Test-Retest Reliability and Responsiveness of the Machine Learning-Based Short-Form of the Berg Balance Scale in Persons With Stroke.
Journal
Archives of physical medicine and rehabilitation
ISSN
1532-821X
Date Issued
2024-11-09
Author(s)
Abstract
To examine the test–retest reliability, responsiveness, and clinical utility of the machine learning-based short form of the Berg Balance Scale (BBS-ML) in persons with stroke. Design: Repeated-measures design. Setting: A department of rehabilitation in a medical center. Participants: This study recruited 2 groups: 50 persons who were more than 6 months post-stroke to examine the test–retest reliability, and 52 persons who were within 3 months post-stroke to examine the responsiveness. Test–retest reliability was investigated by administering assessments twice at a 2-week interval. Responsiveness was investigated by gathering data at admission and discharge from the hospital. Interventions: Not applicable. Main Outcome Measure: BBS-ML. Results: The BBS-ML exhibited excellent test–retest reliability (intraclass correlation coefficient=0.99), acceptable minimal random measurement error (minimal detectable change %=13.6%), and good responsiveness (Kazis’ effect size and standardized response mean values≥1.34). On average, the participants completed the BBS-ML in around 6 minutes per administration. Conclusions: Our findings indicate that the BBS-ML appears an efficient measure with excellent test–retest reliability and responsiveness. Moreover, the BBS-ML may be used as a substitute for the original BBS to monitor the progress of balance function in persons with stroke.
Subjects
Berg Balance Scale
Clinical utility
Machine learning
Rehabilitation
Responsiveness
Stroke
Test–retest reliability
SDGs
Type
journal article