Search

Introduction: Driver drowsiness detection technology that assesses eye blinks is increasingly being used as a safety intervention in the transport industry. It is unclear how alcohol consumption to common legal driving limits impacts upon this technology. The aim of the study was to assess the impact of a blood alcohol content (BAC) of 0.05% and of 0.08% on drowsiness detection technology during simulated driving. Methods: Participants completed a 60‐min driving simulation and sleepiness questionnaire under three conditions: 1–0.00% BAC, 2–0.05% BAC and 3–0.08% BAC. During the driving simulation task participants wore a commercial eye blink drowsiness detection technology (Optalert) with the drowsiness alarms silenced. Results: Twelve participants (3 female) completed all alcohol conditions. Relative to baseline, all eye blink parameters were affected at 0.08% BAC (all p < 0.05), whereas 0.05% BAC only affected the composite eye blink drowsiness measure (the Johns Drowsiness Scale). Conclusions: Alcohol consumption to 0.08% BAC impaired eye blink measures to a level that would be considered a moderate drowsiness risk. Therefore, employers should be aware that drowsiness alerts from these technologies may increase after alcohol consumption. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

Summary: This study aimed to examine the impact of break duration between consecutive shifts, time of break onset, and prior shift duration on total sleep time (TST) between shifts in heavy vehicle drivers (HVDs), and to assess the interaction between break duration and time of break onset. The sleep (actigraphy and sleep diaries) and work shifts (work diaries) of 27 HVDs were monitored during their usual work schedule for up to 9 weeks. Differences in TST between consecutive shifts and days off were assessed. Linear mixed models (followed by pairwise comparisons) assessed whether break duration, prior shift duration, time of break onset, and the interaction between break duration and break onset were related to TST between shifts. Investigators found TST between consecutive shifts (mean [SD] 6.38 [1.38] h) was significantly less than on days off (mean [SD] 7.63 [1.93] h; p < 0.001). Breaks starting between 12:01 and 8:00 a.m. led to shorter sleep (p < 0.05) compared to breaks starting between 4:01 and 8:00 p.m. Break durations up to 7, 9, and 11 h (Australian and European minimum break durations) resulted in a mean (SD) of 4.76 (1.06), 5.66 (0.77), and 6.41 (1.06) h of sleep, respectively. The impact of shift duration prior to the break and the interaction between break duration and time of break were not significant. HVDs' sleep between workdays is influenced independently by break duration and time of break onset. This naturalistic study provides evidence that current break regulations prevent sufficient sleep duration in this industry. Work regulations should evaluate appropriate break durations and break onset times to allow longer sleep opportunities for HVDs. [ABSTRACT FROM AUTHOR]