App-Based Salt Reduction Intervention in School Children and Their Families (AppSalt) in China: Protocol for a Mixed Methods Process Evaluation
Notice bibliographique
Résumé
BACKGROUND: The app-based salt reduction intervention program in school children and their families (AppSalt) is a multicomponent mobile health (mHealth) intervention program, which involves multiple stakeholders, including students, parents, teachers, school heads, and local health and education authorities. The complexity of the AppSalt program highlights the need for process evaluation to investigate how the implementation will be achieved at different sites. OBJECTIVE: This paper presents a process evaluation protocol of the AppSalt program, which aims to monitor the implementation of the program, explain its causal mechanisms, and provide evidence for scaling up the program nationwide. METHODS: A mixed methods approach will be used to collect data relating to five process evaluation dimensions: fidelity, dose delivered, dose received, reach, and context. Quantitative data, including app use logs, activity logs, and routine monitoring data, will be collected alongside the intervention process to evaluate the quantity and quality of intervention activities. The quantitative data will be summarized as medians, means, and proportions as appropriate. Qualitative data will be collected through semistructured interviews of purposely selected intervention participants and key stakeholders from local health and education authorities. The thematic analysis technique will be used for analyzing the qualitative data with the support of NVivo 12. The qualitative data will be triangulated with the quantitative data during the interpretation phase to explain the 5 process evaluation dimensions. RESULTS: The intervention activities of the AppSalt program were initiated at 27 primary schools in three cities since October 2018. We have completed the 1-year intervention of this program. The quantitative data for this study, including app use log, activity logs, and the routine monitoring data, were collected and organized during the intervention process. After completing the intervention, we conducted semistructured interviews with 32 students, 32 parents, 9 teachers, 9 school heads, and 8 stakeholders from local health and education departments. Data analysis is currently underway. CONCLUSIONS: Using mHealth technology for salt reduction among primary school students is an innovation in China. The findings of this study will help researchers understand the implementation of the AppSalt program and similar mHealth interventions in real-world settings. Furthermore, this process evaluation will be informative for other researchers and policy makers interested in replicating the AppSalt program and designing their salt reduction intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19430.
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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 | 0,011 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 |
| 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 ».