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.     

Sleepiness among night-time drivers: relationship to blood alcohol concentration and other factors

OBJECTIVE: The study aimed to explore the distribution and correlates of subjective sleepiness among the general night-time driving population. METHODS: The survey took place in three British Columbia communities in June 2003 between 21:00 hours to 03:00 hours. Sites and vehicles were selected randomly. Surveyors obtained information on several demographic and situational variables including self-assessed degree of sleepiness and self-reported hours asleep and awake, as well as an objective measure of blood alcohol concentration obtained from a hand-held breath-testing device. RESULTS: The total compliance rate among intercepted drivers was 85%. Among the 2335 drivers responding to the questionnaire, 68.4% indicated that they were wide awake, 27.6% were somewhat sleepy, and 4.1% were very sleepy. Logistic regression quantified the independent contributions of the various factors to subjective sleepiness. Male drivers with positive blood alcohol concentrations under 50 mg% were more likely to report feeling sleepy than those with either higher or with zero blood alcohol concentration. Greater relative risk of sleepiness was also associated with being female, being under age 55, and advanced hour of night. Driving with passengers of the same gender was associated with lower reported sleepiness. CONCLUSIONS: A substantial proportion of night-time drivers are driving while sleepy, especially at late night and early morning hours. The combination of alcohol and sleepiness compounds impairment in experimental studies and deserves greater attention in crash risk studies and as a topic for public education and awareness.     

Working conditions and sleepiness while driving among truck drivers

Objective: The objective of this study is to identify the role of working conditions as predictors of sleepiness while driving among truck drivers.

Methods: This was a cross-sectional study carried out among truck drivers who transported grains to Paranaguá Port, Paraná, Brazil. The truck drivers were interviewed and completed a self-administered questionnaire to collect data on sociodemographic and behavioral variables, working conditions, consumption of illicit psychoactive substances, and sleep patterns. Drivers were considered to be sleepy while driving if they reported a medium or high probability of napping while driving at night, during the daytime, or while stopped in traffic. The statistical analysis used logistic regression models progressively adjusted for age, behavioral variables, sleep duration, and other working conditions.

Results: In total, 670 male drivers, with a mean age of 41.9 (±11.1) years, were enrolled. The prevalence of sleepiness while driving was 31.5%. After model adjustments, the following working conditions were associated with sleepiness while driving: Distance from the last shipment of more than 1,000?km (odds ratio [OR]?=?1.54; 95% confidence interval [CI], 1.07–2.23) and a formal labor contract with a productivity-based salary (OR = 2.65; 95% CI, 1.86–3.78). Consumption of illicit psychoactive substances (OR = 1.99; 95% CI, 1.14–3.47) was also associated with sleepiness while driving.

Conclusions: Distance traveled and a formal labor contract with productivity-based earnings were the working conditions associated with sleepiness while driving, regardless of other working or behavioral characteristics, age, consumption of illicit psychoactive substances, and sleep duration.     

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.     

真实条件下长时间作业的驾驶安全性影响研究

为研究长时间单调驾驶对驾驶员疲劳、嗜睡、反应时间和驾驶速度的确切影响,以合作企业3系重卡为操作对象进行了一组真实驾驶条件试验。在自愿的基础上随机地从合作企业物流公司选择12名经验丰富的驾驶员作为试验的驾驶员样本。试验采用主观评分方法记录试验者的嗜睡和疲劳状况,所有数据分析基于统计学软件PASW Statistic 18.0。试验结果表明长时间单调驾驶会导致驾驶员疲劳和嗜睡,而疲劳和嗜睡在一定的程度上会导致驾驶速度的加快,却没有导致驾驶员反应时间的显著变化。在持续驾驶3 h之后驾驶员的疲劳和驾驶绩效发生了明显劣化,故从安全的角度考虑应以2~3 h为界合理安排司机的作业负荷和绩效考评。     

Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue

Introduction: Fatigue is one of the most crucial factors that contribute to a decrease of the operating performance of aircraft pilots and car drivers and, as such, plays a dangerous role in transport safety. To reduce fatigue-related tragedies and to increase the quality of a healthy life, many studies have focused on exploring effective methods and psychophysiological indicators for detecting and monitoring fatigue. However, those fatigue indicators rose many discrepancies among simulator and field studies, due to the vague conceptualism of fatigue, per se, which hinders the development of fatigue monitoring devices. Method: This paper aims to give psychological insight of the existing non-invasive measures for driver and pilot fatigue by differentiating sleepiness and mental fatigue. Such a study helps to improve research results for a wide range of researchers whose interests lie in the development of in-vehicle fatigue detection devices. First, the nature of fatigue for drivers/pilots is elucidated regarding fatigue types and fatigue responses, which reshapes our understanding of the fatigue issue in the transport industry. Secondly, the widely used objective neurophysiological methods, including electroencephalography (EEG), electrooculography (EOG), and electrocardiography (ECG), physical movement-based methods, vehicle-based methods, fitness-for-duty test as well as subjective methods (self-rating scales) are introduced. On the one hand, considering the difference between mental fatigue and sleepiness effects, the links between the objective and subjective indicators and fatigue are thoroughly investigated and reviewed. On the other hand, to better determine fatigue occurrence, a new combination of measures is recommended, as a single measure is not sufficient to yield a convincing benchmark of fatigue. Finally, since video-based techniques of measuring eye metrics offer a promising and practical method for monitoring operator fatigue, the relationship between fatigue and these eye metrics, that include blink-based, pupil-based, and saccade-based features, are also discussed. To realize a pragmatic fatigue detector for operators in the future, this paper concludes with a discussion on the future directions in terms of methodology of conducting operator fatigue research and fatigue analysis by using eye-related parameters.     

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.     

基于EEG频谱特征的驾驶员疲劳监测研究

研究表明疲劳驾驶是引发交通伤亡事故的重要原因之一,因此有必要采取相应的预防措施。脑电是公认的睡眠(疲劳)金指标,因此论文提出了基于脑电频谱特征的驾驶员疲劳预测方法。采用了驾驶模拟实验中记录的三路驾驶员脑电信号,并利用驾驶员自评与专家评定两种方式相结合的方法将驾驶数据分为疲劳和清醒。针对脑电中眼电噪声很强的特点,对记录的脑电进行了自适应滤波消噪处理,结果显示可有效滤除眼电伪迹;然后根据脑电的频域特征比较突出且与疲劳相关的特点,从去噪后的脑电中提取出了的75个频谱特征;最后利用这些频谱特征,采用朴素贝叶斯分类的方法建立了驾驶员疲劳监测模型。实验结果表明,该方法能监测出驾驶员84%的疲劳状态。     

铁路机车乘务员睡眠状况调研

为进一步了解机车乘务员睡眠状况及影响因素,以更好改善职工睡眠状况、为保证行车安全提供依据。采用阿森斯失眠量表及睡眠呼吸障碍问卷等心理测量法,选取某铁路局集团公司864名机车乘务员开展相关调研。结果表明,机车乘务员睡眠问题较严重,有失眠和可疑失眠的机车乘务员合计占调查人员总数的96.5%;睡眠问题主要表现为睡不好和睡觉中打呼噜,分别占调查中有失眠问题机车乘务员总数的86.7%和65.3%;工作环境和工作压力大导致的睡眠问题是机车乘务员失眠的主要原因,体重指数超标与患阻塞性睡眠呼吸暂停风险的机车乘务员占有失眠问题人员总数比例较高。进而提出加大睡眠健康宣传、降低职工体重指数及患阻塞性睡眠呼吸暂停的风险、改善机车乘务员工作环境、提高职工自我心理调节能力等改善职工睡眠质量的建议。     

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.     

Freight transport and non-driving work duties as predictors of falling asleep at the wheel in urban areas of Crete

BACKGROUND: This study investigated the impact of subjective reports of drowsy driving and non-driving duties on the falling asleep responses and road crash involvement of professional drivers in Crete. An attempt was also made to elucidate other driving parameters, such as freight transportation, which could be potential predictors of risky driving, after controlling for lifestyle patterns. METHOD: A sample of 317 professional drivers was studied through personal interviews. The interview questionnaire included items about sleep and fatigue as contributing factors to falling asleep probability and crash risk. In addition, the drivers reported the type of freight they carried in their last trip, as well as practices such as smoking and alcohol consumption. RESULTS: The first logistic regression analysis showed that the most significant predictors of falling asleep at the wheel were transportation of fruits/vegetables and livestock, non-driving hours of work, insufficient hours of sleep, and smoking. The second logistic regression analysis revealed all the previous items as powerful factors of crash probability, including the transportation of express freight and freezer. IMPACT: The findings of the current study are discussed as they pertain to directions for future studies and for the development of fatigue countermeasures.     

