Association of Daily Driving Behaviors With Mild Cognitive Impairment in Older Adults Followed Over 10 Years

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BACKGROUND AND OBJECTIVES: Driving integrates multiple cognitive, sensory, and motor systems and may serve as a real-world indicator of functional decline in aging. Older adults with mild cognitive impairment (MCI) often experience subtle driving changes before formal dementia diagnosis, but longitudinal, real-world evidence is limited. This study examined whether naturalistic driving data can differentiate older adults with MCI from those with normal cognition (NC) over time and evaluated the discriminative ability of driving features compared with conventional risk factors. METHODS: We conducted a prospective, observational cohort study of community-dwelling older drivers enrolled in the Driving Real-World In-Vehicle Evaluation System Project at Washington University. Participants underwent annual Clinical Dementia Rating assessment, neuropsychological testing, and apolipoprotein ε4 (APOE ε4) genotyping. Driving behaviors were captured daily for up to 40 months using global positioning system-enabled in-vehicle dataloggers, recording trip frequency, duration, distance, time of day, speeding, hard braking, and spatial mobility (entropy, maximum distance, radius of gyration). Longitudinal changes were analyzed using linear mixed-effect models, adjusting for age, sex, race, education, and APOE ε4. Logistic regression with reciever operator curve analysis evaluated discrimination between older adults with MCI and those with NC, compared with conventional sociodemographic and genetic markers. RESULTS: < 0.001). Key features such as medium trip distance, speeding events, entropy, and maximum distance distinguished drivers with MCI from those with NC (area under the curve [AUC] 0.82, 95% CI 0.75-0.89). Adding demographics, APOE ε4, and cognitive composite improved AUC to 0.87 (95% CI 0.81-0.93). DISCUSSION: MCI was associated with progressive declines in driving frequency, complexity, and spatial range, supporting naturalistic driving data as a potential unobtrusive digital biomarker for early cognitive decline. Limitations of the study include a predominantly White, highly educated sample and a lack of external validation, warranting cautious interpretation. Continuous monitoring could augment clinical assessments, inform driving safety decisions, and guide interventions to preserve mobility in aging.

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