Ingesting A Sports Drink Enhances Simulated Ice Hockey Performance While Reducing Perceived Effort
Bibliographic record
Abstract
Abstract This study determined whether ingesting a carbohydrate-electrolyte solution (CES) vs. progressive dehydration affected skeletal muscle glycogen use and performance in ice hockey players during simulated ice hockey exercise comprised of 3 active “periods”. Seven males (21.3±0.3 years, 184.7±1.2 cm, 84.2±3.9 kg, and 49.6±1.8 mL·kg−1·min−1) performed a hockey-specific protocol on two occasions and either dehydrated progressively (NF), or stayed well-hydrated by ingesting a CES. Muscle biopsies were taken at rest, before the 3rd period (P3), and after the final sprint in the protocol. Compared to dehydration in the NF trial (−1.8% BM), CES ingestion enhanced voluntary performance (151.0±8.0 vs. 144.1±8.7 kJ) and glycogen use (177.5±31.1 vs. 103.5±16.2 mmol·kg dm−1), and reduced perceived exertion (16±1 vs. 18±1) in P3. Mean core temperature was reduced by CES ingestion throughout the protocol (38.0±0.2 vs. 38.1±0.1°C). These results suggest that compared to progressive dehydration, staying hydrated by ingesting a CES helps preserve performance, while reducing thermal and perceptual strains, in P3 of cycle-based simulation of ice hockey exercise. These benefits are observed despite greater glycogen use in P3 with CES ingestion.
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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 | 0.000 |
| 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) | 0.001 | 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".