Literature DB >> 29071439

Cognition is associated with gait variability in individuals with multiple sclerosis.

Katherine L Hsieh1, Ruopeng Sun1, Jacob J Sosnoff2.   

Abstract

Fluctuations in gait, or gait variability, are closely related to cognitive function in various clinical populations. However, there are limited data on this relationship in multiple sclerosis (MS) patients. This investigation determined whether cognitive function as measured by processing speed is associated with gait variability in individuals with MS. This secondary analysis included 191 individuals with MS who underwent gait assessment and cognitive assessment. Cognitive processing speed was index by symbol digit modalities test (SDMT). Gait variability was indexed by step length and step time coefficient of variation (CV). Hierarchical linear regressions were performed to examine whether SDMT scores would predict step length and step time CV. After adjusting for age, gender, and disability, we found that SDMT was a significant predictor of step time CV (p < 0.001) and step length CV (p = 0.03). Overall, slower cognitive processing speed was significantly associated with greater gait variability. It is speculated that neural damage in MS patients impairs both cognitive processing speed and gait control. This study provides further evidence that motor and cognitive functions are interrelated.

Entities:  

Keywords:  Cognitive processing speed; Gait variability; Multiple sclerosis; Walking

Mesh:

Year:  2017        PMID: 29071439     DOI: 10.1007/s00702-017-1801-0

Source DB:  PubMed          Journal:  J Neural Transm (Vienna)        ISSN: 0300-9564            Impact factor:   3.575


  39 in total

Review 1.  Cognitive motor interference while walking: a systematic review and meta-analysis.

Authors:  Emad Al-Yahya; Helen Dawes; Lesley Smith; Andrea Dennis; Ken Howells; Janet Cockburn
Journal:  Neurosci Biobehav Rev       Date:  2010-09-15       Impact factor: 8.989

2.  Gait variability measures reveal differences between multiple sclerosis patients and healthy controls.

Authors:  Jeffrey P Kaipust; Jessie M Huisinga; Mary Filipi; Nicholas Stergiou
Journal:  Motor Control       Date:  2012-04       Impact factor: 1.422

3.  Multiple sclerosis; earning a living.

Authors:  L Scheinberg; N Holland; N Larocca; P Laitin; A Bennett; H Hall
Journal:  N Y State J Med       Date:  1980-08

Review 4.  Axonal and neuronal degeneration in multiple sclerosis: mechanisms and functional consequences.

Authors:  C Bjartmar; B D Trapp
Journal:  Curr Opin Neurol       Date:  2001-06       Impact factor: 5.710

5.  Gait variability and fall risk in community-living older adults: a 1-year prospective study.

Authors:  J M Hausdorff; D A Rios; H K Edelberg
Journal:  Arch Phys Med Rehabil       Date:  2001-08       Impact factor: 3.966

6.  Dual task training in persons with Multiple Sclerosis: a feasability randomized controlled trial.

Authors:  Jacob J Sosnoff; Douglas A Wajda; Brian M Sandroff; Kathleen L Roeing; JongHun Sung; Robert W Motl
Journal:  Clin Rehabil       Date:  2017-03-20       Impact factor: 3.477

7.  Is speed of processing or working memory the primary information processing deficit in multiple sclerosis?

Authors:  John DeLuca; Gordon J Chelune; David S Tulsky; Jean Lengenfelder; Nancy D Chiaravalloti
Journal:  J Clin Exp Neuropsychol       Date:  2004-06       Impact factor: 2.475

8.  Gait variability and disability in multiple sclerosis.

Authors:  Michael J Socie; Robert W Motl; John H Pula; Brian M Sandroff; Jacob J Sosnoff
Journal:  Gait Posture       Date:  2012-11-13       Impact factor: 2.840

9.  Myelin breakdown mediates age-related slowing in cognitive processing speed in healthy elderly men.

Authors:  Po H Lu; Grace J Lee; Todd A Tishler; Michael Meghpara; Paul M Thompson; George Bartzokis
Journal:  Brain Cogn       Date:  2012-11-27       Impact factor: 2.310

10.  Gait variability and multiple sclerosis.

Authors:  Michael J Socie; Jacob J Sosnoff
Journal:  Mult Scler Int       Date:  2013-03-03
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  5 in total

1.  The relationship between gait variability and cognitive functions differs between fallers and non-fallers in MS.

Authors:  Alon Kalron; Roy Aloni; Mark Dolev; Lior Frid; Uri Givon; Shay Menascu
Journal:  J Neural Transm (Vienna)       Date:  2018-01-19       Impact factor: 3.575

2.  Using Body-Worn Sensors to Detect Changes in Balance and Mobility After Acute Aerobic Exercise in Adults with Multiple Sclerosis.

Authors:  Susan L Kasser; Jesse V Jacobs; Jeremy Sibold; Avery Marcus; Laurel Cole
Journal:  Int J MS Care       Date:  2020 Jan-Feb

3.  The Effect of Body Composition on Gait Variability Varies with Age: Interaction by Hierarchical Moderated Regression Analysis.

Authors:  Yungon Lee; Sunghoon Shin
Journal:  Int J Environ Res Public Health       Date:  2022-01-21       Impact factor: 3.390

4.  The application of principal component analysis to characterize gait and its association with falls in multiple sclerosis.

Authors:  Andrew S Monaghan; Jessie M Huisinga; Daniel S Peterson
Journal:  Sci Rep       Date:  2021-06-17       Impact factor: 4.996

5.  Gait Variability Is Associated With the Strength of Functional Connectivity Between the Default and Dorsal Attention Brain Networks: Evidence From Multiple Cohorts.

Authors:  On-Yee Lo; Mark A Halko; Kathryn J Devaney; Peter M Wayne; Lewis A Lipsitz; Brad Manor
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2021-09-13       Impact factor: 6.591

  5 in total

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