Abstract Details

Title Digital Driving Metrics Index Early Mobility Declines in Patients At Risk for Alzheimer’s Disease

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P18 - Poster Session 18 (5:30 PM-6:30 PM)

Poster/Presentation
Number 3-002

Objective

Develop methods to screen early mobility decline in patients at risk for developing Alzheimer’s disease (AD) and related dementias.

Background Mobility decline in AD and related degenerative disorders may flag warning signs of disease burden, social isolation, depression, caregiver burden, and quality of life. Real-world, sensor-based, digital behavioral data can track mobility decline indexed by reduced driver mobility. Data can inform caregivers on patient risk and potential needs for intervention, even before dementia development.

Design/Methods Real-world (naturalistic) driving was collected from 68 active, non-demented, older drivers (age: mean = 75.39 years, range: 65-89) across two 3-month periods, spaced one year apart. Each year, drivers completed neuropsychological tests of abilities (executive, visuospatial, speed, memory) relevant to aging and driving. Scores ≤1.5 SDs below test means were classified as cognitively impaired, indicating probable mild cognitive impairment (MCI). Seven drivers showed signs of MCI at year 1 and 16 at year 2. Driver mobility (miles driven) overall and across roadway type, traffic density (high vs. low), and lighting (day vs. night) was assessed with mixed effect linear regression models controlling for driver demographics.

Results 13,2803 drives were recorded across 24,9104 miles. Probable MCI drivers showed reduced mobility, driving per person on average 719 miles less per year than drivers without MCI (p = 0.0195) at baseline and one year later. Probable MCI drivers also reduced driving on interstate roads (p = 0.0241), but not high traffic or nighttime roads.

Conclusions Results show promise for using objective, real-world, digital behavior profiles like driver monitoring to screen warnings signs of early neurodegeneration like mobility decline. Study findings show longitudinal reliability and ability to link data to standard, clinical measures. Passive collection of real-world data from patients’ daily lives advances efforts to develop disease biomarkers to identify AD in early stages for effective clinical intervention.

Authors/Disclosures
Jennifer Merickel PRESENTER	No disclosure on file
	No disclosure on file
Jun Ha Chang, PhD (University of Nebraska Medical Center)	Jun Ha Chang has nothing to disclose.
Matthew Rizzo, MD, FAAN (University of Nebraska Medical Center)	The institution of Dr. Rizzo has received research support from NIH.
	No disclosure on file

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