You are what you eat: Longitudnal associations between the neighborhood food envi

你吃什么就是什么：邻近食物环境之间的纵向关联

• 批准号: 7478981
• 负责人: Kiyah Duffey
• 金额: $ 2.96万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7478981
• 关键词: what; eat; Longitudnal; associations; between

DESCRIPTION (provided by applicant): The physical food environment is hypothesized to play a critical role in influencing dietary patterns, obesity, and diabetes; however research on this topic has been largely cross-sectional. Consequently, little is known about the long-term associations between access to away-from-home food (restaurants and fast food places), patterns of away-from-home eating, and health outcomes. Objectives: This study seeks to examine longitudinal changes in the physical food environment and determine their effect on diet and health outcomes. Specifically, we hope to (1) determine factors in the built environment that are associated with the number of away-from-home food places in an individual's residential neighborhood; (2) determine the relationship between the number of away-from-home eating places in an individual's residential neighborhood and frequency of away-from-home food consumption; and (3) determine the longitudinal associations between the frequency of away-from-home eating with 13-year changes in weight and glucose status. Methods: Data collected from the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a longitudinal cohort of 5,115 young adults, will be used in these analyses. Individual-level health and diet data have been linked by residential location to a Geographic Information Systems (GIS)-derived database which includes measures of the food environment (i.e. location of restaurants), environmental features (i.e. road networks), and census measures (i.e. block-group average household income), resulting in a complex database of both individual and environmental factors. We will use a combination of multivariate, hierarchical linear regression ([HLM] a form of multilevel modeling), and longitudinal fixed-effect models. HLM allows us to examine the independent contribution of both individual and environmental factors to the frequency of away-from-home eating. Fixed effect models, used to examine inter-individual changes in behaviors and health outcomes over time, have the additional benefit of providing inherent control for endogeneity (unobserved, time-invariant heterogeneity within individuals). Implications: Weight loss and weight gain prevention efforts focused solely on individual-level factors have been met with limited success. Understanding influences of the community food environment on dietary choice and health-related outcomes may be necessary for promoting healthy lifestlyes. Results from this study will identify factors that could be targeted for the design and development of community environments that improve individuals' diets and support healthy behaviors.

描述（由申请人提供）：物理食物环境被假设在影响饮食模式、肥胖和糖尿病方面起着关键作用;然而，关于这一主题的研究在很大程度上是横断面的。因此，很少有人知道获得远离家乡的食物（餐馆和快餐店），远离家乡的饮食模式和健康结果之间的长期关联。目的：本研究旨在研究物理食物环境的纵向变化，并确定其对饮食和健康结果的影响。具体而言，我们希望（1）确定与个人居住区中的外出就餐场所数量相关的建筑环境因素;（2）确定个人居住区中的外出就餐场所数量与外出就餐频率之间的关系;（3）确定个人居住区中的外出就餐场所数量与外出就餐频率之间的关系。以及（3）确定外出就餐频率与13年来体重和血糖状态变化之间的纵向关联。研究方法：这些分析将使用从年轻人冠状动脉风险发展（CARDIA）研究（一项包含5，115名年轻人的纵向队列研究）收集的数据。个人一级的健康和饮食数据按居住地点与地理信息系统衍生的数据库相联系，该数据库包括食物环境（即餐馆的位置）、环境特征（即道路网）和普查措施（即街区组平均家庭收入），从而形成了一个复杂的个人和环境因素数据库。我们将使用多变量、分层线性回归（[HLM]是多水平建模的一种形式）和纵向固定效应模型的组合。HLM使我们能够检查个人和环境因素对离家吃饭频率的独立贡献。固定效应模型，用于检查个体间的行为和健康结果随时间的变化，有额外的好处，提供内在的控制endofilm（未观察到的，时间不变的异质性在个人）。影响：体重减轻和体重增加的预防工作仅仅集中在个人层面的因素已经遇到了有限的成功。了解社区食物环境对饮食选择和健康相关结果的影响可能是促进健康生活方式的必要条件。这项研究的结果将确定可用于社区环境设计和开发的因素，以改善个人的饮食和支持健康行为。