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Multiple organizations and task forces have called for a reliable and valid method to identify older drivers who are medically unfit to drive. The development of a clinical decision rule for this type of screening requires data from a longitudinal prospective cohort of older drivers. The aim of this article is to identify potential design, sampling and data collection barriers to such studies based on an analysis of the Canadian Safe Driving Study-phase I pilot (Candrive I). A convenience sample of 100 active older drivers aged 70 years or more was recruited through the aid of a seniors' organization, 94 of whom completed the full study (retention rate 94%). Data were collected over the course of 1 year on various driving behaviours, as well as on cognitive, physical and mental functioning. Driving patterns were recorded using driving diaries, logs and electronic devices. Driving records from the Ministry of Transportation of Ontario (MTO) were obtained for the 3-year period preceding the study initiation and up to 1 year following study completion. An increased burden of illness was observed as the number of medical diagnoses and medication use increased over the study period. Study participants were involved in a total of five motor vehicle collisions identified through MTO records, which was comparable to the Ontario annual collision rate of 4.1% for drivers aged 75 years or older. In sum, many of the relevant logistical and practical barriers to studying a large sample of older drivers longitudinally have been shown to be addressable, supporting the feasibility of completing a large prospective cohort study of older drivers.

Given that sleep problems and serious motor vehicle collisions are increasingly prevalent in older adults, even minor drowsiness could potentially contribute to driving patterns in older drivers. To date, it is unknown whether less serious problems with sleep influence driving frequency and ability in older adults. We investigated the influence of everyday sleep disturbances on driving practices and driver perceptions in a large cohort of healthy older drivers. Selfreported measures of sleep problems were used to investigate the influence of sleep disturbance on self-reported driving practices and perceived driving abilities. On two measures of self-reported driving outcomes, participants with problems with rated themselves more poorly. However, this relationship disappeared when health and demographic variables were entered prior in hierarchical regression analyses. Our results show that the relationship between sleep problems, driving frequency and perceived abilities is better explained by mediating demographic, health, and cognitive factors.

The present study sought to investigate the influence of subclinical sleep disturbances on driving practices and driver perceptions in a large cohort of healthy older drivers. Participants from the Candrive II prospective cohort study were investigated. Self-reported measures of sleep problems were used to determine the influence of sleep disturbance on self-reported driving practices and perceived driving abilities, as measured by the Situational Driving Frequency, Situational Driving Avoidance, and Perceived Driving Abilities scales. Hierarchical regression analyses were used to estimate whether mild self-reported sleep problems were predictive of driving restrictions and perceived abilities, while controlling for a variety of health-related factors and demographic variables known to mediate sleep problems or to impact driving. Cross-sectional analysis of baseline data from the Candrive II study suggests that subclinical sleep problems do not significantly influence self-reported driving patterns or perceived driving abilities in older drivers once control variables are considered. The relationship between sleep problems, driving frequency, avoidance and perceived abilities is better explained by mediating demographic, health, and cognitive factors. Further research examining sleep disturbances and driving should include objective measures of driving practices (exposure, patterns) and outcomes (crashes, violations) and should take in consideration the severity of sleep problems.