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Record W2475029813 · doi:10.3233/ies-160620

Upper body muscle activation during low-versus high-load resistance exercise in the bench press

2016· article· en· W2475029813 on OpenAlexaff
Brad J. Schöenfeld, Bret Contreras, Andrew D. Vigotsky, Dan Ogborn, Fabio Fontana, Gül Tiryaki-Sönmez

Bibliographic record

VenueIsokinetics and Exercise Science · 2016
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBench pressElectromyographyMedicineResistance trainingDeltoid musclePhysical medicine and rehabilitationPhysical therapyAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to compare activation of the upper body musculature during the barbell bench press at varying training intensities. METHODS: Twelve young, resistance-trained men performed sets of the bench press to momentary muscular failure with two different loads: a high-load(HIGH) set at 80% of 1RM and a low-load (LOW) set at 50% 1RM. Exercise order was counterbalanced so that half the subjects performed the LOW condition first and the other half performed the HIGH first. Surface electromyography (EMG) was used to assess mean, peak, and iEMG muscle activation of the anterior deltoid, triceps brachii, and sternal and clavicular heads of the pectoralis major. RESULTS: The main effects for trials were significant for mean EMG (p < 0.001) and iEMG matched (p < 0.001) favoring HIGH and iEMG total favoring LOW(p = 0.001) across all muscle groups in both conditions with varying effect sizes. All other main effects and interactions were not statistically significant. CONCLUSION: Despite similarities in peak EMG amplitude, the greater results for mean and iEMG matched in HIGH suggests that heavier loads may produce greater muscle activation.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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