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Enregistrement W2981337582 · doi:10.2196/15146

Usability and Utility of a Mobile App to Improve Medication Adherence Among Ambulatory Care Patients in Malaysia: Qualitative Study

2019· article· en· W2981337582 sur OpenAlexvenueno aff
Sara Chew, Pauline Siew Mei Lai, Chirk Jenn Ng

Notice bibliographique

RevueJMIR mhealth and uhealth · 2019
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensnon disponible
Organismes subventionnairesUniversiti Malaya
Mots-clésUsabilityMedicineSystem usability scalemHealthQualitative researchHealth careFamily medicineNursingWeb usabilityPsychological interventionComputer science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: To date, several medication adherence apps have been developed. However, the existing apps have been developed without involving relevant stakeholders and were not subjected to mobile health app guidelines. In addition, the usability and utility of these apps have not been tested with end users. OBJECTIVE: This study aimed to describe the usability and utility testing of a newly developed medication adherence app-Med Assist-among ambulatory care patients in Malaysia. METHODS: The Med Assist app was developed based on the Theory of Planned Behavior and the Nielson usability model. Beta testing was conducted from March to May 2016 at a primary care clinic in Kuala Lumpur. Ambulatory care patients who scored ≥40% on the electronic health literacy scale, were aged ≥21 years, and were taking two or more long-term medications were recruited. Two rounds of in-depth interviews were conducted with each participant. The first interview, which was conducted upon participant recruitment, was to assess the usability of Med Assist. Participants were asked to download Med Assist on their phone and perform two tasks (register themselves on Med Assist and enter at least one medication). Participants were encouraged to "concurrently think aloud" when using Med Assist, while nonverbal cues were observed and recorded. The participants were then invited for a second interview (conducted ≥7 days after the first interview) to assess the utility of Med Assist after using the app for 1 week. This was done using "retrospective probing" based on a topic guide developed for utilities that could improve medication adherence. RESULTS: Usability and utility testing was performed for the Med Assist app (version P4). A total of 13 participants were recruited (6 men, 7 women) for beta testing. Three themes emerged from the usability testing, while three themes emerged from the utility testing. From the usability testing, participants found Med Assist easy to use and user friendly, as they were able to complete the tasks given to them. However, the details required when adding a new medication were found to be confusing despite displaying information in a hierarchical order. Participants who were caregivers as well as patients found the multiple-user support and pill buddy utility useful. This suggests that Med Assist may improve the medication adherence of patients on multiple long-term medications. CONCLUSIONS: The usability and utility testing of Med Assist with end users made the app more patient centered in ambulatory care. From the usability testing, the overall design and layout of Med Assist were simple and user friendly enough for participants to navigate through the app and add a new medication. From the participants' perspectives, Med Assist was a useful and reliable tool with the potential to improve medication adherence. In addition, utilities such as multiple user support and a medication refill reminder encouraged improved medication management.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,033
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,047
Tête enseignante GPT0,472
Écart entre enseignants0,425 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations41
Publié2019
Routes d'admission1
Résumé présentoui

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