## 4 Discussion  
This study was designed to compare the goodness-of-fit between the individualized and generalized %Rep-%VL relationships as well as to explore the effect of fatigue on the accuracy of these relationships to estimate the %Rep when a moderate 20%VL is achieved during the Smith machine parallel back-squat. The main findings of the present study revealed (i) a high R\({}^{2}\) for generalized and individualized %Rep-%VL relationships, but the SEE was generally high, (ii) a lower goodness-of-fit for the generalized compared to the individualized %Rep-%VL relationships, (iii) a low (absolute errors > 10%) and comparable accuracy in the %Rep estimation for both %Rep-%VL relationships, and (iv) the overestimation of %Rep was progressively higher as the number of sets increased when using the MV\({}_{\text{fastest}}\) of the first set but not the MV\({}_{\text{fastest}}\) specific to each set. These results suggest that neither the generalized nor individualized %Rep-%VL equations are able to provide accurate estimations of %Rep during a parallel back-squat training session consisting of multiple sets to fatigue. The strength of the generalized %Rep-%VL relationship observed in our study for the parallel back-squat exercise (R\({}^{2}\) = 0.82; SEE = 12.4%Rep) was weaker compared to that previously reported for other RT exercises performed against similar relative loads such as the full back squat at 70-80%1 RM (R\({}^{2}\) = 0.93; SEE = 7.1 to 7.2%Rep) [6], bench press at 75%1 RM (R\({}^{2}\) = 0.97; SEE = 5.2%Rep) [5], prone bench pull at 75%1 RM (R\({}^{2}\) \(\geq\) 0.99; SEE not reported) [19], and shoulder press at 75%1 RM (R\({}^{2}\) \(\geq\) 0.99; SEE not reported) [19]. Furthermore, supporting our hypothesis, the individualized %Rep-%VL relationships (R\({}^{2}\) = 0.84-0.99) provided greater goodness-of-fit than the generalized %Rep-%VL relationship. Our findings support the results from [PERSON] et al. [15], who revealed that the goodness-of-fit was superior for individualized %Rep-%VL relationships (R\({}^{2}\) = 0.97-0.99) compared to generalized %Rep-%VL relationships (R\({}^{2}\) = 0.80-0.94) in the bench press also performed in a Smith machine. Therefore, although the goodness-of-fit of the %Rep-%VL relationships obtained in the present study was somewhat lower than  
Figure 3: Raw errors between the actual percentage of performed repetitions in relation to the maximum number of repetitions performed to failure (%Rep) and the predicted %Rep from the generalized (**upper panels**) and individualized (**lower panels**) %Rep-%VL equations determined in set 1 when a velocity loss magnitude (%VL) of 20% is reached. The %VL was computed considering both the fastest repetition mean velocity specific to each set (MV\({}_{\text{fastest}}\); **left panels**) and the MV\({}_{\text{fastest}}\) of the first set (**right panels**). those reported in previous studies [5; 6; 15; 16; 19], our findings provide additional evidence confirming the better goodness-of-fit for individualized %Rep-%VL relationships. The present study is the first to examine the accuracy of %Rep-%VL equations for predicting %Rep during a RT session consisting of multiple sets of the same exercise. For this purpose, we determined the raw and absolute differences between the actual %Rep completed during the second, third, and fourth set of back-squats after reaching a 20%VL and the %Rep predicted using %Rep-%VL relationships obtained with the data collected in the first set. Our findings suggest that, independently of the regression model considered, the accuracy in the prediction of the %Rep was always low (absolute errors > 10%). These results are in disagreement with previous studies reporting an acceptable consistency (coefficient of variation < 7%) and high stability (mean difference < 2.4%) of the %Rep estimated using individualized %Rep-%VL equations obtained during the bench press exercise at 60%1 RM [5] and the pull-up performed without external loads [16].