781 2880 Board #166 May 30, 3:30 PM - 5:00 PM The Onset of Puberty and its Impact on Activity Levels Renee Jeffreys 1 , Susan M. Pinney 1 , Paul Succop 1 , Barbara Sternfeld, FACSM 2 , Susan L. Teitelbaum 3 , Maida P. Galvez 3 , Mary S. Wolff 4 , Lawrence H. Kushi 2 , Frank M. Biro 5 . 1 University of Cincinnati, Cincinnati, OH. 2 Kaiser Permanente Northern California, Oakland, CA. 3 Icahn School of Medicine at Mount Sinai, New Yord, NY. 4 Icahn School of Medicine at Mount Sinai, New York, NY. 5 Cincinnati Children’s Hospital Medical Center, Cincinnati, OH. (No relationships reported) Low levels of physical activity are associated with increased risks of obesity, heart disease, and certain cancers, however physical activity significantly decreases during puberty, especially for girls. The puberty studies of the Breast Cancer and the Environment Research Programs (BCERP) are longitudinal observational studies employing Tanner staging combined with serial clinical and paper based measurements, including detailed physical activity questions. Clinical data have been used in survival statistical models to estimate a date of thelarche for each girl. PURPOSE: 1) Quantify physical activity level change at different time windows post-thelarche (breast development). 2) Determine an optimal time for an intervention to maintain physical activity level during adolescence. METHODS: Sliding time windows post-thelarche (estimated date) were identified from 0-1 year to 1-2 years. Statistical analyses using mixed models incorporating the repeated measurements of physical activity were conducted to determine the effect of age, at each time window, on annualized change in time spent performing total, moderate, or vigorous physical activity. Covariates included age (at time of questionnaire), race, BMI, and socioeconomic status. RESULTS: 1,071 girls with pubertal staging from three sites (Ohio, New York, California) were included in the analysis. The analysis covers data from girls aged 6 to 14 obtained over a 7-year period and included 6,496 physical activity volume measures (questionnaire records). Overall, total hours per week increased 0.05% with each month of age (p=0.03). When the cohort was stratified by the median value of pre-thelarche activity, girls with lower physical activity levels increased their activity during the year after thelarche (p=0.0009) and those with higher physical activity decreased their activity in the window 1.75-2.75 years after (p=0.04). CONCLUSIONS: Girls vary in the pattern of physical activity change with age and thelarche. Supported by NIEHS & NCI to BCERP # U01ES012770, U01ES012771, U01ES012800, U01ES012801, U01ES019435, U01ES019453, U01ES019454, U01ES019457; NEIHS # R827039, P30ES06096 & P01ES009584; NCI #CSTA-UL1RR029887. 2881 Board #167 May 30, 3:30 PM - 5:00 PM Influence Of The Environment On Cardiovascular Risk Factors Ana Jose A. Rodrigues, Bebiana C. Sabino, Carina P. Basílio, Joana V. Teixeira, Ricardo L. Vasconcelos, Maria João C. Almeida. University of Madeira, Funchal, Portugal. (Sponsor: Sara Wilcox, FACSM) (No relationships reported) PURPOSE: Several international organizations have reported the influence of the environment on health behaviors such as physical activity, sedentary lifestyles, eating behaviors, and cardiovascular indicators in children and adolescents. The aims of the study were to determine the differences between urban and rural areas and the effect of the area of residence on health behaviors and cardiovascular indicators. METHODS: Participants were 1832 adolescents (888 boys and 944 girls), between 10 and 17.9 years of age (13.21 ± 2.12 years). All participants were asked about their physical activity (Crocker et al., 1997), sedentary activities and measured for weight, height, 20m pacer (The Cooper Institute, 2010) and metabolic parameters (blood pressure, glucose, HDL and triglycerides). Area of residence was classified according to criteria by the Portuguese National Institute of Statistics (Monteiro, 2000). Diagnosis for overweight and obesity were based on Cole et al. (2000) standards and the metabolic syndrome according to guidelines by Cook et al. (2003). RESULTS: Overall, 1 in 3 participants were overweight or obese, 3.5% had metabolic syndrome and 50.7 % scored below the aerobic capacity healthy fitness zone (HFZ). For both genders, significant differences were found between participants from urban and rural areas (p.05). Regression analysis showed that residence area explained 8% of the adiposity variance in boys and 9% in girls, whereas overall physical activity explained 7% and 5% (p<.01), respectively. Adiposity accounted for 27.5% of the aerobic fitness variance in boys and 16.8% in girls (p<.001), and regarding metabolic syndrome, it accounted for 8.1% of the variance in boys and 10.8% in girls (p<.001). CONCLUSIONS: These results suggest the need to develop different intervention programs according to the environment, with a greater need in rural areas. 2882 Board #168 May 30, 3:30 PM - 5:00 PM Impact Of Physical Inactivity On Hospitalizations And Costs Resulting From Major Non-communicable Diseases In Brazil Leandro Rezende 1 , Fabiana Rabacow 1 , Juliana Viscondi 1 , Olinda Luiz 1 , Victor Matsudo 2 , I-Min Lee, FACSM 3 . 1 Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 2 Center of Studies of Physical Fitness Research Laboratory from São Caetano do Sul, CELAFISCS, São Caetano do Sul, Brazil. 3 Harvard Medical School, Boston, MA. (No relationships reported) PURPOSE: To evaluate the impact of physical inactivity on hospital admissions and costs in Brazil that result from major non-communicable diseases. METHODS:The population attributable fraction (PAF) due to physical inactivity was calculated from the prevalence of inactivity and relative risks of coronary heart disease (CHD), type 2 diabetes (T2D), breast and colon cancer. The PAF equation used was: PAF=P(RRadj-1)/RRadj where P is the proportion of physical inactivity among cases of disease, obtained from the Brazilian National Household Sample 2008 and RRadj the relative risk of CHD, T2D, breast and colon cancer from published systematic reviews. We performed Monte Carlo simulation (10,000 simulations) to estimate the 95% confidence interval for each PAF. An individual was considered physically inactive if s/he reported no physical activity in any of the domains asked about: commuting to work, occupational physical activity, cleaning the home, and leisure-time physical activity. Since the PAF estimates the proportion of disease attributable to inactivity, we applied the PAF for each NCD to the number of hospital admissions and costs (US$) for that NCD, obtained from the Hospital Information Service of the Brazilian Health System for 2008. Copyright © 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.