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Ocular indicators of sleepiness: implications for drowsy driving prevention in shift workers

posted on 16.02.2017, 05:26 by Ftouni, Suzanne
In Australia, approximately 20% of motor vehicle collisions result from fatigue or sleepiness-related driving, making it one of the most common causes of preventable motor vehicle crashes (MVC). The shift working population is found to be overrepresented in sleepiness-related vehicle crashes due to the effect of circadian misalignment and sleep loss, resulting in excessive sleepiness and neurobehavioural performance decline during waking hours. It is therefore essential that effective countermeasures to the deleterious effects of shift work are developed. A significant barrier to the development of effective countermeasures is the lack of real-time and practical measures of alertness. A number of methods have been developed to objectively assess sleepiness or alertness levels while driving, including infrared oculography. This method utilises eye blink characteristics to quantify physiological sleepiness, as eye blinks associated with the onset of sleep display unique properties that reflect sleep- and wake-related CNS changes. Oculometrics have also shown potential in assessing sleepiness that is associated with attentional performance impairment. The dissertation examined, i) the utility of oculometrics as objective indicators of sleepiness in relation to validated measures of performance, vigilance, and subjective sleepiness, in healthy male volunteers under a highly controlled laboratory environment; ii) the response of ocular measures of sleepiness and attentional performance in night shift workers who were exposed to a simulated laboratory night shift during their circadian day, compared to those tested in their circadian night; and iii) the association between oculometrics and unsafe driving events during commutes to and from night shift work. The first study demonstrated the influence of homeostatic and circadian variation on oculometrics with sleep deprivation. The temporal dynamics of oculometrics were strongly phase-locked with validated measures of performance, vigilance, and subjective sleepiness, and established their potential to detect sleepiness-related attentional lapses. The second study in this dissertation showed the utility of oculometrics to measure sleepiness that is associated with circadian misalignment in night shift workers. The study found that shift workers who did not biologically adapt to the night shift displayed higher levels of sleepiness as measured by oculometrics, as well as increases in objective vigilance (EEG delta power frequency band), subjective sleepiness, and neurobehavioural performance, compared to those who showed biological adaptation. The study is the first to demonstrate the sleepiness-related ocular responses to circadian misalignment in shift work. Lastly, the final study in the dissertation demonstrated the potential of oculometrics to detect sleepiness-related driving performance impairment following night shift work. The study found that ocular measures of sleepiness were associated with increased risks of adverse (hazardous, inattentive, and sleep-related) driving events in shift workers commuting to and from night shift work. With the demand for a 24 hour society comes the likely increase in adverse occupational health and safety outcomes. This dissertation has demonstrated the accuracy and potential utility of oculometrics (specifically JDS, PosAVR, BTD, %TEC) to assess sleepiness in healthy participants, and the shift working population. It has further demonstrated the application of oculometrics in identifying performance impairment as a result of sleepiness and circadian misalignment. The dissertation presents the scientific basis for the future utilisation of oculometrics in field in the prevention of sleepiness-related MVCs, and as a measure in intervention trials, particularly in the high-risk shift working population.


Principal supervisor

Shanthakumar M Wilson Rajaratnam

Year of Award


Department, School or Centre

Psychological sciences

Campus location



Doctor of Philosophy

Degree Type



Faculty of Medicine Nursing and Health Sciences