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Alcohol use patterns and their association with sober driver vehicle control in high fidelity driving simulation
Barbara C Banz1, Timothy L Brown2, Deepa R Camenga1
1Department of Emergency Medicine, Yale Developmental Neurocognitive Driving Simulation Research Center (DrivSim Lab), Yale University School of Medicine, New Haven, Connecticut.
Objective:
To examine the relationship between patterns of alcohol use, as determined by the Alcohol Use Disorders Identification Test, and vehicle control measures in high fidelity driving simulation among adult sober drivers.
Methods:
Baseline data (BAC = 0.00%; N = 108) from a larger study aimed at using high-fidelity driving simulation (National Advanced Driving Simulator) to evaluate the feasibility of vehicle-based sensors to identify alcohol impairment were analyzed. Driving simulation scenarios included driving on urban, interstate, and rural roadways. The independent variable was the pattern of alcohol use measured with the Alcohol Use Disorder Identification Test (AUDIT). Dependent variables included one lateral vehicle control measure (i.e., standard deviation of lane position (SDLP)) and one longitudinal vehicle control measure (i.e., average speed relative to the speed limit) in high fidelity driving simulation. Multivariable linear regression was used to examine the associations between patterns of alcohol use and vehicle control measures.
Results:
Total AUDIT scores ≥8 was positively associated with SDLP. Increased frequency of drinking was associated with decreased SDLP and increased average speed relative to the speed limit. Increased reports of blackouts and alcohol-related injury were associated with increased average speed relative to the speed limit. Driver performance (SDLP, average speed relative to the speed limit) was related to additional factors such as driver experience, age, marital status, and driving context.
Conclusions:
The findings support our hypothesis that the AUDIT score and responses to individual AUDIT questions, among sober drivers, relates to vehicle control measures. Overall, our data highlight two important themes: 1) a need to further integrate alcohol use metrics with high-fidelity driving simulation studies to understand how drinking experience can relate to driver behavior and vehicle control and 2) the opportunity to integrate clinical perspectives with driving simulation research to strengthen clinically oriented alcohol-misuse prevention efforts.
