Abstract
Purpose
While 10–20% of heavy vehicle crashes (HVDs) are drowsiness-related, the contributions of subsequent shifts to chronic drowsiness in HVDs is largely unknown. Eye-blink parameters indicate driver drowsiness reliably. This study examined the association of consecutive shifts and real-time drowsiness in HVDs.
Methods
Habitual sleep-wake of HVDs (all males, aged 49.5 ± 8 years) was monitored objectively (Philips Actiwatch, N=15) for 5 weeks (5.75± 1.4 hours). Johns Drowsiness Score (JDS, a composite eye-blink parameter in one-min intervals) was monitored for 4 weeks in HVDs (N=14) using an infrared oculography (Optalert, Melbourne, Australia) device. We assessed the association of drowsiness events (JDS equal or larger than 2.6) with consecutive shift types via mixed linear regression models.
Results
Eigth consecutive shifts increased drowsiness by 1.06 times compared to 2 shifts (8.37 events/h vs 6.77 events/h, P= 0.03). Consecutive shift sequences included afternoons (9%), mornings (29%), nights (5%), mixed rotating shifts (28%), forward-rotating shifts (11%) and backward-rotating shifts (12%). Drowsiness event rates were 1.23 times greater during night consecutive shifts relative to afternoon shifts (8.37 events/h vs 6.67 events/h, P= 0.03). Backward-rotating shifts (morning-night-evening- afternoon) elevated daytime drowsiness between 10 am and 3 pm by 1.55 times (10.01 events/h vs 6.47 events/h, P= 0.016).
Conclusions
Regardless of the number of consecutive shifts, sequential night shifts increase real-time drowsiness in HVDs, with backward rotating shifts resulting in higher rates of drowsiness events during daytime. The interaction of schedule features should inform the work scheduling of HVDs to reduce the risk of drowsiness.
Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm
