The effects of a sport drink containing carbohydrates and electrolytes with or without caffeine on 20km cycling time trial performance in men and women
Bibliographic record
Abstract
Caffeine is a commonly used substance by athletes and has become more widespread since it's legalization by the World Anti Doping Association (WADA) and the International Olympic Committee (IOC). Extensive research has been done on the effect of caffeine on exercise performance, however, most studies were done at low intensity, and none compared differences in performance between men and women. Thus, 18 university aged (9 men and 9 women) individuals were recruited to perform three 20km cycling time trials including a familiarization trial (FAM), a carbohydrate plus placebo condition (CHO+P) and carbohydrate plus caffeine (5mg/kg) condition (CHO+C). Time to complete the 20km distance was significantly decreased in the CHO+C trial compared to the CHO+P trial in both men and women. This was concomitant with an increase in post-exercise blood glucose levels in the CHO+C trial but not in the CHO+P trial. However, ratings of perceived exertion (RER), respiratory exchange ratio (RER), tympanic temperature and a novel measure of arousal (long term excitement, LTE) were not different between trials. It is concluded that caffeine can improve performance when combined carbohydrate over carbohydrate alone. Moreover, these performance improvements are not different between sexes. Of the subjective (RPE), physiological (HR, RER) and excitement (LTE) variables that were measured, it is unclear why caffeine had this effect. Nonetheless, these findings are intriguing, especially for those athletes looking to gain an advantage in activity that requires intense work over the span of 20-35min, as well as WADA in their decisions to examine the use of caffeine during competition. Caffeine is currently a legal ergogenic aid and these data add to the significant body of literature indicating its powerful ergogenic properties in many activities.
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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 | 0.000 | 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".