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Record W2131984946 · doi:10.1080/02640414.2010.534805

Changes in running mechanics using conventional shoelace versus elastic shoe cover

2010· article· en· W2131984946 on OpenAlexaff
Youlian Hong, Lin Wang, Jing Xian Li, Ji He Zhou

Bibliographic record

VenueJournal of Sports Sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCushioningForefootPlantar pressureHeelMathematicsOrthodonticsBiomechanicsPhysical medicine and rehabilitationPhysical therapyMedicineStructural engineeringEngineeringPressure sensorSurgeryMechanical engineering

Abstract

fetched live from OpenAlex

The purpose of this study was to determine whether there are differences in the perceived comfort, plantar pressure, and rearfoot motion between laced running shoes and elastic-covered running shoes. Fifteen male amateur runners participated in the study. Each participant was assigned laced running shoes and elastic-covered running shoes for use during the study. The perceived comfort, plantar loading, and rearfoot motion control of each type of shoes during running were recorded. When the laced running shoes and elastic-covered running shoes were compared, the elastic-covered running shoes were given a lower perceived comfort rating in terms of shoe length, width, heel cup fitting, and forefoot cushioning. The elastic-covered running shoes also recorded higher peak plantar pressure in the lateral side of the forefoot, as well as larger maximum rearfoot pronation. Overall, shoelaces can help runners obtain better foot-shoe fit. They increase the perceived comfort, and decrease the maximum pronation and plantar pressure. Moreover, shoelaces may help prevent injury in running by allowing better control of the aforementioned factors.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.517

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 communication0.0000.000
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 head0.033
GPT teacher head0.265
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations19
Published2010
Admission routes1
Has abstractyes

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