A Combination of Amino Acids and Caffeine Enhances Sprint Running Capacity in a Hot, Hypoxic Environment
Bibliographic record
Abstract
Heat and hypoxia exacerbate central nervous system (CNS) fatigue. We therefore investigated whether essential amino acid (EAA) and caffeine ingestion attenuates CNS fatigue in a simulated team sport-specific running protocol in a hot, hypoxic environment. Subelite male team sport athletes (n = 8) performed a repeat sprint running protocol on a nonmotorized treadmill in an extreme environment on 4 separate occasions. Participants ingested one of four supplements: a double placebo, 3 mg.kg-1 body mass of caffeine + placebo, 2 x 7 g EAA (Musashi Create)+placebo, or caffeine + EAA before each exercise session using a randomized, double-blind crossover design. Electromyography (EMG) activity and quadriceps evoked responses to magnetic stimulation were assessed from the dominant leg at preexercise, halftime, and postexercise. Central activation ratio (CAR) was used to quantify completeness of quadriceps activation. Oxygenation of the prefrontal cortex was measured via near-infrared spectroscopy. Mean sprint work was higher (M = 174 J, 95% CI [23, 324], p < .05, d = 0.30; effect size, likely beneficial) in the caffeine + EAA condition versus EAAs alone. The decline in EMG activity was less (M = 13%, 95% CI [0, 26]; p < .01, d = 0.58, likely beneficial) in caffeine + EAA versus EAA alone. Similarly, the pre- to postexercise decrement in CAR was significantly less (M = -2.7%, 95% CI [0.4, 5.4]; p < .05, d = 0.50, likely beneficial) when caffeine + EAA were ingested compared with placebo. Cerebral oxygenation was lower (M = -5.6%, 95% CI [1.0, 10.1]; p < .01, d = 0.60, very likely beneficial) in the caffeine + EAA condition compared with LNAA alone. Co-ingestion of caffeine and EAA appears to maintain muscle activation and central drive, with a small improvement in running performance.
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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 | 0.000 |
| 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) | 0.001 | 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".