1. The Effect of Longitudinal Changes in Driving-related Attitudes on Older Adults’ Driving Patterns
Paweena Sukhawathanakul
Michelle M. Porter
Centre on Aging, University of Manitoba
Holly Tuokko
Institute on Aging and Lifelong Health, University of Victoria

2. Introduction
Aging and age-related diseases may put older adults at increased risk of crashes and other unsafe driving behaviors.
Studies have demonstrated that some older adults voluntarily regulate their driving
Psychological processes like attitudes and beliefs play important roles in driving self-regulation among older adults.
E.g., related to avoidance of challenging driving situations (Jouk et al., 2016), mediates health and driving outcome (Tuokko et al., 2016).

3. Driving-related Attitudes and Objective Measures of Driving
Association between driving-related attitudes and driving practices has relied mainly on self-reported driving measures.
Self-reported measures of driving behaviours have been shown to be susceptible to memory inaccuracies when compared to objective measures (Porter et al., 2015).
More longitudinal research with objectively measured driving patterns is needed to better understand how driving patterns change over time within individuals, and the role that attitudes play in determining these changes.

4. The Current Study
The current study uses objective GPS data to determine how changes in negative and positive attitudes about driving influence actual driving patterns in older adults over time using four years of longitudinal data from the nationwide Canadian Driving Research Initiative for Vehicular Safety in the Elderly (Candrive)

5. 928 older adults (Mage = 76.21, SD = 4.85).
7 cities across 4 provinces (Marshall et al., 2013).
Annual driving assessments include measures of sensory, physical and cognitive function, and information about psychosocial factors, driving habits and behaviours, and health status.

6. In-vehicle Recording Device
Collects continuous information from the vehicle including time/date of trip, speed, distance travelled and vehicle parameters (e.g., throttle position).
A GPS antenna mounted on the dash and a receiver in the main device box allows vehicle location information to be collected.
Contains a radio frequency identifier system (antenna plus key chain fob) that can identify the participant as the vehicle driver

7. Attitude Measures
Decisional Balance Scale based on the Transtheoretical Model of Behaviour Change (Sukhawathanakul et al., 2015; Tuokko et al., 2006).
Participants rate their responses to statements concerning positive and negative attitudes towards driving relevant for the individual (i.e., self) and how the individual perceives others view their driving (i.e., other).
Pro-self: “Being able to drive is important to me.”
Con-self: “I am experiencing increasing apprehension about driving.”
Pro-other: “Others count on me being able to drive”
Con-other: “Some people think I should stop driving”

8. Data Analytic Strategy
Driving data selection: Data were aggregated by week with a minimum of 20 consecutive weeks between May to October over 4 years.
Driving pattern outcomes: total kms, number of trips, speeding (over 10km), and average daily maximum speed per week during day and night.
Analysis: Multilevel models examining change in driving patterns over time with changes in attitudes as predictors of variability in driving.
All 4 attitude subtypes were entered simultaneously in the model to determine their unique effects.
Covariates: age, gender, and education (SES) were controlled.

9. Results: Driving patterns over time

10. Results: Driving patterns over time
Weekly and Yearly Changes
On average, number of total trips, number of day trips, average daily maximum speed and maximum speed decline across the 4 years.
Total day km, number of total trips, number of day trips, and maximum speed decline across weeks within each year.
Number of night trips increased across weeks within each year but did not change across years.

11. Results: Attitudes over time
Negative attitudes increase slightly over time (βs = .10 & .16, ps < .05).
Positive attitudes remain stable over time (βs = -.04 & -.07, ps > .05).

12. Results: Association between attitudes and driving patterns over time
Among all driving outcomes (km, n trips, speeding, and avg. daily max. velocity), changes in con-other were associated with total kms during the day.
Individuals who held increasingly negative attitudes towards other in relation to their driving reported driving fewer kms over time.

13. Con-other and total day-time kms

14. Discussion
Consistent with self-report (Jouk et al., 2016), negative attitudes emerged as predictor of changes in driving patterns, in particular frequency of driving (total kms).
May need to account for other mediating factors such as health status (Tuokko et al., 2016) or cognitive functioning (Rapoport et al., 2016).
Other attitudes may also contribute such as comfort levels (Blanchard & Myers, 2010; Myers et al., 2008).
Other driving outcomes may be more sensitive to changes in attitudes such as sudden starts/stops, driving under favourable/unfavourable conditions.

15. Thank you