## 2 Materials and Methods
### Statistical Analysis  
Means and standard deviations (SD) were calculated for each variable. Normal distribution for all parameters (Shapiro-Wilk test) and the homogeneity of variances (Levene's test) were analyzed and confirmed (\(p>0.05\)). In Phase A, a multiple linear regression model (stepwise method) was conducted to examine the relationship between each time-shortened WAnT (including MPO and FI as predictor variables) and the 30 s WAnT. The following statistics were reported for each general model: adjusted coefficient of determination (R\({}^{2}\)), standard error of the estimation (SEE), and F-value (ANOVA). Furthermore, the raw (B) and standardized (8) coefficients, and \(t\)-value (\(t\)) were used to describe each predictor variable within the model. The variance inflation factor (VIF) was included to inspect multicollinearity. If the VIF value exceeded 4.0, a high correlation between predictor variables was considered [27]. Residuals from each regression model were examined in terms of normality ([PERSON]'s test and P-P plot), independence ([PERSON]'s test), linearity, and homoscedasticity (Q-Q plot). In Phase B, besides the Pearson's correlation coefficient (r), intraclass correlation coefficient (ICC), and paired samples \(t\)-test, the cross-validation analysis included calculations about the magnitude of error:  
* Standard error of measurement (SEM) calculated from the square root of the mean square error term in repeated-measurements ANOVA [28]. Results were presented both in absolute (W) and relative terms as a coefficient of variation (CV = 100 SEM/mean).
* Smallest detectable change (SDC) derived from the SEM (\(\surd{2}\times\text{SEM}\times 1.96\)) as an indicator of sensitivity [29].
* Bland-Altman plots and the calculation of systematic bias and its 95% limits of agreement (LoA = bias \(\pm\) 1.96 SD) were also used to analyze the level of agreement between paired (estimated vs. criterion) MPO measurements [30].  
In Phase C, training effects were analyzed by means of a 2 \(\times\) 2 repeated measures ANOVA with Bonferroni post-hoc adjustments using one inter-factor (group: TRAIN vs. CONTROL) and one intra-factor (time: Pre (T0) vs. Post (T10)). Sensitivity of each time-shortened WAnT model was examined by comparing the SDC result in the cross-validation analysis (Phase B) against each subject's change in MPO after the training intervention. Statistical significance was accepted at \(p<0.05\). The required sample size for the intervention was determined by statistical power calculation [31] on the basis of previous studies that described the variability on the WAnT MPO in a similar sample [6] and determined the changes after a training intervention [13]. Ten subjects would be required to detect changes of 30 \(\pm\) 25 W in the WAnT MPO, with a power of 0.90 and two-tailed \(\alpha\) level set at 0.05. For the validity analysis, 12 pairs of data would be enough to identify differences below 30 W with an expected mean difference of 10 \(\pm\) 5 W [32]. Calculations were performed using SPSS software version 17.0 (SPSS, Chicago, IL, USA), MedCalc Statistical Software version 18.2.1 (MedCalc Software bvba, Ostend, Belgium; [[http://www.medcalc.org](http://www.medcalc.org)]([http://www.medcalc.org](http://www.medcalc.org)); 2018), and GraphPad Prism 6.0 (GraphPad Software Inc., CA, USA).