142 research outputs found
Racial differences in the built environment—body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods
Background: Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers). Methods: Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran’s I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools. Results: We found significant positive spatial autocorrelation in the built environment features examined (Global Moran’s I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran’s I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer. Conclusion: Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents
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School-Based Programs: Lessons Learned from CATCH, Planet Health, and Not-On-Tobacco
Establishing healthy habits in youth can help prevent many chronic health problems later in life that are attributable to unhealthy eating, sedentary lifestyle, and overweight. For this reason, many public health professionals are interested in working with school systems to reach children in school settings. However, a lack of familiarity with how schools operate can be a substantial impediment to developing effective partnerships with schools. We describe lessons learned from three successful school health promotion programs that were developed and disseminated through collaborations between public health professionals, academic institutions, and school personnel. The programs include two focused on physical activity and good nutrition for elementary and middle school children — Coordinated Approach to Child Health (CATCH) and Planet Health — and one focused on smoking cessation among adolescents — Not-On-Tobacco (N-O-T). Important features of these school health programs include 1) identification of staff and resources required for program implementation and dissemination; 2) involvement of stakeholders (e.g., teachers, students, other school personnel, parents, nonprofit organizations, professional organizations) during all phases of program development and dissemination; 3) planning for dissemination of programs early in the development and testing process; and 4) rigorous evaluation of interventions to determine their effectiveness. The authors provide advice based on lessons learned from these programs to those who wish to work with young people in schools
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Identifying Sources of Children’s Consumption of Junk Food in Boston After-School Programs, April–May 2011
Introduction: Little is known about how the nutrition environment in after-school settings may affect children’s dietary intake. We measured the nutritional quality of after-school snacks provided by programs participating in the National School Lunch Program or the Child and Adult Care Food Program and compared them with snacks brought from home or purchased elsewhere (nonprogram snacks). We quantified the effect of nonprogram snacks on the dietary intake of children who also received program-provided snacks during after-school time. Our study objective was to determine how different sources of snacks affect children’s snack consumption in after-school settings. Methods: We recorded snacks served to and brought in by 298 children in 18 after-school programs in Boston, Massachusetts, on 5 program days in April and May 2011. We measured children’s snack consumption on 2 program days using a validated observation protocol. We then calculated within-child change-in-change models to estimate the effect of nonprogram snacks on children’s dietary intake after school. Results: Nonprogram snacks contained more sugary beverages and candy than program-provided snacks. Having a nonprogram snack was associated with significantly higher consumption of total calories (+114.7 kcal, P < .001), sugar-sweetened beverages (+0.5 oz, P = .01), desserts (+0.3 servings, P < .001), and foods with added sugars (+0.5 servings; P < .001) during the snack period. Conclusion: On days when children brought their own after-school snack, they consumed more salty and sugary foods and nearly twice as many calories than on days when they consumed only program-provided snacks. Policy strategies limiting nonprogram snacks or setting nutritional standards for them in after-school settings should be explored further as a way to promote child health
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Play Across Boston: A Community Initiative to Reduce Disparities in Access to After-School Physical Activity Programs for Inner-City Youths
Background: In 1999, the Centers for Disease Control and Prevention (CDC) funded Play Across Boston to address disparities in access to physical activity facilities and programs for Boston, Mass, inner-city youths. Context: Local stakeholders worked with the Harvard School of Public Health Prevention Research Center and Northeastern University's Center for the Study of Sport in Society to improve opportunities for youth physical activity through censuses of facilities and programs and dissemination of results. Methods: Play Across Boston staff conducted a facility census among 230 public recreational complexes and a program census of 86% of 274 physical activity programs for Boston inner-city youths aged 5 to 18 years during nonschool hours for the 1999 to 2000 school year and summer of 2000. Comparison data were collected from three suburban communities: one low income, one medium income, and one high income. Consequences: Although Boston has a substantial sports and recreational infrastructure, the ratio of youths to facilities in inner-city Boston was twice the ratio found in the medium- and high-income suburban comparison communities. The low-income suburban comparison community had the highest number of youths per recreational facility with 137 youths per facility, followed by Boston with 117 youths per facility. The ratio of youths to facilities differed among Boston neighborhoods. Boston youths participated less in school-year physical activities than youths in medium- and high-income communities, and less advantaged Boston neighborhoods had lower levels of participation than more advantaged Boston neighborhoods. Girls participated less than boys. Interpretation: Play Across Boston successfully developed and implemented a rigorous needs assessment with local relevance and important implications for public health research on physical activity and the environment. Boston Mayor Thomas M. Menino called the Play Across Boston report a "playbook" for future sports and recreation planning by the city of Boston and its community partners
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Informal Training in Staff Networks to Support Dissemination of Health Promotion Programs
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Validity of a practitioner-administered observational tool to measure physical activity, nutrition, and screen time in school-age programs
Background: Nutrition and physical activity interventions have been effective in creating environmental changes in afterschool programs. However, accurate assessment can be time-consuming and expensive as initiatives are scaled up for optimal population impact. This study aims to determine the criterion validity of a simple, low-cost, practitioner-administered observational measure of afterschool physical activity, nutrition, and screen time practices and child behaviors. Methods: Directors from 35 programs in three cities completed the Out-of-School Nutrition and Physical Activity Observational Practice Assessment Tool (OSNAP-OPAT) on five days. Trained observers recorded snacks served and obtained accelerometer data each day during the same week. Observations of physical activity participation and snack consumption were conducted on two days. Correlations were calculated to validate weekly average estimates from OSNAP-OPAT compared to criterion measures. Weekly criterion averages are based on 175 meals served, snack consumption of 528 children, and physical activity levels of 356 children. Results: OSNAP-OPAT validly assessed serving water (r = 0.73), fruits and vegetables (r = 0.84), juice >4oz (r = 0.56), and grains (r = 0.60) at snack; sugary drinks (r = 0.70) and foods (r = 0.68) from outside the program; and children’s water consumption (r = 0.56) (all p <0.05). Reports of physical activity time offered were correlated with accelerometer estimates (minutes of moderate and vigorous physical activity r = 0.59, p = 0.02; vigorous physical activity r = 0.63, p = 0.01). The reported proportion of children participating in moderate and vigorous physical activity was correlated with observations (r = 0.48, p = 0.03), as were reports of computer (r = 0.85) and TV/movie (r = 0.68) time compared to direct observations (both p < 0.01). Conclusions: OSNAP-OPAT can assist researchers and practitioners in validly assessing nutrition and physical activity environments and behaviors in afterschool settings. Trial registration Phase 1 of this measure validation was conducted during a study registered at clinicaltrials.gov NCT01396473. Electronic supplementary material The online version of this article (doi:10.1186/s12966-014-0145-5) contains supplementary material, which is available to authorized users
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