Please use this identifier to cite or link to this item: https://doi.org/10.3390/nu13114073
Title: Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
Authors: Suppiah, Haresh T
Ng, Ee Ling 
Wee, Jericho 
Taim, Bernadette Cherianne
Huynh, Minh
Gastin, Paul B
Chia, Michael 
Low, Chee Yong
Lee, Jason KW 
Keywords: Science & Technology
Life Sciences & Biomedicine
Nutrition & Dietetics
sport
training intensity
hypohydration
dehydration
young sportsmen/women
SOCCER PLAYERS
EXERCISE
RESPONSES
BALANCE
THIRST
ELITE
HYPOHYDRATION
REQUIREMENTS
SWEAT
HEAT
Issue Date: 1-Nov-2021
Publisher: MDPI
Citation: Suppiah, Haresh T, Ng, Ee Ling, Wee, Jericho, Taim, Bernadette Cherianne, Huynh, Minh, Gastin, Paul B, Chia, Michael, Low, Chee Yong, Lee, Jason KW (2021-11-01). Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics. NUTRIENTS 13 (11). ScholarBank@NUS Repository. https://doi.org/10.3390/nu13114073
Abstract: There are limited data on the fluid balance characteristics and fluid replenishment behav-iors 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.
Source Title: NUTRIENTS
URI: https://scholarbank.nus.edu.sg/handle/10635/225616
ISSN: 2072-6643
DOI: 10.3390/nu13114073
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