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A logistic regression analysis for locomotive engineer self report sleep quality and on -duty alertness
Abstract
Railroad crews sometimes have had to work work/rest random schedules. Such schedules impact the ability of the locomotive engineer to gain enough sleep. Humans normally have regular circadian rhythms. These rhythms require about the normal 24-hour cycle. The random work schedule can seriously affect the ability of train crew to have enough sleep. This research intended to explore matters of how the random work scheduling affects the locomotive engineers' on-duty alertness and their sleep quality. The main contribution of this research was to develop an index which captured the day by day randomness of the 24 hour work/rest circadian patterns of locomotive engineers. The index is called Calculated Cumulative Circadian Randomness (TCR). During the creation of TCR, a multivariate statistical analysis method was employed. The TCR was used in an attempt to quantitatively measure locomotive engineer's cumulative circadian randomness. After a multiple logistic regression was used to study the effects of circadian randomness on the locomotive engineers' on-duty alertness and sleep quality. It indicate that, (1) The randomness of the work schedule affected the locomotive engineer's on-duty alertness. (a) TCR is a significant predictor for locomotive engineers' on-duty alertness. (b) The estimated probability of having on-duty alertness problems goes up as TCR increases. (2) The randomness of the work schedule also affected the locomotive engineer's sleep quality (ability to "stay asleep"). (a) TCR is a significant predictor for locomotive engineers' ability to "stay asleep". (b) The estimated probability of having a "stay asleep" problem goes up as TCR increases.
Subject Area
Industrial engineering
Recommended Citation
Ding, Xuedong, "A logistic regression analysis for locomotive engineer self report sleep quality and on -duty alertness" (2007). ETD collection for University of Nebraska-Lincoln. AAI3239364.
https://digitalcommons.unl.edu/dissertations/AAI3239364