Chronic effects of stretching on range of motion with consideration of potential moderating variables: A systematic review with meta-analysis
Bibliographic record
Abstract
BACKGROUND: It is well known that stretch training can induce prolonged increases in joint range of motion (ROM). However, to date more information is needed regarding which training variables might have greater influence on improvements in flexibility. Thus, the purpose of this meta-analysis was to investigate the effects of stretch training on ROM in healthy participants by considering potential moderating variables, such as stretching technique, intensity, duration, frequency, and muscles stretched, as well as sex-specific, age-specific, and/or trained state-specific adaptations to stretch training. METHODS: We searched through PubMed, Scopus, Web of Science, and SportDiscus to find eligible studies and, finally, assessed the results from 77 studies and 186 effect sizes by applying a random-effect meta-analysis. Moreover, by applying a mixed-effect model, we performed the respective subgroup analyses. To find potential relationships between stretch duration or age and effect sizes, we performed a meta-regression. RESULTS: = 74.97). Subgroup analysis showed a significant difference between the stretching techniques (p = 0.01) indicating that proprioceptive neuromuscular facilitation and static stretching produced greater ROM than did ballistic/dynamic stretching. Moreover, there was a significant effect between the sexes (p = 0.04), indicating that females showed higher gains in ROM compared to males. However, further moderating analysis showed no significant relation or difference. CONCLUSION: When the goal is to maximize ROM in the long term, proprioceptive neuromuscular facilitation or static stretching, rather than ballistic/dynamic stretching, should be applied. Something to consider in future research as well as sports practice is that neither volume, intensity, nor frequency of stretching were found to play a significant role in ROM yields.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".