Non-Pharmacological Interventions for Chronic Pain Management: A Narrative Review
Bibliographic record
Abstract
Background: Chronic pain is a pervasive and debilitating condition affecting millions globally, leading to significant distress, disability, and economic burden. Characterized by persistent or recurring pain lasting beyond three months, it profoundly impacts quality of life, mental health, and socioeconomic well-being. Objectives: This comprehensive review critically evaluates the efficacy and safety of various non-pharmacological interventions for chronic pain management. Methods: A systematic search was conducted across PubMed, Scopus, CINAHL, and Web of Science databases for studies published in English between 2000 and 2024. Keywords related to chronic pain and non-pharmacological interventions were identified, including physical therapies, psychological therapies, and complementary and integrative medicine (CIM) modalities. Study selection focused on randomized controlled trials, controlled clinical trials, systematic reviews, cohort studies, and case-control studies involving adults with chronic pain. Results: Physical therapies, such as manual therapy, exercise therapy, and therapeutic modalities, demonstrate benefits in improving physical function and reducing chronic pain. Psychological therapies, including cognitive behavioural therapy (CBT), mindfulness-based stress reduction (MBSR), and acceptance and commitment therapy (ACT), are crucial for addressing the psychological and emotional dimensions of chronic pain. CIM modalities such as yoga, Tai Chi, acupuncture, and massage therapy offer complementary approaches. The strength of evidence varies across interventions, with CBT and exercise therapy supported by robust evidence, while other modalities require further investigation. Conclusions: This review underscores the importance of a multimodal approach to chronic pain management, integrating physical, psychological, and CIM therapies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.006 | 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".