P237 Technophobia is not the most significant patient-reported barrier to accepting a digital adherence package: an analysis of 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 digital 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 screened for eligibility for the UK-based MAGNIFY trial (Price et al 2021 doi: 10.2147/POR.S302809), with main inclusion criteria being 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 (Propeller Health). Patients unwilling/unable to accept the package were asked to provide reasons. Results 87 participating practices had a total COPD list size of 33211 patients, of which 1833 patients met the trial eligibility criteria. Pharmacists excluded 541 patients following electronic medical record review, and were unable to contact a further 111 patients. Of the 1181 patients contacted, 73 were clinically unsuitable for the adherence package. Of the remaining 1108 patients, 395 (36%) were unwilling/unable to accept the adherence package; reasons included: no smartphone/incompatible phone (n=273), unwilling to change inhaler (n=71), unwilling to use the support package (n=19), life events (n=12), partially sighted (n=2), no reason (n=18). Patient demographics are reported in table 1. Conclusions The main reasons for not accepting the adherence package were due to lacking a compatible smartphone or not wanting to change inhaler, rather than unwillingness to use technology. Though this is data from a single trial, the patients are from multiple diverse practices. The data suggest that technophobia may not be the most important barrier to patients accepting digital adherence support. A quarter of invited patients did not have a smartphone, highlighting the need for future implementation to ensure equitable access to digital 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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".