Early Childhood Physical Activity: A Dynamic Systems Approach to Reducing Health Disparities

项目摘要 几乎一半的美国成年人患有可预防的慢性病，其中大部分可以改善 定期进行身体活动（PA）。这些比例在种族/族裔多样化的人口中甚至更高 在慢性病和PA行为中出现差异。重要的是，成人每天约20分钟 体力活动比不活动的人全因死亡的风险低33%，我们知道， 成年人的体育活动模式在儿童时期就开始发展。虽然我们知道 大概什么时候，具体是如何影响这些模式是多因素的。直接的“因果”模型是 不足以容纳图案形成中涉及的复杂层。这种复杂性包括 多个动态系统之间和内部的相互作用，影响儿童的PA行为，包括 建筑环境，社会环境（包括家庭内外），以及不同的认知过程 在儿童发展过程中。更深入地了解这些动态可以推动干预措施， 儿童PA行为和长期健康差距减少的政策。 为了完成这项任务，我们将利用生物医学领域以外更常用的方法， 研究，在生态学和社会科学等领域，并汇集了跨学科和跨部门 复杂系统建模方法（布鲁金斯研究所）和儿科PA和健康方面的专家团队 差异（范德比尔特），以建立一个病因代理为基础的模型（ABM），确定哪些可修改的 决定因素可能对不同幼儿的PA模式形成有最大的影响。该项目将 利用一个独立的数据集收集的成长权利对健康（GROW）试验的儿童-父母对 通知反弹道导弹系统所有这些家庭都代表了田纳西州各种服务不足的人口。种植 试验（总共N=610名3-8岁的儿童）收集了详细的客观PA数据（通过加速度计），每年4次- 在研究期间（对于儿童-父母对）的分数，以及儿童社会环境的数据， 环境和认知过程。在这种情况下使用ABM利用了这种多样性和丰富性， 纵向数据集，以建立具有经验得出的参数估计值的模型， 支持不同人群的幼儿保护。ABM允许我们检查如何，何时以及为谁 PA行为是动态塑造的宏观层面的影响，如建筑环境，其中 儿童居住，中观层面的影响，如家庭内外的社会环境，以及微观层面的影响， 影响，如儿童早期发展中的个人认知过程。我们将研究 这些影响在儿童特征（包括性别、种族/民族、BMI和BMI）中的异质性 随着时间的推移而改变。该项目的成果将是一套基于数据的政策建议， 研究儿童PA行为的复杂系统方法，可应用于现实世界 社区设置。

PROJECT SUMMARY Almost half of American adults have a preventable chronic disease, most of which could be improved with regular physical activity (PA). These proportions are even higher for racially/ethnically diverse populations where disparities emerge in both chronic disease and PA behavior. Importantly, adults with ~20 minutes/day of physical activity have a 33% lower risk for all-cause mortality than those who are inactive We know that physical activity patterns for adults have their developmental beginnings in childhood. Although we know roughly when, specifically how to affect these patterns is multi-factorial. Direct “cause and effect” models are insufficient to accommodate the layers of complexity involved in pattern formation. Such complexity includes multiple dynamic systems with inter- and intra- interactions that influence children’s PA behaviors, including the built environment, the social environment (both inside and outside the home), and cognitive processes that vary during- child development. Providing a deeper understanding of these dynamics can advance interventions and policies for childhood PA behaviors and long-term health disparities reduction. To accomplish this task, we will leverage approaches more commonly used outside of biomedical research, in fields such as ecology and social science, and bring together a trans-disciplinary and cross-sector team of experts in complex systems modeling approaches (Brookings Institution) and pediatric PA and health disparities (Vanderbilt) to build an etiologic Agent-Based Model (ABM) that identifies which modifiable determinants may have the biggest impact on PA pattern formation for diverse young children. This project will utilize an independent dataset collected by the Growing Right Onto Wellness (GROW) Trial of child-parent pairs to inform the ABM. All of these families represented diverse under-served populations in Tennessee. The GROW Trial (total N=610 children ages 3-8) collected detailed objective PA data (via accelerometry) at four annual time- points over the study period (for child-parent pairs), as well as data on the child’s social environment, built environment, and cognitive processes. Using ABM in this context leverages the diversity and richness of this longitudinal dataset to build a model with empirically derived parameter estimates to generate new insights into supporting early childhood PA in diverse populations. ABMs allows us to examine how, when, and for whom PA behaviors are dynamically shaped by macro-level influences such as the built environment in which children reside, meso-level influences such as social environments both in and out of the home, and micro-level influences such as individual cognitive processes in early childhood development. We will examine the potential heterogeneity in these influences across child characteristics including gender, race/ethnicity, BMI, and BMI change over time. The result of this project will be a set of data-driven policy recommendations, based on a complex systems approach to studying childhood PA behaviors, that can be applied in real-world community settings.