Effects Of Protein Pre-run On Glucose And Perceived Exertion - A Pilot Study
Bibliographic record
Abstract
PURPOSE: Protein recommendations pre-running have yet to be established and will need to consider performance responses as well as the potential for exercise-induced gastrointestinal symptoms. The purpose of this study was to examine the impact of a high protein (HP) shake consisting of 0.4 g/kg body weight (BW) protein vs. a low protein shake (LP) 0.15 g/kg BW protein pre-run on glucose, gut fullness, and perceived exertion. METHODS: Five (n=2 male) endurance trained runners were administered a HP or LP shake one hour prior to a 10 km run in a randomized cross-over design. Carbohydrate and water intakes remained consistent across trials. Blood glucose was measured at fasting, 30, and 60 minutes post-shake and post-run using a glucose meter. Perceived exertion was measured using Borg’s scale. Exercise induced gastrointestinal symptoms were measured at fasting, pre-run and post-run using a 10-point questionnaire. Gut fullness was measured using a visual analogue scale at fasting, 15, 30, 60 minutes post-shake and post-run. RESULTS: Blood glucose peaked at 30 minutes post-shake and there was no difference between the HP and LP shakes. There was a significant interaction between time and shake (p=0.044), however no main effect of time or shake. There was no difference in perceived exertion between the two interventions. Gut fullness changed over time (p=0.005), however, was not affected by the composition of the shake. There was no difference in the number of exercise-induced gastrointestinal symptoms experienced on the HP and LP shakes. CONCLUSION: The results from this pilot study suggest that the inclusion protein in the pre-run meal is feasible and provides support for a fully powered trial. Supported by Mount Royal University Innovation 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".