## Materials and Methods
### Magnitude-Based Decision (MBD) to Monitor Individuals with Gait Analysis
The MBD method provides the probability that a change (defined by the confidence interval of the difference, _Cldif_) exceeds a specific threshold (\(-\delta\), \(+\delta\)) selected by researchers in accordance with their objectives [36; 37]. This method is not exempt from controversy; thus, some authors support [38; 39; 40; 41] and others oppose [42; 43] its application. However, we assumed this method to transmit a simple and interesting idea, considering a change'relevant' if it exceeds a specific threshold. This idea may not be applicable in all fields but it makes sense in individual patient monitoring. The MBD method has, for example, been applied to elite athletes (e.g., [44]). We found similarities between elite athletes and the patients with spasticity because, in both cases, an individualized evaluation is required due to the uniqueness or heterogeneity of each participant, and it is difficult to compare a participant with a reference database. This idea has also been illustrated by [PERSON] et al. [45], who applied MBD to the individual evaluation of patients with vertiginous pathologies. Therefore, the application of the MBD method requires facing the challenge of establishing an adequate threshold \(\delta\). According to the MBD basis, the ideal or optimal solution would be to use as the threshold \(\delta\) the minimal important difference (MID) [46], the smallest worthwhile change [47], the smallest clinically important value [37], or any other combination of the terms used in the literature to identify changes that have clinical or practical relevance (i.e., changes that have an effect on quality of life). Nevertheless, to the best of our knowledge, no one has proposed MID indexes for gait variables resulting from a MoCap system for the same population or treatment. This would be a challenge requiring in-depth further research beyond the scope of this study. Thus, until further research provides these MID values, it will be reasonable to affirm that a change is significant if it overcomes at least the errors inherent in the test. In this regard, it will not be known if a change influenced a participant's quality of life, but at least, it will be known that the change existed and was not the result of a measurement error. This conclusion could be useful for clinicians, especially if they combine it with the rest of the clinical information. In this regard, [PERSON] et al. [17] recently summarized the error sources of a gait analysis test in the following groups: participant intrinsic variation, soft tissue movement, relative movement between the device and skin, positioning, instrument accuracy, gait event detection, and anatomical calibration. As shown in other studies [2; 48; 49; 50], the magnitude of these errors in our experiment could be estimated for each participant using [PERSON] and [PERSON]'s limits of agreement [51] (see Equation (1)). Using these errors as threshold \(\delta\), the MBD method provides the probability that a change had been more than zero. This is the probability that a patient had undergone'real' changes. Reflections about the implication of using this threshold \(\delta\) will be explored in the Discussion section. In this manner, the threshold \(\delta\) was calculated using Equation (1):  
\[\delta=Z_{1-\alpha/2}\ \sqrt{2}\ SE_{diff};\ SE_{diff}=\sqrt{\frac{SD_{1}^{2}}{n _{1}}+\frac{SD_{2}^{2}}{n_{2}}}\, \tag{1}\]  
where \(Z_{1-\alpha/2}\) is the value of the normal distribution at \(1-\alpha/2\) (as we stabilized a confidence level of 95%, \(\alpha\) was 0.05 and the \(Z_{1-\alpha/2}\) value was 1.96), \(\sqrt{2}\) accounts for errors between two measurements [51; 52; 53; 54], _SEdif_ is the standard error of the difference between the means, \(n_{1}\) and \(n_{2}\) are the respective samples of the pre- and post-tests, and \(SD_{1}\) and \(SD_{2}\) are the respective standard deviations of the pre- and post-tests.