Mobile Apps for Medication Management: Review and Analysis
Bibliographic record
Abstract
BACKGROUND: Pharmacotherapy remains one of the major interventional strategies in medicine. However, patients from all age groups and conditions face challenges when taking medications, such as integrating them into the daily routine, understanding their effects and side effects, and monitoring outcomes. In this context, a reliable medication management tool adaptable to the patient's needs becomes critical. As most people have a mobile phone, mobile apps offer a platform for such a personalized support tool available on the go. OBJECTIVE: This study aimed to provide an overview of available mobile apps, focusing on those that help patients understand and take their medications. We reviewed the existing apps and provided suggestions for future development based on the concept understand and manage, instead of the conventional adhere to medication. This concept aims to engage and empower patients to be in charge of their health, as well as see medication as part of a broader clinical approach, working simultaneously with other types of interventions or lifestyle changes, to achieve optimal outcomes. METHODS: We performed a Web search in the iOS Apple App Store and Android Google Play Store, using 4 search terms: medication management, pill reminder, medication health monitor, and medication helper. We extracted information from the app store descriptions for each eligible app and categorized into the following characteristics: features, author affiliation, specialty, user interface, cost, and user rating. In addition, we conducted Google searches to obtain more information about the author affiliation. RESULTS: A total of 328 apps (175 Android and 153 iOS) were categorized. The majority of the apps were developed by the software industry (73%, 11/15), a minority of them were codeveloped by health care professionals (15%, 3/20) or academia (2.1%; 7/328). The most prevalent specialty was diabetes (23 apps). Only 7 apps focused on mental health, but their content was highly comprehensive in terms of features and had the highest prevalence of the education component. The most prevalent features were reminder, symptom tracker, and ability to share data with a family member or doctor. In addition, we highlighted the features considered innovative and listed practical suggestions for future development and innovations. CONCLUSIONS: We identified detailed characteristics of the existing apps, with the aim of informing future app development. Ultimately, the goal was to provide users with effective mobile health solutions, which can be expected to improve their engagement in the treatment process and long-term well-being. This study also highlighted the need for improved standards for reporting on app stores. Furthermore, it underlined the need for a platform to offer health app users an ongoing evaluation of apps by health professionals in addition to other users and to provide them with tools to easily select an appropriate and trustworthy app.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".