Background and Purpose: Previous evidence that the Postural Assessment Scale for Stroke (PASS) and the Berg Balance Scale (BBS) have similar responsiveness is doubtful. Compared with the BBS, the PASS has more items assessing basic balance abilities (such as postural transition during lying and sitting), so it should be more likely to detect changes in patients with severe balance deficits. We aimed to compare the responsiveness of the PASS and the BBS in patients with stroke who have severe balance deficits. Methods: The PASS and BBS scores of 49 patients with severe balance deficits at 14 and 30 days after stroke were retrieved. The group-level responsiveness was examined with the standardized response mean (SRM). The individual-level responsiveness was examined by the proportion of patients who achieved clinically significant improvements (ie, their pre-post change scores in the PASS/BBS exceeded the minimal detectable change with 95% confidence of each measure). The responsiveness of the 2 measures was compared using the bootstrap approach. Results and Discussion: The comparisons of responsiveness showed significant differences between the PASS and the BBS at both the group and individual levels. At the group level, the PASS indicated moderate changes in balance function (SRM = 0.79), but the BBS indicated only small changes (SRM = 0.39). At the individual level, the PASS showed that 42.9% of patients had clinically significant improvements, while the BBS showed that only 6.1% of patients had clinically significant improvements. Conclusions: Compared with the BBS, the PASS was better able to detect balance improvements in patients having severe balance deficits. The PASS is recommended as an outcome measure to detect change in balance in patients with stroke who have severe balance deficits.

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Objective The Fugl-Meyer motor scale is a well-validated measure for assessing upper extremity and lower extremity motor functions in people with stroke. The Fugl-Meyer Assessment (FM) motor scale contains numerous items (50), which reduces its clinical usability. The purpose of this study was to develop a short form of the FM for people with stroke using a machine learning methodology (FM-ML) and compare the efficiency (ie, number of items) and psychometric properties of the FM-ML with those of other FM versions, including the original FM, the 37-item FM, and the 12-item FM. Methods This observational study with follow-up used a secondary data analysis. For developing the FM-ML, the random lasso method of ML was used to select the 10 most informative items (in terms of index of importance). Next, the scores of the FM-ML were calculated using an artificial neural network. Finally, the concurrent validity, predictive validity, responsiveness, and test–retest reliability of all FM versions were examined. Results The FM-ML used fewer items (80% fewer than the FM, 73% fewer than the 37-item FM, and 17% fewer than the 12-item FM) to achieve psychometric properties comparable to those of the other FM versions (concurrent validity: Pearson r = 0.95–0.99 vs 0.91–0.97; responsiveness: Pearson r = 0.78–0.91 vs 0.33–0.72; and test–retest reliability: intraclass correlation coefficient = 0.88–0.92 vs 0.93–0.98). Conclusion The findings preliminarily support the efficiency and psychometric properties of the 10-item FM-ML. Impact The FM-ML has potential to substantially improve the efficiency of motor function assessments in patients with stroke.

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