Sleep disorders and risk of traffic crashes: A naturalistic driving study analysis

被引:18
作者
Bharadwaj, Nipjyoti [1 ]
Edara, Praveen [2 ]
Sun, Carlos [2 ]
机构
[1] Fed Highway Adm, Turner Fairbank Highway Res Ctr, 6300 Georgetown Pike, Mclean, VA 22101 USA
[2] Univ Missouri, Dept Civil & Environm Engn, C2640 Lafferre Hall, Columbia, MO 65211 USA
关键词
Sleep disorder; Naturalistic driving study; SHRP; 2; Crash risk; Random-effect logistic regression; POPULATION; ACCIDENTS; EVENTS; APNEA;
D O I
10.1016/j.ssci.2021.105295
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Sleep disorders and various acute and chronic medical conditions affect the quality and quantity of an individual's sleep causing excessive day time sleepiness. This study examined the relationship between clinical sleep disorders (i.e., shift work sleep disorder (SWSD), sleep apnea and insomnia) and crashes using naturalistic driving study data. Using data from 1892 events that occurred in six different US states, random effect binary logistic regression models were developed to estimate crash risk associated with SWSD, sleep apnea and insomnia. The study found that sleep disorders elevate crash risk in drivers. Drivers with SWSD exhibited the highest crash risk (OR = 2.96). Older drivers with SWSD were found to be more vulnerable and prone to crashes than other drivers. The analysis showed that drivers with sleep apnea and insomnia were 29% and 33% more likely, respectively, to be involved in a crash or a near-crash. For drivers with insomnia, factors such as age and sleep quality can elevate the associated crash risk. Decrease in sleep quality increases the likelihood of crash or near crash. The study also found that individuals with a sleep disorder are 29% more likely to be inattentive while driving as compared to drivers without a sleep disorder. The OR values found in this study were lower than those reported in studies using self-reported data and driver simulator experiments. In addition to medical diagnosis and treatment, transportation agencies could also consider alleviation strategies such as providing sufficient rest areas on highways and using technology such as in-vehicle and personal technologies that monitor drowsy in-vehicle behavior to increase driver attention. The use of transportation modes such as taxi, transportation network companies (e.g. Uber), or public transit, can be an alternative for shift workers returning home after completing their shift.
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页数:7
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