Driving Navigation in Older Adults Across the Alzheimer's Disease Continuum

BACKGROUND AND OBJECTIVES: To examine whether time from amyloid positivity (amyloid time), a continuous measure of biological Alzheimer's disease (AD) progression, is associated with differences in navigation-related driving behaviors among older adults. METHODS: This longitudinal cohort study was drawn from the DRIVES participants who had at least one PET Pittsburgh compound-B (PiB) scan. AD timeline was assessed using amyloid time, estimated with the Sampled Iterative Local Approximation (SILA) approach. SILA aligns individuals relative to the timing of amyloid positivity. Naturalistic driving data were continuously collected using in-vehicle data loggers. Navigation-related behaviors were quantified using trip-chaining and entropy metrics. Linear mixed-effects models examined associations between amyloid time and longitudinal differences in trip chaining behaviors, adjusting for demographic factors. Growth mixture models were used to explore latent trajectory classes, and logistic regression was used to explore demographic characteristics, self-reported medical history, and medication use as potential risk and resilience factors associated with membership in trajectory classes. RESULTS: Greater amyloid time was associated with higher counts and proportions of chained trips and less entropy, indicating more predictable driving patterns over time. Growth mixture modeling identified two distinct trajectory classes for both trip chaining and entropy. Insomnia was associated with a faster increase in trip chaining, whereas psychiatric conditions, such as having a diagnosis of depression or anxiety, were associated with a slower decline in entropy. DISCUSSION: Amyloid time is associated with gradual differences in real-world navigation behaviors among cognitively normal older adults. More trip chaining and less entropy may reflect compensatory planning and reduced driving space in the context of early AD pathology. These findings highlight the utility of naturalistic driving data as ecologically valid, scalable markers of early functional change and underscore the importance of continuous biomarker measures for capturing disease progression prior to clinical symptom onset.