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Record W4410790440 · doi:10.1080/14763141.2025.2508244

Effects of selected features of advanced footwear technology on lower limb joint work

2025· article· en· W4410790440 on OpenAlexaff
Ashna Subramanium, Jordyn Vienneau, Sandro Nigg, Benno Maurus Nigg

Bibliographic record

VenueSports Biomechanics · 2025
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsJoint (building)Work (physics)Physical medicine and rehabilitationLower limbComputer scienceMathematicsPhysical therapyMedicineEngineeringSurgeryMechanical engineeringStructural engineering

Abstract

fetched live from OpenAlex

The objective of this study was to isolate the effects of two key components of Advanced Footwear Technology, the curved carbon fibre plate and the midsole material, on lower-limb joint work. Sixteen male recreational runners ran overground at a speed of 3.9 (±5%) metres per second in three shoe conditions: a standard Nike Vaporfly 4% (Original VP4), a modified version without the curved carbon fibre plate (No Plate) and a version with the PEBA midsole material replaced with EVA foam (EVA). Motion capture and force platform data were recorded to determine positive and negative metatarsophalangeal, ankle, knee, and hip joint work, and positive and negative foot + footwear work across the different conditions. Removing the carbon fibre plate significantly increased negative work at the metatarsophalangeal joint and positive work at the ankle. Replacing PEBA with EVA significantly reduced positive foot + footwear work. The findings of this study highlighted a group effect of the curved carbon fibre plate, which redistributed positive lower-limb joint work from the ankle to the metatarsophalangeal joint. Conversely, the results highlighted subject-specific differences in response to the midsole material, emphasising the importance of considering individual variability in footwear design elements to optimise athletic performance.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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