Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Materiel Command, Fort Detrick, MD, USA.
J Theor Biol. 2013 Aug 21;331:66-77. doi: 10.1016/j.jtbi.2013.04.013. Epub 2013 Apr 24.
Performance prediction models based on the classical two-process model of sleep regulation are reasonably effective at predicting alertness and neurocognitive performance during total sleep deprivation (TSD). However, during sleep restriction (partial sleep loss) performance predictions based on such models have been found to be less accurate. Because most modern operational environments are predominantly characterized by chronic sleep restriction (CSR) rather than by episodic TSD, the practical utility of this class of models has been limited. To better quantify performance during both CSR and TSD, we developed a unified mathematical model that incorporates extant sleep debt as a function of a known sleep/wake history, with recent history exerting greater influence. This incorporation of sleep/wake history into the classical two-process model captures an individual's capacity to recover during sleep as a function of sleep debt and naturally bridges the continuum from CSR to TSD by reducing to the classical two-process model in the case of TSD. We validated the proposed unified model using psychomotor vigilance task data from three prior studies involving TSD, CSR, and sleep extension. We compared and contrasted the fits, within-study predictions, and across-study predictions from the unified model against predictions generated by two previously published models, and found that the unified model more accurately represented multiple experimental studies and consistently predicted sleep restriction scenarios better than the existing models. In addition, we found that the model parameters obtained by fitting TSD data could be used to predict performance in other sleep restriction scenarios for the same study populations, and vice versa. Furthermore, this model better accounted for the relatively slow recovery process that is known to characterize CSR, as well as the enhanced performance that has been shown to result from sleep banking.
基于经典睡眠调节双加工模型的表现预测模型在预测完全睡眠剥夺(TSD)期间的警觉性和神经认知表现方面相当有效。然而,在睡眠限制(部分睡眠损失)期间,基于这些模型的表现预测被发现准确性较低。由于大多数现代作业环境主要以慢性睡眠限制(CSR)为特征,而不是以间歇性 TSD 为特征,因此这类模型的实际应用受到限制。为了更好地量化 CSR 和 TSD 期间的表现,我们开发了一个统一的数学模型,该模型将已知的睡眠/觉醒历史作为睡眠债务的函数纳入其中,其中最近的历史影响更大。这种将睡眠/觉醒历史纳入经典双加工模型的方法,捕捉了个体在睡眠期间恢复能力作为睡眠债务的函数,并且通过在 TSD 情况下简化为经典双加工模型,自然地在 CSR 和 TSD 之间架起了一座桥梁。我们使用涉及 TSD、CSR 和睡眠延长的三项先前研究的心理运动警觉任务数据验证了所提出的统一模型。我们将统一模型的拟合、内部研究预测和跨研究预测与两个先前发表的模型的预测进行了比较和对比,发现统一模型更准确地代表了多个实验研究,并且比现有模型更一致地预测了睡眠限制情况。此外,我们发现,通过拟合 TSD 数据获得的模型参数可用于预测相同研究人群中其他睡眠限制情况下的表现,反之亦然。此外,该模型更好地解释了众所周知的 CSR 所具有的相对缓慢的恢复过程,以及睡眠储蓄所导致的性能增强。