Abstract
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.
Keywords
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Article info
Publication history
Published online: January 31, 2023
Accepted:
January 10,
2023
Received in revised form:
November 28,
2022
Received:
August 31,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
This work was supported by the Ministry of Science and Technology, Taiwan (grant numbers 109-2314-B-002-110-MY3, 110-2811-B-002-547, and 110-2628-B-038-013) and the Taipei Medical University (grant number TMU109-AE1-B06).
Disclosures: none.
List of Abbreviations: FMA-UE, Fugl-Meyer Assessment of Upper Extremity; FMA-UE-ML, FMA-UE short form using a machine learning approach; ICC, intraclass correlation coefficient; LEAPS, Locomotor Experience Applied Post-stroke; SRM, standardized response mean; XGBoost, Extreme Gradient Boosting
Identification
Copyright
© 2023 by the American Congress of Rehabilitation Medicine.
