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

Gong Hong Lin, Inga Wang, Shih Chieh Lee, Chien Yu Huang, Yi Ching Wang, Ching Lin Hsieh

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Objective: 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. Design: 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. Setting: Rehabilitation units in hospitals. Participants: A total of 408 individuals post-stroke (N=408). Interventions: Not applicable. Main Outcome Measures: The 30-item FMA-UE. Results: 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). Conclusions: 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.

Original languageEnglish
Pages (from-to)1219-1226
Number of pages8
JournalArchives of Physical Medicine and Rehabilitation
Issue number8
Publication statusPublished - Aug 2023


  • Machine learning
  • Motor skills
  • Patient outcome assessment
  • Rehabilitation
  • Stroke
  • Upper extremity

ASJC Scopus subject areas

  • Physical Therapy, Sports Therapy and Rehabilitation
  • Rehabilitation


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