## 4 Discussion  
This study examined the validity and sensitivity of two time-shortened versions (15 s and 20 s) of the original 30 s WAnT by directly measuring the main variable (power output) using a power meter device. The main finding is that both WAnT\({}_{15}\) and WAnT\({}_{20}\) are useful strategies to replace the traditional 30 s WAnT. In particular, the WAnT\({}_{20}\) showed the highest fit and lowest magnitude of error when compared with the criterion (30 s MPO). Furthermore, the WAnT\({}_{20}\) model was found to be more sensitive than the WAnT\({}_{15}\) in identifying changes in MPO after a training or detraining period. The high agreement found by the current study for both time-shortened WAnT models is in line with previous investigations examining the capability of the 15 s and 20 s WAnT to predict 30 s MPO. In this regard, [PERSON] et al. [6] found a close relationship (R\({}^{2}\) = 0.98) between a time-reduced 20 s WAnT and the traditional 30 s WAnT. Similarly, [PERSON] et al. [16] reported a high agreement between MPO estimated by a 20 s WAnT model and the 30 s MPO (R\({}^{2}\) = 0.99). Moreover, in accordance with our findings, [PERSON] et al. [16] found the 20 s model closer to the traditional WAnT than the 15 s model (R\({}^{2}\) = 0.97). For the first time, our prediction models include the FI as a predictor variable to estimate MPO. The FI relates to the time-course power decline during the test [2], so it seems a useful parameter to reduce the possible overestimation bias and increase the accuracy of the prediction (Figure 3). When the cross-validation of the two regression models was examined (Phase B), estimated MPO by both time-shortened proposals, especially the WAnT\({}_{20}\), showed a high agreement and reduced magnitude of error compared to the 30 s MPO (Table 2). This superior predictive capability of the WAnT\({}_{20}\) could be due to the additional 5 s included in this proposal. Regarding the error generated by each model, a time-shortened WAnT could be considered useful if it has sufficient sensitivity to detect changes in the variable of interest (i.e., SDC \(\leq\) change in MPO) [25; 26]. For the first time, in order to practically interpret this sensitivity, the current research compared the SDC obtained by each time-shortened model in the cross-validation analysis with the changes in MPO after a resistance training or detraining period. After the 10-week intervention, the TRAIN group increased the MPO by 22.1 W, whereas the CONTROL group decreased the MPO by 13.5 W. When the changes were interpreted individually, it was found that the WAnT\({}_{20}\) model was able to detect MPO changes in 11/11 and 9/11 participants in the TRAIN and CONTROL groups, respectively; whereas this sensitivity substantially decreased for the WAnT\({}_{15}\) (4/11 subjects in the TRAIN group and 2/11 subjects in the CONTROL group) (Figure 4). Extending this practical interpretation (based on SDC) to other long-term interventions, both models would be sensitive to detect changes in MPO after programs including low/moderate-level subjects [13; 33]. On the contrary, the WAnT\({}_{15}\) would fail to identify changes in high-level athletes, whose changes in MPO are commonly slighter [34; 35]. Similarly, whereas both time-shortened WAnTs would be useful to identify the effects on performance of some ergogenic aids [36; 37; 38; 39; 40; 41] or the circadian rhythms [42], the WAnT\({}_{15}\) would lack the capability to accurately detect the acute effects of some pre-exercise strategies like the post-activation potentiation or the foam roller warm-up [43; 44]. Thus, the higher sensitivity of the WAnT\({}_{20}\) makes it the best candidate, among those analyzed, to replace the traditional 30 s WAnT.