Low and moderate doses of caffeine late in exercise improve performance in trained cyclists
Bibliographic record
Abstract
The aim of the present study was to assess if low and moderate doses of caffeine delivered in a carbohydrate-electrolyte solution (CES) late in exercise improved time-trial (TT) performance. Fifteen (11 male, 4 female) cyclists (age, 22.5 ± 0.9 years; body mass, 69.3 ± 2.6 kg; peak oxygen consumption, 64.6 ± 1.9 mL·min(-1)·kg(-1)) completed 4 double-blinded randomized trials. Subjects completed 120 min of cycling at ∼60% peak oxygen consumption with 5 interspersed 120-s intervals at ∼82% peak oxygen consumption, immediately followed by 40-s intervals at 50 W. Following 80 min of cycling, subjects either ingested a 6% CES (PL), a CES with 100 mg (low dose, 1.5 ± 0.1 mg·kg body mass(-1)) of caffeine (CAF1), or a CES with 200 mg (moderate dose, 2.9 ± 0.1 mg·kg body mass(-1)) of caffeine (CAF2). Following the 120-min cycling challenge, cyclists completed a 6-kJ·kg body mass(-1) TT. There was no difference between respiratory, heart rate, glucose, free fatty acid, body mass, hematocrit, or urine specific gravity measurements between treatments. The CAF2 (26:36 ± 0:22 min:s) TT was completed faster than CAF1 (27:36 ± 0:32 min:s, p < 0.05) and both CAF1 and CAF2 TTs were completed faster than PL (28:41 ± 0:38 min:s, p < 0.05). Blood lactate was similar between trials and rose to a greater extent during the TT (p < 0.05). In summary, both doses of caffeine delivered late in exercise improved TT performance over the PL trial and the moderate dose (CAF2) improved performance to a greater extent than the low dose (CAF1).
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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 | 0.001 | 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".