Nicotine Supplementation Does Not Influence Performance of a 1h Cycling Time-Trial in Trained Males
Bibliographic record
Abstract
The use of nicotine amongst professional and elite athletes is high, with anecdotal evidence indicating increased prevalence amongst cycling sports. However, previous investigations have not used high-validity or -reliability protocols nor trained cyclists. Therefore, the present study sought to determine whether nicotine administration proved ergogenic during a self-paced ~1h cycling time-trial (TT). Ten well-trained male cyclists (34 ± 9 y; 71 ± 8 kg; O2max: 71 ± 6 ml·kg-1·min-1) completed three work-dependent TT following ~30 min administration of 2 mg nicotine gum (GUM), ~10 h administration of 7 mg·24 h-1 nicotine patch (PAT) or color- and flavor-matched placebos (PLA) in a randomized, crossover and double blind design. Measures of nicotine’s primary metabolite (cotinine), core body temperature, heart rate, blood biochemistry (pH, HCO3-, La-) and Borg’s rating of perceived exertion (RPE) accompanied performance measures of time and power output. Plasma concentrations of cotinine were highest for PAT, followed by GUM, then PLA, respectively (p 0.46) or RPE with mean values of 16.7 ± 0.9, 16.8 ± 0.7 and 16.8 ± 0.8 (p = 0.89) for GUM, PAT and PLA, respectively. In conclusion: i) nicotine administration, whether via gum or transdermal patch, did not exert an ergogenic or ergolytic effect on self-paced cycling performance of ~1hr; ii) systemic delivery of nicotine was greatest when using a transdermal patch; and iii) nicotine administration did not alter any of the psycho-physiological measures observed.
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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) | 0.000 | 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".