## 4 Discussion
In this regard, increasing the _StepLgth_ of the healthy leg is particularly positive. In the patients in this study, the _StepLgth_ was usually greater in the affected leg because the affected leg moves to its maximum range when the healthy leg supports the full body weight. Thus, increasing the _StepLgth_ of the healthy leg means that the affected leg is capable of supporting the full body weight over a more extended range. * Decreasing the percentage of _Double.Supp_ implies that the patient can spend more time in mono-pedal support, which results in increased confidence and security. * Reducing the _Step.Wdth_ means reducing the base of support, which can be associated with improvement in terms of stability and confidence [56]. However, it is necessary to check whether the change is causing instabilities (i.e., increase in the _Pelvic.Tilt_ or _Chest.Tilt_). * Reducing the _Ankle.InvEv_ is a positive change, as it can imply a reduction of the equinovarus foot effect, which hemiplegic spasticity usually produces [27; 28; 29]. Notably, in some cases, it is impossible to determine the nature of one change in isolation. Instead, it is necessary to consider a change in combination with other changes and with the increase or decrease in variability. These guidelines were explored during the study of a limited and specific sample; thus, we encourage improvements to them or the addition of more guidelines based on further research. In addition to the proposed analysis and guidelines, we examined whether the samples (25 strides) recorded per patient per session were sufficient. With this sample size, the statistical comparison achieved a mean power of \(89.8\pm 8.4\%\), indicating a high probability of detecting effects and avoiding Type II errors (false negatives). Additionally, the 25 strides recorded were higher than the \(18\pm 6.5\) strides that would be necessary to achieve a power of 80%. Both of these conclusions affirmed that the selected number of strides was reasonable. In relation to these results, the number of statistical tests per patient was high (23 variables were statistically compared for each patient), which increased the risk of making a Type I error (see the section on multiple inferences in Hopkins [61]). As established in the Methods section, this probability was 5%, since we defined a confidence level of 95%. This scenario suggested the need for a more conservative approach to evaluating the statistical tests, such as the Bonferroni [79] adjustment. However, we agreed with others (e.g., [PERSON] [80]) who have advised against using the Bonferroni adjustment, instead asserting that each variable must be assessed in its own right. 'Evidence in data is what the data say--other considerations, such as how many other tests were performed, are irrelevant' [80], and the probability to commit an error with each inference must be assumed by the data interpreter. In any event, the MBD approach does not prevent including such adjustments if they are needed. To accomplish this, the \(\alpha\) variable of the _Cldif_ can be adjusted. In light of the goal of this study, we must mention that in recent years, data management options based on machine learning have been gaining special relevance. In gait analysis, machine learning techniques are usually applied to classify types of gait, identify human physical activity, or detect gait events [8; 81; 82]. In other healthcare fields, these techniques are also being used to monitor individual characteristics [83; 84]. Thus, future research could focus on feed machine learning models with the results that our approach would provide. This could lend insight to both the exposed interpretation process and to clinicians' decisions. In summary, the MBD approach to monitoring individuals allowed us to assess the progression of 21 patients with hemiplegic spasticity. This approach necessitates thoughtful decisions, but it can also provide a standardized, automated, realistic option for data processing.