Theory-Based Determinants of Stopping Drowsy Driving Behavior in College Students: A Cross-Sectional Study.

Drowsy driving among college students is a critical public health issue due to its significant impact on road safety. This cross-sectional study aimed to investigate the determinants of stopping drowsy driving behavior among college students using the multi-theory model (MTM) of health behavior change. Data for this study were collected from September to October 2023 via a 42-item psychometric valid, web-based survey disseminated via Qualtrics, involving 725 students from a large southwestern university. Nearly half of the participants (49.38%) reported drowsy driving in the past month. Hierarchical multiple regression analysis revealed that participatory dialogue (p = 0.0008) and behavioral confidence (p < 0.0001) significantly predicted the initiation of refraining from drowsy driving, with the final model explaining 36.4% of the variance. Similarly, emotional transformation (p < 0.0001) and practice for change (p = 0.0202) significantly predicted the sustenance of behavior change, with the final model accounting for 40.6% of the variance. These findings underscore the importance of targeted MTM-based interventions focusing on enhancing students' awareness and confidence in managing drowsiness to mitigate drowsy driving, ultimately improving road safety and student well-being.

"I know when to stop": Sentinels and counter-strategies against drowsy driving in narcolepsy and idiopathic hypersomnia.

STUDY OBJECTIVES: To collect prodromal symptoms experienced by participants with narcolepsy and idiopathic hypersomnia (considered "hypersomnolence experts") prior to drowsy driving and counter-strategies used to maintain alertness. METHODS: Systematic, face-to-face interview (using a semi-structured questionnaire), including clinical measures, frequency of car accidents/near misses, and symptoms experienced before impending drowsy driving episodes and counter-strategies. RESULTS: Among 61 participants (32 with narcolepsy, 29 with idiopathic hypersomnia; 56 drivers), 61% of drivers had at least one lifetime accident/near miss. They had a higher sleepiness score (14 ± 4 vs. 11 ± 5, P<0.04) than those without an accident/near miss, but no other differences in demographics, driving experience, medical conditions, symptoms, sleep tests, and treatment. All but three participants experienced prodromal symptoms of drowsy driving, which included postural and motor changes (86.9%: axial hypotonia - e.g., eyelid droop, stereotyped movements), cognitive impairment (53.3%: automatic steering, difficulty concentrating/shifting, dissociation, mind wandering, dreaming), sensory (65%: paresthesia, pain, stiffness, heaviness, blunted perceptions such as a flat dashboard with loss of 3D, illusions and hallucinations), and autonomic symptoms (10%, altered heart/breath rate, penile erection). Counterstrategies included self-stimulation from external sources (pain, cold air, music, drinks, driving with bare feet), motor changes (upright posture, movements), and surprise (sudden braking). CONCLUSIONS: Drowsy driving symptoms can result from "local" NREM, entry in N1 sleep, and hybrid wake/REM sleep states. These rich qualitative insights from participants with narcolepsy and idiopathic hypersomnia, as well as sophisticated counter-strategies, can be gathered to reduce the crash risk in this population, but also in inexperienced healthy drivers.

Subjective awareness of sleepiness while driving in younger and older adults.

Understanding whether drivers can accurately assess sleepiness is essential for educational campaigns advising drivers to stop driving when feeling sleepy. However, few studies have examined this in real-world driving environments, particularly among older drivers who comprise a large proportion of all road users. To examine the accuracy of subjective sleepiness ratings in predicting subsequent driving impairment and physiological drowsiness, 16 younger (21-33 years) and 17 older (50-65 years) adults drove an instrumented vehicle for 2 h on closed loop under two conditions: well-rested and 29 h sleep deprivation. Sleepiness ratings (Karolinska Sleepiness Scale, Likelihood of Falling Asleep scale, Sleepiness Symptoms Questionnaire) were obtained every 15min, alongside lane deviations, near crash events, and ocular indices of drowsiness. All subjective sleepiness measures increased with sleep deprivation for both age groups (p < 0.013). While most subjective sleepiness ratings significantly predicted driving impairment and drowsiness in younger adults (OR: 1.7-15.6, p < 0.02), this was only apparent for KSS, likelihood of falling asleep, and "difficulty staying in the lane for the older adults" (OR: 2.76-2.86, p = 0.02). This may be due to an altered perception of sleepiness in older adults, or due to lowered objective signs of impairment in the older group. Our data suggest that (i) younger and older drivers are aware of sleepiness; (ii) the best subjective scale may differ across age groups; and (iii) future research should expand on the best subjective measures to inform of crash risk in older adults to inform tailored educational road safety campaigns on signs of sleepiness.

