Cognitive components of simulated driving performance: Sleep loss effects and predictors

Estimating vigilance from the pre-work shift sleep using an under-mattress sleep sensor

Predicting vigilance impairment in high-risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi-night, often manually entered, sleep data. This study developed a machine learning model for predicting vigilance errors based on a single prior sleep period, derived from an under-mattress sensor. Twenty-four healthy volunteers (mean [SD] age = 27.6 [9.5] years, 12 male) attended the laboratory on two separate occasions, 1 month apart, to compare wake performance and sleep under two different lighting conditions. Each condition occurred over an 8 day protocol comprising a baseline sleep opportunity from 10 p.m. to 7 a.m., a 27 h wake period, then daytime sleep opportunities from 10 a.m. to 7 p.m. on days 3-7. From 12 a.m. to 8 a.m. on each of days 4-7, participants completed simulated night shifts that included six 10 min psychomotor vigilance task (PVT) trials per shift. Sleep was assessed using an under-mattress sensor. Using extra-trees machine learning models, PVT performance (reaction times <500 ms, reaction, and lapses) during each night shift was predicted based on the preceding daytime sleep. The final extra-trees model demonstrated moderate accuracy for predicting PVT performance, with standard errors (RMSE) of 19.9 ms (reaction time, 359 [41.6]ms), 0.42 reactions/s (reaction speed, 2.5 [0.6] reactions/s), and 7.2 (lapses, 10.5 [12.3]). The model also correctly classified 84% of trials containing ≥5 lapses (Matthews correlation coefficient = 0.59, F1 = 0.83). Model performance is comparable to current fatigue prediction models that rely upon self-report or manually entered data. This efficient approach may help to manage fatigue and safety in non-standard work schedules.

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.

The effect of sleep restriction, with or without high-intensity interval exercise, on behavioural alertness and mood state in young healthy males

Mood state and alertness are negatively affected by sleep loss, and can be positively influenced by exercise. However, the potential mitigating effects of exercise on sleep-loss-induced changes in mood state and alertness have not been studied comprehensively. Twenty-four healthy young males were matched into one of three, 5-night sleep interventions: normal sleep (NS; total sleep time (TST) per night = 449 ± 22 min), sleep restriction (SR; TST = 230 ± 5 min), or sleep restriction and exercise (SR + EX; TST = 235 ± 5 min, plus three sessions of high-intensity interval exercise (HIIE)). Mood state was assessed using the profile of mood states (POMS) and a daily well-being questionnaire. Alertness was assessed using psychomotor vigilance testing (PVT). Following the intervention, POMS total mood disturbance scores significantly increased for both the SR and SR + EX groups, and were greater than the NS group (SR vs NS; 31.0 ± 10.7 A.U., [4.4-57.7 A.U.], p = 0.020; SR + EX vs NS; 38.6 ± 14.9 A.U., [11.1-66.1 A.U.], p = 0.004). The PVT reaction times increased in the SR (p = 0.049) and SR + EX groups (p = 0.033) and the daily well-being questionnaire revealed increased levels of fatigue in both groups (SR; p = 0.041, SR + EX; p = 0.026) during the intervention. Despite previously demonstrated physiological benefits of performing three sessions of HIIE during five nights of sleep restriction, the detriments to mood, wellness, and alertness were not mitigated by exercise in this study. Whether alternatively timed exercise sessions or other exercise protocols could promote more positive outcomes on these factors during sleep restriction requires further research.

Factors Affecting Medical Residents' Decisions to Work After Call

BACKGROUND

Accreditation Council for Graduate Medical Education (ACGME) work-hour restrictions (WHRs) are intended to improve patient safety by reducing resident fatigue. Compliance with ACGME WHRs is not universal.

PURPOSE

The purpose of this study was to identify factors that influence residents' decisions to take a postcall day (PCD) off according to ACGME WHRs.

METHODS

Residents (N = 433) at one university were emailed a link to a survey in 2019. The survey included demographic details and a Discrete Choice Experiment examining influences on resident decisions to take a PCD off.

RESULTS

One hundred seventy-five residents (40.4%) responded to the survey; 113 residents (26%) completed the survey. Positive feedback from attending physicians about taking PCDs off in the past had the greatest impact on respondents' decisions to take a PCD off, increasing the probability by 27.3%, followed by chief resident comments about the resident looking tired (16.6% increase), and having never heard their attendings comment about PCDs off as either positive or negative (13.9% increase). Factors that had the largest effect on decreasing the probability of taking a PCD were negative feedback about taking PCDs off (14.3% decrease), continuity of care concerns (10.8% decrease), and whether the resident was looking forward to an assignment (7.9% decrease).

CONCLUSIONS

The most important influencer of residents' decisions to take a PCD off was related to feedback from their attending physicians, suggesting that compliance with WHRs can be improved by focusing on the residency program's safety culture.

Effects of Sleep Deprivation and Hazard Types on the Visual Search Patterns and Hazard Response Times of Taxi Drivers

The present study attempted to explore the effects of sleep deprivation on the visual search patterns and hazard response times of taxi drivers when they encountered different types of hazards. A two (driver groups: sleep deprivation or control) × two (hazard types: covert hazard or overt hazard) mixed experimental design was employed. A total of 60 drivers were recruited, half of whom were in the sleep-deprived group and half of whom were in the control group. A validated video-based hazard perception test that either contained covert hazards (12 video clips) or overt hazards (12 video clips) filmed from the drivers' perspective was presented to participants. Participants were instructed to click the left mouse button quickly once they detected a potentially dangerous situation that could lead to an accident. Participants' response time and eye movements relative to the hazards were recorded. The sleep-deprived group had a significantly longer response time and took a longer time to first fixate on covert hazards than the control group, while they had a shorter response time to overt hazards than the control group. The first fixation duration of sleep-deprived drivers was longer than that of the control group for overt hazards, while the duration of the first fixation of the two driver groups was similar for covert hazards. Sleep deprivation affects the visual search patterns and response times to hazards, and the adverse effects of sleep deprivation were worse in relation to covert hazards. The findings have some implications for classifying and evaluating high-risk taxi drivers whose hazard perception ability might be affected by insufficient sleep.

Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.

