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A Meta-Analysis Of The Effect Of Quercetin Supplementation On Endurance Performance And Maximal Oxygen Consumption

2011· article· en· W2334914867 on OpenAlexaff
Eric Goulet, Audrey Asselin, Guillaume Lacerte

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsRandom effects modelVO2 maxStatisticsMeta-analysisPlaceboConfidence intervalInclusion and exclusion criteriaMedicineMathematicsLimits of agreementPhysical therapyInternal medicineNuclear medicine

Abstract

fetched live from OpenAlex

Recently, the effect of quercetin supplementation (QS) upon endurance performance (EP) and maximal oxygen consumption (VO2max) has been the object of several studies. To this day, however, the effect of QS on these parameters remains unclear. PURPOSE: Determine the magnitude of the effect of QS on EP and VO2max using a meta-analytic approach. METHODS: Database searches and cross-referencing used to locate articles. Inclusion criteria: 1) data necessary to compute the effect estimates and variances; 2) protocols were placebo-controlled, double-blinded; 3) laboratory-based exercise protocols; 4) exercise protocols duration > 5 min (for EP); 5) studies published in English and in peer-reviewed journals. Exclusion criteria: 1) QS < 5 days; 2) research with animals. All EP outcomes were put on the same scale and converted to mean changes in power output (PO), when necessary. A random-effects model was used to determine the mean weighted summary effects, along with a random-effects meta-regression (method-of-moments) to establish the relationship between fitness level and QS-induced EP changes. Ninety-five percent confidence intervals (CI) were calculated and exclusion of 0 indicated a statistically significant effect. Magnitude-based inferential statistics were also used: the smallest worthwhile % change in VO2max was set at 2.5%, and for half-marathon and marathon-runners and long-distance cyclists the changes in PO were set at 2.15, 2.7 and 1.6%, respectively. RESULTS: Ten research articles were retrieved, providing 4 (VO2max) and 9 (EP) effect estimates from 4 and 6 research articles meeting the inclusion criteria, respectively. Supplementation duration was 21 ± 18 days (EP) and 18 ± 23 days (VO2max), with a daily amount of quercetin of 1000 mg. For EP, mean exercise duration was 82 ± 87 min. QS increased mean PO by 0.73% (95% CI: 0.08-1.37%) and VO2max by 2.06% (95% CI: 0.43-3.70%), compared with the placebo. The meta-regression established no relationship between the changes in EP and VO2max (P=0.74). Under real-world conditions, the effect of QS on VO2max, half-marathon, marathon and long-distance cycling performances is very likely to be trivial. CONCLUSION: QS (1000 mg/day) for 18-21 days is very unlikely to provide an EP advantage or incur a meaningful physiological change in VO2max under field conditions.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0110.046
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.056
GPT teacher head0.291
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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