Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/23107
Title: The impact of heart rate-based drowsiness monitoring on adverse driving events in heavy vehicle drivers under naturalistic conditions.
Authors: Wolkow, Alexander P;Rajaratnam, Shantha M W;Wilkinson, Vanessa E;Shee, Dexter;Baker, Angela;Lillington, Teri;Roest, Peter;Marx, Bernd;Chew, Carmen;Tucker, Andrew;Haque, Shamsul;Schaefer, Alexandre;Howard, Mark E
Affiliation: Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, Victoria, Australia
Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Clayton, VIC, Australia
Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, Australia
Neurobusiness Behavioural Laboratory, Monash University Malaysia, Building 6B, Kuala Lumpur, Malaysia
Shell International, Carel van Bylandtlaan 16, The Hague, the Netherlands
Neurobusiness Behavioural Laboratory, Monash University Malaysia, Building 6B, Kuala Lumpur, Malaysia
Jeffrey Cheah School of Medicine and Health Sciences, Department of Psychology, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor Darul Ehsan, Malaysia
Neurobusiness Behavioural Laboratory, Monash University Malaysia, Building 6B, Kuala Lumpur, Malaysia
Issue Date: 24-Apr-2020
EDate: 2020-04-24
Citation: Sleep health 2020; online first: 24 April
Abstract: This study examined the influence of a wrist-worn heart rate drowsiness detection device on heavy vehicle driver safety and sleep and its ability to predict driving events under naturalistic conditions. Prospective, non-randomized trial. Naturalistic driving in Malaysia. Heavy vehicle drivers in Malaysia were assigned to the Device (n = 25) or Control condition (n = 34). Both conditions were monitored for driving events at work over 4-weeks in Phase 1, and 12-weeks in Phase 2. In Phase 1, the Device condition wore the device operated in the silent mode (i.e., no drowsiness alerts) to examine the accuracy of the device in predicting driving events. In Phase 2, the Device condition wore the device in the active mode to examine if drowsiness alerts from the device influenced the rate of driving events (compared to Phase 1). All participants were monitored for harsh braking and harsh acceleration driving events and self-reported sleep duration and sleepiness daily. There was a significant decrease in the rate of harsh braking events (Rate ratio = 0.48, p < 0.05) and a fall in subjective sleepiness (p < 0.05) when the device was operated in the active mode (compared to the silent mode). The device predicted when no driving events were occurring (specificity=98.81%), but had low accuracy in detecting when a driving event did occur (sensitivity=6.25%). Including drowsiness detection devices in fatigue management programs appears to alter driver behaviour, improving safety despite the modest accuracy. Longer term studies are required to determine if this change is sustained.
URI: http://ahro.austin.org.au/austinjspui/handle/1/23107
DOI: 10.1016/j.sleh.2020.03.005
ORCID: 0000-0002-1807-9189
PubMed URL: 32340910
Type: Journal Article
Appears in Collections:Journal articles

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