Multi-sensor driver monitoring for drowsiness prediction.

OBJECTIVE: Driver monitoring systems are growing in importance as well as capability. This paper reports drowsy driving detection models that use vehicular, behavioral, and physiological data. The objectives were to augment camera-based system with vehicle-based and heart rate variability measures from a wearable device and compare the performance of drowsiness detection models that use these data sources. Timeliness of the models in predicting drowsiness is analyzed. Timeliness refers to how quickly a model can identify drowsiness and, by extension, how far in advance of an adverse event a classification can be given. METHODS: Behavioral data were provided by a production-type Driver Monitoring System manufactured by Aisin Technical Center of America. Vehicular data were recorded from the National Advanced Driving Simulator's large-excursion motion-base driving simulator. Physiological data were collected from an Empatica E4 wristband. Forty participants drove the simulator for up to three hours after being awake for at least 16 hours. Periodic measurements of drowsiness were recorded every ten minutes using both observational rating of drowsiness by an external rater and the self-reported Karolinska Sleepiness Scale. Nine binary random forest models were created, using different combinations of data sources and ground truths. RESULTS: The classification accuracy of the nine models ranged from 0.77 to 0.92 on a scale from 0 to 1, with 1 indicating a perfect model. The best-performing model included physiological data and used a reduced dataset that eliminated missing data segments after heartrate variability measures were computed. The most timely model was able to detect the presence of drowsiness 6.7 minutes before a drowsy lane departure. CONCLUSIONS: The addition of physiological measures added a small amount of accuracy to the model performance. Models trained on observational ratings of drowsiness detected drowsiness earlier than those based only on Karolinska Sleepiness Scale, making them more timely in detecting the onset of drowsiness.

PERCLOS-based technologies for detecting drowsiness: current evidence and future directions.

Drowsiness associated with sleep loss and circadian misalignment is a risk factor for accidents and human error. The percentage of time that the eyes are more than 80% closed (PERCLOS) is one of the most validated indices used for the passive detection of drowsiness, which is increased with sleep deprivation, after partial sleep restriction, at nighttime, and by other drowsiness manipulations during vigilance tests, simulated driving, and on-road driving. However, some cases have been reported wherein PERCLOS was not affected by drowsiness manipulations, such as in moderate drowsiness conditions, in older adults, and during aviation-related tasks. Additionally, although PERCLOS is one of the most sensitive indices for detecting drowsiness-related performance impairments during the psychomotor vigilance test or behavioral maintenance of wakefulness test, no single index is currently available as an optimal marker for detecting drowsiness during driving or other real-world situations. Based on the current published evidence, this narrative review suggests that future studies should focus on: (1) standardization to minimize differences in the definition of PERCLOS between studies; (2) extensive validation using a single device that utilizes PERCLOS-based technology; (3) development and validation of technologies that integrate PERCLOS with other behavioral and/or physiological indices, because PERCLOS alone may not be sufficiently sensitive for detecting drowsiness caused by factors other than falling asleep, such as inattention or distraction; and (4) further validation studies and field trials targeting sleep disorders and trials in real-world environments. Through such studies, PERCLOS-based technology may contribute to preventing drowsiness-related accidents and human error.

Understanding the drowsy driving crash patterns from correspondence regression analysis.

Drowsy driving-related crashes have been a key concern in transportation safety. In Louisiana, 14% (1,758 out of 12,512) of police-reported drowsy driving-related crashes during 2015-2019 resulted in injury (fatal, severe, or moderate). Amid the calls for action against drowsy driving by national agencies, it is of paramount importance to explore the key reportable attributes of drowsy driving behaviors and their potential association with crash severity. METHOD: This study used 5-years (2015-2019) of crash data and utilized the correspondence regression analysis method to identify the key collective associations of attributes in drowsy driving-related crashes and interpretable patterns based on injury levels. RESULTS: Several drowsy driving-related crash patterns were identified through crash clusters - afternoon fatigue crashes by middle-aged female drivers on urban multilane curves, crossover crashes by young drivers on low-speed roadways, crashes by male drivers during dark rainy conditions, pickup truck crashes in manufacturing/industrial areas, late-night crashes in business and residential districts, and heavy truck crashes on elevated curves. Several attributes - scattered residential areas indicating rural areas, multiple passengers, and older drivers (aged more than 65 years) - showed a strong association with fatal and severe injury crashes. PRACTICAL APPLICATIONS: The findings of this study are expected to help researchers, planners, and policymakers in understanding and developing strategic mitigation measures to prevent drowsy driving.

