The Combination of Plyometric and Balance Training Improves Sprint and Shuttle Run Performances More Often Than Plyometric-Only Training With Children
Bibliographic record
Abstract
Because balance is not fully developed in children and studies have shown functional improvements with balance only training studies, a combination of plyometric and balance activities might enhance static balance, dynamic balance, and power. The objective of this study was to compare the effectiveness of plyometric only (PLYO) with balance and plyometric (COMBINED) training on balance and power measures in children. Before and after an 8-week training period, testing assessed lower-body strength (1 repetition maximum leg press), power (horizontal and vertical jumps, triple hop for distance, reactive strength, and leg stiffness), running speed (10-m and 30-m sprint), static and dynamic balance (Standing Stork Test and Star Excursion Balance Test), and agility (shuttle run). Subjects were randomly divided into 2 training groups (PLYO [n = 14] and COMBINED [n = 14]) and a control group (n = 12). Results based on magnitude-based inferences and precision of estimation indicated that the COMBINED training group was considered likely to be superior to the PLYO group in leg stiffness (d = 0.69, 91% likely), 10-m sprint (d = 0.57, 84% likely), and shuttle run (d = 0.52, 80% likely). The difference between the groups was unclear in 8 of the 11 dependent variables. COMBINED training enhanced activities such as 10-m sprints and shuttle runs to a greater degree. COMBINED training could be an important consideration for reducing the high velocity impacts of PLYO training. This reduction in stretch-shortening cycle stress on neuromuscular system with the replacement of balance and landing exercises might help to alleviate the overtraining effects of excessive repetitive high load activities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".