Repercussões do período pandêmico no consumo de fast-foods no Brasil: revisão integrativa.

Abstract

COVID-19 emerged in China and soon spread around the world, becoming a public health problem. In 2020, the World Health Organization declared that COVID-19 was now considered a pandemic. To contain the advance of the disease, social isolation measures were decreed, however, such measures may have been responsible for several changes in the lifestyle of the population, especially eating habits, given that the coronavirus pandemic has contributed to the increased consumption of fast food and industrialized foods through delivery, which has a great influence on the onset of diseases such as obesity, hypertension and diabetes. Therefore, the present study aims to analyze the literature and identify, through integrative review, the repercussions of the pandemic on the consumption of fast foods in Brazil. The articles were selected in November 2022, through an automatic search in the literature, through databases such as: Google Scholar, Scientific Electronic Library Online (SciELO), PubMED, Virtual Health Library (VHL), among others, using keywords according

to health sciences descriptors (DeCS) in Portuguese: "Comportamento Alimentar", "Covid- 19"; and in English: "Fast-food", "Feeding Behavior" and "Sars-Cov-2", 11 papers were

selected after the applicability of the exclusion and inclusion criteria. It is notorious that during the period of home confinement, caused by coronavirus that the eating habits of the Brazilian population and the lifestyle of families have changed. In most of the studies mentioned in this review, the population reported less healthy eating habits during the isolation period, as the increase in the consumption of fast-foods and industrialized foods. Thus, it is concluded that future studies should be conducted in order to deepen more knowledge about the subject in certain regions of the country, with larger samples, crossing data on income, schooling and cultural differences, in order to optimize the data available in the scientific literature.