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Area of Science:

  • Gerontology
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Cognitive decline in older adults necessitates accurate monitoring for timely interventions.
  • Wearable devices offer potential for collecting data (e.g., activity, sleep) linked to cognitive function.
  • Machine learning can leverage sensor data to develop cognitive monitoring systems.

Purpose of the Study:

  • To develop and validate machine learning models for differentiating normal versus poor cognition in older adults using wearable-derived data.
  • To identify specific cognitive domains and associated sensor-based metrics most predictive of cognitive impairment.
  • To establish the feasibility of wearable-based systems for cognitive health monitoring.

Main Methods:

  • Utilized data from over 2400 older adults in the National Health and Nutrition Examination Survey (NHANES).
  • Trained and cross-validated machine learning models (CatBoost, XGBoost, Random Forest) to predict cognitive status based on cognitive test outcomes.
  • Analyzed associations between activity, sleep parameters, and different cognitive subdomains.

Main Results:

  • Machine learning models achieved high accuracy (median AUCs ≥0.82) in predicting poor cognition, particularly for processing speed, working memory, and attention.
  • Activity and sleep parameters showed stronger associations with deficits in processing speed, working memory, and attention.
  • Demonstrated that data like age, education, sleep, activity, and light exposure are collatable via wearables to differentiate cognitive statuses.

Conclusions:

  • Wearable device data, including activity and sleep patterns, can effectively differentiate between normal and poor cognitive function in older adults.
  • Processing speed, working memory, and attention are key cognitive domains amenable to monitoring via wearable sensors.
  • This study provides a proof of concept for wearable-based cognitive monitoring systems and identifies metrics for future causal research on sleep, activity, and cognition.