The effects of passive stretching plus vibration on strength and activation of the plantar flexors
Bibliographic record
Abstract
This study examined the effects of passive stretching only (PS+CON) and passive stretching with the addition of continuous vibration (VIB) during post-passive stretching tests (PS+VIB) on peak torque (PT), percent voluntary inactivation (%VI), single stimulus twitch torque (TTSINGLE), and doublet stimuli twitch torque (TTDOUBLET) of the plantar flexors at a short (20° plantar flexion (PF)) and long muscle length (15° dorsiflexion (DF)). Fourteen healthy men (age = 22 ± 3 years) performed isometric maximal voluntary contractions at PF and DF, and passive range of motion (PROM) assessments before and after 8 × 30-s passive stretches without (PS+CON) or with VIB (PS+VIB) administered continuously throughout post-passive stretching tests. The passive properties of the muscle tendon unit were assessed pre- and post-passive stretching via PROM, passive torque (PASSTQ), and musculotendinous stiffness (MTS) measurements. PT, TTSINGLE, and TTDOUBLET decreased, whereas, %VI increased following passive stretching at PF and DF (P < 0.05) with no significant differences between PS+CON and PS+VIB. PASSTQ and MTS decreased while PROM increased post-passive stretching during both trials (P < 0.05). The stretching-induced force/torque deficit and increases in %VI were evident following passive stretching at short and long muscle lengths. Although not statistically significant, effect size calculations suggested large and moderate differences in the absolute changes in PT (Cohen's d = 1.14) and %VI (Cohen's d = 0.54) from pre- to post-passive stretching between treatments, with PS+VIB having greater decreases of PT and higher %VI than PS+CON. The decrement in PT following passive stretching may be primarily neural in origin.
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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) | 0.001 | 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".