Development of a 13-item Short Form for Fugl-Meyer Assessment of Upper Extremity Scale Using a Machine Learning Approach

  • Gong-Hong Lin
    International Ph.D. Program in Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan
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  • Inga Wang
    Department of Rehabilitation Sciences & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI
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  • Shih-Chieh Lee
    Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan, Taiwan

    School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan

    Institute of Long-Term Care, MacKay Medical College, New Taipei City, Taiwan
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  • Chien-Yu Huang
    School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan

    Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan
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  • Yi-Ching Wang
    School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
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  • Ching-Lin Hsieh
    Corresponding author Ching-Lin Hsieh, Rm. 418, 4F, No. 17, Xuzhou Rd, School of Occupational Therapy, Taipei, Taiwan (R.O.C.).
    School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan

    Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan

    Department of Occupational Therapy, College of Medical and Health Science, Asia University, Taichung, Taiwan
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  • Author Footnotes
    1 Ching-Lin Hsieh and Chien-Yu Huang contributed equally to the work as co-corresponding authors.
Published:January 31, 2023DOI:



      To develop and validate a short form of the Fugl-Meyer Assessment of Upper Extremity Scale (FMA-UE) using a machine learning approach (FMA-UE-ML). In addition, scores of items not included in the FMA-UE-ML were predicted.


      Secondary data from a previous study, which assessed individuals post-stroke using the FMA-UE at 4 time points: 5-30 days post-stroke screen, 2-month post-stroke baseline assessment, 6-month post-stroke assessment, and 12-month post-stroke assessment.


      Rehabilitation units in hospitals.


      A total of 408 individuals post-stroke (N=408).


      Not applicable.

      Main Outcome Measures

      The 30-item FMA-UE.


      We established 29 candidate versions of the FMA-UE-ML with different numbers of items, from 1 to 29, and examined their concurrent validity and responsiveness. We found that the responsiveness of the candidate versions obviously declined when the number of items was less than 13. Thus, the 13-item version was selected as the FMA-UE-ML. The concurrent validity was good (intra-class correlation coefficients ≥0.99). The standardized response means of the FMA-UE-ML and FMA-UE were 0.54-0.88 and 0.52-0.91, respectively. The Pearson's rs between the change scores of the FMA-UE-ML and those of the FMA-UE were 0.96-0.98. The predicted item scores had acceptable to good accuracy (Kappa=0.50-0.92).


      The FMA-UE-ML seems a promising short form to improve administrative efficiency while retaining good concurrent validity and responsiveness. In addition, the FAM-UE-ML can provide all item scores of the FMA-UE for users.


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