# Power Profile Index: An Adjustable Metric for Load Monitoring in Road Cycling
## 4 Discussion
### Understanding the Differences Compared to Other Load Metrics  
The large variability found between the different quantification indexes (Table 4 and Figure 2) could be explained by the items used in the respective equations. In this sense, as it is well known, RPE, sRPE, eTRIMP, or TSS have their strengths and also different weaknesses. While TRIMP, RPE, and TSS are able to identify differences in loading for different stage types, PPi is shown to be a more stable index. This could be interpreted negatively for PPi, as it would not be able to identify differences between stage types. However, we believe that this stability in its values is due to the higher weighting of high intensity with respect to volume, which is not the case for TRIMP, TSS, and sRPE. The differences between stages identified by the rest of the indices are mainly due to the duration of the effort. Hence, the flat stage (although the RPE for this stage is clearly lower than for the rest of the stages), obtains much higher score values via TRIMP, sRPE,and TSS compared to the time trials. In fact, the high intensity of a time trial causes fatigue comparable to mountain stages. While the other indices are not able to adequately quantify the workload of a time trial and its consequent fatigue, PPi is more consistent. These similarities between TRIMP, sRPE, and TSS can be seen in the strong correlation between the three indices (Table 5). In all three, a common element that may explain the strong correlation is the importance of volume in their calculation. On the other hand, the higher weighting of volume over intensity in the calculation of these three indices may explain the lack of statistical significance in the correlation between them and the RPE (Table 5), which is greater in high-intensity efforts such as time trials. In contrast, PPi shows a strong correlation with RPE, while it also moderately correlates with TSS and sRPE. This could be interpreted as a strength of the PPi and would be a consequence of integrating the RPE into its calculation, weighting high intensity efforts more heavily in relation to volume. The lack of a significant correlation between PPi and eTRIMP could be due to the difficulty of this index to identify short and high intensity efforts because of the HR delay [11], as well as the excessive weighting of volume in its calculation. According to [PERSON] et al., using power-based metrics to quantify cycling intensity provides solid advantages compared to other methods (e.g., TRIMP). These include the possibility for mechanical power to be analyzed in depth from the data available at any time during training or competition, including the evaluation of the cyclists' endurance capacity outside laboratory conditions [66]. Nevertheless, power-based metrics have associated limitations. These include the influence that some factors (e.g., drafting, team tactics, environmental conditions, or terrain types) may have on the fact that the highest PO recorded (in competition or training) may not be the maximum achievable by the cyclist [60]. Moreover, differences between subjects in their ability to attenuate fatigue-induced declines in mean maximal power (MMP) values, which is a key determinant for endurance performance [33], must be considered. Therefore, we cannot claim that the use of power to quantify workload is better than the HR or RPE methods. In fact, the ideal would be to integrate or consider all indices, including the HRV. This will provide an overview of the external and internal loading and the balance between fatigue and recovery. However, we do believe that PPi corrects some aspects existing in other indices, such as TSS or eTRIMP, although at no time do we claim that it is better than the rest, but just another index to consider. One of its advantages lies in combining elements of external load (power) and internal load (perceived fatigue).