A narrative review of velocity-based training best practice: the importance of contraction intent versus movement speed
Bibliographic record
Abstract
Explosive movements requiring high force and power outputs are integral to many sports, posing distinct challenges for the neuromuscular system. Traditional resistance training can improve muscle strength, power, endurance, and range of motion; however, evidence regarding its effects on athletic performance, such as sprint speed, agility, and jump height, remains conflicting. The specificity of resistance training movements, including velocity, contraction type, and joint angles affects performance outcomes, demonstrates advantages when matching training modalities with targeted sports activities. However, independent of movement speed, the intent to contract explosively (ballistic) has also demonstrated high velocity-specific training adaptations. The purpose of this narrative review was to assess the impact of explosive or ballistic contraction intent on velocity-specific training adaptations. Such movement intent may predominantly elicit motor efferent neural adaptations, including motor unit recruitment and rate coding enhancements. Plyometrics, which utilize rapid stretch-shortening cycle movements, may augment high-speed movement efficiency and muscle activation, possibly leading to improved motor control through adaptations like faster eccentric force absorption, reduced amortization periods, and quicker transitions to explosive concentric contractions. An optimal training paradigm for power and performance enhancement might involve a combination of maximal explosive intent training with heavier loads and plyometric exercises with lighter loads at high velocities. This narrative review synthesizes key literature to answer whether contraction intent or movement speed is more critical for athletic performance enhancement, ultimately advocating for an integrative approach to resistance training tailored for sports-specific explosive action.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".