Carbohydrate mouth rinse improves resistance exercise capacity in the glycogen-lowered state
Bibliographic record
Abstract
The effect of carbohydrate mouth rinse (CHO MR) on resistance exercise performance is equivocal and may be moderated by carbohydrate availability. This study determined the effect of CHO MR on low-load resistance exercise capacity completed in a fed but glycogen-lowered state. Twelve resistance-trained men (age: 22 ± 4 years; height: 1.79 ± 0.05 m; mass: 78.7 ± 7.8 kg; bench press one-repetition maximum (1RM): 87 ± 21 kg; squat 1RM: 123 ± 19 kg) completed two fed-state resistance exercise bouts consisting of six sets of bench press and six sets of squat to failure at 40% 1RM. Each bout was preceded by glycogen-depleting cycling the evening before, with feeding controlled to create acute energy deficit and maintain low muscle glycogen. During resistance exercise, participants rinsed with either a 6% CHO MR solution or a taste-matched placebo (PLA) between sets. Total volume workload was greater with CHO MR (9354 ± 2051 vs. 8525 ± 1911 kg, p = 0.010). Total number of repetitions of squat were greater with CHO MR (107 ± 26 vs. 92 ± 16, p = 0.017); the number of repetitions of bench press were not significantly different (CHO MR: 120 ± 24 vs. PLA: 115 ± 22, p = 0.146). This was independent of differences in feeling or arousal. CHO MR may be an effective ergogenic aid for athletes completing resistance exercise when in energy deficit and with low carbohydrate availability. Novelty: CHO MR can increase low-load resistance exercise capacity undertaken in a glycogen-lowered but fed state. This effect was driven by a greater number of repetitions-to-failure in the squat — using muscles lowered in glycogen content with exhaustive cycling on the evening prior to resistance exercise — but not bench press.
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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 | 0.000 |
| 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) | 0.002 | 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".