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纵向队列中的自然主义睡眠跟踪:短时间采样中的不确定性和偏差
Balaji Goparaju1, Glen de Palma1, Matt T Bianchi1
1Apple Inc., Cupertino, California, United States of America.
PloS one
|November 3, 2025
概括
追踪短睡眠时间导致研究中的偏见和不确定性. 较长的睡眠监测 (例如7晚) 显著减少了错误分类,并提高了睡眠健康研究的准确性.
科学领域:
- 睡眠科学 睡眠科学
- 生物统计学 生物统计学
- 可穿戴技术是可穿戴的技术.
背景情况:
- 传统的睡眠持续时间测量 (多睡眠学,动图学) 通常仅限于短时间 (1 - 几个晚上).
- 这种限制导致睡眠研究描述性统计数据的推断性不确定性.
研究的目的:
- 调查亚样本对睡眠时间和阶段指标准确性的影响.
- 确定睡眠监测的最佳持续时间,以减少睡眠健康研究中的偏见和不确定性.
主要方法:
- 对果心脏和运动研究中超过35,000名参与者的观察数据进行了回顾性分析.
- 使用模拟数据和真实世界睡眠数据 (30-365晚) 的综合方法.
- 基于子样本大小,数据分布和统计指标 (平均值,中位数,SD,IQR) 的偏差和不确定性分析.
主要成果:
- 模拟显示,子样本大小,数据分布 (正常与偏差) 和选择的统计数据 (平均值,中位数,SD,IQR) 显著影响偏差和不确定性.
- 现实世界的数据分析反映了模拟结果,表明睡眠时间的偏差和收率相似.
- 亚样本睡眠阶段也带来了与真实值的显著差异;较长的观察期 (例如7晚) 减轻了错误分类的风险.
结论:
- 由于分样采集,睡眠研究中的偏见和不确定性取决于观察窗口的大小,使用的统计措施和数据分布.
- 延长睡眠时间的跟踪 (例如30-365晚) 在减少偏差和不确定性方面提供了显著的优势.
- 较长的监测时间对于提高睡眠健康研究结果的可靠性至关重要.
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