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Updated: Jun 12, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
The 10-year risk of verified motor vehicle crashes in relation to physiologic sleepiness
Christopher Drake1, Timothy Roehrs, Naomi Breslau
1Sleep Disorders and Research Center, Henry Ford Hospital, 2799 West Grand Blvd, CFP3, Detroit, MI 48202, USA. cdrake1@hfhs.org
Study Objectives:
The purpose of this study was to determine the risk of DMV documented crashes as a function of physiological sleepiness in a population-based sample.
Design:
24-hour laboratory assessment (nocturnal polysomnogram and daytime MSLT) and 10-year crash rate based on DMV obtained accident records.
Participants:
618 individuals (mean age = 41.6 +/- 12.8; 48.5% male) were recruited from the general population of southeastern Michigan using random-digit dialing techniques.
Results:
Subjects were divided into 3 groups based on their average MSLT latency (in minutes) as follows: excessively sleepy, 0.0 to < or = 5.0 (n = 69); moderately sleepy, 5.0 to < or = 10.0 (n = 204); and alert, > 10 (n = 345). Main outcome measures were DMV data on accidents from 1995-2005. Rates for all accidents in the 3 MSLT groups were: excessively sleepy = 59.4%, moderately sleepy = 52.5%, alert = 47.3%. Excessively sleepy subjects were at significantly greater risk of an accident over the 10-year period compared to alert subjects. A similar relation was observed when we limited the database to those accident victims with severe injury (excessively sleepy = 4.3%, moderately sleepy = 0.5%, alert = 0.6%; P = 0.028). When the victim was the only occupant of the car, subjects in the lowest MSLT group (highest sleepiness) had the greatest crash rate compared with alert individuals (excessively sleepy = 52.2%, moderately sleepy = 42.2%, alert = 37.4%; P = 0.022).
Interventions:
N/A.
Conclusions:
These data demonstrate that the MSLT, a physiological measure of sleepiness, is predictive of an increased risk of DMV documented automotive crashes in the general population.
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