A Smartphone App to Increase Immunizations in the Pediatric Solid Organ Transplant Population: Development and Initial Usability Study
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».