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

基本信息

批准号：
10671049
负责人：
Shari Barkin
金额：
$ 39.82万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
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/10671049
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 Disparity Disparity population Ecology Elements Ethics Ethnic Origin Ethnic Population Etiology Family Future Gender Guidelines Health Health Promotion Heterogeneity Home Individual Institution Intervention Literature Metabolic Minority Groups Modeling Outcome Parameter Estimation Parents Pathway interactions Pattern Pattern Formation Physical activity Physical assessment Policies Population Population Heterogeneity Prevention strategy Race Recommendation Reduce health disparities Risk Shapes Social Environment Social Sciences System Target Populations Tennessee Testing Time Underserved Population Variant aged behavioral study bone built environment cardiovascular health cognitive process community setting disorder prevention disparity reduction dynamic system early childhood ethnic diversity exercise intervention health disparity improved insight longitudinal dataset low socioeconomic status mortality novel physical conditioning policy recommendation prevent psychosocial racial diversity racial population response skeletal

项目摘要

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.

项目摘要几乎一半的美国成年人患有可预防的慢性疾病，其中大多数可以改善对于种族/种族的种族，定期体育锻炼（PA）更高在慢性病和PA行为中都出现差异。与不活动的人相比，身体活动的全因死亡率低33％，我们知道那是虽然我们知道，虽然直接的“原因和效果”是直接的。不足以适应模式形成的复杂性层具有影响儿童PA行为的多个动态系统，包括他们的行为建筑环境，社会环境（内部和外部）以及不同的认知过程在儿童发展期间，对这些动态有更深入的了解儿童PA行为的政策和长期健康差异降低。为了完成这项任务，我们将利用更常用的生物医学之外使用的方法研究，在诸如生态和社会科学等领域，并将跨学科和跨科学汇集在一起复杂系统建模方法（Brookings Institute）和PA和Health的专家团队差异（范德比尔特）建立一个基于病因的模型（ABM），该模型可修改决定因素可能会对多个幼儿的PA模式形成产生最大的影响。利用由生长右生长到健康对的健康（成长）试验的独立数据集告知ABM的所有家庭都在田纳西州抑制了多样化的人口试验（总n = 610个儿童屁股3-8）在四个年度时收集的详细目标PA数据（通过加速度计）在研究期间（对于儿童对）以及有关儿童社会环境的数据环境和认知过程。纵向数据集以构建具有经验得出参数的模型，以生成新的INTOS 支持儿童早期PA PA行为是由宏观影响动态影响的，例如建筑环境Inwich 儿童居住，中层的影响像在家中和外面的！影响诸如幼儿发展中个人认知过程之类的影响。这些影响的异质性在儿童特征跨性别，种族/种族，BMI和BMI等影响随着时间的推移，该项目的结果将是一组数据驱动的策略关于研究儿童PA行为的复杂系统方法，可以应用于现实世界中社区设置。