Failure of Protein to Improve Time Trial Performance when Added to a Sports Drink
Bibliographic record
Abstract
INTRODUCTION: Recent studies have reported that adding approximately 2% protein to a carbohydrate sports drink increased cycle endurance capacity compared with carbohydrate alone. However, the practical implications of these studies work are hampered by the following limitations: (a) the rate of carbohydrate ingestion was less than what is considered optimal for endurance performance, and (b) the performance test (exercise time to fatigue) did not mimic the way in which athletes typically compete (i.e., a race in which a fixed distance or set amount of work is performed as quickly as possible). PURPOSE: We tested the hypothesis that adding 2% protein to a 6% carbohydrate drink (CHO-PRO) would improve 80-km cycling time trial performance, as compared with a 6% carbohydrate drink (CHO) and a nonenergetic sweetened placebo (PLAC). METHODS: Ten trained male cyclists (24 +/- 2 yr; VO2peak = 63 +/- 2 mL.kg(-1).min(-1); mean +/- SE) performed an 80-km laboratory time trial (TT) on three occasions separated by 7 d. In a double-blind crossover manner, subjects ingested CHO-PRO, CHO, or PLAC at a rate of 250 mL every 15 min with no temporal, verbal, or physiological feedback. RESULTS: Time to complete the TT was 4.4% lower (P < 0.002) during CHO (135 +/- 9 min) and CHO-PRO (135 +/- 9) compared with PLAC (141 +/- 10), with no difference between CHO and CHO-PRO (P = 0.92). CONCLUSION: Ingesting 6% carbohydrate at a rate of 1 L.h(-1) (60 g.h(-1)) improved an 80-km TT performance in trained male cyclists. However, adding 2% protein to a 6% carbohydrate drink provided no additional performance benefit during a task that closely simulated the manner in which athletes typically compete.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".