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Relationship between lifestyle and level of physical activity of schoolchildren during Physical Education classes

Grant number: 18/24567-3
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Start date: March 01, 2019
End date: December 31, 2019
Field of knowledge:Health Sciences - Physical Education
Principal Investigator:Ismael Forte Freitas Júnior
Grantee:Giovanna Goes Sant Ana
Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil

Abstract

The present study aims to analyze the level of physical activity during physical education classes and its interface with the lifestyle content of 6th and 7th grade students from public schools (10 to 13 years). This is an epidemiological cross-sectional, analytical and qualitative-quantitative approach. Students will be evaluated from state public schools of partial education of the city of Presidente Prudente. The data relating to physical activity level will be obtained using tri axial accelerometer, Actigraph (ActiGraph LLC, Pensacola, Fla, USA) Model + GT3X and cutting points Evenson et al. (2008) will be adopted to categorize levels of physical activity. For the variables related to lifestyle and sociodemographic will be collected through a self-reported questionnaire whose main questions involve the evaluation of behaviors related to diet, physical activity in leisure, social support for physical activity practice, use of smartphone (Smartphone Inventary for Adolescent's (SIA) and sedentary behavior. The answers obtained will be tabulated in an optical reader using SPHYNX Survey software version 5.1.0.8. For the statistical analysis, the SPSS Program for Windows, version 25.0 will be used, adopting a significance level of 5%. Association measures will be applied by the Chi-square test (x2) and Fisher's Exact for the association of variables, and to estimate the association between the primary outcome and the secondary outcomes, the Odds Ratio (RO) will be used by means of Binary Logistic Regression.

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