An Evaluation of Sleepiness, Performance, and Workload Among Operators During a Real-Time Reactive Telerobotic Lunar Mission Simulation

OBJECTIVE

We assessed operator performance during a real-time reactive telerobotic lunar mission simulation to understand how daytime versus nighttime operations might affect sleepiness, performance, and workload.

BACKGROUND

Control center operations present factors that can influence sleepiness, neurobehavioral performance, and workload. Each spaceflight mission poses unique challenges that make it difficult to predict how long operators can safely and accurately conduct operations. We aimed to evaluate the performance impact of time-on-task and time-of-day using a simulated telerobotic lunar rover to better inform staffing and scheduling needs for the upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission.

METHODS

We studied seven trained operators in a simulated mission control environment. Operators completed two five-hour simulations in a randomized order, beginning at noon and midnight. Performance was evaluated every 25 minutes using the Karolinska Sleepiness Scale, Psychomotor Vigilance Task, and NASA Task Load Index.

RESULTS

Participants rated themselves as sleepier (5.06 ± 2.28) on the midnight compared to the noon simulation (3.12 ± 1.44; p < .001). Reaction time worsened over time during the midnight simulation but did not vary between simulations. Workload was rated higher during the noon (37.93 ± 20.09) compared to the midnight simulation (32.09 ± 21.74; p = .007).

CONCLUSION

Our findings suggest that work shifts during future operations should be limited in duration to minimize sleepiness. Our findings also suggest that working during the day, when distractions are present, increases perceived workload. Further research is needed to understand how working consecutive shifts and taking breaks within a shift influence performance.

The Effect of Partial Sleep Deprivation and Time-on-Task on Young Drivers' Subjective and Objective Sleepiness

Despite sleepiness being considered one of the main factors contributing to road crashes, and even though extensive efforts have been made in the identification of techniques able to detect it, the assessment of fitness-to-drive regarding driving fatigue and sleepiness is still an open issue. In the literature on driver sleepiness, both vehicle-based measures and behavioral measures are used. Concerning the former, the one considered more reliable is the Standard Deviation of Lateral Position (SDLP) while the PERcent of eye CLOSure over a defined period of time (PERCLOS) seems to be the most informative behavioral measure. In the present study, using a within-subject design, we assessed the effect of a single night of partial sleep deprivation (PSD, less than 5 h sleeping time) compared to a control condition (full night of sleep, 8 h sleeping time) on SDLP and PERCLOS, in young adults driving in a dynamic car simulator. Results show that time-on-task and PSD affect both subjective and objective sleepiness measures. Moreover, our data confirm that both objective and subjective sleepiness increase through a monotonous driving scenario. Considering that SDLP and PERCLOS were often used separately in studies on driver sleepiness and fatigue detection, the present results have potential implications for fitness-to-drive assessment in that they provide useful information allowing to combine the advantages of the two measures for drowsiness detection while driving.

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving

Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver's recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment.

Comparison of Eye and Face Features on Drowsiness Analysis

Drowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep can cause property damage and sometimes lead to grave consequences of injuries and fatality. This study aims to design learning models to recognize drowsiness through human facial features. In addition, this work analyzes the attentions of individual neurons in the learning model to understand how neural networks interpret drowsiness. For this analysis, gradient-weighted class activation mapping (Grad-CAM) is implemented in the neural networks to display the attention of neurons. The eye and face images are processed separately to the model for the training process. The results initially show that better results can be obtained by delivering eye images alone. The effect of Grad-CAM is also more reasonable using eye images alone. Furthermore, this work proposed a feature analysis method, K-nearest neighbors Sigma (KNN-Sigma), to estimate the homogeneous concentration and heterogeneous separation of the extracted features. In the end, we found that the fusion of face and eye signals gave the best results for recognition accuracy and KNN-sigma. The area under the curve (AUC) of using face, eye, and fusion images are 0.814, 0.897, and 0.935, respectively.

Effects of heat strain on cognitive function among a sample of miners

Heat stress is associated with workplace injuries, likely through a combination of fatigue, reduced cognitive function, and thermal discomfort. The purpose of this study was to evaluate four cognitive tasks for sensitivity to heat stress. Eight participants performed treadmill exercise followed by assessments of serial reaction time (RT), Stroop effect, verbal delayed memory, and continuous performance working memory in an environmental chamber. A control (21.1 °C) trial, and "Hot 1" and "Hot 2" (both 37.8 °C) trials were run sequentially on two separate days to evaluate the four cognitive tasks. Heat strain (comparing Hot 1 and Hot 2 with the control trial) resulted in impairments in the serial RT test response and Stroop accuracy. Delayed memory was impacted only in the Hot 2 trial compared with the control trial. Given the demonstrated impact of heat on cognitive processes relevant to workers' real-world functioning in the workplace, understanding how to assess and monitor vigilant attention in the workplace is essential.

Association between early stage N2 sleep spindle burst characteristics and vigilance groups: an observational study on patients from a tertiary sleep centre

OBJECTIVE

Obstructive Sleep Apnoea (OSA) is associated with impaired vigilance. This paper examines the hypothesis that sleep spindle (Sp) characteristics during nocturnal sleep can be mapped to vigilance deficits measured by the Psychomotor Vigilance Task (PVT) in patients with OSA.

APPROACH

The PVT was performed prior to In-laboratory Polysomnography for 250 patients. PVT outcomes were clustered into three Vigilance Groups (VGs). Spindles were scored manually for a Training Cohort of 55 patients, (9491 Sps) across different blocks of NREM sleep (SBs) and validated in a Test Cohort (25 patients, 4867 Sps). We proposed a novel set of Sp features including a Spindle Burst Index (SBI), which quantifies the burst characteristics of spindles and constructed models mapping them to VGs. We also explored the performance of conventional Sp features (such as Sp number and density) in our modelling approach.

MAIN RESULTS

In the Training Cohort, we observed statistically significant differences in the SBI across VGs and SBs independent of OSA severity (1st Stage N2 SBI; p=<0.001 across VGs). In the Test Cohort, a Model based on the proposed SBI predicted VG membership with 88% accuracy. A model based on conventional Sp features mapped to VGs with 70.7% accuracy, and a model using mixed burst and conventional features reached an accuracy of 88%.

