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Driving the neural exposome: Latent mobility states from naturalistic GPS data in older adults
Kenan Li1, Enbal Shacham2, Yiqi Zhu3
1Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, St. Louis, Missouri, USA.
Introduction:
Naturalistic driving provides real-world behavioral indicators of early cognitive and functional changes. This study integrated naturalistic driving GPS trajectories collected from in-vehicle sensors with points of interest (POIs) to quantify daily environmental engagement among older adults who were cognitively normal at enrollment.
Methods:
Data from 438 participants enrolled in the Driving Real-world in-Vehicle Evaluation System Project were used to generate daily POI share vectors and model latent engagement patterns using a logistic-normal hidden Markov model (HMM). Thirteen latent states described distinct modes of environmental interaction. From each participant's inferred state sequence, we derived mobility features - state occupancy, dwell time, transition entropy, and self-transition probability - and examined their differences across clinical status groups and associations with Preclinical Alzheimer's Cognitive Composite (PACC) performance.
Results:
Transition entropy and several state-specific occupancy and dwelling metrics differed across clinical groups, but none of the mobility features were significantly associated with PACC scores.
Discussion:
Mobility-derived behavioral features differentiate clinical status groups and may reflect early functional changes preceding cognitive decline.
