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Record W18798005 · doi:10.1123/ijsnem.17.5.433

Effect of Red Bull Energy Drink on Repeated Wingate Cycle Performance and Bench-Press Muscle Endurance

2007· article· en· W18798005 on OpenAlexaff
Scott C. Forbes, Darren G. Candow, Jonathan P. Little, Charlene R.A. Magnus, Philip D. Chilibeck

Bibliographic record

VenueInternational Journal of Sport Nutrition and Exercise Metabolism · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBench pressWingate testCrossover studyPlaceboAnaerobic exerciseAnimal scienceCaffeineMedicineCyclingPhysical therapyInternal medicineResistance trainingBiology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the effects of Red Bull energy drink on Wingate cycle performance and muscle endurance. Healthy young adults (N = 15, 11 men, 4 women, 21 +/- 5 y old) participated in a crossover study in which they were randomized to supplement with Red Bull (2 mg/kg body mass of caffeine) or isoenergetic, isovolumetric, noncaffeinated placebo, separated by 7 d. Muscle endurance (bench press) was assessed by the maximum number of repetitions over 3 sets (separated by 1-min rest intervals) at an intensity corresponding to 70% of baseline 1-repetition maximum. Three 30-s Wingate cycling tests (load = 0.075 kp/kg body mass), with 2 min recovery between tests, were used to assess peak and average power output. Red Bull energy drink significantly increased total bench-press repetitions over 3 sets (Red Bull = 34 +/- 9 vs. placebo = 32 +/- 8, P %%%lt; 0.05) but had no effect on Wingate peak or average power (Red Bull = 701 +/- 124 W vs. placebo = 700 +/- 132 W, Red Bull = 479 +/- 74 W vs. placebo = 471 +/- 74 W, respectively). Red Bull energy drink significantly increased upper body muscle endurance but had no effect on anaerobic peak or average power during repeated Wingate cycling tests in young healthy adults.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.235
Teacher spread0.231 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations145
Published2007
Admission routes1
Has abstractyes

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