SIGNIFICANCE

Spindle features measured during diagnostic In-laboratory PSG can be mapped to PVT outcomes. The novel SBI proved useful for exploring the relationship between PVT outcomes and sleep. Further studies in larger populations are needed to verify these conclusions.

Sleep, alertness and performance across a first and a second night shift in mining haul truck drivers

This study examined the impact of first and second night shift work on sleep and performance in mining haul truck drivers. Sleep-wake patterns were monitored using wrist actigraphy. The Karolinska Sleepiness Scale (KSS), Psychomotor Vigilance Test (PVT) and a truck simulator were administered at the start and end of the first (N1) or second (N2) night shift (19:00-07:00 h). Participants were categorised into those who demonstrated a decline in performance (increase of one or more PVT lapses [reaction time >500 msec] from the start to the end of shift) or those who did not demonstrate a decline in performance (no increase in lapses) from the start to the end of shift. Total sleep time (TST) was longer in the 24 h prior to N1 (9.05 ± 1.49 h) compared to N2 (5.38 ± 1.32 h). PVT lapses and the slowest 10% of reaction times were similar at the start and end of N1, while greater impairments on these outcomes were observed at the end of N2 compared to the end of N1 (p < .05). In contrast, subjective sleepiness was equally impaired at the end of both night shifts. PVT performance (lapses and slowest 10% of reaction times) and drive violations demonstrated a similar direction of change on N1 and N2. Participants who demonstrated a decline in performance showed reduced TST in the 48 h prior to shifts compared to those who demonstrated no decline in performance across the shift. Likely due to short sleep prior, the end of N2 was associated with pronounced performance impairments on the PVT and drive violations compared to the start of the shift. The findings suggest that drive violations may be more sensitive to sleep loss compared to the other driving measures examined in this study. This study also emphasizes the need for adequate recovery sleep between night shifts.

Mapping Sleep Spindle Characteristics to Vigilance Outcomes in Patients with Obstructive Sleep Apnea

Obstructive Sleep Apnea (OSA) is a sleep disorder associated with reduced vigilance. Vigilance status is often measured using the Psychomotor Vigilance Task (PVT). This paper investigates modelling strategies to map sleep spindle (Sp) characteristics to PVT metrics in patients with OSA. Sleep spindles (n=2305) were manually detected across blocks of sleep for 20 patients randomly selected from a cohort of 190 undergoing Polysomnography (PSG) for suspected OSA. Novel Sp metrics based on runs or "bursts" of Sps were used to model Sp characteristics to standardized (z) Lapse and Median Reaction Time (MdRT) scores, and to Groups based on zLapse and zMdRT scores. A model employing Sp Burst characteristics mapped to MdRT Group membership with an accuracy of 91.9%, (95% C.I. 90.8-93.0). The model had a sensitivity of 88.9%, (95% C.I. 87.5-89.0) and specificity of 89.1% (95% C.I. 87.3-90.5) for detecting patients with the lowest MdRTs in our cohort.Clinical Relevance- Based on these results it may be possible to use Sp data collected during overnight diagnostic PSG for OSA to detect patients at risk for attention deficits. This would improve triage for OSA therapy by identifying at risk patients at the time of OSA diagnosis and would remove the need to employ additional testing to assess vigilance status.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

Supervision of a self-driving vehicle unmasks latent sleepiness relative to manually controlled driving

Human error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

Impact of Safety Culture Implementation on Driving Performance among Oil and Gas Tanker Drivers: A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach

This research aims to develop a safety culture model by investigating the relationship between safety culture and driving performance. In previous studies, safety culture has been one of the factors that determine safety issues. These issues were then contextually transformed via a pilot study and organized in the form of a theoretical model. The data were collected from 307 oil and gas tanker drivers in Malaysia through questionnaire surveys. Consequently, structural equation models of partial least squares (PLS-SEM) were applied to statistically assess the final model of this study. The results showed that the implementation of safety culture contributes to driving performance at a substantial level; there is a strong association with an effect of 67.3%. The findings of this research would serve as a benchmark for decision-makers in the oil and gas transportation sector, as promoting an awareness of safety culture should boost the efficiency of drivers. This research fills a gap in knowledge by identifying that positive safety culture practices and mindset are direct antecedents for the improvement of driver performance and, thus, the avoidance of road accidents.

Differential interactions of age and sleep deprivation in driving and spatial perception by male drivers in a virtual reality environment

We determined the effects of age and sleep deprivation on driving and spatial perception in a virtual reality environment. Twenty-two young (mean age: 22 years, range: 18-35) and 23 old (mean age: 71 years, range: 65-79) participants were tested after a normal night of sleep and a night of sleep deprivation. The participants drove a virtual car while responding to uni- and bilateral visual and auditory stimuli. Driving errors (crossing the lane borders), reaction times and accuracy to visual and auditory stimuli, performance in psychological tests, and subjective driving ability and tiredness were measured. Age had no effect on the number of driving errors, whereas sleep deprivation increased significantly especially the number of left lane border crossings. Age increased the number of stimulus detection errors, while sleep deprivation increased the number of errors particularly in the young and in the auditory modality as response omissions. Age and sleep deprivation together increased the number of response omissions in both modalities. Left side stimulus omissions suggest a bias to the right hemispace. The subjective evaluations were consistent with the objective measures. The psychological tests were more sensitive to the effects of age than to those of sleep deprivation. Driving simulation in a virtual reality setting is sensitive in detecting the effects of deteriorating factors on both driving and simultaneous spatial perception.

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.

Perceptions of Safety Climate and Fatigue Related to ACGME Residency Duty Hour Restrictions in Otolaryngology Residents

OBJECTIVE

To compare otolaryngology residents' perceptions of safety climate with respect to duty hour compliance and self-perceived fatigue.

STUDY DESIGN

Cross-sectional study.

SETTING

Forty-one otolaryngology residencies distributed across the United States.

METHODS

A national sample of otolaryngology residents was surveyed electronically in 2019. The survey included demographic details, on-call descriptors, an 18-point Safety Climate Survey (SCS) modified to measure perceptions of program attitudes and practices around resident duty hour compliance, and the 33-point Chalder Fatigue Questionnaire (CFQ).

