A Mobile Health Platform for Self-Management of Pediatric Cystic Fibrosis: Qualitative Study of Adaptation to Stakeholder Needs and Integration in Clinical Settings
Notice bibliographique
Résumé
BACKGROUND: Cystic fibrosis (CF) is an inherited chronic condition that requires extensive daily care and quarterly clinic visits with a multidisciplinary care team. The limited exchange of information outside of the quarterly clinic visits impedes optimal disease self-management, patient engagement, and shared decision making. OBJECTIVE: The aim of this study is to adapt a mobile health (mHealth) app originally developed in Sweden to the needs of patients, families, and health care providers in a CF center in the United States and to test it as a platform for sharing patient-generated health data with the CF health care team. METHODS: Focus groups with health care providers of patients with CF, adolescents with CF, and caregivers of children with CF were conducted to determine what modifications were necessary. Focus group data were analyzed using a thematic analysis, and emergent themes were ranked according to desirability and technical feasibility. The mHealth platform was then modified to meet the identified needs and preferences, and the flow of patient-generated health data to a secure Research Electronic Data Capture database was tested. Protocols for data management and clinical follow-up were also developed. RESULTS: A total of 5 focus groups with 21 participants were conducted. Recommended modifications pertained to all functionalities of the mHealth platform, including tracking of symptoms, treatments, and activities of daily care; creating and organizing medication lists and setting up reminders; generating reports for the health care team; language and presentation; sharing and privacy; and settings and accounts. Overall, health care providers recommended changes to align the mHealth platform with US standards of care, people with CF and their caregivers requested more tracking functionalities, and both groups suggested the inclusion of a mental health tracker as well as more detailed response options and precise language. Beta testers of the modified platform reported issues related to translatability to US environment and various bugs. CONCLUSIONS: This study demonstrated the importance of identifying the needs and preferences of target users and stakeholders before adopting existing mHealth solutions. All relevant perspectives, including those of clinicians, patients, and caregivers, should be thoroughly considered to meet both end users' needs and evidence-based practice recommendations.
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 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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».