ASU Food Policy and Environment Research Group
A collection of scholarly work created by the ASU Food Policy and Environment Research Group under the leadership of Professor Punam Ohri-Vachaspati. The group examines policies, programs, and environments that influence food consumption and physical activity behaviors and health outcomes in disadvantaged populations. We aim to improve the health of children and families through comprehensive policy and environmental approaches.
The maps in this chartbook describe the physical activity environment in Vineland in terms of geographic distribution of parks and physical activity facilities. Research shows that people who have access to these facilities are more likely to be physically active.
• The maps in this chartbook were created using physical activity facilities data from a commercial database (lnfoUSA, 2008), data from city departments, as well as information obtained from systematic web searches. The maps present data for the city of Vineland and for a 1 mile buffer area around Vineland.
• Physical activity centers include private and public facilities which offer physical activity opportunities for children 3-18 years of age.
• Physical activity environment maps are compared with Census 2000 data to visualize accessibility of physical activity opportunities in neighborhoods with different characteristics.
• Poverty level presented in this chartbook are based on the 2000 Federal Poverty Guidelines.
• Crime rates in Vineland are presented at the census block group level as relative crime risk (CrimeRisk) obtained from a commercial data source (Applied Geographic Solutions, 2008). CrimeRisk - an index value derived from modeling the relationship between crime rates and demographics data - is expressed as the risk of crime occurring in a specific block group relative to the national average. For this chartbook, data on total CrimeRisk, which includes personal and property crimes, are reported.
The maps in this chartbook describe the physical activity environment in Newark in terms of geographic distribution of parks and physical activity facilities. Research shows that people who have access to these facilities are more likely to be physically active.
• The maps in this chartbook were created using physical activity facilities data from a commercial database (lnfoUSA, 2008), data from city departments, as well as information obtained from systematic web searches. The maps present data for the city of Newark and for a 1 mile buffer area around Newark.
• Physical activity centers include private and public facilities which offer physical activity opportunities for children 3-18 years of age.
• Physical activity environment maps are compared with Census 2000 data to visualize accessibility of physical activity opportunities in neighborhoods with different characteristics.
• Poverty level presented in this chartbook are based on the 2000 Federal Poverty Guidelines.
• Crime rates in Newark are presented at the census block group level as relative crime risk (CrimeRisk) obtained from a commercial data source (Applied Geographic Solutions, 2008). CrimeRisk - an index value derived from modeling the relationship between crime rates and demographics data - is expressed as the risk of crime occurring in a specific block group relative to the national average. For this chartbook, data on total CrimeRisk, which includes personal and property crimes, are reported.
The maps in this chartbook describe the physical activity environment in Trenton in terms of geographic distribution of parks and physical activity facilities. Research shows that people who have access to these facilities are more likely to be physically active.
• The maps in this chartbook were created using physical activity facilities data from a commercial database (lnfoUSA, 2008), data from city departments, as well as information obtained from systematic web searches. The maps present data for the city of Trenton and for a 1 mile buffer area around Trenton.
• Physical activity centers include private and public facilities which offer physical activity opportunities for children 3-18 years of age.
• Physical activity environment maps are compared with Census 2000 data to visualize accessibility of physical activity opportunities in neighborhoods with different characteristics.
• Poverty level presented in this chartbook are based on the 2000 Federal Poverty Guidelines.
• Crime rates in Trenton are presented at the census block group level as relative crime risk (CrimeRisk) obtained from a commercial data source (Applied Geographic Solutions, 2008). CrimeRisk - an index value derived from modeling the relationship between crime rates and demographics data - is expressed as the risk of crime occurring in a specific block group relative to the national average. For this chartbook, data on total CrimeRisk, which includes personal and property crimes, are reported.
The New Jersey Childhood Obesity Study was designed to provide vital information for planning, implementing, and evaluating interventions aimed at preventing childhood obesity in five New Jersey municipalities: Camden, Newark, New Brunswick, Trenton, and Vineland. These five communities are being supported by the Robert Wood Johnson Foundation’s New Jersey Partnership for Healthy Kids program to plan and implement policy and environmental change strategies to prevent childhood obesity. Effective interventions for addressing childhood obesity require community-specific information on
who is most at risk and on contributing factors that can be addressed through tailored interventions that meet the needs of the community. Based on comprehensive research, a series of reports are being prepared for each community to assist in planning effective interventions.
