Effects of Plyometric Jump Training on Electromyographic Activity and Its Relationship to Strength and Jump Performance in Healthy Trained and Untrained Populations: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
ABSTRACT: Ramirez-Campillo, R, Garcia-Pinillos, F, Chaabene, H, Moran, J, Behm, DG, and Granacher, U. Effects of plyometric jump training on electromyographic activity and its relationship to strength and jump performance in healthy trained and untrained populations: a systematic review of randomized controlled trials. J Strength Cond Res 35(7): 2053-2065, 2021-This systematic review analyzed the effects of plyometric jump training (PJT) on muscle activation assessed with surface electromyography during the performance of strength and jumping tasks in healthy populations across the lifespan. A systematic literature search was conducted in the electronic databases PubMed/MEDLINE, Web of Science, and SCOPUS. Only randomized controlled studies were eligible to be included in this study. Our search identified 17 studies comprising 23 experimental groups and 266 subjects aged 13-73 years, which were eligible for inclusion. The included studies achieved a median Physiotherapy Evidence Database score of 6. No injuries were reported among the included studies. Significant PJT-related improvements were reported in 7 of 10 studies and in 6 of 10 studies for measures of muscle activation during the performance of strength and jumping tasks, respectively. Moreover, a secondary correlational analysis showed significant positive relationships (r = 0.86; p = 0.012; r2 = 0.74) between changes in muscle activation and changes in jump performance. However, from the total number (n = 287) of muscle activation response variables analyzed for strength and jumping tasks, ∼80% (n = 226) were reported as nonsignificant when compared with a control condition. In conclusion, PJT may improve muscle activation during the performance of strength and jumping tasks. However, conflicting results were observed probably arising from (a) studies that incorporated a large number of outcomes with reduced sensitivity to PJT, (b) methodological limitations associated to muscle activation measurement during strength and jumping tasks, and (c) limitations associated with PJT prescription. Future studies in this field should strive to solve these methodological shortcomings.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".