How do prolonged wakefulness and alcohol compare in the decrements they produce on a simulated driving task?

Sleep Behavior in Royal Australian Navy Shift Workers by Shift and Exposure to the SleepTank App

INTRODUCTION

Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule.

MATERIALS AND METHODS

Royal Australian Navy volunteers agreed to wear a Fitbit Versa 2 with the SleepTank app (SleepTank condition), or without the SleepTank app (Controls), for up to 10 weeks from May to July 2023 during the trial of a novel shift rotation schedule. Participants from across 6 units worked a combination of early (6:00 AM to 2:00 PM), day (7:30 AM to 4:30 PM), late (4:00 PM to 12:00 AM), and night shifts (12:00 AM to 6:00 AM) or stable day shifts (6:00 AM to 4:00 PM). Differences in sleep behavior (time in bed, total sleep time, bedtime, wake time, sleep efficiency [SE]) between conditions and shift types were tested using Analysis of Variance. This study was approved by the Australian Departments of Defence and Veterans' Affairs Human Research Ethics Committee.

RESULTS

Thirty-four participants completed the full study (n = 17 Controls; n = 17 SleepTank). There was a significant effect of shift type on 24-hour time in bed (TIB24; F(4,9) = 8.15, P < .001, η2 = 0.15) and total sleep time (TST24; F(4,9) = 8.54, P < .001, η2 = 0.18); both were shorter in early shifts and night shifts compared to other shift types. TIB24 and TST24 were not significantly different between conditions, but there was a trend for greater SE in the SleepTank condition relative to Controls (F(1,9) = 2.99, P = .08, η2 = 0.11).

CONCLUSIONS

Sleep data collected by Fitbit Versa 2s indicated shorter sleep duration (TIB24, TST24) for Royal Australian Navy workers during early and late shifts relative to stable day shifts. Access to the SleepTank app did not greatly influence measures of sleep duration but may be protective against fatigue by affecting SE. Further research is needed to evaluate the utility of the SleepTank app as a means of improving sleep hygiene in real-world, shift-working environments.

Calibrated to drive: Measuring self-assessed driving ability and perceived workload after prolonged sitting and sleep restriction

Self-assessed driving ability may differ from actual driving performance, leading to poor calibration (i.e., differences between self-assessed driving ability and actual performance), increased risk of accidents and unsafe driving behaviour. Factors such as sleep restriction and sedentary behaviour can impact driver workload, which influences driver calibration. This study aims to investigate how sleep restriction and prolonged sitting impact driver workload and driver calibration to identify strategies that can lead to safer and better calibrated drivers. Participants (n = 84, mean age = 23.5 ± 4.8, 49 % female) undertook a 7-day laboratory study and were randomly allocated to a condition: sitting 9-h sleep opportunity (Sit9), breaking up sitting 9-h sleep opportunity (Break9), sitting 5-h sleep opportunity (Sit5) and breaking up sitting 5-h sleep opportunity (Break5). Break9 and Break5 conditions completed 3-min of light-intensity walking on a treadmill every 30 min between 09:00-17:00 h, while participants in Sit9 and Sit5 conditions remained seated. Each participant completed a 20-min simulated commute in the morning and afternoon each day and completed subjective assessments of driving ability and perceived workload before and after each commute. Objective driving performance was assessed using a driving simulator measuring speed and lane performance metrics. Driver calibration was analysed using a single component and 3-component Brier Score. Correlational matrices were conducted as an exploratory analysis to understand the strength and direction of the relationship between subjective and objective driving outcomes. Analyses revealed participants in Sit9 and Break9 were significantly better calibrated for lane variability, lane position and safe zone-lane parameters at both time points (p < 0.0001) compared to Sit5 and Break5. Break5 participants were better calibrated for safe zone-speed and combined safe zone parameters (p < 0.0001) and speed variability at both time points (p = 0.005) compared to all other conditions. Analyses revealed lower perceived workload scores at both time points for Sit9 and Break9 participants compared to Sit5 and Break5 (p = <0.001). Breaking up sitting during the day may reduce calibration errors compared to sitting during the day for speed keeping parameters. Future studies should investigate if different physical activity frequency and intensity can reduce calibration errors, and better align a driver's self-assessment with their actual performance.

Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving

The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their "surroundings." However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its "surroundings" but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.

Occupational Injuries and Illnesses among Paramedicine Clinicians: Analyses of US Department of Labor Data (2010 - 2020)

OBJECTIVE

Paramedicine clinicians (PCs) in the United States (US) respond to 40 million calls for assistance every year. Their fatality rates are high and their rates of nonfatal injuries are higher than other emergency services personnel, and much higher than the average rate for all US workers. The objectives of this paper are to: describe current occupational injuries among PCs; determine changes in risks over time; and calculate differences in risks compared to other occupational groups.

METHODS

This retrospective open cohort study of nonfatal injuries among PCs used 2010 through 2020 data from the US Department of Labor (DOL), Bureau of Labor Statistics; some data were unavailable for some years. The rates and relative risks (RRs) of injuries were calculated and compared against those of registered nurses (RNs), fire fighters (FFs), and all US workers.

RESULTS

The annual average number of injuries was: 4,234 over-exertion and bodily reaction (eg, motion-related injuries); 3,935 sprains, strains, and tears; 2,000 back injuries; 580 transportation-related injuries; and over 400 violence-related injuries. In this cohort, women had an injury rate that was 50% higher than for men. In 2020, the overall rate of injuries among PCs was more than four-times higher, and the rate of back injuries more than seven-times higher than the national average for all US workers. The rate of violence-related injury was approximately six-times higher for PCs compared to all US workers, seven-times higher than the rate for FFs, and 60% higher than for RNs. The clinicians had a rate of transportation injuries that was 3.6-times higher than the national average for all workers and 2.3-times higher than for FFs. Their overall rate of cases varied between 290 per 10,000 workers in 2018 and 546 per 10,000 workers in 2022.

CONCLUSIONS

Paramedicine clinicians are a critical component of the health, disaster, emergency services, and public health infrastructures, but they have risks that are different than other professionals.This analysis provides greater insight into the injuries and risks for these clinicians. The findings reveal the critical need for support for Emergency Medical Services (EMS)-specific research to develop evidence-based risk-reduction interventions. These risk-reduction efforts will require an enhanced data system that accurately and reliably tracks and identifies injuries and illnesses among PCs.

Improving performance on night shift: a study of resident sleep strategies

STUDY OBJECTIVES

To identify sleep strategies of internal medicine residents transitioning to night shift and report their effect on performance.

