Effect of Carbohydrate Mouth Rinse on Performance after Prolonged Submaximal Cycling
Bibliographic record
Abstract
Previous studies have shown improved shorter duration (∼1 h) performance with carbohydrate (CHO) mouth rinsing (WASH), especially in overnight fasted/non-fuelled subjects. PURPOSE: To determine the effect of WASH on cycling time trial (TT) performance and muscle activity (EMG) after 2 h of submaximal cycling while receiving CHO (FED). METHODS: In a double-blind, placebo-controlled crossover design, 10 well-trained males cyclists (V˙O2max: 65 mL·kg·min) completed two experimental trials. Each trial consisted of a standardized pretrial snack (2 h prior) followed by 120 min of steady-state (SS) cycling (∼60% V˙O2max) followed by an approximately 30-min TT, randomized as follows: 1) 30 g CHO·h during SS + WASH during TT (every 20% of TT) (FEDWASH); 2) 30 g CHO·h during SS + placebo (PLA) wash during TT (FEDPLA). RESULTS: Although FEDWASH was not significantly different than FEDPLA (P = 0.51), there was a 1.7% (90% confidence interval, +6.4% to -3.2%; ES, 0.21) decrease in TT time (35 s) for FEDWASH compared with FEDPLA, with qualitative probabilities of a 60% positive and 23% trivial outcome. For EMG, soleus showed significant increase, whereas medial gastrocnemius showed significant decrease in muscle recruitment from the beginning 20% TT segment to the last 20% only in the FEDPLA condition, which coincided with a slower (P = 0.01) last 20% of the TT in FEDPLA versus FEDWASH. CONCLUSIONS: Contrary to previous studies, this investigation utilized conditions of high ecological validity including a pretrial snack and CHO during SS. Significant changes in muscle recruitment and time over the last 20% of the TT, along with an average 1.7% improvement in TT time, suggest CHO mouth rinse helps maintain power output late in TT compared with placebo. Although marginal gains were achieved with a CHO mouth rinse (35 s), small performance effects can have significant outcomes in real-world competitions.
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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) | 0.002 | 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".