Acute Effects of Dynamic Stretching, Static Stretching, and Light Aerobic Activity on Muscular Performance in Women
Bibliographic record
Abstract
The purpose of this study was to compare three warm-up protocols--static stretching, dynamic stretching, and light aerobic activity--on selected measures of range of motion and power in untrained females and to investigate the sustained effects at 5 and 30 minutes after warm-up. A total of 24 healthy females (ages 23-29 years) attended one familiarization session and three test sessions on nonconsecutive days within 2 weeks. A within-subject design protocol with the testing investigators blinded to the subjects' warm-up was followed. Each session started with 5 minutes of light aerobic cycling followed by pretest baseline measures. Another 5 minutes of light aerobic cycling was completed and followed by one of the three randomly selected warm-up interventions (static stretching, dynamic stretching, or light aerobic activity). The following posttest outcome measures were collected 5 and 30 minutes following the intervention: modified Thomas test, countermovement jump, and isometric time to peak force knee extension measured by dynamometer. Analysis of the data revealed significant time effects on range of motion and countermovement jump changes. No significant differences (p > 0.05) were found between the warm-up conditions on any of the variables. The variation in responses to warm-up conditions emphasizes the unique nature of individual reactions to different warm-ups; however, there was a tendency for warm-ups with an active component to have beneficial effects. The data suggests dynamic stretching has greater applicability to enhance performance on power outcomes compared to static stretching.
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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) | 0.000 | 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".