Cargando…

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

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 success...

Descripción completa

Detalles Bibliográficos
Autores principales: Palacin, Florent, Poinsard, Luc, Pycke, Jean Renaud, Billat, Véronique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453470/
https://www.ncbi.nlm.nih.gov/pubmed/37628149
http://dx.doi.org/10.3390/e25081119
_version_ 1785095943837712384
author Palacin, Florent
Poinsard, Luc
Pycke, Jean Renaud
Billat, Véronique
author_facet Palacin, Florent
Poinsard, Luc
Pycke, Jean Renaud
Billat, Véronique
author_sort Palacin, Florent
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10453470
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104534702023-08-26 A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon Palacin, Florent Poinsard, Luc Pycke, Jean Renaud Billat, Véronique Entropy (Basel) Article 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. MDPI 2023-07-26 /pmc/articles/PMC10453470/ /pubmed/37628149 http://dx.doi.org/10.3390/e25081119 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Palacin, Florent
Poinsard, Luc
Pycke, Jean Renaud
Billat, Véronique
A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon
title A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon
title_full A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon
title_fullStr A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon
title_full_unstemmed A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon
title_short A Pilot Study Using Entropy for Optimizing Self-Pacing during a Marathon
title_sort pilot study using entropy for optimizing self-pacing during a marathon
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453470/
https://www.ncbi.nlm.nih.gov/pubmed/37628149
http://dx.doi.org/10.3390/e25081119
work_keys_str_mv AT palacinflorent apilotstudyusingentropyforoptimizingselfpacingduringamarathon
AT poinsardluc apilotstudyusingentropyforoptimizingselfpacingduringamarathon
AT pyckejeanrenaud apilotstudyusingentropyforoptimizingselfpacingduringamarathon
AT billatveronique apilotstudyusingentropyforoptimizingselfpacingduringamarathon
AT palacinflorent pilotstudyusingentropyforoptimizingselfpacingduringamarathon
AT poinsardluc pilotstudyusingentropyforoptimizingselfpacingduringamarathon
AT pyckejeanrenaud pilotstudyusingentropyforoptimizingselfpacingduringamarathon
AT billatveronique pilotstudyusingentropyforoptimizingselfpacingduringamarathon