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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.
Affiliation
  • Suppiah HT; Sport and Exercise Science, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, VIC 3086, Australia.
  • Ng EL; National Youth Sports Institute, Singapore 397778, Singapore.
  • Wee J; National Youth Sports Institute, Singapore 397778, Singapore.
  • Taim BC; National Youth Sports Institute, Singapore 397778, Singapore.
  • Huynh M; Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
  • Gastin PB; National Youth Sports Institute, Singapore 397778, Singapore.
  • Chia M; Department of Sport and Health Sciences, Technological University of the Shannon, Midlands Midwest, Athlone Campus, N37 HD68 Athlone, Ireland.
  • Low CY; SHE Research Group, Technological University of the Shannon: Midlands Midwest, Athlone Campus, N37 HD68 Athlone, Ireland.
  • Lee JKW; Sport and Exercise Science, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, VIC 3086, Australia.
Nutrients ; 13(11)2021 Nov 15.
Article in En | MEDLINE | ID: mdl-34836328
ABSTRACT
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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sports / Drinking Behavior / Athletes / Machine Learning / Organism Hydration Status Type of study: Diagnostic_studies / Etiology_studies / Guideline / Prognostic_studies Limits: Adolescent / Female / Humans / Male Country/Region as subject: Asia Language: En Journal: Nutrients Year: 2021 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sports / Drinking Behavior / Athletes / Machine Learning / Organism Hydration Status Type of study: Diagnostic_studies / Etiology_studies / Guideline / Prognostic_studies Limits: Adolescent / Female / Humans / Male Country/Region as subject: Asia Language: En Journal: Nutrients Year: 2021 Document type: Article Affiliation country: