活动睡眠协调方法 (MASH):一种新的方法,用于协调两个可穿戴设备的数据,以估计24小时的睡眠和清醒周期
Erin E Dooley1, J F Winkles2, Alicia Colvin3
1Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA.
Journal of activity, sedentary and sleep behaviors
|September 11, 2023
概括
活动睡眠协调方法 (MASH) 通过协调来自部和手腕可穿戴设备的数据来改善24小时睡眠-清醒周期的估计. 这个过程最大限度地减少了数据丢失和纠正差异,提高了时间利用流行病学研究的准确性.
科学领域:
- * 可穿戴式传感器技术
- * 生物医学数据分析
- * 时代生物学和睡眠科学
背景情况:
- *准确估计24小时的睡眠-清醒周期对于健康研究至关重要.
- *研究人员对可穿戴设备类型和放置的偏好使数据收集复杂化.
- *来自多个设备的数据丢失或相互矛盾,妨碍了准确的行为构成.
研究的目的:
- * 引入活动睡眠协调方法 (MASH) 以协调可穿戴传感器数据.
- * 解决因设备限制和数据丢失而导致的每日睡眠-清醒周期估计的挑战.
- *为了提高24小时行为组合的准确性,用于流行病学研究.
主要方法:
- *MASH协调了同时佩戴的部 (活动) 和手腕 (睡眠) 设备的数据.
- *它采用一个双层的过程:得分睡眠数据或一维卷积神经网络 (1D CNN).
- * 1D CNN 模型预测唤醒间隔,并调和数据分歧,创建日级配对.
主要成果:
- *MASH表现出色,ROC-AUC得分为0.991 (使用ActiGraph + Actiwatch) 和0.983 (仅使用ActiGraph).
- * 排除后,分析了8560天,其中46.0%需要MASH干预因不同意或1D CNN估计.
- *MASH纠正了清醒间隔,平均减少了18分钟的久坐行为.
结论:
- *MASH有效地最大限度地减少数据丢失,并纠正可穿戴传感器数据中的设备不一致.
- *协调过程提高了24小时睡眠-清醒周期数据的准确性.
- * MASH通过提供更可靠的行为成分来提高时间使用流行病学的实用性.
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