METHODS

Residents logged hours of sleep and work starting 3 days prior to the first night shift and continuing through the next 8 days. Cohorts were defined by sleep logs and compared separately by transition strategy, total hours of sleep, amount of sleep occurring at work, weekend sleep schedule, and residency training year. Data from logs were entered into the Fatigue Avoidance Scheduling Tool to measure predicted Performance Effectiveness (PE) during each night shift.

RESULTS

Twenty-three residents were evaluated. The Sleep Banking transition strategy (n = 2) had higher PE (mean = 88.6%) than all other sleep strategies combined (n = 21, mean = 80.9%; P = .016). Additionally, residents who slept an average of 8-9 hours daily during their week of night shifts had a higher mean PE compared to those who slept < 6 hours (86.8% vs 78.6%; P = .014).

CONCLUSIONS

Residents who engaged in Sleep Banking prior to the first night shift had higher PE and spent less time above a 0.05% blood alcohol concentration equivalent compared to all other strategies. Similarly, PE and time spent above a 0.05% blood alcohol concentration equivalent improved with increased average hours slept per day during the week of night shifts. Optimizing performance on night shift through the adoption of efficacious sleep strategies is imperative to mitigate patient safety issues that may result from poor alertness and cognitive abilities.

CITATION

Cushman P, Scheuller HS, Cushman J, Markert RJ. Improving performance on night shift: a study of resident sleep strategies. J Clin Sleep Med. 2023;19(5):935-940.

Pilot fatigue survey: A study of the mutual influence among fatigue factors in the "work" dimension

Background

Fatigue risk management for pilots has received increasing attention. The existing fatigue management systems have detailed descriptions of the factors and the mutual influences among the factors that affect the dimension of "sleep", which is one of the most important causes of fatigue. However, the analysis of the influencing factors of the "work" dimension of fatigue causes has not been very detailed or accurate, especially the exploration of the mutual influence among many fatigue-influencing factors in the "work" dimension.

Objective

The purpose of this study was to explore the mutual influence among fatigue-influencing factors related to the "work" dimension in the analysis of pilot fatigue causes.

Methods

This study designed a questionnaire on the dimension of "work" in the causes of pilot fatigue and collected a total of 270 feedback data points from international flight pilots. Based on the questionnaires and data, descriptive statistical analysis, exploratory factor analysis and confirmatory factor analysis were performed to explore the influencing factors and their mutual influences on the "work" dimension of pilot fatigue.

Results

There is a strong, mutual influence relationship among the fatigue causes of long-haul flight pilots - working status, working conditions and working schedules - in the dimension of "work". The workload only has a strong correlation with the working schedule, and the interaction relationships with the working status or working conditions are weak.

Conclusion

This study analyses the mutual influence among the influencing factors of the "work" dimension of pilot fatigue, and we expect to provide empirical data for pilot fatigue risk management and to help improve fatigue risk management systems.

Forecasting crew fatigue risk on international flights under different policies in China during the COVID-19 outbreak

To predict the risk of fatigue for flight crews on international flights under the new operating model policy of the civil aviation exemption approach policy during the COVID-19 outbreak, and to provide scientific validation methods and ideas for the exemption approach policy. This paper uses the change in flight crew alertness as a validation indicator, and then constructs an alertness assessment model to predict flight crew fatigue risk based on the SAFTE model theory. Then, the corresponding in-flight rotation plans for the flight is designed according to the exemption approach policy issued by the CAAC, the CCAR-121 part policy and the real operational requirements of the airline, respectively, and finally the simulation results is compared by comparing the pilot alertness and cockpit crew alertness under the exemption approach policy and the CCAR-121 part policy with the flight duration. The results show that the flight crew alertness level for the flight in-flight rotation plan simulation designed under the exemption approach policy is higher or closer to the pilot alertness level for operational flights under the CCAR-121 Part policy. This validates the reasonableness and safety of the exemption approach policy issued by the CAAC to meet the requirements of epidemic prevention and control, and provides scientific support and solutions for fatigue monitoring and management.

The effects of cannabis and alcohol on driving performance and driver behaviour: a systematic review and meta-analysis

BACKGROUND AND AIMS

Cannabis and alcohol are frequently detected in fatal and injury motor vehicle crashes. While epidemiological meta-analyses of cannabis and alcohol have found associations with an increase in crash risk, convergent evidence from driving performance measures is insufficiently quantitatively characterized. Our objectives were to quantify the magnitude of the effect of cannabis and alcohol-alone and in combination-on driving performance and behaviour.

METHODS

Systematic review and meta-analysis. We systematically searched Academic Search Complete, CINAHL, Embase, Scopus, Google Scholar, MEDLINE, PsycINFO, SPORTDiscus and TRID. Of the 616 studies that underwent full-text review, this meta-analysis represents 57 studies and 1725 participants. We extracted data for hazard response time, lateral position variability, lane deviations or excursions, time out of lane, driving speed, driving speed variability, speed violations, time speeding, headway, headway variability and crashes from experimental driving studies (i.e. driving simulator, closed-course, on-road) involving cannabis and/or alcohol administration. We reported meta-analyses of effect sizes using Hedges' g and r.

RESULTS

Cannabis alone was associated with impaired lateral control [e.g. g = 0.331, 95% confidence interval (CI) = 0.212-0.451 for lateral position variability; g = 0.198, 95% CI = 0.001-0.395 for lane excursions) and decreased driving speed (g = -0.176, 95% CI = -0.298 to -0.053]. The combination of cannabis and alcohol was associated with greater driving performance decrements than either drug in isolation [e.g. g = 0.480, 95% CI = 0.096-0.865 for lateral position variability (combination versus alcohol); g = 0.525, 95% CI = 0.049-1.002 for time out of lane (versus alcohol); g = 0.336, 95% CI = 0.036-0.636 for lateral position variability (combination versus cannabis; g = 0.475, 95% CI = 0.002-0.949 for time out of lane (combination versus cannabis)]. Subgroup analyses indicated that the effects of cannabis on driving performance measures were similar to low blood alcohol concentrations. A scarcity of data and study heterogeneity limited the interpretation of some measures.

CONCLUSIONS

This meta-analysis indicates that cannabis, like alcohol, impairs driving, and the combination of the two drugs is more detrimental to driving performance than either in isolation.

Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents

OBJECTIVE

To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue.

DESIGN

8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness").

SETTING

Greater Washington, DC area hospitals RESULTS: As shift lengths increased, effectiveness scores decreased and the time spent below criterion increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts carried excess sleep debt. Sleep prediction was similar to actual sleep, and both predicted similar performance (p ≤ 0.001).

