A community focused approach toward making healthy and affordable daily diet recommendations

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2023-11-06 DOI:10.3389/fdata.2023.1086212
Joe Germino, Annalisa Szymanski, Ronald Metoyer, Nitesh V. Chawla
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Abstract

Introduction Maintaining an affordable and nutritious diet can be challenging, especially for those living under the conditions of poverty. To fulfill a healthy diet, consumers must make difficult decisions within a complicated food landscape. Decisions must factor information on health and budget constraints, the food supply and pricing options at local grocery stores, and nutrition and portion guidelines provided by government services. Information to support food choice decisions is often inconsistent and challenging to find, making it difficult for consumers to make informed, optimal decisions. This is especially true for low-income and Supplemental Nutrition Assistance Program (SNAP) households which have additional time and cost constraints that impact their food purchases and ultimately leave them more susceptible to malnutrition and obesity. The goal of this paper is to demonstrate how the integration of data from local grocery stores and federal government databases can be used to assist specific communities in meeting their unique health and budget challenges. Methods We discuss many of the challenges of integrating multiple data sources, such as inconsistent data availability and misleading nutrition labels. We conduct a case study using linear programming to identify a healthy meal plan that stays within a limited SNAP budget and also adheres to the Dietary Guidelines for Americans. Finally, we explore the main drivers of cost of local food products with emphasis on the nutrients determined by the USDA as areas of focus: added sugars, saturated fat, and sodium. Results and discussion Our case study results suggest that such an optimization model can be used to facilitate food purchasing decisions within a given community. By focusing on the community level, our results will inform future work navigating the complex networks of food information to build global recommendation systems.
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以社区为中心的方法,提供健康和负担得起的日常饮食建议
维持负担得起的营养饮食可能具有挑战性,特别是对那些生活在贫困条件下的人来说。为了实现健康饮食,消费者必须在复杂的食品环境中做出艰难的决定。决策必须考虑健康和预算限制、当地杂货店的食品供应和价格选择以及政府服务部门提供的营养和份量指南等方面的信息。支持食品选择决策的信息往往不一致,很难找到,这使得消费者难以做出明智的最佳决定。对于低收入家庭和参加补充营养援助计划(SNAP)的家庭来说尤其如此,这些家庭有额外的时间和成本限制,影响了他们的食品购买,最终使他们更容易营养不良和肥胖。本文的目的是演示如何将来自地方杂货店和联邦政府数据库的数据整合起来,以帮助特定社区应对其独特的健康和预算挑战。我们讨论了整合多个数据源的许多挑战,如数据可用性不一致和误导营养标签。我们进行了一个案例研究,使用线性规划来确定一个健康的膳食计划,保持在有限的SNAP预算内,并遵守美国人的膳食指南。最后,我们探讨了当地食品成本的主要驱动因素,重点关注美国农业部确定的营养成分:添加糖、饱和脂肪和钠。结果和讨论我们的案例研究结果表明,这种优化模型可以用于促进特定社区内的食品购买决策。通过关注社区层面,我们的结果将为未来导航复杂的食品信息网络以构建全球推荐系统的工作提供信息。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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