Sleep–wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue

Author:

Riedy Samantha M12,Roach Gregory D3,Dawson Drew3

Affiliation:

1. Sleep and Performance Research Center, Washington State University, Spokane, WA

2. Elson S. Floyd College of Medicine, Washington State University, Spokane, WA

3. Appleton Institute, Central Queensland University, Wayville, South Australia, Australia

Abstract

Abstract Study Objectives 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. Methods 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. Results 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. Conclusions 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.

Funder

Australian Research Council

Publisher

Oxford University Press (OUP)

Subject

Physiology (medical),Neurology (clinical)

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