Detection of Risky Driving Behaviors in the Naturalistic Environment in Healthy Older Adults and Mild Alzheimer's Disease.

Analyzing naturalistic driving behavior recorded with in-car cameras is an ecologically valid method for measuring driving errors, but it is time intensive and not easily applied on a large scale. This study validated a semi-automated, computerized method using archival naturalistic driving data collected for drivers with mild Alzheimer's disease (AD; n = 44) and age-matched healthy controls (HC; n = 16). The computerized method flagged driving situations where safety concerns are most likely to occur (i.e., rapid stops, lane deviations, turns, and intersections). These driving epochs were manually reviewed and rated for error type and severity, if present. Ratings were made with a standardized scoring system adapted from DriveCam®. The top eight error types were applied as features to train a logistic model tree classifier to predict diagnostic group. The sensitivity and specificity were compared among the event-based method, on-road test, and composite ratings of two weeks of recorded driving. The logistic model derived from the event-based method had the best overall accuracy (91.7%) and sensitivity (97.7%) and high specificity (75.0%) compared to the other methods. Review of driving situations where risk is highest appears to be a sensitive data reduction method for detecting cognitive impairment associated driving behaviors and may be a more cost-effective method for analyzing large volumes of naturalistic data.

Brain amyloid in preclinical Alzheimer's disease is associated with increased driving risk.

INTRODUCTION: Postmortem studies suggest that fibrillar brain amyloid places people at higher risk for hazardous driving in the preclinical stage of Alzheimer's disease (AD). METHODS: We administered driving questionnaires to 104 older drivers (19 AD, 24 mild cognitive impairment, and 61 cognitive normal) who had a recent 18F-florbetapir positron emission tomography scan. We examined associations of amyloid standardized uptake value ratios with driving behaviors: traffic violations or accidents in the past 3 years. RESULTS: The frequency of violations or accidents was curvilinear with respect to standardized uptake value ratios, peaking around a value of 1.1 (model r2  = 0.10, P = .002); moreover, this relationship was evident for the cognitively normal participants. DISCUSSION: We found that driving risk is strongly related to accumulating amyloid on positron emission tomography, and that this trend is evident in the preclinical stage of AD. Brain amyloid burden may in part explain the increased crash risk reported in older adults.