Olympic Weightlifting and Plyometric Training With Children Provides Similar or Greater Performance Improvements Than Traditional Resistance Training
Bibliographic record
Abstract
A number of organizations recommend that advanced resistance training (RT) techniques can be implemented with children. The objective of this study was to evaluate the effectiveness of Olympic-style weightlifting (OWL), plyometrics, and traditional RT programs with children. Sixty-three children (10-12 years) were randomly allocated to a 12-week control OWL, plyometric, or traditional RT program. Pre- and post-training tests included body mass index (BMI), sum of skinfolds, countermovement jump (CMJ), horizontal jump, balance, 5- and 20-m sprint times, isokinetic force and power at 60 and 300° · s(-1). Magnitude-based inferences were used to analyze the likelihood of an effect having a standardized (Cohen's) effect size exceeding 0.20. All interventions were generally superior to the control group. Olympic weightlifting was >80% likely to provide substantially better improvements than plyometric training for CMJ, horizontal jump, and 5- and 20-m sprint times, whereas >75% likely to substantially exceed traditional RT for balance and isokinetic power at 300° · s(-1). Plyometric training was >78% likely to elicit substantially better training adaptations than traditional RT for balance, isokinetic force at 60 and 300° · s(-1), isokinetic power at 300° · s(-1), and 5- and 20-m sprints. Traditional RT only exceeded plyometric training for BMI and isokinetic power at 60° · s(-1). Hence, OWL and plyometrics can provide similar or greater performance adaptations for children. It is recommended that any of the 3 training modalities can be implemented under professional supervision with proper training progressions to enhance training adaptations in children.
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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) | 0.000 | 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) | 0.004 | 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".