Introduction: Biomathematical models of fatigue (BMMF) predict fatigue during a work-rest schedule on the basis of sleep-wake histories. In the absence of actual sleep-wake histories, sleep-wake histories are predicted directly from work-rest schedules. The predicted sleep-wake histories are then used to predict fatigue. It remains to be determined whether workers organize their sleep similarly across operations and thus whether sleep predictions generalize.

Methods: Officers (n = 173) enrolled in the Buffalo Cardio-Metabolic Occupational Police Stress study were studied. Officers’ sleep-wake behaviors were measured using wrist-actigraphy and predicted using a BMMF (FAID Quantum) parameterized in aviation and rail. Sleepiness (i.e. Karolinska Sleepiness Scale (KSS) ratings) was predicted using actual and predicted sleep-wake data. Data were analyzed using sensitivity analyses.

Results: During officers’ 16.0 ± 1.9 days of study participation, they worked 8.6 ± 3.1 shifts and primarily worked day shifts and afternoon shifts. Across shifts, 7.0 h ± 1.9 h of actual sleep were obtained in the prior 24 h and associated peak KSS ratings were 5.7 ± 1.3. Across shifts, 7.2 h ± 1.1 h of sleep were predicted in the prior 24 h and associated peak KSS ratings were 5.5 ± 1.2. The minute-by-minute predicted and actual sleep-wake data demonstrated high sensitivity (80.4%). However, sleep was observed at all hours-of-the-day, but sleep was rarely predicted during the daytime hours.

Discussion: The sleep-wake behaviors predicted by a BMMF parameterized in aviation and rail demonstrated high sensitivity with police officers’ actual sleep-wake behaviors. Additional night shift data are needed to conclude whether BMMF sleep predictions generalize across operations.

简介：疲劳的生物数学模型（BMMF）根据睡眠-唤醒历史来预测工作休息时间表中的疲劳。在没有实际的睡眠-唤醒历史的情况下，睡眠-唤醒历史直接根据工作休息时间表进行预测。然后使用预测的睡眠-唤醒历史来预测疲劳。有待确定的是，工人在整个操作过程中是否以类似的方式组织他们的睡眠，从而确定睡眠预测是否能一概而论。

方法：研究了参加水牛心脏代谢职业警察压力研究的人员（n = 173）。军官的睡眠-觉醒行为是通过腕部活动记录仪测量的，并使用在航空和铁路中参数化的BMMF（FAID Quantum）进行预测。使用实际和预测的睡眠/苏醒数据来预测嗜睡（即Karolinska嗜睡量表（KSS）等级）。使用敏感性分析来分析数据。

结果：在官员参加16.0±1.9天学习期间，他们工作8.6±3.1班，主要工作是白班和下午班。在所有班次中，在之前的24小时内获得了7.0 h±1.9 h的实际睡眠，并且相关的KSS峰值为5.7±1.3。在所有班次中，预计在前24小时内睡眠为7.2 h±1.1 h，相关的KSS峰值为5.5±1.2。逐分钟的预测和实际的睡眠唤醒数据显示出很高的灵敏度（80.4％）。但是，一天中的所有小时都观察到睡眠，但是白天很少有人预测到睡眠。

讨论：由在航空和铁路中参数化的BMMF预测的觉醒行为表现出对警官实际觉醒行为的高度敏感性。需要额外的夜班数据来推断BMMF睡眠预测是否会在整个操作中推广。