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Multiuser equipment approved in grant 2015/03333-6: bioimpedance ImpediMed ImpTM SFB7

Grant number: 19/07717-4
Support type:Multi-user Equipment Program
Duration: August 01, 2019 - July 31, 2026
Field of knowledge:Health Sciences - Collective Health - Public Health
Principal Investigator:Patricia Helen de Carvalho Rondó
Grantee:Patricia Helen de Carvalho Rondó
Home Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:15/03333-6 - The relationship between maternal adiposity and adiposity of the offspring in the fetal, neonatal and infant periods: a prospective population-based study, AP.TEM
As informações de acesso ao Equipamento Multiusuário são de responsabilidade do Pesquisador responsável
EMU web page: Página do Equipamento Multiusuário não informada
Tipo de equipamento:Caracterização de Materiais - Imageamento - Elétrico (eletrocardiograma, eletro-encefalograma)
Caracterização e Análises de Amostras - Biomédica - Corporal
Fabricante: Fabricante não informado
Modelo: Modelo não informado


This project is part of a prospective population-based cohort study (process 2015 / 03333-6) that aims to evaluate the nutritional status, body composition and metabolic factors of women in the 3 trimesters of gestation and to relate to adiposity of the fetus, neonate and child in the first year of life. Two thousand (2000) women attending the antenatal care of the 33 Health Units of Araraquara, São Paulo, are being followed in the 3 trmesters of gestation until delivery, investigated monthly up to 6 months of age and twice from 6 to 12 months of age. To evaluate the body composition of the fetus, neonate and child with 1 year of life will be used, respectively, ultrasonography, plethysmography and bioimpedance (BIA). The child's BIA will be assessed by the ImpediMed Imp TM SFB7 equipment and compared with dual emission densitometry (DXA) in a subsample of children. The impact of the maternal factors investigated on adiposity of the fetus, neonate and child will be analyzed by multiple linear regression and recent methods for analysis of longitudinal data and multivariate modeling. (AU)