Metabolomics-based Sleepiness Markers for Risk Prevention and Traffic Safety (ME-SMART): a monocentric, controlled, randomized, crossover trial.

BACKGROUND: Too little sleep and the consequences thereof are a heavy burden in modern societies. In contrast to alcohol or illicit drug use, there are no quick roadside or workplace tests for objective biomarkers for sleepiness. We hypothesize that changes in physiological functions (such as sleep-wake regulation) are reflected in changes of endogenous metabolism and should therefore be detectable as a change in metabolic profiles. This study will allow for creating a reliable and objective panel of candidate biomarkers being indicative for sleepiness and its behavioral outcomes. METHODS: This is a monocentric, controlled, randomized, crossover, clinical study to detect potential biomarkers. Each of the anticipated 24 participants will be allocated in randomized order to each of the three study arms (control, sleep restriction, and sleep deprivation). These only differ in the amount of hours slept per night. In the control condition, participants will adhere to a 16/8 h wake/sleep regime. In both sleep restriction and sleep deprivation conditions, participants will accumulate a total sleep deficit of 8 h, achieved by different wake/sleep regimes that simulate real-life scenarios. The primary outcome is changes in the metabolic profile (i.e., metabolome) in oral fluid. Secondary outcome measures will include driving performance, psychomotor vigilance test, d2 Test of Attention, visual attention test, subjective (situational) sleepiness, electroencephalographic changes, behavioral markers of sleepiness, changes in metabolite concentrations in exhaled breath and finger sweat, and correlation of metabolic changes among biological matrices. DISCUSSION: This is the first trial of its kind that investigates complete metabolic profiles combined with performance monitoring in humans over a multi-day period involving different sleep-wake schedules. Hereby, we aim to establish a candidate biomarker panel being indicative for sleepiness and its behavioral outcomes. To date, there are no robust and easily accessible biomarkers for the detection of sleepiness, even though the vast damage on society is well known. Thus, our findings will be of high value for many related disciplines. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT05585515, released on 18.10.2022; Swiss National Clinical Trial Portal SNCTP000005089, registered on 12 August 2022.

Driver drowsiness detection methods using EEG signals: a systematic review.

Electroencephalography (EEG) is a complex signal that may require several years of training, advanced signal processing, and feature extraction methodologies to interpret correctly. Recently, many methods have been used to extract and classify EEG data. This study reviews 62 papers that used EEG signals to detect driver drowsiness, published between January 2018 and 2022. We extract trends and highlight interesting approaches from this large body of literature to inform future research and formulate recommendations. To find relevant papers published in scientific journals, conferences, and electronic preprint repositories, researchers searched major databases covering the domains of science and engineering. For each investigation, many data items about (1) the data, (2) the channels used, (3) the extraction and classification procedure, and (4) the outcomes were extracted. These items were then analyzed one by one to uncover trends. Our analysis reveals that the amount of EEG data used across studies varies. We saw that more than half the studies used simulation driving experimental. About 21% of the studies used support vector machine (SVM), while 19% used convolutional neural networks (CNN). Overall, we can conclude that drowsiness and fatigue impair driving performance, resulting in drivers who are more exposed to risky situations.

Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study.

Purpose: Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the "gold standard brain biophysiological signal" and facial expression digital data. Methods: The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated. Results: The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively). Conclusion: The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.

Drowsy Driving Among Nurses: Potential Impetus to Support Napping.

