In Archives of physical medicine and rehabilitation ; h5-index 61.0
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.
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.
Lin Gong-Hong, Wang Inga, Lee Shih-Chieh, Huang Chien-Yu, Wang Yi-Ching, Hsieh Ching-Lin
2023-Feb-01
Upper limb, machine learning, motor skills, short form, stroke rehabilitation
