Test-retest Reliability and Responsiveness of the Machine Learning-based Short-form of the Berg Balance Scale in Persons with Stroke.

Abstract

Objective: 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 two 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 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 utilized as a substitute for the original BBS to monitor the progress of balance function in persons with stroke.