Test-Retest Reliability and Responsiveness of the Machine Learning-Based Short-Form of the Berg Balance Scale in Persons With Stroke.

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. Repeated-measures design. A department of rehabilitation in a medical center. 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. Not applicable. BBS-ML. 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. 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.