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Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics
Open accessAbstract
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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Overview
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TL;DR
This field study characterized hydration status and fluid balance in 105 heat-acclimatized high-performance adolescent athletes across 11 sports, assessing first-morning and pre-training hydration and ad libitum fluid intake during low- and high-intensity sessions. Hypohydration was common but few athletes exceeded 2% body-mass loss, and k-means clustering identified three hydration-profile clusters (heavy sweaters with sufficient/insufficient compensatory habits and light sweaters with sufficient habits).
Key findings
- First-morning urine specific gravity (USG) classified 20% (USG > 1.025) and 44% (USG > 1.020) of athletes as hypohydrated; before low-intensity training, 21% and 44% began hypohydrated, and before high-intensity training, 15% and 34% began hypohydrated. Post-training, 2% of athletes in the low-intensity session and 0% in the high-intensity session showed excessive dehydration (>2% body-mass loss).
- Athletes who arrived hypohydrated consumed significantly more fluid during training than euhydrated athletes (main effect: F(1, 183.85) = 5.91, p = 0.016; mean difference 82.1 mL/h, 95% CI 15.5–148.7, p < 0.05).
- Linear mixed models found sport × session interactions for sweat rate (F(10, 94) = 2.901, p = 0.003) and fluid consumption per hour (F(10, 94) = 2.297, p = 0.018), plus a main effect of sport on percentage body-mass change (F(10, 94) = 3.558, p = 0.001). Sex main effects were found for sweat rate (F(1, 103) = 4.64, p = 0.034) and percentage body-mass change (F(1, 103) = 6.347, p = 0.013).
- K-means cluster analyses converged on three clusters: “Heavy sweaters with sufficient compensatory hydration habits” (HSSH), “Heavy sweaters with insufficient compensatory hydration habits” (HSIH), and “Light sweaters with sufficient compensatory hydration habits” (LSSH).
Methods
- Descriptive field study. A total of 199 Singaporean adolescent athletes from 11 sports were recruited by convenience sampling from a high-performance sports academy; 105 (male: 66, female: 39; age 14.1 ± 1.0 y) completed all protocols and were analyzed, with 94 unable to complete sessions due to academic commitments.
- Hydration status was assessed on three occasions: first-morning urine, and 30 min before low- and high-intensity training sessions held 1 week apart between 1500 and 1800 h. Hypohydration was defined by USG > 1.020 and, secondarily, USG > 1.025; post-training dehydration was defined as ≥2.0% body-mass change. Fluid consumption was measured by bottle weighing with ad libitum drinking; sweat rate was calculated from body mass and urine output.
- Training intensity was measured by session RPE (CR10 scale), training load as RPE × duration, and thirst on a 9-point scale. Analyses included linear mixed models with participant as random effect, Pearson correlations, and K-means clustering (optimal n = 3 by elbow method) with UMAP projection.
Limitations
- Paper-stated: hydration status was assessed using USG rather than plasma osmolality; USG is recognized to produce higher false positives for dehydration, so a secondary USG > 1.025 threshold was reported.
- Paper-stated: low- and high-intensity training sessions were classified according to coaches’ interpretation and execution of training plans, not by a standardized research-imposed intensity.
- Evident from the text: the study used convenience sampling from one high-performance academy in Singapore, had substantial attrition (199 recruited, 105 analyzed), and was an observational field study, limiting generalizability and causal inference.
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In the paper
“The LMM identified a sport by session interaction for sweat rate (F (10.94) = 2.901, p = 0.003) and fluid consumption (per hour) (F (10.94) = 2.297, p = 0.018) (Table 3)”
§ Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics > 3. Results > 3.1. Hydration Status and Fluid Balance Characteristics
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In the paper
“In total, 2% of participants in the low-intensity and 0% in the high-intensity session exhibited excessive dehydration, based on ACSM classifications of more than 2% body mass loss [19], following both low- and high-intensity training sessions.”
§ Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics > 3. Results > 3.1. Hydration Status and Fluid Balance Characteristics
What the sources say
- Not checked yetcites [19] Sawka, 2007, American College of Sports Medicine position stand. Exercise and fluid replacement, Med. Sci. Sports Exerc. source
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In the paper
“The LMM analysis did not identify an interaction between pre-training USG hydration classification x training session (p = 0.881).”
§ Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics > 3. Results > 3.2. Fluid Balance Characteristics between Euhydrated and Hypohydrated Athletes
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In the paper
“There was no interaction of sport by session (p = 0.173) for percentage body mass change.”
§ Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics > 3. Results > 3.1. Hydration Status and Fluid Balance Characteristics
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The LMM identified a sport by session interaction for sweat rate (F (10.94) = 2.901, p = 0.003) and fluid consumption (per hour) (F (10.94) = 2.297, p = 0.018) (Table 3)”
§ Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics > 3. Results > 3.1. Hydration Status and Fluid Balance Characteristics
plainstates an association- Asserted: passes — The authors put it forward as true.
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Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics > 2. Materials and Methods > 2.3. Procedure
“where participants with ≥2.0% body mass change were categorized as dehydrated”
§ Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics > 3. Results > 3.1. Hydration Status and Fluid Balance Characteristics
“based on ACSM classifications of more than 2% body mass loss”
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Metadata
- Authors
- Suppiah, H. T., Ng, E. L., Wee, J., Taim, B. C., Huynh, M., Gastin, P. B., Chia, M., Low, C. Y., & Lee, J. K. W.
- Affiliations
- La Trobe University (AU)
- National Youth Sports Institute, Singapore 397778, Singapore
- National University of Singapore (SG)
- Technological University of the Shannon: Midlands Midwest (IE)
- Nanyang Technological University (SG)
- Agency for Science, Technology and Research (SG)
- Corresponding author
- Suppiah, Haresh T.
- ORCID
- Venue
- Nutrients
- Publisher
- MDPI AG
- Published
- 2021-11-15
- Volume
- 13
- Issue
- 11
- Pages
- 4073
- ISSN
- 2072-6643 (ISSN-L)
- PMC
- PMC8625246
- OpenAlex
- W3213732001
- Cited by
- 31
- References
- 37
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 7 · 2023: 3 · 2022: 3
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nutrients (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- NIE Digital Repository (National Institute of Education, Nanyang Technological University) (publishedVersion)
- National University of Singapore (submittedVersion)
- Figshare (submittedVersion, cc-by)
- La Trobe University (cc-by)
- Topics
- Health Sciences › Medicine › Physiology (medical) › Thermoregulation and physiological responses
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports injuries and prevention
- Keywords
- hydration status, fluid replacement, youth athletes, hypohydration, sweat loss, exercise intensity, elite athletes, machine learning clustering
- MeSH terms
- Machine Learning, Organism Hydration Status, Adolescent, Dehydration, Drinking, Drinking Behavior, Female, Humans, Male, Singapore, Sports, Water-Electrolyte Balance, Cluster Analysis, Athletic Performance, Athletes
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