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Variables predicting sleeping at the wheel among long-haul truckers.

Heaton-KL; Brownmg-S; Anderson-D
Sleep 2006 Jun; 29(Abstract Suppl):A141
Introduction: Long-haul truckers who fall asleep at the wheel pose a threat to themselves and others who share the highways with them. Motor vehicle crashes are the cause of most occupational deaths among truck drivers An expected 20% job growth in the U.S. trucking industry between the years 2000-2010 makes this an especially timely Issue m occupational safety and health. Predictor variable identification may lead to the design and implementation of meaningful sleep-related motor vehicle crash prevention strategies. Methods: Data were collected m this cross-sectional study from a convenience sample of long-haul truckers (N = 843) at truck shows across the U S and truck stops in Kentucky. Binary logistic regression analysis using backward stepwise likelihood ratio methods was used to determine whether the initial eight independent variables predicted the probability of falling asleep at the wheel at either 30 days or 12 months. Results: Statistically Significant predictive models were derived for falling asleep at the wheel within 30 days (chi square = 35.32, p < .001) and 12 months (chi square = 43.08, p < .001) Five predictor variables were retained in the final models: Epworth Sleepiness Scale score > 10; driving more than six hours at night, sleeping less than six hours per night and the use of medication to stay awake. Overall rates of correct classification for the final models were 94 6% for sleeping at the wheel within 30 days and 91 3% for sleeping at the wheel within 12 months. Hosmer and Lemeshow Goodness-of-Flt testing indicated a good fit of the models to the data. Conclusion: Findings from this study indicate focus areas for assessment of sleep-related motor vehicle crash risk Focal points for sleep hygiene education; work redesign strategies and policy changes may be drawn from the study.
Accident-potential; Accident-prevention; Accident-rates; Accidents; Accident-statistics; Mathematical-models; Mortality-data; Mortality-rates; Motor-vehicles; Occupational-accidents; Occupational-hazards; Risk-factors; Sleep-deprivation; Sleep-disorders; Statistical-analysis; Time-weighted-average-exposure; Truck-drivers; Trucking; Work-analysis; Work-operations; Work-practices
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Sleep. 20th Anniversary Meeting of the Associated Professional Sleep Societies, Salt Lake City, Utah, June 17-22, 2006
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University of Kentucky