Anthropometric, Nutritional, and Lifestyle Factors Involved in Predicting Food Addiction: An Agnostic Machine Learning Approach.

IF 3 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Alejandro Díaz-Soler, Cristina Reche-García, Juan José Hernández-Morante
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引用次数: 0

Abstract

Food addiction (FA) is an emerging psychiatric condition that presents behavioral and neurobiological similarities with other addictions, and its early identification is essential to prevent the development of more severe disorders. The aim of the present study was to determine the ability of anthropometric measures, eating habits, symptoms related to eating disorders (ED), and lifestyle features to predict the symptoms of food addiction. Methodology: A cross-sectional study was conducted in a sample of 702 university students (77.3% women; age: 22 ± 6 years). The Food Frequency Questionnaire (FFQ), the Yale Food Addiction Scale 2.0 (YFAS 2.0), the Eating Attitudes Test (EAT-26), anthropometric measurements, and a set of self-report questions on substance use, physical activity level, and other questions were administered. A total of 6.4% of participants presented symptoms compatible with food addiction, and 8.1% were at risk for ED. Additionally, 26.5% reported daily smoking, 70.6% consumed alcohol, 2.9% used illicit drugs, and 29.4% took medication; 35.3% did not engage in physical activity. Individuals with food addiction had higher BMI (p = 0.010), waist circumference (p = 0.001), and body fat (p < 0.001) values, and a higher risk of eating disorders (p = 0.010) compared to those without this condition. In the multivariate logistic model, non-dairy beverage consumption (such as coffee or alcohol), vitamin D deficiency, and waist circumference predicted food addiction symptoms (R2Nagelkerke = 0.349). Indeed, the machine learning approaches confirmed the influence of these variables. Conclusions: The prediction models allowed an accurate prediction of FA in the university students; moreover, the individualized approach improved the identification of people with FA, involving complex dimensions of eating behavior, body composition, and potential nutritional deficits not previously studied.

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人体测量,营养和生活方式因素参与预测食物成瘾:一个不可知论的机器学习方法。
食物成瘾(FA)是一种新兴的精神疾病,与其他成瘾具有行为和神经生物学上的相似性,其早期识别对于防止更严重疾病的发展至关重要。本研究的目的是确定人体测量、饮食习惯、与饮食失调(ED)相关的症状和生活方式特征预测食物成瘾症状的能力。方法:采用横断面研究方法,对702名大学生进行抽样调查,其中女性77.3%,年龄22±6岁。采用食物频率问卷(FFQ)、耶鲁食物成瘾量表2.0 (YFAS 2.0)、饮食态度测试(EAT-26)、人体测量、一套关于物质使用、身体活动水平和其他问题的自我报告问题。总共6.4%的参与者表现出与食物成瘾相一致的症状,8.1%的参与者有ED的风险。此外,26.5%的参与者报告每天吸烟,70.6%的参与者饮酒,2.9%的参与者使用非法药物,29.4%的参与者服用药物;35.3%的人没有参加体育活动。与没有食物成瘾的人相比,食物成瘾者的BMI (p = 0.010)、腰围(p = 0.001)和体脂(p < 0.001)值更高,饮食失调的风险也更高(p = 0.010)。在多元logistic模型中,非乳制品饮料(如咖啡或酒精)、维生素D缺乏和腰围预测食物成瘾症状(R2Nagelkerke = 0.349)。事实上,机器学习方法证实了这些变量的影响。结论:该预测模型能准确预测大学生FA;此外,个体化方法提高了对FA患者的识别,涉及饮食行为、身体成分和潜在营养缺陷的复杂维度,这些都是以前没有研究过的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
0.80
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0.00%
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