通过结合纵向数据和个性化的数学模型来确定睡眠表型是否由内生昼夜节律或环境光驱动的方法
Anne C Skeldon1,2, Thalia Rodriguez Garcia1,2,3, Sean F Cleator1,2,3
1School of Mathematics & Physics, University of Surrey, Guildford, United Kingdom.
PLoS computational biology
|December 22, 2023
概括
一个新的恒温-日间循环-光 (HCL) 模型使用可穿戴传感器数据量化个人睡眠模式. 它揭示了内部生物因素和外部光线暴露都显著影响睡眠时间,从而使个性化干预成为可能.
科学领域:
- 时间生物学 时间生物学
- 计算生理学计算生理学
- 数字健康数字健康
背景情况:
- 睡眠时间是可变的,并受到内部生理学,光线和行为的影响.
- 现有的模型往往无法从数量上捕捉个人的睡眠模式.
- 了解睡眠表型的驱动因素对于个性化干预至关重要.
研究的目的:
- 为分析个人睡眠数据引入一种新的,计算效率高的恒温-循环-光 (HCL) 模型.
- 评估内源性和外源性因素对个人睡眠现象型的贡献.
- 为了证明模型在开发个性化睡眠干预措施中的实用性.
主要方法:
- 开发了一种简化的恒温-循环-光 (HCL) 模型.
- 利用可穿戴传感器的纵向睡眠和光照数据.
- 执行了灵敏度分析和参数识别能力评估.
- 通过配合两个参数量化内源驱动因素和使用新型指标量化外源驱动因素.
主要成果:
- 该HCL模型准确地描述了34名老年人的个人睡眠表型.
- 结合内源和外源驱动因素解释了64%的平均中期睡眠时间变异 (调整R平方).
- 个别的驱动因素甚至在类似的睡眠表型中也存在差异.
结论:
- HCL模型提供了一种透明和高效的方法来定量分析个人的睡眠模式.
- 内源性和外源性因素都至关重要,但它们的相对重要性在个体之间有所不同.
- 这些发现支持开发个性化的数字健康干预措施,以调节睡眠和清醒.
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