Subjective awareness of sleepiness while driving in younger and older adults

Cai, Anna W.T.; Manousakis, Jessica E.; Singh, Bikram; Francis‐Pester, Elly; Kuo, Jonny; Jeppe, Katherine J.; Rajaratnam, Shantha M. W.; Lenné, Michael G.; Howard, Mark E.; Anderson, Clare · 2023 · OpenAlex-citations

DOI: 10.1111/jsr.13933

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Summary

This study investigates the accuracy of subjective sleepiness assessments in predicting driving impairment and physiological drowsiness, specifically comparing younger and older adults. The research addresses a critical gap in road safety literature: while public health campaigns encourage drivers to stop when they "feel sleepy," few studies have evaluated whether drivers can accurately self-monitor drowsiness in real-world driving environments, particularly among older adults who represent a significant portion of road users. The authors hypothesized that subjective ratings would predict adverse driving outcomes for both age groups, but the strength and nature of these associations might differ by age. The methodology involved 33 participants: 16 younger adults (aged 21–33) and 17 older adults (aged 50–65). Participants drove an instrumented vehicle on a closed loop for two hours under two counterbalanced conditions: well-rested and after 29 hours of sleep deprivation. Subjective sleepiness was measured every 15 minutes using the Karolinska Sleepiness Scale (KSS) and Likelihood of Falling Asleep (LFA) scale, and every 30 minutes using the Sleepiness Symptoms Questionnaire (SSQ). Objective measures included lane deviations, near-crash events requiring instructor intervention, and ocular indices of drowsiness (blink duration, long eye closures, and PERCLOS) recorded via a driver monitoring system. Statistical analyses included linear mixed models to assess the impact of sleep loss and age, and binary logistic regression with receiver operating characteristic (ROC) analysis to determine the predictive capacity of subjective measures on driving outcomes. The results demonstrated that all subjective sleepiness measures significantly increased with sleep deprivation for both age groups. In younger adults, most subjective ratings significantly predicted subsequent driving impairment and physiological drowsiness, with odds ratios ranging from 1.7 to 15.6. Specifically, measures such as difficulty keeping to the middle of the road and slow reactions showed excellent accuracy (AUC ≥ 0.85) in predicting near-crash events. In contrast, for older adults, significant predictive associations were limited to the KSS, the LFA scale, and the SSQ item regarding difficulty staying in the lane. Older adults generally reported lower subjective sleepiness scores than younger adults across most measures, except for KSS, struggling to keep eyes open, and blurred vision. The study found that while younger drivers exhibited stronger correlations between subjective feelings and objective impairment, older drivers still showed some predictive validity for specific metrics, though with lower overall accuracy. The significance of these findings lies in their implications for tailored road safety interventions. The study concludes that both younger and older drivers are aware of their sleepiness, but the specific subjective scales that best predict crash risk differ between age groups. This suggests that a "one-size-fits-all" approach to drowsy driving education may be ineffective. The authors recommend that future research focus on identifying the most reliable subjective measures for older adults to inform targeted educational campaigns. Understanding these age-related differences in self-awareness and perception of sleepiness is crucial for developing strategies that effectively reduce drowsy driving crashes across the entire driving population.

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Topics

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• drowsiness
• sleep deprivation
• drowsiness detection algorithms
• older drivers

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• Empirical Findings: physiological data