Benefits and Barriers of Caregiver App Engagement for Supporting Diverse Children With Asthma: Mixed Methods Study
Notice bibliographique
Résumé
Background: Asthma is one of the most common pediatric conditions affecting millions of US children. Digital health apps may provide children and their caregivers (parents or legal guardians) with ways to manage asthma and improve health and educational outcomes. Objective: As digital health technology becomes more prevalent to help manage chronic conditions, like asthma, this study examined the reported benefits and barriers of caregiver interactions with an asthma-specific app. The app, created by physicians and digital health development professionals, was designed to educate, inform, and help caregivers manage the health of their child. We evaluated app logins and feature use (collectively defined as "app engagement") for caregivers of children with asthma aged 5-11 years. We examined whether (1) app engagement differed due to children's demographic and asthma health characteristics, (2) themes about app engagement emerged from caregiver-reported app experiences, (3) these themes correlated with demographic and asthma health characteristics, and (4) engagement with the app was associated with reduced school absences. Methods: Eighty caregivers and their children with asthma participated between September 2019 and November 2020. Pretest (Time 1) and posttest (Time 2) data were collected over 6 months on caregiver and child demographic and health characteristics, health care usage, app engagement, and app experiences. Additionally, caregiver app engagement data and child health care data were collected retrospectively, 2 years prior to the start of the study. We used a mixed methods design, which included correlation, regression, chi-square, and content analysis to examine caregiver app engagement. Results: Most caregivers were mothers (76/80, 95%) and had a college degree (45/80, 56%). Children's mean age was 8.76 (SD 1.79), and all were English speakers (80/80, 100%). About half of the participants were White children (43/80, 54%), and 26% (21/80) of them had uncontrolled asthma. Logistic regression revealed that caregivers of White children (OR [odds ratio] 8.57, 95% CI 1.68-43.65) with uncontrolled asthma (OR 17.81, 95% CI 2.36-134.24) who earned a college degree (OR 6.94, 95% CI 1.38-34.87) were statistically significantly more likely to use the app than caregivers of children of other races with controlled asthma without a college degree (P<.001). Qualitative findings support and expand on the logistic regression results. Five themes regarding app engagement emerged, including relevancy, acceptability and understandability, technology limitations, educational barriers, and information and communication benefits. Caregivers also identified specific app features that may promote child health and education. Conclusions: Understanding caregiver and child experiences in using digital health technologies for managing asthma may inform ways to support app engagement among caregivers and their children in the effort to improve patient health outcomes.
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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 | 0,017 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».