In Physical therapy ; h5-index 46.0
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.
Lin Gong-Hong, Huang Chien-Yu, Lee Shih-Chieh, Chen Kuan-Lin, Lien Jenn-Jier James, Chen Mei-Hsiang, Huang Yu-Hui, Hsieh Ching-Lin
Machine Learning, Psychometrics, Stroke