The Impact of Self-acupressure on the Pain, Joint Stiffness, and Physical Functioning of Patients With Knee Osteoarthritis
Bibliographic record
Abstract
Background: People with knee osteoarthritis have a low quality of life due to joint pain and stiffness, severely limiting their daily activities. This study aims to investigate the impact of self-acupressure on the pain, joint stiffness, and physical functioning of patients with knee osteoarthritis. Methods: This randomized clinical trial was conducted on 78 patients aged 50 to 70 with knee osteoarthritis, referred to Imam Khomeini Hospital and private orthopedic clinics in Ahvaz City, Iran, in 2018. The patients were recruited based on the inclusion criteria and then randomly assigned to three groups: self-acupressure (n=26), sham (n=26), and control (n=26). Patients in the intervention group applied daily self-acupressure to 5 specific points around their knees for 8 consecutive weeks. The sham group applied pressure on the points different from those used by the intervention group. The control group received no intervention. The study data were collected using the Western Ontario and McMaster osteoarthritis index (WOMAC), visual analog scale (VAS), and a checklist for daily recordings of pain medication. The obtained data were analyzed using the chi-square test and analysis of variance in SPSS software, version 20. The significance level was set at P<0.05. Results: The comparison of changes within the group showed that the intensity of pain in the intervention group decreased over time (P<0.0001). Also, the joint stiffness, physical functioning, and total WOMAC score significantly decreased in the intervention group (P<0.0001). The frequency of analgesic use was also reduced in the intervention group over time (P=0.026). Conclusion: According to the results, self-acupressure effectively reduces the intensity of pain and joint stiffness and improves the physical performance of older adults with knee osteoarthritis. Overall, this easy and affordable intervention is recommended for this group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".