Suppr超能文献

在线食品配送应用对阿拉伯地区饮食模式紊乱的影响。

The impact of online food delivery applications on dietary pattern disruption in the Arab region.

作者信息

Qasrawi Radwan, Thwib Suliman, Issa Ghada, Amro Malak, AbuGhoush Razan, Hoteit Maha, Khairy Sahar, Al-Awwad Narmeen Jamal, Bookari Khlood, Allehdan Sabika, Alkazemi Dalal, Al Sabbah Haleama, Al Maamari Salima, Malkawi Asma H, Tayyem Reema

机构信息

Department of Computer Science, Al-Quds University, Jerusalem, Palestine.

Department of Computer Engineering, Istinye University, Istanbul, Türkiye.

出版信息

Front Public Health. 2025 Jun 10;13:1569945. doi: 10.3389/fpubh.2025.1569945. eCollection 2025.

Abstract

BACKGROUND

While online food delivery applications (OFDAs) offer convenient food accessibility, their impact on dietary behaviors remains insufficiently explored, especially in the Arab region. This study applies machine learning (ML) techniques to identify the key behavioral and nutritional factors contributing to dietary disruption linked to OFD platforms.

METHODS

We conducted a cross-sectional study which involved 7,370 adults across 10 Arab countries using a comprehensive online survey. The study employed an ensemble ML approach, comparing Random Forest, XGBoost, CatBoost, and LightGBM tree-based models to analyze 31 features across six domains: demographics, ordering frequency, food preferences, nutritional perceptions, behavioral factors, and service attributes. Model performance was evaluated using multiple metrics, including sensitivity, precision, F1-score, and AUC. Clear interpretation of the risk factors was explained using partial dependence plots.

RESULTS

The findings revealed that the strongest predictors of dietary disruption were excessive food consumption, altered meal routines, and preferences for fatty foods. Younger individuals, males, and those with higher BMI reported higher disruption rates. Lebanon and Bahrain showed the highest rates for notable disruption, while Oman reported the lowest. ML analysis demonstrated high predictive performance, with Random Forest achieving the highest sensitivity (94.3%) and F1-score (89.3%). Feature importance analysis identified behavioral factors as more influential than socioeconomic indicators.

CONCLUSION

OFDAs offer valuable convenience and market expansion while simultaneously posing significant challenges to maintaining optimal dietary health. With strategic interventions and public health collaborations, these platforms can shift from being disruptors of healthy dietary habits to catalysts for improved nutrition and well-being in the Arab region and beyond.

摘要

背景

虽然在线食品配送应用程序(OFDAs)提供了便捷的食品获取途径,但其对饮食行为的影响仍未得到充分研究,尤其是在阿拉伯地区。本研究应用机器学习(ML)技术来识别与OF平台相关的饮食紊乱的关键行为和营养因素。

方法

我们进行了一项横断面研究,通过全面的在线调查,涉及10个阿拉伯国家的7370名成年人。该研究采用了集成ML方法,比较了随机森林、XGBoost、CatBoost和基于LightGBM树的模型,以分析六个领域的31个特征:人口统计学、订购频率、食物偏好、营养认知、行为因素和服务属性。使用多种指标评估模型性能,包括敏感性、精确性、F1分数和AUC。使用部分依赖图对风险因素进行清晰解释。

结果

研究结果显示,饮食紊乱的最强预测因素是过度进食、用餐习惯改变和对高脂肪食物的偏好。年轻人、男性和BMI较高的人报告的紊乱率较高。黎巴嫩和巴林的显著紊乱率最高,而阿曼的报告最低。ML分析显示出较高的预测性能,随机森林的敏感性最高(94.3%),F1分数最高(89.3%)。特征重要性分析表明,行为因素比社会经济指标更具影响力。

结论

OFDAs在提供宝贵便利和市场扩张的同时,也对维持最佳饮食健康构成了重大挑战。通过战略干预和公共卫生合作,这些平台可以从健康饮食习惯的破坏者转变为阿拉伯地区及其他地区改善营养和福祉的催化剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4bc/12185297/ebba8bb10a79/fpubh-13-1569945-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验