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

幼儿体育活动：减少健康差异的动态系统方法

• 批准号: 10517199
• 负责人: Shari Barkin
• 金额: $ 43.09万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-25 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10517199
• 关键词: Accelerometer; Adult; Affect; Age; American; Behavior; Biological Models; Biomedical Research; Body mass index; Characteristics; Child; Child Development; Childhood; Chronic Disease; Cognitive; Complex; Data; Data Set; Development; Ecology; Elements; Ethics; Ethnic Origin; Ethnic group; Etiology; Family; Future; Gender; Guidelines; Health; Health Promotion; Heterogeneity; Home; Individual; Institution; Intervention; Literature; Metabolic; Minority Groups; Modeling; Outcome; Parents; Pathway interactions; Pattern; Pattern Formation; Physical activity; Policies; Population; Population Heterogeneity; Prevention strategy; Race; Reduce health disparities; Risk; Social Environment; Social Sciences; System; Target Populations; Tennessee; Testing; Time; Underserved Population; Variant; aged; base; behavioral study; bone; built environment; cardiovascular health; cognitive process; community setting; disorder prevention; disparity reduction; dynamic system; early childhood; exercise intervention; health disparity; improved; insight; longitudinal dataset; low socioeconomic status; mortality; novel; physical conditioning; policy recommendation; prevent; psychosocial; racial and ethnic; response; skeletal

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.

项目总结 几乎一半的美国成年人患有可预防的慢性病，其中大部分是可以改善的 有规律的身体活动(PA)。对于种族/民族多元化的人口来说，这一比例甚至更高 在慢性病和PA行为方面出现差异的地方。重要的是，成年人每天大约20分钟 体力活动比不运动的人全因死亡率低33%我们知道 成年人的身体活动模式是从童年开始发育的。尽管我们知道 大概在什么时候，具体是如何影响这些模式是多因素的。直接的“因果”模型是 不足以适应图案形成过程中涉及的复杂层次。这种复杂性包括 具有影响儿童PA行为的内部和内部交互的多个动态系统，包括 建筑环境、社会环境(包括室内和室外)以及不同的认知过程 儿童时期的发展。更深入地了解这些动态可以促进干预和 儿童PA行为和长期健康差距缩小的政策。 为了完成这项任务，我们将利用生物医学以外更常用的方法 在生态和社会科学等领域进行研究，并汇集跨学科和跨部门的 复杂系统建模方法专家小组(布鲁金斯学会)和儿科PA与健康 差异(Vanderbilt)构建基于病因的模型(ABM)，以确定哪些是可修改的 决定因素可能对不同幼儿PA模式的形成有最大的影响。这个项目将 利用由Growing Right On Wellness(Growth)儿童-父母配对试验收集的独立数据集 通知ABM。所有这些家庭代表了田纳西州不同的未得到充分服务的人群。《成长》 试验(总计N=610名3-8岁儿童)收集了详细的客观PA数据(通过加速度计)，在四个年度时间- 研究期间的点数(对于孩子-父母对)，以及关于孩子的社会环境的数据，建立 环境和认知过程。在这种情况下使用ABM可以充分利用它的多样性和丰富性 纵向数据集，用于使用经验派生的参数估计构建模型，以生成新的见解 支持不同人群的早期儿童PA。ABMS允许我们检查如何、何时以及为谁服务 PA的行为是由宏观层面的影响动态形成的，例如 儿童居住，中观层面的影响，如家庭内外的社会环境，微观层面的影响 儿童早期发展中的个体认知过程等影响。我们将研究潜在的 这些影响在性别、种族/民族、BMI和BMI等儿童特征中的异质性 随着时间的推移而改变。该项目的结果将是一套数据驱动的政策建议，基于 关于研究儿童PA行为的复杂系统方法，可应用于现实世界 社区设置。