Pacing Strategy Affects the Sub-Elite Marathoner's Cardiac Drift and Performance

Véronique Louise Billat; Florent Palacin; Matthieu Correa; Jean-Renaud Pycke

doi:10.3389/fpsyg.2019.03026

Pacing Strategy Affects the Sub-Elite Marathoner's Cardiac Drift and Performance

Front Psychol. 2020 Feb 19:10:3026. doi: 10.3389/fpsyg.2019.03026. eCollection 2019.

Authors

Véronique Louise Billat¹, Florent Palacin¹, Matthieu Correa¹, Jean-Renaud Pycke^{1

2}

Affiliations

¹ IEA 3625/Institut des Sciences du Sport/I3SP, Université de Paris, Paris, France.
² Laboratoire de Mathématiques et Modélisation d'Evry, CNRS, Université Evry, Université Paris-Saclay, Evry, France.

Abstract

The question of cardiac strain arises when considering the emerging class of recreational runners whose running strategy could be a non-optimal running pace. Heart rate (HR) monitoring, which reflects exercise intensity and environmental factors, is often used for running strategies in marathons. However, it is difficult to obtain appropriate feedback for only the HR value since the cardiovascular drift (CV drift) occurs during prolonged exercise. The cardiac cost (CC: HR divided by running velocity) has been shown to be a potential index for evaluation of CV drift during the marathon race. We sought to establish the relationship between recreational marathoners' racing strategy, cardiac drift, and performance. We started with looking for a trend in the speed time series (by Kendall's non-parametric rank correlation coefficient) in 280 (2 h30-3 h40) marathoners. We distinguished two groups, with the one gathering the large majority of runners (n = 215, 77%), who had a significant decrease in their speed during the race that appeared at the 26th km. We therefore named this group of runners the "fallers." Furthermore, the fallers had significantly lower performance (p = 0.006) and higher cardiac drift (p < 0.0001) than the non-fallers. The asymmetry indicator of the faller group runners' speed is negative, meaning that the average speed of this category of riders is below the median, indicating that they ran more than the half marathon distance (56%) above their average speed before they "hit the wall" at the 26th km. Furthermore, we showed that marathon performance was correlated with the amplitude of the cardiac drift (r = 0.18, p = 0.0018) but not with those of the increase in HR (r = 0.01, p = 0.80). In conclusion, for addressing the question of the cardiac drift in marathon, which is very sensitive to the running strategy, we recommend to utilize the cardiac cost, which takes into account the running speed and that could be implemented in the future, on mobile phone applications.

Keywords: Kendall; Strava; big data; endurance; running.