驾驶机舱LED灯光色温与人员警觉度关系仿真实验研究

为了探讨驾驶机舱内LED灯光色温与人员警觉度的联系,在A320模拟驾驶舱内设置2 500～2 700 K,3 000～3 200 K,6 000～6 500 K 3种不同色温的LED灯光环境,采用卡斯罗林嗜睡量表（KSS）、闪光融合临界频率仪（CFF）、视觉舒适度评分量表（VCS）,对5名被试人员在不同灯光环境的疲劳程度和视觉舒适度进行实时测量。针对测量结果,采用方差分析及描述性统计的数据分析方法,评价被试人员于不同灯光环境下的疲劳程度、视觉舒适度的差异性及变化情况。研究结果表明:高色温LED灯光环境下,人员的主观嗜睡感受及客观疲劳感受较低,且有着较低的疲劳感受增长率;高色温LED光源下人员的视觉舒适度也较高;增高LED灯光色温可一定程度上提升人员的警觉性。     

A novel auditory working-memory vigilance task for mental fatigue assessment

An auditory working-memory vigilance task (AWVT), which involves higher mental abilities of a person, like working-memory and decision-making, in addition to vigilance, is presented for measuring human mental fatigue in this paper. A 25-h sleep deprivation study, with hourly testing by AWVT (3 min), PalmPVT (5 min) and self-report of sleepiness, is conducted on eight healthy subjects. The trend of mental fatigue level as measured by a specially proposed score, AWVT Fatigue Index (AFI), during the 25-h study shows very similar pattern to those of PalmPVT lapses and self-report sleepiness. AFI even shows closer correspondence to self-report sleepiness than PalmPVT lapses. This suggests that AWVT is able to measure performance decrement due to sleep deprivation, and it can even provide better measurement of mental fatigue than PalmPVT. AWVT shows a learning curve of less than 3 trials indicating that no skill is required in performing AWVT. Furthermore, repeat studies are done on five of the eight subjects. Pearson’s correlation analysis and other statistical exhibits suggest that AWVT has good test–retest reliability and within subject consistency, which are even better than those of PalmPVT. These results suggest that the AWVT can be used as a reliable objective measure of mental fatigue, and it can even track mental fatigue more accurately than PalmPVT in the real world where most tasks require not just a reaction time type response, but also higher mental abilities.     

Drowsy drivers: The effect of light and circadian rhythm on crash occurrence

Drowsiness has been recognized as a pervasive problem for drivers, with effects comparable to alcohol. Alcohol, however, has a clear legal limit for impairment; there are no comparable criteria to suggest sleepiness. Drowsiness has been associated with light and circadian rhythm. To investigate the joint effects of these factors on crash occurrence, along with other factors, single vehicle crashes reported in Ontario (1999–2004) were analyzed. Crashes occurring at four times of day, when light varies and circadian rhythm is low (2–5 a.m. and 2–4 p.m.) and with similar light conditions and higher circadian rhythm (9–11 p.m. and 10 a.m.–12 noon). Logistic regression was used to see how light and other factors are associated with single vehicle crashes occurring at times of low circadian rhythm, when drowsiness is more likely.Initial results indicated many circumstances associated with occurrence at these times: the age and sex of the driver and reported driver condition as well as weather. There is, however, an interaction between light and presumed alertness. In separate analyses for daytime and night time crashes most variables were significant for nighttime crashes but not for daytime events. The effects of alcohol and youth remained. A lack of light may exacerbate the effects of other factors at times of low alertness; this should be further investigated in controlled environments such as sleep laboratories and/or driving simulators.     

生理因素对驾驶人行为及风险感知影响研究综述

为全面认识驾驶人行为及风险感知并分析行为成因,从驾驶人生理因素着手,从驾驶人视力、阻塞性睡眠呼吸暂停综合征和肌肉骨骼疾患3个方面梳理国内外驾驶人行为及风险感知的研究成果与研究不足。研究结果表明:驾驶人的生理疾病对其驾驶能力和危险感知存在显著影响;生理疾病严重程度与异常驾驶行为中的一般性失误、危险性失误行为之间具有显著正相关关系,疾病严重程度越高,出行失误行为的频率越高;之后可从理论和实验两方面着手,针对驾驶人的不同属性进行研究。     

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.     

