The effect of massage on localized lumbar muscle fatigue
Bibliographic record
Abstract
BACKGROUND: There is not enough evidence to support the efficacy of massage for muscle fatigue despite wide utilization of the modality in various clinical settings. This study investigated the influence of massage application on localized back muscle fatigue. METHODS: Twenty-nine healthy subjects participated in two experimental sessions (massage and rest conditions). On each test day, subjects were asked to lie in the prone position on a treatment table and perform sustained back extension for 90 seconds. Subjects then either received massage on the lumbar region or rested for a 5 minute duration, then repeated the back extension movement. The median frequency (MDF), mean power frequency (MNF), and root mean square (RMS) amplitude of electromyographic signals during the 90 second sustained lumbar muscle contraction were analyzed. The subjective feeling of fatigue was then evaluated using the Visual Analogue Scale (VAS). RESULTS: MDF and MNF significantly declined with time under all conditions. There was no significant difference in MDF, MNF or RMS value change between before and after massage, or between rest and massage conditions. There was a significant increase in fatigue VAS at the end of the 2nd back extension with rest condition. There was a significant difference in fatigue VAS change between massage and rest condition. CONCLUSIONS: A significant difference was observed between massage and rest condition on VAS for muscle fatigue. On EMG analysis, there were no significant differences to conclude that massage stimulation influenced the myoelectrical muscle fatigue, which is associated with metabolic and electrical changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.003 | 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".