Planting Seeds for the Future: Scoping Review of Child Health Promotion Apps for Parents
Notice bibliographique
Résumé
BACKGROUND: Increasingly, parents use child health promotion apps to find health information. An overview of child health promotion apps for parents currently does not exist. The scope of child health topics addressed by parent apps is thus needed, including how they are evaluated. OBJECTIVE: This scoping review aims to describe existing reported mobile health (mHealth) parent apps of middle- to high-income countries that promote child health. The focus centers on apps developed in the last 5 years, showing how the reported apps are evaluated, and listing reported outcomes found. METHODS: A scoping review was conducted according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews) guidelines to identify parent apps or web-based programs on child health promotion published between January 2016 and June 2021 in 5 databases: PubMed, ERIC, IEEE Xplore, Web of Science, and Google Scholar. Separate sources were sought through an expert network. Included studies were summarized and analyzed through a systematic and descriptive content analysis, including keywords, year of publication, country of origin, aims/purpose, study population/sample size, intervention type, methodology/method(s), broad topic(s), evaluation, and study outcomes. RESULTS: In total, 39 studies met the inclusion criteria from 1040 database and 60 expert-identified studies. Keywords reflected the health topics and app foci. About 64% (25/39) of included studies were published after 2019 and most stemmed from the United States, Australian, and European-based research. Studies aimed to review or evaluate apps or conducted app-based study interventions. The number of participants ranged from 7 to 1200. Quantitative and qualitative methods were used. Interventions included 28 primary studies, 6 app feasibility studies, and 5 app or literature reviews. Eight separate topics were found: parental feeding and nutrition, physical activity, maternal-child health, parent-child health, healthy environment, dental health, mental health, and sleep. Study intervention evaluations cited behavior change theories in 26 studies and evaluations were carried out with a variety of topic-specific, adapted, self-developed, or validated questionnaires and evaluation tools. To evaluate apps, user input and qualitative evaluations were often combined with surveys and frequently rated with the Mobile App Rating Scale. Outcomes reported some positive effects, while several intervention studies saw no effect at all. Effectively evaluating changes in behavior through apps, recruiting target groups, and retaining app engagement were challenges cited. CONCLUSIONS: New parents are a key target group for child health apps, but evaluating child health promotion apps remains a challenge. Whether tailored to parent needs or adapted to the specific topic, apps should be rooted in a transparent theoretical groundwork. Applicable lessons for parent apps from existing research are to tailor app content, include intuitive and adaptive features, and embed well-founded parameters for long-term effect evaluation on child health promotion.
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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 | 0,033 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,009 |
| Bibliométrie | 0,020 | 0,016 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».