一种以社区为中心的方法,以制定健康和负担得起的日常饮食建议
Joe Germino1, Annalisa Szymanski1, Ronald Metoyer1
1Department of Computer Science and Engineering, Lucy Family Institute, University of Notre Dame, Notre Dame, IN, United States.
Frontiers in big data
|November 29, 2023
概括
这项研究表明,如何整合杂货和政府数据可以为低收入家庭创建负担得起的,健康的饮食计划. 优化模型有助于SNAP受益人做出更好的食物选择,尽管预算和时间限制.
科学领域:
- 营养科学 营养科学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 负担得起和营养丰富的饮食是很困难的,特别是对于低收入家庭和补充营养援助计划 (SNAP) 的家庭.
- 消费者面临着复杂的食品环境,不一致的信息和预算限制的挑战,增加了对营养不良和肥胖的易感性.
研究的目的:
- 展示如何将当地杂货店数据与联邦数据库集成,可以帮助社区应对健康和预算挑战.
- 开发一个优化模型,在SNAP预算限制范围内创建健康的饮食计划.
主要方法:
- 讨论了数据整合的挑战,包括不一致的可用性和误导性的营养标签.
- 在一个案例研究中使用线性编程来识别符合美国人饮食指南的健康饮食计划,在有限的SNAP预算内.
- 分析了当地食品的成本驱动因素,重点关注添加糖,和脂肪和.
主要成果:
- 案例研究结果表明,优化模型可以促进社区一级的食品购买决策.
- 该模型成功地在预算限制范围内确定了健康的饮食计划.
结论:
- 综合数据和优化模型可以使社区能够做出明智的食物选择.
- 这种方法为开发全球推系统提供了信息,用于导航食品信息网络.
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