Periodized versus Non-periodized Stretch Training on Gymnasts Flexibility and Performance
Bibliographic record
Abstract
Static stretching (SS) can increase joint range of motion (ROM), due to neural, morphological, and physio-psychological factors. Periodized training programs (PD) (e. g., strength, power) are adopted to induce greater adaptations while avoiding overtraining. However, the effectiveness of periodized stretch training adaptations are unknown. Therefore, the objective of this study was to compare the effects of periodized and non-periodized (NP) stretching programs on flexibility, hamstrings stiffness and muscle performance. Sixteen gymnasts were allocated to either periodized or non-periodized SS training and tested pre- and post-8 weeks for countermovement jump height, hip flexors, hip extensors and dorsiflexors ROM, hamstrings stiffness and hamstrings and quadriceps peak torque. Both stretch training groups significantly and similarly increased hip extensor (33.2%), hip flexor (25.2%), and dorsiflexor (23.8%) ROM, hamstrings peak torque (7.9%) and jump height (8.1%) from pre - to post- training. Both groups decreased hamstrings stiffness across the last ten angles (32.1%). PD elicited consistently large magnitude flexibility effect size changes compared to small and moderate magnitude changes for the non-periodized. Therefore, 8-week PD and NP SS programs can decrease young gymnasts' muscle-tendon stiffness and increase muscle performance. However, effect sizes indicate that PD stretch training was more advantageous to increasing flexibility and improving performance.
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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 | 0.000 |
| 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) | 0.002 | 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".