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Record W3006994157 · doi:10.1123/jab.2019-0115

Effect of Forefoot and Midfoot Bending Stiffness on Agility Performance and Foot Biomechanics in Soccer

2020· article· en· W3006994157 on OpenAlexaff
Daniel J. Brinkmann, Harald Koerger, Albert Gollhofer, Dominic Gehring

Bibliographic record

VenueJournal of Applied Biomechanics · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsForefootBiomechanicsBending stiffnessStiffnessTorsion (gastropod)OrthodonticsStructural engineeringBendingMedicineEngineeringAnatomySurgery

Abstract

fetched live from OpenAlex

Footwear bending stiffness is known to positively affect performance in agility maneuvers due to improved energy storage and propulsion based on a stiffer foot-shoe complex. However, the functional properties of the forefoot and midfoot differ. Therefore, the present study investigates the effect of the interface of longitudinal bending stiffness and the ratio of forefoot to midfoot bending stiffness on agility performance and foot biomechanics. A total of 18 male soccer players performed 2 agility tasks in footwear conditions that were systematically modified in forefoot and midfoot bending stiffness. Results revealed that higher longitudinal bending stiffness caused more foot exorotation at the initial ground contact (P < .05), less torsion (P < .001), and an anterior shift in the point of force application during push off (P = .01). In addition, the authors observed decreased forefoot bending (P < .05) and increased torsion (P < .01) in footwear with a higher forefoot-midfoot ratio. Finally, the agility performance was significantly impaired by 1.3% in the condition with the highest forefoot-midfoot ratio (P < .01). The high forefoot-midfoot ratio, that is, a stiff forefoot in combination with a soft midfoot, seemed to shift the flex line from anterior to posterior that may explain the performance impairment.

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 score0.582
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.262
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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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