Continuous gait monitoring discriminates community‐dwelling mild Alzheimer's disease from cognitively normal controls
Vijay R. Varma, Rahul Ghosal, Inbar Hillel, Dmitri Volfson, Jordan Weiss, Jacek Urbanek, Jeffrey M. Hausdorff, Vadim Zipunnikov, Amber Watts
Abstract
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.