A Comparison of Ballistic-Movement and Ballistic-Intent Training on Muscle Strength and Activation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".