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Record W2130068596 · doi:10.1080/02640414.2015.1072640

Ankle muscle strength influence on muscle activation during dynamic and static ankle training modalities

2015· article· en· W2130068596 on OpenAlexaff
Angel Gabriel Lucas‐Cuevas, Jennifer Baltich, Hendrik Enders, Sandro Nigg, Benno M. Nigg

Bibliographic record

VenueJournal of Sports Sciences · 2015
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
FundersMinisterio de Educación, Cultura y Deporte
KeywordsBarefootAnklePeroneus longusIsometric exercisePhysical medicine and rehabilitationMedicinePhysical therapyElectromyographyPsychologyAnatomy

Abstract

fetched live from OpenAlex

Muscle weakness is considered a risk factor for ankle injury. Balance training and barefoot running have been used in an attempt to strengthen the muscles crossing the ankle. It is expected that training tasks that successfully strengthen the ankle would elicit increased muscular activity. However, it is unknown how an individual's ankle strength will influence the muscle activity used during a given task. Twenty-six participants performed dynamic (shod, barefoot running) and static tasks (squat on ground, squat on ®Bosu Ball) believed to strengthen the muscles surrounding the ankle. Electromyographic signals of the tibialis anterior, peroneus longus, gastrocnemius lateralis (GL) and gastrocnemius medialis (GM) were recorded and analysed using a non-linearly scaled wavelet analysis. Participants were divided into a strong group and a weak group according to their isometric plantar-flexion torque. The weak group required more relative GL and GM muscle activity during each training task compared to the strong group. No difference was observed between shod and barefoot running. There was a significant effect of training task on muscle activation level for the weak group. Differences in ankle strength had a significant impact on 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 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)0.001
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 score0.702
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.247
Teacher spread0.217 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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