The impact of alcohol consumption on commercial eye blink drowsiness detection technology.

INTRODUCTION: Driver drowsiness detection technology that assesses eye blinks is increasingly being used as a safety intervention in the transport industry. It is unclear how alcohol consumption to common legal driving limits impacts upon this technology. The aim of the study was to assess the impact of a blood alcohol content (BAC) of 0.05% and of 0.08% on drowsiness detection technology during simulated driving. METHODS: Participants completed a 60-min driving simulation and sleepiness questionnaire under three conditions: 1-0.00% BAC, 2-0.05% BAC and 3-0.08% BAC. During the driving simulation task participants wore a commercial eye blink drowsiness detection technology (Optalert) with the drowsiness alarms silenced. RESULTS: Twelve participants (3 female) completed all alcohol conditions. Relative to baseline, all eye blink parameters were affected at 0.08% BAC (all p < 0.05), whereas 0.05% BAC only affected the composite eye blink drowsiness measure (the Johns Drowsiness Scale). CONCLUSIONS: Alcohol consumption to 0.08% BAC impaired eye blink measures to a level that would be considered a moderate drowsiness risk. Therefore, employers should be aware that drowsiness alerts from these technologies may increase after alcohol consumption.

Assessing the validity of eyelid parameters to detect impairment due to benzodiazepines.

OBJECTIVE: Benzodiazepines impair driving ability and psychomotor function. Eyelid parameters accurately reflect drowsiness; however, the effects of benzodiazepines on these measures have not been extensively studied. The aim of this study was to investigate the effect of benzodiazepines on eyelid parameters and evaluate their accuracy for detecting psychomotor impairment. METHODS: Eyelid parameters were recorded during a psychomotor vigilance task (PVT) and driving simulation over 2 days, baseline, and after 20-mg oral temazepam. The utility of eyelid parameters for detecting PVT lapses was evaluated using receiver operating characteristic curves, and cut-off levels indicating impairment (≥1 and ≥2 PVT lapses per min) were identified. The accuracy of these cut-off levels for detecting driving simulator crashes was then examined. RESULTS: PVT and driving simulator performance was significantly impaired following benzodiazepine administration (p < .05). Average eyelid closure duration (inter-event duration) was a reliable indicator of PVT lapses (area under the curve [AUC] of 0.87-0.90). The cut-off value of eyelid closure duration derived from PVT AUC was able to predict driving simulator crashes with moderately high sensitivity and specificity (76.23% and 75.00%). CONCLUSIONS: Eyelid parameters were affected by benzodiazepines and accurately detected the psychomotor impairment. In particular, eyelid closure duration is a promising real-time indicator of benzodiazepine impairment.

Prolonged Eyelid Closure Episodes during Sleep Deprivation in Professional Drivers.

STUDY OBJECTIVES: Real life ocular measures of drowsiness use average blink duration, amplitude and velocity of eyelid movements to reflect drowsiness in drivers. However, averaged data may conceal the variability in duration of eyelid closure episodes, and more prolonged episodes that indicate higher levels of drowsiness. The current study aimed to describe the frequency and duration of prolonged eyelid closure episodes during acute sleep deprivation. METHODS: Twenty male professional drivers (mean age ± standard deviation = 41.9 ± 8.3 years) were recruited from the Transport Workers Union newsletter and newspaper advertisements in Melbourne, Australia. Each participant underwent 24 hours of sleep deprivation and completed a simulated driving task (AusEd), the Psychomotor Vigilance Task, and the Karolinska Sleepiness Scale. Eyelid closure episodes during the driving task were recorded and analyzed manually from digital video recordings. RESULTS: Eyelid closure episodes increased in frequency and duration with a median of zero s/h of eyelid closure after 3 h increasing to 34 s/h after 23 h awake. Eyelid closure episodes were short and infrequent from 3 to 14 h of wakefulness. After 17 h of sleep deprivation, longer and more frequent eyelid closure episodes began to occur. Episodes lasting from 7 seconds up to 18 seconds developed after 20 h of wakefulness. Length of eyelid closure episodes was moderately to highly correlated with the standard deviation of lateral lane position, braking reaction time, crashes, impaired vigilance, and subjective sleepiness. CONCLUSIONS: The frequency and duration of episodes of prolonged eyelid closure increases during acute sleep deprivation, with very prolonged episodes after 17 hours awake. Automated devices that assess drowsiness using averaged measures of eyelid closure episodes need to be able to detect prolonged eyelid closure episodes that occur during more severe sleep deprivation.

