Unsupervised physical activity interventions for people with COPD: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Unsupervised PA interventions might have a role in the management of chronic obstructive pulmonary disease (COPD) but their effectiveness is largely unknown. Thus, we aimed to identify and synthesise data on the effects of unsupervised PA interventions in people with COPD. MATERIAL AND METHODS: Databases were systematically searched in April 2020, with weekly updates until September 2021. Randomised controlled trials and quasi-experimental studies comparing unsupervised PA with usual care, were included. Primary outcomes were dyspnoea, exercise capacity and physical activity. The effect direction plot was performed to synthesise results. Meta-analysis with forest plots were conducted for the Chronic Respiratory Disease questionnaire - dyspnoea domain (CRQ-D), 6-minute walk distance (6MWD) and incremental shuttle walk distance (ISWD). RESULTS: 63.7±15.8% predicted) were included. All interventions were conducted at home, most with daily sessions, for 8-12 weeks. Walking was the most common component. The effect direction plot showed that unsupervised PA interventions improved emotional function, fatigue, health-related quality of life, muscle strength and symptoms of anxiety and depression. Meta-analysis showed statistical, but not clinical, significant improvements in dyspnoea (CRQ-D, MD=0.12, 95% CI 0.09-0.15) and exercise capacity, measured with 6MWD (MD=13.70, 95% CI 3.58-23.83). Statistical and clinical significant improvements were observed in exercise capacity, measured with ISWD (MD=58.59, 95% CI 5.79-111.39). None to minor adverse events and a high adherence rate were found. CONCLUSIONS: Unsupervised PA interventions benefits dyspnoea and exercise capacity of people with COPD, are safe and present a high adherence rate. Unsupervised PA interventions should be considered for people with COPD who cannot or do not want to engage in supervised PA interventions or as a maintenance strategy of PA levels.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".