OBJECTIVE: Media images of men and women have become increasingly muscular, and muscle-enhancing techniques are available to youth. Identifying populations at risk for unhealthy muscle-enhancingbehaviors is of considerable public health importance. The current study uses a large and diverse population-based sample of adolescents to examine the prevalence of muscle-enhancing behaviors and differences across demographic characteristics, weight status, and sports team involvement. METHODS: Survey data from 2793 diverse adolescents (mean age = 14.4) were collected at 20 urban middle and high schools. Use of 5 muscle-enhancing behaviors was assessed (changing eating, exercising, protein powders, steroids and other muscle-enhancing substances), and a summary score reflecting use of 3 or more behaviors was created. Logistic regression was used to test for differences in each behavior across age group, race/ethnicity, socioeconomic status, BMI category, and sports team participation. RESULTS: Muscle-enhancing behaviors were common in this sample for both boys and girls. For example, 34.7% used protein powders or shakes and 5.9% reported steroid use. Most behaviors were significantly more common among boys. In models mutually adjusted for all covariates, grade level, Asian race, BMI category, and sports team participation were significantly associated with the use of muscle-enhancing behaviors. For example, overweight (odds ratio = 1.45) and obese (odds ratio = 1.90) girls had significantly greater odds of using protein powders or shakes than girls of average BMI. CONCLUSIONS: The use of muscle-enhancing behaviors is substantially higher than has been previously reported and is cause for concern. Pediatricians and other health care providers should ask their adolescent patients about muscle-enhancing behaviors.

BACKGROUND The adverse effects of blood transfusion after cancer surgery have been recently challenged in older anemic persons or those with substantial intraoperative blood loss. We hypothesized that intraoperative blood transfusions continue to adversely impact short-term cancer surgery outcomes regardless of age or preoperative hematocrit levels. METHODS Using the 2005 to 2008 American College of Surgeons National Surgical Quality Improvement Program, we identified 38,926 patients who underwent cancer surgery. Pre-, intra-, and postoperative factors were compared by units of blood transfusion a patient received. Stratified multivariable analyses, by age and hematocrit level, were performed to assess the impact of blood transfusion on operative outcomes, adjusting for covariates. RESULTS Fourteen percent of patients received an intraoperative blood transfusion. Of those, >60% received only 1 to 2 units of blood. Receipt of intraoperative blood transfusion was associated with higher rates of 30-day operative mortality, major complications, total number of complications, and prolonged length of stay across age groups and in persons with low to normal hematocrit levels. CONCLUSION The present study shows that intraoperative blood transfusion adversely impacts short-term operative cancer surgery outcomes across all age groups and in those with low to normal hematocrit levels. These findings provide insightful implications on the patterns of blood transfusion during cancer surgery that deserve further investigation.

We examined the association between sleep duration and BMI in young adults, and, specifically, in possible gender differences. The population-based sample included 955 young men and 1051 young women (mean age = 25.3 years, s.d. = 1.7) who participated in Project EAT-III (Eating and Activity in Teens and Young Adults)-III. In 2008-2009, study participants completed a survey, on which they reported their weight, height, and typical bed and awakening times. Gender-specific regression models estimated cross-sectional associations between sleep duration and weight status, adjusting for age, race, SES, family structure, depressive symptoms, physical activity, and sedentary and dietary behaviors. In multivariable-adjusted linear regression models, an hour increase in sleep was associated with a -0.38 (-0.70, -0.048) BMI in men. Men who slept <7 h had a 1.4 unit higher mean BMI (27.9; 95% confidence interval (CI): 26.9, 28.9) than men who slept 7-9 h/day (26.5; 95% CI: 26.1, 27.0). Prevalence estimates of overweight (BMI ≥25) and obesity (BMI ≥30) were also inversely associated with sleep duration among men. Sleep duration was not associated with BMI, overweight, or obesity in women. Among women, but not men, there was a statistically significant positive association between trouble falling or staying asleep and mean BMI. Sleep may be an important modifiable risk factor for obesity, particularly in young adult men.