Dissertation
Modeling Cannabis driving impairment: the role of individual differences in predicting safety outcomes
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Summer 2023
DOI: 10.25820/etd.006959
Abstract
Epidemiological research from the past twenty years has consistently shown cannabis- impaired driving doubles the risk of a car crash (Hartman & Huestis 2013). This is primarily due to the predominant psychoactive cannabinoid found in cannabis, ∆9-tetrahydrocannabinol (THC), which can impair numerous safety-critical driving tasks, such as divided attention, reaction time, decision making, and risk-taking. However, efforts to predict driving impairment are challenged by individual variability in response to cannabis.
The causes of individual variation are broadly explained by frequency of use, biological/genetic differences, and psychological vulnerabilities, how these manifest in driving performance is less clear. The underlying challenge is a single model which explains performance amongst all cannabis users does not exist.
The objective for this body of work is to develop a model using a systems-based approach to research individual variation in cannabis-induced driving impairment. Chapter 1 lays out the objective, background, problem statement, and current state of research. Chapter 2 introduces the prospect of using subjective perceptions of state to predict driving performance, as these perceptions are one of many manifestations of individual differences. Chapter 3 examines the relationship between subjective effects and driving performance across a broader range of users. Chapter 4 examines the relationship between brain activity, interactions between subjective effects, and driving performance. Chapter 5 lays out the key findings, implications of these findings, and future research goals.
The key findings of this research are as thus. To understand driving impairment, you need to understand driver’s perception of impairment and their physical impairment. Generalizability of individual perceptions of impairment is lacking, but interactive effects show potential. Acute cannabis intoxication manifests as an increase in the frequency and duration of driving errors. Subjective effects and brain activity jointly explain more of the differences in simulated driving performance, than either in isolation.
The significance of this research is that the complex interactions between subjective perceptions and brain activity may predict cannabis impairment across a wider range of users. Furthermore, the examination of error frequency and duration, as opposed to continuous measures of driving performance, is a novel approach to modeling driving performance. The results provide guidance for future modeling efforts, suggesting error frequency may be an effective tool for modeling impairment, whereas error duration is not.
Details
- Title: Subtitle
- Modeling Cannabis driving impairment: the role of individual differences in predicting safety outcomes
- Creators
- Thomas S Burt
- Contributors
- Dan V McGehee (Advisor)Timothy L Brown (Committee Member)Gary Milavetz (Committee Member)Geb W Thomas (Committee Member)Thomas Schnell (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Industrial Engineering
- Date degree season
- Summer 2023
- Publisher
- University of Iowa
- DOI
- 10.25820/etd.006959
- Number of pages
- xi, 85 pages
- Copyright
- Copyright 2023 Thomas S. Burt
- Language
- English
- Date submitted
- 07/24/2023
- Description illustrations
- illustrations, tables, graphs, map
- Description bibliographic
- Includes bibliographical references (pages 68-83).
- Public Abstract (ETD)
- Epidemiological research from the past twenty years has consistently shown cannabis-impaired driving doubles the risk of a car crash (Hartman & Huestis 2013). This is primarily due to the predominant psychoactive cannabinoid found in cannabis, Δ9-tetrahydrocannabinol (THC), which can impair numerous safety-critical driving tasks, such as divided attention, reaction time, decision making, and risk-taking. However, efforts to predict driving impairment are challenged by individual variability in response to cannabis.
The objective for this body of work is to develop a model using a systems-based approach to research individual variation in cannabis-induced driving impairment. Chapter 1 lays out the objective, background, problem statement, and current state of research. Chapter 2 introduces the prospect of using subjective perceptions of state to predict driving performance, as these perceptions are one of many manifestations of individual differences. Chapter 3 examines the relationship between subjective effects and driving performance across a broader range of users. Chapter 4 examines the relationship between brain activity, interactions between subjective effects, and driving performance. Chapter 5 lays out the key findings, implications of these findings, and future research goals.
The significance of this research is that the complex interactions between subjective perceptions and brain activity may predict cannabis impairment across a wider range of users. Furthermore, the examination of error frequency and duration, as opposed to continuous measures of driving performance, is a novel approach to modeling driving performance. The results provide guidance for future modeling efforts, suggesting error frequency may be an effective tool for modeling impairment, whereas error duration is not.
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984454642402771
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