## Materials and Methods
### Magnitude-Based Decision (MBD) to Monitor Individuals with Gait Analysis  
Concerning individual monitoring, the human gait is a repetitive cyclical movement; thus, in a measurement session, a variable produces a multitude of samples (e.g., 25 samples of the _StepLight_ variable for Patient S001 in a measurement session). Therefore, to monitor an individual, it is possible to compare two groups of measurements, namely the group of measurements from a pre-treatment session (\(n_{1}\) samples, \(X_{1}\) mean, and \(SD_{1}\) standard deviation) and the group of measurements from a post-treatment session (\(n_{2}\) samples, \(X_{2}\) mean, and \(SD_{2}\) standard deviation). Thus, according to traditional statistical theory, this approach is a two-mean comparison of independent samples because gait cycles, despite being obtained from the same patient, are not pair related. Regarding the sample of strides recorded in each session, more strides would obviously provide better statistical normality and better precision. However, it was necessary to balance the number of steps to be recorded, the time that the test would take in the daily clinic, and the fatigue that the test could cause in certain patients if they walked for a long time. Thus, we decided to record 25 strides per patient per session. We did this to satisfy the central limit theorem and especially because [PERSON] et al. [33] ensured that 23 strides usually achieve statistical normality and stability. As will be explained later in this sub-section, to prove that the stride sample was sufficient, we calculated the power of each statistical comparison. To conduct the comparison between the pre- and post-sessions, applying a student's \(t\)-test of the independent samples to each specific variable could be valid. Applying this test, a \(p\)-value lower than 0.05 would indicate that a difference existed between the pre- and pot-sessions. Nevertheless, as [PERSON] et al. [34] stated, although it is currently accepted that an effect is significant if the \(p\)-value does not exceed a value of 0.05, this generates a dichotomy that is far from reality. According to the _Nature Research Journal_ this statement about the \(p\)-value has been supported by more than 800 researchers [34]. Thus, to infer a conclusion, researchers must delve deeper into the results. For this purpose, the magnitude-based inference method, which has recently been rebranded as the MBD method, addresses this need by using a more realistic threshold than a \(p\)-value of 0.05. Information about the MBD approach can be found in Excel spreadsheets, presentations, notes, and articles, all of \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Variable** & **Description** \\ \hline \multirow{6}{*}{**Spatiotemporal Variables**} & _StepLight_ (cm) & Distance between feet in the sagittal plane at initial contact \\ \cline{2-3}  & _StepWidth_ (cm) & Distance between feet in the frontal plane at initial contact \\ \cline{2-3}  & _FullSupp_ (\%) & Percentage of support throughout the stride length \\ \cline{2-3}  & _Double.Supp_ (\%) & Percentage of bipedal support throughout the stride length \\ \cline{2-3}  & _GaitSpeed_ (cm/s) & Mean of the gait speed throughout the stride length \\ \hline \multirow{6}{*}{**Kinematic Variables**} & _Pelvic.Tilt_ & Range of Pelvic tilt \\ \cline{2-3}  & _Hip.FlexExt_ & Range of Hip flexo-extension \\ \cline{2-3}  & _Hip.AbAdd_ & Range of Hip adduction-abduction \\ \cline{2-3}  & _Knee.FlexExt_ & Range of Knee flexo-extension \\ \cline{2-3}  & _Ankle.FlexExt_ & Range of Ankle flexo-extension \\ \cline{2-3}  & _Ankle.InvEv_ & Range of Ankle inversion-evers \\ \cline{2-3}  & _Chest.Tilt_ & Range of chest tilt \\ \hline \hline \end{tabular}  
* Spatiotemporal Variables: Dimensions that depend on whole-body movement. Kinematic Variables: Dimensions that depend on the movement of each body segment.  
\end{table} 
Table 2: Variables considered in the study. which are available from sportsci.org [35].