Patient-reported barriers to accepting a technological adherence package in the MAGNIFY trial.
Bibliographic record
Abstract
Introduction: COPD exacerbations lead to increased mortality and disease progression. Maintenance inhaled therapies can reduce exacerbation risk amongst COPD patients, but non-adherence reportedly ranges from 20-60% in this population. The ongoing cluster randomised trial MAGNIFY is investigating the use of technological adherence support as a solution to this problem, but there is little evidence regarding patients’ willingness to accept such devices. Aims and objectives: To explore patient-reported barriers to accepting a technological adherence package. Methods: COPD patients were eligible for MAGNIFY if aged 40 years or above, with ≥2 moderate/severe exacerbations in the last two years and with ≤50% adherence to mono/dual therapy. Eligible patients received a phone call from a pharmacist who conducted a remote patient review and invited them to use the digital support package, comprising an Ultibro Breezhaler and adherence support technology. Patients declining the package were asked to provide reasons. Results: Out of 331 patients clinically suitable for the adherence package, 113 (34.1%) declined the adherence package. Reasons for declining included: no smartphone/not compatible phone (n=89), unwilling to change inhaler (n=8), unwilling to use inhalers regularly (n=5), life events (n=2), another health condition (n=1), no reason (n=8). Conclusions: Most patients declined the adherence package for practical reasons, such as lacking a compatible smartphone, rather than unwillingness to use technology. Though this is interim data from a single trial, it suggests that technophobia may not be an important barrier to patients accepting technological adherence support.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".