ASU Food Policy and Environment Research Group

Relationships between food and physical activity (PA) environments and children's related behaviors are complex.

Latent class analyses derived patterns from proximity to healthy and unhealthy food outlets, PA facilities and parks, and counts of residential dwellings and intersections. Regression analyses examined whether derived classes were related to food consumption, PA, and overweight among 404 low-income children.

Compared to children living in Low PA-Low Food environments, children in High Intersection&Parks-Moderate Density&Food, and High Density-Low Parks-High Food environments, had significantly greater sugar-sweetened beverage consumption (ps<0.01) and overweight/obesity (ps<0.001). Children in the High Density-Low Parks-High Food environments were more likely to walk to destinations (p = 0.01)

Recognizing and leveraging beneficial aspects of neighborhood patterns may be more effective at positively influencing children's eating and PA behaviors compared to isolating individual aspects of the built environment.

Background

The United States Department of Agriculture’s Supplemental Nutrition Assistance Program (SNAP) is the country’s largest nutrition assistance program for low-income populations. Although SNAP has been shown to reduce food insecurity, research findings on the diet quality of program participants are inconsistent.

Objective

This study evaluated whether the community food environment is a potential moderator of the association between SNAP participation and eating behaviors.

Design

This cross-sectional study used participant data from a telephone survey of 2,211 households in four cities in New Jersey. Data were collected from two cross-sectional panels from 2009 to 2010 and 2014. Food outlet data were purchased from commercial sources and classified as supermarkets, small grocery stores, convenience stores, or limited service restaurants.

Participants/setting

Analysis is limited to 983 respondents (588 SNAP participants) with household incomes below 130% of the federal poverty level.

Main outcome measures

Eating behaviors were assessed as frequency of consumption of fruit, vegetables, salad, and sugar-sweetened beverages.

Statistical analyses performed

Interaction and stratified analyses using gamma regression determined the differences in the association between SNAP participation and eating behaviors by the presence or absence of food outlets adjusted for covariates.

Results

SNAP participation was associated with a higher frequency of consuming sugar-sweetened beverages (P<0.05) when respondents lived within ¼ to ½ mile of a small grocery store, supermarket, and limited service restaurant. SNAP participants who did not live close to a convenience store reported a lower frequency of sugar-sweetened beverage consumption (P=0.01), and those living more than ½ mile away from a supermarket reported a lower frequency of fruit consumption (P=0.03).

Conclusions

The findings from this study suggest that the community food environment may play a role in moderating the association between SNAP participation and eating behaviors. Although SNAP participation is associated with some unhealthy behaviors, this association may only hold true when respondents live in certain food environments.

Beginning in March 2020, the COVID-19 pandemic triggered a sudden and severe economic downturn and led to disruptions in domestic and international food systems and supply chains. Over the first few months of the pandemic, in the United States, many stores had empty shelves, bars and restaurants closed, and children could no longer go to school. The unemployment rate increased from 3.5% in February 2020 to 14.8% in April 2020, leading to economic instability for many households. As a result, household food insecurity, defined as having limited or inconsistent access to nutritious and affordable food, increased rapidly.

During the first months of 2021, vaccinations began rolling out, more individuals returned to in-person work, children to schools, and restrictions were gradually phased out. Unemployment has decreased since the April 2020 peak to 5.4% in July 2021, but remains above pre-pandemic levels. This brief describes the prevalence of household food insecurity, job disruptions, and food-related behaviors as reported by a nationally representative sample of 1,643 U.S. adults, both in the year prior to the COVID-19 pandemic (March 2019 – March 2020) and during the first four months of 2021 (January – April 2021), a period representing approximately one year since the onset of the pandemic.

Objective: The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children's weight status.

Design: Cross-sectional survey of randomly selected households with children living in low-income diverse communities.

Setting: A telephone survey conducted in 2009-2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child's residence to food and physical activity outlets.

Subjects: Analysis based on 560 children whose parents participated in the survey and provided measured heights and weights.

Results: Multiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children's weight status.

Conclusions: The approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.