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Investigating The Mechanistic Role Of Muscle Growth On Strength In Response To Isometric Handgrip Training

2024· article· en· W4402662354 on OpenAlexaff
Vickie Wong, Robert W. Spitz, Jun Seob Song, Yujiro Yamada, Ryo Kataoka, William B. Hammert, Anna Kang, Aldo Seffrin, Zachary W. Bell, Jeremy P. Loenneke

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2024
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcGill University
Fundersnot available
KeywordsIsometric exercisePhysical medicine and rehabilitationTraining (meteorology)MedicinePhysical therapyPhysics

Abstract

fetched live from OpenAlex

Muscle growth is a purported mechanism for exercise-induced changes in strength. Prior research in the elbow flexors was unable to identify muscle growth as a mechanism for strength gains. It is possible that ‘learning” the elbow flexion movement may have masked the ability to detect a role of growth on strength. If muscle growth is indeed playing a role in strength adaptations, then its importance should be able to be demonstrated in a simpler movement. PURPOSE: Determine if muscle growth mediates strength changes associated with isometric handgrip (HG) training. METHODS: A total of 179 participants completed a 6-week study, with 135 individuals performing isometric HG training over 18 sessions (3x/week). Participants were randomly assigned to one of four groups: 1) low-intensity (4x2 minutes of 30% MVC; LI, n = 47), 2) low-intensity with blood flow restriction (LI + 50% arterial occlusion pressure; LI-BFR, n = 41), 3) maximal effort (4x5 seconds of ≥100% MVC; MAX, n = 47), and 4) non-exercise control (CON, n = 44). Forearm muscle thickness (MTH) and HG strength were measured before and after the intervention. A mediation model (adjusted for pre-MTH, and pre-strength) was constructed to determine if changes in MTH might explain changes in muscle strength. Effects of each training group were evaluated relative to a control. Data are presented as coefficient (95% CI). RESULTS: Only LI-BFR had evidence of an increase in MTH from training (a1-3 paths) [LI: -.0113 (-.060, .037) cm, LI-BFR: .0630 (.012, .113) cm, MAX: .0166 (-.032, .065) cm]. No evidence suggested a relationship between the change in MTH and maximal HG strength (b path) [ΔMTH: .5083 (-3.679, 4.696) cm]. Importantly, there were no significant mediations via indirect effects (a1-3 x b) on the change in MTH mediating a change in strength for any of the training groups [LI: -0.0057 (-0.141, 0.141) kg, LI-BFR: .0320 (-.289, .390) kg, MAX: .0084 (-.157, .165) kg]. CONCLUSION: Despite what others consider to be a mechanism, our data suggests that the augmentation of muscle size through resistance training does not influence the change in strength. This remains applicable even if the exercise is simplified such as HG training and suggests that the changes in maximal strength observed herein were driven by mechanisms other than growth.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.297
Teacher spread0.268 · 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 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
Published2024
Admission routes1
Has abstractyes

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