Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study.

Xuan Di,David W Eby,Linda L Hill,Rongye Shi,Carolyn Diguiseppi,Minjae Kim,Lisa J Molnar,David Strogatz,Howard F Andrews,Guohua Li,Barbara H Lang,Thelma J Mielenz,Terry E Goldberg

doi:10.3390/geriatrics6020045

Abstract

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.

Highlights

IntroductionAccording to the US Census Bureau, there were over 49 million older adults (aged 65 years and older) in the United States in 2016, accounting for 15% of the population [1]
As aging of the US population accelerates, the number of older drivers continues to rise.According to the US Census Bureau, there were over 49 million older adults in the United States in 2016, accounting for 15% of the population [1]
Eligibility criteria were established to ensure that study participants were relatively healthy, active drivers aged 65–79 years at the time of enrollment, who would likely be available to be assessed annually through the duration of the study. Among those excluded from the LongROAD study were drivers with Six-Item Screener score < 4, having significant cognitive impairment or being diagnosed with degenerative medical conditions, such as Alzheimer’s disease (AD), Huntington’s disease, and Parkinson’s disease [14]

Summary

Introduction

According to the US Census Bureau, there were over 49 million older adults (aged 65 years and older) in the United States in 2016, accounting for 15% of the population [1]. The number of older adults with a driver’s license in the United States is expected to increase from 42 million (or 85% of the older adult population) in 2016 to 63 million in 2030 [2]. While driving allows older adults to meet their mobility needs and to stay independent, age-related functional declines, medical conditions, and side effects of medications can compromise driving abilities and lead to heightened crash risk. Atypical changes in driving behaviors may be early signals of cognitive function declines and dementia. To determine whether a recent history of unsafe driving was associated with cognitive impairment, Ott et al [3] recorded traffic violations and crashes in the previous

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