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Background: Multiple sclerosis (MS) is a progressive and neurodegenerative disease of the central nervous system. Its symptoms vary greatly, which makes its diagnosis complex, expensive, and time-consuming. One of its most prevalent symptoms is muscle fatigue. It occurs in about 92% of patients with MS (PwMS) and is defined as a decrease in maximal strength or energy production in response to contractile activity. This article aims to compare the behavior of a healthy control (HC) with that of a patient with MS before and after muscle fatigue. Methods: For this purpose, a static baropodometric test and a dynamic electromyographic analysis are performed to calculate the area of the stabilometric ellipse, the remitting MS (RMS) value, and the sample entropy (SampEn) of the signals, as a proof of concept to explore the feasibility of this test in the muscle fatigue quantitative analysis; in addition, the statistical analysis was realized to verify the results. Results: According to the results, the ellipse area increased in the presence of muscle fatigue, indicating a decrease in postural stability. Likewise, the RMS value increased in the MS patient and decreased in the HC subject and the opposite behavior in the SampEn was observed in the presence of muscle fatigue. Conclusion: Thus, this study demonstrates that SampEn is a viable parameter to estimate muscle fatigue in PwMS and other neuromuscular diseases. [ABSTRACT FROM AUTHOR]

Hierarchical Bayesian models are used in data modeling in different areas in which hierarchical structures are reflected through random effects. Usually the Normal distribution is used to model the random effects. The Inverse-gamma distribution is used as prior distribution for scale parameters with very small values, this selection has been criticized, some authors comment that unstable posterior distributions can be obtained, which causes not robust inference. Distributions such as half-Cauchy, Scaled Beta2 (SBeta2) and Uniform are considered as alternatives by many authors to model the scale parameter. In the present research work, the behavior of the random effects estimators in a hierarchical model with a Bayesian approach was examined. It was assumed random effects distribution t-Student and scale parameter distributions half-Cauchy, SBeta2 and Uniform. A simulation study was carry on to evaluate the behavior of the random effects estimators. Based on the obtained results, and under differen scenarios, it was possible to examine the shrinkage of the posterior parameters of the model. We concluded that in presence of atypical values, the shrinkage is lower when the effects are modeled with a t-Student distribution compared with those obtained when a Normal distribution is associated to the random effects, under the same prior distribution for the scale parameter. [ABSTRACT FROM AUTHOR]