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

Abstract

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.

Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning

“…Previous studies have used regression algorithms to develop a shortened version of the scale without a cutoff (or call it a threshold) [22,[24][25][26], making it difficult for teachers and clinicians to categorize participants as positive or negative cases based on the shortened version of the scale alone. In contrast, the BWAQ-ML is a shortened version of the BWAQ that has been developed based on machine learning classification algorithms with an explicit cutoff, and the results that can be derived from its use are easier to understand and interpret.…”

“…Morrison reduced the Cognitive Distortions Questionnaire (which involves 15 items) into a 5-item ultrashort version, which was reduced by 67% from the original questionnaire, with an R 2 of between 78.2 and 85.5% [25]. Lin reduced the Fugl-Meyer motor scale (which contains 50 items) into a short version of 10 items, a reduction of 80% in number, with Pearson's correlation coefficient (r) ranging between 0.88 and 0.98 with the original measurement tool [26]. Machine learning techniques are a reliable method capable of simplifying psychometric instruments.…”

Development of a Berg Balance Scale Short-Form Using a Machine Learning Approach in Patients With Stroke

“…First, an ML-based short-form measure can provide scores comparable with those of the original one. 36,37 Such an ML-based shortened measure can be an alternative to the original measure to improve the efficiency of assessments. Second, the administrative methods (eg, performance rating for the BBS) and score interpretation of ML-based short-form measures are identical to those of the original measures.…”

“…Two advantages of ML-based short-form measures have been demonstrated. First, an ML-based short-form measure can provide scores comparable with those of the original one 36,37. Such an ML-based shortened measure can be an alternative to the original measure to improve the efficiency of assessments.…”