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Record W2333677325 · doi:10.1123/jab.2014-0295

Influence of Position and Power Output on Upper Limb Kinetics in Cycling

2015· article· en· W2333677325 on OpenAlexaff
Antony Costes, Nicolas A. Turpin, David Villeger, Pierre Moretto, Bruno Watier

Bibliographic record

VenueJournal of Applied Biomechanics · 2015
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsInstitut de Readaptation Gingras Lindsay de MontrealJewish Rehabilitation Hospital
Fundersnot available
KeywordsCyclingCrankInverse dynamicsUpper limbKinematicsElbowLower limbMathematicsPhysical medicine and rehabilitationPosition (finance)Power (physics)PhysicsMedicineAnatomyGeometrySurgeryEconomics

Abstract

fetched live from OpenAlex

Several suggestions on the upper limb involvement in cycling exist but, to date, no study has quantified upper limb kinetics in this task. The aim of this study was to determine how crank power and pedaling position (seated or standing) affect upper limb kinetics. Handlebar loadings and upper limb kinematics were collected from 17 participants performing seated or standing pedaling trials in a random order at 6 crank powers ranging from 20% (112 ± 19 W) to 120% (675 ± 113 W) of their spontaneous sit-to-stand transition power. An inverse dynamics approach was used to compute 3D moments, powers, and works at the wrist, elbow, and shoulder joints. Over 29 parameters investigated, increases in crank power were associated with increases in the magnitudes of 23 and 20 of the kinetic variables assessed in seated and standing positions, respectively. The standing position was associated with higher magnitudes of upper limb kinetics. These results suggest that both upper and lower limbs should be considered in future models to better understand whole body coordination in cycling.

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 score0.208
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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