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

Gong-Hong Lin¹, Chien-Yu Huang², Shih-Chieh Lee^3,4

¹Master Program in Long-term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan.

Physical Therapy

|January 29, 2021

Summary

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.

Keywords:

Machine Learning Psychometrics Stroke

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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.