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

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.

Race Analysis of the World’s Best Female and Male Marathon Runners

“…This study demonstrated that females ran at a constant pace considering their Kendall's τ was between −0.05 and 0.05 [6]. It was also highlighted that world records are broken using a running strategy based on running speeds below median speed, unlike popular marathoners who run more distance at speeds above the median [9]. This may be due to the fact that lower level runners (>2 h 20 min) run at too high a target that they cannot maintain beyond the 26th km, where the average speed (i.e., final performance) is reduced and is therefore lower than the median speed.…”

Concurrent Evolution of Biomechanical and Physiological Parameters With Running-Induced Acute Fatigue

“…Time-domain HRV metrics included beats per minute (BPM), SDNN, and RMSSD. Following this, the cardiac cost (CC) i.e., the ratio between BPM and running speed, was computed (Billat et al, 2020). Frequency domain analysis covered the low (0.04−0.15 Hz-LF) and high (0.04−1.5 Hz-HF) frequency components, as well as the ratio between the two bands (LF/HF ratio).…”

“…This approach has an implicit assumption that different participants develop similar levels of fatigue at similar distances during the run, which may not be true for a heterogeneous participant group employing a variety of pacing strategies. Similarly, existing research on the continuous monitoring of heart rate dynamics and cardiac drift (Billat et al, 2019;Gronwald and Hoos, 2020;Gronwald et al, 2021) has generally considered their evolution over the distance of the run. Combined together, these studies investigate the neuromuscular and cardiovascular response to acute fatigue, but not their concurrent evolution and association.…”

Continuous Analysis of Marathon Running Using Inertial Sensors: Hitting Two Walls?

“…factors. This "wall" is generally believed in the marathon community to occur around the 30 th km but there are some small differences in the scientific literature; i. e., at km 26 [5], km 30 [6] or with the highest probability at km 32 [7]. Although it seems simplistic to determine a single breakpoint, due to the paucity of the available means for measuring continuously the runner's responses (except for heart rate with heart rate monitors and pacing with Global Navigation Satellite System (GNSS)), the influence of the fatigue-induced biomechanical alterations on the occurrence and magnitude of the "wall" remains unclear.…”