Using Long-Duration Static Stretch Training to Counteract Strength and Flexibility Deficits in Moderately Trained Participants
Bibliographic record
Abstract
Many sports injuries result in surgery and prolonged periods of immobilization, which may lead to significant atrophy accompanied by loss of maximal strength and range of motion and, therefore, a weak-leg/strong-leg ratio (as an imbalance index ∆ ) lower than 1. Consequently, there are common rehabilitation programs that aim to enhance maximal strength, muscle thickness and flexibility; however, the literature demonstrates existing strength imbalances after weeks of rehabilitation. Since no study has previously been conducted to investigate the effects of long-duration static stretch training to treat muscular imbalances, the present research aims to determine the possibility of counteracting imbalances in maximal strength and range of motion. Thirty-nine athletic participants with significant calf muscle imbalances in maximal strength and range of motion were divided into an intervention group (one-hour daily plantar flexors static stretching of the weaker leg for six weeks) and a control group to evaluate the effects on maximal strength and range of motion with extended and bent knee joint. Results show significant increases in maximal strength (d = 0.84–1.61, p < 0.001–0.005) and range of motion (d = 0.92–1.49, p < 0.001–0.002) following six weeks of static stretching. Group * time effects (p < 0.001–0.004, η² = 0.22–0.55) revealed ∆ changes in the intervention group from 0.87 to 1.03 for maximal strength and from 0.92 to 1.11 in range of motion. The results provide evidence for the use of six weeks of daily, one hour stretching to counteract muscular imbalances. Related research in clinical settings after surgery is suggested.
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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 | 0.000 |
| 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".