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Record W2168585389 · doi:10.1123/ijspp.2.4.386

A Comparison of Ballistic-Movement and Ballistic-Intent Training on Muscle Strength and Activation

2007· article· en· W2168585389 on OpenAlexaff
Nicole Agnes Dinn, David G. Behm

Bibliographic record

VenueInternational Journal of Sports Physiology and Performance · 2007
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMovement (music)Physical medicine and rehabilitationTraining (meteorology)Muscle strengthMedicinePhysicsAcoustics

Abstract

fetched live from OpenAlex

PURPOSE: Studies have both supported and refuted the concept that it is the intent to perform ballistic contractions that determines velocity-specific gains in resistance training. The purpose of this investigation was to determine whether ballistic intent is as effective as ballistic movement in improving muscle activation, force, movement time, and reaction time. METHODS: Subjects completed 8 wk of punch training. A dynamic (DYN) group trained with elastic resistance bands, and the isometric (ISO) group trained with an unyielding strap. A control (CTRL) group was also tested. Pretesting and posttesting measures included isometric force; electromyography (EMG) of triceps, biceps, pectoralis major, and latissimus dorsi; movement and reaction time of both arms; and a quick-hands test of coordination. RESULTS: Triceps iEMG increased by 63% in the ISO group (P = .03). Pectoralis major iEMG increased by 65% in the DYN group (P = .007). Movement time decreased 17.6% in the DYN training group (P = .001). Isometric force did not improve in either training group or in the CTRL group. CONCLUSIONS: Because of its specificity of movement, dynamic training might be a more appropriate method to improve punching speed for martial artists and boxers. The intent to contract explosively over a short duration does not appear to be beneficial in increasing force production or speed of movement in punching.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.235

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 head0.028
GPT teacher head0.333
Teacher spread0.305 · 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

Citations21
Published2007
Admission routes1
Has abstractyes

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