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. 2025 Jun 10:13:1569945.
doi: 10.3389/fpubh.2025.1569945. eCollection 2025.

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

Radwan Qasrawi  1   2 , Suliman Thwib  1 , Ghada Issa  1 , Malak Amro  1 , Razan AbuGhoush  1 , Maha Hoteit  3   4 , Sahar Khairy  5 , Narmeen Jamal Al-Awwad  6   7 , Khlood Bookari  8 , Sabika Allehdan  9 , Dalal Alkazemi  10 , Haleama Al Sabbah  11 , Salima Al Maamari  12 , Asma H Malkawi  13 , Reema Tayyem  14
Affiliations

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

Radwan Qasrawi et al. Front Public Health. .

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.

Keywords: dietary disruptions; dietary patterns; food delivery applications; machine learning; online food delivery.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Methodological framework for identifying key predictors of dietary disruption using ensemble machine learning models.
Figure 2
Figure 2
Trade-off between sensitivity and prediction time for machine learning models in detecting dietary disruption.
Figure 3
Figure 3
Predictive performance of the random forest model in identifying individuals at risk of dietary disruption, measured by the area under the ROC curve (AUC).
Figure 4
Figure 4
Key predictors of dietary disruption ranked by their influence in the random forest model.
Figure 5
Figure 5
Marginal effects of key predictors on dietary disruption, as revealed by partial dependence plots (PDPs).
See this image and copyright information in PMC

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