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:	Haresh T. Suppiah, Ee Ling Ng, Jericho Wee, Bernadette Cherianne Taim, Minh Huynh, Paul B. Gastin, Michael Chia, Chee Yong Low, Jason K. W. Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sport training intensity hypohydration dehydration young sportsmen/women Nutrition. Foods and food supply TX341-641
Acceso en línea:	https://doaj.org/article/0c2c39e75dd84328baae77ff30e8df50
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spelling	oai:doaj.org-article:0c2c39e75dd84328baae77ff30e8df502021-11-25T18:36:33ZHydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics10.3390/nu131140732072-6643https://doaj.org/article/0c2c39e75dd84328baae77ff30e8df502021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6643/13/11/4073https://doaj.org/toc/2072-6643There 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, (<i>p</i> = 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.Haresh T. SuppiahEe Ling NgJericho WeeBernadette Cherianne TaimMinh HuynhPaul B. GastinMichael ChiaChee Yong LowJason K. W. LeeMDPI AGarticlesporttraining intensityhypohydrationdehydrationyoung sportsmen/womenNutrition. Foods and food supplyTX341-641ENNutrients, Vol 13, Iss 4073, p 4073 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sport training intensity hypohydration dehydration young sportsmen/women Nutrition. Foods and food supply TX341-641
spellingShingle	sport training intensity hypohydration dehydration young sportsmen/women Nutrition. Foods and food supply TX341-641 Haresh T. Suppiah Ee Ling Ng Jericho Wee Bernadette Cherianne Taim Minh Huynh Paul B. Gastin Michael Chia Chee Yong Low Jason K. W. Lee Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
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, (<i>p</i> = 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.
format	article
author	Haresh T. Suppiah Ee Ling Ng Jericho Wee Bernadette Cherianne Taim Minh Huynh Paul B. Gastin Michael Chia Chee Yong Low Jason K. W. Lee
author_facet	Haresh T. Suppiah Ee Ling Ng Jericho Wee Bernadette Cherianne Taim Minh Huynh Paul B. Gastin Michael Chia Chee Yong Low Jason K. W. Lee
author_sort	Haresh T. Suppiah
title	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_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_sort	hydration status and fluid replacement strategies of high-performance adolescent athletes: an application of machine learning to distinguish hydration characteristics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0c2c39e75dd84328baae77ff30e8df50
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Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics

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