Neural adaptations in quadriceps muscle after 4 weeks of local vibration training in young versus older subjects
Bibliographic record
Abstract
This study investigated the effects of a 4-week local vibration training (LVT) on the function of the knee extensors and corticospinal properties in healthy young and older subjects. Seventeen subjects (9 young and 8 older) performed 3 testing sessions: before (PRE1) and after (PRE2) a 4-week resting period to control the repeatability of the data as well as after the LVT (POST). Jump performance, maximal voluntary contraction (MVC) and electromyographic (EMG) activity on vastus lateralis and rectus femoris muscles were assessed. Single-pulse transcranial magnetic stimulation (TMS) allowed evaluation of cortical voluntary activation (VATMS), motor evoked potential (MEP) area, and silent period (SP) duration. All training adaptations were similar between young and older subjects (p > 0.05) and the following results reflect the pooled sample of subjects. MVC (+11.9% ± 8.0%, p < 0.001) and VATMS (+3.6% ± 5.2%, p = 0.004) were significantly increased at POST compared with PRE2. Maximal vastus lateralis EMG was significantly increased at POST (+21.9% ± 33.7%, p = 0.03). No changes were reported for MEPs on both muscles (p > 0.05). SPs recorded during maximal and submaximal contractions decreased in both muscles at POST (p < 0.05). Vertical jump performance was increased at POST (p < 0.05). LVT seems as effective in young as in older subjects to improve maximal functional capacities through neural modulations occurring at least partly at the supra-spinal level. Local vibration may be used as an efficient alternative training method to improve muscular performance in both healthy young and older subjects.
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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".