Characterizing Navigational Changes in Preclinical Alzheimer’s Disease: A Route Complexity Metric Derived from Naturalistic Driving Data
Author(s) -
Kelly Long,
Ganesh M. Babulal,
Sayeh Bayat
Publication year - 2025
Publication title -
ieee journal of translational engineering in health and medicine
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.653
H-Index - 24
eISSN - 2168-2372
DOI - 10.1109/jtehm.2025.3619802
Subject(s) - bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , signal processing and analysis , robotics and control systems , general topics for engineers
Objective: To examine how early pathophysiological changes in Alzheimer’s disease (AD) affect navigational decision-making by analyzing the complexity of driving routes in older adults with and without preclinical AD. Methods: We developed a novel route complexity metric based on the number of left and right turns and the deviation from the most direct path, accounting for cognitive load during navigation. Naturalistic GPS driving data were collected for a year from 111 older adults aged 65–85, with preclinical AD status determined via cerebrospinal fluid amyloid biomarkers. A multiple linear regression model was used to assess the relationship between age, preclinical AD status, and route complexity. Results: The findings of this study indicate that preclinical AD may influence the navigational abilities of older adults. After controlling for age, participants with preclinical AD chose routes with higher baseline complexity than the control group. It further revealed that participants with preclinical AD selected routes with lower complexity as they aged—a trend not observed in healthy controls. Conclusion: Preclinical AD is associated with changes in spatial decision-making that are observable in real-world driving behaviours. The age-related decline in route complexity among those with preclinical AD may reflect compensatory strategies or progressive cognitive changes. Clinical Impact: This study presents a non-invasive, behaviour-based metric that could support early detection of cognitive decline. It may also inform the design of personalized mobility interventions and dementia-friendly mobility systems.
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