# Identification of cognitive decline and dementia: Prediction by everyday driving behaviors and physiological responses

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $1,506,851

## Abstract

As the population continues to age and rates of late-life cognitive impairment rise, early detection of cognitive
impairment is increasingly important for the timely implementation of interventions and safety initiatives. This
may be particularly important in individuals found to have high brain amyloid burden, putting them at particular
risk for Alzheimer’s disease and related disorders (ADRD) of the brain. Performance changes in challenging,
complex, high-stakes daily activities, such as driving, and accompanying physiological responses may together
provide an inexpensive avenue for early detection. This may serve the dual purpose of alerting individuals or
health care providers to early cognitive impairment, as well as to potential safety issues. Sophisticated in-car
technology that is increasingly becoming standard in new vehicles may provide the means to unobtrusively
capture sensitive information about naturalistic driving behaviors and potentially assist with early detection of
cognitive impairment. The proposed study will apply a novel approach to unobtrusively monitor older drivers in
(a) naturalistic, (b) fixed course, and (c) simulator driving situations. Machine learning approaches will be used
to select key features of driving behaviors and physiological measures of arousal in all driving scenarios and
eye tracking measures from fixed and simulator drives to predict drivers’ clinical diagnosis: young adult drivers,
healthy older drivers with and without high amyloid burden, and drivers with mild cognitive impairment with
evident amyloid burden. The participants will be followed longitudinally in the Michigan Alzheimer’s Disease
Research Center (MADRC) with annual cognitive and neurological evaluations, as well as repeat driving and
physiological testing at two years from baseline. Understanding and identifying changes in driving behaviors
and how these predict who will develop clinically identifiable cognitive impairment will lead to the development
of a model for early detection of cognitive decline and ADRD.

## Key facts

- **NIH application ID:** 10044799
- **Project number:** 1R01AG068338-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** BRUNO GIORDANI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,506,851
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10044799

## Citation

> US National Institutes of Health, RePORTER application 10044799, Identification of cognitive decline and dementia: Prediction by everyday driving behaviors and physiological responses (1R01AG068338-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10044799. Licensed CC0.

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