Marisa M Tsai1, Edward A Frongillo2, Lorrene D Ritchie1, Gail Woodward-Lopez1, Lauren E Au3. 1. Nutrition Policy Institute, Division of Agriculture and Natural Resources, University of California, Oakland, CA, USA. 2. Department of Health Promotion, Education, and Behavior, University of South Carolina, Columbia, SC, USA. 3. Department of Nutrition, University of California, Davis, CA, USA.
Abstract
BACKGROUND: Although it has been recommended that schools be the hub of efforts to improve child nutrition, research describing school nutrition environments in US public schools and their associations with child health is limited. OBJECTIVE: This study aimed to evaluate the applicability of factor analysis methods to characterize school nutrition environments by identifying underlying factors, or dimensions, in the observed data and to examine the relation between school nutrition environment dimensions and child anthropometric and dietary outcomes. METHODS: This study examined a cross-sectional sample of 4635 US children aged 4-15 y from 386 US elementary and middle schools from the Healthy Communities Study (2013-2015). Data collected from schools were used to create 34 variables that assessed the school nutrition environment. To identify dimensions of school nutrition environments, exploratory factor analysis was conducted with orthogonal rotation, and factor scores were derived using methods to account for sporadic missing data. Mixed-effects regression models adjusted for child- and community-level variables and clustered by community and school examined the associations of school nutrition environment dimensions with child anthropometric and dietary outcomes. RESULTS: Six dimensions of school nutrition environments were derived: nutrition education, food options, wellness policies, dining environment, unhealthy food restriction, and nutrition programs. The unhealthy food restriction dimension was negatively associated with added sugar intake (β = -1.13, P < 0.0001), and the wellness policies dimension was positively associated with waist circumference (β = 0.57, P = 0.01). CONCLUSIONS: This study demonstrates how factor analysis can reduce multiple measures of complex school nutrition environments into conceptually cohesive dimensions for purposes of assessing the relation of these dimensions to student health-related outcomes. Findings were mixed and indicate that the restriction of unhealthy foods in school is associated with lower added sugar intake. Additional, longitudinal studies are needed to substantiate the utility of this method for identifying promising school nutrition environments.
BACKGROUND: Although it has been recommended that schools be the hub of efforts to improve child nutrition, research describing school nutrition environments in US public schools and their associations with child health is limited. OBJECTIVE: This study aimed to evaluate the applicability of factor analysis methods to characterize school nutrition environments by identifying underlying factors, or dimensions, in the observed data and to examine the relation between school nutrition environment dimensions and child anthropometric and dietary outcomes. METHODS: This study examined a cross-sectional sample of 4635 US children aged 4-15 y from 386 US elementary and middle schools from the Healthy Communities Study (2013-2015). Data collected from schools were used to create 34 variables that assessed the school nutrition environment. To identify dimensions of school nutrition environments, exploratory factor analysis was conducted with orthogonal rotation, and factor scores were derived using methods to account for sporadic missing data. Mixed-effects regression models adjusted for child- and community-level variables and clustered by community and school examined the associations of school nutrition environment dimensions with child anthropometric and dietary outcomes. RESULTS: Six dimensions of school nutrition environments were derived: nutrition education, food options, wellness policies, dining environment, unhealthy food restriction, and nutrition programs. The unhealthy food restriction dimension was negatively associated with added sugar intake (β = -1.13, P < 0.0001), and the wellness policies dimension was positively associated with waist circumference (β = 0.57, P = 0.01). CONCLUSIONS: This study demonstrates how factor analysis can reduce multiple measures of complex school nutrition environments into conceptually cohesive dimensions for purposes of assessing the relation of these dimensions to student health-related outcomes. Findings were mixed and indicate that the restriction of unhealthy foods in school is associated with lower added sugar intake. Additional, longitudinal studies are needed to substantiate the utility of this method for identifying promising school nutrition environments.
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