Sleepy driving and risk of obstructive sleep apnea among truck drivers in Saudi Arabia

Objective: Truck drivers represent a group at a particularly higher risk of motor vehicle accidents (MVAs). Sleepy driving and obstructive sleep apnea (OSA) among truck drivers are major risk factors for MVAs. No study has assessed the prevalence of sleepy driving and risk of OSA among truck drivers in Saudi Arabia. Therefore, this study aimed to assess sleepy driving and risk of OSA among these truck drivers.

Methods: This study included 338 male truck drivers working in Saudi Arabia. A validated questionnaire regarding sleepy driving and OSA was used. The questionnaire included sociodemographic assessment, the Epworth Sleepiness Scale (ESS), the Berlin Questionnaire (BQ), and driving-related items.

Results: The drivers had a mean age of 42.9?±?9.7 years. The majority (94.7%) drove more than 5?h a day. A history of MVAs during the last 6 months was reported by 6.5%. Approximately 95% of the participants reported that they had accidentally fallen asleep at least once while driving over the past 6 months, and 49.7% stated that this had happened more than 5 times during the last 6 months. Based on the BQ score, a high risk of OSA was detected in 29% of the drivers. “Not getting good-quality sleep” (odds ratio [OR]?=?2.89; 95% confidence interval [CI], 1.08–7.75; P = .014) and driving experience from 6 to 10 years (OR = 3.37; 95% CI, 1.28–8.91; P = .034) were the only independent predictors of MVAs in the past 6 months.

Conclusions: Sleepy driving and a high risk of OSA was prevalent among the study population of male truck drivers in Saudi Arabia. Not getting good-quality sleep and driving experience from 6 to 10 years contributes to the accident risk among these truck drivers.     

The on-road experiences and awareness of sleepiness in a sample of Australian highway drivers: A roadside driver sleepiness study

Objective: Driver sleepiness contributes substantially to road crash incidents. Simulator and on-road studies clearly reveal an impairing effect from sleepiness on driving ability. However, the degree to which drivers appreciate the dangerousness of driving while sleepy is somewhat unclear. This study sought to determine drivers' on-road experiences of sleepiness, their prior sleep habits, and personal awareness of the signs of sleepiness.

Methods: Participants were a random selection of 92 drivers traveling on a major highway in the state of Queensland, Australia, who were stopped by police as part of routine drink driving operations. Participants completed a brief questionnaire that included demographic information, sleepy driving experiences (signs of sleepiness and on-road experiences of sleepiness), and prior sleep habits. A modified version of the Karolinska Sleepiness Scale (KSS) was used to assess subjective sleepiness in the 15 min prior to being stopped by police.

Results: Participants' ratings of subjective sleepiness were quite low, with 90% reporting being alert to extremely alert on the KSS. Participants were reasonably aware of the signs of sleepiness, with many signs of sleepiness associated with on-road experiences of sleepiness. Additionally, the number of hours spent driving was positively correlated with the drivers' level of sleep debt.

Conclusions: The results suggest that participants had moderate experiences of driving while sleepy and many were aware of the signs of sleepiness. The relationship between driving long distances and increased sleep debt is a concern for road safety. Increased education regarding the dangers of sleepy driving seems warranted.     

Roadside survey on alcohol and drug use among drivers in the Arctic county of Finnmark (Norway)

Objective: The objective of this study was to determine the prevalence of alcohol and potentially impairing drugs among the general driving population in Finnmark and to compare the prevalence among Norwegian, Russian, and other foreign drivers by analyzing samples of oral fluid.

Methods: In collaboration with local police, drivers were selected for a voluntary and anonymous study using a multistage cluster sampling procedure (selection of roads, time intervals, and drivers within each interval) from September 2014 to October 2015. Age, gender, citizenship, time, and geographical site were recorded. Samples of oral fluid were collected using the Quantisal device. The samples were analyzed for alcohol with an enzymatic method and for 12 illicit drugs and 16 medicinal drugs and some metabolites using ultra-high-performance liquid chromatography with tandem mass spectrometry detection.

