Comparison of Supervised versus Self-Administered Stretching on Bench Press Maximal Strength and Force Development
Bibliographic record
Abstract
Purpose: While there is reported superior effectiveness with supervised training, it usually requires specialized exercise facilities and instructors. It is reported in the literature that high-volume stretching improves pectoralis muscles strength under supervised conditions while practical relevance is discussed. Therefore, the study objective was to compare the effects of volume equated, supervised- and self-administered home-based stretching on strength performance. Methods: Sixty-three recreational participants were equally assigned to either a supervised static stretching, home-based stretching, or control group. The effects of 15 min pectoralis stretching, 4 days per week for 8 weeks, were assessed on dynamic and isometric bench press strength and force development. Results: While there was a large magnitude maximal strength increase (p < 0.001–0.023, ƞ2 = 0.118–0.351), force development remained unaffected. Dynamic maximal strength in both groups demonstrated large magnitude increases compared to the control group (p < 0.001–0.001, d = 1.227–0.905). No differences between the intervention group for maximal strength (p = 0.518–0.821, d = 0.101–0.322) could be detected. Conclusions: The results could potentially be attributed to stretch-induced tension (mechanical overload) with subsequent anabolic adaptations, and alternative explanatory approaches are discussed. Nevertheless, home-based stretching seems a practical alternative to supervised training with potential meaningful applications in different settings.
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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) | 0.002 | 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".