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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]

• Ocular parameters (Johns Drowsiness Scores, JDS) could detect real-time drowsiness in HVDs on the road. • The risk of drowsiness in HVDs was measured by the occurrence of drowsiness events (JDS ≥ 2.6 events/hour) and assessed by logistic regression models. • Night-time drive, shift start time in the early afternoon, prolonged work hours, short break durations (7–9 h) and short sleep time (<6 h) increased the risk of drowsiness after controlling for the effects of other work schedule metrics. • The magnitude of driver drowsiness was measured using hourly rates of drowsiness events (JDS ≥ 2.6 events/hour) and assessed by mixed linear regression models. • Night-time drive, shift start time in the early morning, prolonged work hours, break times (<9 h), and shift of 8–15 h increased the hourly rate of drowsiness events after adjusting for the effects of other work schedules metrics. • Night-time drives (9 pm- 2 am), 18 to 21 h into the shift, or breaks shorter than 7 h elevated drowsiness event rates substantially relative to "alert state (4.9 events/hour) after controlling for the effects of other work schedule metrics. While drowsiness contributes to 20% of heavy vehicle crashes, the impact of work schedules on heavy vehicle driver (HVD) drowsiness is unclear. This study explored the impact of work schedules on drowsiness (measured by infrared oculography) in HVDs. Work and drowsiness monitoring (Optalert, Australia) of ten HVDs, aged 37–62 years collected nearly 2430 h of work and 1068 h of oculography data during four weeks of naturalistic drives. Drowsiness events were defined as a John's Drowsiness Scores ≥ 2.6. Nine HVDs slept for 5.82 ± 1.37 h during five weeks of actigraphy. Association of driving schedule characteristics and drivers' continuous eye-blink parameters were observed using logistic and mixed linear regression analyses. Combination of time of day (10 pm- 2 am), shift start time (2 pm-3 pm), hours into the shift (16–21 h), break duration (7–9 h), and sleep time (<6 h) increased the likelihood of drowsiness events when controlling for other covariates. A combination of night times (6 pm to 2 am), 18 to 21 h into the shift, shift start times (6 am to 7 am), shifts lengths (8 to15 h) and break times (<7 to 9 h) increased the hourly rate of drowsiness significantly when adjusting for other covariates. The combination of prolonged work hours, night-time drives, early-morning shift starts and short breaks increase drowsiness rates in HVDs. Large datasets are needed to examine the interplay between sleep time, consecutive shifts and shift order and type with this combination. [ABSTRACT FROM AUTHOR]