Community pharmacists’ mobile Health application recommendations for medication adherence in chronic diseases: a mixed-methods pilot study
Bibliographic record
Abstract
Aim: To explore the effect of pharmacists’ mobile health application (mHealth applications) recommendations on medication adherence and their perspectives on using a curated library of applications (AppGuide) to support their interventions for chronic disease-related non-adherence. Method: A mixed-methods pilot study was conducted, recruiting patients with chronic conditions through community pharmacies, and assigning them to an intervention group (usual care plus application recommendation) or a control group (usual care only). Medication adherence was measured using MARS-5 at baseline, 2, and 3 months. Semi-structured interviews were conducted with pharmacists and pharmacy students who had recommended an application with AppGuide. Interviews were transcribed and thematically analyzed using the Technology Acceptance Model. Results: Out of 91 eligible patients, 40 consented to participate (21 intervention, 19 control). Attrition was 12.5%. No significant differences in MARS-5 scores were observed between baseline and 3 months between groups (difference in mean differences = 0.6; P =0.23). Six pharmacists and pharmacy students were interviewed. Eight themes emerged: ease of use, place of mHealth application recommendation in pharmacy practice, perceived usefulness, perceived results, changes in practice, future use, facilitators, and barriers. They described AppGuide as user-friendly and useful, particularly for patient education and health data tracking. They also reported developing tailored strategies to recommend applications, but faced barriers such as patient reluctance and concerns about efficacy. Conclusion: While no significant adherence differences were observed, AppGuide was well-received by pharmacists, suggesting that mHealth applications may support medication adherence counselling in an evolving digital healthcare landscape.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".