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. 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. 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. This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows sleep-wake data, combined with prior mood episode history, can effectively predict mood episodes, enhancing mood disorder management.

可穿戴设备可以被动收集睡眠、心率和步数数据，为情绪障碍患者的情绪发作预测提供了潜力。然而，当前的模型通常需要各种数据类型，这限制了实际应用。在这里，我们开发了仅使用睡眠-觉醒数据预测未来发作的模型，这些数据在对个人的睡眠-觉醒历史和过去的情绪发作进行训练时，可以通过智能手机和可穿戴设备轻松收集。使用数学模型对 168 名患者的纵向数据 （587 天平均临床随访，267 天可穿戴数据） 进行数学建模，我们得出了 36 个睡眠和昼夜节律特征。这些功能能够准确预测抑郁、躁狂和轻躁狂发作的第二天发作 （AUC： 0.80 、 0.98 、 0.95 ）。值得注意的是，每日昼夜节律相移是最重要的预测因子：与抑郁发作相关的延迟，进展到躁狂发作。这项前瞻性观察队列研究 （ClinicalTrials.gov： NCT03088657， 2017-3-23） 显示睡眠-觉醒数据，结合既往心境发作史，可以有效预测心境发作，加强心境障碍管理。