RESULTS

Of 397 surveyed residents, 205 (51.6%) responded. The mean modified SCS score was 11.29 out of 18 (95% CI, 10.76-11.81). Respondents were most likely to disagree with "Residents are told when they are at risk of working beyond ACGME [Accreditation Council for Graduate Medical Education] duty hour restrictions," where 100 (48.8%) disagreed or strongly disagreed. The mean CFQ score was 15.99 of 33 (95% CI, 15.17-16.81). As the modified SCS score improved, CFQ scores decreased, indicating an inverse relationship between duty hour safety climate and fatigue. Having a protected postcall day off and having the program director, chief resident, or senior resident decide that a resident should take a postcall day off were all associated with higher modified SCS scores.

CONCLUSION

Otolaryngology residents perceived a safety climate that is suboptimal with regard to duty hour restriction issues. Additionally, an inverse relationship between fatigue and modified SCS scores suggests that fatigue among residents may be lower in programs where residents perceive that ACGME duty hour compliance is more important.

Driver monitoring systems (DMS): The future of impaired driving management?

OBJECTIVE

Driver monitoring systems (DMS) are the next generation of vehicle safety technology. Broadly, these refer to the embedded, aftermarket wearable or vehicle-mounted devices that collect observable information about the operator to make real-time assessment of their capacity to perform the driving task. Integrating biobehavioral monitoring (primarily ocular metrics) with driving performance assessments, these systems function to infer driver state in real time to identify operator conditions that negatively affect driving (such as fatigue, inattention, or distraction).

METHOD

We review available methods used to infer driver state, as referenced against accepted models for optimal performance. Modeling our observations on deviation from predetermined performance thresholds used to trigger graded safety alerts, we suggest that many psychoactive substances produce alterations to biobehavioral processes including attentional and motor control, which affect performance indices in a manner already arguably captured by these technologies.

RESULTS

Using these existing frameworks, there is considerable potential to similarly catalogue the effect of many common intoxicants known to negatively affect driving ability. This will provide safety-relevant and practical biological models for the development of next-generation multimodal DMS that integrate ocular and physiological variables sensitive to the effects of common and emergent psychoactive substances.

CONCLUSION

These devices have tangible potential application across all areas of transportation, including aviation, rail, and all commercial and private vehicle systems.

How much sleep do you need? A comprehensive review of fatigue related impairment and the capacity to work or drive safely

In developed countries, deaths attributable to driving or working while intoxicated have steadily declined over recent decades. In part, this has been due to (a) public education programs about the risks and (b) the deterrence value associated with penalties and prosecutions based on an individual being 'deemed impaired' if they exceed a proscribed level of blood alcohol or drug concentration while driving/working. In contrast, the relative proportion of fatigue-related accidents have remained stubbornly high despite significant public and workplace education. As such, it may be useful to introduce the legal principle of 'deemed impaired' with respect to fatigue and/or sleep loss. A comprehensive review of the impairment and accident literature was performed, including 44 relevant publications. Findings from this review suggests that a driver or worker might reasonably be 'deemed impaired' once the amount of sleep falls below five hours in the prior 24. Building on the legal principles first outlined in recent New Jersey legislation (Maggie's Law), this review argues that an individual can reasonably be 'deemed impaired' based on prior sleep wake behaviour. In Maggie's Law, a driver can be indirectly 'deemed impaired' if they have not slept in the prior 24 h. Based on the extant literature, we argue that, relative to drug and alcohol intoxication, this may be overly conservative. While roadside measurement of fatigue and prior sleep-wake behavior is not yet possible, we suggest that public education programs should provide specific guidance on the amount of sleep required and that post-accident forensic examination of prior sleep wake behaviours may help the community to determine unsafe behaviours and liability more objectively than is currently the case.

Effect of Sleep and Biobehavioral Patterns on Multidimensional Cognitive Performance: Longitudinal, In-the-Wild Study

Background

With nearly 20% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand how these metrics correlate with cognitive performance measures across a diverse population are lacking, and claims made by device manufacturers are vague. While there has been extensive research leading to a variety of theories on how physiological measures affect cognitive performance, virtually all such studies have been conducted in highly controlled settings and their validity in the real world is poorly understood.

Objective

We seek to bridge this gap by evaluating prevailing theories on the effects of a variety of sleep, activity, and heart rate parameters on cognitive performance against data collected in real-world settings.

Methods

We used a Fitbit Charge 3 and a smartphone app to collect different physiological and neurobehavioral task data, respectively, as part of our 6-week-long in-the-wild study. We collected data from 24 participants across multiple population groups (shift workers, regular workers, and graduate students) on different performance measures (vigilant attention and cognitive throughput). Simultaneously, we used a fitness tracker to unobtrusively obtain physiological measures that could influence these performance measures, including over 900 nights of sleep and over 1 million minutes of heart rate and physical activity metrics. We performed a repeated measures correlation (r_rm) analysis to investigate which sleep and physiological markers show association with each performance measure. We also report how our findings relate to existing theories and previous observations from controlled studies.

Results

Daytime alertness was found to be significantly correlated with total sleep duration on the previous night (r_rm=0.17, P<.001) as well as the duration of rapid eye movement (r_rm=0.12, P<.001) and light sleep (r_rm=0.15, P<.001). Cognitive throughput, by contrast, was not found to be significantly correlated with sleep duration but with sleep timing—a circadian phase shift toward a later sleep time corresponded with lower cognitive throughput on the following day (r_rm=–0.13, P<.001). Both measures show circadian variations, but only alertness showed a decline (r_rm=–0.1, P<.001) as a result of homeostatic pressure. Both heart rate and physical activity correlate positively with alertness as well as cognitive throughput.

Conclusions

Our findings reveal that there are significant differences in terms of which sleep-related physiological metrics influence each of the 2 performance measures. This makes the case for more targeted in-the-wild studies investigating how physiological measures from self-tracking data influence, or can be used to predict, specific aspects of cognitive performance.

Increased nocturnal arterial pulsation frequencies of obstructive sleep apnoea patients is associated with an increased number of lapses in a psychomotor vigilance task

Objectives

Besides hypoxaemia severity, heart rate variability has been linked to cognitive decline in obstructive sleep apnoea (OSA) patients. Thus, our aim was to examine whether the frequency domain features of a nocturnal photoplethysmogram (PPG) can be linked to poor performance in the psychomotor vigilance task (PVT).