BACKGROUND: As shift workers, nurses are at an increased risk of drowsy driving because of long hours at work and/or short sleep periods between shifts. METHODS: In this study, a descriptive cross-sectional cohort design was used to examine the prevalence of drowsy driving among nurses. FINDINGS: An electronic survey was sent to 7,217 nurses of which 2,205 (30.5%) completed the survey. Thirty percent (672 nurses) reported having dozed off while driving during their commute to work. In addition, 44.6% (976) of nurses disclosed feeling unsafe or uncomfortable during their commute due to drowsiness or fatigue. CONCLUSION/APPLICATION TO PRACTICE: As shift workers, nurses are subject to drowsy driving and its untoward effects. Healthcare leaders and nurse executives are in a position to evaluate and explore fatigue mitigation strategies such as napping, as this may prove to be beneficial in supporting nurse well-being and public safety.

On the road again: A cross-sectional survey examining work schedules, commuting time, and driving-related outcomes among U.S. oil and gas extraction workers.

BACKGROUND: Oil and gas extraction (OGE) workers in the United States experience high fatality rates, with motor vehicle crashes the leading cause of death. Land-based OGE workers drive frequently to remote and temporary worksites. Limited information is available on factors that may influence crash risk for this workforce. METHODS: A cross-sectional survey of 500 land-based OGE workers examined work schedules and hours, commuting, sleep, employer policies, and their relationship to potentially harmful events while driving. RESULTS: Over 60% of participants worked 12 or more hours per day. The mean daily roundtrip commuting time was 1.82 h. Longer daily commutes, nonstandard work schedules, less sleep on workdays, and lack of employer policies were associated with one or more risky driving-related outcomes. CONCLUSIONS: Implementation and evaluation of OGE employer policies and programs to limit long work hours, reduce long daily commutes, promote sufficient sleep, and reduce drowsy driving among U.S. OGE workers are needed.

The Effects of Drowsiness Detection Technology and Education on Nurses' Beliefs and Attitudes toward Drowsy Driving.

Occupational ApplicationsNurses' perceived health threat from driving drowsy along with their attitude toward an intervention can be targeted to improve nurses' intentions to avoid this dangerous behavior. The evidence presented in this paper suggests that educational interventions that raise awareness of the risks of drowsy driving and its consequences (e.g., fatalities or injuries), as well as peer stories about their experiences, may positively affect nurses' perceived health threat and attitudes toward drowsy driving interventions.

Background Drowsy driving is prevalent among night-shift nurses, yet there is a gap in understanding nurses' beliefs and attitudes that may affect their intention to avoid drowsy driving.Objectives The objectives of the study were twofold: 1) investigate how behavioral constructs such as beliefs and attitudes may affect nurses' intention to avoid drowsy driving; and 2) assess changes in such beliefs and attitudes during a study that evaluated the effectiveness of educational and technological interventions.Methods Three-hundred night-shift nurses were recruited from a large hospital in Texas to participate in a randomized controlled trial. Participants were randomly assigned to three groups: 1) control; 2) educational intervention; and 3) combined educational and technological intervention. The study utilized an integrated model drawing from the constructs of the Theory of Planned Behavior and the Health Belief Model to elicit attitudes, beliefs, and intentions to use in-vehicle drowsiness detection technologies. Each group was surveyed pre- intervention and at post-intervention around 3 months later to assess changes in beliefs and attitudes. Structural equation models and path analysis were used to analyze changes in beliefs.Results Seventy-nine participants completed the pre-intervention questionnaire, and 44 nurses completed the pre- and post-intervention surveys. Intention was predicted primarily by attitude and perceived health threat. Perceived health threat also mediated the relationship between behavioral intention and the influence of subjective norms as well as perceived behavioral control. Participants who received education about drowsy driving had positive changes in beliefs.Conclusions Nurses' perceived health threat from driving drowsy and their attitude toward our intervention were important motivators to avoid drowsy driving. Interventions aiming at raising awareness of the risks associated with drowsy driving may be effective at motivating nurses to avoid drowsy driving.

Drivers' subjective perceptions of the contextual factors influencing fatigue: A qualitative study of suburban bus drivers in Iran.

