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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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Identifying and Predicting Cognitive Decline Using Multi-Modal Sensor Data and Machine Learning Approach.

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Summary
This summary is machine-generated.

Naturalistic driving behavior and sleep patterns show promise as non-invasive digital biomarkers for detecting cognitive decline in Alzheimer

Keywords:
Alzheimer’sDigital BiomarkerLeave-One-Subject-Out ApproachMachine LearningMild Cognitive ImpairmentNaturalistic Driving

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Area of Science:

  • Neuroscience
  • Gerontology
  • Digital Health

Background:

  • Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) pose significant global health challenges, necessitating early detection methods.
  • Current diagnostic tools for AD and MCI are often invasive and costly, driving the need for scalable, non-invasive biomarkers.
  • Naturalistic driving behavior and sleep patterns are increasingly recognized for their potential as digital biomarkers.

Purpose of the Study:

  • To investigate the efficacy of naturalistic driving behavior and sleep data as digital biomarkers for identifying cognitive decline.
  • To develop and validate a multi-modal framework for classifying and predicting cognitive status in individuals at risk for AD and MCI.
  • To assess the potential of this approach for early detection and monitoring of neurodegenerative conditions.

Main Methods:

  • A cohort of 118 participants (8 AD, 65 MCI, 45 cognitively healthy) was studied.
  • Multi-modal data including demographics, cognitive assessments, 3-month naturalistic driving data, and sleep patterns (actigraphy) were collected.
  • An XGBoost-based framework with a two-phase validation (classification and 1-year prediction) using Leave-One-Subject-Out Cross-Validation (LOSO-CV) was implemented.

Main Results:

  • The multi-modal classifier achieved strong classification performance (accuracy 68.64%, precision 73.97%, F1-score 74.48%) and prediction performance (accuracy 70.48%, precision 71.88%, F1-score 74.80%).
  • Models incorporating demographics and driving features showed the highest recall (76.39%) for classification.
  • Key predictive features included sex, mean awakening duration, age, average acceleration, and sleep efficiency.

Conclusions:

  • Naturalistic driving behavior and sleep characteristics serve as valuable, non-invasive digital biomarkers for cognitive assessment.
  • The developed multi-modal framework demonstrates robust capabilities in classifying and predicting cognitive decline.
  • This methodology offers a scalable and generalizable approach for early detection and monitoring of neurodegenerative diseases and other chronic conditions.