Caffeine and carbohydrate supplementation during exercise when in negative energy balance: effects on performance, metabolism, and salivary cortisol
Bibliographic record
Abstract
The ingestion of carbohydrate (+CHO) and caffeine (+CAF) during exercise is a commonly used ergogenic practice. Investigations are typically conducted with subjects who are in a rested state after an overnight fast. However, this state of positive energy balance is not achieved during many work and exercise circumstances. The aim of this study was to evaluate the substrate use and performance effects of caffeine and carbohydrate consumed alone and in combination while participants were in negative energy balance. Male participants (n = 9; 23 +/- 3 years; 74.1 +/- 10.6 kg) completed 4 trials in random order: -CAF/-CHO, -CAF/+CHO, +CAF/-CHO, and +CAF/+CHO. Diet and exercise were prescribed for 2 days before each trial to ensure negative energy balance. For each trial, before and after 2 h of cycling at 50% of maximal watts, a saliva sample and a muscle biopsy (vastus lateralis) were obtained. A simulated 20 km time trial was then performed. The respiratory exchange ratio was higher (p < 0.05) in +CHO trials and lower (p < 0.05) in the +CAF/+CHO trial than in the -CAF/+CHO trial. Salivary cortisol response was higher (p < 0.05) in the +CAF/-CHO trial than in any of the other trials. Muscle glycogen and heart rates were similar in all trials. Performance in the 20 km time trial was better in the -CAF/+CHO trial than in the -CAF/-CHO trial (p < 0.05), but the +CAF/+CHO trial was no better than the +CAF/-CHO trial (p > 0.05), or any of the other trials. When co-ingested with carbohydrate, caffeine increased fat use and decreased nonmuscle glycogen carbohydrate use over carbohydrate alone when participants are in negative energy balance; however, caffeine had no effect on the 20 km cycling 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 | 0.000 |
| 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".