Interventions for Depressive Symptoms in People Living with Chronic Pain: A Systematic Review of Meta-Analyses
Bibliographic record
Abstract
OBJECTIVE: This review investigated the effectiveness of clinical interventions on depressive symptoms in people with all types of chronic pain. METHODS: We searched seven electronic databases and reference lists on September 15, 2020, and included English-language, systematic reviews and meta-analyses of trials that examined the effects of clinical interventions on depressive outcomes in chronic pain. Two independent reviewers screened, extracted, and assessed the risk of bias. PROSPERO registration: CRD42019131871. RESULTS: Eighty-three reviews were selected and included 182 meta-analyses. Data were summarized visually and narratively using standardized mean differences with 95% confidence intervals as the primary outcome of interest. A large proportion of meta-analyses investigated fibromyalgia or mixed chronic pain, and psychological interventions were most commonly evaluated. Acceptance and commitment therapy for general chronic pain, and fluoxetine and web-based psychotherapy for fibromyalgia showed the most robust effects and can be prioritized for implementation in clinical practice. Exercise for arthritis, pharmacotherapy for neuropathic pain, self-regulatory psychotherapy for axial pain, and music therapy for general chronic pain showed large, significant effects, but estimates were derived from low- or critically low-quality reviews. CONCLUSIONS: No single intervention type demonstrated substantial superiority across multiple pain populations. Other dimensions beyond efficacy, such as accessibility, safety, cost, patient preference, and efficacy for non-depressive outcomes should also be weighed when considering treatment options. Further effectiveness research is required for common pain types such as arthritis and axial pain, and common interventions such as opioids, anti-inflammatories and acupuncture.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".