Co-Design and Development of the SmilesUp Text Messaging Intervention Using Behavioral Theory to Support Parents of Children With Early Childhood Caries: Mixed Methods Study
Notice bibliographique
Résumé
Background: Early childhood caries (ECC) remains a common childhood condition that affects 600 million children worldwide. Providing parents with support for oral health behavior change can address ECC risk factors and complement preventive clinical care. Mobile health (mHealth) text message programs that are co-designed and evaluated by parents and health professionals using behavior theory have been shown to be effective in improving oral health outcomes. Objective: This study aimed to describe the co-design process, development, and content evaluation of a text message program designed to promote oral health behavior change among parents of children diagnosed with ECC using the Behavior Change Wheel (BCW) framework. Methods: The SmilesUp mHealth program was co-designed with parents in 2 stages using the BCW, a widely used theoretical framework to underpin mHealth programs, recommended by the World Health Organization. Through focus groups with parents in phase 1, the BCW was used to understand parental perspectives by identifying barriers and enablers and selecting target behaviors that could be feasibly delivered within a mHealth intervention. Barriers and enablers were mapped to the relevant theoretical domains and behavior change technique (BCT) of the BCW. Phase 2 evaluated content acceptability, measured by understandability, usefulness, and appropriateness of the program through questionnaires with parents and health professionals. Highly rated messages were finalized into an algorithm for the SMS text message program. Results: In phase 1, the overall target behavior was parental behavior change to support good oral health, including oral hygiene, reduced dietary sugar intake, and bedtime routines for their children. The 5 intervention functions focused on education, modeling, persuasion, environmental restructuring, and enablement, and 16 BCTs focused on addressing the motivational enablers and knowledge gap barriers identified by the parents. A total of 111 draft health messages were developed and mapped to the BCTs. In phase 2, a total of 2045 reviews of the 111 draft messages were completed by parents (14/31, 45.2%) and health professionals (17/31, 54.8%). Parents rated 77.4% (86/111) and health professionals rated 61.2% (68/111) of the messages as understandable, useful, and accepted. The messages that were considered understandable, useful, and appropriate by both groups were incorporated into the SmilesUp 12-week semipersonalized SMS message program. Conclusions: The SmilesUp mHealth program uses behavioral theory to address knowledge gaps in tooth brushing, diet, and bedtime routines identified by parents. It provides parents with convenient, bite-sized nudges of information to support oral health-promoting behaviors in the home context. Robust content development and evaluation are crucial initial steps before further investments are made to conduct a clinical trial to assess the effectiveness of the program.
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 | 0,019 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| 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 ».