Mathematical Modeling for Optimized Design of Food Nutrition Labels and Analysis of Consumer Behavior Management

Food nutrition labels serve as essential data-driven information transmission carriers that connect food manufacturers, regulatory authorities, and consumers. They play a crucial role in guiding rational dietary choices and mitigating the global rise of diet-related chronic diseases, such as obesity and type 2 diabetes. However, existing label designs frequently suffer from severe information overload, inconsistent structural formats, and poor visual readability. These deficiencies result in low consumer utilization rates and severely limited impacts on actual dietary behavior. To systematically address these critical issues, this study proposes a novel integrated framework combining multi-objective mathematical modeling and consumer behavior management principles. The proposed framework constructs a robust optimization model incorporating information accessibility, visual cognition efficiency, and personalized dietary matching. Furthermore, it quantifies core design parameters, such as nutrient display priority and color contrast, while predicting consumer behavior via logistic regression and structural equation modeling. Behavioral intervention mechanisms, grounded in the theory of planned behavior and strategic nudging techniques, are seamlessly integrated to promote sustained dietary changes. Comprehensive validation results demonstrate that the optimized labels significantly increase the consumer reading rate by 37 ± 3%, improve nutrient comprehension accuracy by 42 ± 2%, and enhance 12-week dietary adherence by 29 ± 4%. Ultimately, this data-driven, scalable solution successfully balances information comprehensiveness with user-friendliness, providing highly valuable academic insights and practical support for public health promotion and food industry regulation.