Effects of Chronic Static Stretching on Maximal Strength and Muscle Hypertrophy: A Systematic Review and Meta-Analysis with Meta-Regression
Bibliographic record
Abstract
Abstract Background Increases in maximal strength and muscle volume represent central aims of training interventions. Recent research suggested that the chronic application of stretch may be effective in inducing hypertrophy. The present systematic review therefore aimed to syntheisize the evidence on changes of strength and muscle volume following chronic static stretching. Methods Three data bases were sceened to conduct a systematic review with meta-analysis. Studies using randomized, controlled trials with longitudinal (≥ 2 weeks) design, investigating strength and muscle volume following static stretching in humans, were included. Study quality was rated by two examiners using the PEDro scale. Results A total of 42 studies with 1318 cumulative participants were identified. Meta-analyses using robust variance estimation showed small stretch-mediated maximal strength increases (d = 0.30 p < 0.001) with stretching duration and intervention time as significant moderators. Including all studies, stretching induced small magnitude, but significant hypertrophy effects (d = 0.20). Longer stretching durations and intervention periods as well as higher training frequencies revealed small (d = 0.26–0.28), but significant effects (p < 0.001–0.005), while lower dosage did not reach the level of significance (p = 0.13–0.39). Conclusions While of minor effectiveness, chronic static stretching represents a possible alternative to resistance training when aiming to improve strength and increase muscle size. As a dose-response relationship may exist, higher stretch durations and frequencies as well as long program durations should be further elaborated.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.051 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".