A Cluster Randomized Trial of an mHealth Intervention for Adolescents With Congenital Heart Disease: Rationale and Design of the READYorNot CHD Study
Bibliographic record
Abstract
The population of adolescents with congenital heart disease (CHD) is growing exponentially and requires transition preparation for adult-oriented health care. Nurse-led transition programs are effective in improving CHD knowledge and self-management skills. However, many clinical programs lack the human resources needed to provide transition services. Mobile health applications have the potential to prepare transition-age youth for entering adult health care. However, there are no outcome data on the impact and effectiveness of CHD transition applications. Accordingly, in partnership with a Youth Advisory Council, we developed the MyREADY Transition CHD App (the App) designed to enhance youth CHD knowledge and self-management skills. The READYorNot CHD study is a multicenter, cluster randomized noninferiority clinical trial that is evaluating the efficacy of the App plus limited nurse teaching (intervention), vs comprehensive nurse-only teaching (control) for 16- to 17-year-olds with moderate or complex CHD. Participants are being enrolled in clusters based on week of attendance in the pediatric cardiology clinic, with a 1:1 allocation between intervention vs control and target recruitment of 204 participants. The primary outcome is the change in Transition Readiness Assessment Questionnaire score from baseline to 18 months. Secondary outcomes are change in CHD knowledge score, self-efficacy, and time to first adult CHD appointment. Semistructured interviews will provide additional insights into the advantages and disadvantages of the App vs nurse-only teaching. This study will inform patients, pediatric cardiology programs, and policy makers in judging whether this mobile health intervention warrants widespread availability in clinical settings to improve transition outcomes of adolescents with CHD. Clinical Trial Registration: NCT04463446.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".