BACKGROUND: Drivers' drowsiness is a significant issue globally known as a contributing factor to crashes in various transportation operations. Although there is evidence that suburban bus drivers experience drowsy driving, most previous studies are quantitative, which means that drivers experiencing drowsiness have not had the opportunity to explain their direct views and thoughts. OBJECTIVES: This qualitative exploratory study subjectively investigates the contextual factors influencing fatigue among suburban bus drivers. METHODS: Collecting data was conducted through 14 in-depth interviews with suburban bus drivers working in Tehran province's transportation system, Iran. The interview recording was transcribed by the research team and entered into the qualitative data analysis software. Two independent coders with qualitative content analysis and thematic analysis approach analyzed transcripts. RESULTS: Four themes emerged, including human factors (with categories of individual characteristic and lifestyle), vehicle factors (with categories of design and performance), job factors (with categories of task requirement, quantity and quality of sleep, and circadian rhythm,) and environmental factors (with categories of the physical and economic environment). We found a more significant number of codes and categories and thus more contextual factors associated with job factors. The participants emphasized the importance of sleep deprivation, long driving hours, and even time of the day as factors influencing fatigue. CONCLUSIONS: The study results can provide beneficial information for both ergonomists and car manufacturers in developing more accurate fatigue detection models and effective educational and technical interventions to maintain road user's health and reduce road accidents and mortality rates due to drowsiness.

Drowsy driving and teen motor vehicle crashes: Impact of changing school start times.

INTRODUCTION: Motor vehicle crashes (MVC) are the second leading cause of death for adolescents in the United States, with drowsy driving a major contributing factor. Early school start times have been identified as a significant factor that reduces adolescent sleep duration, which in turn contributes to drowsy driving and MVC. This paper examined the longitudinal impact of delaying secondary school start times on self-reported student drowsy driving and teen MVC. METHODS: Secondary school students (10th and 11th grade, 51.7% female, 67.8% White) in the United States completed annual surveys 1 year before and 2 years after implementation of later school start times (70-min delay, n range 1642-2452 per year), reporting frequency of drowsy driving (less than once/week vs. at least once/week). Teen (16-18 years) MVC data from the Colorado Department of Transportation for the 2 years before and 2 years after later start time implementation were compared for Arapahoe County (where start times changed) and neighboring Adams County and Douglas County (where start times did not change). RESULTS: With later start times, there was a significant drop in the percent of students who reported frequent drowsy driving (pre-change: 32.6%, post-change: 21.9%, follow-up: 22.8%). Weekday teen MVC rates went down in Arapahoe County (p = .04) during the school year, while no change or increases in MVC rates were seen in neighboring counties. CONCLUSIONS: Healthy school start times are important for adolescent health and safety, with study findings highlighting the downstream effects of increased sleep duration following a 70-min delay in secondary school start times on adolescent drowsy driving and teen MVC rates.

The EEG correlates and dangerous behavioral consequences of drowsy driving after a single night of mild sleep deprivation.

OBJECTIVE: Here, we investigated the behavioral, cognitive, and electrophysiological impact of mild, acute sleep loss via simultaneously recorded behavioral and electrophysiological measures of vigilance during a "real-world", simulated driving task. METHODS: Participants (N = 34) visited the lab for two testing days where their brain activity and vigilance were simultaneously recorded during a driving simulator task. The driving task lasted approximately 70 mins and consisted of tailgating the lead car at high speed, which braked randomly, requiring participants to react quickly to avoid crashing. The night before testing, participants either slept from 12am-9am (Normally Rested), or 1am-6am (Sleep Restriction). RESULTS: After a single night of mild sleep restriction, sleepiness was increased, participants took longer to brake, missed more braking events, and crashed more often. Brain activity showed more intense alpha burst activity and significant changes in EEG spectral power frequencies related to arousal (e.g., delta, theta, alpha). Importantly, increases in amplitude and number of alpha bursts predicted delays in reaction time when braking. CONCLUSIONS: The findings of this study suggest that a single night of mild sleep loss has significant, negative consequences on driving performance and vigilance, and a clear impact on the physiology of the brain in ways that reflect reduced arousal. SIGNIFICANCE: Understanding neural and cognitive changes associated with sleep loss may lead to important advancements in identifying and preventing potentially dangerous sleep-related lapses in vigilance.

The relationship between burnout, commuting crashes and drowsy driving among hospital health care workers.