The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness.

Slowed eyelid closure coupled with increased duration and frequency of closure is associated with drowsiness. This study assessed the utility of two devices for automated measurement of slow eyelid closure in a standard poor performance condition (alcohol) and following 12-h sleep deprivation. Twenty-two healthy participants (mean age=20.8 (SD 1.9) years) with no history of sleep disorders participated in the study. Participants underwent one baseline and one counterbalanced session each over two weeks; one 24-hour period of sleep deprivation, and one daytime session during which alcohol was consumed after a normal night of sleep. Participants completed a test battery consisting of a 30-min simulated driving task, a 10-min Psychomotor Vigilance Task (PVT) and the Karolinska Sleepiness Scale (KSS) each in two baseline sessions, and in two randomised, counterbalanced experimental sessions; following sleep deprivation and following alcohol consumption. Eyelid closure was measured during both tasks using two automated devices (Copilot and Optalert™). There was an increase in the proportion of time with eyelids closed and the Johns Drowsiness Score (incorporating relative velocity of eyelid movements) following sleep deprivation using Optalert (p<0.05 for both). These measures correlated significantly with crashes, PVT lapses and subjective sleepiness (r-values 0.46-0.69, p<0.05). No difference between the two sessions for PERCLOS recorded during the PVT or the driving task as measured by the Copilot. The duration of eyelid closure predicted frequent lapses following sleep deprivation (which were equivalent to the average lapses at a blood alcohol concentration of 0.05% - area under curve for ROC curve 0.87, p<0.01). The duration of time with slow eyelid closure, assessed by the automated devices, increased following sleep deprivation and was associated with deterioration in psychomotor performance and subjective sleepiness. Comprehensive algorithms inclusive of ocular parameters may be a better indicator of performance impairment following sleep loss.

Deterioration in driving performance during sleep deprivation is similar in professional and nonprofessional drivers.

OBJECTIVE: There is some suggestion in the literature that professional drivers might self-select to be more resistant to the effects of sleep deprivation; however, this question has not been directly examined. The current laboratory study aimed to compare performance changes during acute sleep deprivation between professional and nonprofessional drivers. METHODS: Twenty volunteer male professional drivers and 20 nonprofessional drivers performed a simulated driving task (AusEd) and the Psychomotor Vigilance Task (PVT) during 24 hours of continuous wakefulness. Ratings of subjective sleepiness were also examined. RESULTS: There was a progressive and significant increase in lateral lane position and speed variability on the simulated driving task and an increase in PVT reaction times and lapses after participants had been awake for 17 to 24 hours (Ps < .01). There was no difference in performance changes between the professional and nonprofessional drivers. CONCLUSIONS: Professional drivers in this study had the same susceptibility to sleep deprivation as nonprofessional drivers. This finding does not support the concept that professional drivers are resistant to sleep loss.

Specific sleepiness symptoms are indicators of performance impairment during sleep deprivation.

