Four Days of Blueberry Powder Supplementation Lowers the Blood Lactate Response to Running But Has No Effect on Time-Trial Performance
Bibliographic record
Abstract
Blueberries are abundant with anthocyanins possessing antioxidant and anti-inflammatory properties. As these properties combat fatigue and promote recovery, blueberry supplementation may enhance performance and recovery. Thus, the objectives were to examine the effects of two blueberry supplementation protocols on running performance, physiological responses, and short-term recovery. Using a randomized, double-blind, placebo (PLA)-controlled crossover design, 14 runners completed an 8-km time trial (TT) after supplementation with 4 days of blueberries (4DAY), 4 days of a PLA, or 2 days of placebo followed by 2 days of blueberries (2DAY). Heart rate and ratings of perceived exertion were monitored during the TT. Blood lactate, vertical jump, reactive strength index, and salivary markers were assessed before and after. No significant differences were observed for time to complete the TT (PLA: 3,010 ± 459 s; 2DAY: 3,014 ± 488 s; 4DAY: 3,011 ± 423 s), heart rate, ratings of perceived exertion, or any of the salivary markers. An interaction effect (p = .027) was observed for blood lactate, with lower post-TT concentrations in 4DAY (5.4 ± 2.0 mmol/L) than PLA (6.6 ± 2.5 mmol/L; p = .038) and 2DAY (7.4 ± 3.4 mmol/L; p = .034). Post-TT decreases in vertical jump height were not different, whereas the decline in reactive strength index was less following 4DAY (-6.1% ± 13.5%) than the other conditions (PLA: -12.6% ± 10.1%; 2DAY: -11.6% ± 11.5%; p = .038). Two days of supplementation did not influence performance or physiological stress. Although 4 days of supplementation did not alter performance, it blunted the increase in blood lactate, perhaps reflecting altered lactate production and/or clearance, and offset the decrease in dynamic muscle function post-TT, as indicated by the reactive strength index differences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".