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360 Background: Genetic counseling (GC) and genetic testing (GT) for prostate cancer (PCA) is growing rapidly for men with or at risk for PCA. GC provides opportunities to promote a healthy lifestyle and is understudied in men. To inform future lifestyle interventions, we assessed adherence to physical activity (PA) guidelines among a sample of men undergoing PCA GC/GT at two academic institutions. Methods: Participants enrolled in the Genetic Evaluation of Men study completed a structured lifestyle questionnaire to assess frequency of PA over the past year, including intensity of aerobic activity and strength-based activities. Data were evaluated as M±SD by PCA status, PCA aggressiveness (Gleason>7, T3, or metastatic disease), family history, and body mass index (BMI) with Chi-Square contingency analyses and adjusted residuals. Demographic characteristics predictive of PA adherence with current Department of Health and Human Services and American College of Sports Medicine PA guidelines were assessed using logistic regression analyses. Alpha levels were set a priori to p

e19020 Background: The rate of obesity in adults is 34% in the city of Philadelphia, with rates being highest among non-Hispanic blacks and Hispanics. We sought to evaluate how BMI varies by age and race/ethnicity at time of initial encounter for a breast cancer (BC) diagnosis (dx), and investigate factors predictive of BMI change in the first 2 years of dx. Methods: We identified 1,833 patients (pts) in our electronic medical record, (1/2015-present), who had a BC dx at their first hospital or ambulatory encounter using ICD-10 code C50.X, and had BMI recorded at baseline (BL) (6 mo pre-and up to 3 mo post), at 1 yr +/- 3 mo and 2 yr +/- 6 mo. We summarized demographic data (age, gender, marital status, race/ethnicity) with mean and standard deviation for continuous variables and with count and percentage for categorical variables. Descriptive statistics were summarized by 3-level BMI categories: underweight/normal (≤ 24.9), overweight (25 to 29.9), and obese (>30) and compared with ANOVA test, Chi-square-test or Fisher’s exact test as appropriate. Mixed effect regression analysis was conducted to adjust for the effects of time (i.e., BL, 1 yr, 2 yr), gender, race/ethnicity and the interaction of time and race and ethnicity in estimating mean BMI. We calculated the mean change of BMI between the BL and later time points by race/ethnicity. The significance level of all tests was set a priori to the 0.05 level. Results: 99.4% of pts were female; mean age was 63 yrs; White (60 %), Black/AA (27 %). By BMI category, we did not observe a substantial difference in the mean age of dx and gender (p = 0.18 for age and p = 0.55 for gender). We observed a substantial difference in race/ethnicity among BMI categories (p < .0001); the highest percentage of obese pts were Black/AA. In the mixed effect model, the reference level was set as a white female with an average (avg) systolic blood pressure of 137.8. The estimated avg BL BMI of this reference group was 28.8. We observed that among Asian/Pacific Islander (API) pts the avg BL BMI is lower by 3.5 ( p < .0001) and among female Black/AA pts the avg BL BMI is higher by 2.6 ( p < .0001) compared to reference group. No significant change was noted in mean BMI between BL and 1 yr for any race and ethnicity group, and only the Hispanic/Latino group had a declining trend for change in BMI between BL and 2 yr (estimate: -1.9; 95% CI: -5.9, 2.1). Conclusions: In our cohort, Black/AA pts likely have higher BL BMI and API pts likely have lower BL BMI compared to White pts. We found no significant difference in change of BMI by race and ethnicity group during the first 2 years from an initial encounter for a BC dx.