The Impact of Online Food Delivery Applications on Dietary Pattern Disruption in the Arab Region

Radwan Qasrawi^1,2*Suliman Thwib¹Ghada Issa¹Malak Amro¹Maha Hoteit^3,4Sahar Khairy⁵Narmeen Al-Awwad^6,7Khlood Bookari⁸Allehdan Sabika⁹Dalal Usamah Zaid Alkazemi¹⁰Haleama Al Sabbah¹¹Salima Al Maamari¹²Asma H Malkawi¹³Reema Fayez Tayyem^13*

• ¹Al-Quds University, Jerusalem, Palestine
• ²University of Istinye, Istanbul, Türkiye
• ³National Council for Scientific Research, Beirut, Beyrouth, Lebanon
• ⁴Lebanese University, Beirut, Lebanon
• ⁵National Nutrition Institute, Cairo, Egypt
• ⁶Hashemite University, Zarqa, Zarqa, Jordan
• ⁷Middle East University, Amman, Amman, Jordan
• ⁸Taibah University, Medina, Al Madinah, Saudi Arabia
• ⁹University of Bahrain, Sakhir, Southern Governorate, Bahrain
• ¹⁰Kuwait University, Kuwait City, Kuwait
• ¹¹Abu Dhabi University, Abu Dhabi, United Arab Emirates
• ¹²Ministry of Health (Oman), Muscat, Oman
• ¹³Qatar University, Doha, Qatar

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. Conclusions: 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. Young adults (18-30 years) are a high-risk group for experiencing dietary disruption through OFDAs.  Behavioral factors, particularly excessive consumption, disrupted meal routines, and preference for fatty foods are the strongest predictors of dietary disruption.  Healthier foods have a protective effect.