A Diabetes Education App for People Living With Type 2 Diabetes: Co-Design Study
Notice bibliographique
Résumé
BACKGROUND: Type 2 diabetes (T2D) is a growing global health concern, including in Singapore. Diabetes education programs have been shown to be effective in improving health outcomes and diabetes self-management skills. Mobile health apps have emerged as useful tools for diabetes education; however, their use and acceptance by the target population remain inconsistent. Therefore, end-user participation in the design and development of a mobile health app is crucial for designing an acceptable app that can improve outcomes for populations with a chronic disease. OBJECTIVE: The objective of this study was to apply an end-user participatory approach to co-design a diabetes education app prototype for people living with T2D by exploring their perceptions, acceptance, and usability of an app prototype, as well as their diabetes experience and perspectives on digital diabetes education. METHODS: A total of 8 people with T2D, who were recruited from diabetes management Facebook groups, participated in 4 web-based surveys via Qualtrics and 2 structured interviews via Zoom (Zoom Video Communications, Inc) between August 20, 2021, and January 28, 2022. Descriptive statistics and thematic analyses of the discussion and iterative feedback on the app prototype were used to assess the participants' perceptions of living with T2D, attitudes toward digital diabetes education, and acceptance of the prototype. RESULTS: Analyses of the surveys and interview data revealed 3 themes: challenges of living with T2D; validation, acceptability, and usability of the diabetes education app prototype; and perspectives on digital diabetes education. In the first theme, participants highlighted the importance of solitary accountability, translating knowledge into practice, and developing pragmatic self-consciousness. The second theme indicated that the diabetes education app prototype was acceptable, with information and appearance being key; revealed ambivalent and polarized opinions toward the chatbot; and confirmed potential impact of the app on diabetes self-management skills and practice. The third theme comprised the necessity of using a variety of information-seeking strategies and recommendations for desired content and app qualities, including accessibility, adaptability, autonomy, evidence-based design and content, gamification, guidance, integration, personalization, and up-to-date content. The findings were used to reiterate the app design. CONCLUSIONS: Despite a small sample size, the study demonstrated the feasibility of engaging and empowering people living with T2D to consider digital therapeutics for diabetes self-management skills and practice. Participants gave rather positive feedback on the design and content of the app prototype, with some recommendations for improvements. The findings suggest that incorporating end-user feedback into app design can lead to the creation of feasible and acceptable tools for diabetes education, potentially improving outcomes for populations with a chronic disease. Further research is needed to test the impact of the refined diabetes education app prototype on diabetes self-management skills and practice and quality of life.
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 | 0,026 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».