Drivers are not always aware that they are becoming impaired as a result of sleepiness. Using specific symptoms of sleepiness might assist with recognition of drowsiness related impairment and help drivers judge whether they are safe to drive a vehicle, however this has not been evaluated. In this study, 20 healthy volunteer professional drivers completed two randomized sessions in the laboratory - one under 24h of acute sleep deprivation, and one with alcohol. The Psychomotor Vigilance Task (PVT) and a 30min simulated driving task (AusEdTM) were performed every 3-4h in the sleep deprivation session, and at a BAC of 0.00% and 0.05% in the alcohol session, while electroencephalography (EEG) and eye movements were recorded. After each test session, drivers completed the Karolinska Sleepiness Scale (KSS) and the Sleepiness Symptoms Questionnaire (SSQ), which includes eight specific sleepiness and driving performance symptoms. A second baseline session was completed on a separate day by the professional drivers and in an additional 20 non-professional drivers for test-retest reliability. There was moderate test-retest agreement on the SSQ (r=0.59). Significant correlations were identified between individual sleepiness symptoms and the KSS score (r values 0.50-0.74, p<0.01 for all symptoms). The frequency of all SSQ items increased during sleep deprivation (χ(2) values of 28.4-80.2, p<0.01 for all symptoms) and symptoms were related to increased subjective sleepiness and performance deterioration. The symptoms "struggling to keep your eyes open", "difficulty maintaining correct speed", "reactions were slow" and "head dropping down" were most closely related to increased alpha and theta activity on EEG (r values 0.49-0.59, p<0.001) and "nodding off to sleep" and "struggling to keep your eyes open" were related to slow eye movements (r values 0.67 and 0.64, p<0.001). Symptoms related to visual disturbance and impaired driving performance were most accurate at detecting severely impaired driving performance (AUC on ROC curve of 0.86-0.91 for detecting change in lateral lane position greater than the change at a BAC of 0.05%). Individual sleepiness symptoms are related to impairment during acute sleep deprivation and might be able to assist drivers in recognizing their own sleepiness and ability to drive safely.

The accuracy of eyelid movement parameters for drowsiness detection.

STUDY OBJECTIVES: Drowsiness is a major risk factor for motor vehicle and occupational accidents. Real-time objective indicators of drowsiness could potentially identify drowsy individuals with the goal of intervening before an accident occurs. Several ocular measures are promising objective indicators of drowsiness; however, there is a lack of studies evaluating their accuracy for detecting behavioral impairment due to drowsiness in real time. METHODS: In this study, eye movement parameters were measured during vigilance tasks following restricted sleep and in a rested state (n = 33 participants) at three testing points (n = 71 data points) to compare ocular measures to a gold standard measure of drowsiness (OSLER). The utility of these parameters for detecting drowsiness-related errors was evaluated using receiver operating characteristic curves (ROC) (adjusted by clustering for participant) and identification of optimal cutoff levels for identifying frequent drowsiness-related errors (4 missed signals in a minute using OSLER). Their accuracy was tested for detecting increasing frequencies of behavioral lapses on a different task (psychomotor vigilance task [PVT]). RESULTS: Ocular variables which measured the average duration of eyelid closure (inter-event duration [IED]) and the ratio of the amplitude to velocity of eyelid closure were reliable indicators of frequent errors (area under the curve for ROC of 0.73 to 0.83, p < 0.05). IED produced a sensitivity and specificity of 71% and 88% for detecting ≥ 3 lapses (PVT) in a minute and 100% and 86% for ≥ 5 lapses. A composite measure of several eye movement characteristics (Johns Drowsiness Scale) provided sensitivities of 77% and 100% for detecting 3 and ≥ 5 lapses in a minute, with specificities of 85% and 83%, respectively. CONCLUSIONS: Ocular measures, particularly those measuring the average duration of episodes of eye closure are promising real-time indicators of drowsiness.

The interactive effects of extended wakefulness and low-dose alcohol on simulated driving and vigilance.

