Caffeine increases peripheral fatigue in low- but not in high-performing cyclists
Bibliographic record
Abstract
The influence of cyclists’ performance levels on caffeine-induced increases in neuromuscular fatigue after a 4-km cycling time trial (TT) was investigated. Nineteen cyclists performed a 4-km cycling TT 1 h after ingesting caffeine (5 mg·kg −1 ) or placebo (cellulose). Changes from baseline to after exercise in voluntary activation (VA) and potentiated 1 Hz force twitch (Q tw,pot ) were used as markers of central and peripheral fatigue, respectively. Participants were classified as “high performing” (HP, n = 8) or “low performing” (LP, n = 8) in accordance with their performance in a placebo trial. Compared with placebo, caffeine increased the power, anaerobic mechanical power, and anaerobic work, reducing the time to complete the trial in both groups (p < 0.05). There was a group versus supplement and a group versus supplement versus trial interaction for Q tw,pot , in which the postexercise reduction was greater after caffeine compared with placebo in the LP group (Q tw,pot = −34% ± 17% vs. −21% ± 11%, p = 0.02) but not in the HP group (Q tw,pot = −22% ± 8% vs. −23% ± 10%, p = 0.64). There was no effect of caffeine on VA, but there was a group versus trial interaction with lower postexercise values in the LP group than in the HP group (p = 0.03). Caffeine-induced improvement in 4-km cycling TT performance seems to come at the expense of greater locomotor muscle fatigue in LP but not in HP cyclists. Novelty Caffeine improves exercise performance at the expense of a greater end-exercise peripheral fatigue in low-performing athletes. Caffeine-induced improvement in exercise performance does not affect end-exercise peripheral fatigue in high-performing athletes. High-performing athletes seem to have augmented tolerance to central fatigue during a high-intensity time trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".