Abstract Details

Title Objective Real-world Dyadic Driving: A Novel Social Biomarker to Predict Cognitive Function in Drivers with MCI or AD

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P3 - Poster Session 3 (5:30 PM-6:30 PM)

Poster/Presentation
Number 9-017

Objective Investigate prevalence of real-world dyadic driving as a longitudinal predictor of cognitive function in urban and rural dwellers with mild cognitive impairment (MCI) or Alzheimer’s disease (AD).

Background Research on older drivers at risk for cognitive decline due to MCI has focused on individuals. Cognitive decline affects driver performance, exposure, and agency. As disease progresses, a “co-pilot” (family member or friend) may accompany the driver, creating a verifiable social dyad, and a potential real-world social biomarker of the natural history and progression of disease.

Design/Methods 35 participants (mean age=76.9; male=24, female=11; MCI=31, AD=4) completed demographic questionnaires (including rurality, indexed by home ZIP code). A composite score, derived from neuropsychological tests spanning five cognitive domains, indexed cognition at study start (baseline) and one year later. Unobtrusive video-electronic systems, installed in each driver’s own vehicle (for 3 months at baseline), captured real-world solo and dyadic driving (percentage of drives with a passenger seat confederate, confirmed by video analysts).

Results We analyzed 9,553 videos across all participants. Pearson correlational analyses showed a curvilinear relationship between dyadic driving and cognitive function at baseline (p=0.11) and one year later (p=0.06). These relationships were significant after controlling for driver age and sex (ps<0.03). Moderation analyses showed an effect of driver home ZIP code (p<0.10); lower cognitive scores were associated with increased dyadic driving for urban, not rural, drivers.

Conclusions

This study underscores the potential value of objective real-world dyadic driving as a novel social biomarker to index and predict cognitive function in drivers with MCI or AD. Dyadic driving appears to predict cognitive functions in drivers with MCI or AD at baseline and one year later. Accessibility and need for a “copilot” appear to differ across urban and rural living communities, which may evolve with modern trends of vehicle automation support.

Authors/Disclosures
Matthew Rizzo, MD, FAAN (University of Nebraska Medical Center) PRESENTER	The institution of Dr. Rizzo has received research support from NIH.
Sam B. Shipley (University of Nebraska Medical Center)	Mr. Shipley has nothing to disclose.
Karla Lynch	No disclosure on file
Jeremy Robson (University of Nebraska Medical Center)	No disclosure on file
Elizabeth Vlock	No disclosure on file
Jun Ha Chang, PhD (University of Nebraska Medical Center)	Jun Ha Chang has nothing to disclose.
Kuan-Hua Chen (University of Nebraska Medical Center)	No disclosure on file

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