Effectiveness of Mobile Health–Based Self-Management Programs on Health-Related Outcomes in Patients With Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The progression of chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality, emphasizing the need for effective self-management. Challenges such as accessibility, cost, and patient engagement hinder self-management efforts, underscoring the need for evidence-based mobile health (mHealth) interventions. Objective: This meta-analysis evaluated randomized controlled trials (RCTs) on the effectiveness of mHealth self-management programs for COPD, focusing on the modified Medical Research Council (mMRC) dyspnea scale, the 6-minute walking test (6MWT), and the St. George's Respiratory Questionnaire (SGRQ) score. The secondary outcomes include quality-adjusted life years and costs as economic outcomes; exacerbation, hospitalization, and emergency room and clinic visits as clinical outcomes; and self-efficacy as a humanistic outcome. Methods: The inclusion criteria encompassed RCTs involving patients with COPD aged 18 years and older, comparing mHealth-based self-management programs to non-mHealth interventions, with outcomes measured using the mMRC dyspnea scale, 6MWT, and SGRQ score. Exclusion criteria included observational studies, reviews, qualitative research, protocols, and non-English publications. A comprehensive search was conducted across PubMed, Embase, CINAHL, Web of Science, Cochrane, and Scopus using predefined keywords and MeSH terms for studies published between January 2015 and September 2024. The risk of bias was assessed using the Cochrane Risk-of-Bias 2 tool. Data extraction encompassed study characteristics, interventions, comparators, and outcomes. Meta-analyses were performed for outcomes reported in at least 3 RCTs using R software (version 4.2.2; R Foundation for Statistical Computing). Results: This systematic review included 36 RCTs from diverse geographical regions, encompassing 5606 patients. The meta-analysis revealed significant improvements in the mMRC dyspnea scale (mean difference -0.65, 95% CI -1.14 to -0.16; P=.02) and 6MWT (mean difference 25.96 m, 95% CI 10.05 m to 41.87 m; P=.004) in the mHealth intervention group compared to controls. However, no statistical significance was observed in the SGRQ total score (mean difference -3.56, 95% CI -7.39 to 0.27; P=.07). A total of 2 studies reported economic results, with a possible statistically significant decrease in the mean cost per patient (€3547 vs €4831 [US $4118.4 vs US $5609.24]; P=.01), but no statistically significant difference in quality-adjusted life years (0.485 vs 0.491; P=.73). A total of 5 studies reported substantial reductions in hospital admissions. Additionally, 1 study each reported significant improvements in time to first readmission for COPD exacerbations, clinic visits, mortality rates, and exacerbation frequencies. A single study reported a significant improvement in self-efficacy, as measured by the Pulmonary Rehabilitation Adapted Index of Self-Efficacy scores. Conclusions: This review supports the Global Initiative for Chronic Obstructive Lung Disease 2025 recommendations, highlighting mHealth as a supplementary clinical tool requiring patient education, ethical compliance, and informed consent. Further large-scale studies are needed to refine mHealth tools, ensuring accessibility, long-term safety, and effectiveness across diverse populations and outcome domains.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| 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".