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Contact Time Predicts Coupling Time in Slow Stretch-Shortening Cycle Jumps

2011· article· en· W2808130199 on OpenAlexaff
Dylan Kobsar, John M. Barden

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2011
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsJumpConcentricCoupling (piping)MechanicsKinematicsContact forceEccentricAccelerationAcceleration timeJumpingStretch shortening cyclePhysicsMaterials scienceMathematicsGeometryClassical mechanicsMedicineComposite material

Abstract

fetched live from OpenAlex

PURPOSE: This study was designed to examine the relationship between contact time and coupling time in slow stretch-shortening cycle (SSC) jump conditions. Coupling time has been defined as the transition time between the eccentric and concentric phases of the SSC. Short coupling times are thought to be related to a more efficient utilization of elastic energy in the SSC. Similarly, short contact times (i.e., duration of the SSC) are also important for maximizing the potentiating effects of the SSC. While it has been suggested that coupling time and contact time are linearly related, the exact relationship has not been adequately defined. METHODS: Eight plyometrically trained male varsity athletes performed a series of six slow SSC jumps. Lower limb kinetic and kinematic data were collected by way of a force plate and a six-camera motion analysis system sampling at 800 Hz and 200 Hz, respectively. The slow SSC jump was operationally defined as a drop jump involving a rebounding vertical jump for maximum height, with a contact time greater than 0.25 seconds. Contact time was calculated as the period in which ground reaction forces were recorded in force plate data during the vertical jump rebound. Knee angular acceleration curves were analyzed for all jumps, with coupling time calculated as the time between the peak eccentric and concentric phase angular accelerations. RESULTS: Contact times and coupling times ranged from 0.27 - 0.62 and 0.05 - 0.39 seconds, respectively. Coupling time was found to be significantly positively correlated to contact time (p < 0.01). CONCLUSIONS: The results demonstrated that 81.3% of the variance in coupling time can be accounted for by contact time, and as such coupling time can be accurately predicted from contact time in slow SSC jump conditions. Slow SSC jumps displayed a distinct peak deceleration of the knee joint during the eccentric phase and a peak acceleration of the knee joint during the concentric phase near joint reversal, depicting coupling time. APPLICATION: Defining coupling time as the difference between the peak eccentric and concentric phase knee angular accelerations is an effective method which can be utilized in future SSC jump studies. The findings demonstrate that contact time can be used to predict coupling time, thus providing trainers with the opportunity to use a force plate or contact mat to estimate coupling time in evaluating athletes. Furthermore, the study observed unique changes in the angular acceleration of the knee joint during a slow SSC jump. This finding adds to our knowledge of the SSC, but still raises further questions to its explanation it in terms of joint moments. On the other hand, this information can be immediately important in the modern training setting. Understanding that a decreased level of angular acceleration occurs throughout the reversal of the knee joint motion during a slow SSC jump, directs trainers to eliminate or minimize this in their athletes, with the goal of creating a more effective SSC.

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)0.000
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
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)0.0030.001

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 head0.045
GPT teacher head0.320
Teacher spread0.276 · 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".

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Citations2
Published2011
Admission routes1
Has abstractyes

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