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Record W2982661986 · doi:10.5539/jel.v8n6p110

The Effects of Whole Body Vibration Application on Jump and Balance Performance in University Students

2019· article· en· W2982661986 on OpenAlexvenueno aff
Barış Gürol, Gülsün Güven, Elvin Onarıcı Güngör

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsnot available
Fundersnot available
KeywordsBalance (ability)JumpWhole body vibrationSignificant differenceVibrationSquatDynamic balancePhysical therapyMedicineMathematicsAnimal scienceInternal medicinePhysicsAcousticsEngineeringBiologyMechanical engineering

Abstract

fetched live from OpenAlex

The aim of this research was to investigate the effect of vibration applications on muscle strength and balance. As the subjects to the research, nineteen male sport science students (age: 21.45±2.16 years, height: 177.55±7.24 cm, body mass: 71.62±11.02 kg) voluntarily participated in the study. The students were exposed to vibration in squat exercise position before they were exposed to vibration at 25Hz, 50Hz frequency and control (no-vibration) on three different application days. Before and after the vibration exposure, jump tests and balance tests were administered right after the 5th, 10th and 15th min in the wake of the vibration. A statistically significant difference was found in active and squat jump heights administered following the 25Hz and 50 Hz frequency applications (p≤0.05). No significant differences were found in the jump heights in control group (p>0.05). While there was no significant difference found in balance tests performed after 25Hz and 50Hz vibration applications (p>0.05), a significant difference was found after the control application (p≤0.05). As a result, there was an increase in jump heights following the acute vibration application however, no change was seen in balance scores. Practicing acute vibration applications can be recommended to increase jump height especially before the competitions and applications.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.112

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 head0.003
GPT teacher head0.285
Teacher spread0.282 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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