Effectiveness of remote home monitoring for patients with Chronic Obstructive Pulmonary Disease (COPD): systematic review
Bibliographic record
Abstract
BACKGROUND: Although remote home monitoring (RHM) has the capacity to prevent exacerbations in patients with chronic obstructive pulmonary disease (COPD), evidence regarding its effectiveness remains unclear. The objective of this study was to determine the effectiveness of RHM in patients with COPD. METHODS: A systematic review of the scholarly literature published within the last 10 years was conducted using internationally recognized guidelines. Search strategies were applied to several electronic databases and clinical trial registries through March 2020 to identify studies comparing RHM to 'no remote home monitoring' (no RHM) or comparing RHM with provider's feedback to RHM without feedback. To critically appraise the included randomized studies, the Cochrane Collaboration risk of bias tool (ROB) was used. The quality of included non-randomized interventional and comparative observational studies was evaluated using the ACROBAT-NRSI tool from the Cochrane Collaboration. The quality of evidence relating to key outcomes was assessed using Grading of Recommendations, Assessment, Development and Evaluations (GRADE) on the following: health-related quality of life (HRQoL), patient experience and number of exacerbations, number of emergency room (ER) visits, COPD-related hospital admissions, and adherence as the proportion of patients who completed the study. Three independent reviewers assessed methodologic quality and reviewed the studies. RESULTS: Seventeen randomized controlled trials (RCTs) and two comparative observational studies were included in the review. The primary finding of this systematic review is that a considerable amount of evidence relating to the efficacy/effectiveness of RHM exists, but its quality is low. Although RHM is safe, it does not appear to improve HRQoL (regardless of the type of RHM), lung function or self-efficacy, or to reduce depression, anxiety, or healthcare resource utilization. The inclusion of regular feedback from providers may reduce COPD-related hospital admissions. Though adherence RHM remains unclear, both patient and provider satisfaction were high with the intervention. CONCLUSIONS: Although a considerable amount of evidence to the effectiveness of RHM exists, due to heterogeneity of care settings and the low-quality evidence, they should be interpreted with caution.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".