CONCLUSIONS

Surgical resident sleep and shift patterns may create fatigue risk. Biomathematical modeling can aid the prediction of resident sleep patterns and performance. This approach provides an important tool to help educators in creating work-schedules that minimize fatigue risk.

Evaluating sleep deprivation and time-of-day influences on crash avoidance maneuvers of young motorcyclists using a dynamic simulator

INTRODUCTION

Motorcyclists are particularly at risk of being injured when involved in a road traffic accident. To avoid such crashes, emergency braking and/or swerving maneuvers are frequently performed. The recent development of dynamic motorcycle simulators may allow to study the influences of various disturbance factors such as sleep deprivation (SD) and time-of-day (TOD) in safe conditions.

METHODS

Twelve young healthy males took part in 8 tests sessions at 06:00 h, 10:00 h, 14:00 h, 18:00 h after a night with or without sleep, in a random order. Participants had to perform an emergency braking and a swerving maneuver, both realized at 20 and 40 kph on a motorcycle dynamic simulator. For each task, the total distance/time necessary to perform the maneuver was recorded. Additional analysis was conducted on reaction and execution distance/time (considered as explanatory variables).

RESULTS

Both crash avoidance maneuvers (emergency braking and swerving) were affected by increased speed, resulting in longer time and distance at 40 kph than at 20 kph. Emergency braking was mainly influenced by sleep deprivation, which significantly increased the total distance necessary to stop at 40 kph (+1.57 m; + 20%; p < 0.01). These impaired performances can be linked to an increase in reaction time (+21%; p < 0.01). Considering the swerving maneuver, TOD and SD influences remained limited. TOD only influenced the reaction time/distance measured at 40 kph with poorer performance in the early morning (+30% at 06:00 h vs 18:00 h; p < 0.05).

DISCUSSION

Our results confirm that crash avoidance capabilities of young motorcyclists were influenced by the lack of sleep, mainly because of increased reaction times. More complex tasks (swerving maneuver) remained mostly unchanged in this paradigm. Practical Applications: Prevention campaigns should focus on the dangers of motorcycling while sleepy. Motorcycling simulators can be used to sensitize safely with sleep deprivation and time-of-day influences.

Sleep-deprived car-following: Indicators of rear-end crash potential

Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG'), safety critical time headway and speed variability with respect to leading vehicle's speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG' by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG') significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.

Alcohol and melatonin

Investigation of the pathogenesis of alcoholism in humans using different methodological approaches has facilitated detection of important biological factors of consequent metabolic diseases, endocrine disorders, and other medical conditions, such as alcoholic cardiomyopathy, alcoholic hypertension, heart and vascular lesions, alcoholic liver disease, alcoholic pancreatitis, etc. Alcohol abuse leads to damage to the nervous system, which can result in neurological and mental disorders, including alcoholic polyneuropathy, psychosis, and alcohol dementia. The complexity and versatility of the harmful effects of regular alcohol consumption on the human body can be considered in the perspective of a chronobiological approach, because alcohol is chronotoxic to biological processes. As a rhythm regulator, melatonin exerts a wide range of different effects: circadian rhythm regulation, thermoregulation, sleep induction, antioxidant, immunomodulatory, and anti-stress ones. This review presents from a chronobiological perspective the impact of melatonin on alcohol intoxication in terms of mental disorders, sleep and inflammation, hepatic injury, and mitochondrial function. It discusses the main clinical effects of melatonin on alcohol injury and the main targets as a therapy for alcohol disorders. Chronobiological effects of ethanol are related to melatonin suppression that has been associated with, among others, cancer risk. Exogenous melatonin seems to be a promising hepato- and immune-protector due to its antioxidant and anti-inflammatory properties, which in combination with other medicines makes it useful to prevent alcoholic organ damage. The reason for the scientific interest in melatonin as a treatment for alcoholism is obvious; the number of cases of this pathology that gives rise to metabolic syndrome, and its subsequent transformation into steatohepatitis, liver fibrosis, and cirrhosis, is increasing worldwide. Melatonin not only exerts antioxidant effects but it exerts various other effects contributing to the management of liver conditions. This review discusses the interaction between normal and pathological processes caused by alcohol consumption and the relationship between alcohol and melatonin in these conditions.

Comparison of Royal Canadian Navy Watchstanding Schedules

INTRODUCTION

Life on board a naval vessel is exceptionally demanding. Workdays for naval sailors can quite easily become 18+ hours long when watch schedules, training, and drills/evolutions are taken into account. Rotating watches and short off-watch periods can force sailors into a biphasic sleep pattern that is not sufficiently restful or a rotating pattern that is impossible to adapt to.

MATERIALS AND METHODS

Six different watch systems were evaluated over four separate at-sea trials. Engineering and tactical/combat departments have had different watch systems in the past because of constraints related to the specific environment in which they work. Therefore, two of the watch systems were engineering-specific watch evaluations, three of the systems were specific to tactical/combat departments, and one watch system was evaluated with the entire company of the naval vessel.

RESULTS

Both two-section (1-in-2) watch systems and three-section (1-in-3) watch systems were evaluated, which involve two or three shifts of sailors rotating through a full continuous 24-h day, respectively. Moving beyond three rotations of sailors is impossible on Canadian naval vessels due to bunk space and other limitations. The best watch system that we evaluated with respect to fatigue and quality of life at sea was the 1-in-3 straight 8-h shift system that was tested for the entire ships' company. The system has a single 8-h daily watch obligation (red watch, 4:00 am-12:00 pm; white watch 12:00 pm-8:00 pm; and blue watch, 8:00 pm-4:00 am). The best 1-in-2 system was the 8-4-4-8 system in which sailors are on-watch for 8 h, off-watch for 4 h, on-watch for 4 h, and then rest for 8 h. Both of these two systems have the advantage of equitably sharing the Window of Circadian Low (from about midnight to about 8:00 am), especially when melatonin concentration in the body is usually at its peak, between 2:00 am and 6:00 am.

CONCLUSIONS

The goal of this work was to comprehensively evaluate both submarine and surface fleet watch systems. We were able to develop alternative watch systems that increased Royal Canadian Navy operational readiness and improved the quality of life of our sailors at sea.

Factors Associated with Insomnia Among Truck Drivers in Japan

PURPOSE

Truck drivers with insomnia are at a high risk of traffic accidents. We aimed to investigate the prevalence of insomnia and identify its associated factors among truck drivers in Japan.

