Effectiveness of self-management interventions in inflammatory arthritis: a systematic review informing the 2021 EULAR recommendations for the implementation of self-management strategies in patients with inflammatory arthritis
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review (SR) on the effectiveness of self-management interventions, in order to inform the European League Against Rheumatism Recommendations for its implementation in patients with inflammatory arthritis (IA). METHODS: The SR was conducted according to the Cochrane Handbook and included adults (≥18 years) with IA. The search strategy was run in Medline through PubMed, Embase, Cochrane Library, CINAHL Plus with Full Text, and PEDro. The assessment of risk of bias, data extraction and synthesis were performed by two reviewers independently. A narrative Summary of Findings was provided according to the Grading of Recommendations, Assessment, Development and Evaluation. RESULTS: From a total 1577 references, 57 were selected for a full-text review, and 32 studies fulfilled the inclusion criteria (19 randomised controlled trials (RCTs) and 13 SRs). The most studied self-management components were specific interactive disease education in ten RCTs, problem solving in nine RCTs, cognitive-behavioural therapy in eight RCTs, goal setting in six RCTs, patient education in five RCTs and response training in two RCTs. The most studied interventions were multicomponent or single exercise/physical activity in six SRs, psychosocial interventions in five SRs and education in two SRs. Overall, all these specific components and interventions of self-management have beneficial effects on IAs-related outcomes. CONCLUSIONS: The findings confirm the beneficial effect of the self-management interventions in IA and the importance of their implementation. Further research should focus on the understanding that self-management is a complex intervention to allow the isolation of the effectiveness of its different components.
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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 | 0.064 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".