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New research aims to reduce road accidents caused by driver sleepiness
04 September 2023
The Road Safety Trust is funding a new research project which aims to reduce road traffic accidents caused by driver sleepiness.
The three-year project called SleepiEST, which is being carried out by Nottingham Trent University (NTU) and the National Police Wellbeing Service, will monitor police shift-workers who drive either to commute or as part of their work.
Data will be collected in three phases:
- A nationwide survey of serving officers and police employees will be conducted to collect information on sleep, fatigue, shift patterns and driving behaviour.
- Information will them be collected from officers in a two-week diary study and online vigilance assessment.
- This will then be combined with other data such as telematics.
The overall findings will then be used to estimate and predict driver fatigue levels, in order to create a fatigue management tool which will be made publicly available online. Whilst the research will focus on police employees, the tool is intended to support any shift worker.
NTU School of Social Sciences psychologist and sleep expert Dr Pilkington-Cheney said, “Due to various shift patterns and circadian related factors, sleepiness is prevalent within shift working populations, and has the potential to be particularly dangerous when safety critical tasks are involved.
“Our research aims to explore the risk of sleepiness when driving, both during shifts, but also before and after shifts, as commuting can often be overlooked in terms of risk.”
This integrated approach will aim to develop a publicly available online tool to enable the ‘sleepiness risk’ to be estimated.
NTU Sleep Well Science consultancy founder Professor Groeger said, “Our approach is going to be a little different because we will gather the data, develop and test the tool and assess whether the results are meaningful and useful, all within the same complex work setting.
“The national spread and diversity of the police workforce, officers and other employees, will enable us to incorporate the effects of a broad range of individual differences into the underlying model.”
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