Developing eHealth Interventions to Improve Diabetes Management in Emerging Adulthood: Qualitative Formative Study
Notice bibliographique
Résumé
Background: Emerging adulthood is a high-risk period during which many with type 1 diabetes demonstrate suboptimal diabetes management and glycemic control. There is a need for effective, scalable interventions designed specifically for this population. Technology-based approaches are readily accessible to this age group. Furthermore, interventions consistent with self-determination theory-which posits that the fulfillment of psychological needs for autonomy, self-efficacy, and relatedness promotes intrinsic motivation for change-may resonate well with emerging adults' developmental needs for establishing independence and autonomy, and growing their social network. Objective: This study aimed to enhance the potential relevance, sustainability, and efficacy of 3 self-determination theory-informed mobile health intervention components and content for emerging adults with type 1 diabetes. Key areas of interest included emerging adults' perspectives on the use of cultural tailoring, developmental relevance of content, and delivery preferences. Methods: In this qualitative formative study, 20 emerging adults reviewed and provided feedback on 3 newly developed intervention components via individual interviews. Ten reviewed the motivation enhancement system, a 2-session counseling intervention grounded in motivational interviewing and designed to enhance emerging adults' autonomy and self-efficacy for diabetes self-management. Ten reviewed the SMS text messaging reminder intervention (one-way text message reminders to complete diabetes care) and the question prompt list (a list of questions related to diabetes care designed to increase patients' active participation during medical visits). Interviews were analyzed using framework matrix analysis, an efficient approach to inductive thematic analysis. Results: Emerging adults found all 3 interventions acceptable and helpful. They noted the interventions' integration into the technology they already use as a strength. Across interventions, emerging adults also expressed a preference for culturally tailored intervention content, including intervention examples, actors, and language representing their illness experience, identity, and personal preferences. Intervention-specific feedback suggested emerging adults liked motivation enhancement system intervention elements that were engaging (videos) and relatable (peer testimonials), and supported their growing autonomy and independence. For SMS text messaging reminders, emerging adults appreciated the straightforward nature of the reminders and recommended more directive messages. They appreciated the range of topics and variety of messages. Suggestions included making the messages more impactful (eg, direct, personalized, and engaging, such as using emojis). Emerging adults saw the question prompt list content areas as relevant and well-aligned with their concerns highlighting the topic of transitioning to adult life with diabetes as particularly salient. Conclusions: Emerging adult feedback supports the acceptability and use of these intervention components and will be used to refine the interventions. Feedback was especially positive regarding cultural and other tailoring efforts, as well as content directed at their pending transition to full independence. At the same time, their input suggests the need for multiple specific modifications, highlighting the importance of intensive and detailed feedback from end users.
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 | 0,021 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».