Statistical Analysis: To analyse the data, visual analyses will be done. This kind of analyses is really important for single subject design research to determine: whether evidence of a relation between an independent variable and an outcome variable exists and the strength or the magnitude of that relation. The visual analyses will show the behavior of each participant before, during, and after the intervention. The 2-standard deviation band method (2-SD) will be used to analyze the behavior of each participant on the outcome of interest before, during, and after the treatment. The 2-SD band will be performed as described in the literature: the mean and the standard deviation of the baseline data points will be computed, then, bands representing 2 SD will be drawn on the graph, above and below the mean of the baseline data points. A significant change will be present when two or more consecutive data points in the treatment period fall outside the 2SD bands (α=0.05). The minimally important difference (MID) levels in order to determine clinical relevance will be done. The mean difference will be calculated by subtracting the mean value of the Phase 3 data points from the mean value of the baseline data points (md = mp3 - mb). Two effect size indexes will be used to analyse the data. The first one will be the percentage of non-overlapping data (PND), that is obtained by counting the number of data points in the intervention phase which do not overlap with the highest or lowest data points in the baseline phase; this number then is divided by the total number of data points in the treatment phase, multiplied by 100. The second effect size index will be the percentage of improvement rate difference (IRD), that considers the difference in successful performance between baseline and intervention phases. The main purpose of using these two effect sizes is to explore the effects of treatment and to describe the phase contrast and design context.