METHODS

Participants were 2927 male truck drivers younger than 65 years old. Self-administered questionnaires were used to assess insomnia symptoms, State-Trait Anxiety Inventory, drinking, smoking habits, body mass index, caffeine intake, as well as daily driving hours, consecutive days away from home, and driving distance. Insomnia symptoms included difficulty initiating sleep, maintaining sleep and early morning awakening. Insomnia was defined when any of these symptoms were observed with daily tiredness.

RESULTS

The prevalence of insomnia among the subjects was 13.3% (n=356), of which 13.5% had difficulty initiating sleep, 78% had difficulty maintaining sleep, and 26.4% had early morning awakening. After adjusting for covariates, drinking habits, daily driving hours, and STAI score were significantly and linearly associated with insomnia; the adjusted odds ratio (OR) of drinking habits for insomnia was 1.74 [95% confidence interval (CI), 1.23-2.47] for heavy drinkers compared to non-drinkers (trend p<0.001); the adjusted OR of daily driving hours was 1.87 (95% CI, 1.00-3.49) for 12 hours or longer in a day compared to <8 hours in a day (trend p<0.001); the adjusted OR of STAI quartiles was 5.30 (95% CI, 3.66-7.67) for the highest quartile compared to the lowest quartile (trend p<0.001).

CONCLUSION

The present study demonstrated that insomnia is prevalent among truck drivers in Japan, and its risk factors include drinking habits, daily driving hours, and anxiety.

Driving after pediatric traumatic brain injury: Impact of distraction and executive functioning

OBJECTIVE

The objective of the current study was to examine the driving performance of young drivers with a history of moderate to severe traumatic brain injury (TBI) compared with an uninjured control group. The impact of cell phone related distraction (conversation and texting) and executive functioning (EF) were also explored.

METHOD

Individuals aged 16-25 years with (n = 19) and without (n = 19) a history of TBI engaged in a simulated drive under 3 distraction conditions (no distraction, cell phone conversation, and texting). Mean speed, maximum speed, standard deviation of speed, standard deviation of lane position, and crash rates were used as outcomes. The Global Executive Composite (GEC) from the Behavior Rating Inventory of Executive Functioning (BRIEF) was used to measure EF.

RESULTS

Significant Injury × Distraction × GEC interaction effects were noted on max speed and speed variability, with a trending Distraction × GEC interaction noted for lane position variability. The effect of distraction was most notable among individuals with greater GEC scores, across both injury groups.

CONCLUSIONS

A history of pediatric TBI did not specifically impact driving performance independent of EF, with EF playing a central role in functioning across domains of driving performance. Consistent effect of EF suggests that deficits in driving performance may be associated with EF specifically, with individuals with EF difficulties following TBI at greater risk for poor driving performance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Gender differences in driving control of young alcohol-impaired drivers

BACKGROUND

Male and female drivers exhibit different degrees of vehicle control while driving under the influence of alcohol. However, this interaction between alcohol and gender is understudied. The present study examined the effects of different alcohol levels on the driving control of male and female drivers with the help of driving simulator experiments in heterogeneous traffic conditions.

METHOD

Forty young drivers (20 males and 20 females) completed simulated driving at four Blood Alcohol Concentration (BAC) levels: 0% (control), 0.03%, 0.05% and 0.08%. Driving impairment in vehicle control was measured in terms of average speed, acceleration variability and reaction time of drivers. Repeated-measures ANOVA tests were conducted and regression models were developed for male and female drivers to quantify the effects of BAC levels and driver characteristics on the driving control measures.

RESULTS

Significant effects of gender were observed for average speed (p <  0.001) and acceleration variability (p = 0.015) but not for reaction time of drivers (p =  0.891). Further, the effect of BAC was significant in all the three measures of vehicle control (p <  0.001). Driving control improved with increasing age of male drivers while caffeine consumption was observed as an alcohol-antagonizing factor in female drivers.

CONCLUSION

The findings suggest that vehicle control of female drivers is more likely to get affected even at low BAC levels, providing evidence that they belong to critical section of driving community in terms of alcohol-related impairment. The findings may help in discouraging drinking and driving among male and female drivers.

Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment

Study Objectives: Microsleep episodes (MSEs) are short fragments of sleep (1–15 s) that can cause dangerous situations with potentially fatal outcomes. In the diagnostic sleep-wake and fitness-to-drive assessment, accurate and early identification of sleepiness is essential. However, in the absence of a standardised definition and a time-efficient scoring method of MSEs, these short fragments are not assessed in clinical routine. Based on data of moderately sleepy patients, we recently developed the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring of MSEs and corresponding machine learning algorithms for automatic MSE detection, both mainly based on the electroencephalogram (EEG). The present study aimed to investigate the relationship between automatically detected MSEs and driving performance in a driving simulator, recorded in parallel with EEG, and to assess algorithm performance for MSE detection in severely sleepy participants.

Methods: Maintenance of wakefulness test (MWT) and driving simulator recordings of 18 healthy participants, before and after a full night of sleep deprivation, were retrospectively analysed. Performance of automatic detection was compared with visual MSE scoring, following the BERN criteria, in MWT recordings of 10 participants. Driving performance was measured by the standard deviation of lateral position and the occurrence of off-road events.

Results: In comparison to visual scoring, automatic detection of MSEs in participants with severe sleepiness showed good performance (Cohen’s kappa = 0.66). The MSE rate in the MWT correlated with the latency to the first MSE in the driving simulator (r_s = −0.54, p < 0.05) and with the cumulative MSE duration in the driving simulator (r_s = 0.62, p < 0.01). No correlations between MSE measures in the MWT and driving performance measures were found. In the driving simulator, multiple correlations between MSEs and driving performance variables were observed.

Conclusion: Automatic MSE detection worked well, independent of the degree of sleepiness. The rate and the cumulative duration of MSEs could be promising sleepiness measures in both the MWT and the driving simulator. The correlations between MSEs in the driving simulator and driving performance might reflect a close and time-critical relationship between sleepiness and performance, potentially valuable for the fitness-to-drive assessment.

Acute sleep deprivation and culpable motor vehicle crash involvement

Study Objectives

To quantify the relationship between acute sleep deprivation and culpable involvement in motor vehicle crashes.

Methods

Participants were 6845 drivers involved in a representative sample of crashes investigated by the US Department of Transportation in years 2005-2007. A modified case-control study design was used to compare self-reported hours of sleep in the 24 hr before crashing between drivers deemed culpable versus nonculpable. Analyses controlled for fatigue-related, driver-related, and environmental factors. Specific errors that led to crashes were also examined.

