Sleep and Performance Research Center, Washington State University, Spokane, WA.
Elson S. Floyd College of Medicine, Washington State University, Spokane, WA.
Sleep. 2020 Sep 14;43(9). doi: 10.1093/sleep/zsaa049.
To compare rail workers' actual sleep-wake behaviors in normal operations to those predicted by a biomathematical model of fatigue (BMMF). To determine whether there are group-level residual sources of error in sleep predictions that could be modeled to improve group-level sleep predictions.
The sleep-wake behaviors of 354 rail workers were examined during 1,722 breaks that were 8-24 h in duration. Sleep-wake patterns were continuously monitored using wrist-actigraphy and predicted from the work-rest schedule using a BMMF. Rail workers' actual and predicted sleep-wake behaviors were defined as split-sleep (i.e. ≥2 sleep periods in a break) and consolidated-sleep (i.e. one sleep period in a break) behaviors. Sleepiness was predicted from the actual and predicted sleep-wake data.
Consolidated-sleep behaviors were observed during 1,441 breaks and correctly predicted during 1,359 breaks. Split-sleep behaviors were observed during 280 breaks and correctly predicted during 182 breaks. Predicting the wrong type of sleep-wake behavior resulted in a misestimation of hours of sleep during a break. Relative to sleepiness predictions derived from actual sleep-wake data, predicting the wrong type of sleep-wake behavior resulted in a misestimation of sleepiness predictions during the subsequent shift.
All workers with the same work-rest schedule have the same predicted sleep-wake behaviors; however, these workers do not all exhibit the same sleep-wake behaviors in real-world operations. Future models could account for this group-level residual variance with a new approach to modeling sleep, whereby sub-group(s) may be predicted to exhibit one of a number of sleep-wake behaviors.
将铁路工人在正常作业中的实际睡眠-觉醒行为与疲劳生物数学模型(BMMF)的预测进行比较。确定睡眠预测中是否存在群体水平的剩余误差源,可以对其进行建模以提高群体水平的睡眠预测。
在持续 8-24 小时的 1722 次休息期间,检查了 354 名铁路工人的睡眠-觉醒行为。使用腕动描记术连续监测睡眠-觉醒模式,并使用 BMMF 根据工作-休息时间表进行预测。铁路工人的实际和预测的睡眠-觉醒行为被定义为分段睡眠(即在休息期间有≥2 个睡眠期)和整合睡眠(即在休息期间有 1 个睡眠期)行为。从实际和预测的睡眠-觉醒数据中预测困倦程度。
在 1441 次休息中观察到整合睡眠行为,在 1359 次休息中正确预测。在 280 次休息中观察到分段睡眠行为,在 182 次休息中正确预测。预测错误类型的睡眠-觉醒行为会导致对休息期间睡眠时间的错误估计。与从实际睡眠-觉醒数据中得出的困倦预测相比,预测错误类型的睡眠-觉醒行为会导致对随后轮班的困倦预测的错误估计。
具有相同工作-休息时间表的所有工人都有相同的预测睡眠-觉醒行为;然而,这些工人在现实作业中并不都表现出相同的睡眠-觉醒行为。未来的模型可以通过一种新的睡眠建模方法来解释这种群体水平的剩余方差,其中可以预测亚组(多个)表现出多种睡眠-觉醒行为之一。