Dynamic balance and instrumented gait variables are independent predictors of falls following stroke

Kelly Bower; Shamala Thilarajah; Yong-Hao Pua; Gavin Williams; Dawn Tan; Benjamin Mentiplay; Linda Denehy; Ross Clark

doi:10.1186/s12984-018-0478-4

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Dynamic balance and instrumented gait variables are independent predictors of falls following stroke

Journal article

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Dynamic balance and instrumented gait variables are independent predictors of falls following stroke

Kelly Bower, Shamala Thilarajah, Yong-Hao Pua, Gavin Williams, Dawn Tan, Benjamin Mentiplay, Linda Denehy and Ross Clark

Journal of NeuroEngineering and Rehabilitation, Vol.16, 3

2019

DOI: https://doi.org/10.1186/s12984-018-0478-4

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Abstract

stroke

outcome assessment

postural balance

gait

accidental falls

Background: Falls are common following stroke and are frequently related to deficits in balance and mobility. This study aimed to investigate the predictive strength of gait and balance variables for evaluating post-stroke falls risk over 12 months following rehabilitation discharge. Methods: A prospective cohort study was undertaken in inpatient rehabilitation centres based in Australia and Singapore. A consecutive sample of 81 individuals (mean age 63 years; median 24 days post stroke) were assessed within one week prior to discharge. In addition to comfortable gait speed over six metres (6mWT), a depth-sensing camera (Kinect) was used to obtain fast-paced gait speed, stride length, cadence, step width, step length asymmetry, gait speed variability, and mediolateral and vertical pelvic displacement. Balance variables were the step test, timed up and go (TUG), dual-task TUG, and Wii Balance Board-derived centre of pressure velocity during static standing. Falls data were collected using monthly calendars. Results: Over 12 months, 28% of individuals fell at least once. The faller group had increased TUG time and reduced stride length, gait speed variability, mediolateral and vertical pelvic displacement, and step test scores (P < 0.001-0.048). Significant predictors, when adjusted for country, prior falls and assistance (i.e., physical assistance and/or gait aid use) were stride length, step length asymmetry, mediolateral pelvic displacement, step test and TUG scores (P < 0.040; IQR-odds ratio(OR) = 1.37-7.85). With comfortable gait speed as an additional covariate, to determine the additive benefit over standard clinical assessment, only mediolateral pelvic displacement, TUG and step test scores remained significant (P = 0.001-0.018; IQR-OR = 5.28-10.29). Conclusions: Reduced displacement of the pelvis in the mediolateral direction during walking was the strongest predictor of post-stroke falls compared with other gait variables. Dynamic balance measures, such as the TUG and step test, may better predict falls than gait speed or static balance measures.

Details

Title: Dynamic balance and instrumented gait variables are independent predictors of falls following stroke
Authors: Kelly Bower (Author) - University of Melbourne
Shamala Thilarajah (Author) - University of the Sunshine Coast - School of Health & Sports Sciences
Yong-Hao Pua (Author) - Singapore General Hospital
Gavin Williams (Author) - University of Melbourne
Dawn Tan (Author) - Singapore General Hospital, Singapore
Benjamin Mentiplay (Author) - La Trobe University
Linda Denehy (Author) - University of Melbourne
Ross Clark (Author) - University of the Sunshine Coast - School of Health & Sports Sciences
Publication details: Journal of NeuroEngineering and Rehabilitation, Vol.16, 3; 9
Publisher: BioMed Central Ltd.
Date published: 2019
DOI: 10.1186/s12984-018-0478-4
ISSN: 1743-0003
Copyright note: Copyright © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Organisation Unit: University of the Sunshine Coast, Queensland; School of Health and Sport Sciences - Legacy; School of Health and Behavioural Sciences - Legacy; School of Health - Public Health
Language: English
Record Identifier: 99450797402621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Engineering, Biomedical; Neurosciences; Rehabilitation

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