Effect of Dark Chocolate Supplementation on Tissue Oxygenation, Metabolism, and Performance in Trained Cyclists at Altitude
Bibliographic record
Abstract
Dark chocolate (DC) is high in flavonoids and has been shown to increase nitric oxide in the blood. Increased nitric oxide has the potential to improve delivery of oxygen to muscle, especially in hypoxic conditions, such as altitude. Our aim was to assess the impact of DC supplementation on cycling performance at altitude. Twelve healthy, trained cyclists (n = 2 females, n = 10 males; age = 35 [12] years; height = 177 [7] cm; mass = 75.2 [11.0] kg; VO2max = 55 [6] ml·kg-1·min-1) were randomized to supplement with 60 g of DC or placebo twice per day for 14 days in a double-blind crossover study. After the 2 weeks of supplementation, the participants attended a laboratory session in which they consumed 120 g of DC or placebo and then cycled for 90 min at 50% peak power output, followed immediately by a 10-km time trial (TT) at simulated altitude (15% O2). The plasma concentration of blood glucose and lactate were measured before and at 15, 30, 60, and 90 min during the steady-state exercise and post TT, while muscular and prefrontal cortex oxygenation was measured continuously throughout exercise using near-infrared spectroscopy. DC resulted in a higher concentration of blood glucose (5.5 [0.5] vs. 5.3 [0.9] mmol/L) throughout the trial and lower blood lactate concentration following the TT (7.7 [1.92] vs. 10.0 [4.6] mmol/L) compared with the placebo. DC had no effect on the TT performance (19.04 [2.16] vs. 19.21 ± 1.96 min) or oxygenation status in either the prefrontal cortex or muscle. The authors conclude that, although it provided some metabolic benefit, DC is not effective as an ergogenic aid during TT cycling at simulated altitude.
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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) | 0.001 | 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 | 0.001 |
| 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".