Critical power: Evidence‐based robustness

Recently, Gorostiaga et al.1 published an article entitled “Over 55 years of critical power: Fact or artifact?” with the objective of “generating an evidencebased debate” regarding the validity of the critical power (CP) as a physiological goldstandard parameter. The researchers critically examined the concept of the CP model under four main arguments; “(1) the inconsistency in experimentally demonstrating the classical definition of CP, (2) the wide range of relative intensities at which CP was identified to occur, (3) the choice of duration of exercise trials used to assess CP, and (4) inadequate choice of the hyperbolic function model, despite the power function model, to describe the relationship between velocity and time.” These arguments were originally presented using the best race time performances of the top 10 male and female Spanish runners who completed 1.5 km to 10 km distance races in the same outdoor competitive season (Year 2019). In this way, the critical speed values (CS, analogous to CP) were determined using the race times up to 5 km (CS1.5– 5km, 1.5, 3, and 5 km) and 10 km (CS1.5– 10km, 1.5, 3, 5, and 10 km). In addition, the researchers added the halfmarathon and marathon performance times of other athletes to the model. However, we have identified some serious limitations with the data on which their conclusions are based. Our objectives are to point out important limitations and misconceptions in the original article by Gorostiaga et al.,1 which distorted the models and conclusions, and highlight the robustness of the CP model as the lowest intensity (speed) that represents the severeintensity domain, when using a correct and standardized methodology.2 Using the values (time and distance) provided in the article, we recalculated the CS and determined the D′ values (fixed and limited distance capacity above the CS) through the same model used (distance– time). CS (CS1.5– 5km, 5.45 ± 0.38 m/s vs. CS1.5– 10km, 5.19 ± 0.39 m/s; p = 0.02) and D′ (CS1.5– 5km, 140.6 ± 60.1 m vs. CS1.5– 10km, 272.2 ± 110.2 m; p = 0.001) values were significantly different (paired ttest). In fact, the influence of duration / distance of predictive trials on the CP/CS parameters has been previously deomonstrated.3 Interestingly, during modeling we noticed that the average speed of four athletes (TN, MM, CC, and CV) in the 3 km race was lower than their average speed in the 5 km race. This is interesting because the hyperbolic relationship between speed and time dictates that speed should decrease with increasing distance up to the CS. Yet, in the best performance of these athletes, they ran at a faster average speed in a race with greater distance. In this case, several factors, such as “pacing,” may have prevented the complete depletion of the D′ above the CS, thus, confounding the CS1.55km model prediction. In fact, it was not possible to predict the 3 km running performance for these athletes (i.e., TN, MM, CC, and CV) using CS1.5– 5km parameters. In the 10km race, only two athletes (DP and MJ) ran at average speeds above CS1.55Km, showing that all other athletes were at intensities below CS, justifying the running time (average, 31.54 minutes). The potential overestimation of D′ may explain why the actual times (Mean ± SD: 1.5 km, 247.5 ± 22.8, 3 km, 533.2 ± 44.0, 5 km, 893.0 ± 67.4, and 10 km, 1892.3 ± 144.6 s) were statistically (ttest, p = 0.05– 0.0002) different from the predicted times (Mean ± SD: 299.2 ± 59.26, 566,0.9 ± 75.1, 652.3 ± 131, and 2215.7 ± 304.9 s, respectively) when used CS1.5– 10km model. In this sense, the inclusion of execution times at intensities slightly below the CS (10 km, halfmarathon, and marathon) introduces errors in the mathematical modeling, which do not represent real physiological consequences on performance, possibly by reaching domains of different intensity. In addition, due to numerous influences during a competitive sporting season (e.g., time of day, environmental conditions, training status, and performance level of competing athletes),4,5 it does not seem correct and valid to use different sporting events for CS modeling, particularly paced race times.6 There is evidence that the time trial protocol, similar to the time to exhaustion protocol, is valid for determining the CP/CS model, as long as it induces exhaustion to a maximal of ~15 min, aiming both to deplete D′ and to attain VO2max. 7 Indeed, several key parameters of aerobic fitness (VO2max/VO2peak, blood lactate response to exercise, running economy, and oxygen uptake kinetics) are also protocoldependent, that is, data obtained by different protocols should not be used interchangeably. Thus, it is important to emphasize that (1) Gorostiaga et al.1 used severeintensity race efforts that were performed submaximally in several cases (40% of athletes in the 3 km race); (2) 80% of the athletes were below the CS in the 10 km race (according to the model with 3 distances);