Literature DB >> 28390935

Evaluation of Clinical Gait Analysis parameters in patients affected by Multiple Sclerosis: Analysis of kinematics.

Giacomo Severini1, Mario Manca2, Giovanni Ferraresi2, Luisa Maria Caniatti2, Michela Cosma2, Francesco Baldasso2, Sofia Straudi2, Monica Morelli2, Nino Basaglia2.   

Abstract

BACKGROUND: Clinical Gait Analysis is commonly used to evaluate specific gait characteristics of patients affected by Multiple Sclerosis. The aim of this report is to present a retrospective cross-sectional analysis of the changes in Clinical Gait Analysis parameters in patients affected by Multiple Sclerosis.
METHODS: In this study a sample of 51 patients with different levels of disability (Expanded Disability Status Scale 2-6.5) was analyzed. We extracted a set of 52 parameters from the Clinical Gait Analysis of each patient and used statistical analysis and linear regression to assess differences among several groups of subjects stratified according to the Expanded Disability Status Scale and 6-Minutes Walking Test. The impact of assistive devices (e.g. canes and crutches) on the kinematics was also assessed in a subsample of patients.
FINDINGS: Subjects showed decreased range of motion at hip, knee and ankle that translated in increased pelvic tilt and hiking. Comparison between the two stratifications showed that gait speed during 6-Minutes Walking Test is better at discriminating patients' kinematics with respect to Expanded Disability Status Scale. Assistive devices were shown not to significantly impact gait kinematics and the Clinical Gait Analysis parameters analyzed.
INTERPRETATION: We were able to characterize disability-related trends in gait kinematics. The results presented in this report provide a small atlas of the changes in gait characteristics associated with different disability levels in the Multiple Sclerosis population. This information could be used to effectively track the progression of MS and the effect of different therapies.
Copyright © 2017. Published by Elsevier Ltd.

Entities:  

Keywords:  6MWT; Clinical Gait Analysis; EDSS; Kinematics; Multiple sclerosis

Mesh:

Year:  2017        PMID: 28390935     DOI: 10.1016/j.clinbiomech.2017.04.001

Source DB:  PubMed          Journal:  Clin Biomech (Bristol, Avon)        ISSN: 0268-0033            Impact factor:   2.063


  5 in total

1.  Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway.

Authors:  Wenting Hu; Owen Combden; Xianta Jiang; Syamala Buragadda; Caitlin J Newell; Maria C Williams; Amber L Critch; Michelle Ploughman
Journal:  Biomed Eng Online       Date:  2022-03-30       Impact factor: 2.819

2.  Profiling walking dysfunction in multiple sclerosis: characterisation, classification and progression over time.

Authors:  Linard Filli; Tabea Sutter; Christopher S Easthope; Tim Killeen; Christian Meyer; Katja Reuter; Lilla Lörincz; Marc Bolliger; Michael Weller; Armin Curt; Dominik Straumann; Michael Linnebank; Björn Zörner
Journal:  Sci Rep       Date:  2018-03-21       Impact factor: 4.379

3.  The Timing of Kinematic and Kinetic Parameters during Gait Cycle as a Marker of Early Gait Deterioration in Multiple Sclerosis Subjects with Mild Disability.

Authors:  Francisco Molina-Rueda; Diego Fernández-Vázquez; Víctor Navarro-López; Juan Carlos Miangolarra-Page; María Carratalá-Tejada
Journal:  J Clin Med       Date:  2022-03-29       Impact factor: 4.241

4.  Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis.

Authors:  Wenting Hu; Owen Combden; Xianta Jiang; Syamala Buragadda; Caitlin J Newell; Maria C Williams; Amber L Critch; Michelle Ploughman
Journal:  Front Artif Intell       Date:  2022-09-29

Review 5.  Gait Pattern in People with Multiple Sclerosis: A Systematic Review.

Authors:  María Coca-Tapia; Alicia Cuesta-Gómez; Francisco Molina-Rueda; María Carratalá-Tejada
Journal:  Diagnostics (Basel)       Date:  2021-03-24
  5 in total

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