Literature DB >> 30326756

Walking activity and its determinants in free-living ambulatory people in a chronic phase after stroke: a cross-sectional study.

Ingrid van de Port1, Michiel Punt2, Jan Willem Meijer1.   

Abstract

Background: Free-living walking activity and its contributing factors in ambulatory people with stroke is poorly investigated.Objective: Evaluating free-living walking activity and identifying factors associated with free-living walking activity.
Methods: In this cross-sectional study, participants wore an accelerometer to measure their level of walking activity. They also completed the Berg Balance Scale (BBS) and the Timed Up and Go test for functional balance, the Falls Efficacy Scale, the 10-Metre Walk Test and the Geriatric Depression Scale to investigate the relation between the performance tests and walking activity.
Results: The 38 analyzed participants were on average 62 (±11.4) years old and 66 (IQR 64.8) months post stroke. They took an average of 3048.3 ± 1983.1 steps, had 123.3 ± 61.3 walking bouts a day and walked for 32.5 ± 18.2 min a day. Their average speed was 90.3 ± 13.8 steps a minute. The multivariate linear analysis showed that the BBS was the only determinant that was significantly related to all outcomes, except walking bouts.
Conclusion: Free-living walking activity levels in ambulatory people with chronic stroke are low. The BBS is an independent significant predictor of free-living walking activity.Implications for rehabilitationFree-living walking activity can be expressed in different outcomes measured by accelerometry.Free-living walking activity levels in ambulatory people with chronic stroke are low, therefore support to sustain walking in the own environment should be part of the rehabilitation program after stroke.Balance is an important related factor to free-living walking activity which needs attention during rehabilitation after stroke.

Entities:  

Keywords:  Stroke; accelerometer; balance; walking activity

Mesh:

Year:  2018        PMID: 30326756     DOI: 10.1080/09638288.2018.1504327

Source DB:  PubMed          Journal:  Disabil Rehabil        ISSN: 0963-8288            Impact factor:   3.033


  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.  Effect of Virtual Reality Gait Training on Participation in Survivors of Subacute Stroke: A Randomized Controlled Trial.

Authors:  Ilona J M de Rooij; Ingrid G L van de Port; Michiel Punt; Pim J M Abbink-van Moorsel; Michiel Kortsmit; Ruben P A van Eijk; Johanna M A Visser-Meily; Jan-Willem G Meijer
Journal:  Phys Ther       Date:  2021-05-04

3.  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

4.  Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk.

Authors:  Christopher Buckley; M Encarna Micó-Amigo; Michael Dunne-Willows; Alan Godfrey; Aodhán Hickey; Sue Lord; Lynn Rochester; Silvia Del Din; Sarah A Moore
Journal:  Sensors (Basel)       Date:  2019-12-19       Impact factor: 3.576

5.  Arm impairment and walking speed explain real-life activity of the affected arm and leg after stroke.

Authors:  Sofi A Andersson; Anna Danielsson; Fredrik Ohlsson; Jan Wipenmyr; Margit Alt Murphy
Journal:  J Rehabil Med       Date:  2021-06-23       Impact factor: 2.912

  5 in total

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