Diagnosing dementia from driving patterns

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Columbia University research project uses artificial intelligence (AI) to identify driving behavior that indicates early cognitive impairment.

June 16, 2021

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Staying a bit closer to home when driving, making fewer stops per trip, and braking more aggressively when driving could all indicate mild cognitive impairment or dementia in older drivers, a study from Columbia University’s Mailman School of Public Health and Columbia’s Fu Foundation School of Engineering and Applied Science has found.

Researchers at the New York university developed highly accurate algorithms for detecting mild cognitive impairment and dementia in older drivers using naturalistic driving data, information captured through in-vehicle recording devices or other technologies in a real-world setting. These data could be processed to measure driving exposure, space, and performance in great detail. The findings are published in the journal Geriatrics.

The researchers developed random forests models, a statistical technique widely used in artificial intelligence (AI) for classifying disease status. 

“Based on variables derived from the naturalistic driving data and basic demographic characteristics, such as age, sex, race/ethnicity, and education level, we could predict mild cognitive impairment and dementia with 88% accuracy,” says Sharon Di, associate professor of civil engineering and engineering mechanics at Columbia Engineering and the study’s lead author.

The investigators constructed 29 variables using the naturalistic driving data captured by in-vehicle recording devices from 2,977 participants of the Longitudinal Research on Aging Drivers (LongROAD) project, a multisite cohort study sponsored by the AAA Foundation for Traffic Safety. The participants were active drivers aged 65-to-79 years and had no significant cognitive impairment or degenerative medical conditions. Data in this study was collected from August 2015 through March 2019.

Among the 2,977 participants, 33 were newly diagnosed with mild cognitive impairment and 31 with dementia by April 2019. The researchers trained machine learning models to detect mild cognitive impairment/dementia and found that the model based on driving variables and demographic characteristics was 88% accurate, much better than models based on demographic characteristics only (29%) and driving variables only (66%).

Further analysis revealed that age was most predictive of mild cognitive impairment 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 =0.35g.

“Driving is a complex task involving dynamic cognitive processes and requiring essential cognitive functions and perceptual motor skills. Our study indicates that naturalistic driving behaviors can be used as comprehensive and reliable markers for mild cognitive impairment and dementia,” says Guohua Li, MD, DrPH, professor of epidemiology and anesthesiology at Columbia Mailman School of Public Health and Vagelos College of Physicians and Surgeons, and senior author. “If validated, the algorithms developed in this study could provide a novel, unobtrusive screening tool for early detection and management of mild cognitive impairment and dementia in older drivers.”

While LongROAD participants used cars equipped with specialized recording devices, many modern cars have advanced driver assistance systems (ADAS) that feed similar data to monitoring systems, raising the possibility of mass-market implementations of tracking systems. 

AAA Foundation for Traffic Safety 

Columbia University Mailman School of Public Health