A Voice App Design for Heart Failure Self-management: Proof-of-Concept Implementation Study
Notice bibliographique
Résumé
BACKGROUND: Voice user interfaces are becoming more prevalent in health care and are commonly being used for patient engagement. There is a growing interest in identifying the potential this form of interface has on patient engagement with digital therapeutics (DTx) in chronic disease management. Making DTx accessible through an alternative interaction model also has the potential to better meet the needs of some patients, such as older adults and those with physical and cognitive impairments, based on existing research. OBJECTIVE: This study aimed to evaluate how participants with heart failure interacted with a voice app version of a DTx, Medly, through a proof-of-concept implementation study design. The objective was to understand whether the voice app would enable the participants to successfully interact with the DTx, with a focus on acceptability and feasibility. METHODS: A mixed methods concurrent triangulation design was used to better understand the acceptability and feasibility of the use of the Medly voice app with the study participants (N=20) over a 4-week period. Quantitative data included engagement levels, accuracy rates, and questionnaires, which were analyzed using descriptive statistics. Qualitative data included semistructured interviews and were analyzed using a qualitative descriptive approach. RESULTS: The overall average engagement level was 73% (SD 9.5%), with a 14% decline between results of weeks 1 and 4. The biggest difference was between the average engagement levels of the oldest and youngest demographics, 84% and 43%, respectively, but these results were not significant-Kruskal-Wallis test, H(2)=3.8 (P=.14). The Medly voice app had an overall accuracy rate of 97.8% and was successful in sending data to the clinic. From an acceptability perspective, the voice app was ranked in the 80th percentile, and overall, the users felt that the voice app was not a lot of work (average of 2.1 on a 7-point Likert scale). However, the overall average score for whether users would use it in the future declined by 13%. Thematic analysis revealed the following: the theme feasibility of clinical integration had 2 subthemes, namely users adapted to the voice app's conversational style and device unreliability, and the theme voice app acceptability had 3 subthemes, namely the device integrated well within household and users' lives, users blamed themselves when problems arose with the voice app, and voice app was missing specific, desirable user features. CONCLUSIONS: In conclusion, participants were largely successful in using the Medly voice app despite some of the barriers faced, proving that an app such as this could be feasible to be deployed in the clinic. Our data begin to piece together the patient profile this technology may be most suitable for, namely those who are older, have flexible schedules, are confident in using technology, and are experiencing other medical conditions.
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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 | 0,014 | 0,019 |
| 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 | 0,002 | 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 ».