective observational cohort study shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management