BACKGROUND: Burnout and work satisfaction have been shown to be associated with risk of commuting crashes and drowsy driving. Although health care workers (HCWs) were found to have high burnout, no study has yet examined the relationship between burnout and commuting crashes in this occupational group. OBJECTIVE: The objective of this study was to examine the relationships between burnout, commuting crashes and drowsy driving among HCWs. METHODS: A cross-sectional study was conducted among 291 HCWs in a tertiary hospital, using an online survey focusing on burnout subscales, work satisfaction, commuting crashes, and drowsy driving to and from work. RESULTS: One third of the sample population reported commuting crashes that led to physical, mental, and quality-of-life harms in more than half of them. Burnout was not associated with commuting crashes; however, it was associated with increased drowsy driving. Nurses reported on more physical, emotional, and quality-of-life harms, and administrative staff reported on more physical harm. Low work satisfaction was significantly associated with higher severity of reported mental harm (p = 0.01). CONCLUSIONS: Burnout and commuting crashes are more common among physician and nurses, compared to other HCWs. Work satisfaction and sense of personal accomplishment can reduce the negative outcomes of commuting crashes and may contribute to recovery of HCWs after commuting crashes.

An Efficient Approach for Driver Drowsiness Detection at Moderate Drowsiness Level Based on Electroencephalography Signal and Vehicle Dynamics Data.

Background: Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data. Methods: A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data. Results: According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier. Conclusion: According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.

Driver drowsiness detection using facial thermal imaging in a driving simulator.

Driver drowsiness causes fatal driving accidents. Thermal imaging is a suitable drowsiness detection method as it is non-invasive and robust against changes in the ambient light. In this paper, driver drowsiness is detected by measuring the forehead temperature at the region covering the supratrochlear artery and also the cheek temperature. About 30 subjects drove on a highway in a driving simulator in two sessions. A thermal camera was used to monitor the facial temperature pattern. The subjects' drowsiness levels were estimated by three human observers. The forehead and the cheek regions were located and tracked in each frame. The forehead and the cheek skin temperatures were obtained at three levels of drowsiness. The Support Vector Machine, the K-Nearest Neighbor, and the regression tree classifiers were used. From wakefulness to extreme drowsiness, the forehead skin temperature and the absolute cheek-forehead skin temperature gradient decreased by 0.46°C and 0.81°C, respectively. But the cheek skin temperature increased by 0.35°C in two sessions. The gradient difference is on average 50% higher than the forehead or the cheek temperature change alone. The results indicate that drowsiness can be detected with an accuracy of 82%, sensitivity of 85%, specificity of 90%, and precision of 84%. Driver drowsiness can be detected by monitoring changes in the forehead and the cheek temperature signal. Also, the temperature gradient can be used as a more robust and sensitive indicator of drowsiness.

Obstructive sleep apnea screening, diagnosis, and treatment in the transportation industry.

Obstructive sleep apnea (OSA) is a common, identifiable, and treatable disorder with serious health, safety, and financial implications-including sleepiness- related crashes and incidents-in workers who perform safety-sensitive functions in the transportation industry. Up to one-third of crashes of large trucks are attributable to sleepiness, and large truck crashes result in more than 4,000 deaths annually. For each occupant of a truck who is killed, 6 to 7 occupants of other vehicles are killed. Treatment of OSA is cost-effective, lowers crash rates, and improves health and well-being. A large body of scientific evidence and expert consensus supports the identification and treatment of OSA in transportation operators. An Advanced Notice of Proposed Rulemaking regarding the diagnosis and treatment of OSA in commercial truck and rail operators was issued by the Federal Motor Carrier Safety Administration and Federal Railroad Administration, but it was later withdrawn. This reversal of the agencies' position has caused confusion among some, who have questioned whether efforts to identify and treat the disorder are warranted. In response, we urge key stakeholders, including employers, operators, legislators, payers, clinicians, and patients, to engage in a collaborative, patient-centered approach to address the disorder. At a minimum, stakeholders should follow the guidelines issued by a medical review board commissioned by the Federal Motor Carrier Safety Administration in 2016 alone, or in combination with the 2006 criteria, "Sleep Apnea and Commercial Motor Vehicle Operators," a Statement from the Joint Task Force of the American College of Chest Physicians, the American College of Occupational and Environmental Medicine, and the National Sleep Foundation developed by a joint task force. As research in this area continues to evolve, waiting is no longer an option, and the current standard of care demands action to mitigate the burden of serious health and safety risks due to this common, treatable disorder. CITATION: Das AM, Chang JL, Berneking M, Hartenbaum NP, Rosekind M, Gurubhagavatula I. Obstructive sleep apnea screening, diagnosis, and treatment in the transportation industry. J Clin Sleep Med. 2022;18(10):2471-2479.