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Sleep-wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue.
Riedy, Samantha M; Roach, Gregory D; Dawson, Drew.
Afiliación
  • Riedy SM; Sleep and Performance Research Center, Washington State University, Spokane, WA.
  • Roach GD; Elson S. Floyd College of Medicine, Washington State University, Spokane, WA.
  • Dawson D; Appleton Institute, Central Queensland University, Wayville, South Australia, Australia.
Sleep ; 43(9)2020 09 14.
Article en En | MEDLINE | ID: mdl-32215552
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tolerancia al Trabajo Programado / Trastornos del Sueño del Ritmo Circadiano Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sleep Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tolerancia al Trabajo Programado / Trastornos del Sueño del Ritmo Circadiano Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sleep Año: 2020 Tipo del documento: Article