Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD.
The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD.
Sleep. 2021 May 14;44(5). doi: 10.1093/sleep/zsaa263.
Planning effective sleep-wake schedules for civilian and military settings depends on the ability to predict the extent to which restorative sleep is likely for a specified sleep period. Here, we developed and validated two mathematical models, one for predicting sleep latency and a second for predicting sleep duration, as decision aids to predict efficacious sleep periods.
We extended the Unified Model of Performance (UMP), a well-validated mathematical model of neurobehavioral performance, to predict sleep latency and sleep duration, which vary nonlinearly as a function of the homeostatic sleep pressure and the circadian rhythm. To this end, we used the UMP to predict the time course of neurobehavioral performance under different conditions. We developed and validated the models using experimental data from 317 unique subjects from 24 different studies, which included sleep conditions spanning the entire circadian cycle.
The sleep-latency and sleep-duration models accounted for 42% and 84% of the variance in the data, respectively, and yielded acceptable average prediction errors for planning sleep schedules (4.0 min for sleep latency and 0.8 h for sleep duration). Importantly, we identified conditions under which small shifts in sleep onset timing result in disproportionately large differences in sleep duration-knowledge that may be applied to improve performance, safety, and sustainability in civilian and military operations.
These models extend the capabilities of existing predictive fatigue-management tools, allowing users to anticipate the most opportune times to schedule sleep periods.
为了制定民用和军事环境中有效的睡眠-觉醒时间表，需要具备预测特定睡眠时间内恢复性睡眠可能性的能力。在这里，我们开发并验证了两种数学模型，一种用于预测睡眠潜伏期，另一种用于预测睡眠持续时间，作为预测有效睡眠期的决策辅助工具。
我们扩展了性能统一模型（UMP），这是一种经过充分验证的神经行为表现数学模型，用于预测睡眠潜伏期和睡眠持续时间，这两个变量随内稳态睡眠压力和昼夜节律的非线性变化而变化。为此，我们使用 UMP 来预测不同条件下神经行为表现的时间过程。我们使用来自 24 项不同研究的 317 名独特受试者的实验数据来开发和验证模型，这些研究包括跨越整个昼夜节律周期的睡眠条件。
睡眠潜伏期和睡眠持续时间模型分别解释了数据中 42%和 84%的方差，并且对睡眠计划的平均预测误差可接受（睡眠潜伏期为 4.0 分钟，睡眠持续时间为 0.8 小时）。重要的是，我们确定了睡眠起始时间的小变化会导致睡眠持续时间产生不成比例的大差异的条件——这一知识可应用于提高民用和军事行动中的绩效、安全性和可持续性。
这些模型扩展了现有预测疲劳管理工具的功能，使用户能够预测安排睡眠期的最佳时机。
