Abstract:

Driving among older adults is a critical and timely public health issue. Thirty-eight million licensed drivers are aged 65 years or older, and the number of older adults is growing rapidly. Motor vehicle crashes are a leading cause of injury and death in older adults. Driving fatalities of adults 55 years and older are higher compared to younger adults. Also, fatalities increase with older adult age, such that the risk of death in a crash for drivers 85 years or older is nine times greater than it is for drivers of 69 years or younger. Identifying who will be at most risk of driving decline and predicting when the decline will occur will inform early driving safety intervention trials for older adults.

Alzheimer's disease (AD) is recognized as a major contributor to driving risks. This talk will cover the underlying associations between symptomatic as well as preclinical Alzheimer's Disease and driving. Our cross-sectional results show that cognitively normal persons with more abnormal AD biomarker values at baseline obtain worse scores on an on-road driving test, and our longitudinal analyses show that AD biomarkers predict time to a road test rating of Marginal or Fail. Our most recent results show that using machine learning we can distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD, from their daily driving data collected using in-vehicle GPS devices.