Dementia impacts a person’s ability to complete day-to-day activities like familiar tasks at work or at home. What if we could identify these changes in everyday behaviors early enough to identify preclinical Alzheimer’s disease? That’s what Dr. Sayeh Bayat, an assistant professor at the University of Calgary, looked to find out. Dr. Bayat is the lead author of a recent paper highlighting how driving behaviors such as braking, following the speed limit and the number of trips taken could predict preclinical Alzheimer’s disease. Dr. Bayat joined the podcast to share findings from the paper and discuss some of the ways engineering and machine learning can help us discover more about dementia and aging.

Guest: Sayeh Bayat, PhD, assistant professor, Department of Geomatics Engineering, University of Calgary

1:05 - What led you to study this intersection of engineering and aging?

3:23 - What inspired you to study the topic of driving and aging?

5:30 - Who was involved in the study, and how long were these participants monitored?

7:01 - What did you find?

7:50 - Can you explain machine learning?

11:10 - Different health and life factors can impact driving. Is that something you’re looking to control for in future studies?

14:59 - How do you account for people who are just bad drivers without any cognitive change?

15:48 - What is the direction for your research in the future?

Learn more about Dr. Sayeh Bayat’s study in the New York Times article, “Seeking Early Signals of Dementia in Driving and Credit Scores” and in the BBC article, “How your driving might reveal early signs of Alzheimer’s”.

Find a free PDF of Dr. Bayat’s paper, “GPS driving: a digital biomarker for preclinical Alzheimer disease,” through the National Library of Medicine.