Feasibility, Acceptability, and Effectiveness of a Smartphone App to Increase Pretransplant Vaccine Rates: Usability Study
Bibliographic record
Abstract
Background: Vaccine-preventable infections result in significant morbidity, mortality, and costs in pediatric transplant recipients. Despite intensive medical care in the pretransplant period, less than 20% of children are up to date for age-appropriate vaccines at the time of transplant. Mobile health apps have the potential to improve pretransplant vaccine rates. Objective: This paper aimed to perform phase 2 beta testing of the smartphone app, Immunize PediatricTransplant, to determine (1) if it was effective in achieving up-to-date vaccine status by the time of transplant in a cohort of children awaiting transplants and (2) if the app was feasible and acceptable to parent and transplant provider users. Methods: We recruited 25 dyads of parents and providers of a child awaiting a liver, kidney, or heart transplant at Children's Hospital Colorado, Ann and Robert H. Lurie Children's Hospital, and the Children's Hospital of Philadelphia. Parents and providers filled out an entry questionnaire before app use to gather baseline information. A research team member entered the child's vaccine records into the app. The parent and provider downloaded and used the app until the transplant to view vaccine records, read vaccine education, communicate with team members, and receive overdue vaccine reminders. After the transplant (or on April 1, 2024, the conclusion of the study), the parent and provider filled out an exit questionnaire to explore feasibility and acceptability of the app. The child's vaccine records were reviewed to determine if the child was up to date on vaccines at the time of transplant. Results: Twenty-five parent and provider dyads were enrolled; 56% (14/25) had a child awaiting a liver transplant, 28% (7/25) had a child awaiting a kidney transplant, and 16% (4/25) had a child awaiting a heart transplant. At the conclusion of the study, 96% (24/25) of the children were up to date on vaccines. Of the 36 parents and providers who filled out an exit questionnaire, 97% (n=35) agreed or strongly agreed that they felt knowledgeable about pretransplant vaccine use and 86% (n=31) agreed or strongly agreed that communication around vaccines was good after using the app. Further, 91% (20/22) of parents and 79% (11/14) of providers recommended the app to future parents and providers of transplant candidates. Parents and providers suggested that in the future the app should connect directly to the electronic medical record or state vaccine registries to obtain vaccine data. Conclusions: The overwhelming majority of children whose parents and providers used the Immunize PediatricTransplant app were up to date on vaccines at the time of transplant. The majority of app users felt the app was feasible and acceptable. In future iterations of the app and subsequent clinical trials, we will explore whether application programming interfaces might be used to extract vaccine data from the electronic medical record. If implemented broadly, this app has the potential to improve pretransplant vaccine rates, resulting in fewer posttransplant infections and improved posttransplant outcomes.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".