Physiological signal analysis for fatigue level of experienced and inexperienced drivers

Objective: We studied the changes in driving fatigue levels of experienced and inexperienced drivers at 3 periods of the day: 9:00 a.m.–12:00 p.m., 12:00 p.m.–2:00 p.m., and 4:00 p.m.–6:00 p.m.

Methods: Thirty drivers were involved in 120-min real-car driving, and sleepiness ratings (Stanford Sleepiness Scale, SSS; Hoddes et al. 1973 Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiology. 1973;10:431–436.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), electroencephalogram (EEG) signals, and heart rates (HRs) were recorded. Together with principal component analysis, the relationship between EEG signals and HR was explored and used to determine a comprehensive indicator of driving fatigue. Then the comprehensive indicator was assessed via paired t test.

Results: Experienced and inexperienced drivers behaved significantly differently in terms of subjective fatigue during preliminary trials. At the beginning of trials and after termination, subjective fatigue level was aggravated with prolonged continuous driving. Moreover, we discussed the changing rules of EEG signals and HR and found that with prolonged time, the ratios of δ and β waves significantly declined, whereas that of the θ wave significantly rose. The ratio of (α + θ)/β significantly rose both before trials and after termination, but HR dropped significantly. However, one-factor analysis of variance shows that driving experience significantly affects the θ wave, (α + θ)/β ratio, and HR.

Conclusions: We found that in a monotonous road environment, fatigue symptoms occurred in inexperienced drivers and experienced drivers after about 60 and 80 min of continuous driving, respectively. Therefore, as for drivers with different experiences, restriction on continuous driving time would avoid fatigued driving and thereby eliminate traffic accidents. We find that the comprehensive indicator changes significantly with fatigue level. The integration of different indicators improves the recognition accuracy of different driving fatigue levels.     

生理疲劳和心理疲劳对车辆驾驶的影响对比

针对现有疲劳驾驶预警和干预技术研究鲜有对生理疲劳和心理疲劳进行区分考虑的问题,为对比这两类典型疲劳态对驾驶员车辆驾驶过程的事故倾向影响,分别从性别、年龄和驾龄的角度分3批次共招募90位驾驶员进行状态诱发和驾驶实验。结果表明:尽管生理疲劳和心理疲劳都会如传统研究所述导致各驾驶员的驾驶违规倾向增加和驾驶能力降低,但是二者对于各类别驾驶员的驾驶影响程度和规律存在差异甚至迥异。研究疲劳驾驶相关问题时有必要首先判断驾驶员是生理疲劳还是心理疲劳,这是一个被普遍忽视而又可能影响研究结论准确性和有效性的重要因素。     

Task-induced fatigue and collisions in adult drivers with attention deficit hyperactivity disorder

OBJECTIVE: This study compares collision involvement between adult drivers with attention deficit hyperactivity disorder (ADHD) and control participants in a simulation experiment designed to enhance the effects of fatigue. Because the effects of ADHD include difficulties in maintaining attention, drivers with ADHD were hypothesized to be more susceptible to the effects of fatigue while driving. METHODS: Data are drawn from a validated driving simulation study, portions of which were focused on enhancing the effects of fatigue. The simulator data are supplemented with written questionnaire data. Drivers with ADHD were compared with controls. RESULTS: The self-report data indicated that drivers with ADHD were more likely to report having been involved in an accident within the previous five years. Simulation data showed that time of day of participation in the experiment were significantly related to likelihood of collision, and that these effects were further exacerbated by ADHD status. Participants with ADHD were more likely than controls to be involved in a crash in the simulator regardless of time of day, but the effects were particularly pronounced in the morning, and the rate of increase in accident involvement from the late afternoon into the evening was greater among participants with ADHD. No differences in self-reported sleep patterns or caffeine use were found between participants with ADHD and controls. CONCLUSIONS: The results suggest that drivers with ADHD became fatigued more quickly than controls. Such drivers thus face greater risk of involvement in accidents on highways or open roadways where the visual and task monotony of the environment contribute to greater driver fatigue.