Marathon Performance Depends on Pacing Oscillations between Non Symmetric Extreme Values

Pycke, Jean-Renaud; Billat, Véronique

doi:10.3390/ijerph19042463

Cited by 10 publications

(9 citation statements)

References 42 publications

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“…Indeed, a marathon was recently run in less than 2 h by a man who ran the three fastest marathons ever recorded in a span of three years-Eliud Kipchoge-in the Tokyo Olympic games. We have demonstrated in a prior paper [43] that the best marathons were run according to a pace distribution that is statistically not constant and with negative asymmetry. The utilization of extreme values and oscillations allows for recovery and optimization of the complementary aerobic and anaerobic metabolisms.…”

Section: Discussionmentioning

confidence: 92%

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Heart Rate Does Not Reflect the %VO2max in Recreational Runners during the Marathon

Billat

Palacin

Poinsard

et al. 2022

IJERPH

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Exercise physiologists and coaches prescribe heart rate zones (between 65 and 80% of maximal heart rate, HRmax) during a marathon because it supposedly represents specific metabolic zones and the percentage of O2max below the lactate threshold. The present study tested the hypothesis that the heart rate does not reflect the oxygen uptake of recreational runners during a marathon and that this dissociation would be more pronounced in the lower performers’ group (>4 h). While wearing a portable gas exchange system, ten male endurance runners performed an incremental test on the road to determine O2max, HRmax, and anaerobic threshold. Two weeks later, the same subjects ran a marathon with the same device for measuring the gas exchanges and HR continuously. The %HRmax remained stable after the 5th km (between 88% and 91%, p = 0.27), which was not significantly different from the %HRmax at the ventilatory threshold (89 ± 4% vs. 93 ± 6%, p = 0.12). However, the %O2max and percentage of the speed associated with O2max decreased during the marathon (81 ± 5 to 74 ± 5 %O2max and 72 ± 9 to 58 ± 14 %vO2max, p < 0.0001). Hence, the ratio between %HRmax and %O2max increased significantly between the 5th and the 42nd km (from 1.01 to 1.19, p = < 0.001). In conclusion, pacing during a marathon according to heart rate zones is not recommended. Rather, learning about the relationship between running sensations during training and racing using RPE is optimal.

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Section: Discussionmentioning

confidence: 92%

“…We showed that marathon performance over a period between 3 and 5 h did not depend on the factors of performance measured in an incremental test. Rather, it depended more on the running pace early in the first 5th km of the race [12,43]. Big data has shown the consistency of pacing profiles according to performance level.…”

Section: Discussionmentioning

confidence: 99%

Heart Rate Does Not Reflect the %VO2max in Recreational Runners during the Marathon

Billat

Palacin

Poinsard

et al. 2022

IJERPH

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“…Even when marathon runners’ self-pace their race based on the rating of perceived exertion (RPE), instead of heart rate (HR) as instructed in this experiment, a significant decrease in speed is commonly observed after the 26th kilometer on average [ 4 ]. This decline has been attributed to a lack of speed variability and the inability to recover without compromising average speed [ 21 ]. This suggests that runners may lack the ability to effectively pace their running, possibly due to insufficient information from physiological and mechanical cues.…”

Section: Discussionmentioning

confidence: 99%

“…The same watch used for the RABIT ® test was used for the marathon to measure V, SL, and HR. Given that performance in a marathon has recently been shown to be dependent on pacing oscillations [ 21 ], we encouraged runners to self-pace their run without focusing on the cardio-GPS whose dial was hidden. During the marathon, refreshment points (offering water, dry and fresh fruit, and sugar) were located every 5 km and at the finish line.…”

Section: Methodsmentioning

confidence: 99%

A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon

Palacin

Poinsard

Pycke

et al. 2023

Entropy

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A new group of marathon participants with minimal prior experience encounters the phenomenon known as “hitting the wall,” characterized by a notable decline in velocity accompanied by the heightened perception of fatigue (rate of perceived exertion, RPE). Previous research has suggested that successfully completing a marathon requires self-pacing according to RPE rather than attempting to maintain a constant speed or heart rate. However, it remains unclear how runners can self-pace their races based on the signals received from their physiological and mechanical running parameters. This study aims to investigate the relationship between the amount of information conveyed in a message or signal, RPE, and performance. It is hypothesized that a reduction in physiological or mechanical information (quantified by Shannon Entropy) affects performance. The entropy of heart rate, speed, and stride length was calculated for each kilometer of the race. The results showed that stride length had the highest entropy among the variables, and a reduction in its entropy to less than 50% of its maximum value (H = 3.3) was strongly associated with the distance (between 22 and 40) at which participants reported “hard exertion” (as indicated by an RPE of 15) and their performance (p < 0.001). These findings suggest that integrating stride length’s Entropy feedback into new cardioGPS watches could improve marathon runners’ performance.

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“…Based on the experiments on the real races 29,34,37,44 , various observable such as velocity and density waves, speed and velocity distribution of the participants, distribution of density in the system as well as density and velocity auto-correlation can be examined to explore the dynamics and characteristics of the moving crowd. These observable are valuable tools for characterizing the dynamic behavior of participants during the races.…”

Section: ■■■✳ |❊❙❯▲❚❙mentioning

confidence: 99%

Polarised crowd in motion: Insights into statistical and dynamical behavior

Jena,

Mishra

2024

Preprint

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The collection of active agents often exhibits intriguing statistical and dynamical properties, particularly when consid- ering human crowds. In this study, we have developed a computational model to simulate the recent experiment on real marathon races by Bain et al., [Science 363, 46–49 (2019)]. Our primary goal is to investigate the impact of race staff on crowd dynamics. By comparing simulated races with and without the presence of race staff, our study reveals that the local velocity and density of participants display a wave pattern akin to real races for both the cases. The observed traveling wave in the crowd consistently propagates at a constant speed, regardless of the system size under consid- eration. The participants’ dynamics in the longitudinal direction primarily contribute to velocity fluctuations, while fluctuations in the transverse direction are suppressed. In the absence of race staff, density and velocity fluctuations weaken without significantly affecting other statistical and dynamic characteristics of the crowd. Through this research, we aim to deepen our understanding of crowd motion, providing insights that can inform the development of effective crowd management strategies and contribute to the successful control of such events.

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Marathon Performance Depends on Pacing Oscillations between Non Symmetric Extreme Values

Cited by 10 publications

References 42 publications

Heart Rate Does Not Reflect the %VO2max in Recreational Runners during the Marathon

Heart Rate Does Not Reflect the %VO2max in Recreational Runners during the Marathon

A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon

Polarised crowd in motion: Insights into statistical and dynamical behavior

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