Improving Transitions of Care for Young Adults With Congenital Heart Disease: Mobile App Development Using Formative Research
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".