Akerstedt Torbjörn, Ingre Michael, Kecklund Göran, Folkard Simon, Axelsson John
Stress Research Institute, University of Stockholm, Stockholm, Sweden.
Chronobiol Int. 2008 Apr;25(2):309-19. doi: 10.1080/07420520802110613.
Mathematical models designed to predict alertness or performance have been developed primarily as tools for evaluating work and/or sleep-wake schedules that deviate from the traditional daytime orientation. In general, these models cope well with the acute changes resulting from an abnormal sleep but have difficulties handling sleep restriction across longer periods. The reason is that the function representing recovery is too steep--usually exponentially so--and with increasing sleep loss, the steepness increases, resulting in too rapid recovery. The present study focused on refining the Three-Process Model of alertness regulation. We used an experiment with 4 h of sleep/night (nine participants) that included subjective self-ratings of sleepiness every hour. To evaluate the model at the individual subject level, a set of mixed-effect regression analyses were performed using subjective sleepiness as the dependent variable. These mixed models estimate a fixed effect (group mean) and a random effect that accounts for heterogeneity between participants in the overall level of sleepiness (i.e., a random intercept). Using this technique, a point was sought on the exponential recovery function that would explain maximum variance in subjective sleepiness by switching to a linear function. The resulting point explaining the highest amount of variance was 12.2 on the 1-21 unit scale. It was concluded that the accumulation of sleep loss effects on subjective sleepiness may be accounted for by making the recovery function linear below a certain point on the otherwise exponential function.
旨在预测警觉性或工作表现的数学模型主要是作为评估偏离传统日间作息的工作和/或睡眠-清醒时间表的工具而开发的。一般来说，这些模型能够很好地应对异常睡眠导致的急性变化，但在处理较长时间段的睡眠限制方面存在困难。原因在于代表恢复的函数过于陡峭——通常呈指数形式——并且随着睡眠不足的增加，陡峭程度会加剧，导致恢复速度过快。本研究聚焦于完善警觉性调节的三过程模型。我们进行了一项实验，让参与者每晚睡4小时（共9名参与者），并要求他们每小时对困倦程度进行主观自我评分。为了在个体层面评估该模型，我们以主观困倦程度为因变量进行了一系列混合效应回归分析。这些混合模型估计了一个固定效应（组均值）和一个随机效应，该随机效应解释了参与者在整体困倦水平上的异质性（即随机截距）。使用这种技术，我们在指数恢复函数上寻找一个点，通过切换为线性函数来解释主观困倦程度的最大方差。在1至21的量表上，解释最大方差量的结果点为12.2。研究得出结论，通过使恢复函数在原本指数函数的某一点以下变为线性，可以解释睡眠不足对主观困倦程度的累积影响。
