Development of a novel driving behavior adaptations questionnaire.

BACKGROUND: Driving a car requires adapting one's behavior to current task demands taking into account one's capacities. With increasing age, driving-relevant cognitive performance may decrease, creating a need for risk-reducing behavioral adaptations. Three different kinds of behavioral adaptations are known: selection, optimization, and compensation. These can occur on the tactical and the strategic level. Risk-reducing behavioral adaptations should be considered when evaluating older drivers' traffic-related risks. METHODS: A questionnaire to assess driving-related behavioral adaptations in older drivers was created. The questionnaire was administered to 61 years older (age 65-87 years; mean age = 70.2 years; SD = 5.5 years; 30 female, 31 male) and 31 younger participants (age 22-55 years; mean age = 30.5 years; SD = 6.3 years; 16 female and 15 male) to explore age and gender differences in behavioral adaptations. RESULTS: Two factors were extracted from the questionnaire, a risk-increasing factor and a risk-reducing factor. Group comparisons revealed significantly more risk-reducing behaviors in older participants (t(84.5) = 2.21, p = 0.013) and females (t(90) = 2.52, p = 0.014) compared, respectively, to younger participants and males. No differences for the risk-increasing factor were found (p > 0.05). CONCLUSIONS: The questionnaire seems to be a useful tool to assess driving-related behavioral adaptations aimed at decreasing the risk while driving. The possibility to assess driving-related behavioral adaptations in a systematic way enables a more resource-oriented approach in the evaluation of fitness to drive in older drivers.

Age-dependent visual exploration during simulated day- and night driving on a motorway: a cross-sectional study.

BACKGROUND: Central and peripheral vision is needed for object detection. Previous research has shown that visual target detection is affected by age. In addition, light conditions also influence visual exploration. The aim of the study was to investigate the effects of age and different light conditions on visual exploration behavior and on driving performance during simulated driving. METHODS: A fixed-base simulator with 180 degree field of view was used to simulate a motorway route under daylight and night conditions to test 29 young subjects (25-40 years) and 27 older subjects (65-78 years). Drivers' eye fixations were analyzed and assigned to regions of interests (ROI) such as street, road signs, car ahead, environment, rear view mirror, side mirror left, side mirror right, incoming car, parked car, road repair. In addition, lane-keeping and driving speed were analyzed as a measure of driving performance. RESULTS: Older drivers had longer fixations on the task relevant ROI, but had a lower frequency of checking mirrors when compared to younger drivers. In both age groups, night driving led to a less fixations on the mirror. At the performance level, older drivers showed more variation in driving speed and lane-keeping behavior, which was especially prominent at night. In younger drivers, night driving had no impact on driving speed or lane-keeping behavior. CONCLUSIONS: Older drivers' visual exploration behavior are more fixed on the task relevant ROI, especially at night, when driving performance becomes more heterogeneous than in younger drivers.

Non-illness-related factors contributing to traffic safety in older drivers: a literature review.

UNLABELLED: BACKGROUND/STUDY CONTEXT: Older drivers are at increased risk of becoming involved in car crashes. Contrary to well-studied illness-related factors contributing to crash risk, the non-illness-related factors that can influence safety of older drivers are underresearched. METHODS: Here, the authors review the literature on non-illness-related factors influencing driving in people over age 60. We identified six safety-relevant factors: road infrastructure, vehicle characteristics, traffic-related knowledge, accuracy of self-awareness, personality traits, and self-restricted driving. RESULTS: The literature suggests that vehicle preference, the quality of traffic-related knowledge, the location and time of traffic exposure, and personality traits should all be taken into account when assessing fitness-to-drive in older drivers. Studies indicate that self-rating of driving skills does not reliably predict fitness-to-drive. CONCLUSIONS: Most factors discussed are adaptable or accessible to training and collectively may have the potential to increase traffic safety for older drivers and other road users.

A novel computer test to assess driving-relevant cognitive functions--a pilot study.

