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.
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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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».