Research Papers

Dementia-Specific Gait Profile: A Computational Approach Using Signal Detection Theory and Introduction of an Index to Assess Response to Cognitive Perturbations

[+] Author and Article Information
T. Karakostas

Rehabilitation Institute of Chicago,
345 E. Superior, Suite 1466,
Chicago, IL 60611;
College of Health Professions,
Medical University of South Carolina,
77 President Street, Charleston, SC 29425
e-mail: tkarakosta@ric.org

B. Davis

Applied Linguistics/English,
University of North Carolina Charlotte,
9201 University City Blvd., Charlotte, NC 28223;
College of Nursing, Medical University of South Carolina,
Charleston, SC 29425
e-mail: bdavis@uncc.edu

S. Hsiang

Department of Industrial Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: simon.hsiang@ttu.edu

M. Maclagan

Department of Communication Disorders,
University of Canterbury,
PB 4800, Christchurch, 8140, New Zealand
e-mail: margaret.maclagan@canterbury.ac.nz

D. Shenk

Gerontology Program, Department of Anthropology,
University of North Carolina Charlotte,
9201 University City Blvd., Charlotte, NC 28223
e-mail: dshenk@uncc.edu

Contributed by the Design Engineering Division of ASME for publication in the Journal of Computational and Nonlinear Dynamics. Manuscript received August 1, 2011; final manuscript received August 30, 2012; published online November 15, 2012. Assoc. Editor: Aki Mikkola.

J. Comput. Nonlinear Dynam 8(2), 021017 (Nov 15, 2012) (6 pages) Paper No: CND-11-1122; doi: 10.1115/1.4007857 History: Received August 01, 2011; Revised August 30, 2012

Elderly diagnosed with dementia are three times more likely to fall and over three times more likely to have severe injury compared to cognitively unimpaired elderly. Consequently, there is a need to identify biomarkers that can facilitate early detection, diagnosis, and progression of dementia. One of the characteristics of dementia is the inability to allocate attentional resources to concurrent tasks. Consequently, recent studies have used walking gait in conjunction with another cognitive or motor task to identify biomarkers related to the disease. However, in every study all temporal-spatial gait descriptors are being evaluated and, typically, the nonspecific velocity, double limb support, and stride variability are reported as significant. The purpose, therefore, of this investigation was to use a computational approach to first establish a dementia-specific gait profile irrespective of walking condition (talking, without talking) using the minimum number of temporal-spatial gait descriptors, second to investigate the effect of condition, and third to investigate the effect of an everyday realistic cognitive perturbation, resulting in potential falls, by constructing an index of responsiveness. Six normal elderly and seven diagnosed with dementia walked on an instrumented walkway: (i) without talking, (ii) conversing with an investigator, and (iii) conversing with an investigator, but including as part of the conversation a cognitive perturbation in the form of an unexpected direct question. To accomplish the first two goals we implemented signal detection theory combined with receiver operator characteristic curves. Based on these results we constructed the index of responsiveness that we compared between the two cohorts. Only six of thirteen gait variables were needed to distinguish individuals with dementia from normally aging, irrespective of whether gait was used as a stand-alone task, i.e., without talking, or under a dual-task paradigm, i.e., combined with a conversation. Double limb support was the most sensitive variable to describe adaptation to walking condition. The index of responsiveness was significantly larger for individuals with dementia. The six discriminating temporal-spatial gait descriptors provide new focus for health care professionals involved in diagnosis and treatment of elderly with dementia. The index of responsiveness can be used to describe a bandwidth of safety, identifying individuals with dementia at risk of falling.

Copyright © 2013 by ASME
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Grahic Jump Location
Fig. 1

A representation in the time domain of two strides (left foot strike to left foot strike and right foot strike to right foot strike) during walking. Each stride has a stance phase (approximately 60% of the stride/gait cycle time) and a swing phase (approximately 40% of the stride/gait cycle time). Each stance phase is further divided in a single stance phase (only one foot is on the ground, approximately 50% of the stride/gait cycle time) and a double stance phase (both feet are on the ground, approximately 10% of the stride/gait cycle time).

Grahic Jump Location
Fig. 2

The ROC curve when all temporal-spatial variables (All variables) are used to discriminate the individuals with dementia from those of normal aging plotted against the respective ROC curve for the minimum six gait descriptors (STEP DISC).

Grahic Jump Location
Fig. 3

The ROC curve when all temporal-spatial variables (All variables) are used to discriminate the walking task, plotted against the respective ROC curve for the double support time (STEP DISC)




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