Methods

PPG signals from 567 suspected OSA patients, extracted from Type 1 diagnostic polysomnography, and corresponding results of PVT were retrospectively examined. The frequency content of complete PPGs was determined, and analyses were conducted separately for men (n=327) and women (n=240). Patients were grouped into PVT performance quartiles based on the number of lapses (reaction times ≥500 ms) and within-test variation in reaction times. The best-performing (Q1) and worst-performing (Q4) quartiles were compared due the lack of clinical thresholds in PVT.

Results

We found that the increase in arterial pulsation frequency (APF) in both men and women was associated with a higher number of lapses. Higher APF was also associated with higher within-test variation in men, but not in women. Median APF (β=0.27, p=0.01), time spent under 90% saturation (β=0.05, p<0.01), female sex (β=1.29, p<0.01), older age (β=0.03, p<0.01) and subjective sleepiness (β=0.07, p<0.01) were significant predictors of belonging to Q4 based on lapses. Only female sex (β=0.75, p<0.01) and depression (β=0.91, p<0.02) were significant predictors of belonging to Q4 based on the within-test variation.

Conclusions

In conclusion, increased APF in PPG provides a possible polysomnography indicator for deteriorated vigilance especially in male OSA patients. This finding highlights the connection between cardiorespiratory regulation, vigilance and OSA. However, our results indicate substantial sex-dependent differences that warrant further prospective studies.

Mindfulness, sleep, and post-traumatic stress in long-haul truck drivers

BACKGROUND

The high stress culture and demands associated with long-haul truck driving place truckers at risk for mental health and sleep disorders, and thereby, increased risk for accidents, injuries, and fatality. Hours-of-service regulations have proven insufficient as a stand-alone intervention to protect the welfare of long-haul truckers, impacting those working in the industry and those sharing our nation's roads. Interventions to increase mindfulness have been used across occupational and personal domains to improve sleep quality, mental health, awareness of the environment, and reaction time.

OBJECTIVE

The purpose of this study was to examine the relationships between sleep, mental health, health care utilization, and mindfulness in long-haul truck drivers in the United States.

METHODS

Participants (N = 140) were recruited to complete a web-based survey. Descriptive statistics, bivariate analysis, and regression analysis were used to examine variables of interest.

RESULTS

Post-traumatic Stress Disorder (PTSD) symptomology and daytime sleepiness predicted mental health care utilization in the past year. Mindfulness was inversely correlated with PTSD symptomology, however in the full regression model, mindfulness failed to predict mental health care utilization.

CONCLUSIONS

Occupational health professionals should utilize mindfulness screenings as an adjunctive component to traditional mental health screenings and refer drivers for advanced care as appropriate.

Temporal stability of the factors related to the severity of drivers' injuries in rear-end collisions

Most of the previous studies that investigated the factors increasing the severity of rear-end collisions were based on analyzing collision reports from multiple years and combining them into a single dataset for analysis. Analyzing pooled data from multiple years carries the risk of introducing aggregation bias in the analysis. Those aggregated models might be structurally unstable, and the significance of the risk factors identified using those aggregated models might change over time due to the ongoing changes in vehicle technologies, law-enforcement technologies, and drivers' attitudes. This study demonstrates the importance of testing the temporal stability of pooled data by utilizing logistic regression modeling to analyze all rear-end collisions that occurred in North Carolina for the period from January 1, 2004 to December 31, 2015. Separate models were developed for each year to model injury severity of striking and struck drivers. The year-wise models were compared together to identify the most temporally stable factors, and it was found that older and female drivers are usually more severely injured, but they do not increase injury severity of the drivers they collide with. It was also found that compared to other light-duty vehicles, passenger cars are usually associated with increased injury severity to their drivers and reduced injury severity to the drivers of the vehicles they collide with. The increased age of a vehicle was found to increase the injury severity of its driver as well as the driver of the vehicle it collides with. Dark conditions were found to increase drivers' injury severity, but adverse weather conditions have no similar effect. For comparison, aggregated models were also developed by pooling data from all analysis years (from 2004 to 2015) and were found to return significant factors that were found by the year-wise models to be temporally unstable. Chow tests were performed on the data, and it was found that pooling data for four years or more generally returned structurally unstable models.

Effects of Partial Sleep Deprivation on Braking Response of Drivers in Hazard Scenarios

This study aimed at modeling the Response Time (RT) and Total Braking Time (TBT) of drivers under Partial Sleep Deprivation (PSD). Fifty male participants drove the driving simulator in three experimental conditions: two test sessions and a baseline. The two test sessions were conducted after one and two nights of PSD (sleep = 4.25 ± 0.5 h), respectively. Sleep reduction was recorded using a wrist-worn Actiwatch. The baseline session was conducted after full rest (7-8 h sleep/day for a week). The order of test sessions and baseline was randomized. Each test included two hazard events: 1) pedestrians crossing a road and 2) parked vehicles merging into a roadway. Karolinska Sleepiness Scale (KSS) and Sleepiness Symptoms Questionnaire (SSQ) ratings were also recorded during each drive. Four separate models using parametric accelerated failure time (AFT) with Weibull distribution were developed for RT and TBT in the two events. The models were chosen with clustered heterogeneity to account for intra-group heterogeneity due to repeated measures across tests. In the case of pedestrians crossing event, RT increased by 10% in the first test session and no significant effect observed on RT in the second test session. The overall TBT reduced by 25% and 28% during the first and second PSD sessions, respectively. In the case of vehicle merging event, both response time and total braking time delayed by 44% and 17% respectively after PSD. Other factors such as age, experience, work-rest hours, KSS and SSQ rating, often exercising, approaching speed and braking force were also found significant in the analysis. The parametric AFT approach adopted in this study showed the change in 'response time' and 'total braking time' concerning the type of hazard scenario and partial sleep-deprivation.

Severe desaturations increase psychomotor vigilance task-based median reaction time and number of lapses in obstructive sleep apnoea patients

Current diagnostic parameters estimating obstructive sleep apnoea (OSA) severity have a poor connection to the psychomotor vigilance of OSA patients. Thus, we aimed to investigate how the severity of apnoeas, hypopnoeas and intermittent hypoxaemia is associated with impaired vigilance.

