The Effect of Pre-Run Foods on Athletic Performance
Bibliographic record
Abstract
Ergogenic aids such as sodium bicarbonate that buffer against metabolic acidosis during long sprint or strength endurance races can enhance performance but have known drawbacks. Whether foods that contain high levels of potassium can also provide buffering capacity and improve performance is not known. PURPOSE: To determine if a small meal containing high levels of potassium supplied by whole foods when consumed before a maximal effort can improve performance in competitive track athletes. METHODS: Seven healthy track athletes (3 women), 19-22 years, were tested on a treadmill with 5% grade for VO2peak. They were then randomly assigned to consume one of two small meals before running to volitional exhaustion on a treadmill with a 5% grade at the final speed achieved on their VO2peak test. A week later, they consumed the other meal before an identical test. The meals were formulated to have either high or low potassium content, and contained 18% of each athlete’s daily energy requirement with the macronutrients providing 56%, 23%, and 21% of energy from carbohydrate, fat, and protein respectively. Urine samples were analyzed for hydration and pH before consumption of each meal and for pH before and after each running test. Time to volitional exhaustion and the pre- to post-run drop in urinary pH after the two meals were compared using paired t-tests. Results: All athletes ran longer to volitional fatigue after the high versus the low potassium meal (161±59s vs. 148±63s; p=0.003). Changes in pH from pre-meal to pre-test, from pre-meal to post-test, and from pre-test to post-test were not different between treatments. All athletes were well hydrated, with a urinary specific gravity less than 1.020 for all tests. CONCLUSION: High potassium foods may provide benefits to performance in sports that generate metabolic acidosis but whether that benefit is due to buffering the body against acidosis will require further research. Whole foods have potential to be ergogenic aids for some athletes.
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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 | 0.001 |
| 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) | 0.003 | 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".