Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study
Xuan Di,
Rongye Shi,
Carolyn DiGuiseppi,
David W. Eby,
Linda L. Hill,
Thelma J. Mielenz,
Lisa J. Molnar,
David Strogatz,
Howard F. Andrews,
Terry E. Goldberg,
Barbara H. Lang,
Minjae Kim,
Guohua Li
Affiliations
Xuan Di
Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA
Rongye Shi
Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA
Carolyn DiGuiseppi
Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
David W. Eby
Transportation Research Institute, University of Michigan, Ann Arbor, MI 48109, USA
Linda L. Hill
School of Public Health, University of California San Diego, La Jolla, CA 92093, USA
Thelma J. Mielenz
Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
Lisa J. Molnar
Transportation Research Institute, University of Michigan, Ann Arbor, MI 48109, USA
David Strogatz
Bassett Research Institute, Cooperstown, NY 13326, USA
Howard F. Andrews
Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
Terry E. Goldberg
Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
Barbara H. Lang
Department of Anesthesiology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
Minjae Kim
Department of Anesthesiology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
Guohua Li
Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F1 score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers.
