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Free Content Subjective sleepiness and accident risk avoiding the ecological fallacy

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Abstract:

Summary

The present study of sleepiness and accident risk in a HI-FI car simulator aimed to provide subject-level relative risks (RR) with 95% confidence intervals (CI) for different levels of subjective sleepiness measured with the Karolinska Sleepiness Scale (KSS), 1 = very alert, 9 = very sleepy, fighting sleep, an effort to staying awake. Five male and five female shift workers, mean age 37 years, participated with a 2-h drive (08:00–10:00 hours) in a dynamic high-fidelity moving base driving simulator, after a night of work and after a night of sleep. Subjective sleepiness was measured with KSS every 5 min and events of incidents (two wheels outside the right lane), accidents (two wheels off the road or four wheels in opposite lane) and crashes (four wheels off the road) were recorded. The probability of an accident was modelled with a Generalized Linear Mixed Model approach to estimate subject-specific effects, rather than group average effects, to avoid the ecological fallacy. The results showed that sleepiness was strongly related to accident risk. An average subject was estimated at 28.2 times (95% CI RR = 10.7–74.1) increased risk at KSS = 8 and at 185 times (95% CI RR = 42–316) at KSS = 9 compared with KSS = 5. There were large individual differences in event propensity that complicates the prediction of absolute accident risk for individual subjects.

Keywords: Generalized Linear Mixed Models

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1365-2869.2006.00517.x

Affiliations: 1: National Institute for Psychosocial Medicine (IPM), & Department of Psychology, Stockholm University, Stockholm, Sweden 2: National Institute for Psychosocial Medicine (IPM), & Karolinska Institutet, Stockholm, Sweden 3: Swedish Road and Transport Research Institute, Linköping, Sweden 4: National Institute for Psychosocial Medicine (IPM), Stockholm, Sweden 5: School of Epidemiology and Health Sciences & Centre for Census and Survey Research, University of Manchester, Manchester, UK

Publication date: 2006-06-01

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