Results

Drivers who reported having slept for 6, 5, 4, and less than 4 hr in the 24 hr before crashing had 1.3 (95% confidence interval [CI] = 1.04 to 1.7), 1.9 (1.1 to 3.2), 2.9 (1.4 to 6.2), and 15.1 (4.2 to 54.4) times the odds, respectively, of having been culpable for their crashes, compared with drivers who reported 7-9 hr of sleep. Drivers who had slept less than 4 hr had 3.4 (95% CI = 2.1 to 5.6) times the increase in odds of culpable involvement in single-vehicle crashes compared with multiple-vehicle crashes. Recent change in sleep schedule, typically feeling drowsy upon waking, and driving for 3+ hr were also associated with culpability (all p ≤ 0.013). Assuming nonculpable drivers comprised a representative sample of all drivers present where crashes occurred, these odds ratios approximate incidence rate ratios for culpable crash involvement per unit of time driving.

Conclusions

Driving after having slept less than 7 hr in a 24 hr period is associated with elevated risk of culpable crash involvement. Risk is greatest for drivers who have slept less than 4 hr and is manifested disproportionately in single-vehicle crashes.

Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study

It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of "alert" and "abnormal" driving was 90.9%, and that of "fatigued" and "drunk" driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states.

Detection and prediction of driver drowsiness using artificial neural network models

Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110min under conditions optimized to induce drowsiness. We measured physiological and behavioral indicators such as heart rate and variability, respiration rate, head and eyelid movements (blink duration, frequency and PERCLOS) and recorded driving behavior such as time-to-lane-crossing, speed, steering wheel angle, position on the lane. Different combinations of this information were tested against the real state of the driver, namely the ground truth, as defined from video recordings via the Trained Observer Rating. Two models using artificial neural networks were developed, one to detect the degree of drowsiness every minute, and the other to predict every minute the time required to reach a particular drowsiness level (moderately drowsy). The best performance in both detection and prediction is obtained with behavioral indicators and additional information. The model can detect the drowsiness level with a mean square error of 0.22 and can predict when a given drowsiness level will be reached with a mean square error of 4.18min. This study shows that, on a controlled and very monotonous environment conducive to drowsiness in a driving simulator, the dynamics of driver impairment can be predicted.

To Sleep, Perchance to Dream: Acute and Chronic Sleep Deprivation in Acute Care Surgeons

BACKGROUND

Acute and chronic sleep deprivation are significantly associated with depressive symptoms and are thought to be contributors to the development of burnout. In-house call inherently includes frequent periods of disrupted sleep and is common among acute care surgeons. The relationship between in-house call and sleep deprivation among acute care surgeons has not been previously studied. The goal of this study was to determine prevalence and patterns of sleep deprivation in acute care surgeons.

STUDY DESIGN

A prospective study of acute care surgeons with in-house call responsibilities from 2 level I trauma centers was performed. Participants wore a sleep-tracking device continuously over a 3-month period. Data collected included age, sex, schedule of in-house call, hours and pattern of each sleep stage (light, slow wave, and rapid eye movement [REM]), and total hours of sleep. Sleep patterns were analyzed for each night, excluding in-house call, and categorized as normal, acute sleep deprivation, or chronic sleep deprivation.

RESULTS

There were 1,421 nights recorded among 17 acute care surgeons (35.3% female; ages 37 to 65 years, mean 45.5 years). Excluding in-house call, the average amount of sleep was 6.54 hours, with 64.8% of sleep patterns categorized as acute sleep deprivation or chronic sleep deprivation. Average amount of sleep was significantly higher on post-call day 1 (6.96 hours, p = 0.0016), but decreased significantly on post-call day 2 (6.33 hours, p = 0.0006). Sleep patterns with acute and chronic sleep deprivation peaked on post-call day 2, and returned to baseline on post-call day 3 (p = 0.046).

CONCLUSIONS

Sleep patterns consistent with acute and chronic sleep deprivation are common among acute care surgeons and worsen on post-call day 2. Baseline sleep patterns were not recovered until post-call day 3. Future study is needed to identify factors that affect physiologic recovery after in-house call and further elucidate the relationship between sleep deprivation and burnout.

Train drivers' subjective perceptions of their abilities to perceive and control fatigue

Issues about train driver fatigue are important in the safety of railway systems. This study investigates the subjective perception of fatigue of conventional railway system drivers. The multidimensional Rasch model was used to measure two subjective latent constructs, namely, perceived fatigue awareness and perceived fatigue control. Analytical results show that 21% of the train drivers are unable to control fatigue. Randomly assigned vehicles with various cabin control systems for work shifts is the most unlikely scenario for drivers to perceive and control fatigue. Our results demonstrate that a driver who is unmarried, holds a university degree and has limited driving experience exhibits a low perceived ability to control fatigue. Thus, segmented programs for fatigue risk mitigation should be developed for specific drivers. The findings of this study can help railway safety managers and government regulators in developing and evaluating a management system for driver fatigue risk.

Transporte rodoviário de carga: acidentes de trabalho fatais e fiscalização trabalhista

Resumo Introdução: um grande número de acidentes de trabalho fatais (ATF) ocorre no Transporte Rodoviário de Cargas (TRC). Jornada de trabalho excessiva e descanso inadequado dos motoristas são apontados entre os principais fatores contribuintes. É atribuição do Ministério do Trabalho fiscalizar essas condições. Objetivos: avaliar se a ação da fiscalização do trabalho no setor de TRC recebeu atenção proporcional à magnitude dos indicadores de mortalidade por acidente de trabalho nessa atividade e analisar a inclusão dos fatores jornada e descanso nas inspeções. Métodos: estudo exploratório, quantitativo, descritivo, com base documental e bibliográfica e utilização de dados oficiais de ATF e da Fiscalização do Trabalho, de 2008 a 2012. Resultados: entre as 20 atividades/ocupações com mais mortes, o TRC (1430 óbitos; 37,97 mortes/100 mil vínculos) e a ocupação de motorista de caminhão de longas distâncias (1098 óbitos; 55,33 mortes/100 mil vínculos, em 2011) se destacaram. Contudo, as ações de fiscalização no setor representaram 1,4% do total no período investigado e a inclusão da jornada e descanso dos motoristas se deu somente em metade dessas ações. Conclusão: é necessário incremento no número de fiscalizações no TRC e na abordagem dos fatores contribuintes para os ATF dos motoristas de caminhão.

Sleep- and Wake-Promoting Drugs: Where Are They Being Sourced, and What Is Their Impact?

