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Record W2900455852 · doi:10.3390/sports6040149

Using Portable Force Plates to Assess Vertical Jump Performance: A Metrological Appraisal

2018· article· en· W2900455852 on OpenAlexaff
François Raymond, B. Lussier, François Dugas, Mathieu Charbonneau, Félix Croteau, Cory Kennedy, Nicolas Berryman

Bibliographic record

VenueSports · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsBishop's UniversityMcGill University
Fundersnot available
KeywordsIntraclass correlationReliability (semiconductor)JumpCoefficient of variationContext (archaeology)MathematicsSimulationStatisticsComputer scienceReproducibilityPhysics

Abstract

fetched live from OpenAlex

The purpose of this study was to verify the metrological properties of portable force plates that are used to assess countermovement jump performance. While 88 participants (38 males, 50 females) were included in the agreement analyses, 84 participants (37 males and 47 females) completed the reliability part of the study. This randomized crossover design suggests that portable force plates could be used interchangeably with a reference system. Indeed, the differences between both devices were all considered trivial (effect size (ES) < 0.20), and the mean bias was never greater than 3.41% in comparison to the reference system. In addition, the absolute and relative reliability parameters were found to be acceptable for clinical use, even when used on different floor surfaces. However, it was found that the ratio between flight time and contraction time (FTCT) showed questionable reliability when tests were conducted on different surfaces (intraclass correlation coefficient = 0.49; coefficient of variation = 26.72%). Therefore, practitioners should be careful when installing the portable device on different floor surfaces in order to optimize the reliability and the ability to detect real change in the context of a countermovement jump monitoring process.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

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)0.0010.000

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 head0.101
GPT teacher head0.379
Teacher spread0.278 · 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.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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