Sleep-related crash characteristics: Implications for applying a fatigue definition to crash reports

Filtness, Ashleigh J.; Armstrong, Kerry; Watson, Angela; Smith, Simon S.

doi:10.1016/j.aap.2015.11.024

Cited by 26 publications

(6 citation statements)

References 14 publications

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“…Sleepiness while driving is a leading cause of car crashes. 18 The findings of the current study indicated that sleepiness while driving was fairly common among private car drivers in Oman, with just over onequarter of the drivers reporting that they experienced daytime sleepiness while driving at least once a month. In addition, a significantly greater proportion of male drivers experienced daytime sleepiness while driving compared to females.…”

Section: Discussionmentioning

confidence: 61%

Daytime Sleepiness Among Young Adult Omani Car Drivers

Abri

Al-Adawi

Al-Abri

et al. 2018

Sultan Qaboos Univ Med J

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Section: Discussionmentioning

confidence: 61%

Daytime Sleepiness Among Young Adult Omani Car Drivers

Abri

Al-Adawi

Al-Abri

et al. 2018

Sultan Qaboos Univ Med J

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“…Drowsiness during driving is considered a major threat that endangers the life of drivers, Studies have repeatedly shown that traffic accidents due to drowsy driving has become a serious problem in the society (Chen et al 2015;Filtness et al 2017;He et al 2017). In many countries, drowsy driving is identified as a contributing factor in a significant proportion of road transport accidents and the number of collisions and fatalities caused by this risky behavior has remained high over the past decades (Tefft 2012;Vanlaar et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness

et al. 2018

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A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states. The filtered EEG signals are then decomposed into multiple frequency bands by wavelet packet transform. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase lag index (PLI). Based on this, PLI-weighted networks are subsequently calculated, from which minimum spanning trees are constructed-a graph method that corrects for comparison bias. Statistical analyses are performed on graph-derived metrics as well as on the PLI connectivity values. The major finding is that significant differences in the delta frequency band for three graph metrics and in the theta frequency band for five graph metrics suggesting network integration and communication between network nodes are increased from alertness to drowsiness. Together, our findings also suggest a more line-like configuration in alert states and a more star-like topology in drowsy states. Collectively, our findings point to a more proficient configuration in drowsy state for lower frequency bands. Graph metrics relate to the intrinsic organization of functional brain networks, and these graph metrics may provide additional insights on driver drowsiness detection for reducing and preventing traffic accidents and further understanding the neural mechanisms of driver drowsiness.

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“…Consistent with previous research, the findings concur that the most prevalent collisions occur when there are heightened opportunities for conflicts between road users, including busy urban roads [ 45 ], and during peak travel times when there are higher levels of traffic congestion [ 46 ]. Inclement weather conditions also play a role in collision risk, particularly precipitation [ 46 ], winter storms and icy roads [ 47 ] and impaired visibility conditions [ 48 ]. However, inclement conditions did not differentiate well between the collision classes in this study.…”

Section: Discussionmentioning

confidence: 99%

Driver, Collision and Meteorological Characteristics of Motor Vehicle Collisions among Road Trauma Survivors

Giummarra

Lee

et al. 2021

IJERPH

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Road trauma remains a significant public health problem. We aimed to identify sub-groups of motor vehicle collisions in Victoria, Australia, and the association between collision characteristics and outcomes up to 24 months post-injury. Data were extracted from the Victorian State Trauma Registry for injured drivers aged ≥16 years, from 2010 to 2016, with a compensation claim who survived ≥12 months post-injury. People with intentional or severe head injury were excluded, resulting in 2735 cases. Latent class analysis was used to identify collision classes for driver fault and blood alcohol concentration (BAC), day and time of collision, weather conditions, single vs. multi-vehicle and regional vs. metropolitan injury location. Five classes were identified: (1) daytime multi-vehicle collisions, no other at fault; (2) daytime single-vehicle predominantly weekday collisions; (3) evening single-vehicle collisions, no other at fault, 36% with BAC ≥ 0.05; (4) sunrise or sunset weekday collisions; and (5) dusk and evening multi-vehicle in metropolitan areas with BAC < 0.05. Mixed linear and logistic regression analyses examined associations between collision class and return to work, health (EQ-5D-3L summary score) and independent function Glasgow Outcome Scale - Extended at 6, 12 and 24 months. After adjusting for demographic, health and injury characteristics, collision class was not associated with outcomes. Rather, risk of poor outcomes was associated with age, sex and socioeconomic disadvantage, education, pre-injury health and injury severity. People at risk of poor recovery may be identified from factors available during the hospital admission and may benefit from clinical assessment and targeted referrals and treatments.

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Sleep-related crash characteristics: Implications for applying a fatigue definition to crash reports

Cited by 26 publications

References 14 publications

Daytime Sleepiness Among Young Adult Omani Car Drivers

Daytime Sleepiness Among Young Adult Omani Car Drivers

Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness

Driver, Collision and Meteorological Characteristics of Motor Vehicle Collisions among Road Trauma Survivors

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