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Record W2113302745 · doi:10.1139/apnm-2015-0109

The effects of adding single-joint exercises to a multi-joint exercise resistance training program on upper body muscle strength and size in trained men

2015· article· en· W2113302745 on OpenAlexaffvenue
Henrique França, Paulo Alexandre Nordeste Branco, Dilmar Pinto Guedes, Paulo Gentil, James Steele, Cauê Vazquez La Scala Teixeira

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsNorthwestern Polytechnic
Fundersnot available
KeywordsMedicineElbowResistance trainingPhysical therapyOne-repetition maximumStrength trainingElbow flexionMuscle strengthLower bodySignificant differenceInternal medicineSurgery

Abstract

fetched live from OpenAlex

The aim of this study was compare changes in upper body muscle strength and size in trained men performing resistance training (RT) programs involving multi-joint plus single-joint (MJ+SJ) or only multi-joint (MJ) exercises. Twenty young men with at least 2 years of experience in RT were randomized in 2 groups: MJ+SJ (n = 10; age, 27.7 ± 6.6 years) and MJ (n = 10; age, 29.4 ± 4.6 years). Both groups trained for 8 weeks following a linear periodization model. Measures of elbow flexors and extensors 1-repetition maximum (1RM), flexed arm circumference (FAC), and arm muscle circumference (AMC) were taken pre- and post-training period. Both groups significantly increased 1RM for elbow flexion (4.99% and 6.42% for MJ and MJ+SJ, respectively), extension (10.60% vs 9.79%, for MJ and MJ+SJ, respectively), FAC (1.72% vs 1.45%, for MJ and MJ+SJ, respectively), and AMC (1.33% vs 3.17% for MJ and MJ+SJ, respectively). Comparison between groups revealed no significant difference in any variable. In conclusion, 8 weeks of RT involving MJ or MJ+SJ resulted in similar alterations in muscle strength and size in trained participants. Therefore, the addition of SJ exercises to a RT program involving MJ exercises does not seem to promote additional benefits to trained men, suggesting MJ-only RT to be a time-efficient approach.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.029
GPT teacher head0.272
Teacher spread0.243 · 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 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

Citations60
Published2015
Admission routes2
Has abstractyes

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