Effects of carbohydrate mouth rinse and caffeine on high-intensity interval running in a fed state
Bibliographic record
Abstract
The current study aims to identify if mouth rinsing with a 6% carbohydrate mouth-rinse (CMR) solution and mouth rinsing and ingestion of caffeine (CMR+CAFF) can affect exercise performance during steady-state (SS) running and high-intensity intervals (HIIT) in comparison with a 0% control solution (PLA) when in a fed state. Eight recreationally trained males completed 3 trials (CMR, CMR+CAFF, and PLA) of 45 min SS running and an HIIT protocol (90% peak treadmill velocity) until fatigue in a double blinded, repeated-measures study. Participants ingested a capsule of either CAFF or PLA before and after SS. Participants received a 25-mL bolus of carbohydrate solution (CMR and CMR+CAFF trials) or taste-matched PLA (PLA trial) prior to HIIT protocol and after every second effort. Heart rate and lactate responses were recorded throughout the SS and HIIT protocol. CMR+CAFF was significantly different when compared with PLA (p = 0.001; Cohens d = 1.34) and CMR (p = 0.031; Cohens d = 0.87) in relation to distance covered before fatigue. Although there was no significant difference between CMR and PLA, there was a small benefit for CMR (p = 0.218; Cohens d = 0.46). Results indicate that CMR and ingestion of CAFF leads to improvements in performance during interval sessions while participants were in a fed state. These findings indicate that the regular use of CMR can decrease the risk of gastrointestinal distress reported by athletes, which can be applicable to athletes in a real-world setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".