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A 10-item Fugl-Meyer Motor Scale Based on Machine Learning
Gong-Hong Lin1, Chien-Yu Huang2, Shih-Chieh Lee3,4
1Master Program in Long-term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan.
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.
Insights
A new 10-item Fugl-Meyer motor scale (FM-ML) developed using machine learning offers comparable psychometric properties to longer versions. This efficient tool can significantly improve motor function assessments for stroke patients.
Area of Science:
- Neurology
- Rehabilitation Medicine
- Biomedical Engineering
Background:
- The Fugl-Meyer motor scale (FM) is a standard for assessing motor function post-stroke.
- Its extensive item count (50 items) limits practical clinical application.
Purpose of the Study:
- To develop a machine learning-derived short form of the FM (FM-ML) for stroke patients.
- To evaluate the efficiency and psychometric properties of the FM-ML against existing FM versions.
Main Methods:
- A random lasso machine learning method identified 10 key items for the FM-ML.
- Artificial neural networks were used to calculate FM-ML scores.
- Concurrent validity, predictive validity, responsiveness, and test-retest reliability were assessed for all FM versions.
Main Results:
- The 10-item FM-ML demonstrated comparable psychometric properties to longer FM versions.
- FM-ML achieved high concurrent validity (r=0.95-0.99), responsiveness (r=0.78-0.91), and test-retest reliability (ICC=0.88-0.92).
- FM-ML used significantly fewer items, offering a 80% reduction compared to the original FM.
Conclusions:
- The 10-item FM-ML shows promising efficiency and robust psychometric properties.
- This tool has the potential to enhance the speed and effectiveness of motor function evaluations in stroke rehabilitation.
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