Exploring the Fit Between the Outputs of Freely Available Medication Adherence Apps and Users’ Needs: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Medication nonadherence is a significant barrier to therapy success. Smartphone apps represent reasonable tools for simple adherence-enhancing interventions. Many adherence apps are available in app stores with diverse content, quality, and outputs. We define "output of an adherence app" as the processing and visualization of data recorded by the user and related to adherence. In 2016, Santo et al defined 5 desirable features in the output of adherence apps: tracking history, charts, statistics, rewards, and an exportable file. With this, a reference point to evaluate outputs of adherence apps was delivered. Identifying and fulfilling users' needs are essential when developing an adherence app for patients' self-management and professional adherence services, such as therapy support provided by health care professionals (HCPs). OBJECTIVE: We aimed to investigate the smartphone app market regarding desirable features in the outputs, explore the users' needs, and evaluate the concordance. METHODS: We searched for smartphone adherence apps in the 2 largest commercial app repositories by using keywords. Search results were screened for eligibility by applying inclusion and exclusion criteria. Eligible, freely available apps were tested regarding desirable features in their output. We conducted 2 focus groups and a cross-sectional online survey to explore users' needs. Survey participants rated their desire for features on a 7-point Likert scale. Focus groups were analyzed using the previously reported framework method. Descriptive statistics were calculated by median and IQR or mean and SD. We compared survey subgroups with a 2-tailed t test. A P value <.05 was considered statistically significant. RESULTS: We screened 80 apps for eligibility and included 9 in our analyses. All desirable features were present, with tracking history being the most frequent feature (in 8/9 apps). Other desirable features were observed in 3 or fewer of the apps. Eight individuals participated in the 2 focus groups. During the focus groups, a total of 13 categories of desired features emerged. All 5 desirable features were rated as important in adherence apps. Three additional features were mentioned: (1) professional feedback regarding therapy or intake course, (2) additional recommendations based on intake course, and (3) option to discuss the data with an HCP. A total of 42 individuals participated in the online survey. Tracking history was the most desired (mean rating of 5.29) and rewards the least desired feature (mean rating of 2.81) in the output. There was ambivalence regarding professional feedback, statistics, and charts. Participants with or without regular medication use showed no significant differences. CONCLUSIONS: The outputs delivered by freely available smartphone adherence apps only partly match users' needs. Users showed a special interest in the interpretation of their data with an HCP. Therefore, adherence apps cannot substitute for the HCP but can be used to enhance current patient care.
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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 | 0,037 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».