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Duncan D.T.,Harvard University | Duncan D.T.,Harvard Prevention Research Center on Nutrition and Physical Activity | Wolin K.Y.,University of Washington | Scharoun-Lee M.,Duke University | And 6 more authors.
International Journal of Behavioral Nutrition and Physical Activity | Year: 2011

Background: Weight misperception might preclude the adoption of healthful weight-related attitudes and behaviors among overweight and obese individuals, yet limited research exists in this area. We examined associations between weight misperception and several weight-related attitudes and behaviors among a nationally representative sample of overweight and obese US adults.Methods: Data from the 2003-2006 National Health and Nutrition Examination Survey (NHANES) were used. Analyses included non-pregnant, overweight and obese (measured body mass index ≥ 25) adults aged 20 and older. Weight misperception was identified among those who reported themselves as "underweight" or "about the right weight". Outcome variables and sample sizes were: weight-loss attitudes/behaviors (wanting to weigh less and having tried to lose weight; n = 4,784); dietary intake (total energy intake; n = 4,894); and physical activity (meets 2008 US physical activity recommendations, insufficiently active, and sedentary; n = 5,401). Multivariable regression models were stratified by gender and race/ethnicity. Analyses were conducted in 2009-2010.Results: These overweight/obese men and women who misperceived their weight were 71% (RR 0.29, 95% CI 0.25-0.34) and 65% (RR 0.35, 95% CI 0.29-0.42) less likely to report that they want to lose weight and 60% (RR 0.40, 95% CI 0.30-0.52) and 56% (RR 0.44, 95% CI 0.32-0.59) less likely to have tried to lose weight within the past year, respectively, compared to those who accurately perceived themselves as overweight. Blacks were particularly less likely to have tried to lose weight. Weight misperception was not a significant predictor of total energy intake among most subgroups, but was associated with lower total energy intake among Hispanic women (change -252.72, 95% CI -433.25, -72.18). Men who misperceived their weight were less likely (RR 0.68, 95% CI 0.52-0.89) to be insufficiently active (the strongest results were among Black men) and women who misperceived their weight were less likely (RR 0.74, 95% CI 0.54, 1.00, p = 0.047) to meet activity recommendations compared to being sedentary.Conclusion: Overall, weight misperception among overweight and obese adults was associated with less likelihood of interest in or attempts at weight loss and less physical activity. These associations varied by gender and race/ethnicity. This study highlights the importance of focusing on inaccurate weight perceptions in targeted weight loss efforts. © 2011 Duncan et al; licensee BioMed Central Ltd.


Duncan D.T.,Harvard University | Duncan D.T.,Harvard Prevention Research Center on Nutrition and Physical Activity | Castro M.C.,Harvard University | Blossom J.C.,Harvard University | And 4 more authors.
Geospatial Health | Year: 2011

Geocoding, the process of matching addresses to geographic coordinates, is a necessary first step when using geographical information systems (GIS) technology. However, different geocoding methodologies can result in different geographic coordinates. The objective of this study was to compare the positional (i.e. longitude/latitude) difference between two common geocoding methods, i.e. ArcGIS (Environmental System Research Institute, Redlands, CA, USA) and Batchgeo (freely available online at http://www.batchgeo.com). Address data came from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5-11 years and their families participating in YMCAadministered, after-school programmes located in four geographically diverse metropolitan areas in the USA. Our analyses include baseline addresses (n = 748) collected from the parents of the children in the after school sites. Addresses were first geocoded to the street level and assigned longitude and latitude coordinates with ArcGIS, version 9.3, then the same addresses were geocoded with Batchgeo. For this analysis, the ArcGIS minimum match score was 80. The resulting geocodes were projected into state plane coordinates, and the difference in longitude and latitude coordinates were calculated in meters between the two methods for all data points in each of the four metropolitan areas. We also quantified the descriptions of the geocoding accuracy provided by Batchgeo with the match scores from ArcGIS. We found a 94% match rate (n = 705), 2% (n = 18) were tied and 3% (n = 25) were unmatched using ArcGIS. Forty-eight addresses (6.4%) were not matched in ArcGIS with a match score ≥80 (therefore only 700 addresses were included in our positional difference analysis). Six hundred thirteen (87.6%) of these addresses had a match score of 100. Batchgeo yielded a 100% match rate for the addresses that ArcGIS geocoded. The median for longitude and latitude coordinates for all the data was just over 25 m. Overall, the range for longitude was 0.04-12,911.8 m, and the range for latitude was 0.02-37,766.6 m. Comparisons show minimal differences in the median and minimum values, while there were slightly larger differences in the maximum values. The majority (>75%) of the geographic differences were within 50 m of each other; mostly <25 m from each other (about 49%). Only about 4% overall were ≥400 m apart. We also found geographic differences in the proportion of addresses that fell within certain meter ranges. The match-score range associated with the Batchgeo accuracy level "approximate" (least accurate) was 84-100 (mean = 92), while the "rooftop" Batchgeo accuracy level (most accurate) delivered a mean of 98.9 but the range was the same. Although future research should compare the positional difference of Batchgeo to criterion measures of longitude/latitude (e.g. with global positioning system measurement), this study suggests that Batchgeo is a good, free-of-charge option to geocode addresses.


Duncan D.T.,Harvard University | Duncan D.T.,Harvard Prevention Research Center on Nutrition and Physical Activity | Castro M.C.,Harvard University | Gortmaker S.L.,Harvard University | And 5 more authors.
International Journal of Health Geographics | Year: 2012

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. © 2012 Duncan et al.; licensee BioMed Central Ltd.


Duncan D.T.,Harvard University | Duncan D.T.,Harvard Prevention Research Center on Nutrition and Physical Activity | Aldstadt J.,State University of New York at Buffalo | Whalen J.,State University of New York at Buffalo | And 3 more authors.
International Journal of Environmental Research and Public Health | Year: 2011

Neighborhood walkability can influence physical activity. We evaluated the validity of Walk Score® for assessing neighborhood walkability based on GIS (objective) indicators of neighborhood walkability with addresses from four US metropolitan areas with several street network buffer distances (i.e., 400-, 800-, and 1,600-meters). Address data come from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5-11 years and their families participating in YMCA-administered, after-school programs located in four geographically diverse metropolitan areas in the US (n = 733). GIS data were used to measure multiple objective indicators of neighborhood walkability. Walk Scores were also obtained for the participant's residential addresses. Spearman correlations between Walk Scores and the GIS neighborhood walkability indicators were calculated as well as Spearman correlations accounting for spatial autocorrelation. There were many significant moderate correlations between Walk Scores and the GIS neighborhood walkability indicators such as density of retail destinations and intersection density (p < 0.05). The magnitude varied by the GIS indicator of neighborhood walkability. Correlations generally became stronger with a larger spatial scale, and there were some geographic differences. Walk Score® is free and publicly available for public health researchers and practitioners. Results from our study suggest that Walk Score® is a valid measure of estimating certain aspects of neighborhood walkability, particularly at the 1600-meter buffer. As such, our study confirms and extends the generalizability of previous findings demonstrating that Walk Score is a valid measure of estimating neighborhood walkability in multiple geographic locations and at multiple spatial scales. © 2011 by the authors; licensee MDPI, Basel, Switzerland.

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