A Smartphone App to Increase Immunizations in the Pediatric Solid Organ Transplant Population: Development and Initial Usability Study
Bibliographic record
Abstract
BACKGROUND: Vaccine-preventable infections result in significant morbidity, mortality, and costs in pediatric transplant recipients. However, at the time of transplant, less than 20% of children are up-to-date for age-appropriate immunizations that could prevent these diseases. Smartphone apps have the potential to increase immunization rates through their ability to provide vaccine education, send vaccine reminders, and facilitate communication between parents and a multidisciplinary medical group. OBJECTIVE: The aim of this study was to describe the development of a smartphone app, Immunize PediatricTransplant, to promote pretransplant immunization and to report on app functionality and usability when applied to the target population. METHODS: We used a mixed methods study design guided by the Mobile Health Agile Development and Evaluation Lifecycle. We first completed a formative research including semistructured interviews with transplant stakeholders (12 primary care physicians, 40 parents or guardians of transplant recipients, 11 transplant nurse coordinators, and 19 transplant subspecialists) to explore the acceptability of an immunization app to be used in the pretransplant period. Based on these findings, CANImmunize Inc developed the Immunize PediatricTransplant app. We next held 2 focus group discussions with 5-6 transplant stakeholders/group (n=11; 5 parents of transplant recipients, 2 primary care physicians, 2 transplant nurse coordinators, and 2 transplant subspecialists) to receive feedback on the app. After the app modifications were made, alpha testing was conducted on the functional prototype. We then implemented beta testing with 12 stakeholders (6 parents of transplant recipients, 2 primary care doctors, 2 transplant nurse coordinators, and 2 transplant subspecialists) to refine the app through an iterative process. Finally, the stakeholders completed the user version of the Mobile Application Rating Scale (uMARS) to assess the functionality and quality of the app. RESULTS: A new Android- and Apple-compatible app, Immunize PediatricTransplant, was developed to improve immunization delivery in the pretransplant period. The app contains information about vaccine use in the pretransplant period, houses a complete immunization record for each child, includes a communication tool for parents and care providers, and sends automated reminders to parents and care providers when immunizations are due. During usability testing, the stakeholders were able to enter a mock vaccine record containing 16 vaccines in an average of 8.1 minutes (SD 1.8) with 87% accuracy. The stakeholders rated engagement, functionality, aesthetics, and information quality of the app as 4.2/5, 4.5/5, 4.6/5, and 4.8/5, respectively. All participants reported that they would recommend this app to families and care teams with a child awaiting solid organ transplant. CONCLUSIONS: Through a systematic, user-centered, agile, iterative approach, the Immunize PediatricTransplant app was developed to improve immunization delivery in the pretransplant period. The app tested well with end users. Further testing and agile development among patients awaiting transplant are needed to understand real-world acceptability and effectiveness in improving immunization rates in children awaiting transplant.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".