Menthol Mouth Rinsing Improves Cycling Performance in Trained Adolescent Males Under Heat Stress
Bibliographic record
Abstract
PURPOSE: This study investigated the effect of menthol (MEN) mouth rinsing (MR) on cycling performance during a modified variable cycle test (M-VCT) in adolescent athletes under hot conditions (31.4 ± 0.9 °C, 23.4 ± 3.7% relative humidity). METHODS: Trained adolescent male cyclists (n = 11, 16.7 ± 1.3 years, height 176.6 ± 8.8 cm, body mass 65.8 ± 11.6 kg, maximal oxygen uptake 62.97 ± 7.47 ml·kg-1·min-1) voluntarily completed three trials (familiarization and two experimental) of a 30-min M-VCT, which included five 6-min laps consisting of three 6-s accelerations and three 10-s sprints throughout each lap. In a randomized crossover design, MEN (0.01%) or placebo (PLA) (crystal-light), was swilled for 5 s before the start of each lap (total of 6 MR). Power output, distance (in kilometers), core temperature, heart rate, perceptual exertion, thermal stimulation (thermal comfort and thermal sensation), and blood lactate concentration were recorded. RESULTS: MEN MR significantly improved M-VCT mean power output by 1.81 ± 1.57% compared to PLA (MEN, 177.8 ± 31.4 W; PLA, 174.7 ± 30.5 W, p < .001, 95% confidence interval [1.73, 4.46], d = 1.53). For maximal intermittent sprints, 6- and 10-s mean power output was significantly higher with MEN than PLA (6 s, p = .041, 95% confidence interval [0.73, 27.19], d = 0.71; 10 s, p = .002, 95% confidence interval [11.08, 35.22], d = 1.29). There was no significant difference in core temperature, heart rate, blood lactate concentration, or any perceptual measure between trials (p > .05) despite significantly higher work with MEN. CONCLUSION: 64% of athletes (7/11) improved M-VCT performance with MEN. The results of this investigation suggest that a MEN MR may improve power output during a sport-specific stochastic cycling task in elite adolescent male cyclists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".