Ontology highlight
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.
SUBMITTER: Varma VR
PROVIDER: S-EPMC7864220 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Varma Vijay R VR Ghosal Rahul R Hillel Inbar I Volfson Dmitri D Weiss Jordan J Urbanek Jacek J Hausdorff Jeffrey M JM Zipunnikov Vadim V Watts Amber A
Alzheimer's & dementia (New York, N. Y.) 20210205 1
<h4>Introduction</h4>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.<h4>Methods</h4>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 l ...[more]