在预测食物成方面涉及的人类测量,营养和生活方式因素:一种不可知机器学习方法
Alejandro Díaz-Soler1, Cristina Reche-García2, Juan José Hernández-Morante1,3
1Eating Disorders Research Unit, Universidad Católica de Murcia, Guadalupe, 30107 Murcia, Spain.
Diseases (Basel, Switzerland)
|August 27, 2025
概括
使用饮料摄入量,维生素D水平和腰围等因素可以预测食物成. 通过了解饮食行为和营养缺陷,
科学领域:
- 行为科学
- 营养科学
- 精神病学
背景情况:
- 食物成是一种新兴的精神疾病,与其他成有相似之处.
- 早期发现FA对于预防更严重疾病的发展至关重要.
研究的目的:
- 确定人体测量,饮食习惯,饮食障碍 (ED) 症状和生活方式特征是否可以预测FA症状.
- 在大学学生群体中开发和验证FA的预测模型.
主要方法:
- 这是一项涉及702名大学生的横截面研究.
- 数据收集包括耶鲁食品成量2.0 (YFAS 2.0),饮食态度测试 (EAT-26),食物频率问卷 (FFQ),人体测量和生活方式问卷.
- 用多变量逻辑回归和机器学习方法进行分析.
主要成果:
- 6. 4%的参与者表现出与FA相容的症状; 8. 1%的人有ED的风险.
- 患有FA的个体BMI,腰围和身体脂肪都较高,ED的风险也较高.
- 非乳制品饮料消费,维生素D缺乏和腰围显著预测了FA症状 (R2Nagelkerke=0. 349).
结论:
- 预测模型在大学生中准确地识别了FA.
- 一个个别化的方法, 考虑饮食行为, 身体组成, 和营养不足, 改善了FA的识别.
- 这项研究强调了导致食物成的复杂因素.
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