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

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 & GE;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.Archives of Physical Medicine and Rehabilitation 2023;104:1219-26 & COPY; 2023 by the American Congress of Rehabilitation Medicine.

Automated summary

The study developed and validated a shortened form of the Fugl-Meyer Assessment of Upper Extremity Scale (FMA-UE) using a machine learning approach (FMA-UE-ML). Through data analysis, it was determined that there were significant differences among different versions of FMA-UE-ML, with a 13-item version selected as the final version. The FMA-UE-ML demonstrated good concurrent validity and responsiveness, and it can provide all item scores of the FMA-UE for users.