OBJECTIVES: Sleep deprivation and alcohol both impair driving performance. This study assessed the interactive effect of low-dose alcohol and extended wakefulness. DESIGN: Repeated-measures, crossover design evaluating psychomotor and driving function in a non-sleep-deprived state and after extended wakefulness with and without low-dose alcohol. SETTING: Teaching hospital sleep laboratory. PARTICIPANTS: Nineteen volunteer professional drivers. INTERVENTION & MEASUREMENTS: Driving simulation (AusEd) and the Psychomotor Vigilance Task (PVT) were measured in a rested state (12-15 hours awake) and after extended wakefulness (18-21 hours awake) during two sessions. Alcohol was administered during one session, with performance measured at blood alcohol concentrations (BAC) of 0.00%, 0.03%, and 0.05% in a non-sleep-deprived state, and at 0.03% after extended wakefulness (at 01:00 and 03:00). During the second session, tests were performed at the same times without alcohol. RESULTS: The combination of extended wakefulness and low-dose alcohol had significant deleterious effects on reaction time and lapses (PVT) and variation in lane position and speed (AusEd). Extended wakefulness (18-21 hours awake) combined with low-dose alcohol (0.03% BAC) resulted in more lapses (t = -2.75, P < 0.05) and greater variation in lane position (t = -3.94, P < 0.01) and speed (t = -2.79, P < 0.05) than did a BAC of 0.05% in a rested state. CONCLUSION: The combination of legal low-dose alcohol and extended wakefulness results in impairment worse than that at an alcohol level known to increase accident risk. Avoiding alcohol when driving after extended wakefulness may reduce accident risk.

Drugs in oral fluid in randomly selected drivers.

There were 13,176 roadside drug tests performed in the first year of the random drug-testing program conducted in the state of Victoria. Drugs targeted in the testing were methamphetamines and Delta(9)-tetrahydrocannabinol (THC). On-site screening was conducted by the police using DrugWipe, while the driver was still in the vehicle and if positive, a second test on collected oral fluid, using the Rapiscan, was performed in a specially outfitted "drug bus" located adjacent to the testing area. Oral fluid on presumptive positive cases was sent to the laboratory for confirmation with limits of quantification of 5, 5, and 2 ng/mL for methamphetamine (MA), methylenedioxy-methamphetamine (MDMA), and THC, respectively. Recovery experiments conducted in the laboratory showed quantitative recovery of analytes from the collector. When oral fluid could not be collected, blood was taken from the driver and sent to the laboratory for confirmation. These roadside tests gave 313 positive cases following GC-MS confirmation. These comprised 269, 118, and 87 cases positive to MA, MDMA, and THC, respectively. The median oral concentrations (undiluted) of MA, MDMA, and THC was 1136, 2724, and 81 ng/mL. The overall drug positive rate was 2.4% of the screened population. This rate was highest in drivers of cars (2.8%). The average age of drivers detected with a positive drug reading was 28 years. Large vehicle (trucks over 4.5 t) drivers were older; on average at 38 years. Females accounted for 19% of all positives, although none of the positive truck drivers were female. There was one false positive to cannabis when the results of both on-site devices were considered and four to methamphetamines.

Sleepiness, sleep-disordered breathing, and accident risk factors in commercial vehicle drivers.

Sleep-disordered breathing and excessive sleepiness may be more common in commercial vehicle drivers than in the general population. The relative importance of factors causing excessive sleepiness and accidents in this population remains unclear. We measured the prevalence of excessive sleepiness and sleep-disordered breathing and assessed accident risk factors in 2,342 respondents to a questionnaire distributed to a random sample of 3,268 Australian commercial vehicle drivers and another 161 drivers among 244 invited to undergo polysomnography. More than half (59.6%) of drivers had sleep-disordered breathing and 15.8% had obstructive sleep apnea syndrome. Twenty-four percent of drivers had excessive sleepiness. Increasing sleepiness was related to an increased accident risk. The sleepiest 5% of drivers on the Epworth Sleepiness Scale and Functional Outcomes of Sleep Questionnaire had an increased risk of an accident (odds ratio [OR] 1.91, p = 0.02 and OR 2.23, p < 0.01, respectively) and multiple accidents (OR 2.67, p < 0.01 and OR 2.39, p = 0.01), adjusted for established risk factors. There was an increased accident risk with narcotic analgesic use (OR 2.40, p < 0.01) and antihistamine use (OR 3.44, p = 0.04). Chronic excessive sleepiness and sleep-disordered breathing are common in Australian commercial vehicle drivers. Accident risk was related to increasing chronic sleepiness and antihistamine and narcotic analgesic use.

