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Record W3041967542 · doi:10.1080/14763141.2020.1778777

Ankle bracing’s effects during a modified agility task: analysis of sEMG, impulse, and time to complete using a crossover, repeated measures design

2020· article· en· W3041967542 on OpenAlexaff
Zachariah Henderson, Paolo Sanzo, Carlos Zerpa, Derek Kivi

Bibliographic record

VenueSports Biomechanics · 2020
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsNOSM UniversityLakehead UniversityUniversity of Manitoba
Fundersnot available
KeywordsAnkleImpulse (physics)Physical medicine and rehabilitationBraceTask (project management)Crossover studyComputer scienceSimulationMathematicsMedicinePhysical therapyStructural engineeringEngineeringSurgeryPhysics

Abstract

fetched live from OpenAlex

This study explored the effects of no braces, softshell (AE), and semi-rigid (T1) ankle braces on time to complete a modified agility task, as well as lower extremity muscle activity and impulse during the change of direction component of the task. Thirty-nine healthy, active individuals completed a modified agility task under the three brace conditions. Time to complete the modified agility task, along with mean surface electromyographic activity (sEMG) and impulse during the deceleration and propulsive phases of the task were measured. There were no significant differences across conditions with respect to sEMG or impulse measures during the deceleration or propulsive phases. There was a significant change in time to complete the modified agility task, F(2,76) = 17.242, p< 0.001, ηp2 = 0.312. Post-hoc analysis revealed a significant increase in time to complete the modified agility task when wearing the AE (0.16 (95% CI, 0.062 to 0.265) seconds, p< 0.001) and T1 (0.20 (95% CI, 0.113 to 0.286) seconds, p< 0.001) ankle braces compared to no braces. It appears that performance on a modified agility task may be diminished when wearing ankle braces, although sEMG activity and impulse are unaffected.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.272
Teacher spread0.240 · 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 designBench or experimental
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

Citations9
Published2020
Admission routes1
Has abstractyes

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