: The Fugl-Meyer Assessment (FMA) is widely used as the gold standard in stroke research. However, the FMA has not been used in general clinical practice, which may be related to the fact that the FMA is a time-consuming measurement. Therefore, the FMA (upper extremity motor section) has already been shortened to a 6-item version using Rasch analysis for routine assessments of patients with low endurance. Although the shortened FMA has already demonstrated sound clinical utility, data on its psychometric properties remain insufficient.: This study aimed to investigate the psychometric properties of the shortened FMA for the affected upper extremity in patients following stroke.: A retrospective single-center study involving 30 patients was conducted. This study was registered in 2018 as a pre-initiation condition. The data used in this study were obtained from a study conducted between 2016 and 2017. The FMA (33- and 6-item versions) and the Action Research Arm Test, the Box-and-Block Test, and the Motor Activity Log were employed, and inter-rater reliability/agreement, validity, and internal consistency were assessed.: Regarding inter-rater reliability, the intraclass correlation coefficient was 0.994 (95% confidence interval: 0.988-0.997; < .001). The mean differences between the raters of the shortened FMA were 0.07, and the limits of agreement were calculated to be between -0.81 and 0.95. Regarding the motor-related measurements, Spearman's rho were all higher than 0.91. On the other hand, regarding the sensation and joint motion/pain domain, Spearman's rho ranged from 0.25 to 0.50, and Cronbach's alpha was 0.92.: The shortened FMA can reliably assess the affected upper extremity in patients with hemiparesis after stroke.
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http://dx.doi.org/10.1080/10749357.2019.1701176 | DOI Listing |
Background: In Alzheimer's Disease (AD) trials, clinical scales are used to assess treatment effect in patients. Minimizing statistical uncertainty of trial outcomes is an important consideration to increase statistical power. Machine learning models can leverage baseline data to create AI-generated digital twins - individualized predictions (or prognostic scores) of how each patient's clinical outcomes may change during a trial assuming they received placebo.
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