Blueberry supplementation reduces the blood lactate response to running in normobaric hypoxia but has no effect on performance in recreational runners
Bibliographic record
Abstract
Background Blueberries are concentrated with anthocyanins possessing antioxidant properties. As these properties counter fatigue, blueberry supplementation may improve performance and recovery, particularly in hypoxia, where oxidative stress is elevated.Methods This study examined the effects of blueberry supplementation on running performance, physiological responses, and recovery in normobaric hypoxia. Eleven experienced runners completed a 30-minute time-trial (TT) in normobaric hypoxia (%O2 = 15.5 %) on separate days after supplementation with four days of blueberries (BLU) or four days of placebo (PLA). Heart rate (HR), oxygen saturation (SaO2) and ratings of perceived exertion (RPE) were monitored during the TT. Blood lactate and fraction of exhaled nitric oxide (FENO) were assessed pre-TT, post-TT, and during recovery.Results No significant differences were observed in the distance run during the TT, HR, SaO2, and RPE. The post-TT increase in blood lactate was significantly lower in BLU than PLA (p = 0.036). Pre-TT and post-TT FENO did not differ between conditions. Blood lactate recovery following the TT was similar between conditions.Conclusions Four days of blueberry supplementation did not alter running performance or cardiovascular and perceptual responses in normobaric hypoxia. Supplementation lowered the blood lactate response to running, however, the significance of this finding is uncertain given the absence of an ergogenic effect.
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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) | 0.002 | 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".