通过潜在类分析和成年人的确认因素分析确定的习惯性和特定饮食模式的比较:一个横截面研究
Ahmad Jayedi1, Mahdi Shafiei Neyestanak2, Mostafa Qorbani3
1Social Determinants of Health Research Center, Semnan University of Medical Sciences, Semnan, Iran; Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.
Nutrition (Burbank, Los Angeles County, Calif.)
|August 27, 2023
概括
隐性类分析 (LCA) 有效地确定了成年人独特的习惯性和特定于饮食的饮食模式. 这些发现表明,LCA是将个人分类为特定的饮食摄入次组的合适方法.
科学领域:
- 营养科学 营养科学
- 统计建模 统计建模
- 公共卫生 公共卫生
背景情况:
- 了解饮食模式对于公共卫生干预至关重要.
- 区分习惯性饮食摄入量和特定于一餐的饮食摄入量可以提供细微的见解.
- 隐性类分析 (LCA) 和确认因素分析 (CFA) 是用于模式识别的统计方法.
研究的目的:
- 为了比较由LCA和CFA识别的习惯性和特定饮食模式.
- 评估LCA在将个人分为不同的饮食子组的有效性.
- 通过不同的分析方法来评估饮食模式的特征.
主要方法:
- 一项对伊朗德黑兰778名成年人进行的横截面研究.
- 评估饮食摄入量使用三个24小时回忆.
- LCA用于根据饮食摄入量分组参与者;CFA用于识别习惯性和特定饮食模式.
主要成果:
- CFA确定了三种习惯性饮食模式:水果和蔬菜,混合和西方.
- LCA确定了四个习惯性摄入子组:水果和蔬菜,混合,西方和低消费者.
- 通过LCA识别的类别显示出相应的CFA衍生模式的平均得分显著更高,无论是习惯性摄入量还是特定于餐点的摄入量.
结论:
- 通过LCA识别的习惯性和特定于一餐的饮食类别与CFA衍生的模式保持一致.
- LCA 显示出作为一种强大的统计方法来将个人分类为特定的饮食摄入子组的潜力.
- 这些发现支持LCA在营养流行病学和饮食评估中的有用性.
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