The efficacy of objective and subjective predictors of driving performance during sleep restriction and circadian misalignment

Fatigue is a significant contributor to motor-vehicle accidents and fatalities. Shift workers are particularly susceptible to fatigue-related risks as they are often sleep-restricted and required to commute around the clock. Simple assays of performance could provide useful indications of risk in fatigue management, but their effectiveness may be influenced by changes in their sensitivity to sleep loss across the day. The aim of this study was to evaluate the sensitivity of several neurobehavioral and subjective tasks to sleep restriction (SR) at different circadian phases and their efficacy as predictors of performance during a simulated driving task. Thirty-two volunteers (M ± SD; 22.8 ± 2.9 years) were time-isolated for 13-days and participated in one of two 14-h forced desynchrony protocols with sleep opportunities equivalent to 8 h/24 h (control) or 4 h/24 h (SR). At regular intervals during wake periods, participants completed a simulated driving task, several neurobehavioral tasks, including the psychomotor vigilance task (PVT), and subjective ratings, including a self-assessment measure of ability to perform. Scores transformed into standardized units relative to baseline were folded into circadian phase bins based on core body temperature. Sleep dose and circadian phase effect sizes were derived via mixed models analyses. Predictors of driving were identified with regressions. Performance was most sensitive to sleep restriction around the circadian nadir. The effects of sleep restriction around the circadian nadir were larger for simulated driving and neurobehavioral tasks than for subjective ratings. Tasks did not significantly predict driving performance during the control condition or around the acrophase during the SR condition. The PVT and self-assessed ability were the best predictors of simulated driving across circadian phases during SR. These results show that simple performance measures and self-monitoring explain a large proportion of the variance in driving when fatigue-risk is high