McCauley Mark E, McCauley Peter, Riedy Samantha M, Banks Siobhan, Ecker Adrian J, Kalachev Leonid V, Rangan Suresh, Dinges David F, Van Dongen Hans P A
Sleep and Performance Research Center, Washington State University Health Sciences Spokane.
Elson S. Floyd College of Medicine, Washington State University Health Sciences Spokane.
Transp Res Part F Traffic Psychol Behav. 2021 May;79:94-106. doi: 10.1016/j.trf.2021.04.006. Epub 2021 May 12.
Biomathematical models of fatigue can be used to predict neurobehavioral deficits during sleep/wake or work/rest schedules. Current models make predictions for objective performance deficits and/or subjective sleepiness, but known differences in the temporal dynamics of objective versus subjective outcomes have not been addressed. We expanded a biomathematical model of fatigue previously developed to predict objective performance deficits as measured on the Psychomotor Vigilance Test (PVT) to also predict subjective sleepiness as self-reported on the Karolinska Sleepiness Scale (KSS). Four model parameters were re-estimated to capture the distinct dynamics of the KSS and account for the scale difference between KSS and PVT. Two separate ensembles of datasets - drawn from laboratory studies of sleep deprivation, sleep restriction, simulated night work, napping, and recovery sleep - were used for calibration and subsequent validation of the model for subjective sleepiness. The expanded model was found to exhibit high prediction accuracy for subjective sleepiness, while retaining high prediction accuracy for objective performance deficits. Application of the validated model to an example scenario based on cargo aviation operations revealed divergence between predictions for objective and subjective outcomes, with subjective sleepiness substantially underestimating accumulating objective impairment, which has important real-world implications. In safety-sensitive operations such as commercial aviation, where self-ratings of sleepiness are used as part of fatigue risk management, the systematic differences in the temporal dynamics of objective versus subjective measures of functional impairment point to a potentially significant risk evaluation sensitivity gap. The expanded biomathematical model of fatigue presented here provides a useful quantitative tool to bridge this previously unrecognized gap.
疲劳的生物数学模型可用于预测睡眠/清醒或工作/休息时间表期间的神经行为缺陷。当前的模型可预测客观性能缺陷和/或主观嗜睡，但客观与主观结果在时间动态方面的已知差异尚未得到解决。我们扩展了先前开发的用于预测心理运动警觉性测试（PVT）所测量的客观性能缺陷的疲劳生物数学模型，使其也能预测卡罗林斯卡嗜睡量表（KSS）自我报告的主观嗜睡。重新估计了四个模型参数，以捕捉KSS的独特动态，并考虑KSS和PVT之间的量表差异。从睡眠剥夺、睡眠限制、模拟夜间工作、小睡和恢复睡眠的实验室研究中提取的两个独立数据集用于校准和随后对主观嗜睡模型的验证。结果发现，扩展后的模型对主观嗜睡表现出较高的预测准确性，同时对客观性能缺陷保持较高的预测准确性。将经过验证的模型应用于基于货运航空运营的示例场景，结果显示客观和主观结果的预测存在差异，主观嗜睡大大低估了累积的客观损伤，这具有重要的现实意义。在商业航空等对安全敏感的运营中，嗜睡自评被用作疲劳风险管理的一部分，功能损伤的客观与主观测量在时间动态上的系统差异表明存在潜在的重大风险评估敏感性差距。本文提出的扩展疲劳生物数学模型提供了一个有用的定量工具，以弥合这一先前未被认识到的差距。
