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Volume 15 No. 09
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Accepted Papers





Commentary
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An Objective Measure of Drowsy Driving: Are We There Yet?

Dorrie Rizzo, PhD1,2; Marc Baltzan, MDCM, FRCPC, FAASM2,3,4
1Lady Davis Institute for Medical Research (Jewish General Hospital), Montreal, Canada; 2McGill University, Montreal, Canada; 3Mount-Sinai Hospital, Montreal, Canada; 4OSR Medical, Montreal, Canada

ABSTRACT

Rizzo D, Baltzan M. An objective measure of drowsy driving: are we there yet? J Clin Sleep Med. 2019;15(9):1191–1192.


Policies regarding driving with OSA are currently being decided all around the world, but clear guidelines are limited, and few are evidence based.1 To date, we have no accurate metric for establishing level of driver risk. What is obvious today is that specific guidelines to address these challenges are needed, but fitness-to-drive recommendations and ethical aspect of this directive is of concern. The difficulty also lies in translating research findings into fitness-to-drive recommendations.

Driving a motor vehicle is a complex task that relies on driver’s cognitive abilities, such as attention, reaction time and vigilance. Dr Soleimanloo and colleagues' meticulous work examining accuracy of eye-closure parameters for detecting driver drowsiness, reminds us that drivers often combine sleep deprivation and driving, which is associated with injury and death on the road.2 Recent studies, which included examining brain activity, have shown that sleep-deprived drivers are more likely to experience lasting or momentary losses of alertness behind the wheel, making it difficult to maintain the lateral position of the vehicle on the road.3-6

Dr Soleimanloo and colleagues working in Queensland, Australia, also contribute in this issue of the Journal of Clinical Sleep Medicine significant findings that address the challenge of a suitable objective measure of drowsiness in healthy sleep-deprived drivers.7 While evaluating oculomotor changes during naturalistic drives and identifying those ocular parameters that best identify drowsiness-related driving impairments, they found that eye-blink parameters are sensitive to acute sleep deprivation and prolonged driving performance task. This latter “time-on-task” deterioration of driver performance is an important observation, the implications of which deserve greater research attention. More specifically, this study shows that impaired daytime drowsiness induces lateral deviations during driving—circumstances frequently linked to sleep-related accidents.

Nevertheless, the authors could not demonstrate a clear-cut threshold beyond which driver error would soon occur. The authors did not measure fatigue as separate from sleepiness, which might have both theoretical and practical implications for assessment of (driver state) cause and (driving performance) effect in a driving task that requires sustained attention for long periods of time.8 Dr Soleimanloo and colleagues' study makes an important contribution in the practical objective measurement of drowsiness and the associated implication for reducing road accidents linked to sleep-deprivation. Further research is clearly required to determine a metric using this and other technology to inform the driver that it is time to stop driving and rest.

This study provides very interesting research and the authors propose integrating alert monitoring technology into future industry frameworks, but how far are we from a practical in-vehicle ‘sleep-o-meter’ that guides the driver when it is time to stop? Like the speedometer tells you it is time to slow down, this would inform the driver that a period of rest (or sleep) would be recommended. Important studies have previously shown that a great inter subject variability is observed between sleep-deprived drivers, likely explained by differences in waking systems.9,10 Advancement of technology is certainly advantageous; however, this monitor would have to be personalized to each driver likely including specifications on driver’s circadian rhythm. This advancement presents its technical challenges. We expect in the future that several warning systems may need to be in play simultaneously to best warn drivers, such as the alerting systems currently offered by some automobile manufacturers when the vehicle is driving out of lane, one of a whole suite of advanced driver assistance systems (ADAS).11

This study certainly contributes to the improvement of driver safety. What remains to “get there” is the identification of characteristics associated with higher sensitivity to sleep-deprivation. For now, campaigns centered on informing drivers of the effects of sleep deprivation and promoting breaks while driving long distances still remain essential, both as part of media campaigns as well as the curriculum of the education programs for new drivers.

DISCLOSURE STATEMENT

The authors report no conflicts of interest.

REFERENCES

1 

Rizzo D, Libman E, Creti L, et al. Determinants of policy decisions for non-commercial drivers with OSA: an integrative review. Sleep Med Rev. 2018;37:130–137. [PubMed]

2 

Sagaspe P, Taillard J, Bayon V, et al. Sleepiness, near‐misses and driving accidents among a representative population of French drivers. J Sleep Res. 2010;19(4):578–584. [PubMed]

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Bermudez EB, Klerman EB, Czeisler CA, Cohen DA, Wyatt JK, Phillips AJ. Prediction of vigilant attention and cognitive performance using self-reported alertness, circadian phase, hours since awakening, and accumulated sleep loss. PLoS One. 2016;11(3):e0151770[PubMed Central][PubMed]

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Åkerstedt T, Peters B, Anund A, Kecklund G. Impaired alertness and performance driving home from the night shift: a driving simulator study. J Sleep Res. 2005;14(1):17–20. [PubMed]

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Aksan N, Dawson J, Tippin J, Lee JD, Rizzo M. Effects of fatigue on real-world driving in diseased and control participants. Poster presented at: 8th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design; June 22-25, 2015; Salt Lake City, Utah.

6 

Verster JC, Roth T. Standard operation procedures for conducting the on-the-road driving test, and measurement of the standard deviation of lateral position (SDLP). Int J Gen Med. 2011;4:359[PubMed Central][PubMed]

7 

Shekari Soleimanloo S, Wilkinson VE, Cori JM, et al. Eye-blink parameters detect on-road track-driving impairment following severe sleep deprivation. J Clin Sleep Med. 2019;15(9):1271–1284

8 

Philip P, Sagaspe P, Moore N, et al. Fatigue, sleep restriction and driving performance. Accid Anal Prev. 2005;37(3):473–478. [PubMed]

9 

Aksan N, Schall M, Anderson S, Dawson J, Tippin J, Rizzo M. Can intermittent video sampling capture individual differences in naturalistic driving? Poster presented at: 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design; June 17-20, 2013; Bolton, New York.

10 

Saper CB, Fuller PM. Wake–sleep circuitry: an overview. Curr Opin Neurobiol. 2017;44:186–192. [PubMed Central][PubMed]

11 

Hamid UZA, Pushkin K, Zamzuri H, Gueraiche D, Rahman MAA. Current collision mitigation technologies for advanced driver assistance systems–a survey. PERINTIS eJournal. 2016;6(2):77–90