Moderate-to-Severe OSA and Objective Drowsiness Are Associated with Driving-Related Accidents, but Not Subjective Sleepiness: A Simulator Study

Background/Objectives: Obstructive sleep apnea (OSA) is associated with an increased risk of motor vehicle accidents, traditionally attributed to excessive daytime sleepiness (EDS). However, subjective sleepiness may be underreported and does not consistently reflect functional impairment. We aimed to examine the association between objectively measured drowsiness during simulated driving and traffic accidents in OSA, and to compare the predictive roles of objective drowsiness, subjective sleepiness, and OSA severity. Methods: Fifty-one male drivers underwent overnight polysomnography followed by a 50-min driving simulation. OSA severity was categorized as moderate-to-severe (AHI ≥ 15 events/h) or no/mild (AHI < 15 events/h). A frontal camera captured facial expressions. Drowsiness was quantified using eye-closure-based metrics: PERCLOS, the ratio of frames with closed eyes to total observable frames, and CLOSDUR, representing eye-closure duration. Drowsiness was defined as PERCLOS ≥ 0.3 or CLOSDUR ≥ 2 s. Drowsiness-related traffic accidents were recorded. Multivariable logistic regression models were adjusted for age, body mass index, Epworth Sleepiness Scale (ESS), and total sleep time. Results: Drowsiness-related traffic accidents occurred more frequently in participants with moderate-to-severe OSA than in those with no or mild OSA (72.2% vs. 40.0%, p = 0.030). Drowsiness duration, but not ESS, was positively associated with the number of traffic accidents (r = 0.46, p = 0.001). While continuous AHI was not associated with accidents, moderate-to-severe OSA was independently associated with higher accident risk (adjusted OR 6.45, 95% CI 1.43–29.13; p = 0.015). Conclusions: Driving-related accident risk in OSA was associated with objectively measured drowsiness and moderate-to-severe disease, whereas subjective sleepiness assessed by the ESS showed no significant association. These findings suggest that functional impairment, rather than self-reported symptoms, may be more relevant for identifying high-risk drivers.