Results: A total of 3,228 drivers were asked to participate in the study. The refusal rate was 6.2%. Of the 3,027 participants in the study, 111 (3.7%) were Russian and 204 (6.7%) had citizenship other than Norwegian or Russian. The total prevalence of psychoactive substances was 4.3%. Alcohol was detected in 0.3%, psychoactive medicinal drugs in 2.5%, and illicit drugs in 1.6% of the samples. The most commonly found substances were the sleeping agent zopiclone (1.1%), tetrahydrocannabinol (THC; 1.1%), and the analgesic agent codeine (0.6%). Illicit drugs were detected significantly more often in samples from drivers of citizenship other than Norwegian or Russian. The prevalence of alcohol was somewhat higher among Russian drivers but not statistically significant. There were large differences between age groups and genders concerning illicit drugs and psychoactive medicinal drugs; illicit drugs were more frequently in samples from young male drivers, whereas psychoactive medicinal drugs were more frequently in samples from elderly female drivers.

Conclusion: The total prevalence of alcohol and drugs among the general driving population in Finnmark was low and similar to previous Norwegian roadside surveys. Illicit drugs were detected significantly more often in samples from drivers with citizenship other than Russian and Norwegian and among young male drivers.     

Trends in the use of psychoactive substances by truck drivers in São Paulo State,Brazil: A time-series cross sectional roadside survey (2009–2016)

Objective: Truck drivers represent a group that is susceptible to the use of stimulant substances to reduce the symptoms of fatigue, which may be caused by a stressful and exhausting work environment. The use of psychoactive substances may increase the risk for involvement in road traffic crashes. Previous studies have demonstrated that amphetamine, cocaine, and cannabis are the 3 main drugs used by Brazilian truck drivers. We studied the prevalence of amphetamine, benzoylecgonine (indicating use of cocaine), and Δ-9-tetrahydrocannabinol-9-carboxylic acid (THC-COOH; indicating use of cannabis) in urine samples from truck drivers in the state of São Paulo, Brazil, using the same methodology during 8 years (2009–2016).

Methods: Samples were collected during a health program supported by the Federal Highway Police. Toxicological analyses were performed using immunoassays and gas chromatography–mass spectrometry.

Results: The total prevalence of illicit drugs was 7.8%. Benzoylecgonine was the most prevalent substance (3.6%), followed by amphetamine (3.4%) and THC-COOH (1.6%). We found the highest drug prevalence in 2010 (11.3%) and the lowest in 2011 (6.1%). We could detect a slight change in the pattern of stimulant use: until 2010, amphetamine was the most prevalent substance; however, in 2011 benzoylecgonine became the most frequently detected substance. This lasted until 2015, probably due to changes in Brazilian legislation regarding appetite suppressants; the most common one is metabolized to amphetamine.

Conclusion: These data show that the use of psychoactive substances by truck drivers in Brazil did not decrease during the study period. This reinforces the need for further preventive measures to reduce drug use among drivers, which could lead to a decrease in traffic crashes in Brazil.     

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

Objective: Slow eyelid closure is recognized as an indicator of sleepiness in sleep-deprived individuals, although automated ocular devices are not well validated. This study aimed to determine whether changes in eyelid closure are evident following acute sleep deprivation as assessed by an automated device and how ocular parameters relate to performance after sleep deprivation.

Methods: Twelve healthy professional drivers (45.58 ± 10.93 years) completed 2 randomized sessions: After a normal night of sleep and after 24 h of total sleep deprivation. Slow eye closure (PERCLOS) was measured while drivers performed a simulated driving task.

Results: Following sleep deprivation, drivers displayed significantly more eyelid closure (P < .05), greater variation in lane position (P < .01) and more attentional lapses (P < .05) compared to after normal sleep. PERCLOS was moderately associated with variability in both vigilance performance (r = 0.68, P < .05) and variation in lane position on the driving task (r = 0.61, P < .05).

Conclusions: Automated ocular measurement appears to be an effective means of detecting impairment due to sleep loss in the laboratory.     

Adaptive response criteria in road hazard detection among older drivers

Objectives: The majority of existing investigations on attention, aging, and driving have focused on the negative impacts of age-related declines in attention on hazard detection and driver performance. However, driving skills and behavioral compensation may accommodate for the negative effects that age-related attentional decline places on driving performance. In this study, we examined an important question that had been largely neglected in the literature linking attention, aging, and driving: can top-down factors such as behavioral compensation, specifically adaptive response criteria, accommodate the negative impacts from age-related attention declines on hazard detection during driving?

