Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland.
Behavioral Biology Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland.
J Sleep Res. 2018 Oct;27(5):e12711. doi: 10.1111/jsr.12711. Epub 2018 May 28.
Sleep loss, which affects about one-third of the US population, can severely impair physical and neurobehavioural performance. Although caffeine, the most widely used stimulant in the world, can mitigate these effects, currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits. In this work, we provide an optimization algorithm, suited for mobile computing platforms, to determine when and how much caffeine to consume, so as to safely maximize neurobehavioural performance at the desired time of the day, under any sleep-loss condition. The algorithm is based on our previously validated Unified Model of Performance, which predicts the effect of caffeine consumption on a psychomotor vigilance task. We assessed the algorithm by comparing the caffeine-dosing strategies (timing and amount) it identified with the dosing strategies used in four experimental studies, involving total and partial sleep loss. Through computer simulations, we showed that the algorithm yielded caffeine-dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64% while using the same total amount of caffeine as in the original studies. In addition, the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65%. Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep-loss condition.
睡眠不足影响了大约三分之一的美国人口，会严重损害他们的身体和神经行为表现。尽管世界上使用最广泛的兴奋剂咖啡因可以减轻这些影响，但目前还没有工具可以指导咖啡因的摄入时间和剂量，以优化其益处。在这项工作中，我们提供了一种适合移动计算平台的优化算法，以确定何时以及摄入多少咖啡因，以便在任何睡眠不足的情况下，安全地在一天中期望的时间最大限度地提高神经行为表现。该算法基于我们之前经过验证的表现综合模型，该模型预测了咖啡因摄入对精神运动警觉任务的影响。我们通过将其确定的咖啡因剂量策略（时间和剂量）与涉及完全和部分睡眠不足的四项实验研究中使用的剂量策略进行比较，来评估该算法。通过计算机模拟，我们表明，该算法产生的咖啡因剂量策略可以将预测的精神运动警觉任务的表现提高多达 64%，而使用的咖啡因总量与原始研究相同。此外，该算法确定了可以达到与实验研究相同的性能的策略，同时将咖啡因的消耗减少多达 65%。我们的工作提供了第一个用于设计有效策略的定量咖啡因优化工具，可以在任何任意的睡眠不足情况下最大限度地提高神经行为表现，并避免过度摄入咖啡因。
