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Related Experiment Video

Updated: Aug 30, 2025

Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients

Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients

Published on: May 17, 2020

Development of a Berg Balance Scale Short-Form Using a Machine Learning Approach in Patients With Stroke.

Inga Wang¹, Pei-Chi Li, Shih-Chieh Lee

¹Department of Rehabilitation Sciences & Technology (I.W.), University of Wisconsin-Milwaukee, Milwaukee, Wisconsin; School of Occupational Therapy (P.-C.L., S.-C.L., C.-L.H.), College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Occupational Therapy (S.-C.L.), College of Medicine, National Cheng Kung University, Tainan City, Taiwan; Department of Occupational Therapy (Y.-C.L., C.-L.H.), College of Medical and Health Science, Asia University, Taichung, Taiwan; Institute of Long-Term Care (S.-C.L.), MacKay Medical College, New Taipei City, Taiwan; Department of Physical Therapy (C.-H.W.) and Physical Therapy Room (C.-H.W.), Chung Shan Medical University Hospital, Taichung, Taiwan; and Department of Physical Medicine and Rehabilitation (C.-L.H.), National Taiwan University Hospital, Taipei, Taiwan.

Journal of Neurologic Physical Therapy : JNPT

|September 1, 2022

Summary

Researchers developed a 6-item Berg Balance Scale (BBS-ML) using machine learning for stroke patients. This short form offers improved administrative efficiency while maintaining high predictive power for balance assessment.

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Area of Science:

Rehabilitation Science
Biomedical Engineering
Clinical Assessment Tools

Background:

The Berg Balance Scale (BBS) is a widely used clinical tool for assessing balance in individuals, particularly post-stroke.
Traditional BBS administration can be time-consuming in routine clinical and research settings.
This study aimed to develop a machine learning-derived short form of the BBS (BBS-ML) to enhance efficiency.

Discussion:

The 6-item BBS-ML demonstrated strong psychometric properties, including high predictive power (R2) and narrow limits of agreement (LoA).
The developed BBS-ML offers a promising alternative for improving administrative efficiency in balance assessments.
Preliminary external validation in an independent stroke sample supports the generalizability of the BBS-ML.

Key Insights:

A 6-item Berg Balance Scale (BBS-ML) was successfully developed using an artificial neural network model and feature selection.
The BBS-ML achieved high predictive accuracy (R2=0.97) and a narrow 95% limit of agreement (LoA=9.7) in the development sample.
External validation confirmed the BBS-ML's robust performance in a separate cohort of individuals with stroke (R2=0.99, LoA=10.6).

Outlook:

Further research is recommended to comprehensively evaluate the psychometric properties of the 6-item BBS-ML.
Investigating the clinical utility and application of the BBS-ML across diverse patient populations and settings is warranted.
The BBS-ML has the potential to streamline balance assessments, facilitating more efficient patient management and research.