Using a driving simulator to identify older drivers at inflated risk of motor vehicle crashes.
OBJECTIVE
To develop appropriate assessment criteria to measure the performance of older drivers using an interactive PC-based driving simulator, and to determine which measures were associated with the occurrence of motor-vehicle crash.


METHODS
One hundred and twenty-nine older drivers residing in a metropolitan city volunteered to participate in this retrospective cohort study. Using the driving simulator, appropriate driving tasks were devised to test the older drivers, whose performances were assessed by 10 reliable assessment criteria. Logistic regression analysis was then undertaken to determine those criteria that influence the self-reported crash outcome.


RESULTS
As expected, driving skill of older drivers was found to decline with age. Over 60% of the sample participants reported having at least one motor-vehicle crash during the past year. Adjusting for age in a logistic regression analysis, the cognitive abilities associated with the crash occurrence were working memory, decision making under pressure of time, and confidence in driving at high speed.


CONCLUSIONS
The findings of this retrospective study indicated those individuals at inflated risk of vehicle crashes could be identified using the PC-based interactive driving simulator. Prospective studies need to be undertaken to determine whether the driving simulator can predict future crash events.


CONCLUSIONS
This study demonstrated an economical driving simulator approach to screen out problematic or unsafe older drivers before a more detailed but expensive road test is considered.
Lee
Hoe C
HC
Curtin University Driving Assessment and Consultancy, Perth, Australia. Andy.Lee@cutin.edu.au
Lee
Andy H
AH
Cameron
Don
D
Li-Tsang
Cecilia
C
D000063:Accidents, Traffic / Q000517:prevention & control*; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001334:Automobile Driving / Q000523:psychology*; D015331:Cohort Studies; D003198:Computer Simulation; D003657:Decision Making; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018986:Motor Vehicles; D011597:Psychomotor Performance; D018570:Risk Assessment / Q000379:methods*; D014505:Urban Population