Caffeine bars used as pre-exercise supplements influence endurance performance, energy metabolism and perception of effort in trained cyclists
Bibliographic record
Abstract
Background: In contrast to caffeine bars, the effect of caffeine intake from tablets and energy drinks on endurance performance has already been investigated. Therefore, the aim of the study is to examine the effects of caffeine bars used as pre-exercise supplements on endurance performance in cycling. Methods : The present study was designed as a randomized single-blind cross-over placebo-controlled trial. Nine male, trained cyclists completed endurance exercises on a cycling ergometer under the following conditions: ingestion of water (WAT), placebo bars (PLA) and caffeine bars (CAF; 5 mg caffeine/kg bodyweight), respectively, 45 min prior to the test. After 40 min at a constant intensity of 75% VO 2 max (assessed in a previously performed incremental test) load was increased 10 W/min until exhaustion. Results: CAF compared to PLA resulted in a higher maximal power and longer time to exhaustion ( p = .002). Surprisingly, concentration of free fatty acids was lower at exhaustion ( p = .004), whereas blood lactate levels ( p = .021) and heart rate ( p = .008) were significantly higher after CAF. Subjects also reported lower received perception of effort at warm-up (0.034), 30 min ( p = .026) and 40 min ( p = .041) only after CAF. Conclusions: Caffeine bars are useful pre-exercise supplements. Their performance enhancing effect was rather due to a delayed perception of fatigue than an increased lipolysis, proving caffeine as central nervous system stimulant.
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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) | 0.001 | 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 | 0.001 | 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".