Sleep Status and the Risk Factor of Drowsy-Related Accidents in Commercial Motor Vehicle Drivers

Abstract

Background and ObjectiveaaThe purpose of this study was to describe prevalence of sleep problems including obstructive sleep apnea (OSA), insomnia, and sleep insufficiency in commercial motor vehicle (CMV) drivers, and to quantify risk of drowsy-related accidents in CMV drivers. MethodsaaCMV drivers were invited to participate in this study. Participants were asked to complete a questionnaire about sleep habits, sleep disorders, work environments, and experience of drowsy-related accidents. A home sleep test (Watch-PAT 200) was also undertaken to detect OSA. resultsaaAmong 110 participants, 89 with eligible data of questionnaires and home sleep tests were included in the analysis. 91.0% of participants had OSA with peripheral arterial tonometry respiratory distress index (pRDI) ≥ 5/h and 23.6% had moderate to severe OSA with pRDI ≥ 15/h. 55.1% perceived that their sleep is insufficient. 52.3% of participants experienced drowsy driving. Drowsy driving-related motor vehicle crashes (MVCs) and near miss MVCs were reported by 9.6% and 37.9% of participants, respectively. Multiple regression analysis showed that increased risk of drowsy driving was associated with perceived sleep insufficiency [odds ratio (OR) = 3.48, 95% confidence interval (CI) 1.26-9.64]. Increased risk of drowsy driving-related near miss MVC was associated with perceived sleep insufficiency (OR = 3.06, 95% CI 1.11-8.44) and continuous driving for more than 4 hours (OR = 3.10, 95% CI 1.00-9.58). ConclusionsaaOSA and perceived sleep insufficiency are prevalent in CMV drivers. Perceived sleep insufficiency and longer continuous driving hours increased risk of drowsy-related accidents.

How Tired is Too Tired to Drive? A Systematic Review Assessing the Use of Prior Sleep Duration to Detect Driving Impairment

“… 51 A separate study included crash and near miss rates (though these were not necessarily attributed to fatigue/falling asleep at the wheel). 63 In this study, participants who habitually obtained less than 6 h sleep/night were far more likely to experience a crash in the previous year (24%) than participants who regularly obtained ≥7 h (0%) or between 6 h and 7 h (8%) of sleep per night. Similarly, approximately double the number of individuals who obtained less than 6 h of sleep per night had experienced a near miss in the previous year, when compared with those who obtained 7 h or more sleep each night.…”

“… Odds ratio of falling asleep at wheel at least once on the trail with ≤5 h sleep compared to ≥6 h sleep, 2.12 (95% CI, 1.51–2.98). Hutchens et al (2008) 60 Young adults (n = 506) Age: 17.8 years (range 14–22) Sex: 236f, 270m Cross-sectional Habitual sleep Self-reported sleep duration >8 h vs <8 h Self-reported involvement in crash/es at any time <8 h sleep group 1.284 times more likely to be involved in a crash than >8 h sleep group OR = 1.28, 95% CI = 1.03–1.61, p = 0.029) Ikeda et al (2021) 61 Short haul commercial truck drivers (n = 110) Age = 49.5 ± 11.4 years Sex: 110m Cross-sectional Habitual sleep Self-reported sleep duration <5 h, 5 h, 6 h, ≥7 h Overspeeding Sudden start and acceleration Sudden deceleration Sudden braking Overspeeding (adjusted OR) <5 h: 1.49 (0.47–4.71) 5 h: 0.83 (0.30–2.30) 6 h: 1.0 (reference) ≥7 h: 0.64 (0.19–2.16) Sudden start and acceleration (adjusted OR) <5 h: 1.75 (0.14–22.12) 5 h: 0.38 (0.07–2.13) 6 h: 1.0 (reference) ≥7 h: 0.27 (0.04–1.69) Sudden deceleration (adjusted OR) <5 h: 1.17 (0.31–4.34) 5 h: 0.39 (0.13–1.16) 6 h: 1.0 (reference) ≥7 h: 0.69 (0.19–2.51) Sudden braking (adjusted OR) <5 h: 2.87 (0.87–9.45) 5 h: 1.08 (0.38–3.03) 6 h: 1.0 (reference) ≥7 h: 1.05 (0.31–3.53) Johnson et al (2017) 62 Medical students (n = 307) Age: 26.4 years (range 21–42) Sex: 169f, 111m, 27 did not respond Cross-sectional Habitual sleep Self-reported average weeknight sleep in the previous academic quarter <6 h vs 6–6.9 h vs >7 h sleep/night Self-reported ‘nodding off’ at the wheel in the past year Self-reported ‘nodding off’ while driving in the past year (p=0.01) <6 h (n = 20; 22%) 6–6.9 h (n = 85; 14.5%) >7 h (n = 201; 5.5%) Kim et al (2018) 63 Com...…”

“…[39][40][41][42][43][44][45][46]48,49 One sample of participants was heavy vehicle drivers who had recently crashed (with matched heavy vehicle driver controls who had not crashed) (n = 1). 47 Cross-sectional studies were made up of members of the general driving population (n = 10), [51][52][53][55][56][57]69,[71][72][73] drivers who had recently been in a crash (n = 1), 50 heavy vehicle or commercial drivers (n = 8), 61,[63][64][65][66][67][68]70 nurses (n = 1), 54 young adults (n = 1), 60 university students (n = 2), 62,74 drivers in regional Australia (Queensland) (n = 1), 58 and drill sergeants (n = 1). 59…”

“…A number of these studies were performed in large samples of the general population, [51][52][53][55][56][57]69,71,72 with some performed in occupation-based samples, such as nurses 54 and heavy vehicle drivers. 61,[63][64][65][66][67][68]70 Typically, these studies were based on either interview or survey data and therefore relied on individual recall.…”