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, USA.
The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA.
Sleep. 2024 Oct 11;47(10). doi: 10.1093/sleep/zsae133.
Sleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to suboptimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the day.
We combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm's recommendations against that achieved by following the U.S. Army caffeine guidelines.
Compared to the alertness-impairment levels in the original studies, the algorithm's recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine guidelines.
We provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times.
睡眠不足会导致认知障碍，增加犯错和事故的风险。然而，现有的对抗睡眠不足影响的指南是通用的，没有针对个体特定情况进行设计，导致警觉水平不理想。在这里，我们开发了一种优化算法，可以自动识别睡眠时间表和咖啡因剂量策略，以最大限度地减少由于睡眠不足导致的在特定时间段的警觉性损害。
我们结合了之前分别优化睡眠或咖啡因的算法，以同时确定最佳的睡眠时间表和咖啡因剂量，以最大限度地减少在特定时间段的警觉性损害。该优化算法使用经过充分验证的综合绩效模型的预测来估计大量可能解决方案的有效性和生理可行性，并确定最佳解决方案。为了评估优化算法，我们使用它来确定四个研究中最佳的睡眠时间表和咖啡因剂量策略，这些研究代表了常见的睡眠不足情况，并将使用算法建议所达到的警觉性损害减少程度与遵循美国陆军咖啡因指南所达到的警觉性损害减少程度进行了比较。
与原始研究中的警觉性损害水平相比，算法的建议平均降低了 63%的警觉性损害，比美国陆军咖啡因指南提高了 24 个百分点。
我们提供了一种优化算法，可以同时确定有效和安全的睡眠时间表和咖啡因剂量策略，以最大限度地减少在用户指定时间的警觉性损害。
