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Record W4210777895 · doi:10.1519/jsc.0000000000004215

Quantifying Asymmetry and Performance of Lower Limb Mechanical Muscle Function in Varsity Athletes—Using Non–Countermovement Jumps

2022· article· en· W4210777895 on OpenAlexaff
М. М. Stadnyk, Mohammadali Sepehri, Michael J. Cook, Samer Adeeb, Lindsey Westover

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsJumpCountermovementPhysical medicine and rehabilitationJumpingAthletesTakeoffRehabilitationPhysical therapyPlyometricsMathematicsMedicinePhysicsEngineering

Abstract

fetched live from OpenAlex

ABSTRACT: Stadnyk, M, Sepehri, M, Cook, M, Adeeb, S, and Westover, L. Quantifying asymmetry and performance of lower limb mechanical muscle function in varsity athletes-using non-countermovement jumps. J Strength Cond Res 37(1): 98-106, 2023-The ability to automatically quantify jump performance and lower limb muscle function in athletes would be beneficial for both training and rehabilitation purposes. Countermovement jumps (CMJs) and non-CMJs (NCMJs) are simple, quick, and require relatively inexpensive equipment to effectively and reliably monitor lower limb function. In a previous study, CMJ characteristics were assessed across different varsity sports. This study is a follow-up study to incorporate NCMJs into assessing jump characteristics of the same sports and investigate the additional information provided by the complementary jump type. The main objective of this study was to look at a means of quantifying the lower limb mechanical muscle function automatically to provide information for rehabilitation and performance purposes in athletes of specific sports. Male and female varsity athletes from 4 different sports completed 5 trials of an NCMJ on dual force plates. An analysis program was developed using Wolfram Mathematica to analyze force-time jump data. Various parameters of interest were generated, including peak force, force-time curve shape classification, jump phase lengths, phase-specific kinetic impulse, asymmetry index, takeoff velocity, jump height, phase-specific center of mass displacements, and reactive strength index modified. Results obtained indicate that similar jump characteristics to the CMJ study can be quantified, which can be used for performance enhancement or injury rehabilitation. Additional data found, such as the ability of an athlete to hold a steady squat during an NCMJ, could also be meaningful in aiding trainers to design programs tailored for athletes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.064
GPT teacher head0.342
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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

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