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Record W3210545348 · doi:10.3389/fnhum.2021.764660

Effects of the Proprioceptive Neuromuscular Facilitation Contraction Sequence on Motor Skill Learning-Related Increases in the Maximal Rate of Wrist Flexion Torque Development

2021· article· en· W3210545348 on OpenAlexafffund
Lara A. Green, Jessica McGuire, David A. Gabriel

Bibliographic record

VenueFrontiers in Human Neuroscience · 2021
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsWilfrid Laurier UniversityBrock University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsWristIsometric exercisePhysical medicine and rehabilitationProprioceptionElectromyographyMuscle contractionContraction (grammar)MedicineTorquePhysical therapyPsychologyAnatomyPhysicsInternal medicine

Abstract

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Background: The proprioceptive neuromuscular facilitation (PNF) reciprocal contraction pattern has the potential to increase the maximum rate of torque development. However, it is a more complex resistive exercise task and may interfere with improvements in the maximum rate of torque development due to motor skill learning, as observed for unidirectional contractions. The purpose of this study was to examine the cost-benefit of using the PNF exercise technique to increase the maximum rate of torque development. Methods: Twenty-six participants completed isometric maximal extension-to-flexion (experimental PNF group) or flexion-only (control group) contractions at the wrist. Ten of the assigned contractions were performed on each of three sessions separated by 48-h for skill acquisition. Retention was assessed with 5 contractions performed 2-weeks after acquisition. Torque and surface electromyographic (sEMG) activity were analyzed for evidence of facilitated contractions between groups, as well as alterations in muscle coordination assessed across test sessions. The criterion measures were: mean maximal isometric wrist flexion toque; the maximal rate of torque development (dτ/dtmax); root-mean-square error (RMSE) variability of the rate of torque versus torque phase-plane; the rate of wrist flexion muscle activation (Q30); a coactivation ratio for wrist flexor and extensor sEMG activity; and wrist flexor electromechanical delay (EMD). Results: There were no significant differences between groups with respect to maximal wrist flexion torque, dτ/dtmax or RMSE variability of torque trajectories. Both groups exhibited a progressive increase in maximal strength (+23.35% p < 0.01, η2 = 0.655) and in dτ/dtmax (+19.84% p = 0.08, η2 = 0.150) from the start of acquisition to retention. RMSE was lowest after a 2-week rest interval (−18.2% p = 0.04, η2 = 0.198). There were no significant differences between groups in the rate of muscle activation or the coactivation ratio. There was a reduction in coactivation that was retained after a 2-week rest interval (−32.60%, p = 0.02, η2 = 0.266). Alternatively, EMD was significantly greater in the experimental group (Δ 77.43%, p < 0.01, η2 = 0.809) across all sessions. However, both groups had a similar pattern of improvement to the third consecutive day of testing (−16.82%, p = 0.049, η2 = 0.189), but returned close to baseline value after the 2-week rest interval. Discussion: The wrist extension-to-flexion contraction pattern did not result in a greater maximal rate of torque development than simple contractions of the wrist flexors. There was no difference between groups with respect to motor skill learning. The main adaptation in neuromotor control was a decrease in coactivation, not the maximal rate of 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 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 score0.002
Threshold uncertainty score0.008

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)0.0020.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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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Citations3
Published2021
Admission routes2
Has abstractyes

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