Literature DB >> 20534642

Real-life walking impairment in multiple sclerosis: preliminary comparison of four methods for processing accelerometry data.

Jacob J Sosnoff1, Myla D Goldman, Robert W Motl.   

Abstract

This study further validates accelerometers as a measure of walking impairment in persons with multiple sclerosis. We examined total movement counts and three novel methods of processing accelerometer data (i.e. standard deviation, approximate entropy and detrended fluctuation analysis) for quantifying real-life walking impairment in this population. A total of 70 individuals with a definite diagnosis of multiple sclerosis completed a battery of patient-rated measures of walking impairment and then wore an ActiGraph accelerometer for 7 days. The data were analyzed using multivariate analysis of variance and bivariate correlation analysis. The results indicated that total daily movement counts and standard deviation of daily movement counts differed between groups of persons with mild, moderate, and severe self-reported disability status and who were independently ambulatory or ambulatory with assistance. Those two metrics for the accelerometer data further demonstrated strong correlations with patient-rated measures of walking impairment. By comparison, there were smaller and often non-significant differences in approximate entropy and detrended fluctuation analysis metrics for the accelerometer data as a function of disability and ambulatory status, and only moderate correlations with patient-rated measures of walking impairment. The results confirm that the metric of total daily movement counts correlates with level of disability, ambulatory status, and patient reports of walking impairment in persons with multiple sclerosis. We further demonstrate that variability, indexed by the standard deviation of daily movement counts, correlates with multiple sclerosis-related disability, ambulatory status, and self-reported walking impairment. Such results provide preliminary evidence that variability in accelerometer counts is not simply noise and may provide important information about multiple sclerosis-related walking impairment.

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Mesh:

Year:  2010        PMID: 20534642     DOI: 10.1177/1352458510373111

Source DB:  PubMed          Journal:  Mult Scler        ISSN: 1352-4585            Impact factor:   6.312


  15 in total

1.  Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed.

Authors:  R I Spain; R J St George; A Salarian; M Mancini; J M Wagner; F B Horak; D Bourdette
Journal:  Gait Posture       Date:  2012-01-25       Impact factor: 2.840

2.  Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations.

Authors:  Wanting Xiong; Luca Faes; Plamen Ch Ivanov
Journal:  Phys Rev E       Date:  2017-06-12       Impact factor: 2.529

3.  Accelerometry reveals differences in gait variability between patients with multiple sclerosis and healthy controls.

Authors:  Jessie M Huisinga; Martina Mancini; Rebecca J St George; Fay B Horak
Journal:  Ann Biomed Eng       Date:  2012-11-18       Impact factor: 3.934

Review 4.  The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors.

Authors:  Bruce H Dobkin; Andrew Dorsch
Journal:  Neurorehabil Neural Repair       Date:  2011 Nov-Dec       Impact factor: 3.919

5.  Body-worn sensors capture variability, but not decline, of gait and balance measures in multiple sclerosis over 18 months.

Authors:  Rebecca I Spain; Martina Mancini; Fay B Horak; Dennis Bourdette
Journal:  Gait Posture       Date:  2013-12-21       Impact factor: 2.840

6.  Treatment of walking impairment in multiple sclerosis with dalfampridine.

Authors:  Andrew R Blight
Journal:  Ther Adv Neurol Disord       Date:  2011-03       Impact factor: 6.570

7.  Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study).

Authors:  Layal Shammas; Tom Zentek; Birte von Haaren; Stefan Schlesinger; Stefan Hey; Asarnusch Rashid
Journal:  Biomed Eng Online       Date:  2014-02-06       Impact factor: 2.819

Review 8.  Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review.

Authors:  Valerie A J Block; Erica Pitsch; Peggy Tahir; Bruce A C Cree; Diane D Allen; Jeffrey M Gelfand
Journal:  PLoS One       Date:  2016-04-28       Impact factor: 3.240

9.  A Movement Monitor Based on Magneto-Inertial Sensors for Non-Ambulant Patients with Duchenne Muscular Dystrophy: A Pilot Study in Controlled Environment.

Authors:  Anne-Gaëlle Le Moing; Andreea Mihaela Seferian; Amélie Moraux; Mélanie Annoussamy; Eric Dorveaux; Erwan Gasnier; Jean-Yves Hogrel; Thomas Voit; David Vissière; Laurent Servais
Journal:  PLoS One       Date:  2016-06-07       Impact factor: 3.240

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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