发表机构
Emory University; University of California San Francisco; University of North Carolina at Chapel Hill; Georgia Institute of Technology; Johns Hopkins University（埃默里大学; 旧金山加州大学; 北卡罗来纳大学教堂山分校; 佐治亚理工学院; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用可穿戴数据发现，约8周监测是区分失眠严重程度的最短有效时长，昼夜节律特征与活动计数相当或更优，可作为失眠评估的数字生物标志物。
AI 中文摘要
背景：可穿戴设备提供连续、客观的日常活动测量，为评估睡眠障碍提供了前景。然而，区分失眠严重程度所需的最短监测时长仍不清楚。我们研究了可穿戴设备衍生的行为特征何时开始对区分失眠严重指数（ISI）类别具有信息价值，并考察了活动计数（AC）和昼夜节律衍生特征的贡献。方法：我们分析了创伤后恢复理解推进（AURORA）研究中2,305名参与者的可穿戴数据。使用AC和昼夜节律衍生特征集，在六个随访期为四个ISI类别分别开发了二元分类器。使用准确率、F1分数、精确率、召回率和AUROC评估性能。结果：随着监测时间延长，分类效果有所改善。在ISI类别中，约八周是可穿戴衍生特征持续实现有信息价值区分的最早时间点，此后仅有适度改善。无临床显著失眠的参与者最易识别，AUROC达到0.693。昼夜节律衍生特征的性能与AC特征相当，且在若干情况下更优，表明日常活动的时间组织提供了超出总体活动量的信息。结论：约八周的纵向可穿戴监测可能是区分ISI定义的失眠类别的实际最短时间。更长的监测仅带来增量改善。昼夜节律行为特征有望作为客观失眠评估的数字生物标志物。
英文摘要
Background: Wearable devices provide continuous, objective measures of daily activity and offer promise for assessing sleep disorders. However, the minimum monitoring duration needed to differentiate insomnia severity remains unclear. We investigated when wearable-derived behavioral features become informative for distinguishing Insomnia Severity Index (ISI) categories and examined the contribution of Activity Count (AC) and circadian-derived features. Methods: We analyzed wearable data from 2,305 participants in the Advancing Understanding of Recovery after Trauma (AURORA) study. Separate binary classifiers were developed for four ISI categories across six follow-up periods using AC and circadian-derived feature sets. Performance was evaluated using accuracy, F1-score, precision, recall, and AUROC. Results: Classification improved with longer monitoring. Across ISI categories, approximately eight weeks was the earliest time point at which wearable-derived features consistently achieved informative discrimination, with modest improvements thereafter. Participants without clinically significant insomnia were easiest to identify, reaching an AUROC of 0.693. Circadian-derived features performed comparably to, and in several cases better than, AC features, suggesting that the temporal organization of daily activity provides information beyond overall activity volume. Conclusions: Approximately eight weeks of longitudinal wearable monitoring may represent a practical minimum for differentiating ISI-defined insomnia categories. Longer monitoring provided only incremental improvements. Circadian behavioral features show promise as digital biomarkers for objective insomnia assessment.
