Work- and sleep-related predictors of subjective on-duty alertness in irregular work schedules.

Auteur(s)
Pilcher, J.J. Anderson, J. Edwards, G. & Coplen, M.K.
Jaar
Samenvatting

Many transportation industries operate under conditions that require workers on a 24-h basis. Unfortunately, these working conditions often negatively affect the workers' ability to obtain sufficient sleep and to maintain the necessary alertness while on the job. The work schedules of locomotive engineers provide one example of 24-h-based work. Most engineers, however, not only have to work at different times of the day and night, but also must work under an on-call system, which typically results in highly irregular work hours. The relationship between irregular work schedules as experienced by locomotive engineers and subjective alertness was examined by producing a predictive model of on-duty alertness using work- and sleep-related variables as predictors. One hundred twenty-four locomotive engineers completed a 14-day activity log on work time, time off work, sleep habits, and on-duty alertness. The results from the regression analyses indicated that the single best predictor of on-duty alertness was the time of day that alertness was recorded. This result indicated that the human endogenous rhythm to be alert during the day and sleepy at night was the major predictor of on-duty alertness even in the irregular schedules experienced by most locomotive engineers. Other work- and sleep-related variables such as time that the employee was notified when to report to work, work duration, and sleep duration were also significant predictors of on-duty alertness. Operationally, the current results suggest that nighttime workers will experience less alertness than daytime workers. Thus, specific countermeasures addressing alertness and fatigue during nighttime work should be developed, such as better predictability of work scheduling and on-duty napping.

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Publicatie

Bibliotheeknummer
C 28479 (In: C 28476 S [electronic version only]) /83 / ITRD E821262
Uitgave

In: Human performance : models, intelligent vehicle initiative, traveler advisory and information systems, Transportation Research Record TRR 1803, p. 16-21, 23 ref.

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