Resistance Training Causes the Stretch-Induced Force Deficit—A Randomized Cross-Over Study
Bibliographic record
Abstract
Purpose: Stretch-induced force deficit suggests an acute stretch-specific strength capacity loss, which is commonly attributed to EMG reductions. Since those deficits could also be attributed to general fatigue induced by overloading the muscle, this study aimed to compare stretching with an exhausting calf raise programme to compare strength and stretching responses. Method: This study included 16 participants with different, high-duration calf muscle stretching effects (10, 20, 30 min of stretching) with resistance training (RT) (3 × 12 repetitions) performed until muscle failure, by using a cross-over study design with pre-post comparisons. Strength was tested via isometric plantar flexor diagnostics, while flexibility was assessed using the knee-to-wall test (KtW) and an isolated goniometer test. Results: Using a three-way ANOVA, RT strength decreases were greater compared to 10 and 20 min of stretching (p = 0.01–0.02), but similar to those of 30 min of stretching. ROM in the KtW showed no specific stretch-induced increases, while only the stretching conditions enhanced isolated tested ROM (p < 0.001–0.008). No RT-related isolated ROM increases were observed. Conclusions: The results showed both interventions had similar effects on strength and ROM in the calf muscles. More holistic explanatory approaches such as fatigue and warm-up are discussed in the manuscript and call for further research.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".