We retrospectively examined type I polysomnography data and corresponding psychomotor vigilance tasks (PVTs) of 743 consecutive OSA patients (apnoea–hypopnoea index (AHI) ≥5 events·h⁻¹). Conventional diagnostic parameters (e.g. AHI and oxygen desaturation index (ODI)) and novel parameters (e.g. desaturation severity and obstruction severity) incorporating duration of apnoeas and hypopnoeas as well as depth and duration of desaturations were assessed. Patients were grouped into quartiles based on PVT outcome variables. The odds of belonging to the worst-performing quartile were assessed. Analyses were performed for all PVT outcome variables using binomial logistic regression.

A relative 10% increase in median depth of desaturations elevated the odds (OR_range 1.20–1.37, p<0.05) of prolonged mean and median reaction times as well as increased lapse count. Similarly, an increase in desaturation severity (OR_range 1.26–1.52, p<0.05) associated with prolonged median reaction time. Female sex (OR_range 2.21–6.02, p<0.01), Epworth Sleepiness Scale score (OR_range 1.05–1.07, p<0.01) and older age (OR_range 1.01–1.05, p<0.05) were significant risk factors in all analyses. In contrast, increases in conventional AHI, ODI and arousal index were not associated with deteriorated PVT performance.

These results show that our novel parameters describing the severity of intermittent hypoxaemia are significantly associated with increased risk of impaired PVT performance, whereas conventional OSA severity and sleep fragmentation metrics are not. These results underline the importance of developing the assessment of OSA severity beyond the AHI.

Parameters considering characteristic properties of desaturations have a significant association with impaired vigilance, highlighting the importance of developing methods beyond the AHI for a more detailed assessment of OSA severityhttp://bit.ly/2veqxD9

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.

A pre-drive ocular assessment predicts alertness and driving impairment: A naturalistic driving study in shift workers

Sleepiness is a major contributor to motor vehicle crashes and shift workers are particularly vulnerable. There is currently no validated objective field-based measure of sleep-related impairment prior to driving. Ocular parameters are promising markers of continuous driver alertness in laboratory and track studies, however their ability to determine fitness-to-drive in naturalistic driving is unknown. This study assessed the efficacy of a pre-drive ocular assessment for predicting sleep-related impairment in naturalistic driving, in rotating shift workers. Fifteen healthcare workers drove an instrumented vehicle for 2 weeks, while working a combination of day, evening and night shifts. The vehicle monitored lane departures and behavioural microsleeps (blinks >500 ms) during the drive. Immediately prior to driving, ocular parameters were assessed with a 4-min test. Lane departures and behavioural microsleeps occurred on 17.5 % and 10 % of drives that had pre-drive assessments, respectively. Pre-drive blink duration significantly predicted behavioural microsleeps and showed promise for predicting lane departures (AUC = 0.79 and 0.74). Pre-drive percentage of time with eyes closed had high accuracy for predicting lane departures and behavioural microsleeps (AUC = 0.73 and 0.96), although was not statistically significant. Pre-drive psychomotor vigilance task variables were not statistically significant predictors of lane departures. Self-reported sleep-related and hazardous driving events were significantly predicted by mean blink duration (AUC = 0.65 and 0.69). Measurement of ocular parameters pre-drive predict drowsy driving during naturalistic driving, demonstrating potential for fitness-to-drive assessment in operational environments.

Obstructive sleep apnea: personal, societal, public health, and legal implications

Introduction Obstructive sleep apnea (OSA) is a widely prevalent sleep-related breathing disorder, which leads to several life-threatening diseases. OSA has systemic effects on various organ systems. Untreated OSA is associated with long-term health consequences including hypertension, heart disease, diabetes, depression, metabolic disorders, and stroke. In addition, untreated OSA is reported to be associated with cognitive dysfunction, impaired productivity at the workplace and in an increased risk of motor vehicle accidents (MVAs) resulting in injury and fatality. Other consequences of OSA include, but are not limited to, impaired vigilance, daytime somnolence, performance deficits, morning headaches, mood disturbances, neurobehavioral impairments, and general malaise. Additionally, OSA has become an economic burden on most health systems all over the world. Many driving license regulations have been developed to reduce MVAs among OSA patients. Methods Studies of the personal, societal, public health, and legal aspects of OSA are reviewed. Data were collected through the following databases: MEDLINE, Google Scholar, Scopus, SAGE Research Methods, and ScienceDirect. Conclusion OSA leads to worsening of patients' personal relationships, decreasing work productivity, and increasing occupational accidents as well as MVAs. The costs of undiagnosed and untreated OSA to healthcare organizations are excessive. Thus, proper management of OSA will benefit not only the patient but will also provide widespread benefits to the society as a whole.

Drowsiness measures for commercial motor vehicle operations

Timely detection of drowsiness in Commercial Motor Vehicle (C MV) operations is necessary to reduce drowsiness-related CMV crashes. This is relevant for manual driving and, paradoxically, even more so with increasing levels of driving automation. Measures available for drowsiness detection vary in reliability, validity, usability, and effectiveness. Passively recorded physiologic measures such as electroencephalography (EEG) and a variety of ocular parameters tend to accurately identify states of considerable drowsiness, but are limited in their potential to detect lower levels of drowsiness. They also do not correlate well with measures of driver performance. Objective measures of vigilant attention performance capture drowsiness reliably, but they require active driver involvement in a performance task and are prone to confounds from distraction and (lack of) motivation. Embedded performance measures of actual driving, such as lane deviation, have been found to correlate with physiologic and vigilance performance measures, yet to what extent drowsiness levels can be derived from them reliably remains a topic of investigation. Transient effects from external circumstances and behaviors - such as task load, light exposure, physical activity, and caffeine intake - may mask a driver's underlying state of drowsiness. Also, drivers differ in the degree to which drowsiness affects their driving performance, based on trait vulnerability as well as age. This paper provides a broad overview of the current science pertinent to a range of drowsiness measures, with an emphasis on those that may be most relevant for CMV operations. There is a need for smart technologies that in a transparent manner combine different measurement modalities with mathematical representations of the neurobiological processes driving drowsiness, that account for various mediators and confounds, and that are appropriately adapted to the individual driver. The research for and development of such technologies requires a multi-disciplinary approach and significant resources, but is technically within reach.

