Acute Effects of Various Stretching Techniques on Range of Motion: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
Although stretching can acutely increase joint range of motion (ROM), there are a variety of factors which could influence the extent of stretch-induced flexibility such as participant characteristics, stretching intensities, durations, type (technique), and muscle or joint tested. The objective of this systematic review and meta-analysis was to investigate the acute effects of stretching on ROM including moderating variables such as muscles tested, stretch techniques, intensity, sex, and trained state. A random-effect meta-analysis was performed from 47 eligible studies (110 effect sizes). A mixed-effect meta-analysis subgroup analysis was also performed on the moderating variables. A meta-regression was also performed between age and stretch duration. GRADE analysis was used to assess the quality of evidence obtained from this meta-analysis. The meta-analysis revealed a small ROM standard mean difference in favor of an acute bout of stretching compared to non-active control condition (ES = −0.555; Z = −8.939; CI (95%) −0.677 to −0.434; p < 0.001; I2 = 33.32). While there were ROM increases with sit and reach (P = 0.038), hamstrings (P < 0.001), and triceps surae (P = 0.002) tests, there was no change with the hip adductor test (P = 0.403). Further subgroup analyses revealed no significant difference in stretch intensity (P = 0.76), trained state (P = 0.99), stretching techniques (P = 0.72), and sex (P = 0.89). Finally, meta-regression showed no relationship between the ROM standard mean differences to age (R2 = −0.03; P = 0.56) and stretch duration (R2 = 0.00; P = 0.39), respectively. GRADE analysis indicated that we can be moderately confident in the effect estimates. A single bout of stretching can be considered effective for providing acute small magnitude ROM improvements for most ROM tests, which are not significantly affected by stretch intensity, participants’ trained state, stretching techniques, and sex.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".