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Offering Digital Adherence Support Improves Clinical Outcomes for High-Risk Chronic Obstructive Pulmonary Disease (COPD) Patients With Poor Adherence to Inhaled Therapy

2025· article· en· W4410268434 on OpenAlexaff
D.B. Price, Andy Dickens, William Henley, Derek Skinner, Hilary Pinnock, Job F. M. van Boven, N. Roche, K. Kostikas, K.M. Beeh, OS Usmani, Allan Clark, James D. Chalmers, A. Kaplan, V. Carter, Ursie Smith, Z. Burnett-Kirton, Sara J. White, Stephanie Davis, Natalie J. Hannan, Franklin Igwe, Paul Mastoridis, Karen Mezzi, Pascal Pfister, DMG Halpin

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsTD Bank GroupUniversity of Toronto
Fundersnot available
KeywordsMedicineCOPDPulmonary diseaseIntensive care medicineMedication adherenceInternal medicine

Abstract

fetched live from OpenAlex

Abstract Rationale: Non-adherence to medication is common in COPD and leads to poor outcomes. Digital interventions have improved inhaler adherence in small short-term studies, but there is minimal evidence of their impact on clinical outcomes. Methods: Our 12-month pragmatic cluster randomised trial (MAGNIFY) evaluated the impact of providing primary care with access to a complex intervention for COPD patients designed to enhance adherence (10.2147/POR.S302809). The intervention included identification of high-risk COPD patients (≥2 moderate/severe exacerbations in last 2 years) using Optimum Patient Care electronic medical record (EMR) driven quality improvement algorithms, a remote COPD review and a digital adherence support package (comprising Ultibro® Breezhaler®, Propeller Health electronic inhaler monitoring device and smartphone app). To minimise contamination, primary care centres were randomised to offer the intervention or continue usual care (control). In practices randomized to the intervention arm, high-risk patients aged ≥40 years and deemed clinically suitable and poorly adherent to inhaled therapy at the review, were offered the digital adherence support package and those that accepted were included in the study. In control practices, we identified high-risk COPD patients aged ≥40 years on the basis of LABA/LAMA prescription date. Primary outcome was time to treatment failure (moderate/severe exacerbation, prescription of ICS or other additional COPD therapy, or death) and secondary outcomes were moderate/severe exacerbation rate and time to exacerbation in 12-month follow-up. Analyses were adjusted for three pre-specified confounders: age, baseline exacerbation rate & baseline adherence. Results: 164 primary care centres (87 intervention, 77 control) were included in the trial. 835 patients were offered the digital adherence support package and 656 (78.6%) accepted. There was a 23% reduced risk of treatment failure over the 12-month trial period (adjusted HR 0.77; 95% CI 0.64, 0.94; p=0.009) in patients provided with the digital intervention compared to controls. The median time to treatment failure was 263 days in the intervention and 361 days in the control arm. Exacerbation rates were 11% lower in the intervention arm (adjusted IRR 0.89; 95% CI 0.79, 0.99; p=0.047). In pre-specified subgroups, the intervention was more effective in patients with FEV1<80% predicted, no previous exacerbations, or a Cambridge Multimorbidity Score ≥3 (Table 1). Conclusions: Offering a digital intervention to optimise adherence reduced risk of treatment failure by almost 25% and reduced exacerbation rate by over 10% in high-risk patients with COPD. This is the first large, year-long trial demonstrating improved clinical outcomes in a real-world primary care setting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.037
GPT teacher head0.429
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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