Unilateral static and dynamic hamstrings stretching increases contralateral hip flexion range of motion
Bibliographic record
Abstract
Summary Static (SS) and dynamic stretching (DS) can lead to subsequent performance impairments or enhancement with the stretched limb. Crossover or non‐local muscle fatigue (NLMF) refers to unilateral fatigue‐induced impairments in a contralateral or non‐exercised muscle. Whereas there are conflicting findings in the NLMF literature, there are few studies examining the effect of an acute bout of SS or DS on contralateral flexibility, torque or power. Fourteen highly trained subjects (means ± standard deviations: 18 ± 2 years; 179·4 ± 4·6 cm; 70·5 ± 6·3 kg; %body fat: 10·7 ± 2·5%) were tested before and following separate sessions of eight repetitions of 30 s of unilateral hip flexion SS or DS. Pre‐ and postintervention testing at 1 and 10 min included hip flexor range of motion (ROM), isokinetic leg flexion torque and power at 60°.s−1 and 300°.s−1 of the stretched and contralateral limbs. The stretched limb had a 6·3% (P = 0·01; ES: 0·91) ROM increase with DS at 10 min. The contralateral non‐stretched hip flexors experienced ROM increases with SS of 5·7% (P = 0·02; ES: 0·68) from pretest to 1 min post‐test, whereas DS showed 7·1% (P<0·0001; ES: 1·09) and 8·4% (P = 0·005; ES: 0·89) increases, respectively. There were no relative differences in ROM changes between conditions or limbs nor any stretch‐induced changes in isokinetic torque or power. In conclusion, unilateral SS and DS augment contralateral limb ROM likely through an increased stretch tolerance.
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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 | 0.000 |
| 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) | 0.004 | 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".