Prolonged static stretching causes acute, nonmetabolic fatigue and impairs exercise tolerance during severe-intensity cycling
Bibliographic record
Abstract
We tested the hypothesis that static stretching, an acute, nonmetabolic fatiguing intervention, reduces exercise tolerance by increasing muscle activation and affecting muscle bioenergetics during cycling in the “severe” intensity domain. Ten active men (age, 24 ± 2 years; body mass, 74 ± 11 kg; height, 176 ± 8 cm) participated in identical constant-load cycling tests of equal intensity, of which 2 tests were carried out under control conditions and 2 were done after stretching. This resulted in a 5% reduction of maximal isokinetic sprinting power output. We measured (i) oxygen consumption, (ii) electromyography, (iii) deoxyhemoglobin, (iv) blood lactate concentration; (v) time to exhaustion, and (vi) perception of effort. Finally, oxygen consumption and deoxyhemoglobin kinetics were determined. Force reduction following stretching was accompanied by augmented muscle excitation at a given workload (p = 0.025) and a significant reduction in time to exhaustion (p = 0.002). The time to peak oxygen consumption was reduced by stretching (p = 0.034), suggesting an influence of the increased muscle excitation on the oxygen consumption kinetics. Moreover, stretching was associated with a mismatch between O2 delivery and utilization during the isokinetic exercise, increased perception of effort, and blood lactate concentration; these observations are all consistent with an increased contribution of the glycolytic energy system to sustain the same absolute intensity. These results suggest a link between exercise intolerance and the decreased ability to produce force. Novelty We provided the first characterization of the effects of prolonged stretching on the metabolic response during severe cycling. Stretching reduced maximal force and augmented muscle activation, which in turn increased the metabolic response to sustain exercise.
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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) | 0.001 | 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".