Independent and combined effects of carbohydrate and caffeine ingestion on aerobic cycling performance in the fed state
Bibliographic record
Abstract
The purpose of this study was to examine the independent and combined effects of carbohydrate and caffeine ingestion on performance and various physiological parameters during aerobic cycling (∼1 h). Ten male cyclists (28 ± 9 years, 73 ± 6 kg, 66 ± 9 mL·kg(-1)·min(-1) maximal oxygen consumption) performed 20 min of steady-state (SS) cycling (60% peak power (W(max))) followed by a simulated 20-km time trial (TT) under placebo (PLA), carbohydrate (CHO), caffeine (CAF), and combined CAF-CHO conditions, all of which were performed in the fed state. CAF-CHO improved TT performance by 3.4% ± 2% (84 ± 57 s) compared with PLA (p < 0.05), whereas no differences were detected among CHO, CAF, and PLA. The SS respiratory exchange ratio was elevated in CHO (0.92 ± 0.03), CAF (0.96 ± 0.07), and CAF-CHO (0.95 ± 0.02) compared with PLA (0.89 ± 0.03) (p < 0.05). Post-SS and post-TT blood glucose levels were also elevated in CAF-CHO (88.3 ± 16.7 mg·dL(-1) and 111.2 ± 33.5 mg·dL(-1), respectively) compared with PLA (74.5 ± 9.8 mg·dL(-1) and 85.4 ± 17.6 mg·dL(-1), respectively) (p < 0.05). Treatment conditions did not differentially impact SS pulmonary ventilation, oxygen consumption, heart rate, peak quadriceps muscle strength, rating of perceived exertion, or blood lactate. CAF and CHO improved TT performance when taken together but not independently. Although the present work did not yield any definitive physiological mechanisms for the performance findings, these data suggest that cyclists in the fed state should ingest carbohydrate and caffeine together to improve time trial performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".