Why do team-sport athletes drink fluid in excess when exercising in cool conditions?
Bibliographic record
Abstract
This study assessed the potential physiological and perceptual drivers of fluid intake and thirst sensation during intermittent exercise. Ten male rugby players (17 ± 1 years, stature: 179.1 ± 4.2 cm, body mass (BM): 81.9 ± 8.1 kg) participated in six 6-min small-sided games, interspersed with 2 min rest, where fluid intake was ad libitum during rest periods. Pre- and postmeasurements of BM, subjective ratings (thirst, thermal comfort, thermal sensation, mouth dryness), plasma osmolality (POsm), serum sodium concentration (S[Na+]), haematocrit and haemoglobin (to calculate plasma volume change; PV) were taken. Fluid intake was measured during rest periods. BM change was –0.17 ± 0.59% and fluid intake was 0.88 ± 0.38 L. Pre- to post-POsm decreased (–3.1 ± 2.3 mOsm·kg−1; p = 0.002) and S[Na+] remained similar (–0.3 ± 0.7 mmol·L−1, p = 0.193). ΔPV was 5.84 ± 3.65%. Fluid intake displayed a relationship with pre-POsm (r = –0.640, p = 0.046), prethermal comfort (r = 0.651; p = –0.041), ΔS[Na+] (r = 0.816, p = 0.004), and ΔPV (r = 0.740; p = 0.014). ΔThirst sensation displayed a relationship with premouth dryness (r = 0.861, p = 0.006) and Δmouth dryness (r = 0.878, p = 0.004). Yet a weak positive relationship between Δthirst sensation and fluid intake was observed (r = 0.085, p = 0.841). These data observed in an ambient temperature of 13.6 ± 0.9 °C, suggest team-sport athletes drink in excess of fluid homeostasis requirements and thirst sensation in cool conditions; however, this was not influenced by thermal discomfort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".