Feasibility, Acceptability, and Effectiveness of a Smartphone App to Increase Pretransplant Vaccine Rates: Usability Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».