A 10-item Fugl-Meyer Motor Scale Based on Machine Learning

Abstract

Objective: The Fugl-Meyer motor scale (FM) is a well-validated measure for assessing upper extremity and lower extremity motor functions in people with stroke. The FM 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 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 with 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.

Keywords: Machine Learning; Psychometrics; Stroke.