Lim Dongju, Jeong Jaegwon, Song Yun Min, Cho Chul-Hyun, Yeom Ji Won, Lee Taek, Lee Jung-Been, Lee Heon-Jeong, Kim Jae Kyoung
Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea.
Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea.
NPJ Digit Med. 2024 Nov 18;7(1):324. doi: 10.1038/s41746-024-01333-z.
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.
可穿戴设备能够被动收集睡眠、心率和步数数据，为预测情绪障碍患者的情绪发作提供了可能。然而，当前的模型通常需要各种数据类型，限制了其在现实世界中的应用。在此，我们开发了仅使用睡眠-觉醒数据来预测未来发作的模型，当根据个体的睡眠-觉醒历史和过去的情绪发作进行训练时，这些数据可通过智能手机和可穿戴设备轻松收集。通过对168名患者的纵向数据（平均临床随访587天，可穿戴设备数据267天）进行数学建模，我们得出了36个睡眠和昼夜节律特征。这些特征能够准确预测次日的抑郁、躁狂和轻躁狂发作（曲线下面积：0.80、0.98、0.95）。值得注意的是，每日昼夜相位变化是最显著的预测因素：延迟与抑郁发作相关，提前与躁狂发作相关。这项前瞻性观察性队列研究（ClinicalTrials.gov：NCT03088657，2017年3月23日）表明，睡眠-觉醒数据与先前的情绪发作历史相结合，可以有效预测情绪发作，加强情绪障碍的管理。
