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Titre : | Development of a 13-item Short Form for Fugl-Meyer Assessment of Upper Extremity Scale Using a Machine Learning Approach (2023) |
Auteurs : | Gong-Hong Lin ; Inga Wang ; Shih-Chieh Lee ; Chien-Yu Huang ; Yi-Ching Wang ; Ching-Lin Hsieh |
Type de document : | Article |
Dans : | Archives of Physical Medicine and Rehabilitation (Vol. 104, n° 8, 2023) |
Article en page(s) : | p. 1219-1226 |
Note générale : | https://doi.org/10.1016/j.apmr.2023.01.005 |
Langues: | Anglais |
Descripteurs : |
HE Vinci Accident vasculaire cérébral (AVC) ; Apprentissage machine ; Aptitudes motrices ; Évaluation des résultats des patients ; Membre supérieur ; Réadaptation |
Résumé : | 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. |
Disponible en ligne : | Oui |
En ligne : | https://login.ezproxy.vinci.be/login?url=https://www.sciencedirect.com/science/article/pii/S0003999323000497 |