Effects Of Protein Intake On Gastrointestinal Symptoms In Runners - A Pilot Study
Bibliographic record
Abstract
PURPOSE: Gastrointestinal (GI) symptoms often hinder running performance and are responsive to nutrient intakes. Currently, the recommendation is to “limit” protein intakes to minimize symptoms, but a threshold has not been established. The purpose of this study was to examine the effect of a highprotein (HP) vs a low protein (LP) shake on running induced GI symptoms. METHODS: Five (n=2 male) endurance trained runners were administered a HP (0.4 g/kg body weight) or LP (0.15 g/kg/ body weight) shake one hour prior to a 10 km run at 85% of their race pace in a single-blind, randomizedcross-over design. Carbohydrate and water intakes remained consistent across trials. Exercise induced GI symptoms were measured pre-shake, 60 minutes post-shake, and post-run. Symptoms were rated on a 10 point scale and included six upper abdominal problems, seven lower abdominal problems, and five systemic problems. RESULTS: Symptoms experienced during the LP run included belching (2), stomach cramps (2), intestinal cramps (3), flatulence (1), urge to defecate (1), stitch (1), dizziness (1), muscle cramp (1), urge to urinate (2), and fullness (1). Severity was consistently low with only urge to urinate rated as a 4. Symptoms experienced during the HP run included re-flux (1), belching (2), bloating (1),stomach cramps (2), intestinal cramps (1), flatulence (1), stitch (1), and fullness (1). Severity was consistently low with a maximum of 3. There was no significant difference in the severity of symptoms experienced between the two trials and no difference in the number of symptoms. CONCLUSIONS: A pilot trial indicates no difference in exercise-induced GI symptoms with a HP or LP shake pre-run and suggests intakes up to 0.4g/kg body weight can be well tolerated. Supported by a Mount Royal UniversityInnovation Grant.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".