Exposure Variation Analysis (EVA) method to monitor ability to optimally regulate exercise intensity of professional cyclists during time-trial competitions

T Ouvrad, J Pinot, A Groslambert, F Grappe

Abstract


Introduction

Pacing strategies during endurance performance is becoming one of the main topics in sport sciences. Several studies proved that a constant pacing strategy, adjusted from course main difficulties, is optimal for individual time-trial (ITT) performance. However, the regulation mechanisms that allow athletes to maintain a constant power output (PO) remain poorly studied (Abbiss and Laursen 2008). The “Exposure Variation Analysis” (EVA) was recently proposed as a new innovative method to quantify short-time PO fluctuations during ITT (Abbiss et al. 2010; Peiffer and Abbiss 2011). This method appears to offer an interesting way to investigate the athlete ability to optimally regulate exercise intensity during ITT in order to maintain a constant optimal pacing strategy. The aim of this study was to assess the relationship between the exercise intensity regulation mechanisms quantified thanks to the EVA method and the evolutions of performance of Word-Tour cyclists during an official competitive ITT performed on the same course during two consecutive years.

 

Methods

After a maximal exhaustive aerobic test, a group of physically active individuals and trained cyclists (N= 12; MAP 352 ± 49 W) in randomized order performed one FTP test over 20 minutes (FTP20) and one CP test, comprising of maximal time trial efforts (TT) of 12 min, 7 min and 3 min, interspersed by 30 min rest (9). Tests were performed on a Cyclus2 ergometer (RBM Elektronik GmbH, Leipzig, Germany). During TTs and FTP test, participants utilised a self-pacing strategy were gearing was adjusted throughout efforts using the virtual gear changer mounted to the handlebars. CP was determined using the power-inverse time model (P = W(1/t) + CP) and FTP60 was calculated as 95% of the FTP20 mean PO. Differences of statistical significance between CP and FTP values were tested using a paired samples t-tests. Relationships were assessed using Pearson product moment correlation coefficients. The agreement between CP and FTP60 values was assessed using Limits of Agreement (LoA). Linear regression was used to calculate the standard error of estimate (SEE) associated with predicting FTP60 values as well as for individual CP values.

 

Results

The mean difference between CP and FTP60 values was 5 ± 11 W, which was non-significant (t(11) = 1.7, p = .108). LoA between values were -38 – 29 W (Fig 1A). The standard error associated with the prediction of FTP60 was 18 W (Cl, 13 - 28W), when expressed as percentage error resulted in 7.9% (Cl, 5.8 – 12.9%). The correlation between CP and FTP60 values was r = 0.93, P ≤ 0.001 (Fig 1B). The mean SEE for CP values was 4 ± 2 W.

 

Discussion

Results demonstrate low LoA between CP and FTP60 values and a high prediction error when using a group of mixed trained athletes. This suggests that CP and FTP60 results cannot be used interchangeably. By using more than one data point, an advantage of CP over FTP60 is a reduction in random error (expressed as SEE). This can be useful for coaches as it minimizes the biological re-test variability caused by the athlete. CP testing also allows the determination of W', i.e. the maximum amount of work that can be expended above the intensity of CP. It therefore offers more information about the performance capabilities of an athlete. With some agreement present for the trained cyclists, future studies should focus on this population.

 

Conclusion

For a same ITT, mean PO of a professional cyclist remain extremely stable over years. Accordingly, he will be more able to improve his performance thanks to a better effort regulation than by increasing his mean PO. EVA method appears to be an innovative tool to evaluate cyclists’ ability to optimally regulate exercise intensity during ITT.


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