Background: Recent decades have seen both an increased number of shift workers in order to deliver services 24/7, and increased potential for social interactions at all hours of the day. People have sought to engage in strategies, which either promote vigilance or facilitate sleep, with the use of sleep- and wake-promoting drugs representing one strategy. Methods: We investigated use of sleep- and wake-promoting drugs in participants (n = 377) who completed a survey investigating the type and source of sleep- and wake-promoting drugs, and their impact on sleep and performance outcomes. Results: The most commonly reported wake-promoting drugs were amphetamine and dextroamphetamin salts, modafinil, and illicit substances including methamphetamine and cocaine, while the most commonly reported sleep-promoting drugs were benzodiazepines and antihistamines. Use of a sleep-promoting drug in the past month was associated with higher odds of having poorer sleep quality (OR = 3.15) and moderate-high insomnia (OR = 3.30), while use of a wake-promoting drug was associated with poor sleep quality (OR = 3.76), or making a fatigue-related error (OR = 2.65). Conclusions: These findings represent novel data on the use and source of sleep- and wake-promoting- drugs, and suggest that despite their use, poor sleep and performance outcomes persist, likely representing individuals struggling to keep up with the 24/7 world.

Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness

Monitoring car drivers for drowsiness is crucial but challenging. The high inter-individual variability observed in measurements raises questions about the accuracy of the drowsiness detection process. In this study, we sought to enhance the performance of machine learning models (Artificial Neural Networks: ANNs) by training a model with a group of drivers and then adapting it to a new individual. Twenty-one participants drove a car simulator for 110 min in a monotonous environment. We measured physiological and behavioral indicators and recorded driving behavior. These measurements, in addition to driving time and personal information, served as the ANN inputs. Two ANN-based models were used, one to detect the level of drowsiness every minute, and the other to predict, every minute, how long it would take the driver to reach a specific drowsiness level (moderately drowsy). The ANNs were trained with 20 participants and subsequently adapted using the earliest part of the data recorded from a 21st participant. Then the adapted ANNs were tested with the remaining data from this 21st participant. The same procedure was run for all 21 participants. Varying amounts of data were used to adapt the ANNs, from 1 to 30 min, Model performance was enhanced for each participant. The overall drowsiness monitoring performance of the models was enhanced by roughly 40% for prediction and 80% for detection.

Evaluation method regarding the effect of psychotropic drugs on driving performance: A literature review

Although automobile driving is necessary for many people, including patients with mental disorders, the influence of psychotropic drugs on driving performance remains unclear and requires scientific verification. Therefore, the objective of this study was to conduct a review of the literature in order to aid the development of a valid evaluation method regarding the influence of medication on driving performance. We conducted a literature search using two sets of terms on PubMed. One set was related to psychotropic drugs, and the other to driving tests. We excluded reviews and case studies and added literature found on other sites. A total of 121 relevant reports were found. The experiments were roughly divided into on-the-road tests (ORT) and driving simulators (DS). Although highway driving tests in ORT are most often used to evaluate driving performance, DS are becoming increasingly common because of their safety and low cost. The validity of evaluation methods for alcohol should be verified; however, we found that there were few validated tests, especially for DS. The scenarios and measurement indices of each DS were different, which makes it difficult to compare the results of DS studies directly. No evaluation indices, except for SD of lateral position, were sufficiently validated. Although highway ORT are the gold standard, DS were shown to have an increasing role in evaluating driving performance. The reliability of DS needs to be established, as does their validation with alcohol in order to accumulate more high-quality evidence.

Synchronized drowsiness monitoring and simulated driving performance data under 50-hr sleep deprivation: A double-blind placebo-controlled caffeine intervention

This paper presents the 60-s time-resolution segment from our 50-h total sleep deprivation (TSD) dataset (Aidman et al., 2018) [1] that captures minute-by-minute dynamics of driving performance (lane keeping and speed variability) along with objective, oculography-derived drowsiness estimates synchronised to the same 1-min driving epochs. Eleven participants (5 females, aged 18–28) were randomised into caffeine (administered in four 200 mg doses via chewing gum in the early morning hours) or placebo groups. Every three hours they performed a 40 min simulated drive in a medium fidelity driving simulator, while their drowsiness was continuously measured with a spectacle frame-mounted infra-red alertness monitoring system. The dataset covers 15 driving periods of 40 min each, and thus contains over 600 data points of paired data per participant. The 1-min time resolution enables detailed time-series analyses of both time-since-wake and time-on-task performance dynamics and associated drowsiness levels. It also enables direct examination of the relationships between drowsiness and task performance measures. The question of how these relationships might change under various intervention conditions (caffeine in our case) seems worth further investigation.

Effect of different alcohol levels on take-over performance in conditionally automated driving

Automated driving systems are getting pushed into the consumer market, with varying degrees of automation. Most often the driver's task will consist of being available as a fall-back level when the automation reaches its limits. These so-called take-over situations have attracted a great body of research, focusing on various human factors aspects (e.g., sleepiness) that could undermine the safety of control transitions between automated and manual driving. However, a major source of accidents in manual driving, alcohol consumption, has been a non-issue so far, although a false understanding of the driver's responsibility (i.e., being available as a fallback level) might promote driving under its influence. In this experiment, N = 36 drivers were exposed to different levels of blood alcohol concentrations (BACs: placebo vs. 0.05% vs. 0.08%) in a high fidelity driving simulator, and the effect on take-over time and quality was assessed. The results point out that a 0.08% BAC increases the time needed to re-engage in the driving task and impairs several aspects of longitudinal and lateral vehicle control, whereas 0.05% BAC did only go along with descriptive impairments in fewer parameters.

Assessment of Sleep Deprivation and Fatigue Among Chemical Transportation Drivers in Chonburi, Thailand

BACKGROUND

Fatigue and sleepiness are inter-related and common among road transport drivers. In this study, sleep deprivation and fatigue among chemical transportation drivers were examined.

METHODS

A cross-sectional study surveying 107 drivers from three hazardous types of chemical production and transportation industries (nonflammable gases, flammable gases, and flammable liquids) was conducted. Data on sleep deprivation were collected using questionnaires of the Stanford Sleeping Scale and the Groningen Sleep Quality Scale. Fatigue was assessed using an interview questionnaire and a flicker fusion instrument.

