Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysis

Abstract A comprehensive analysis integrating kinematic, kinetic, and electromyographic data to evaluate balance impairments in patients with stroke is lacking. We investigated balance disparities in patients with balance impairment following stroke using principal component analysis (PCA). The comp...

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Main Authors: Jieun Cho, Sunghe Ha, Jooyoung Lee, Minsuk Kim, Hogene Kim
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-99710-5
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author Jieun Cho
Sunghe Ha
Jooyoung Lee
Minsuk Kim
Hogene Kim
author_facet Jieun Cho
Sunghe Ha
Jooyoung Lee
Minsuk Kim
Hogene Kim
author_sort Jieun Cho
collection DOAJ
description Abstract A comprehensive analysis integrating kinematic, kinetic, and electromyographic data to evaluate balance impairments in patients with stroke is lacking. We investigated balance disparities in patients with balance impairment following stroke using principal component analysis (PCA). The complete waveforms of lower-limb-joint angles, centre of pressure, and muscle activity in 43 stroke patients during four Berg Balance Scale (BBS) standing balance tasks were analysed. Multiple regression analysis using principal components (PCs) was conducted to predict BBS scores. Thirteen patients had balance impairments (BBS score < 45). Significant differences in bilateral standing PCs were observed between patients with and without balance impairments during the standing balance tasks (p < 0.2). The strongest predictor of BBS score was the performance of the paretic leg during quiet standing with open eyes (p < 0.01). Key contributors to balance impairment included bilateral sagittal plane ankle and pelvic joint angles, bilateral vertical ground response forces, and paretic plantar-flexor activation across all standing tasks. These findings highlight that postural control of the paretic limb is a key determinant of balance ability, with distinct balance strategies observed across ability levels. Additionally, PCA effectively quantified balance impairments, revealing significant associations with Fugl-Meyer lower extremity, ankle joint range of motion, and strength. These results emphasize the role of sagittal plane postural control and plantar-flexor activation in stability and suggest that PCA may be a valuable tool for developing targeted rehabilitation strategies.
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spelling doaj-art-965274cf582e475a893ca7e4f80e16fa2025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-99710-5Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysisJieun Cho0Sunghe Ha1Jooyoung Lee2Minsuk Kim3Hogene Kim4Translational Research Centre on Rehabilitation Robots, National Rehabilitation Centre, Ministry of Health & WelfareDepartment of Sports Rehabilitation Medicine, Kyungil UniversityDepartment of Applied Statistics, Chung-Ang UniversityDepartment of Applied Statistics, Chung-Ang UniversityDepartment of Mechanical Engineering, University of MichiganAbstract A comprehensive analysis integrating kinematic, kinetic, and electromyographic data to evaluate balance impairments in patients with stroke is lacking. We investigated balance disparities in patients with balance impairment following stroke using principal component analysis (PCA). The complete waveforms of lower-limb-joint angles, centre of pressure, and muscle activity in 43 stroke patients during four Berg Balance Scale (BBS) standing balance tasks were analysed. Multiple regression analysis using principal components (PCs) was conducted to predict BBS scores. Thirteen patients had balance impairments (BBS score < 45). Significant differences in bilateral standing PCs were observed between patients with and without balance impairments during the standing balance tasks (p < 0.2). The strongest predictor of BBS score was the performance of the paretic leg during quiet standing with open eyes (p < 0.01). Key contributors to balance impairment included bilateral sagittal plane ankle and pelvic joint angles, bilateral vertical ground response forces, and paretic plantar-flexor activation across all standing tasks. These findings highlight that postural control of the paretic limb is a key determinant of balance ability, with distinct balance strategies observed across ability levels. Additionally, PCA effectively quantified balance impairments, revealing significant associations with Fugl-Meyer lower extremity, ankle joint range of motion, and strength. These results emphasize the role of sagittal plane postural control and plantar-flexor activation in stability and suggest that PCA may be a valuable tool for developing targeted rehabilitation strategies.https://doi.org/10.1038/s41598-025-99710-5StrokeBalance impairmentBerg Balance ScalePrincipal component analysisKinematics
spellingShingle Jieun Cho
Sunghe Ha
Jooyoung Lee
Minsuk Kim
Hogene Kim
Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysis
Scientific Reports
Stroke
Balance impairment
Berg Balance Scale
Principal component analysis
Kinematics
title Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysis
title_full Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysis
title_fullStr Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysis
title_full_unstemmed Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysis
title_short Standing balance test predicts the Berg Balance Scale score in patients with stroke using principal component analysis
title_sort standing balance test predicts the berg balance scale score in patients with stroke using principal component analysis
topic Stroke
Balance impairment
Berg Balance Scale
Principal component analysis
Kinematics
url https://doi.org/10.1038/s41598-025-99710-5
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AT jooyounglee standingbalancetestpredictsthebergbalancescalescoreinpatientswithstrokeusingprincipalcomponentanalysis
AT minsukkim standingbalancetestpredictsthebergbalancescalescoreinpatientswithstrokeusingprincipalcomponentanalysis
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