This research brief examines self-reported and objectively derived measures of driving in an older population.
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This research brief used data from the AAA Longitudinal Research on Aging Drivers (LongROAD) study to examine self-reported and objectively derived measures of driving in an older population. Information about older adults’ driving exposure and patterns (i.e., when, where and under what conditions they drive) is important for several reasons. Such information contributes to a better understanding of the crash risk of older drivers relative to other age groups. It also provides a context for understanding the process of self-regulation, whereby older drivers reduce their exposure to driving conditions they find challenging (e.g., at night, during rush-hour traffic, on major highways or long distances from home) or decrease their overall amount of driving (see Molnar et al., 2015 for a review of this literature). Such self-regulation of driving may help older drivers compensate for declining driving-related abilities and extend the period over which they can safely drive. Improving our knowledge of older adults’ driving exposure and patterns will help inform efforts to develop and strengthen educational and training materials for older drivers.
Data for this study came from 2,131 participants in the AAA LongROAD study (see Li et al., 2017 for full study details). LongROAD is a multisite prospective cohort study of drivers ages 65-79. The LongROAD data set, provides an opportunity to compare a set of subjective and objective measures of driving exposure and patterns among a large subset of older drivers.
Objective driving measures were derived from GPS/ datalogger data, following procedures described in previous research (Molnar et al., 2013). The devices automatically recorded driving information when the vehicle was turned on, and also determined whether or not it was the participant who was driving. Subjective measures came from a comprehensive questionnaire administered to participants at baseline that asked them to report their driving exposure, patterns and other aspects of driving.
The subjective driving avoidance questionnaire items were matched to each appropriate GPS-derived objective variable. Similarly, the questionnaire items related to exposure (e.g., number of miles driven) were matched to GPS-derived objective variables.
Several of the GPS-derived variables were recoded from monthly to weekly measures to more closely match the wording used on the questionnaire. Questionnaire items were assessed at a single point in time, but the objective data were collected continuously throughout the study. Only participants with at least 12 months of driving data were included in the analysis for this paper.
To account for differences in exposure and seasonality, the analysis only included participants’ first 12 months of driving. Finally, the GPS variables were averaged across the 12-month period to compare with each of the subjective measures, and the subjective and objective data sets were merged together. Univariate statistics were generated for each of the variables of interest. The analysis was conducted by fitting a series of separate simple regression models, one for each matched pair of subjective and objective behaviors. For each model, the objective measure served as the independent variable, with the subjective measure as the dependent variable. Linear or logistic models were fit, as appropriate, depending upon the outcome variable type.
When looking just at the objective driving measures, the oldest age group (75-79 years old) had the lowest overall driving exposure, in general, of the three age groups. They drove fewer miles and minutes per month, and fewer miles per trip than either of the younger age groups (65-69 and 70-74 years old). They also made fewer trips per month than the youngest age group. In terms of driving patterns, they took a lower percentage of trips on high-speed roads and a higher percentage of trips within 25 miles of home than either of the younger age groups. They also made a lower percentage of trips at night and in p.m. rush-hour traffic than the youngest age group.
Women had lower overall driving exposure than men, driving fewer trips, miles, and minutes per month, and fewer miles per trip. They also made a lower percentage of trips at night, in a.m. rush-hour traffic and on high-speed roads. They also had fewer speeding events and a higher percentage of trips within 25 miles of home than men.
We also compared the objective measures of a subset of driving exposure and patterns to subjective measures based on drivers’ self-reports. An important strength of the study was the large sample size (n=2,131) relative to the few studies of this type that have been conducted to date. With one exception, the results supported our assumption going into the study that for each driving behavior of interest, the subjective measure and its comparable objective measure would be related. For driving exposure, comparisons were statistically significant for both days driving per week and miles driving per week. In both cases, actual driving predicted self-reported driving, albeit not perfectly. For driving patterns, comparisons were statistically significant for driving at night, in unfamiliar areas, during rush-hour traffic and on high-speed roads. For each driving situation, participants’ actual driving predicted the likelihood of reporting trying to avoid that situation. However, the objective measure of the ratio of right to left turns was not significantly related to the subjective measure of avoidance of unprotected left turns.
The lack of correspondence between the subjective and objective measures related to right and left turns may have been due, in part, to the necessity of using the ratio of right to left hand turns as a proxy measure for making left turns across unprotected intersections, based on the idea that drivers who tried to avoid such turns would be more likely to have a higher ratio of right to left hand turns. However, we were not able to identify whether left hand turns occurred at protected or unprotected intersections, although the algorithm did limit turns to intersections and not roundabouts. It is also important to note that the avoidance items in the questionnaire focused on participants’ intent rather than actual behavior; thus, the subjective and objective measures were not identical. However, the significant relationships between the subjective and objective measures for avoidance of driving at night, in unfamiliar areas and on high-speed roads suggest that there is an opportunity to use both types of data in combination to better understand these driving patterns. Improving our knowledge of older adults’ driving exposure and patterns will help inform efforts to develop and strengthen educational and training materials for older drivers.
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