mHealth Apps in the Digital Marketplace for Pediatric Patients With Cancer: Systematic Search and Analysis
Notice bibliographique
Résumé
Background: The substantial increase in smartphone ownership has led to a rise in mobile health (mHealth) app use. Developing tailored features through mHealth apps creates a pathway to address the health care needs of pediatric patients with cancer and their families who have complex care needs. However, few apps are designed specifically to integrate with pediatric cancer care. Objective: This study reports a systematic search and analysis of mHealth apps available on the Apple App (iOS) and Google Play (Android) stores designed for pediatric cancer through a list of features that serve (1) patients, (2) caregivers, or (3) both audiences. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we reviewed apps for pediatric patients with cancer and caregivers available as of January 30, 2024. We searched the Apple App and Google Play stores with a list of keyword combinations focusing on pediatric cancer care. The inclusion criteria were (1) specifically apps targeted toward pediatric patients with cancer, their families, or both; (2) available in either app store; and (3) available in English. Apps were assessed using the Mobile Application Rating Scale (MARS). The MARS is a quality assessment for mHealth apps, including components of engagement, functionality, aesthetics, and informational quality (5-point Likert scale items-1: low and 5: high quality). Results: In total, 22 apps were identified and 17 of those apps were available on both platforms. The most popular features (n=12) were resource sharing, symptom tracking, reminders, care team connections, journaling, community support, medication tracking, data visualizations, and appointment tracking. Features and interfaces were designed for caregivers (n=9) more frequently than the patients (n=7) while a subset of apps created options for both users (n=6). A total of 16 apps received positive reviews (mean 4.4, SD 0.59; Min=3.1, Max=5.0). A small subset (n=3) achieved over 5000 downloads; however, the majority (n=15) had fewer than 500. More than half (n=12) of the apps were not available in English. Apps requested access to a range of device functionalities to operate (mean 2.72, SD 3.13; Min=0, Max=10). Out of 22, a total of 17 apps were publicly accessible. The mean MARS scores for the apps ranged from 1.71 (SD 0.75) to 4.33 (SD 0.82). Overall, apps scored high on functionality (mean 3.72, SD 0.54) but low on engagement (mean 3.02, SD 0.93). Conclusions: Our review highlights the promising yet underdeveloped potential of mHealth apps in pediatric oncology care, underscoring the need for more inclusive, comprehensive, and integrative digital health solutions. Future developments should actively involve key stakeholders from the pediatric oncology community, including patients, families, and health care professionals, to ensure the apps meet specific needs while addressing linguistic and cultural barriers.
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 | 0,010 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,009 |
| Bibliométrie | 0,018 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».