Lee Minki P, Kim Dae Wook, Fang Yu, Kim Ruby, Bohnert Amy S B, Sen Srijan, Forger Daniel B
Department of Mathematics, University of Michigan, Ann Arbor, MI, 48109, USA.
Department of Brain and Cognitive Sciences, KAIST, Daejeon, 34141, Republic of Korea.
NPJ Digit Med. 2024 Dec 5;7(1):355. doi: 10.1038/s41746-024-01348-6.
While circadian disruption is recognized as a potential driver of depression, its real-world impact is poorly understood. A critical step to addressing this is the noninvasive collection of physiological time-series data outside laboratory settings in large populations. Digital tools offer promise in this endeavor. Here, using wearable data, we first quantify the degrees of circadian disruption, both between different internal rhythms and between each internal rhythm and the sleep-wake cycle. Our analysis, based on over 50,000 days of data from over 800 first-year training physicians, reveals bidirectional links between digital markers of circadian disruption and mood both before and after they began shift work, while accounting for confounders such as demographic and geographic variables. We further validate this by finding clinically relevant changes in the 9-item Patient Health Questionnaire score. Our findings validate a scalable digital measure of circadian disruption that could serve as a marker for psychiatric intervention.
虽然昼夜节律紊乱被认为是抑郁症的一个潜在驱动因素,但其在现实世界中的影响却鲜为人知。解决这一问题的关键一步是在实验室环境之外对大量人群进行非侵入性生理时间序列数据收集。数字工具在这一领域具有潜力。在此,我们利用可穿戴设备数据,首先量化了不同内部节律之间以及每个内部节律与睡眠-觉醒周期之间的昼夜节律紊乱程度。我们基于800多名第一年培训医生超过50000天的数据进行分析,揭示了昼夜节律紊乱的数字标志物与他们开始轮班工作前后情绪之间的双向联系,同时考虑了人口统计学和地理变量等混杂因素。我们通过在9项患者健康问卷评分中发现临床相关变化进一步验证了这一点。我们的研究结果验证了一种可扩展的昼夜节律紊乱数字测量方法,该方法可作为精神科干预的标志物。