Reported energy intake and estimated energy needs by predictive equations in adults and older adults in São Paulo.

• Grant number: 24/13946-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2024
• End date: August 31, 2025
• Field of knowledge: Health Sciences - Nutrition - Nutritional Analysis of Population
• Principal Investigator: Regina Mara Fisberg
• Grantee: Thiago Lucena Rodrigues
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 17/05125-7 - Lifestyle, biochemical and genetic markers as cardiometabolic risk factors: Health Survey in São Paulo City, AP.TEM

Abstract

Accurate estimation of energy needs and intake is essential to minimize health consequences across all age groups. Therefore, it is of fundamental importance to develop studies that contribute to the validation and accuracy of the methods used for its measurement. Thus, the objective of the present study is to compare the reported energy intake of adults and older adults with estimates of energy requirements through predictive equations. Data from adults and older adults from the "ISA-Nutrition" study, a cross-sectional, population-based study with a probabilistic sample of residents of the city of São Paulo, who underwent dietary intake assessment and anthropometric data collection, totaling 610 individuals, will be used. Weight and height measurements were collected using standardized procedures, and dietary intake was measured by two 24-hour recalls. Physical activity level was assessed using the International Physical Activity Questionnaire, validated for the Brazilian population, and sociodemographic data such as age and sex were collected through questionnaires. Habitual dietary intake will be compared with the estimates of energy requirements calculated by the Dietary Reference Intakes equations, updated in 2023. For this purpose, Pearson or Spearman correlation coefficients and Bland-Altman plots will be calculated to assess the magnitude of differences and the presence of systematic bias between the measures. Analyses will be performed in RStudio software, considering a significance level of 5%.