Gaze position modulates the effectiveness of forward collision warnings for drowsy drivers

Advanced driver assistance systems (ADAS) have the potential to prevent crashes and reduce their severity. Forward collision warnings (FCW) are quickly becoming standard across vehicle lineups and may prevent frontal crashes by alerting drivers. Previous research has demonstrated the effectiveness of FCW for distracted drivers, but their effectiveness for other types of impairment remains unknown. Like distraction, drowsiness can impair driver response time and lead to crashes. The goal of the present study was to evaluate the effectiveness of FCW for moderately and severely drowsy drivers using a high-fidelity driving simulator. Drowsy drivers were divided into three warning conditions during a revealed stop vehicle forward collision event: An auditory alert, a haptic seat vibration, and a no warning baseline. Results indicate that FCW were effective at speeding drowsy driver response, but only when the drowsy drivers were looking away from the forward roadway at the onset of the event. These results have important implications for ADAS technology and driver state monitoring systems.

The psychomotor vigilance task: Role in the diagnosis of hepatic encephalopathy and relationship with driving ability

BACKGROUND & AIMS

Hepatic encephalopathy (HE) is a syndrome of decreased vigilance and has been associated with impaired driving ability. The aim of this study was to evaluate the psychomotor vigilance task (PVT), which is used to assess both vigilance and driving ability, in a group of patients with cirrhosis and varying degrees of HE.

METHODS

A total of 145 patients (120 males, 59 ± 10 years, model for end-stage liver disease [MELD] score 13 ± 5) underwent the PVT; a subgroup of 117 completed a driving questionnaire and a subgroup of 106 underwent the psychometric hepatic encephalopathy score (PHES) and an electroencephalogram (EEG), based on which, plus a clinical evaluation, they were classed as being unimpaired (n = 51), or as having minimal (n = 35), or mild overt HE (n = 20). All patients were followed up for an average of 13 ± 5 months in relation to the occurrence of accidents and/or traffic offences, HE-related hospitalisations and death. Sixty-six healthy volunteers evenly distributed by sex, age and education served as a reference cohort for the PVT.

RESULTS

Patients showed worse PVT performance compared with healthy volunteers, and PVT indices significantly correlated with MELD, ammonia levels, PHES and the EEG results. Significant associations were observed between neuropsychiatric performance/PVT indices and licence/driving status. PVT, PHES and EEG results all predicted HE-related hospitalisations and/or death over the follow-up period; none predicted accidents or traffic offences. However, individuals with the slowest reaction times and most lapses on the PVT were often not driving despite having a licence. When patients who had stopped driving for HE-related reasons (n = 6) were modelled as having an accident or fine over the subsequent 6 and 12 months, PVT was a predictor of accidents and traffic offences, even after correction for MELD and age.

CONCLUSIONS

The PVT is worthy of further study for the purposes of both HE and driving ability assessment.

LAY SUMMARY

Hepatic encephalopathy (HE) is a complication of advanced liver disease that can manifest as excessive sleepiness. Some patients with HE have been shown to have difficulty driving. Herein, we used a test called the Psychomotor Vigilance Task (PVT), which measures sleepiness and can also be used to assess driving competence. We showed that PVT performance is fairly stable in healthy individuals. We also showed that PVT performance parallels performance in tests which are commonly used in cirrhotic patients to measure HE. We suggest that this test is helpful in quantifying HE and identifying dangerous drivers among patients with cirrhosis.

The Effects of Fatigue on Cognitive Performance in Police Officers and Staff During a Forward Rotating Shift Pattern

Background

Few studies have examined the effects of a forward rotating shift pattern on police employee performance and well-being. This study sought to compare sleep duration, cognitive performance, and vigilance at the start and end of each shift within a three-shift, forward rotating shift pattern, common in United Kingdom police forces.

Methods

Twenty-three police employee participants were recruited from North Yorkshire Police (mean age, 43 years). The participants were all working the same, 10-day, forward rotating shift pattern. No other exclusion criteria were stipulated. Sleep data were gathered using both actigraphy and self-reported methods; cognitive performance and vigilance were assessed using a customized test battery, comprising five tests: motor praxis task, visual object learning task, NBACK, digital symbol substitution task, and psychomotor vigilance test. Statistical comparisons were conducted, taking into account the shift type, shift number, and the start and end of each shift worked.

Results

Sleep duration was found to be significantly reduced after night shifts. Results showed a significant main effect of shift type in the visual object learning task and NBACK task and also a significant main effect of start/end in the digital symbol substitution task, along with a number of significant interactions.

Conclusion

The results of the tests indicated that learning and practice effects may have an effect on results of some of the tests. However, it is also possible that due to the fast rotating nature of the shift pattern, participants did not adjust to any particular shift; hence, their performance in the cognitive and vigilance tests did not suffer significantly as a result of this particular shift pattern.

A prospective study of psychomotor performance of driving among two kinds of shift work in Iran

Background and aim

Driving after a night shift imposes a risk on health care professionals and other road users. The aim of this study was to measure psychomotor performance of driving of night shift nurses compared to day-shift nurses.

Methods

Forty-seven volunteer female nurses working at Sina hospital in Tehran, Iran, with a call in all departments of hospital, participated in this study (23 night shift and 24 day shift nurses) in 2016. The tests included RT for simple reaction time, ATAVT for perceptual speed, LVT for visual orientation and ZBA for time anticipation. Data collection tools were individual characteristics, 11-item circadian type inventory (CTI), Stanford sleepiness scale (SSS), and Swedish occupational fatigue inventory (SOFI-20) questionnaires. Psychomotor driving performance was assessed using validated computerized traffic psychological battery of Vienna Test System (VTS), before and after the shifts. Data analysis was performed using paired-samples t-test and Linear Regression.

Results

The mean age of day and night-shift nurses were 31.4±5.6 and 28.7±3.9 years respectively, no significant difference between two groups. Thirty percent of night shift and 16.7% of day shift nurses reported traffic accidents in the past year. The results revealed that, scores based on viewing times in visual orientation test (p=0.005), and median reaction time score in choice reaction time and reactive stress tolerance test (p=0.045), had a significant association with a 12-hour night shift with a 3-hour nap.

Conclusions

Twelve-hour night shift work impairs choice reaction time and visual orientation in nurses, even though they take a 3- hour nap during the shift. These skills are required for safe driving.

