Allison W Watts1, Jon Miller2, Nicole I Larson2, Marla E Eisenberg3, Mary T Story4, Dianne Neumark-Sztainer2. 1. Division of Epidemiology and Community Health, University of Minnesota, 1300 South 2(nd) Street, Suite 300, Minneapolis, MN 55454, USA. Electronic address: awwatts@umn.edu. 2. Division of Epidemiology and Community Health, University of Minnesota, 1300 South 2(nd) Street, Suite 300, Minneapolis, MN 55454, USA. 3. Department of Pediatrics and Adolescent Health, University of Minnesota, 717 Delaware St SE, Minneapolis, MN 55414, USA. 4. Community and Family Medicine and Global Health, Duke University, Duke Global Health Institute, Box 90519, Durham, NC 27708, USA.
Abstract
PURPOSE: To examine personal, home, peer, school, neighborhood, and media correlates of sugar-sweetened beverage (SSB) intake in a diverse sample of adolescents. METHODS: Cross-sectional, population-based study (EAT 2010: Eating and Activity in Teens) of 2793 adolescents (54% female, mean age [SD] = 14.5 [2.0], 80% nonwhite) attending public secondary schools in Minneapolis-St. Paul, Minnesota. Adolescents completed a food frequency questionnaire and answered survey questions about their diet/health perceptions and behaviors. Socio-environmental data were collected from parents/caregivers, peers, school personnel, Geographic Information Systems (e.g., distance to food outlet), and a content analysis of favorite TV shows. Individual and mutually adjusted mixed-effects regression models examined associations between multi-contextual factors and estimated daily servings of SSB, controlling for relevant covariates. RESULTS: The contextual factors examined accounted for 24% of the variance in adolescents' SSB consumption. The proportion of variance explained by each context was 13% personal, 16% home/family, 3% peer, 1% school, 0.1% media, and 0% neighborhood. The strongest correlate of SSB intake was home soda availability (adjusted for covariates: β = 0.26, p < 0.01; adjusted for all multi-contextual factors: β = 0.18, p < 0.01). Other significant correlates of SSB intake included personal behaviors (e.g., fast food intake, sleep), home/family factors (e.g., parent modeling) and peer influences (e.g., friends' SSB intake). CONCLUSIONS: Public health policies and programs to reduce adolescent SSB intake should target personal behaviors (e.g., limit fast food, encourage adequate sleep), address the home setting (e.g., help parents to reduce SSB availability and model healthy eating habits) and involve peers (e.g., identify and enable peers to model healthy eating behaviors).
PURPOSE: To examine personal, home, peer, school, neighborhood, and media correlates of sugar-sweetened beverage (SSB) intake in a diverse sample of adolescents. METHODS: Cross-sectional, population-based study (EAT 2010: Eating and Activity in Teens) of 2793 adolescents (54% female, mean age [SD] = 14.5 [2.0], 80% nonwhite) attending public secondary schools in Minneapolis-St. Paul, Minnesota. Adolescents completed a food frequency questionnaire and answered survey questions about their diet/health perceptions and behaviors. Socio-environmental data were collected from parents/caregivers, peers, school personnel, Geographic Information Systems (e.g., distance to food outlet), and a content analysis of favorite TV shows. Individual and mutually adjusted mixed-effects regression models examined associations between multi-contextual factors and estimated daily servings of SSB, controlling for relevant covariates. RESULTS: The contextual factors examined accounted for 24% of the variance in adolescents' SSB consumption. The proportion of variance explained by each context was 13% personal, 16% home/family, 3% peer, 1% school, 0.1% media, and 0% neighborhood. The strongest correlate of SSB intake was home soda availability (adjusted for covariates: β = 0.26, p < 0.01; adjusted for all multi-contextual factors: β = 0.18, p < 0.01). Other significant correlates of SSB intake included personal behaviors (e.g., fast food intake, sleep), home/family factors (e.g., parent modeling) and peer influences (e.g., friends' SSB intake). CONCLUSIONS: Public health policies and programs to reduce adolescent SSB intake should target personal behaviors (e.g., limit fast food, encourage adequate sleep), address the home setting (e.g., help parents to reduce SSB availability and model healthy eating habits) and involve peers (e.g., identify and enable peers to model healthy eating behaviors).
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