当直观地吃饭并不总是一个积极的反应时:使用机器学习更好地解开食者概况
Johana Monthuy-Blanc1,2, Usef Faghihi1,3, Mahan Najafpour Ghazvini Fardshad1,3
1Unité de Recherche Loricorps, Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, (CR-IUSMM), 7331, Rue Hochelaga, Montreal, QC H1N 3V2, Canada.
Journal of clinical medicine
|August 26, 2023
概括
这项研究使用机器学习来识别基于身体形象,嗜食症,克制和直观饮食模式的七个不同的饮食者个人资料. 这些个人资料为定制的健康教育和干预提供了洞察力.
科学领域:
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
- 营养科学 营养科学
背景情况:
- 了解不同的饮食行为对于有效的健康干预至关重要.
- 以前的研究还没有充分利用先进的聚类技术来对食者进行分类.
- 识别不同的食者个人资料可以带来更个性化的方法.
研究的目的:
- 通过机器学习集群分析来识别不同的食者配置文件.
- 描述每个已识别的食者个人资料的独特属性.
- 为了解饮食行为的范围提供一个框架.
主要方法:
- 共有317名参与者完成了一项在线调查.
- 测量包括对身体形象的不满,食欲,克制和直觉性饮食.
- 采用集群分析来识别食者个人资料,并使用因果推理方法进行验证.
主要成果:
- 确定了7个集群的食者个人资料模型.
- 详细介绍了每个个人资料的特点,需求和优势.
- 呈现了一系列的食者个人资料,说明了饮食模式的变化.
结论:
- 鉴定到的食者简介为饮食行为提供了一个新的概念化.
- 这一框架可以指导制定有针对性的健康教育计划.
- 针对感知和饮食维度的干预措施可以根据这些配置文件进行细化.
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