Improving Transitions of Care for Young Adults With Congenital Heart Disease: Mobile App Development Using Formative Research
Notice bibliographique
Résumé
Background: Congenital heart diseases (CHDs) are the most common type of birth defects. Improvements in CHD care have led to approximately 1.4 million survivors reaching adulthood. Successful transition and transfer from pediatric to adult care is crucial. Unfortunately, less than 30% of adolescents with CHD successfully transition to adult care; this number is lower for minority and lower socioeconomic status populations. Few CHD programs exist to facilitate successful transition. Objective: The goal of our study was to describe the formative research used to develop a prototype mobile app to facilitate transition to adult care for adolescents with CHD. Methods: A literature search about best practices in transition medicine for CHD was conducted to inform app development. Formative research with a diverse group of CHD adolescents and their parents was conducted to determine gaps and needs for CHD transition to adult care. As part of the interview, surveys assessing transition readiness and CHD knowledge were completed. Two adolescent CHD expert panels were convened to inform educational content and app design. Results: The literature review revealed 113 articles, of which 38 were studies on transition programs and attitudes and 3 identified best practices in transition specific to CHD. A total of 402 adolescents aged 15 to 22 years (median 16 years) participated in semistructured interviews. The group was racially and ethnically diverse (12.6% [51/402] African American and 37.8% [152/402] Latino) and 42.0% (169/402) female; 36.3% (146/402) received public insurance. Most adolescents (313/402, 76.7%) had moderate or severe CHD complexity and reported minimal CHD understanding (79.0% [275/348] of those aged 15 to 17 years and 61.1% [33/54] of those aged 18 to 22 years). Average initial transition readiness score was 50.9/100, meaning that transition readiness training was recommended. When participants with moderate to severe CHD (313/402, 77.9%) were asked about technology use, 94.2% (295/313) reported having access to a mobile phone. Interviews with parents revealed limited interactions with the pediatric cardiologist about transition-related topics: 79.4% (331/417) reported no discussions regarding future family planning, and 55.2% (230/417) reported the adolescent had not been screened for mental health concerns (depression, anxiety). Further, 66.4% (277/417) reported not understanding how health care changes as adolescents become adults. Adolescents in the expert panels (2 groups of 3 adolescents each) expressed interest in a CHD-specific tailored app consisting of quick access to specific educational questions (eg, "Can I exercise?"), a CHD story-blog forum, a mentorship platform, a question and answer space, and a checklist to facilitate transition. They expressed interest in using the app to schedule CHD clinic appointments and receive medication reminders. Based on this data, a prototype mobile app was created to assist in adolescent CHD transition. Conclusions: Formative research revealed that most adolescents with CHD had access to mobile phones, were not prepared for transition to adult care, and were interested in an app to facilitate transition to adult CHD care. Understanding adolescent and parent needs, interests, and concerns helped in the development of a mobile app with a broader, tailored approach for adolescents with CHD.
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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 | 0,048 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».