Effects of Personal Low-Frequency Stimulation Device on Myalgia: A Randomized Controlled Trial
Bibliographic record
Abstract
Electrotherapy is commonly used for myalgia alleviation. Low-frequency stimulation (LFS) is primarily used for controlling acute and chronic pain and is a non-invasive therapy that can be easily performed with electric stimulation applied on the skin. However, little evidence exists regarding the pain alleviation effects of personal low-frequency stimulation device for home use. Moreover, no studies have compared myalgia alleviation effects between personal low-frequency stimulation (PLS) and physical therapy (PT), which are most commonly used for patients with myalgia in hospitals and clinics. Therefore, we aimed to investigate the pain alleviation effects of PLS in patients with myalgia and compare these effects with those of conventional PT (transcutaneous electrical nerve stimulation + ultrasound). In total, 39 patients with myalgia in the neck, shoulder, back, and waist areas were randomly assigned to the personal low-frequency stimulation group (PLSG: n = 20) and physical therapy group (PTG: n = 19). Both groups were treated for 3 weeks (20 min per session and 5 sessions per week). Patients were assessed for pain intensity by surface electromyography (sEMG), visual analogue scale (VAS) and a short-form McGill pain questionnaire (SF-MPQ) before and after the intervention period. Our results showed that PLSG showed a tendency of muscle relaxation with a significant decrease in sEMG in the neck (p = 0.0425), shoulder (p = 0.0425), and back (p = 0.0046) areas compared to the control group. However, there was no significant difference in waist area. Additionally, VAS scores significantly decreased between pre- and post-treatment in both PTG (p = 0.0098), and PLSG (p = 0.0304) groups, but there was no significance difference between the groups. With respect to SF-MPQ, the PLSG showed greater pain alleviation (5.23 ± 0.25) effects than the PTG (6.23 ± 0.25). Accordingly, our results suggest that PLS treatment using a home device might offer positive assistance in pain alleviation for patients with myalgia that is as equally effective as conventional PT treatment. However, further detailed studies are required considering larger samples to fully claim the effectiveness of this device.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".