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Mental and behavioral factors associated with food addiction among university students: A Bangladeshi study
This study investigates the prevalence of food addiction, defined as the compulsive consumption of highly palatable foods, among university students in Bangladesh, and its associations with mental health indicators such as depression, anxiety, stress, and insomnia, as well as behavioral factors including smoking, drug use, alcohol consumption, and pornography use. A cross-sectional survey was conducted involving 1,697 participants from two universities. Food addiction was measured using the Modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Logistic regression analyses and subgroup analyses by gender were performed to examine associations, while six machine learning models (K-nearest neighbors, support vector machine, random forest, gradient boosting machine, XGBoost, and CatBoost) were utilized to enhance predictive accuracy. The findings revealed that 13% of students met the criteria for food addiction, with a higher prevalence among males (14.8%) compared to females (10.4%). In adjusted models, anxiety, stress, and pornography use were identified as significant predictors of food addiction, particularly among male students. Among the machine learning models, K-nearest neighbors achieved the highest accuracy (85.3%), while random forest exhibited the best Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) at 0.697, confirming their efficacy in identifying at-risk individuals. In conclusion, food addiction is prevalent among Bangladeshi university students and is significantly associated with anxiety, stress, and pornography use, especially in males. Recommendations for intervention include cognitive-behavioral therapy, stress management programs, digital hygiene education, and nutritional counseling specifically designed for student populations. Additionally, machine learning-based predictive models, such as random forest and CatBoost, should be integrated into campus health systems to facilitate early identification and personalized interventions. [NPID: Food addiction, depression, anxiety, behavioral factors, Yale Food Addication Scale, pornography, Bangladeshi]
Year: 2025