RESULTS

Chemical drivers had a mean sleeping scale (Stanford Sleeping Scale) of 1.98 (standard deviation 1.00) and had a mean score of 1.89 (standard deviation 2.06) on the Groningen Sleep Quality Scale. High-risk drivers had higher scores in both the Stanford Sleeping Scale and the Groningen Sleep Quality Scale with a mean score of 2.59 and 4.62, respectively, and those differences reached statistical significance (p < 0.05). The prevalence of fatigue, as assessed through a critical flicker fusion analyzer, subjective fatigue question, and either of the instruments, was 32.32%, 16.16%, and 43.43%, respectively. Drivers who slept <7 hours and had poor sleep quality were found to have more fatigue than those who slept enough and well. Drivers who had a more sleepiness score resulted in significantly more objective fatigue than those who had a less sleepiness score.

CONCLUSION

Sleep quality and sleeping hour can affect a driver's fatigue. Optimization of work-rest model should be considered to improve productivity, driver retention, and road safety.

Sleep characteristics associated with drowsy driving

OBJECTIVE

The aim of this study was to evaluate sleep characteristics associated with drowsy driving in an adult population.

METHODS

The study subjects consisted of 1675 adults aged 19 years or older who completed a population-based questionnaire survey on sleep habits. Experiences of drowsy driving were obtained from self-reported data. We investigated sleep-related variables including sleep duration, sleep efficiency, chronotype, subjective sleep perception, daytime sleepiness, sleep quality, and snoring. We performed multivariate logistic regression analysis to determine sleep characteristics independently associated with drowsy driving.

RESULTS

The mean age of the subjects was 43.2 years, and 66.3% were men. The prevalence of self-reported drowsy driving was 23.6% (396 of 1675), and 33.1% of subjects experienced dozing at the wheel at least once a month. Multivariate analysis demonstrated that men, office and manual workers, excessive daytime sleepiness, depression, habitual snoring, and perceived insufficient sleep were independently associated with drowsy driving. Subgroup analyses revealed that reduced weekday sleep duration was a risk factor of drowsy driving in adults with perceived sufficient sleep. On the other hand, frequent alcohol drinking significantly increased risk of drowsy driving in the subgroup with perceived sleep insufficiency. Furthermore, ordinal regression analyses confirmed the association between sleep characteristics and drowsy driving across different drowsy driving frequencies.

CONCLUSION

Excessive daytime sleepiness, depression, habitual snoring, and perceived insufficient sleep were sleep-related risk factors for drowsy driving. In addition to maintaining healthy sleep habits, individuals at high risk should be encouraged to evaluate underlying sleep disorders or psychiatric problems to prevent drowsy driving.

Effects of acute alcohol consumption on measures of simulated driving: A systematic review and meta-analysis

Driving simulators are used in a wide range of research settings to help develop an understanding of driver behavior in complex environments. Acute alcohol impairment is an important research topic for traffic safety and a large number of studies have indicated levels of simulated driving impairment imposed by alcohol across a range of performance outcome variables. The aim of the present study was to examine the impact of acute alcohol consumption on simulated driving performance by conducting a systematic review and meta-analysis of the available evidence. The online databases PubMed (MEDLINE), Web of Science (via Thomas Reuters) and Scopus were searched to identify studies that measured simulated car driving performance under control ('no alcohol' or 'placebo alcohol' ingestion) and intervention (acute alcohol ingestion) conditions, using repeated-measures experimental designs. Primary research outcomes were standard deviation of lane position (SDLP) and standard deviation of speed (SDSP); (total number of lane crossings (LC) and average speed (Speed) were secondary research outcomes). Meta-analytic procedures were used to quantify the effect of acute alcohol consumption on vehicle control, and to determine the influence of methodological variables (i.e. the duration of the simulated driving task, the limb of the BAC curve (ascending vs. descending) and the type of driving simulator employed (i.e. car vs. PC-based)) on the magnitude of the performance change due to alcohol consumption. 423 records were screened, and 50 repeated-measures trials (n=962 participants, 62% male) derived from 17 original publications were reviewed. 37 trials (n=721 participants) used a 'placebo alcohol' comparator to determine the effect of alcohol consumption on SDLP (32/37) and SDSP (22/37). Alcohol consumption significantly increased SDLP by 4.0±0.5cm (95% CI: 3.0, 5.1) and SDSP by 0.38±0.10km⋅h^-1 (95% CI: 0.19, 0.57). Regression analyses indicate BAC (p=0.004) and driving simulator platform (p<0.001) influence the magnitude of the SDLP change, such that higher BAC levels and the use of PC-based driving simulators were associated with larger performance decrements (R²=0.80). The limb of the BAC curve and the duration of the driving task did not significantly alter the magnitude of the performance change. Eleven trials (n=205 participants) used a 'no alcohol' comparator to measure the effect of alcohol consumption on SDLP (10/11); few trials assessed SDSP (3/11). Alcohol consumption resulted in a small significant increase in SDLP under these conditions (standardized difference in means=0.23, 95% CI: 0.06, 0.39). These results demonstrate that lateral (SDLP and LC) and longitudinal (SDSP) vehicle control measures in a driving simulator are impaired with acute alcohol consumption. However, SDLP appears to be a more sensitive indicator of driving impairment than other driving performance variables and the results of the present study support its use as a performance outcome when examining alcohol-induced simulated driving impairment.

Effects of vibration on occupant driving performance under simulated driving conditions

Although much research has been devoted to the characterization of the effects of whole-body vibration on seated occupants' comfort, drowsiness induced by vibration has received less attention to date. There are also little validated measurement methods available to quantify whole body vibration-induced drowsiness. Here, the effects of vibration on drowsiness were investigated. Twenty male volunteers were recruited for this experiment. Drowsiness was measured in a driving simulator, before and after 30-min exposure to vibration. Gaussian random vibration, with 1-15 Hz frequency bandwidth was used for excitation. During the driving session, volunteers were required to obey the speed limit of 100 kph and maintain a steady position on the left-hand lane. A deviation in lane position, steering angle variability, and speed deviation were recorded and analysed. Alternatively, volunteers rated their subjective drowsiness by Karolinska Sleepiness Scale (KSS) scores every 5-min. Following 30-min of exposure to vibration, a significant increase of lane deviation, steering angle variability, and KSS scores were observed in all volunteers suggesting the adverse effects of vibration on human alertness level.

Driving Safety

Driving is a common and hazardous activity that is a prominent cause of death worldwide. Driver behavior represents a predominant cause, contributing to over 90% of crashes. In this review, I will focus on how driver behavior influences driving safety by describing the types of crashes and their general causes, the driving process, the perceptual and cognitive characteristics of drivers, and driver types and impairments. Evidence from each of these perspectives suggests that breakdowns of a multilevel control process are the fundamental factors that undermine driving safety. Drivers adapt and drive safely in a broad range of situations but fail when expectations are violated or when feedback is inadequate. The review concludes by considering driving safety from a societal risk management perspective.

