“I Just Wanted a Dentist in My Phone”—Designing Evidence-Based mHealth Prototype to Improve Preschool Children’s Oral and Dental Health: Multimethod Study of the Codevelopment of an App for Children’s Teeth
Notice bibliographique
Résumé
BACKGROUND: Dental caries in preschool children is a global health concern. With increased access to technology and the disruption of health care during the pandemic, mobile health apps have been of interest as potential vehicles for individuals' health maintenance. However, little is known about caring for their child's teeth and what their preferences would be regarding the content or design of an oral health app. OBJECTIVE: This study aims to co-design the prototype of an app named App for Children's Teeth with parents, providing a source of information for them about caring for their children's teeth and promoting positive dental habits. METHODS: This multimethod study conducted user involvement research with a purposive sample of parents or carers of children aged ≤6 years to (1) understand their use of the internet through the eHealth Literacy Scale and interviews, (2) determine their opinions about content related to children's oral health, and (3) collect feedback about the app's acceptability using the Theoretical Framework of Acceptability. There were three stages: (1) interviews with parents to understand their needs, preferences, and abilities; (2) prototype design with app developers; and (3) parent feedback interviews using the think aloud method for data collection. Data were deductively analyzed using a codebook strategy, whereas data from the think aloud sessions were analyzed inductively using reflexive thematic analysis. RESULTS: The prototype design stage involved 10 parents who reported using the internet for health information but found it to be scattered and contradictory. Parents generally welcomed the App for Children's Teeth but expressed concerns about screen time and practicality. They suggested guidance regarding oral hygiene practices, teething symptoms, and pain relief. Parents appreciated features such as clear fonts, categorization according to their child's age, and "In a Nutshell" bullet points. Topics that resonated with parents included information about teething, finding a dentist, and breastfeeding. They believed that the app aligned with their goals and offered suggestions for future developments, such as outlining the process of finding a dentist and incorporating a forum for parents to communicate and exchange ideas. CONCLUSIONS: The coproduction design approach highlighted parents' need for solutions such as mobile health apps to access reliable information about oral health. Parents identified key design concepts for the app, including a simple and uncluttered interface, content categorization according to their child's age, and practical guidance supported by visual aids. Despite potential challenges related to screen time restrictions, parents provided insights into how such an app could fit seamlessly into their lives. TRIAL REGISTRATION: Open Science Framework; https://osf.io/uj9az.
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 | 0,015 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 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) | 0,002 | 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 ».