Quantitative Modeling of Dietary Behavior, Obesity, and Perceived Stress Among University Students: A Cross-Sectional Study Using Logistic and Multinomial Decision Models

This study applies quantitative decision-making and numerical modeling to analyze interactions between dietary behavior, obesity, and perceived stress among 1,036 students at Ibn Tofail University, Morocco. Using binary and multinomial logistic regression models, key determinants were identified for adopting a balanced diet, developing obesity, and experiencing high stress. Age, home-prepared meals, and health awareness increased the likelihood of a balanced diet, whereas sugary drinks and long engagement time reduced it. Regular physical activity and better nutritional knowledge lowered obesity risk, while prolonged daily engagement increased it. Obesity strongly predicted high perceived stress, supported by an E-value of 6.075, indicating robust associations. These findings highlight the relevance of numerical analysis in understanding student health dynamics and support evidence-based decision-making for targeted university health interventions.