Cori Jennifer M, Wilkinson Vanessa E, Soleimanloo Shamsi Shekari, Westlake Justine, Stevens Bronwyn, Rajaratnam Shantha M W, Howard Mark E
Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, Australia.
Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, Victoria, Australia.
J Sleep Res. 2023 Jun;32(3):e13785. doi: 10.1111/jsr.13785. Epub 2022 Dec 7.
Drowsy driving is a major cause of fatal and serious injury motor vehicle accidents. The inability objectively to assess drowsiness has hindered the assessment of fitness to drive and the development of drowsy driving regulations. This study evaluated whether spontaneous eye blink parameters measured briefly pre- and post-drive could be used to detect drowsy driving impairment. Twelve healthy participants (6 female) drove an instrumented vehicle for 2 h on a closed-loop track during a rested (8-10 h awake) and an extended wake condition (32-34 h awake). Pre- and post-drive, the participants completed a 5 min eye blink task, a psychomotor vigilance task (PVT), and the Karolinska sleepiness scale (KSS). Whole drive impairment was defined as >3.5 lane departures per hour. Severe end of drive impairment was defined as ≥2 lane departures in the last 15 min. The pre-drive % of time with eyes closed best predicted the whole drive impairment (area under the curve [AUC] 0.87). KSS had similar prediction ability (AUC 0.85), while PVT reaction time (AUC 0.72) was less accurate. The composite eye blink parameter, the Johns drowsiness scale was the best retrospective detector of severe end of drive impairment (AUC 0.99). The PVT reaction time (AUC 0.92) and the KSS (AUC 0.93) were less accurate. Eye blink parameters detected drowsy driving impairment with an accuracy that was similar to, or marginally better than, PVT and KSS. As eye blink measures are simple to measure, are objective and have high accuracy, they present an ideal option for the assessment of fitness for duty and roadside drowsiness.
疲劳驾驶是机动车致命和重伤事故的主要原因。无法客观评估疲劳阻碍了对驾驶适宜性的评估以及疲劳驾驶法规的制定。本研究评估了在驾驶前和驾驶后短暂测量的自发眨眼参数是否可用于检测疲劳驾驶损伤。12名健康参与者（6名女性）在休息状态（清醒8 - 10小时）和长时间清醒状态（清醒32 - 34小时）下，在闭环轨道上驾驶一辆仪器化车辆2小时。在驾驶前和驾驶后，参与者完成了一项5分钟的眨眼任务、一项心理运动警觉任务（PVT）和卡罗林斯卡嗜睡量表（KSS）。整个驾驶过程中的损伤定义为每小时>3.5次车道偏离。驾驶结束时的严重损伤定义为在最后15分钟内≥2次车道偏离。驾驶前闭眼时间百分比最能预测整个驾驶过程中的损伤（曲线下面积[AUC]为0.87）。KSS具有相似的预测能力（AUC为0.85），而PVT反应时间（AUC为0.72）的准确性较低。综合眨眼参数约翰斯嗜睡量表是驾驶结束时严重损伤的最佳回顾性检测指标（AUC为0.99）。PVT反应时间（AUC为0.92）和KSS（AUC为0.93）的准确性较低。眨眼参数检测疲劳驾驶损伤的准确性与PVT和KSS相似，或略优于它们。由于眨眼测量简单、客观且准确性高，它们是评估执勤适宜性和路边疲劳的理想选择。
