Holistic Care for Reducing Pain Intensity among Individual with Rheumatoid Arthritis: A Systematic Literature Review
Bibliographic record
Abstract
Introduction: Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by persistent joint inflammation that causes pain, stiffness, and impaired mobility. While pharmacological treatment helps manage symptoms, some patients experience side effects or inadequate pain control. Therefore, holistic approaches involving psychological, lifestyle, and physical therapies are increasingly considered to support overall well-being and complement medical management. Objective: This systematic review aims to assess the effectiveness of holistic interventions in reducing pain intensity among individuals with RA. Method: A systematic search of six databases PubMed, ProQuest, Garuda, JSTOR, ScienceDirect, and Wiley was conducted for studies published between 2020 and 2024. Eligible studies included RA patients whose pain intensity was measured using validated tools such as the Numerical Rating Scale (NRS), Visual Analog Scale (VAS), or McGill Pain Questionnaire (MPQ). Two independent reviewers performed study selection and data extraction, while quality assessment followed PRISMA guidelines and the CASP Checklist. Results: Eleven studies comprising 607 participants met the inclusion criteria. The findings showed that holistic interventions including warm ginger compresses, Swedish massage, hand and foot massage, lavender aromatherapy, eucalyptus inhalation, Reiki therapy, and combined ginger compress with rheumatic exercise, were effective in reducing pain intensity in RA patients. Most studies reported significant improvements, with warm ginger compress being the most consistently effective method across various settings. Conclusion: Holistic approaches demonstrate meaningful benefits in reducing pain and supporting comfort in individuals with RA, particularly among older adults. Consistent application and appropriate duration of therapy enhance effectiveness. Overall, holistic care serves as a valuable complementary strategy that promotes patient-centered pain management and improves quality of life.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".