Effects of a Short Proprioceptive Neuromuscular Facilitation Stretching Bout on Quadriceps Neuromuscular Function, Flexibility, and Vertical Jump Performance
Bibliographic record
Abstract
The inclusion of relatively long bouts of stretching (repeated static stretches of ∼30 seconds) in the warm-up is usually associated with a drop in muscle performance. The purpose of this study was to assess the effect of a novel self-administered proprioceptive neuromuscular facilitation (PNF) paradigm with short periods of stretching and contraction on quadriceps neuromuscular function, vertical jump performance, and articular range of motion (ROM). Twelve healthy men (age: 27.7 ± 7.3 years, height: 178.4 ± 10.4 cm, weight: 73.8 ± 16.9 kg) volunteered to participate in a PNF session and a control session separated by 2-7 days. The PNF stretching lasted 2 minutes and consisted of 4 sets of 5-second isometric hamstring contraction immediately followed by 5 seconds of passive static stretch of the quadriceps immediately followed by 5 seconds isometric quadriceps contraction for each leg. For the control session, the participants were asked to walk at a comfortable speed for 2 minutes. Active ROM of knee flexion, vertical jump performance, and quadriceps neuromuscular function were tested before, immediately after, and 15 minutes after the intervention. The PNF stretching procedure did not affect ROM, squat jump, and countermovement jump performances. Accordingly, we did not observe any change in maximal voluntary contraction force, voluntary activation level, M-wave and twitch contractile properties that could be attributed to PNF stretching. The present self-administered PNF stretching of the quadriceps with short (5-second) stretches is not recommended before sports where flexibility is mandatory for performance.
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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) | 0.002 | 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".