Methods: In the experiment, we used the Drive Aware Task, a task combining the driving context with well-controlled laboratory procedures measuring attention. We compared younger (n = 16, age 21–30) and older (n = 21, age 65–79) drivers on their attentional processing of hazards in driving scenes, indexed by percentage of correct responses and reaction time of hazard detection, as well as sensitivity and response criteria using signal detection analysis.

Results: Older drivers, in general, were less accurate and slower on the task than younger drivers. However, results from this experiment revealed that older, but not younger, drivers adapted their response criteria when the traffic condition changed in the driving scenes. When there was more traffic in the driving scene, older drivers became more liberal in their responses, meaning that they were more likely to report that a driving hazard was detected.

Conclusions: Older drivers adopt compensatory strategies for hazard detection during driving. Our findings showed that, in the driving context, even at an older age our attentional functions are still adaptive according to environmental conditions. This leads to considerations on potential training methods to promote adaptive strategies that may help older drivers maintain performance in road hazard detection.     

Crash risk perception of sleepy driving and its comparisons with drink driving and speeding: Which behavior is perceived as the riskiest?

Objective: Driver sleepiness is a major crash risk factor but may be underrecognized as a risky driving behavior. Sleepy driving is usually rated as less of a road safety issue than more well-known risky driving behaviors, such as drink driving and speeding. The objective of this study was to compare perception of crash risk of sleepy driving, drink driving, and speeding.

Methods: Three hundred Australian drivers completed a questionnaire that assessed crash risk perceptions for sleepy driving, drink driving, and speeding. Additionally, the participants' perceptions of crash risk were assessed for 5 different contextual scenarios that included different levels of sleepiness (low, high), driving duration (short, long), and time of day/circadian influences (afternoon, nighttime) of driving.

Results: The analysis confirmed that sleepy driving was considered a risky driving behavior but not as risky as high levels of speeding (P < .05). Yet, the risk of crashing at 4 a.m. was considered as equally risky as low levels of speeding (10 km over the limit). The comparisons of the contextual scenarios revealed driving scenarios that would arguably be perceived as quite risky because time of day/circadian influences were not reported as high risk.

Conclusions: The results suggest a lack of awareness or appreciation of circadian rhythm functioning, particularly the descending phase of circadian rhythm that promotes increased sleepiness in the afternoon and during the early hours of the morning. Yet, the results suggested an appreciation of the danger associated with long-distance driving and driver sleepiness. Further efforts are required to improve the community's awareness of the impairing effects from sleepiness and, in particular, knowledge regarding the human circadian rhythm and the increased sleep propensity during the circadian nadir.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.     

Can real-life driving ability be predicted by the Maintenance of Wakefulness Test?

Objective: Drowsy driving is a profound road safety issue. In patients with excessive daytime sleepiness (EDS), the Maintenance of Wakefulness Test (MWT) is commonly used to evaluate driving ability. However, there is little evidence that MWT predicts driving performance, and several sleep latency cutoffs have been suggested.

Methods: Based on a retrospective chart analysis of patients with an MWT and a driving ability assessment between January 2006 and November 2014, we identified 63 studies in 60 patients. The driving ability assessment judged the patients as qualified or disqualified for commercial driving. MWT latencies to 3?s of alpha activity, 3?s of drowsiness (microsleep), and sleep onset were compared between qualified and disqualified patients and their validity to identify driving qualification was evaluated.

Results: Disqualified patients had shorter alpha, microsleep, and sleep latencies, but the latency distributions were widely overlapping. MWT accuracy to predict driving performance was poor: two thirds of short sleep latencies were false positives. Adding information from alpha and microsleep latencies added little extra accuracy.

Conclusions: MWT results correlate poorly with driving performance in a 2-h test irrespective of sleep latency cutoff or added alpha/microsleep latency data. Better diagnostic tools are needed to evaluate driving performance in patients with EDS.     

Driving concerns among older adults: Associations with driving skill,behaviors, and experiences

Objectives: The purpose of this investigation was to determine what older adults find most concerning about driving as they age and how these concerns are related to driving skill, behaviors, and experiences.

Methods: In partnership with the Maryland Motor Vehicle Administration, a sample of 751 older adults ages 65 and older completed an online survey between October 2017 and May 2018. A content analysis was used to code open-ended responses about driver concerns, and multivariate logistic regression models were used to analyze the associations between driving concerns and driving skill, behavior, and experiences.

