Changes in Hip Flexor Passive Compliance Do Not Account for Improvement in Vertical Jump Performance After Hip Flexor Static Stretching
Bibliographic record
Abstract
To date, there is limited research investigating stretching of antagonist muscles and its effects on agonist muscle function. The purpose of this research was to investigate the effects of pre-static stretching (pre-SS) of the hip flexor musculature on passive hip extension range of motion (ROM) and vertical jump height. Fifteen subjects reported to the laboratory on 4 separate days (D1, D2, D3, and D4). D1 was for familiarization, while on D2 to D4, subjects randomly completed 1 of 3 intervention conditions; no stretch (CON), hip flexor stretch (HFS), or hip extensor stretch (HES). Subject's pre- and post-intervention hip extension ROM were measured before performing 3 sets of pre- and post-maximal counter-movement vertical jumps. Vertical jump height was normalized to baseline for data analysis. A repeated-measures ANOVA with post hoc paired sample t-tests revealed a significant increase in vertical jump height in the HFS condition (1.74% ± 0.73; p ≤ 0.05) when compared with CON (-1.34% ± 0.96) or HES (-1.74% ± 0.65) conditions. There was also a significant increase in hip extension ROM after the HFS stretching protocol (6.5 ± 2.75%; p ≤ 0.05) when compared with the CON protocol (-1.73 ± 3.26); however, no significant difference when compared with the HES protocol (1.84 ± 2.79). A correlation analysis showed that the relative hip laxity of each subject had no effect on response to either condition nor did the magnitude of hip ROM change predict improvement in vertical jump. These results suggest that performing SS of the hip flexors may enhance vertical jump performance independent of changes in passive compliance of the hip flexor muscular tendon unit.
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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) | 0.001 | 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".