Stationary gaze entropy predicts lane departure events in sleep-deprived drivers

Performance decrement associated with sleep deprivation is a leading contributor to traffic accidents and fatalities. While current research has focused on eye blink parameters as physiological indicators of driver drowsiness, little is understood of how gaze behaviour alters as a result of sleep deprivation. In particular, the effect of sleep deprivation on gaze entropy has not been previously examined. In this randomised, repeated measures study, 9 (4 male, 5 female) healthy participants completed two driving sessions in a fully instrumented vehicle (1 after a night of sleep deprivation and 1 after normal sleep) on a closed track, during which eye movement activity and lane departure events were recorded. Following sleep deprivation, the rate of fixations reduced while blink rate and duration as well as saccade amplitude increased. In addition, stationary and transition entropy of gaze also increased following sleep deprivation as well as with amount of time driven. An increase in stationary gaze entropy in particular was associated with higher odds of a lane departure event occurrence. These results highlight how fatigue induced by sleep deprivation and time-on-task effects can impair drivers’ visual awareness through disruption of gaze distribution and scanning patterns.

The effect of poor sleep and occupational demands on driving safety in medical residents

OBJECTIVE

The Accreditation Council for Graduate Medical Examination recently revised and implemented duty hour standards that increased maximum duty hours for first-year medical residents and reduced the minimal amount of time off between duty periods for all medical residents. Little work has examined driving performance of medical residents at multiple periods surrounding duty, including in reference to off-duty driving performance as a baseline. Certain work-related factors that may be negatively impacted in medical residents, such as sleep quality, fatigue, and stress, are known to affect mental and physical performance and may further exacerbate driving risks. The overall objective of this study was to examine driving performance of medical residents off duty, preduty, and postduty using a high-fidelity driving simulator.

METHOD

Thirty-two medical residents were enrolled and wore sleep tracking devices over several days. Both self-reported and objective estimates of sleep quality, fatigue, and stress were collected at off-duty, preduty, and postduty points of time. The medical residents drove in a high-fidelity driving simulator at each time point to provide objective driving performance metrics.

RESULTS

Findings indicated that medical residents experienced the highest levels of stress and sleep propensity preduty and displayed riskier driving behaviors postduty. Those further into their residency were less affected by the negative effect of stress on driving performance, and those with better sleep quality metrics were also less affected by the negative effects of increased stress on driving outcomes.

CONCLUSIONS

The impact of occupational demands on psychophysiological outcomes requires further investigation to better understand the mechanisms of how work demands affect these psychophysiological outcomes. Understanding how to mitigate high job strain may have several implications in improving psychophysiological functions impacted by occupational demands, namely, sleep quality and stress, and subsequently improving driving safety outcomes that may also be negatively affected by the duty demands.

The impact of total sleep deprivation upon cognitive functioning in firefighters

INTRODUCTION

Firefighters as a profession are required to maintain high levels of attention for prolonged periods. However, total sleep deprivation (TSD) could influence negatively upon performance, particularly when the task is prolonged and repetitive.

PURPOSE

The aim of this study is to examine the influence of TSD on cognitive functioning in a group of firefighters.

SUBJECTS AND METHODS

Sixty volunteers who were active male fire brigade officers were examined with a computerized battery test that consisted of simple reaction time (SRT) (repeated three times), choice reaction time, visual attention test, and delayed matching to sample. Six series of measurements were undertaken over a period of TSD.

RESULTS

Performance in the second attempt in SRT test was significantly worse in terms of increased number of errors and, consequently, decreased number of correct responses during TSD. In contrast, the choice reaction time number of correct responses as well as the visual attention test reaction time for all and correct responses significantly improved compared to initial time points.

CONCLUSION

The study has confirmed that subjects committed significantly more errors and, consequently, noted a smaller number of correct responses in the second attempt of SRT test. However, the remaining results showed reversed direction of TSD influence. TSD potentially leads to worse performance in a relatively easy task in a group of firefighters. Errors during repetitive tasks in firefighting routines could potentially translate into catastrophic consequences.

Mother-Infant Room-Sharing and Sleep Outcomes in the INSIGHT Study

OBJECTIVES

The American Academy of Pediatrics recommends infant-parent room-sharing until age 1. We assessed the association between room-sharing and sleep outcomes.

METHODS

The Intervention Nurses Start Infants Growing on Healthy Trajectories study is an obesity prevention trial comparing a responsive parenting intervention with a safety control among primiparous mother-infant dyads. Mothers completed the Brief Infant Sleep Questionnaire at 4, 9, 12, and 30 months. Reported sleep duration and overnight behaviors, adjusted for intervention group, were compared among early independent sleepers (own room <4 months), later independent sleepers (own room between 4 and 9 months), and room-sharers at 9 months.

RESULTS

At 4 months, reported overnight sleep duration was similar between groups, but compared with room-sharers, early independent sleepers had better sleep consolidation (longest stretch: 46 more minutes, P = .02). At 9 months, early independent sleepers slept 40 more minutes nightly than room-sharers and 26 more minutes than later independent sleepers (P = .008). The longest stretch for early independent sleepers was 100 and 45 minutes more than room-sharers and later independent sleepers, respectively (P = .01). At 30 months, infants sleeping independently by 9 months slept >45 more minutes nightly than those room-sharing at 9 months (P = .004). Room-sharers had 4 times the odds of transitioning to bed-sharing overnight at both 4 and 9 months (P < .01 for both).

CONCLUSIONS

Room-sharing at ages 4 and 9 months is associated with less nighttime sleep in both the short and long-term, reduced sleep consolidation, and unsafe sleep practices previously associated with sleep-related death.

Sleep, Health, and Society

Biological needs for sleep are met by engaging in behaviors that are largely influenced by the environment, social norms and demands, and societal influences and pressures. Insufficient sleep duration and sleep disorders such as insomnia and sleep apnea are highly prevalent in the US population. This article outlines some of these downstream factors, including cardiovascular and metabolic disease risk, neurocognitive dysfunction, and mortality, as well as societal factors such as age, sex, race/ethnicity, and socioeconomics. This review also discusses societal factors related to sleep, such as globalization, health disparities, public policy, public safety, and changing patterns of use of technology.