饮食模式表征方法的进步:一个范围审查
Joy M Hutchinson1, Amanda Raffoul2, Alexandra Pepetone1
1School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
The British journal of nutrition
|March 10, 2025
概括
机器学习和潜在类分析等新的方法为饮食模式和健康提供了更深入的见解. 需要一致的报告来综合发现,并为公共卫生政策提供信息.
科学领域:
- 营养科学 营养科学
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 了解饮食模式对于公共健康至关重要.
- 传统的方法可能无法完全捕捉饮食的复杂性.
- 新的分析方法正在出现,用于饮食模式的表征.
研究的目的:
- 进行从2005年到2022年的文献范围审查.
- 综合应用非传统方法来表征饮食模式.
- 检查新方法衍生饮食模式与健康结果之间的关联.
主要方法:
- 在 MEDLINE,CINAHL 和 Scopus 数据库中进行了搜索.
- 包括在2005年至2022年间发表的研究.
- 专注于诸如潜在类分析和机器学习算法等方法.
主要成果:
- 24项研究符合纳入标准,其中自2020年以来发表的12项研究.
- 九项研究使用了机器学习应用程序;其他研究使用了隐性类分析和类似方法.
- 14项研究将新的饮食模式与健康结果 (癌症,心血管疾病,喘) 联系起来.
结论:
- 新的方法提供了饮食模式的先进特征.
- 这些方法的应用和报告存在显著差异.
- 需要加强报告准则和质量评估工具,以实现一致的综合和政策制定.
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