Assessment of correlation between body composition, muscle strength and spontaneous physical activity in elderly

• Grant number: 11/16444-0
• Support Opportunities: Scholarships in Brazil - Master
• Start date: March 01, 2012
• End date: October 31, 2013
• Field of knowledge: Health Sciences - Nutrition
• Principal Investigator: Eduardo Ferriolli
• Grantee: Juliana Cristina Lemos de Souza Marchesi
• Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

It is estimated that by 2020 Brazil will be the sixth country in the world in number of elderly people, possibly more than 30 million people. Aging involves changes in physiological and nutritional status. Physical inactivity has been shown to be an important risk factor for chronic diseases, disability and frailty and its association with obesity is well established, but recent work of the group of Geriatrics, Faculty of Medicine of Ribeirão Preto, using remote monitoring of spontaneous physical activity suggest that the pattern of fat accumulation and its relation to lean body mass may be in the elderly, related to the pattern of accumulation of hours of rest in the day, in relation to physical activity and intensity. Thus, this study aims to evaluate the correlation between body composition and spontaneous physical activity in older adults. Participants will be selected among the attendees of outpatient clinics and ward of the Geriatric Hospital of the Faculty of Medicine of Ribeirão Preto and among participants in two projects already underway. Be evaluated: food intake, body composition (by analysis of deuterated water, bioelectrical impedance and DEXA, dual energy x-ray absorptiometry or dual beam radioabsorciometria), anthropometric measures, physical activity, muscle strength and power. All data will be submitted to descriptive statistics. It will also be performed paired t-test to verify the difference between the groups, calculating the Pearson correlation coefficient and multiple linear regression.