An App-Based Intervention for Caregivers to Prevent Unintentional Injury Among Preschoolers: Cluster Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: App-based interventions have the potential to reduce child injury in countries with limited prevention resources, but their effectiveness has not been rigorously examined. OBJECTIVE: This study aimed to assess the effectiveness of an app-based intervention for caregivers of preschoolers to prevent unintentional injury among Chinese preschoolers. METHODS: A 6-month cluster randomized controlled trial was conducted from December 2017 to June 2018. Recruitment was conducted through preschools, which were randomly allocated to either the control group (ie, app-based parenting education excluding unintentional injury prevention) or the intervention group (ie, app-based parenting education including unintentional injury prevention). A total of 2920 caregivers of preschoolers aged 3-6 years from 20 preschools in Changsha, China, were recruited offline through the schools. The primary outcome was unintentional injury incidences among preschoolers in the past 3 months; this measure was assessed through an online caregiver-report at the baseline visit and at 3-month and 6-month follow-up visits. Secondary outcome measures included caregivers' self-reported attitudes and behaviors concerning child supervision during the last week. Generalized estimating equations (GEEs) were used to assess the effectiveness of the app-based intervention on responses at 3 and 6 months after adjusting for sociodemographic variables, baseline level of the outcome variable, and engagement with interventions in the assigned group. All analyses were intention-to-treat. A per-protocol sensitivity analysis was also conducted. RESULTS: In total, 1980 of the 2920 caregivers completed the study. The mean age of participants was 32.0 years (SD 5.5) and 68.99% (1366/1980) of them were female. During the 6-month follow-up visit, unintentional injury incidence did not change significantly in either group: incidence in the intervention group went from 8.76% (94/1073) to 8.11% (87/1073), P=.59; incidence in the control group went from 9.4% (85/907) to 7.5% (69/907), P=.15. The changes did not differ between the groups (odds ratio [OR] 1.14, 95% CI 0.80-1.62). Changes in the average score in attitude concerning unintentional injury prevention were also similar between the groups (B .05, 95% CI -0.03 to 0.13). Changes in unintentional injury prevention behaviors were greater in the intervention group than in the control group after the intervention (B .87, 95% CI 0.33-1.42). Analyses of individual injury prevention behaviors showed that the intervention reduced three risky behaviors: unsafe feeding of children (OR 0.73, 95% CI 0.60-0.89); incorrectly placing children in cars (OR 0.73, 95% CI 0.57-0.93); and allowing children to ride bicycles, electric bicycles, or motorcycles unsupervised (OR 0.80, 95% CI 0.64-0.99). The intervention also improved scores on three safety-focused behaviors: testing water temperature before giving children a bath (OR 1.26, 95% CI 1.05-1.52); properly storing sharp objects (OR 1.24, 95% CI 1.01-1.52); and safely storing medicines, detergents, and pesticides (OR 1.24, 95% CI 1.02-1.51). CONCLUSIONS: The app-based intervention did not reduce unintentional injury incidence among preschoolers but significantly improved caregivers' safety behaviors. This app-based intervention approach to improve caregiver behaviors surrounding child injury risk offers promise to be modified and ultimately disseminated broadly. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR-IOR-17010438; http://www.chictr.org.cn/showproj.aspx?proj=17376 (Archived by WebCite at http://www.webcitation.org/75jt17X84). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-018-5790-1.
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 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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».