Sensorimotor Control During Peripheral Muscle Vibration: An Experimental Study
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to determine whether the application of vibration on a postural lower limb muscle altered the sensorimotor control of its joint as measured by isometric force production parameters and to compare present findings with previous work conducted on trunk muscle. METHODS: Twenty healthy adults were asked to reproduce submaximal isometric plantar flexion under 3 different conditions: no vibration and vibration frequencies of 30 and 80 Hz on the soleus muscle. Time to peak torque, variable error, as well as constant error and absolute error in peak torque were calculated and compared across conditions. RESULTS: Under vibration, participants were significantly less accurate in the force reproduction task, as they mainly undershot the target torque. Applying an 80-Hz vibration resulted in a significantly higher negative constant error than lower-frequency vibration (30 Hz) or no-vibration condition. Decreases in isometric force production accuracy under vibration influence were also observed in a previous study conducted on trunk muscle. However, no difference in constant error was found between 30- and 80-Hz vibration conditions. CONCLUSION: The results suggest that acute soleus muscle vibration interferes with plantar flexion torque generation by distorting proprioceptive information, leading to decreases in accuracy of a force reproduction task. Similar results in an isometric trunk extension force reproduction task were found with vibration applied on erector spinae muscle. However, high-frequency vibration applied on soleus muscle elicited higher force reproduction errors than low-frequency stimulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".