Results: Eighty-four percent of participants reported at least one driving concern, with 44% concerned about others’ driving, 34% concerned about their own driving, and 24% concerned about driving conditions. The most frequently mentioned driving concerns were other drivers in general, driving at night, visual ability and awareness, and other drivers being aggressive or reckless. Being concerned with their own driving was significantly associated with decreased perceived driving skill and increased odds of experiencing negative driving experiences in the past year. Being concerned about others’ driving was associated with increased odds of wearing a seat belt (adjusted odds ratio [AOR]?=?2.67; 95% confidence interval [CI], 1.02, 7.00), having high perceived driving skills in emergency situations (AOR = 1.56; 95% CI, 1.14, 2.12), and getting in a near crash or collision in the past year (AOR = 1.50; 95% CI, 1.04, 2.18).

Conclusions: Older adult drivers are frequently concerned about their own driving as well as the driving of others. Implications for future research and health practice are discussed.     

Aggressive driving behaviour in young drivers (aged 16 through 25) involved in fatal crashes

IntroductionWe wished to determine the extent to which number of passengers, driver age, and sex were associated with aggressive driving actions (ADAs) in young drivers involved in a fatal crash.MethodsWe used U.S. fatal-crash data from Fatality Analysis Reporting System (FARS), 1991 –2008. Proxy measures of aggressive driving included ADA presence and speed differential (posted speed limit minus estimated travel speed). We examined the odds of an ADA and speed differential in young drivers (aged 16 to 25) by passenger status.ResultsCompared to driving alone young drivers (aged 16) had increased odds of an ADA between 14% (OR: 1.14; 95% CI: 1.07; 1.22) and 95% (OR: 1.95; 95% CI: 1.40; 2.74) when accompanied by one and five passengers, respectively. Further, carrying a higher number of passengers was a stronger predictor of speeding in younger drivers.ConclusionsThis study supports the use of graduated licensing approaches. Specifically, developing interventions to reduce aggressive driving appear imperative.Impact on IndustryWhile the results of our study support the use of graduated licensing approaches there is room for improvement. Our study indicates that tackling impaired driving is not sufficient to drastically reduce aggressive driving among the youngest drivers. Further research on young drivers is required to understand the influence of peers and the role of gender on driving behavior. Strategies to reduce aggressive driving behaviors among the youngest drivers may not only prevent crashes during their early driving careers but may also translate into a reduced crash risk over their lifetime.     

The use and safety of medications known to affect driving in Jordan: A cross-sectional study

Objectives: The aim of this study was to estimate the main driving-impairing medications used by drivers in Jordan, the reported frequency of medication side effects, the frequency of motor vehicle crashes (MVCs) while using driving-impairing medicines, as well as factors associated with MVCs.

Methods: A cross-sectional study involving 1,049 individuals (age 18–75 years) who are actively driving vehicles and taking at least one medication known to affect driving (anxiolytics, antidepressants, hypnotics, antiepileptics, opioids, sedating antihistamines, hypoglycemic agents, antihypertensives, central nervous system [CNS] stimulants, and herbals with CNS-related effects) was conducted in Amman, Jordan, over a period of 8 months (September 2013–May 2014) using a structured validated questionnaire.

Results: Sixty-three percent of participants noticed a link between a medicine taken and feeling sleepy and 57% stated that they experience at least one adverse effect other than sleepiness from their medication. About 22% of the participants reported having a MVC while on medication. Multiple logistic regression analysis showed that among the participants who reported having a crash while taking a driving-impairing medication, the odds ratios were significantly higher for the use of inhalant substance (odds ratio [OR] = 2.787, P = .014), having chronic conditions (OR = 1.869, P = .001), and use of antiepileptic medications (OR = 2.348, P = .008) and significantly lower for the use of antihypertensives (OR = 0.533, P = .008).

Conclusion: The study results show high prevalence of adverse effects of medications with potential for driving impairment, including involvement in MVCs. Our findings highlight the types of patient-related and medication-related factors associated with MVCs in Jordan, such as inhalant use, presence of chronic conditions, and use of antiepileptics.     

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.

Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.

Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between ?0.0011 and 0.056 for a car driver and between ?0.00086 and 0.067 for a truck driver.

Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.     

