Systems Science Approaches to Improve Access to Healthier Foods: The FRESH Trial

项目摘要 高质量的饮食可以预防癌症。以前的工作，以改善饮食质量，通过改变食物 环境部强调在零售店增加健康食品的供应，但相对较少关注 专注于干预独立拥有的餐厅，这些餐厅有可能为 在以少数民族为主的低收入城市社区进行癌症预防。我们的团队 广泛与这些类型的餐厅，以增加获得和促进健康的主菜，双方， 饮料，导致总收入，销售额和促销食品的采购显着增加。 基于这项工作，本研究报告有两个相互关联的目标， 在独立经营的餐馆推广更健康的食品：1）使用系统科学方法， 完善，实施和测试一种新的干预措施（新鲜：专注于餐厅参与， 加强健康）的饮食质量，以及常客的健康，以及其他结果;以及2）使用 更新形成、基线、实施和结果数据，以开发、参数化和校准系统 动态模型，使利益相关者能够虚拟测试FRESH战略对结果的影响， 他们自己的社区。FRESH是一个多层次的理论和实践为基础的干预独立拥有的 餐厅有3个核心组成部分：食物准备，食物获取和采购，以及消费者营养 环境目标1）使用系统科学方法从我们成功的材料中提炼现有材料 餐厅干预措施，并调整干预两个城市网站（巴尔的摩和DC地铁区）， 主要是低收入和少数民族（非洲裔美国人和拉丁美洲人）。目标2）在24个国家实施FRESH 在16个月的时间里，在两个地点的独立餐厅收集过程数据。目标3） 使用多点随机对照试验评估FRESH对饮食质量的影响（主要 结果）;健康指标;常客的心理社会因素（n=576），并评估其对 单位销售额和每周收入。主要假设：参与FRESH的餐厅的常客 干预社区将显示健康饮食指数得分至少增加5分， 与餐馆顾客相比。目标1- 3的数据将用于开发， 参数化，并校准系统动力学模型，以模拟FRESH干预策略的效果 （4）传播一种规划工具，使利益攸关方能够 在其他城市环境中模拟和虚拟测试FRESH干预策略，可视化潜在影响 单位销售额，收入，客户健康指标和预防癌症的饮食行为，通过基于网络的 用户友好的交互式仪表板。这项研究将产生既新颖又重要的结果，包括 模拟模型来测试基于餐馆的癌症预防策略，从以下方面节省大量成本： 避免了反复试验。

Project Summary High dietary quality is protective against cancer. Previous work to improve dietary quality by changing the food environment has emphasized increasing access to healthy foods in retail stores, but relatively little attention has focused on intervening in independently owned restaurants, which have the potential to contribute to cancer prevention in predominantly minority, low-income urban neighborhoods. Our team has worked extensively with these types of restaurants to increase access to and promote healthier entrees, sides and beverages, leading to significant increases in total revenues, sales, and purchasing of promoted foods. Stemming from that work, this study has two interrelated objectives to sustainably improve access to and promotion of healthier foods in independently owned restaurants: 1) to use systems science approaches to refine, implement and test the effects of a novel intervention (FRESH: Focus on Restaurant Engagement to Strengthen Health) on dietary quality, and health of regular customers, among other outcomes; and 2) to use FRESH formative, baseline, implementation and outcome data to develop, parametrize, and calibrate a system dynamics model that will allow stakeholders to virtually test the effects of FRESH strategies on outcomes in their own communities. FRESH is a multilevel theory- and practice-based intervention for independently owned restaurants with 3 core components: food preparation, food access & procurement, and consumer nutrition environment. Aim 1) To use systems science approaches to refine existing materials from our successful restaurant interventions and adapt the intervention to two urban sites (Baltimore and the DC metro area) that are predominantly low-income and minority (African American and Latinx). Aim 2) To implement FRESH in 24 independently owned restaurants across both sites during 16 months and collect process data. Aim 3) To assess the impact of FRESH using a multisite cluster randomized controlled trial on dietary quality (primary outcome); health indicators; psychosocial factors of regular customers (n=576), and evaluate its impacts on unit sales and weekly revenues. Primary Hypothesis: Regular customers of participating restaurants in FRESH intervention neighborhoods will demonstrate at least a 5-point increase in Healthy Eating Index score, as compared to restaurant customers in comparison neighborhoods. Data from Aims 1- 3 will be used to develop, parameterize, and calibrate a system dynamics model to simulate the effects of FRESH intervention strategies under different scenarios, leading to Aim 4) To disseminate a planning tool that enables stakeholders to simulate and virtually test FRESH intervention strategies in other urban contexts, visualizing potential effects on unit sales, revenues, customer health indicators, and cancer-prevention dietary behaviors, via a web-based user-friendly interactive dashboard. This study will yield results that are both novel and significant, including a simulation model to test restaurant-based cancer-prevention strategies, yielding substantial cost-savings from avoided trial-and-error implementation.