Features of self-management interventions for people with COPD associated with improved health-related quality of life and reduced emergency department visits: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Self-management interventions (SMIs) are recommended for individuals with COPD to help monitor symptoms and optimize health-related quality of life (HRQOL). However, SMIs vary widely in content, delivery, and intensity, making it unclear which methods and techniques are associated with improved outcomes. This systematic review aimed to summarize the current evidence base surrounding the effectiveness of SMIs for improving HRQOL in people with COPD. Methods: Systematic reviews that focused upon SMIs were eligible for inclusion. Intervention descriptions were coded for behavior change techniques (BCTs) that targeted self-management behaviors to address 1) symptoms, 2) physical activity, and 3) mental health. Meta-analyses and meta-regression were used to explore the association between health behaviors targeted by SMIs, the BCTs used, patient illness severity, and modes of delivery, with the impact on HRQOL and emergency department (ED) visits. Results: Data related to SMI content were extracted from 26 randomized controlled trials identified from 11 systematic reviews. Patients receiving SMIs reported improved HRQOL (standardized mean difference =−0.16; 95% confidence interval [CI] =−0.25, −0.07; P =0.001) and made fewer ED visits (standardized mean difference =−0.13; 95% CI =−0.23, −0.03; P =0.02) compared to patients who received usual care. Patients receiving SMIs targeting mental health alongside symptom management had greater improvement of HRQOL (Q=4.37; P =0.04) and fewer ED visits (Q=5.95; P =0.02) than patients receiving SMIs focused on symptom management alone. Within-group analyses showed that HRQOL was significantly improved in 1) studies with COPD patients with severe symptoms, 2) single-practitioner based SMIs but not SMIs delivered by a multidisciplinary team, 3) SMIs with multiple sessions but not single session SMIs, and 4) both individual- and group-based SMIs. Conclusion: SMIs can be effective at improving HRQOL and reducing ED visits, with those targeting mental health being significantly more effective than those targeting symptom management alone. Keywords: self-management, emergency department visits, behavior change techniques, COPD, mental health, meta-analysis
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".