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Record W4301032813

Do physiological measures predict selected CrossFit® benchmark performance?

2015· article· en· W4301032813 on OpenAlexaboutno aff
Butcher SJ, Neyedly TJ, Horvey KJ, Benko CR

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmark (surveying)Computer scienceEnvironmental scienceGeographyCartography
DOInot available

Abstract

fetched live from OpenAlex

Scotty J Butcher,1,2 Tyler J Neyedly,3 Karla J Horvey,1 Chad R Benko2,41Physical Therapy, University of Saskatchewan, 2BOSS Strength Institute, 3Physiology, University of Saskatchewan, 4Synergy Strength and Conditioning, Saskatoon, SK, CanadaPurpose: CrossFit® is a new but extremely popular method of exercise training and competition that involves constantly varied functional movements performed at high intensity. Despite the popularity of this training method, the physiological determinants of CrossFit performance have not yet been reported. The purpose of this study was to determine whether physiological and/or muscle strength measures could predict performance on three common CrossFit "Workouts of the Day" (WODs).Materials and methods: Fourteen CrossFit Open or Regional athletes completed, on separate days, the WODs "Grace" (30 clean and jerks for time), "Fran" (three rounds of thrusters and pull-ups for 21, 15, and nine repetitions), and "Cindy" (20 minutes of rounds of five pull-ups, ten push-ups, and 15 bodyweight squats), as well as the "CrossFit Total" (1 repetition max [1RM] back squat, overhead press, and deadlift), maximal oxygen consumption (VO2max), and Wingate anaerobic power/capacity testing.Results: Performance of Grace and Fran was related to whole-body strength (CrossFit Total) (r=-0.88 and -0.65, respectively) and anaerobic threshold (r=-0.61 and -0.53, respectively); however, whole-body strength was the only variable to survive the prediction regression for both of these WODs (R2=0.77 and 0.42, respectively). There were no significant associations or predictors for Cindy.Conclusion: CrossFit benchmark WOD performance cannot be predicted by VO2max, Wingate power/capacity, or either respiratory compensation or anaerobic thresholds. Of the data measured, only whole-body strength can partially explain performance on Grace and Fran, although anaerobic threshold also exhibited association with performance. Along with their typical training, CrossFit athletes should likely ensure an adequate level of strength and aerobic endurance to optimize performance on at least some benchmark WODs.Keywords: strength, aerobic, anaerobic, high-intensity, training, exercise, functional

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0240.000

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 head0.296
GPT teacher head0.513
Teacher spread0.217 · 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.

Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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