Fatigue, sleep restriction and driving performance

Philip, Pierre; Sagaspe, Patricia; Moore, Nicholas; Taillard, Jacques; André, Charles; Guilleminault, Christian; Bioulac, Bernard

doi:10.1016/j.aap.2004.07.007

Cited by 304 publications

(195 citation statements)

References 30 publications

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“…Specifically, variability in lateral lane position significantly increased when drivers had lowered alertness levels, consistent with previous studies using the AusEd driving simulator (Howard et al, 2007;Vakulin et al, 2007), on other driving simulator tasks (George, 2000;George, Boudreau, & Smiley, 1996;Lenne, Triggs, & Redman, 1998), and during on-road driving (Philip, et al, 2005;Ramaekers & O'Hanlon, 1994). Previous studies have also noted significant variations outside the prescribed speed range with sleep loss (Arnedt, Wilde, Munt, & MacLean, 2001;Lenne, Triggs, & Redman, 1998), consistent with the current study.…”

Section: Discussionsupporting

confidence: 81%

“…Driving simulators pose less pressure and distractions compared to real driving, and therefore our drivers' sleepiness ratings, and alertness and/or motivation levels are likely to differ. It has been noted that driving simulators can produce premature performance and sleepiness impairment when compared to real driving (Philip, et al, 2005). The homogeneity of our sample and use of healthy professional drivers, in addition to the small sample size, reduces the generalizability of our findings, although it provides preliminary evidence of performance impairment associated with sleep loss in a population in who sleepiness is commonly reported (Howard et al, 2004;Howard et al, 2014).…”

Section: Discussioncontrasting

confidence: 42%

“…Previous studies have also noted significant variations outside the prescribed speed range with sleep loss (Arnedt, Wilde, Munt, & MacLean, 2001;Lenne, Triggs, & Redman, 1998), consistent with the current study. Whilst driving simulator performance does not provide an absolute measure of crash risk, it is related to on road driving performance (Philip et al, 2005) and these behaviors are indicative of unsafe driving (Brookhuis, De Waard, & Fairclough, 2003).…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

Jackson

Raj

Croft

et al. 2016

Traffic Injury Prevention

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(2016). Slow eyelid closure as a measure of driver drowsiness and its relationship to performance. Traffic Injury Prevention, 17 (3), 251-257. Slow eyelid closure as a measure of driver drowsiness and its relationship to performance AbstractObjective: Slow eyelid closure is recognized as an indicator of sleepiness in sleep-deprived individuals, although automated ocular devices are not well validated. This study aimed to determine whether changes in eyelid closure are evident following acute sleep deprivation as assessed by an automated device and how ocular parameters relate to performance after sleep deprivation. Methods: Twelve healthy professional drivers (45.58 ± 10.93 years) completed 2 randomized sessions: After a normal night of sleep and after 24 h of total sleep deprivation. Slow eye closure (PERCLOS) was measured while drivers performed a simulated driving task. Results: Following sleep deprivation, drivers displayed significantly more eyelid closure (P < .05), greater variation in lane position (P < .01) and more attentional lapses (P < .05) compared to after normal sleep. PERCLOS was moderately associated with variability in both vigilance performance (r = 0.68, P < .05) and variation in lane position on the driving task (r = 0.61, P < .05). Conclusions: Automated ocular measurement appears to be an effective means of detecting impairment due to sleep loss in the laboratory.

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Section: Discussionsupporting

confidence: 81%

Section: Discussioncontrasting

confidence: 42%

Section: Discussionmentioning

confidence: 99%

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Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

Jackson

Raj

Croft

et al. 2016

Traffic Injury Prevention

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“…SDLP is extremely sensitive to modifications involving psychotropic drugs and fatigue. It is frequently used to quantify the negative effects of these factors in natural or simulated driving (O'Hanlon et al 1982, Brookhuis 1998Arnedt et al 2001;Brookhuis et al 2003;Vermeeren 2004;Philip et al 2005;Verster and Ramaekers 2009;Roth 2012a, Helland et al 2013). The highway driving test also shows a dose-dependent relationship between the alcohol dose and performance degradation (Louwerens et al 1987).…”

Section: Introductionmentioning

confidence: 99%

Effects of alcohol on automated and controlled driving performances

Berthelon¹,

Gineyt²

2013

Psychopharmacology

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Rationale: Alcohol is the most frequently detected substance in fatal automobile crashes, but its precise mode of action is not always clear.Objective: The present study was designed to establish the influence of blood alcohol concentration as a function of the complexity of the scenarios. Road scenarios implying automatic or controlled driving performances were manipulated in order to identify which behavioral parameters were deteriorated.Method: A single blind counterbalanced experiment was conducted on a driving simulator. Conclusion:Performance decrement was greater with driving scenarios involving automated processes than with scenarios involving controlled processes.

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“…Research on sleep-related problems and driving suggests that sleepiness is a significant risk factor for motor vehicle collisions and impaired driving performance (Connor et al, 2002). For instance, both driving simulator and on-road driving performance tests demonstrate that sleep deprivation significantly slows down reaction time (Philip et al, 2003(Philip et al, , 2005Ting et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

The impact of subclinical sleep problems on self-reported driving patterns and perceived driving abilities in a cohort of active older drivers

Hickey¹,

Weegar²,

Kadulina³

et al. 2013

Accident Analysis & Prevention

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The present study sought to investigate the influence of subclinical sleep disturbances on driving practices and driver perceptions in a large cohort of healthy older drivers. Participants from the Candrive II prospective cohort study were investigated. Self-reported measures of sleep problems were used to determine the influence of sleep disturbance on self-reported driving practices and perceived driving abilities, as measured by the Situational Driving Frequency, Situational Driving Avoidance, and Perceived Driving Abilities scales. Hierarchical regression analyses were used to estimate whether mild self-reported sleep problems were predictive of driving restrictions and perceived abilities, while controlling for a variety of health-related factors and demographic variables known to mediate sleep problems or to impact driving.Cross-sectional analysis of baseline data from the Candrive II study suggests that subclinical sleep problems do not significantly influence self-reported driving patterns or perceived driving abilities in older drivers once control variables are considered. The relationship between sleep problems, driving frequency, avoidance and perceived abilities is better explained by mediating demographic, health, and cognitive factors. Further research examining sleep disturbances and driving should include objective measures of driving practices (exposure, patterns) and outcomes (crashes, violations) and should take in consideration the severity of sleep problems.

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Fatigue, sleep restriction and driving performance

Cited by 304 publications

References 30 publications

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

Effects of alcohol on automated and controlled driving performances

The impact of subclinical sleep problems on self-reported driving patterns and perceived driving abilities in a cohort of active older drivers

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