Usability Challenges for Health and Wellness Mobile Apps: Mixed-Methods Study Among mHealth Experts and Consumers
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Notice bibliographique
Résumé
BACKGROUND: By 2019, there will be an estimated 4.68 billion mobile phone users globally. This increase comes with an unprecedented proliferation in mobile apps, a plug-and-play product positioned to improve lives in innumerable ways. Within this landscape, medical apps will see a 41% compounded annual growth rate between 2015 and 2020, but paradoxically, prevailing evidence indicates declining downloads of such apps and decreasing "stickiness" with the intended end users. OBJECTIVE: As usability is a prerequisite for success of health and wellness mobile apps, this paper aims to provide insights and suggestions for improving usability experience of the mobile health (mHealth) app by exploring the degree of alignment between mHealth insiders and consumers. METHODS: Usability-related major themes were selected from over 20 mHealth app development studies. The list of themes, grouped into 5 categories using the Nielsen usability model, was then used as a framework to identify and classify the responses from mHealth expert (insider) interviews. Responses from the qualitative phase were integrated into some questions for a quantitative consumer survey. Subsequently, categorical data from qualitative mHealth insider interviews and numerical data from a quantitative consumer survey were compared in order to identify common usability themes and areas of divergence. RESULTS: Of the 5 usability attributes described in Nielsen model, Satisfaction ranked as the top attribute for both mHealth insiders and consumers. Satisfaction refers to user likability, comfort, and pleasure. The consumer survey yielded 451 responses. Out of 9 mHealth insiders' top concerns, 5 were similar to those of the consumers. On the other hand, consumers did not grade themes such as Intuitiveness as important, which was deemed vital by mHealth insiders. Other concerns of the consumers include in-app charges and advertisements. CONCLUSIONS: This study supports and contributes to the existing pool of mixed-research studies. Strengthening the connectivity between suppliers and users (through the designed research tool) will help increase uptake of mHealth apps. In a holistic manner, this will have a positive overall outcome for the mHealth app ecosystem.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 découle