Citas bibligráficas
Pineda, V., (2019). Capacidad de interpretación del modelo de etiquetado nutricional: "Guía diaria de alimentación (GDA)", "Octógono" y "GDA-Semáforo" [Tesis, Universidad Femenina del Sagrado Corazón]. http://hdl.handle.net/20.500.11955/561
Pineda, V., Capacidad de interpretación del modelo de etiquetado nutricional: "Guía diaria de alimentación (GDA)", "Octógono" y "GDA-Semáforo" [Tesis]. PE: Universidad Femenina del Sagrado Corazón; 2019. http://hdl.handle.net/20.500.11955/561
@mastersthesis{renati/224683,
title = "Capacidad de interpretación del modelo de etiquetado nutricional: "Guía diaria de alimentación (GDA)", "Octógono" y "GDA-Semáforo"",
author = "Pineda Soto, Vannya Inés",
publisher = "Universidad Femenina del Sagrado Corazón",
year = "2019"
}
Given the alarming figures presented by our country of overweight and obesity, which are associated as the main risk factors for developing noncommunicable diseases, three models of nutritional labeling have been proposed in Peru: Guideline Daily Amounts (GDA), GDA-Traffic Light and octagon, the latter being approved by Supreme Decree on June 17, 2017, as an alternative to improve eating behavior. The objective of this thesis was to know the nutritional labeling model that most influences the capacity to interpret the critical nutrient content of industrialized products in adults in Lima, Peru. It is descriptive-comparative level, "field" type, and cross-sectional experimental design. The data was obtained from a sample of 384 adults living in Metropolitan Lima. An online questionnaire of Google Drive was used, which included personal data and ten closed questions in relation to the nutritional labeling model presented. The results were entered into the SPSS v. 25 and t-test, ANOVA and Tukey-test were used. The result was that there are significant differences in the capacity to interpret the critical nutrient content of industrialized products between the labeling models, with the "GDA-Traffic Light" model having the greatest influence; according to the statistical tests applied.
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