PURPOSE: To determine the prevalence and correlates of missing meals among adolescents. DESIGN: The 2010 National Youth Physical Activity and Nutrition Study, a cross-sectional study. SETTING: School based. PARTICIPANTS: A nationally representative sample of 11 429 high school students. MEASURES: Breakfast, lunch, and dinner consumption; demographics; measured and perceived weight status; physical activity and sedentary behaviors; and fruit, vegetable, milk, sugar-sweetened beverage, and fast-food intake. ANALYSIS: Prevalence estimates for missing breakfast, lunch, or dinner on ≥1 day during the past 7 days were calculated. Associations between demographics and missing meals were tested. Associations of lifestyle and dietary behaviors with missing meals were examined using logistic regression controlling for sex, race/ethnicity, and grade. RESULTS: In 2010, 63.1% of students missed breakfast, 38.2% missed lunch, and 23.3% missed dinner; the prevalence was highest among female and non-Hispanic black students. Being overweight/obese, perceiving oneself to be overweight, and video game/computer use were associated with increased risk of missing meals. Physical activity behaviors were associated with reduced risk of missing meals. Students who missed breakfast were less likely to eat fruits and vegetables and more likely to consume sugar-sweetened beverages and fast food. CONCLUSION: Breakfast was the most frequently missed meal, and missing breakfast was associated with the greatest number of less healthy dietary practices. Intervention and education efforts might prioritize breakfast consumption.

OBJECTIVES: We investigated the body mass index (BMI; weight in pounds/[height in inches]2 x 703) of parents whose children participated in Shape Up Somerville (SUS), a community-based participatory research study that altered household, school, and community environments to prevent and reduce childhood obesity. METHODS: SUS was a nonrandomized controlled trial with 30 participating elementary schools in 3 Massachusetts communities that occurred from 2002 to 2005. It included first-, second-, and third-grade children. We used an inverse probability weighting estimator adjusted for clustering effects to isolate the influence of SUS on parent (n = 478) BMI. The model's dependent variable was the change in pre- and postintervention parent BMI. RESULTS: SUS was significantly associated with decreases in parent BMIs. SUS decreased treatment parents' BMIs by 0.411 points (95% confidence interval = -0.725, -0.097) relative to control parents. CONCLUSIONS: The benefits of a community-based environmental change childhood obesity intervention can spill over to parents, resulting in decreased parental BMI. Further research is warranted to examine the effects of this type of intervention on parental health behaviors and health outcomes.

BACKGROUND: Eating more fruits and vegetables adds underconsumed nutrients to diets, reduces the risks for leading causes of illness and death, and helps manage body weight. This report describes trends in the contributions of fruits and vegetables to the diets of children aged 2-18 years. METHODS: CDC analyzed 1 day of 24-hour dietary recalls from the National Health and Nutrition Examination Surveys from 2003 to 2010 to estimate trends in children's fruit and vegetable intake in cup-equivalents per 1,000 calories (CEPC) and trends by sex, age, race/ethnicity, family income to poverty ratio, and obesity status. Total fruit includes whole fruit (all fruit excluding juice) and fruit juice (from 100% juice, foods, and other beverages). Total vegetables include those encouraged in the Dietary Guidelines for Americans, 2010 (i.e., dark green, orange, and red vegetables and legumes), white potatoes, and all other vegetables. RESULTS: Total fruit intake among children increased from 0.55 CEPC in 2003-2004 to 0.62 in 2009-2010 because of significant increases in whole fruit intake (0.24 to 0.40 CEPC). Over this period, fruit juice intake significantly decreased (0.31 to 0.22 CEPC). Total vegetable intake did not change (0.54 to 0.53 CEPC). No socio-demographic group met the Healthy People 2020 target of 1.1 CEPC vegetables, and only children aged 2-5 years met the target of 0.9 CEPC fruits. CONCLUSIONS: Children's total fruit intake increased because of increases in whole fruit consumption, but total vegetable intake remained unchanged. Implications for Public Health Practice: Increased attention to the policies and food environments in multiple settings, including schools, early care and education, and homes might help continue the progress in fruit intake and improve vegetable intake.

To examine trends in weight management goals and behaviors among U.S. high school students during 1999-2009. Data from six biennial cycles (1999-2009) of the national Youth Risk Behavior Survey were analyzed. Cross-sectional, nationally representative samples of 9th-12th grade students (approximately 14,000 students/cycle) completed self-administered questionnaires. Logistic regression models adjusted for grade, race/ethnicity, and obesity were used to test for trends in weight management goals and behaviors among subgroups of students. Combined prevalences and trends differed by sex and by race/ethnicity and weight status within sex. During 1999-2009, the prevalence of female students trying to gain weight decreased (7.6-5.7 %). Among female students trying to lose or stay the same weight, prevalences decreased for eating less (69.6-63.2 %); fasting (23.3-17.6 %); using diet pills/powders/liquids (13.7-7.8 %); and vomiting/laxatives (9.5-6.6 %) for weight control. During 1999-2009, the prevalence of male students trying to lose weight increased (26.1-30.5 %). Among male students trying to lose or stay the same weight, the prevalence of exercising to control weight did not change during 1999-2003 and then increased (74.0-79.1 %) while the prevalence of taking diet pills/powders/liquids for weight control decreased (6.9-5.1 %) during 1999-2009. Weight management goals and behaviors changed during 1999-2009 and differed by subgroup. To combat the use of unhealthy weight control behaviors, efforts may be needed to teach adolescents about recommended weight management strategies and avoiding the risks associated with unhealthy methods.

