Caffeine potentiates low frequency skeletal muscle force in habitual and nonhabitual caffeine consumers
Bibliographic record
Abstract
The mechanism of action underlying the ergogenic effect of caffeine is still unclear. Caffeine increases the force of muscular contraction during low-frequency stimulation by potentiating calcium release from the sarcoplasmic reticulum. Studies have also suggested an enhancement of lipid oxidation and glycogen sparing as potential mechanisms. Given that several studies have found an ergogenic effect of caffeine with no apparent metabolic effects, it is likely that a direct effect upon muscle is important. Twelve healthy male subjects were classified as habitual (n = 6) or nonhabitual (n = 6) caffeine consumers based on a 4-day diet record analysis, with a mean caffeine consumption of 771 and 14 mg/day for each group, respectively. Subjects were randomly allocated to receive caffeine (6 mg/kg) and placebo (citrate) in a double-blind, cross-over fashion approximately 100 min before a 2-min tetanic stimulation of the common peroneal nerve in a custom-made dynamometer (2 trials each of 20 and 40 Hz). Tetanic torque was measured every 30 s during and at 1, 5, and 15 min after the stimulation protocol. Maximal voluntary contraction strength and peak twitch torque were measured before and after the stimulation protocol. Caffeine potentiated the force of contraction during the final minute of the 20-Hz stimulation (P<0.05) with no effect of habituation. There was no effect of caffeine on 40-Hz stimulation strength nor was there an effect on maximal voluntary contraction or peak twitch torque. These data support the hypothesis that some of the ergogenic effect of caffeine in endurance exercise performance occurs directly at the skeletal muscle level.
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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".