Clare Anderson, Anna W T Cai, Michael L Lee, William J Horrey, Yulan Liang, Conor S O'Brien, Charles A Czeisler, Mark E Howard
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Physiological drowsiness was defined by eye closures (Johns drowsiness scores) and EEG-based microsleep events.</p><p><strong>Results: </strong>All subjective ratings increased post night-shift (p < 0.001). No severe drive events occurred without noticeable symptoms beforehand. All subjective sleepiness ratings, and specific symptoms, predicted a severe (emergency brake) driving event occurring in the next 15 minutes (OR: 1.76-2.4, AUC > 0.81, p < 0.009), except \"head dropping down\". Karolinska Sleepiness Scale (KSS), ocular symptoms, difficulty keeping to center of the road, and nodding off to sleep, were associated with a lane deviation in the next 15 minutes (OR: 1.17-1.24, p<0.029), although accuracy was only \"fair\" (AUC 0.59-0.65). All sleepiness ratings predicted severe ocular-based drowsiness (OR: 1.30-2.81, p < 0.001), with very good-to-excellent accuracy (AUC > 0.8), while moderate ocular-based drowsiness was predicted with fair-to-good accuracy (AUC > 0.62). KSS, likelihood of falling asleep, ocular symptoms, and \"nodding off\" predicted microsleep events, with fair-to-good accuracy (AUC 0.65-0.73).</p><p><strong>Conclusions: </strong>Drivers are aware of sleepiness, and many self-reported sleepiness symptoms predicted subsequent driving impairment/physiological drowsiness. Drivers should self-assess a wide range of sleepiness symptoms and stop driving when these occur to reduce the escalating risk of road crashes due to drowsiness.</p>","PeriodicalId":49514,"journal":{"name":"Sleep","volume":" ","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636256/pdf/","citationCount":"0","resultStr":"{\"title\":\"Feeling sleepy? stop driving-awareness of fall asleep crashes.\",\"authors\":\"Clare Anderson, Anna W T Cai, Michael L Lee, William J Horrey, Yulan Liang, Conor S O'Brien, Charles A Czeisler, Mark E Howard\",\"doi\":\"10.1093/sleep/zsad136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study objectives: </strong>To examine whether drivers are aware of sleepiness and associated symptoms, and how subjective reports predict driving impairment and physiological drowsiness.</p><p><strong>Methods: </strong>Sixteen shift workers (19-65 years; 9 women) drove an instrumented vehicle for 2 hours on a closed-loop track after a night of sleep and a night of work. Subjective sleepiness/symptoms were rated every 15 minutes. Severe and moderate driving impairment was defined by emergency brake maneuvers and lane deviations, respectively. Physiological drowsiness was defined by eye closures (Johns drowsiness scores) and EEG-based microsleep events.</p><p><strong>Results: </strong>All subjective ratings increased post night-shift (p < 0.001). No severe drive events occurred without noticeable symptoms beforehand. All subjective sleepiness ratings, and specific symptoms, predicted a severe (emergency brake) driving event occurring in the next 15 minutes (OR: 1.76-2.4, AUC > 0.81, p < 0.009), except \\\"head dropping down\\\". Karolinska Sleepiness Scale (KSS), ocular symptoms, difficulty keeping to center of the road, and nodding off to sleep, were associated with a lane deviation in the next 15 minutes (OR: 1.17-1.24, p<0.029), although accuracy was only \\\"fair\\\" (AUC 0.59-0.65). All sleepiness ratings predicted severe ocular-based drowsiness (OR: 1.30-2.81, p < 0.001), with very good-to-excellent accuracy (AUC > 0.8), while moderate ocular-based drowsiness was predicted with fair-to-good accuracy (AUC > 0.62). KSS, likelihood of falling asleep, ocular symptoms, and \\\"nodding off\\\" predicted microsleep events, with fair-to-good accuracy (AUC 0.65-0.73).</p><p><strong>Conclusions: </strong>Drivers are aware of sleepiness, and many self-reported sleepiness symptoms predicted subsequent driving impairment/physiological drowsiness. 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引用次数: 0
摘要
研究目的:检查驾驶员是否意识到困倦和相关症状,以及主观报告如何预测驾驶障碍和生理性困倦。方法:16名轮班工人(19 ~ 65岁);(9名女性)在一晚的睡眠和一晚的工作后,在一个闭环轨道上驾驶一辆仪表车辆2小时。主观嗜睡/症状每15分钟评分一次。严重和中度驾驶损伤分别由紧急制动机动和车道偏差定义。生理性嗜睡通过闭眼(约翰嗜睡评分)和基于脑电图的微睡眠事件来定义。结果:所有主观评分在夜班后增加(p < 0.001)。没有发生严重的驱动事件,事先没有明显的症状。所有主观嗜睡评分和特定症状都预测在接下来的15分钟内发生严重(紧急制动)驾驶事件(OR: 1.76-2.4, AUC > 0.81, p < 0.009),但“头下降”除外。卡罗林斯卡嗜睡量表(KSS)、眼部症状、难以保持道路中心和打瞌睡与接下来15分钟的车道偏离相关(OR: 1.17-1.24, p 0.8),而中度眼部嗜睡预测具有相当好的准确性(AUC > 0.62)。KSS、入睡可能性、眼部症状和“打盹”预测微睡眠事件具有相当好的准确性(AUC为0.65-0.73)。结论:司机意识到困倦,许多自述的困倦症状预示着随后的驾驶障碍/生理性困倦。司机应该自我评估各种各样的困倦症状,并在出现这些症状时停止驾驶,以减少因困倦而导致的道路交通事故的风险。
Feeling sleepy? stop driving-awareness of fall asleep crashes.
Study objectives: To examine whether drivers are aware of sleepiness and associated symptoms, and how subjective reports predict driving impairment and physiological drowsiness.
Methods: Sixteen shift workers (19-65 years; 9 women) drove an instrumented vehicle for 2 hours on a closed-loop track after a night of sleep and a night of work. Subjective sleepiness/symptoms were rated every 15 minutes. Severe and moderate driving impairment was defined by emergency brake maneuvers and lane deviations, respectively. Physiological drowsiness was defined by eye closures (Johns drowsiness scores) and EEG-based microsleep events.
Results: All subjective ratings increased post night-shift (p < 0.001). No severe drive events occurred without noticeable symptoms beforehand. All subjective sleepiness ratings, and specific symptoms, predicted a severe (emergency brake) driving event occurring in the next 15 minutes (OR: 1.76-2.4, AUC > 0.81, p < 0.009), except "head dropping down". Karolinska Sleepiness Scale (KSS), ocular symptoms, difficulty keeping to center of the road, and nodding off to sleep, were associated with a lane deviation in the next 15 minutes (OR: 1.17-1.24, p<0.029), although accuracy was only "fair" (AUC 0.59-0.65). All sleepiness ratings predicted severe ocular-based drowsiness (OR: 1.30-2.81, p < 0.001), with very good-to-excellent accuracy (AUC > 0.8), while moderate ocular-based drowsiness was predicted with fair-to-good accuracy (AUC > 0.62). KSS, likelihood of falling asleep, ocular symptoms, and "nodding off" predicted microsleep events, with fair-to-good accuracy (AUC 0.65-0.73).
Conclusions: Drivers are aware of sleepiness, and many self-reported sleepiness symptoms predicted subsequent driving impairment/physiological drowsiness. Drivers should self-assess a wide range of sleepiness symptoms and stop driving when these occur to reduce the escalating risk of road crashes due to drowsiness.
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