With obesity affecting approximately 12.5 million American youth, population-level interventions are indicated to help support healthy behaviors. The purpose of this review is to provide a summary of population-level intervention strategies and specific intervention examples that illustrate ways to help prevent and control obesity in children through improving nutrition and physical activity behaviors. Information is summarized within the settings where children live, learn, and play (early care and education, school, community, health care, home). Intervention strategies are activities or changes intended to promote healthful behaviors in children. They were identified from (a) systematic reviews; (b) evidence- and expert consensus-based recommendations, guidelines, or standards from nongovernmental or federal agencies; and finally (c) peer-reviewed synthesis reviews. Intervention examples illustrate how at least one of the strategies was used in a particular setting. To identify interventions examples, we considered (a) peer-reviewed literature as well as (b) additional sources with research-tested and practice-based initiatives. Researchers and practitioners may use this review as they set priorities and promote integration across settings and to find research- and practice-tested intervention examples that can be replicated in their communities for childhood obesity prevention. (Expected final online publication date for the Annual Review of Nutrition Volume 32 is July 17, 2012. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.)

OBJECTIVE: The purpose of this review is to synthesize the scientific literature that has examined the association between school-based physical activity (including physical education) and academic performance (including indicators of cognitive skills and attitudes, academic behaviors, and academic achievement). METHOD: Relevant research was identified through a search of nine electronic databases using both physical activity and academic-related search terms. Forty-three articles (reporting a total of 50 unique studies) met the inclusion criteria and were read, abstracted, and coded for this synthesis. Findings of the 50 studies were then summarized. RESULTS: Across all the studies, there were a total of 251 associations between physical activity and academic performance, representing measures of academic achievement, academic behavior, and cognitive skills and attitudes. Slightly more than half (50.5%) of all associations examined were positive, 48% were not significant, and 1.5% were negative. Examination of the findings by each physical activity context provides insights regarding specific relationships. CONCLUSION: Results suggest physical activity is either positively related to academic performance or that there is not a demonstrated relationship between physical activity and academic performance. Results have important implications for both policy and schools.

BACKGROUND: The cutoffs from the Centers for Disease Control and Prevention (CDC) growth charts and from the Cooper Institute (FitnessGram) are widely used to identify children who have a high body mass index (BMI). OBJECTIVE: We compared the abilities of these 2 systems to identify children who have adverse lipid concentrations and blood pressure measurements and the reliability (consistency) of each classification system over time (mean follow-up: 7 y). DESIGN: A cross-sectional analysis based on data from 22,896 examinations of 5- to 17-y-olds was conducted. Principal components analyses were used to summarize levels of the 5 risk factors, and likelihood ratios and the kappa statistic were used to compare the screening abilities of the 2 systems. Of these children, 3972 were included in longitudinal analyses. RESULTS: There were marked differences in the prevalence of a high FitnessGram BMI by age, with the prevalence among boys increasing from 2.5% to 21% between the ages of 5 and 11 y. The identification of adverse risk factors by the 2 systems was only fair (kappa = 0.25), but there was little difference in the abilities of the CDC and FitnessGram cutoffs to identify high-risk children. Longitudinal analyses, however, indicated that the agreement between initial and follow-up FitnessGram classifications was substantially lower than that based on CDC cutoffs (kappa = 0.28 compared with 0.49). CONCLUSIONS: The FitnessGram and CDC cutoffs have similar abilities to identify high-risk children. However, a high FitnessGram BMI is difficult to interpret because the reliability over time is low, and the prevalence increases markedly with age.

BACKGROUND AND OBJECTIVE: School-based BMI measurement has attracted attention across the nation as a potential approach to address obesity among youth. However, little is known about its impact or effectiveness in changing obesity rates or related physical activity and dietary behaviors that influence obesity. This article describes current BMI-measurement programs and practices, research, and expert recommendations and provides guidance on implementing such an approach. METHODS: An extensive search for scientific articles, position statements, and current state legislation related to BMI-measurement programs was conducted. A literature and policy review was written and presented to a panel of experts. This panel, comprising experts in public health, education, school counseling, school medical care, and parenting, reviewed and provided expertise on this article. RESULTS: School-based BMI-measurement programs are conducted for surveillance or screening purposes. Thirteen states are implementing school-based BMI-measurement programs as required by legislation. Few studies exist that assess the utility of these programs in preventing increases in obesity or the effects these programs may have on weight-related knowledge, attitudes, and behaviors of youth and their families. Typically, expert organizations support school-based BMI surveillance; however, controversy exists over screening. BMI screening does not currently meet all of the American Academy of Pediatrics' criteria for determining whether screening for specific health conditions should be implemented in schools. CONCLUSION: Schools initiating BMI-measurement programs should adhere to safeguards to minimize potential harms and maximize benefits, establish a safe and supportive environment for students of all body sizes, and implement science-based strategies to promote physical activity and healthy eating.