The involvement of drugs in drivers of motor vehicles killed in Australian road traffic crashes.

A multi-center case-control study was conducted on 3398 fatally-injured drivers to assess the effect of alcohol and drug use on the likelihood of them being culpable. Crashes investigated were from three Australian states (Victoria, New South Wales and Western Australia). The control group of drug- and alcohol-free drivers comprised 50.1% of the study population. A previously validated method of responsibility analysis was used to classify drivers as either culpable or non-culpable. Cases in which the driver "contributed" to the crash (n=188) were excluded. Logistic regression was used to examine the association of key attributes such as age, gender, type of crash and drug use on the likelihood of culpability. Drivers positive to psychotropic drugs were significantly more likely to be culpable than drug-free drivers. Drivers with Delta(9)-tetrahydrocannabinol (THC) in their blood had a significantly higher likelihood of being culpable than drug-free drivers (odds ratio (OR) 2.7, 95% CI 1.02-7.0). For drivers with blood THC concentrations of 5 ng/ml or higher the odds ratio was greater and more statistically significant (OR 6.6, 95% CI 1.5-28.0). The estimated odds ratio is greater than that for drivers with a blood alcohol concentration (BAC) of 0.10-0.15% (OR 3.7, 95% CI 1.5-9.1). A significantly stronger positive association with culpability was seen with drivers positive to THC and with BAC > or =0.05% compared with BAC > or =0.05 alone (OR 2.9, 95% CI 1.1-7.7). Strong associations were also seen for stimulants, particularly in truck drivers. There were non-significant, weakly positive associations of opiates and benzodiazepines with culpability. Drivers positive to any psychoactive drug were significantly more likely to be culpable (OR 1.8, 95% CI 1.3-2.4). Gender differences were not significant, but differences were apparent with age. Drivers showing the highest culpability rates were in the under 25 and over 65 age groups.

The incidence of drugs in drivers killed in Australian road traffic crashes.

The incidence of alcohol and drugs in fatally injured drivers were determined in three Australian states; Victoria (VIC), New South Wales (NSW) and Western Australia (WA) for the period of 1990-1999. A total of 3398 driver fatalities were investigated which included 2609 car drivers, 650 motorcyclists and 139 truck drivers. Alcohol at or over 0.05 g/100ml (%) was present in 29.1% of all drivers. The highest prevalence was in car drivers (30.3%) and the lowest in truckers (8.6%). WA had the highest rate of alcohol presence of the three states (35.8%). Almost 10% of the cases involved both alcohol and drugs. Drugs (other than alcohol) were present in 26.7% of cases and psychotropic drugs in 23.5%. These drugs comprised cannabis (13.5%), opioids (4.9%), stimulants (4.1%), benzodiazepines (4.1%) and other psychotropic drugs (2.7%). 8.5% of all drivers tested positive for Delta(9)-tetrahydrocannabinol (THC) and the balance of cannabis positive drivers were positive to only the 11-nor-Delta(9)-tetrahydrocannabinol-9-carboxylic acid (carboxy-THC) metabolite. The range of THC blood concentrations in drivers was 0.1-228 ng/ml, with a median of 9 ng/ml. Opioids consisted mainly of morphine (n=84), codeine (n=89) and methadone (n=33), while stimulants consisted mainly of methamphetamine (n=51), MDMA (n=6), cocaine (n=5), and the ephedrines (n=61). The prevalence of drugs increased over the decade, particularly cannabis and opioids, while alcohol decreased. Cannabis had a larger prevalence in motorcyclists (22.2%), whereas stimulants had a much larger presence in truckers (23%).