Related Experiment Video
Updated: Nov 17, 2025
Home-Based Monitor for Gait and Activity Analysis
Published on: August 8, 2019
Continuous gait monitoring discriminates community-dwelling mild Alzheimer's disease from cognitively normal controls
Vijay R Varma1, Rahul Ghosal2, Inbar Hillel3
1Clinical and Translational Neuroscience Section Laboratory of Behavioral Neuroscience National Institute on Aging (NIA) National Institutes of Health (NIH) Baltimore Maryland USA.
Introduction:
Few studies have explored whether gait measured continuously within a community setting can identify individuals with Alzheimer's disease (AD). This study tests the feasibility of this method to identify individuals at the earliest stage of AD.
Methods:
Mild AD (n = 38) and cognitively normal control (CNC; n = 48) participants from the University of Kansas Alzheimer's Disease Center Registry wore a GT3x+ accelerometer continuously for 7 days to assess gait. Penalized logistic regression with repeated five-fold cross-validation followed by adjusted logistic regression was used to identify gait metrics with the highest predictive performance in discriminating mild AD from CNC.
Results:
Variability in step velocity and cadence had the highest predictive utility in identifying individuals with mild AD. Metrics were also associated with cognitive domains impacted in early AD.
Discussion:
Continuous gait monitoring may be a scalable method to identify individuals at-risk for developing dementia within large, population-based studies.
