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Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics

There are limited data on the fluid balance characteristics and fluid replenishment behaviors of high-performance adolescent athletes. The heterogeneity of hydration status and practices of adolescent athletes warrant efficient approaches to individualizing hydration strategies. This study aimed to...

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Autores principales: Suppiah, Haresh T., Ng, Ee Ling, Wee, Jericho, Taim, Bernadette Cherianne, Huynh, Minh, Gastin, Paul B., Chia, Michael, Low, Chee Yong, Lee, Jason K. W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625246/
https://www.ncbi.nlm.nih.gov/pubmed/34836328
http://dx.doi.org/10.3390/nu13114073
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author Suppiah, Haresh T.
Ng, Ee Ling
Wee, Jericho
Taim, Bernadette Cherianne
Huynh, Minh
Gastin, Paul B.
Chia, Michael
Low, Chee Yong
Lee, Jason K. W.
author_facet Suppiah, Haresh T.
Ng, Ee Ling
Wee, Jericho
Taim, Bernadette Cherianne
Huynh, Minh
Gastin, Paul B.
Chia, Michael
Low, Chee Yong
Lee, Jason K. W.
author_sort Suppiah, Haresh T.
collection PubMed
description There are limited data on the fluid balance characteristics and fluid replenishment behaviors of high-performance adolescent athletes. The heterogeneity of hydration status and practices of adolescent athletes warrant efficient approaches to individualizing hydration strategies. This study aimed to evaluate and characterize the hydration status and fluid balance characteristics of high-performance adolescent athletes and examine the differences in fluid consumption behaviors during training. In total, 105 high-performance adolescent athletes (male: 66, female: 39; age 14.1 ± 1.0 y) across 11 sports had their hydration status assessed on three separate occasions—upon rising and before a low and a high-intensity training session (pre-training). The results showed that 20–44% of athletes were identified as hypohydrated, with 21–44% and 15–34% of athletes commencing low- and high-intensity training in a hypohydrated state, respectively. Linear mixed model (LMM) analyses revealed that athletes who were hypohydrated consumed more fluid (F (1.183.85)) = 5.91, (p = 0.016). Additional K-means cluster analyses performed highlighted three clusters: “Heavy sweaters with sufficient compensatory hydration habits,” “Heavy sweaters with insufficient compensatory hydration habits” and “Light sweaters with sufficient compensatory hydration habits”. Our results highlight that high-performance adolescent athletes with ad libitum drinking have compensatory mechanisms to replenish fluids lost from training. The approach to distinguish athletes by hydration characteristics could assist practitioners in prioritizing future hydration intervention protocols.
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spelling pubmed-86252462021-11-27 Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics Suppiah, Haresh T. Ng, Ee Ling Wee, Jericho Taim, Bernadette Cherianne Huynh, Minh Gastin, Paul B. Chia, Michael Low, Chee Yong Lee, Jason K. W. Nutrients Article There are limited data on the fluid balance characteristics and fluid replenishment behaviors of high-performance adolescent athletes. The heterogeneity of hydration status and practices of adolescent athletes warrant efficient approaches to individualizing hydration strategies. This study aimed to evaluate and characterize the hydration status and fluid balance characteristics of high-performance adolescent athletes and examine the differences in fluid consumption behaviors during training. In total, 105 high-performance adolescent athletes (male: 66, female: 39; age 14.1 ± 1.0 y) across 11 sports had their hydration status assessed on three separate occasions—upon rising and before a low and a high-intensity training session (pre-training). The results showed that 20–44% of athletes were identified as hypohydrated, with 21–44% and 15–34% of athletes commencing low- and high-intensity training in a hypohydrated state, respectively. Linear mixed model (LMM) analyses revealed that athletes who were hypohydrated consumed more fluid (F (1.183.85)) = 5.91, (p = 0.016). Additional K-means cluster analyses performed highlighted three clusters: “Heavy sweaters with sufficient compensatory hydration habits,” “Heavy sweaters with insufficient compensatory hydration habits” and “Light sweaters with sufficient compensatory hydration habits”. Our results highlight that high-performance adolescent athletes with ad libitum drinking have compensatory mechanisms to replenish fluids lost from training. The approach to distinguish athletes by hydration characteristics could assist practitioners in prioritizing future hydration intervention protocols. MDPI 2021-11-15 /pmc/articles/PMC8625246/ /pubmed/34836328 http://dx.doi.org/10.3390/nu13114073 Text en © 2021 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
Suppiah, Haresh T.
Ng, Ee Ling
Wee, Jericho
Taim, Bernadette Cherianne
Huynh, Minh
Gastin, Paul B.
Chia, Michael
Low, Chee Yong
Lee, Jason K. W.
Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
title Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
title_full Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
title_fullStr Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
title_full_unstemmed Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
title_short Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
title_sort hydration status and fluid replacement strategies of high-performance adolescent athletes: an application of machine learning to distinguish hydration characteristics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625246/
https://www.ncbi.nlm.nih.gov/pubmed/34836328
http://dx.doi.org/10.3390/nu13114073
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