Mobile Phone Apps Targeting Medication Adherence: Quality Assessment and Content Analysis of User Reviews (Preprint)
Bibliographic record
Abstract
BACKGROUND With the accessibility and widespread use of mobile phones, mobile phone apps targeting medication adherence may be useful tools to help patients take medications as prescribed. OBJECTIVE Our objectives were to (1) characterize and assess mobile phone medication adherence apps guided by a conceptual framework on the focus of adherence interventions and (2) conduct a content analysis of Web-based reviews to explore users’ perspectives and experiences with mobile phone medication adherence apps. METHODS We searched for mobile phone medication adherence apps using keyword searches in Apple and Android operating systems. We characterized all apps in terms of number of downloads, ratings, languages, cost, and disease target. We categorized apps according to 4 key features of (1) alerting to take medication, (2) tracking medication taking, (3) reminding to refill or indicating amount of medication left, and (4) storing medication information. We then selected representative apps from each operating system for detailed quality assessment and user testing. We also downloaded Web-based reviews for these selected apps and conducted a qualitative content analysis using an inductive approach involving steps of initial open coding, construction of categories, and abstraction into themes. RESULTS We identified 704 apps (443 from Apple and 261 from Android). The majority of apps across both operating systems had 1 or 2 features—specifically, 37.2% (165/443) and 38.1% (169/443) of Apple apps, respectively, and 41.4% (108/261) and 31.4% (108/261) of Android apps, respectively. Quality assessment and user testing of 20 selected apps revealed apps varied in quality and commonly focused on behavioral strategies to enhance medication adherence through alerts, reminders, and logs. A total of 1323 eligible Web-based reviews from these 20 selected apps were analyzed, and the following themes emerged: (1) features and functions appreciated by users, which included the ability to set up customized medication regimen details and reminders, monitor other health information (eg, vitals, supplements, and manage multiple people or pets), support health care visits (eg, having a list of medications and necessary health information in 1 app); (2) negative user experiences that captured technical difficulties (glitches, confusing app navigation, and poor interoperability), dosage schedule, and reminder setup inflexibility; and (3) desired functions and features related to optimization of information input, improvement of reminders, and upgrading app performance (better synchronization or backup of data and interoperability). CONCLUSIONS A large number of mobile phone medication adherence apps are currently available. The majority of apps have features representing a behavioral approach to intervention. Findings of the content analysis offer mostly positive feedback as well as insights into current limitations and improvements that could be addressed in current and future medication adherence apps.
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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 | 0.173 | 0.453 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".