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评估基于应用程序的儿童意外伤害预防干预措施对农村中国学龄前儿童照顾者的有效性:一项群组随机对照试验方案。

Assessing the effectiveness of an app-based child unintentional injury prevention intervention for caregivers of rural Chinese preschoolers: protocol for a cluster randomized controlled trial.

机构信息

Department of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, 410078, China.

Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.

出版信息

BMC Public Health. 2021 Nov 20;21(1):2137. doi: 10.1186/s12889-021-12156-y.

Abstract

BACKGROUND

Compared to urban children, children living in rural areas of most countries, including China, are at higher risk of suffering unintentional injuries. Most proven injury prevention interventions, however, are rarely implemented in rural China due to lack of resources. Mobile health interventions are low-cost and easy-to-implement, facilitating implementing injury prevention in resource-limited areas (e.g., rural areas). This study is designed and implemented to examine the effectiveness of an app-based intervention for unintentional injury prevention among rural preschoolers in China.

METHODS

A single-blind, 18-month, parallel-group cluster randomized controlled trial with 1:1 allocation ratio will be implemented in 2 rural areas of China (Yang County, Shaanxi Province, and Shicheng County, Jiangxi Province). In total, at least 3508 rural caregivers of preschoolers aged 3-6 years old who own a smartphone will be recruited from 24 preschools. Clusters will be randomized at the preschool level and allocated to the control group (receiving routine school-based education plus app-based parenting education excluding unintentional injury prevention) or the intervention group (receiving routine school-based education plus app-based parenting education including unintentional injury prevention). External support strategies will be adopted by local partners to minimize user fatigue, non-compliance, and attrition. Data collection will be conducted at baseline and then every 3 months during the 18-month follow-up time period. Intention-to-treat data analysis will be implemented. Missing values will be imputed by using the Expectation Maximization algorithm. Generalized estimating equation will test the overall effectiveness of the app-based intervention. A per-protocol sensitivity analysis will be conducted to test the robustness of results. Subgroup analyses will follow the strategies for primary analyses. The primary outcome measure is the incidence rate of unintentional injury among preschoolers during the study period. Secondary outcome measures comprise longitudinal changes in caregiver's attitudes, caregiver-reported supervision behaviors, and caregiver-assessed home environment safety surrounding child unintentional injury prevention in the last week using a standardized audit instrument.

DISCUSSION

The app-based intervention is expected to be feasible and effective over the 18-month intervention period. If the app is demonstrated effective as hypothesized, we will initiate processes to generalize and popularize it broadly to rural child caregivers across China.

TRIAL REGISTRATION

ChiCTR2000037606 , registered on August 29, 2020.

摘要

背景

与城市儿童相比,包括中国在内的大多数国家农村地区的儿童更容易遭受意外伤害。然而,由于资源匮乏,大多数经过验证的伤害预防干预措施很少在农村中国实施。移动健康干预措施成本低且易于实施,有利于在资源有限的地区(如农村地区)实施伤害预防。本研究旨在设计和实施一项基于应用程序的干预措施,以预防中国农村学龄前儿童意外伤害。

方法

本研究将在中国两个农村地区(陕西省洋县和江西省石城县)实施一项为期 18 个月的单盲、1:1 分配比例的平行分组集群随机对照试验。总共将从 24 所幼儿园招募至少 3508 名 3-6 岁农村学龄前儿童的农村照顾者。将以幼儿园为单位进行集群随机分组,并分配到对照组(接受常规学校教育加基于应用程序的育儿教育,不包括意外伤害预防)或干预组(接受常规学校教育加基于应用程序的育儿教育,包括意外伤害预防)。当地合作伙伴将采取外部支持策略,以最大限度地减少用户疲劳、不遵守和流失。将在基线时进行数据收集,然后在 18 个月的随访期间每 3 个月进行一次。将采用意向治疗数据分析缺失值。将使用期望最大化算法进行估算。广义估计方程将检验基于应用程序的干预措施的总体效果。将进行方案敏感分析,以检验结果的稳健性。将根据主要分析策略进行亚组分析。主要结局指标是研究期间学龄前儿童意外伤害的发生率。次要结局指标包括使用标准化审核工具在过去一周内照顾者态度、照顾者报告的监督行为以及照顾者评估的儿童意外伤害预防家庭环境安全的纵向变化。

讨论

预计该基于应用程序的干预措施在 18 个月的干预期间是可行且有效的。如果该应用程序如假设的那样有效,我们将启动将其推广到中国农村地区的广大儿童照顾者的过程。

试验注册

ChiCTR2000037606,于 2020 年 8 月 29 日注册。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/199e/8606071/31efe1e205f9/12889_2021_12156_Fig1_HTML.jpg

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