Voluntary fluid intake, hydration status, and aerobic performance of adolescent athletes in the heat
Bibliographic record
Abstract
We determined whether beverage flavoring and composition would stimulate voluntary drink intake, prevent dehydration, and maintain exercise performance in heat-acclimated adolescent males running in the heat. Eight adolescent (age, 13.7 ± 1.1 years) runners (peak oxygen uptake, 59.5 ± 4.0 mL·kg-1·min-1) underwent at least four 80-min exercise heat-acclimation sessions before completing 3 experimental sessions. All sessions were performed at 30 °C and 60%-65% relative humidity. Each experimental session consisted of five 15-min treadmill runs at a speed eliciting 65% peak oxygen uptake, with a 5 min rest prior to each run. Ten minutes after the final run, a time to exhaustion test was performed at a speed eliciting 90% peak oxygen uptake. Counterbalanced experimental sessions were identical, except for fluid intake, which consisted of tap water (W), flavored water (FW), and FW with 6% carbohydrate and 18 mmol·L-1 NaCl (CNa) consumed ad libitum. Fluid intake and body weight were monitored to calculate dehydration. Voluntary fluid intake was similar to fluid losses in W (1032 ± 130 vs. 1340 ± 246 g), FW (1086 ± 86 vs. 1451 ± 253 g), and CNa (1259 ± 119 vs. 1358 ± 234 g). As a result, significant dehydration was avoided in all trials (-0.45% ± 0.68% body weight in W, -0.66% ± 0.50% body weight in FW, and -0.13% ± 0.71% body weight in CNa). Core temperature increased by ~1 °C during exercise, but was not different between trials. Time to exhaustion was not different between trials and averaged 8.8 ± 1.7 min. Under exercise conditions more closely reflecting real-life situations, heat-acclimatized adolescent male runners can appropriately gauge fluid intake regardless of the type of beverage made available, resulting in consistency in exercise performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".