Outputs Ready for Further Research and Development

Led by sleep clinician-researchers from the Woolcock Institute of Medical Research, the SleepFix® mobile application was developed for consumers with poor sleep. The software delivers brief-behavioural therapy targeting insomnia and insomnia-like problems, and is fully integrated with a wearable device (optional).

Algorithm developed to measure and predict alertness state, based on measures captured by ocular monitoring devices, for the purposes of determining fitness-to-drive or performing work duties. The algorithm is based on eye movements that can measure impairment before, during, and after driving. Initial prototype development and validation has commenced for an ambulatory driver operator eye-tracking device for roadside (fatigue management) assessment.

Monash University &The University of Sydney led the development of an individual shift work management tool on the back of extensive user testing and sleep-wake management model development. The tool provides personalised and adaptive sleep health advice and feedback for individuals with variable sleep times imposed by shift work, and is based on a sleep-wake management algorithm that draws on biomathematical modelling to provide adaptive recommendations for individual users (sleep/wake times, countermeasure usage, light exposure and avoidance).

A biological marker of sleep loss, whether that be a blood, saliva or breath, was the overarching goal of this biomarker discovery program. Our vision is to develop a biological test for sleep loss, akin to a breathalyser for fatigue to be deployed across occupational settings, including road safety.

The Metabolomics Biomarker Program was developed to determine if blood-based metabolites could detect how long an individual had been awake, and their associated alertness state. Led by Monash University, in collaboration with Metabolomics Australia, the world’s first blood-based marker for sleep loss was developed. This biomarker had an extremely high accuracy for detecting sleep loss (>99%).

The two-year study evaluated alertness monitoring technology and the impacts of work shifts on driver alertness. It analysed shift start time, the number of consecutive shifts, shift length, shift rotation, rest breaks and their likely impact on driver drowsiness and fatigue. The research involved a study of more than 300 heavy vehicle driver shifts both in-vehicle and in a laboratory, as well as 150,000 samples of retrospective data, and was undertaken in collaboration with the Australian Government, Transport for NSW, Austin Health, Monash University, the Institute for Breathing and Sleep and the heavy vehicle industry.

It was found that slow eye and eyelid movements, longer blink duration and prolonged eye closure are reliable predictors of drowsiness and fatigue. It also confirmed the scientific link between alertness and drowsiness patterns associated with specific work shifts for heavy vehicle driving.