Quadriceps or triceps surae proprioceptive neuromuscular facilitation stretching with post-stretching dynamic activities does not induce acute changes in running economy
Bibliographic record
Abstract
Previous studies reported that both a more compliant quadriceps tendon and a stiffer Achilles tendon are associated with better running economy. While tendon stiffness can be decreased by a single bout of proprioceptive neuromuscular facilitation (PNF), post-stretching dynamic activities (PSA) can counteract the potential stretch-induced force loss. Thus, the purpose of this study was to investigate if a single, moderate duration, (4 × 15 s), bout of PNF stretching of either the quadriceps or triceps surae muscles followed each by PSA, causes either an improvement or impairment in running economy. Eighteen trained male runners/triathletes visited the laboratory five times. The first two visits were to familiarize the participants and to test for maximal oxygen consumption (VO2max) respectively. The further three appointments were randomly assigned to either 1.) quadriceps PNF stretching + PSA or 2.) triceps surae PNF stretching + PSA or 3.) no stretching + PSA. Following the interventions, participants performed a 15-min run on the treadmill with a speed reflecting a velocity of 70% VO2max to assess oxygen consumption (i.e., running economy) and running biomechanics. Our results showed neither a difference in oxygen consumption (p = 0.15) nor a change in any variable of the running biomechanics (p > 0.33) during the steady-state (i.e., last 5 min) of the 15-min run. Athletes can perform moderate duration PNF stretching of the quadriceps or triceps surae + PSA prior to a running event, without affecting running economy. Future studies should emphasize long-term training effects on tendon stiffness adaptations and running economy.
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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) | 0.003 | 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".