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Journal Article
Research Support, U.S. Gov't, P.H.S.
How do older adult drivers self-regulate? Characteristics of self-regulation classes defined by latent class analysis.
Journal of Safety Research 2017 June
PROBLEM: Motor-vehicle crashes were the second leading cause of injury death for adults aged 65-84years in 2014. Some older drivers choose to self-regulate their driving to maintain mobility while reducing driving risk, yet the process remains poorly understood.
METHODS: Data from 729 older adults (aged ≥60years) who joined an older adult ride service program between April 1, 2010 and November 8, 2013 were analyzed to define and describe classes of driving self-regulation. Latent class analysis was employed to characterize older adult driving self-regulation classes using driving frequency and avoidance of seven driving situations. Logistic regression was used to explore associations between characteristics affecting mobility and self-regulation class.
RESULTS: Three classes were identified (low, medium, and high self-regulation). High self-regulating participants reported the highest proportion of always avoiding seven risky driving situations and the lowest driving frequency followed by medium and low self-regulators. Those who were female, aged 80years or older, visually impaired, assistive device users, and those with special health needs were more likely to be high self-regulating compared with low self-regulating.
CONCLUSIONS AND PRACTICAL APPLICATIONS: Avoidance of certain driving situations and weekly driving frequency are valid indicators for describing driving self-regulation classes in older adults. Understanding the unique characteristics and mobility limitations of each class can guide optimal transportation strategies for older adults.
METHODS: Data from 729 older adults (aged ≥60years) who joined an older adult ride service program between April 1, 2010 and November 8, 2013 were analyzed to define and describe classes of driving self-regulation. Latent class analysis was employed to characterize older adult driving self-regulation classes using driving frequency and avoidance of seven driving situations. Logistic regression was used to explore associations between characteristics affecting mobility and self-regulation class.
RESULTS: Three classes were identified (low, medium, and high self-regulation). High self-regulating participants reported the highest proportion of always avoiding seven risky driving situations and the lowest driving frequency followed by medium and low self-regulators. Those who were female, aged 80years or older, visually impaired, assistive device users, and those with special health needs were more likely to be high self-regulating compared with low self-regulating.
CONCLUSIONS AND PRACTICAL APPLICATIONS: Avoidance of certain driving situations and weekly driving frequency are valid indicators for describing driving self-regulation classes in older adults. Understanding the unique characteristics and mobility limitations of each class can guide optimal transportation strategies for older adults.
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