## Materials and Methods
### Magnitude-Based Decision (MBD) to Monitor Individuals with Gait Analysis
Moving from the threshold \(\delta\), another important issue in individualized gait analysis monitoring is that most of the changes in gait variables are not clearly either beneficial or harmful (see the terminology used by [PERSON] and [PERSON] [55]). An increment or decrement in the magnitude of a specific gait variable could be beneficial for one patient but harmful for another. Spasticity causes such alterations to gait that, even if a particular gait variable changes to a value closer to normal, this will not necessarily indicate a beneficial change. In the rehabilitation process of patients with spasticity, the goal is usually to develop, learn, or internalize a gait pattern that is as functional and harmless as possible considering anthropometric, muscular, or cognitive conditions, regardless of the normality of the pattern itself [7; 56; 57; 58]. Therefore, in an experiment like this one, it is necessary to interpret the results for each patient independently. For this reason, we used the'real' changes provided by the MBD method to conduct a process that we called biomechanical interpretation. Three researchers--[PERSON]. (an engineer), [PERSON] (a medical doctor), and [PERSON] (a medical doctor)--independently studied the same results (tables and graphs). They only considered changes that were at least'very likely' (\(>\)95% chance). Following a discussion of their reflections and once a consensus had been reached, the conclusions for each patient were listed in detail (see the Supplementary Materials). These researchers later classified the patients into the following three groups: patients with overall improvement, those with overall impairment, and those without overall change. Finally, based on the information acquired during the interpretation process, the researchers wrote interpretation guidelines applicable to other assessments (see Section 4). After outlining the decisions made in this study regarding the threshold \(\delta\) and the interpretations of the results, we will introduce how we compared the two groups of measurements using the MBD approach. [PERSON] [59] developed a spreadsheet to compare'means of two groups'. Based on this spreadsheet, we developed a script applying the MBD method by using as input the threshold \(\delta\) selected by the researcher, and the measurements taken from the pre- and post-tests of one specific patient. This script was developed using Vizard (6.2 version, WorldViz, Santa Barbara, CA, USA, 2020), which is based on Python 2.7, and the Pandas and Matplotlib libraries. The sequence that this program follows for a two-mean comparison begins with the calculation of the interval of the difference _Cldif_, which defines the lower and upper limits (\(Li\), \(Ls\)) of the change (Equation (2)) [59]:  
\[Cldif=\ t_{\alpha,\ DoF}\ SE_{dif};\left[Li,\ LS\right]=\left[X_{dif}-Cldif,\ X_{dif}+Cldif\right], \tag{2}\]  
\[DoF=\frac{\left(\frac{SD_{1}^{2}}{n_{1}}+\frac{SD_{2}^{2}}{n_{2}}\right)^{2} }{\frac{SD_{1}^{4}}{n_{1}^{2}(n_{1}-1)}+\frac{SD_{2}^{4}}{n_{2}^{2}(n_{2}-1)}}, \tag{3}\]  
\[X_{dif}=X_{2}-X_{1}. \tag{4}\]  
In Equation (2), \(t_{\alpha,DoF}\) is the value of the t distribution for a specific \(\alpha\) selected by the researchers, (we defined \(\alpha\) as 0.05 to achieve a confidence level of 95%) and a specific degree of freedom (\(DoF\)), which is computed with Equation (3) using [PERSON]'s approximation [60], where \(n\) and \(SD\) are the stride sample size and standard deviation, respectively; \(SELif\) is calculated as explained with Equation (1); and the difference between means (\(X_{dif}\)) is computed with Equation (4), where \(X_{2}\) is the post-test mean, and \(X_{1}\) is the pre-test mean. Exposed calculations assume independent samples, non-equal variances, and data that have approximately normal distribution. This data normality assumption is based on work by [PERSON] et al.