A systematic review of resistance training methodologies for the development of lower body concentric mean power, peak power, and mean propulsive power in team-sport athletes
Bibliographic record
Abstract
This study aimed to systematically review training methods prescribed to develop lower-body power, determine their effectiveness for the development of lower-body mechanical power and their implementation in an annual training cycle amongst team-sport athletes. The absolute and relative outcome values of concentric mean power, peak power and mean propulsive power were extracted from 19 studies. Outcomes were assessed using baseline to post intervention percent change, effect sizes, and the level of evidence concerning the method's effectiveness. A thorough analysis of the literature indicated that, based on the high level of evidence, traditional (e.g., strength training alone) and combination training (e.g., complex and contrast) methods should be considered. Further, optimal load and velocity-based training can be implemented if coaches have access to the appropriate equipment to monitor movement velocity and mechanical power in every session. This is of particular importance in periods of the season where high volumes of technical-tactical training and congested fixture periods are present. Also, flywheel, eccentric overload and weightlifting methods have been shown to be effective although the level of evidence is low. Future research should expand on current training practices whilst adequately reporting actual training loads from sport-specific training and games alongside strength-power training protocols.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".