BACKGROUND: The assessment of driving-relevant cognitive functions in older drivers is a difficult challenge as there is no clear-cut dividing line between normal cognition and impaired cognition and not all cognitive functions are equally important for driving. METHODS: To support decision makers, the Bern Cognitive Screening Test (BCST) for older drivers was designed. It is a computer-assisted test battery assessing visuo-spatial attention, executive functions, eye-hand coordination, distance judgment, and speed regulation. Here we compare the performance in BCST with the performance in paper and pencil cognitive screening tests and the performance in the driving simulator testing of 41 safe drivers (without crash history) and 14 unsafe drivers (with crash history). RESULTS: Safe drivers performed better than unsafe drivers in BCST (Mann-Whitney U test: U = 125.5; p = 0.001) and in the driving simulator (Student's t-test: t(44) = -2.64, p = 0.006). No clear group differences were found in paper and pencil screening tests (p > 0.05; ns). BCST was best at identifying older unsafe drivers (sensitivity 86%; specificity 61%) and was also better tolerated than the driving simulator test with fewer dropouts. CONCLUSIONS: BCST is more accurate than paper and pencil screening tests, and better tolerated than driving simulator testing when assessing driving-relevant cognition in older drivers.

Can a novel web-based computer test predict poor simulated driving performance? a pilot study with healthy and cognitive-impaired participants.

BACKGROUND: Driving a car is a complex instrumental activity of daily living and driving performance is very sensitive to cognitive impairment. The assessment of driving-relevant cognition in older drivers is challenging and requires reliable and valid tests with good sensitivity and specificity to predict safe driving. Driving simulators can be used to test fitness to drive. Several studies have found strong correlation between driving simulator performance and on-the-road driving. However, access to driving simulators is restricted to specialists and simulators are too expensive, large, and complex to allow easy access to older drivers or physicians advising them. An easily accessible, Web-based, cognitive screening test could offer a solution to this problem. The World Wide Web allows easy dissemination of the test software and implementation of the scoring algorithm on a central server, allowing generation of a dynamically growing database with normative values and ensures that all users have access to the same up-to-date normative values. OBJECTIVE: In this pilot study, we present the novel Web-based Bern Cognitive Screening Test (wBCST) and investigate whether it can predict poor simulated driving performance in healthy and cognitive-impaired participants. METHODS: The wBCST performance and simulated driving performance have been analyzed in 26 healthy younger and 44 healthy older participants as well as in 10 older participants with cognitive impairment. Correlations between the two tests were calculated. Also, simulated driving performance was used to group the participants into good performers (n=70) and poor performers (n=10). A receiver-operating characteristic analysis was calculated to determine sensitivity and specificity of the wBCST in predicting simulated driving performance. RESULTS: The mean wBCST score of the participants with poor simulated driving performance was reduced by 52%, compared to participants with good simulated driving performance (P<.001). The area under the receiver-operating characteristic curve was 0.80 with a 95% confidence interval 0.68-0.92. CONCLUSIONS: When selecting a 75% test score as the cutoff, the novel test has 83% sensitivity, 70% specificity, and 81% efficiency, which are good values for a screening test. Overall, in this pilot study, the novel Web-based computer test appears to be a promising tool for supporting clinicians in fitness-to-drive assessments of older drivers. The Web-based distribution and scoring on a central computer will facilitate further evaluation of the novel test setup. We expect that in the near future, Web-based computer tests will become a valid and reliable tool for clinicians, for example, when assessing fitness to drive in older drivers.

Effects of age and eccentricity on visual target detection.

The aim of this study was to examine the effects of aging and target eccentricity on a visual search task comprising 30 images of everyday life projected into a hemisphere, realizing a ±90° visual field. The task performed binocularly allowed participants to freely move their eyes to scan images for an appearing target or distractor stimulus (presented at 10°; 30°, and 50° eccentricity). The distractor stimulus required no response, while the target stimulus required acknowledgment by pressing the response button. One hundred and seventeen healthy subjects (mean age = 49.63 years, SD = 17.40 years, age range 20-78 years) were studied. The results show that target detection performance decreases with age as well as with increasing eccentricity, especially for older subjects. Reaction time also increases with age and eccentricity, but in contrast to target detection, there is no interaction between age and eccentricity. Eye movement analysis showed that younger subjects exhibited a passive search strategy while older subjects exhibited an active search strategy probably as a compensation for their reduced peripheral detection performance.