Can variations in visual behavior measures be good predictors of driver sleepiness? A real driving test study

Objective: The primary purpose of this study was to examine the association between variations in visual behavior measures and subjective sleepiness levels across age groups over time to determine a quantitative method of measuring drivers' sleepiness levels.

Method: A total of 128 volunteer drivers in 4 age groups were asked to finish 2-, 3-, and 4-h continuous driving tasks on expressways, during which the driver's fixation, saccade, and blink measures were recorded by an eye-tracking system and the subjective sleepiness level was measured through the Stanford Sleepiness Scale. Two-way repeated measures analysis of variance was then used to examine the change in visual behavior measures across age groups over time and compare the interactive effects of these 2 factors on the dependent visual measures.

Results: Drivers' visual behavior measures and subjective sleepiness levels vary significantly over time but not across age groups. A statistically significant interaction between age group and driving duration was found in drivers' pupil diameter, deviation of search angle, saccade amplitude, blink frequency, blink duration, and closure duration. Additionally, change in a driver's subjective sleepiness level is positively or negatively associated with variation in visual behavior measures, and such relationships can be expressed in regression models for different period of driving duration.

Conclusions: Driving duration affects drivers' sleepiness significantly, so the amount of continuous driving time should be strictly controlled. Moreover, driving sleepiness can be quantified through the change rate of drivers' visual behavior measures to alert drivers of sleepiness risk and to encourage rest periods. These results provide insight into potential strategies for reducing and preventing traffic accidents and injuries.     

Medical investigation after a motorcoach strikes the rear of a tractor semitrailer parked on the highway; 13 fatal

Abstract

Objective: The objective of this study was to examine the medical conditions of 2 commercial drivers and the effects of physical barriers to occupant egress in a crash involving a tractor trailer and a motorcoach in order to assess and identify the factors that caused the crash and had a significant effect on occupant extrication.

Methods: Physical evidence from the scene, video evidence, commercial driver information, phone records, toxicology findings, autopsy results, and personal medical information were reviewed.

Results: On October 23, 2016, at 5:16 a.m., a motorcoach carrying a driver and 42 passengers struck the rear of a stopped semitrailer occupied by its driver in the center-right lane of Interstate 10 at highway speed outside Palm Springs, California. The motorcoach driver and 12 passengers died; 11 passengers were seriously injured.

All traffic had been stopped on I-10 early that morning to allow electrical lines to be strung over the highway. Security camera footage showed that the truck arrived at the end of a traffic queue 2?min before traffic flow resumed. Physical evidence indicated that the truck’s parking brake was still engaged at the time of the collision about 2?min later. The truck driver had a body mass index (BMI) between 45.6 and 50?kg/m², which placed him at very high risk of moderate to severe obstructive sleep apnea; he also inaccurately recalled that he had been stopped for 20–25?min and had placed the vehicle in gear just before the collision.

The motorcoach driver was on the return leg of an overnight trip to a casino. Based on his phone records, known driving time, and security camera footage, at the time of the collision he had had 4?h of sleep opportunity in the preceding 35?h. There was no evidence that the motorcoach driver attempted any evasive action before the collision. In addition, postmortem testing revealed a hemoglobin A1C of 11.4%, indicating poorly controlled diabetes; this was apparently undiagnosed prior to the crash.

The motorcoach was equipped with a single loading door at the front of the vehicle; it was rendered inoperable by the collision. Emergency egress was initially carried out through the emergency exit windows, but they repeatedly swung shut, impeding passengers’ efforts to exit. Emergency responders eventually cut open the bus wall to create a larger means of egress. Overall, it took almost 3?h to extricate the occupants from the vehicle.

Conclusions: The National Transportation Safety Board (NTSB) determined that the probable cause of the accident was the truck driver’s falling asleep, most likely due to undiagnosed moderate-to-severe obstructive sleep apnea, and the motorcoach driver’s failure to identify the stopped truck as a hazard requiring evasive action, most likely as the result of fatigue. Additional easy-to-use emergency exits would have decreased the time to extricate the occupants.     

Self-regulation of driving speed among distracted drivers: An application of driver behavioral adaptation theory

Objective: The adaptive behavior of mobile phone–distracted drivers has been a topic of much discussion in the recent literature. Both simulator and naturalistic studies suggest that distracted drivers generally select lower driving speeds; however, speed adaptation is not observed among all drivers, and the mechanisms of speed selection are not well understood. The aim of this research was to apply a driver behavioral adaptation model to investigate the speed adaptation of mobile phone–distracted drivers.