Exploring background risk factors for fatigue crashes involving truck drivers on regional roadway networks: a case control study in Jiangxi and Shaanxi, China

Background

Fatigue driving is a leading cause of traffic fatalities and injuries in China, especially among heavy truck drivers. The present study tried to examine which and how factors within the human-vehicle-roadway-environment system contribute to the occurrence of crashes involving fatigued truck drivers.

Findings

To reduce such risk on the road, a total of 9168 crashes which occurred in Jiangxi and Shaanxi between 2003 and 2014 were selected to measure the effects of potential factors on fatigue related truck crashes using a case control study. Pearson Chi-square test was used to determine the relationship between crash risk and independent factors, and a stepwise logistic regression model was developed to determine the significant risk factors. According to the data analysis results, driver’s gender, age, driving experience, and overspeeding behavior, vehicle’s commercial status, overloading conditions and brake performance, road’s type, slippery pavement and existence of sharp curve and long steep grade, and time of day, season, weather and visibility conditions, etc. were identified to be significantly associated with fatigue related truck crashes on Jiangxi and Shaanxi highways. Moreover, it is found that (a) in Jiangxi, an employed truck driver has a higher risk of crash involving multi-vehicles or a passenger car at bridge locations, and (b) in Shaanxi, the adult, tunnel location, summer and winter days prohibit statistically significant association with the occurrence of multi-vehicle and single-vehicle run-off-road/rollover crashes.

Conclusions

Young employed male truck drivers with less experience are at high risk, especially while driving across sharp curves, down long steep grades, over bridge or through tunnels, during the midnight period, on rainy, snowy or foggy days in rural areas. All these help recommend potential policy initiatives as well as effective safety promotion strategies at the public health scale for professional truck drivers.

The sensitivity of laboratory tests assessing driving related skills to dose-related impairment of alcohol: A literature review

Laboratory tests assessing driving related skills can be useful as initial screening tools to assess potential drug induced impairment as part of a standardized behavioural assessment. Unfortunately, consensus about which laboratory tests should be included to reliably assess drug induced impairment has not yet been reached. The aim of the present review was to evaluate the sensitivity of laboratory tests to the dose dependent effects of alcohol, as a benchmark, on performance parameters. In total, 179 experimental studies were included. Results show that a cued go/no-go task and a divided attention test with primary tracking and secondary visual search were consistently sensitive to the impairing effects at medium and high blood alcohol concentrations. Driving performance assessed in a simulator was less sensitive to the effects of alcohol as compared to naturalistic, on-the-road driving. In conclusion, replicating results of several potentially useful tests and their predictive validity of actual driving impairment should deserve further research. In addition, driving simulators should be validated and compared head to head to naturalistic driving in order to increase construct validity.

Occupational Sleep Medicine

Sleep and circadian rhythms significantly impact almost all aspects of human behavior and are therefore relevant to occupational sleep medicine, which is focused predominantly around workplace productivity, safety, and health. In this article, 5 main factors that influence occupational functioning are reviewed: (1) sleep deprivation, (2) disordered sleep, (3) circadian rhythms, (4) common medical illnesses that affect sleep and sleepiness, and (5) medications that affect sleep and sleepiness. Consequences of disturbed sleep and sleepiness are also reviewed, including cognitive, emotional, and psychomotor functioning and drowsy driving.

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

OBJECTIVE

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.

Effects of fatigue on driving performance under different roadway geometries: a simulator study

OBJECTIVE

This article examines the effects of fatigue on driving performance under different roadway geometries using a driving simulator.

METHODS

Twenty-four participants each completed a driving scenario twice: while alert and while experiencing fatigue. The driving scenario was composed of straight road segments and curves; there were 6 curves with 3 radius values (i.e., 200, 500, and 800 m) and 2 turning directions (i.e., left and right). Analysis was conducted on driving performance measures such as longitudinal speed, steering wheel movements, and lateral position.

RESULTS

RESULTS confirmed that decremental changes in driving performance due to fatigue varied among road conditions. On straight segments, drivers' abilities to steer and maintain lane position were impaired, whereas on curves we found decremental changes in the quality of longitudinal speed as well as steering control and keeping the vehicle in the lane. Moreover, the effects of fatigue on driving performance were relative to the radius and direction of the curve. Fatigue impaired drivers' abilities to control the steering wheel, and the impairment proved more obvious on curves. The degree varied significantly as the curve radius changed. Drivers tended to drive closer to the right side due to fatigue, and the impairment in maintaining lane position became more obvious as the right-turn curve radius decreased.

CONCLUSIONS

Driver fatigue has detrimental effects on driving performance, and the effects differ under different roadway geometries.

Effects of alcohol on automated and controlled driving performances

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. Sixteen experienced drivers (25.3 ± 2.9 years old, 8 men and 8 women) were tested with 0, 0.3, 0.5, and 0.8 g/l of alcohol. Driving scenarios varied: road tracking, car following, and an urban scenario including events inspired by real accidents. Statistical analyses were performed on driving parameters as a function of alcohol level.

RESULTS

Automated driving parameters such as standard deviation of lateral position measured with the road tracking and car following scenarios were impaired by alcohol, notably with the highest dose. More controlled parameters such as response time to braking and number of crashes when confronted with specific events (urban scenario) were less affected by the alcohol level.

CONCLUSION

Performance decrement was greater with driving scenarios involving automated processes than with scenarios involving controlled processes.

The effects of a single night of sleep deprivation on fluency and prefrontal cortex function during divergent thinking

The dorsal and ventral aspects of the prefrontal cortex (PFC) are the two regions most consistently recruited in divergent thinking tasks. Given that frontal tasks have been shown to be vulnerable to sleep loss, we explored the impact of a single night of sleep deprivation on fluency (i.e., number of generated responses) and PFC function during divergent thinking. Participants underwent functional magnetic resonance imaging scanning twice while engaged in the Alternate Uses Task (AUT) - once following a single night of sleep deprivation and once following a night of normal sleep. They also wore wrist activity monitors, which enabled us to quantify daily sleep and model cognitive effectiveness. The intervention was effective, producing greater levels of fatigue and sleepiness. Modeled cognitive effectiveness and fluency were impaired following sleep deprivation, and sleep deprivation was associated with greater activation in the left inferior frontal gyrus (IFG) during AUT. The results suggest that an intervention known to temporarily compromise frontal function can impair fluency, and that this effect is instantiated in the form of an increased hemodynamic response in the left IFG.