Meeting the Needs of Emerging Adults With Type 1 Diabetes Living in a Rural Area With Mobile Health Interventions: Focus Group Study
Notice bibliographique
Résumé
BACKGROUND: Emerging adults (EAs; age 18-30 years) with type 1 diabetes (T1D) have more challenges with diabetes management and glycemic control than other age groups. Living in a rural community introduces additional unique diabetes care challenges due to limited access to specialty care and ancillary support services. Yet, few interventions have been developed to improve diabetes management in rural-dwelling EAs with T1D. OBJECTIVE: This study aimed to understand the diabetes management experiences of older adolescents and EAs (age 16-25 years) with T1D living in a rural area and to assess their perceptions of the acceptability of 4 fully automated mobile health (mHealth) interventions to support diabetes management. METHODS: EAs were identified by clinical staff through convenience sampling. In total, 8 EAs participated in 1 focus group and 1 EA completed an individual interview; all data were collected over Zoom. Facilitators explored EAs' experiences living in a rural community with T1D and discussed EAs' impressions of, feedback on, and recommendations for improving 4 mHealth interventions to meet the specific needs of EAs with T1D living in rural communities. Discussions were transcribed and analyzed using conventional content analysis. RESULTS: In total, 9 EAs (aged 18.8, SD 2.7 years; 5, 56% men; 8, 89% White) with a duration of diabetes of 8.6 (SD 4.3) years participated. They described experiences with diabetes stigma (attributing diabetes to poor lifestyle choices) and feelings of self-consciousness (hyperawareness) in their rural communities. They attributed these experiences to the small size of their communities ("everyone knows") and community members' lack of knowledge about diabetes (unable to differentiate between type 1 and type 2 diabetes). In contrast, EAs reported high levels of social support for diabetes and diabetes care from family, friends, and other community members, but low support for medical needs. The location of their diabetes care providers and the limited accessibility of diabetes-specific and general medical care services in their local community created a challenging medical care context. Overall, EAs found mHealth interventions appealing due to their digital delivery and highlighted features that increased accessibility (voiceovers and simple, jargon-free language), individualization (ability to tailor intervention content and delivery), and applicability to their own lives and other EAs with T1D (relatability of vignettes and other content). EAs suggestions for improving the interventions included more opportunities to tailor the interventions to their preferences (greater frequency and duration, ability to adapt content to emerging needs), increasing opportunities for peer support within the interventions (friend and significant other as identified support person, connecting with peers beyond their local community), and making the tone of intervention components more casual and engaging. CONCLUSIONS: mHealth interventions aligned with EAs' needs and preferences are a promising strategy to support EAs in communities where social support and resources might be limited. TRIAL REGISTRATION: N/A, not a clinical trial.
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 | 0,004 | 0,004 |
| 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 | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».