Methods: The speed selection behavior of drivers was observed in 3 phone conditions including baseline (no conversation) and hands-free and handheld phone conversations in a high-fidelity driving simulator. Speed adaptation in each phone condition was modeled as a function of secondary task demand and self-reported personal/psychological characteristics with a system of seemingly unrelated equations (SURE) accounting for potential correlations due to repeated measures experiment design.

Results: Speed adaptation is similar between hands-free and handheld phone conditions, but the predictors of speed adaptation vary across the phone conditions. Though perceived workload of secondary task demand, self-efficacy, attitude toward safety, and driver demographics were significant predictors of speed adaptation in the handheld condition, drivers' familiarity with the hands-free interface, attitude toward safety, and sensation seeking were significant predictors in the hands-free condition. Drivers who reported more positive safety attitudes selected lower driving speeds while using phones.

Conclusion: This research confirmed that behavioral adaptation models are suitable for explaining speed adaptation of mobile phone distracted drivers, and future research could be focused on further theoretical refinement.     

Road traffic crashes among farm vehicle drivers in southern China: A cross-sectional survey

Objective: The objective of this study was to identify the prevalence and potential risk factors of farm vehicle–related road traffic crashes among farm vehicle drivers in southern China.

Methods: A cross-sectional study was used to interview 1,422 farm vehicle drivers in southern China. Farm vehicle–related road traffic crashes that occurred from December 2013 to November 2014 were investigated. Data on farm vehicle–related road traffic crashes and related factors were collected by face-to-face interviews.

Results: The prevalence of farm vehicle–related road traffic crashes among the investigated drivers was 7.2%. Farm vehicle–related road traffic crashes were significantly associated with self-reported vision problem (adjusted odds ratio [AOR] = 6.48, 95% confidence interval [CI], 3.86–10.87), self-reported sleep disorders (AOR = 10.03, 95% CI, 6.28–15.99), self-reported stress (AOR = 20.47, 95% CI, 9.96–42.08), reported history of crashes (AOR = 5.40, 95% CI, 3.47–8.42), reported history of drunk driving (AOR = 5.07, 95% CI, 2.97–8.65), and reported history of fatigued driving (AOR = 5.72, 95% CI, 3.73–8.78). The number of road traffic crashes was highest in the daytime and during harvest season. In over 96% of farm vehicle–related road traffic crashes, drivers were believed to be responsible for the crash. Major crash-causing factors included improper driving, careless driving, violating of traffic signals or signs, and being in the wrong lane.

Conclusion: Findings of this study suggest that farm vehicle–related road traffic crashes have become a burgeoning public health problem in China. Programs need to be developed to prevent farm vehicle–related road traffic crashes in this emerging country.     

Methiopropamine in blood samples from drivers suspected of being under the influence of drugs

Objectives: Methiopropamine (MPA; 1-(thiophen-2-yl)-2-methylaminopropane) belongs to the new psychoactive substances (NPS) that have emerged on the drug market in recent years. MPA appeared in 2011 and is an analogue of methamphetamine, sold as, for example, "Slush Eric"and "Blow." It is reported to have effects similar to those of methamphetamine, but the toxicity in humans is not known. Three fatal cases involving MPA have been reported. One analytical confirmed intoxication case has been published, and this supports the symptoms described by the users. The prevalence of recreational use of MPA is unknown, and no studies have reported the prevalence in driving under the influence of drug (DUID) cases.

Methods: We investigated the frequency of MPA in DUID cases received at our institute during a 12-week period and report the analytical method using an ultraperformance liquid chromatography.tandem mass spectrometry for quantification of MPA in whole blood. The analytical findings were compared to the results from a clinical test of impairment performed by a physician shortly after the driving episode. The samples were analyzed for 42 different psychoactive substances.

Results: MPA was detected in 10 DUID cases (0.8% of the cases), only from male drivers. Other drugs were detected concomitantly in all the cases. Two of the cases were traffic accidents.

Conclusions: Our study shows that MPA is found in DUID cases and reveals that NPS are used among drivers and also proven in blood from drivers involved in traffic accidents. More studies are requested regarding the pharmacological and toxicological effects of MPA and other NPS. This is the first article that describes a method for analyzing and quantifying MPA in whole blood samples.