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Record W3208507021 · doi:10.1101/2021.11.01.466761

Muscle activation strategies of the vastus lateralis according to sex

2021· preprint· en· W3208507021 on OpenAlexaff
Yuxiao Guo, Eleanor J. Jones, Thomas B. Inns, Isabel A. Ely, Daniel W. Stashuk, Daniel J. Wilkinson, Kenneth E. Smith, Jessica Piasecki, Bethan E. Phillips, Philip J. Atherton, Mathew Piasecki

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Waterloo
FundersNIHR Nottingham Biomedical Research CentreVersus ArthritisDepartment of Health and Social CareMedical Research CouncilNational Institute for Health and Care Research
KeywordsIsometric exerciseMotor unitElectromyographyMotor unit recruitmentVastus lateralis muscleMedicineInternal medicineMuscle strengthCardiologyPhysical medicine and rehabilitationAnatomyPsychologyPhysical therapySkeletal muscle

Abstract

fetched live from OpenAlex

Abstract Aim Despite men exhibiting greater muscle strength and fatigibility than women, it remains unclear if there are sex-based differences in muscle recruitment strategies e.g. motor unit (MU) recruitment and modulation of firing rate (FR) at normalised forces and during progressive increases in force. Methods Twenty-nine healthy male and thirty-one healthy female participants (18-35 years) were studied. Intramuscular electromyography was used to record individual motor unit potentials (MUPs) and near fibre MUPs from the vastus lateralis (VL) during 10% and 25% maximum isometric voluntary contractions (MVC), and spike-triggered averaging was used to obtain motor unit number estimates (MUNE) of the VL. Multilevel mixed-effects linear regression models were used to investigate the effects of sex at each contraction level. Results Men exhibited greater muscle strength ( p <0.001) and size ( p <0.001) than women, with no difference in force steadiness at 10% or 25% MVC. Women had smaller MUs and higher FR at 10% MVC (both p <0.02), similar to that at 25% MVC in MU size ( p =0.062) and FR ( p =0.031). However, both sexes showed similar increases in MU size and FR when moving from low-to mid-level contractions. There were no sex differences in any near fibre MUP parameters or in MUNE. Conclusion I n the vastus lateralis, women produce muscle force via different neuromuscular recruitment strategies to men which is characterised by smaller MUs discharging at higher rates. However, similar strategies are employed to increase force production from low to moderate contractions. These findings of similar proportional increases between sexes support the use of mixed sex cohorts in studies of this nature. Key points Increases in muscle force production are mediated by motor unit (MU) recruitment, and MU firing rate (FR). Women are underrepresented in studies of human neuromuscular research and markedly differ to men in a number of aspects of neuromuscular function, yet little is known of the recruitment strategies of each. Here we demonstrate men and women have similar vastus lateralis MU number estimates, yet women recruit smaller MUs with higher FR than men at normalised contraction levels. However, increases in force are achieved via similar trajectories of MU recruitment and MU FR in men and women. Although men and women exhibit divergent neuromuscular recruitment strategies to achieve normalised forces, increases in force are achived similarly and support the inclusion of mixed sex cohorts in studies of this nature.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0050.001

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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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