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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Clinical Manifestations.

Sai Santosh Reddy Danda1, Yi Lu Murphy2, Amanda Cook Maher3

  • 1University of Michigan-Dearborn, Dearborn, MI, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

Driving patterns in cognitively normal older adults reveal differences between those with amyloid plaques (Aβ+) and those without (Aβ-). Aβ+ individuals drove more, longer distances, and made fewer turns, suggesting potential early Alzheimer's Disease (AD) detection through driving behavior analysis.

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Area of Science:

  • Neuroscience
  • Gerontology
  • Transportation Safety

Background:

  • Subtle cognitive decline, prodromal to Alzheimer's Disease (AD), is challenging to detect in early stages.
  • Complex tasks like driving integrate multiple cognitive functions, making driving behavior a potential indicator of early cognitive shifts.
  • This study compares driving patterns in cognitively normal older adults with and without brain amyloid plaques (Aβ+), who are at higher risk for AD.

Purpose of the Study:

  • To identify distinct driving behavior patterns between amyloid-positive (Aβ+) and amyloid-negative (Aβ-) cognitively normal older adults.
  • To explore the potential of naturalistic driving data as an early detection method for cognitive decline associated with Alzheimer's Disease.
  • To differentiate individuals at higher risk for AD based on their driving characteristics.

Main Methods:

  • Naturalistic driving data were collected from 30 Aβ+ and 35 Aβ- cognitively normal older adults over 30 days.
  • Vehicular, physiological, and video data were analyzed, focusing on driving attributes (speed, acceleration, jerks, deceleration) and trip characteristics (distance, duration, time of day, turns).
  • Spatial patterns (unique destinations, radius of gyration) were assessed, with group differences analyzed using independent sample t-tests.

Main Results:

  • Aβ+ participants exhibited significantly greater overall driving distance, a higher proportion of longer trips, and a larger radius of gyration compared to Aβ- participants.
  • Aβ+ individuals showed more frequent large negative jerk events and fewer deceleration events, alongside more unique destinations visited.
  • Conversely, Aβ+ participants completed more trips on average but made fewer left and right turns than Aβ- participants.

Conclusions:

  • Observed differences in driving patterns between Aβ+ and Aβ- groups may serve as early warning signals for subtle cognitive changes related to AD.
  • Naturalistic driving features show promise in distinguishing individuals at different risks for AD.
  • Future research will focus on developing machine learning systems to classify Aβ+ and Aβ- individuals using driving data.