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

1
Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence, RI 02903, USA
2
Schepens Eye Research Institute, Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
3
Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI 02912, USA
4
Alzheimer’s Disease and Memory Disorders Center, Rhode Island Hospital, Providence, RI 02903, USA
5
Department of Neurology, Alpert Medical School of Brown University, Providence, RI 02903, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Geriatrics 2018, 3(2), 13; https://doi.org/10.3390/geriatrics3020013
Received: 31 January 2018 / Revised: 13 March 2018 / Accepted: 14 March 2018 / Published: 21 March 2018
(This article belongs to the Special Issue Aging and Driving)
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. View Full-Text
Keywords: Alzheimer’s disease; cognitive impairment; naturalistic driving assessment Alzheimer’s disease; cognitive impairment; naturalistic driving assessment
MDPI and ACS Style

Davis, J.D.; Wang, S.; Festa, E.K.; Luo, G.; Moharrer, M.; Bernier, J.; Ott, B.R. Detection of Risky Driving Behaviors in the Naturalistic Environment in Healthy Older Adults and Mild Alzheimer’s Disease. Geriatrics 2018, 3, 13.

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