ArticleNPJ digital medicine2025
Evaluation of an integrated digital and mobile intervention to improve outcomes for patients with moderate to severe COPD.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Understanding Delivery and Engagement With a Digital Self-Management Intervention for Chronic Obstructive Pulmonary Disease Across Two Clinical Settings: Qualitative Study.JMIR human factors · 2026Article
- Implementing mobile integrated health for adults with intellectual and developmental disabilities: a qualitative readiness assessment.Disability and health journal · 2026Article
- Implementing acute exacerbations of COPD care bundles: evidence, effectiveness and future directions.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Evaluating a Mobile Integrated Health Transitional Care Program to Reduce Readmissions: Findings From a Quasi-Experimental Design.Journal of the American Geriatrics Society · 2026Observational
- Effectiveness of a pharmacist diabetes coaching program: A propensity-matched retrospective analysis.PloS one · 2026Article
- Research on the road: Partnering with community emergency medical services to expand access to clinical trials.Journal of clinical and translational science · 2026Article
- Hospitalizations and inpatient resource consumption of patients suffering from chronic disease - Past trend and forecast for Switzerland.BMC health services research · 2025Article
- Artificial Intelligence Methods and Digital Intervention Strategies for Predicting and Managing Chronic Obstructive Pulmonary Disease Exacerbations: An Umbrella Review.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
- Update of
Authors and funding
26 authors.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD) leads to high rates of emergency department (ED) visits and hospitalizations. This study evaluated a community-based digital health intervention's association with acute care utilization among patients with moderate to severe COPD. In a decentralized, nonrandomized trial, participants received biometric monitoring, symptom tracking, on-demand paramedic services, and digital pulmonary rehabilitation for 6 months. Outcomes were compared to a synthetic control group using weighted optimal matching and multivariable-adjusted regression. The primary outcome was hospitalization; secondary outcomes included readmission rates, ED visits, length of stay, and mortality. Eighty-eight intervention participants (mean age 67 (SD 10)) were compared to a weighted control group of 14,492 (mean age 69 (SD 11)). Intervention participants had lower odds of hospitalization (OR 0.67, 95% CI: 0.46-0.98) and 30-day readmission (OR 0.38, 95% CI: 0.17-0.84). This digital and mobile intervention was associated with reduced acute care use and supports further evaluation of hybrid care models for COPD.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.