Effectiveness of the User-Centered “Healthcare CEO” App for Patients With Type 1 Diabetes Transitioning From Adolescence to Early Adulthood: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Young patients aged 16 to 25 years with type 1 diabetes (T1D) often encounter challenges related to deteriorating disease control and accelerated complications. Mobile apps have shown promise in enhancing self-care among youth with diabetes. However, inconsistent findings suggest that further evidence is necessary to confirm the effectiveness of app-based interventions. OBJECTIVE: This study aims to evaluate the effectiveness of the Healthcare CEO app in patients with T1D transitioning from adolescence to early adulthood. METHODS: A 2 arms, double-blind, randomized controlled trial will be conducted over a 9-month period, with strategies designed to enhance treatment fidelity. The study expects to enroll 96 patients with T1D, aged 16 to 25 years. Participants will be randomly assigned to either the experimental or control group through central randomization. The intervention will be implemented using the Healthcare CEO app, which consists of 11 interfaces. The research will compare differences in disease control outcomes, confidence in self-management, self-care behaviors, emotional distress, quality of life, and specific diabetes-related knowledge between the 2 groups at baseline and 3, 6, and 9 months after intervention. Additionally, changes within the experimental group will be analyzed before and after the intervention. RESULTS: The study was funded in August 2020. It was originally scheduled from August 2020 to July 2022 but was interrupted by the COVID-19 pandemic after enrolling 38 participants, with preliminary results anticipated for publication by November 2024. Recruitment resumed in August 2023, with findings expected to be finalized by July 2025. CONCLUSIONS: The Healthcare CEO app is a comprehensive solution tailored specifically for individuals with T1D transitioning from adolescence to early adulthood. This innovative app has the potential to improve the quality of care for adolescents with T1D during this critical stage and may serve as valuable evidence in support of app-based intervention strategies. TRIAL REGISTRATION: ClinicalTrials.gov NCT05022875; https://www.clinicaltrials.gov/study/NCT05022875. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59871.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.010 |
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".