Mobile Apps for Medication Management: Review and Analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,010 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».