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Velocity-dependent Neural Mechanisms Of Dynamic Ballistic Contractions

2023· article· en· W4387054079 on OpenAlexaff
Jesse Collins, Ryan William Weller, Jayne M. Kalmar

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2023
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsIsometric exerciseConcentricMotor unitVastus medialisMathematicsContraction (grammar)PhysicsElectromyographyAnatomyPhysical medicine and rehabilitationGeometryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Motor unit firing behaviour during isometric ballistic contractions is characterized by a large burst of neural activity with short initial interspike intervals (ISI) and high motor unit discharge rates (MUDR). However, dynamic movements include additional variables such as movement velocity and range of motion that may affect the neural strategies used during this type of ballistic contraction. PURPOSE: To assess discharge and recruitment properties of knee extensor motor units during maximal isokinetic contractions at different velocities. METHODS: Two wireless surface sensors, were used to record four differential sEMG signals from the vastus medialis (VM) and vastus lateralis (VL) of the right leg. Twenty university-aged participants (11 female) performed 3 blocks of maximal isokinetic contractions with 5 m rest between blocks. Each block included 5 contractions at 60°/s, 120°/s, 180°/s, 240°/s, 300°/s (velocities pseudorandomized) with 1 m rest between contractions. Joint angle, torque, average and peak MUDRs were assessed during the movement phase (first discharge to the end of the concentric phase). Repeated-measures ANOVA was used to assess peak and average MUDR, and the first 3 ISIs across the 5 movement velocities, with sex included as a covariate. RESULTS: Average MUDR was lower at faster velocities in VL (60°/s, 13.03 ± 2.83; 300°s,10.84 ± 2.46, p < 0.01) and VM (60°/s, 14.54 ± 2.83; 300°s,12.71 ± 2.03, p < 0.01). Peak MUDR was also lower at faster velocities in VL (60°/s, 16.17 ± 2.98; 300°s, 13.95 ± 3.15, p < 0.01) and VM (60°/s, 17.60 ± 2.68; 300°s, 16.13 ± 2.37, p < 0.01). When torque was included as a covariate, a repeated-measure ANCOVA revealed that there was no effect of velocity on peak VL MUDR (p = 0.32), average VL MUDR (p = 0.67), peak VM MUDR (p = 0.68) or average VM MUDR (p = 0.58). The first 3 ISIs of VL MUs were shorter at higher velocities (60°/s, 0.31 ± 0.01 s; 300°s, 0.26 ± 0.004 s, p < 0.01) while the first 3 ISIs of VM MUs were not velocity-dependent. There were no sex differences with torque as a covariate. CONCLUSIONS: Peak and average MUDR during the movement phase of maximal isokinetic contractions were not velocity-dependent. However, initial interspike intervals were shorter at high velocities.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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