Identifying variables that predict falling asleep at the wheel among long-haul truck drivers.
Analysis of data from 843 long-haul truck drivers was conducted to determine the variables that predicted falling asleep at the wheel. Demographics, sleep-specific questions, and the Epworth Sleepiness Scale were used for analysis. More than 25% of the participants (n = 247) scored 10 or higher on the Epworth Sleepiness Scale, indicating chronic sleepiness. Eight initial predictor variables were included in the logistic regression analysis. Four of the eight original variables were retained in the final model to predict falling asleep at the wheel within the past 12 months. Four variables were retained in the final model to predict falling asleep at the wheel within the past 30 days. Screening for excessive sleepiness using the Epworth Sleepiness Scale and an extensive history of medication use should be conducted for all long-haul truck drivers.
Heaton
Karen
K
University of Alabama at Birmingham School of Nursing, Birmingham, AL, USA.
Browning
Steven
S
Anderson
Debra
D
D000063:Accidents, Traffic / Q000706:statistics & numerical data; D001294:Attitude to Health; D001334:Automobile Driving / Q000706:statistics & numerical data; D005221:Fatigue / Q000453:epidemiology / Q000209:etiology*; D005260:Female; D006801:Humans; D007484:Iowa / Q000453:epidemiology; D007629:Kentucky / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D015999:Multivariate Analysis; D015404:Nursing Methodology Research; D009784:Occupational Diseases / Q000453:epidemiology / Q000209:etiology*; D009785:Occupational Health Nursing; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D020178:Sleep Disorders, Circadian Rhythm / Q000453:epidemiology / Q000209:etiology*; D011795:Surveys and Questionnaires; D013781:Texas / Q000453:epidemiology; D013997:Time Factors; D014186:Transportation / Q000706:statistics & numerical data; D014940:Work Schedule Tolerance