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Record W4229055041 · doi:10.1055/a-1776-7986

Carbon Plate Shoes Improve Metabolic Power and Performance in Recreational Runners

2022· article· en· W4229055041 on OpenAlexaff
Anders Paarup Nielsen, Jesper Franch, Christian Heyde, Mark de Zee, Uwe G. Kersting, Ryan Godsk Larsen

Bibliographic record

VenueInternational Journal of Sports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsRecreationPhysical medicine and rehabilitationMedicineEnvironmental sciencePhysical therapyBiologyEcology

Abstract

fetched live from OpenAlex

This study compared metabolic power (MP) and time trial (TT) running performance between Adidas Adizero Adios (AAA) and Nike VaporFly 4% (NVP). Thirty-seven runners completed three laboratory sessions and two field sessions (n=30). After familiarization (visit 1), participants completed eight 6-min treadmill running bouts (four with each shoe, counterbalanced) at their preferred pace, and MP was assessed using indirect calorimetry (visits 2 and 3). During visits 4 and 5, participants completed two outdoor TTs (~3.5 km) in NVP and AAA (counterbalanced). Compared with AAA, NVP exhibited superior MP (NVP: median=13.88 (Q1-Q3=12.90-15.08 W/kg; AAA: median=14.08 (Q1-Q3=13.12-15.44 W/kg; z=-4.81, p<.001, effect size=.56) and TT (NVP=793±98 s; AAA=802±100 s, p=.001; effect size=.09). However, there was no relationship between changes in MP and changes in TT between shoes (r=.151 p=.425, 95% confidence interval=[-.22; .48]). Our results demonstrate that NVP, compared with AAA, improves MP and TT in recreational runners. The lack of correlation between changes in MP and TT indicates that factors other than improved MP contribute to faster short-distance TT with NVP.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.272
Teacher spread0.261 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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