Effectiveness of Tailored Self-Management Interventions for People with Chronic Musculoskeletal Conditions: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: To evaluate the effectiveness of tailored self-management interventions to improve health and behavioural outcomes for individuals living with chronic musculoskeletal (MSK) conditions. Method: We searched relevant databases and grey literature on January 27, 2022 (CRD42022297624). We included English language randomized controlled trials and quasi-experimental trials that assessed the effect of tailored self-management on health and behavioural outcomes for individuals living with chronic MSK conditions. Eligible studies included: Individually prescribed components based on subjective and/or objective assessments; usual care or non-tailored controls; ≥1 health and behavioural outcome(s). Methodological quality was evaluated using the Cochrane Risk of Bias (RoB 2) tool and certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation. Results: Our search yielded 1558 articles, and 21 studies were included in the review. Compared with usual care/non-tailored controls, positive effects were detected in favour of tailored interventions for pain (standardized mean difference [SMD] = 0.35; 95% CI: 0.20, 0.50; moderate certainty) and quality of life, SF-12/36, (SMD = 0.22; 95% CI: 0.08, 0.37; high certainty) in people with inflammatory arthritis (IA) and chronic pain conditions. Similar effects were detected for pain self-efficacy (SMD = 0.40; 95% CI: 0.20, 0.62; moderate certainty) and quality of life, index score (SMD = 0.19; 95% CI: 0.00, 0.38; moderate certainty) across chronic MSK conditions. Conclusion: Tailored self-management interventions offer modest benefits for select health outcomes; however, clinical significance remains unclear.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.036 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".