Design, Development, and Usability Evaluation of a Voice App Experience for Heart Failure Management (Preprint)
Notice bibliographique
Résumé
BACKGROUND The use of digital therapeutics (DTx) in the prevention and management of medical conditions has increased through the years with an estimated 44 million people using one as part of their treatment plan in 2021, nearly double the amount from last year. DTx are commonly accessed through smartphone apps, but offering these treatments through additional platforms can improve the accessibility of these interventions. Voice apps are an emerging technology in the digital health field; not only have they the potential to improve DTx adherence but also can create a better user experience for some user groups. OBJECTIVE This research aimed to identify the acceptability and feasibility of offering a voice app for a chronic disease self-management program. The objective of this project was to design, develop, and evaluate a voice app of an already existing smartphone-based heart failure self-management program, Medly, to be used as a case study. METHODS A voice app version of Medly was designed and developed through a user-centered design process. We conducted a usability study and semi-structured interviews with representative end users (n=8) at the Peter Munk Cardiac Clinic in Toronto General Hospital to better understand the user experience. A Medly voice app prototype was built using a software development kit in tandem with a cloud computing platform. The voice app was verified prior to the usability study and validated through user interaction and feedback. Both quantitative and qualitative data was collected and analyzed using a mixed methods triangulated convergence design. RESULTS Saturation was successfully achieved with the usability study, which involved eight heart failure participants. Almost all (7 out of the 8) participants were satisfied with the voice app and felt confident using it, although half of the participants were unsure about using it in the future. Six main themes were identified: changes in physical behaviour, preference between voice app and smartphone, importance of music during voice app interaction, lack of privacy concerns, desired reassurances during voice app interaction, and helpful aids during voice app interaction. These findings were triangulated with the quantitative data and concluded the main area for improvement was related to ease of use; design changes were then implemented to better improve the user experience. CONCLUSIONS This work offers preliminary insight into the acceptability and feasibility of a Medly voice app. We believe that offering Medly on multiple platforms (smartphone, voice user interface device) will not only increase user uptake, but also allow some patients to more easily interact with the program. With rapid advancements in voice user interfaces, we believe voice apps will play an integral role when providing access to DTx for chronic disease 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 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 | 0,010 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».