通过一种新的以用户为中心的算法捕捉现实世界的习惯性睡眠模式,以预处理我们所有人的Fitbit数据研究计划:回顾性观察纵向研究
Hiral Master1, Jeffrey Annis1, Jack H Ching2
1Vanderbilt University Medical Center, Nashville, TN, United States.
Journal of medical Internet research
|July 28, 2025
概括
一个新的以用户为中心的算法通过计算个人睡眠模式来改善Fitbit的睡眠跟踪,提供更准确的睡眠持续时间和效率指标. 这种方法增强了用于研究和临床应用的可穿戴睡眠数据分析.
科学领域:
- 睡眠医学 睡眠医学
- 可穿戴技术可穿戴技术
- 数字健康数字健康
背景情况:
- 像Fitbit这样的商业可穿戴设备使用固定的睡眠测量周期,可能会误解个人的睡眠模式.
- 开发了一种新的以用户为中心的算法,以提高可穿戴设备衍生睡眠指标的准确性.
研究的目的:
- 开发和描述一个以用户为中心的睡眠算法.
- 将其性能与默认日历相对算法进行比较.
- 为分析云平台上的Fitbit睡眠数据提供指导.
主要方法:
- 在8563名"我们所有人"参与者的Fitbit数据上实现了默认和以用户为中心的算法.
- 典型睡眠模式的计算变化.
- 使用线性混合效应模型来比较睡眠指标.
主要成果:
- 以用户为中心的算法将4.75%的睡眠日志重新归类为主要睡眠.
- 具有高睡眠模式变异性的参与者在以用户为中心的算法下增加了总睡眠时间 (+17.6分钟) 和入睡后醒来 (+13.9分钟).
- 在这个群体中,以用户为中心的算法使睡眠效率降低了2.0%.
结论:
- 以用户为中心的算法有效地捕捉了自然睡眠时间表的变化.
- 它提供了一种改进的方法,用于预处理和评估与时间表,持续时间和干扰有关的睡眠指标.
- 一个公开可用的R包支持其在临床和翻译研究中的使用.
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