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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere12131
JournalAlzheimer's and Dementia: Translational Research and Clinical Interventions
Issue number1
StatePublished - 2021


FundersFunder number
Millennium Pharmaceuticals
National Institutes of Health90084034
National Institute on Aging5P30AG035982‐3
Institute for Clinical and Translational Research, University of Wisconsin, MadisonUL1TR000001, UL1RR033179
National Center for Advancing Translational Sciences


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