Utility of self-report and performance-based measures of risk for predicting driving behavior in young people |
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| Institution: | 1. Center for Pediatric Clinical Effectiveness;2. Division of General Pediatrics, The Children''s Hospital of Philadelphia, Philadelphia, PA;3. Department of Pediatrics, The Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA;4. PolicyLab;5. Center for Injury Research and Prevention, The Children''s Hospital of Philadelphia, Philadelphia, PA;6. Health Coverage for Low-Income and Uninsured Populations, RTI International, Washington, DC;7. Department of Emergency Medicine and Injury Prevention Center, Alpert Medical School of Brown University and Hasbro Children''s Hospital, Providence, RI;1. Brock University, St. Catharines, Ontario, Canada;2. Trent University, Oshawa, Ontario, Canada;1. German Institute for Japanese Studies (DIJ), Jochi Kioizaka Bldg. 2F, 7-1 Kioicho, Chiyoda-ku, Tokyo 102-0094, Japan;2. International Business School Suzhou (IBSS), Xi''an Jiaotong-Liverpool University (XJTLU), China |
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| Abstract: | Road-traffic injuries are the single biggest killer of young people worldwide. Our study sought to determine whether self-report and performance-based measures of risk could be administered online to predict driving risk in young people (aged 18–25, n = 102). We used a retrospective approach and compared self-reported driving behavior with outcomes on Eysenck's Impulsivity Inventory Impulsiveness subscale, Multidimensional Personality Questionnaire Harm Avoidance subscale, Iowa Gambling Task (IGT), and Balloon Analog Risk Task (BART). As hypothesized, higher levels of driving risk were associated with higher levels of impulsivity (p < .001), and lower levels of harm avoidance (indicating fearlessness; p = .025). These personality measures can be readily incorporated into an online tool for predicting driving risk. An unexpected finding was that the IGT and BART did not significantly predict driving risk (p = .627 and .379). This study contributes to the development of an online tool for predicting driving risk. In order to further develop this tool, future research should assess the utility of other performance-based measures in online driving assessment. Identifying cognitive and psychological characteristics that can predict driving behavior will help direct prevention efforts, such as added driver safety opportunities for youth at the highest crash risk. |
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