Exploring the Usability and Acceptability of a Well-Being App for Adolescents Living With Type 1 Diabetes: Qualitative Study
Notice bibliographique
Résumé
Background: Adolescents living with either type 1 diabetes (T1D) or type 2 diabetes (T2D) have an increased risk of psychological disorders due to the demands of managing a chronic illness and the challenges of adolescence. Psychological disorders during adolescence increase the risk of suboptimal glycemic outcomes and may lead to serious diabetes-related complications. Research shows that digital health interventions may increase access to psychological support for adolescents and improve physical and mental health outcomes for youth with diabetes. To our knowledge, there are no evidence-based, publicly available mental health apps with a focus on improving the psychological well-being of adolescents with diabetes. Objective: This study aimed to explore the acceptability and usability of our evidence-based well-being app for New Zealand adolescents, Whitu: 7 Ways in 7 Days (Whitu), to allow us to further tailor it for youth with diabetes. We interviewed adolescents with T1D and T2D, their parents, and health care professionals to explore their views on the Whitu app and suggestions for tailoring the app for adolescent with diabetes. We also explored the cultural acceptability of the Whitu app for Māori and Pacific adolescents. Methods: A total of 34 participants, comprising 13 adolescents aged 12-16 years (11 with T1D and 2 with T2D), 10 parents, and 11 health care professionals, were recruited from a specialist diabetes outpatient clinic and Facebook diabetes groups. Each participant attended one 1-hour focus group on Zoom, in person, or via phone. Researchers gathered general feedback on what makes an effective and engaging app for adolescents with diabetes, as well as specific feedback about Whitu. Transcribed audio recordings of the focus groups were analyzed using directed content analysis. Results: Adolescents with T1D, their parents, and health care professionals found Whitu to be acceptable and usable. Adolescents with T1D and their parents signaled a preference for more diabetes-specific content. Health care professionals expressed less awareness and trust of digital health interventions and, as such, recommended that they be used with external support. Due to challenges in recruitment and retention, we were unable to include the views of adolescents with T2D in this qualitative study. Conclusions: There appears to be sufficient openness to the use of an app such as Whitu for supporting the well-being of adolescents with T1D, albeit with modifications to make its content more diabetes specific. Based on this qualitative study, we have recently developed a diabetes-specific version of Whitu (called LIFT: Thriving with Diabetes). We are also planning a qualitative study to explore the views of youth with T2D and their perspectives on the new LIFT app, where we are using alternative research approaches to recruit and engage adolescents with T2D and their families.
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Comment cette classification a été obtenuedéplier
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».