The main components of the study were:
• A household telephone survey of 1700 families with 3–18 year old children,
• De-identified heights and weights measured at public schools,
• Assessment of the food and physical activity environments using objective data.
This report presents the results from the household survey. Reports based on school body mass index (BMI) data and food and physical activity environment data are available at www.cshp.rutgers.edu/childhoodobesity.htm.
The New Jersey Childhood Obesity Study was designed to provide vital information for planning, implementing, and evaluating interventions aimed at preventing childhood obesity in five New Jersey municipalities: Camden, Newark, New Brunswick, Trenton, and Vineland. These five communities are being supported by the Robert Wood Johnson Foundation’s New Jersey Partnership for Healthy Kids program to plan and implement policy and environmental change strategies to prevent childhood obesity. Effective interventions for addressing childhood obesity require community-specific information on
who is most at risk and on contributing factors that can be addressed through tailored interventions that meet the needs of the community. Based on comprehensive research, a series of reports are being prepared for each community to assist in planning effective interventions.
The main components of the study were:
• A household telephone survey of 1700 families with 3–18 year old children,
• De-identified heights and weights measured at public schools,
• Assessment of the food and physical activity environments using objective data.
This report presents the results from the household survey. Reports based on school body mass index (BMI) data and food and physical activity environment data are available at www.cshp.rutgers.edu/childhoodobesity.htm.
This brief summarizes the different types of food stores open in New Brunswick, New Jersey and in a one mile radius around the city during 2008 to 2014.
This brief summarizes the different types of food stores open in Newark, New Jersey and in a one mile radius around the city during 2008 to 2014.
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.
Purpose: To develop a valid and feasible short-form corner store audit tool (SCAT) that could be used in-store or over the phone to capture the healthfulness of corner stores.
Design: Nonexperimental.
Setting: Four New Jersey cities.
Subjects: Random selection of 229 and 96 corner stores in rounds 1 and 2, respectively.
Measures: An adapted version of the Nutrition Environment Measures Survey for Corner Stores (NEMS-CS) was used to conduct in-store audits. The 7-item SCAT was developed and used for round 2 phone audits.
Analysis: Exploratory factor analysis and item response theory were used to develop the SCAT.
Results: The SCAT was highly correlated with the adapted NEMS-CS ( r = .79). Short-form corner store audit tool scores placed stores in the same healthfulness categories as did the adapted NEMS-CS in 88% of the cases. Phone response matches indicated that store owners did not distinguish between 2% and low-fat milk and tended to round up the fruit and vegetable count to 5 if they had fewer varieties.
Conclusion: The SCAT discriminates between higher versus lower healthfulness scores of corner stores and is feasible for use as a phone audit tool.
Background: Understanding determinants of high consumption of sugar-sweetened beverages (SSBs), a highly prevalent obesogenic behavior, will help build effective customized public health interventions.
Objective: Our aim was to identify child and parent lifestyle and household demographic factors predictive of high SSB consumption frequency in children from low-income, ethnically diverse communities that may help inform public health interventions.
Design: We used a cross-sectional telephone household survey.
Participants/setting: Participants were 717 boys and 686 girls aged 3 to 18 years old from the New Jersey Childhood Obesity Study living in five low-income cities (Camden, New Brunswick, Newark, Trenton, and Vineland). The adult most knowledgeable about household food shopping completed a questionnaire over the telephone inquiring about their and their child's dietary and physical activity habits, and household-, parent-, and child-level demographics.
Main outcome measures: Child's SSB consumption frequency was measured.
Statistical analysis performed: Multivariate ordered logit models were designed to investigate a variety of variables hypothesized to affect the frequency of SSB consumption. Exploratory stratified analyses by race, sex, and age were also conducted.
Results: Eight percent of our study participants never consumed SSBs, 45% consumed SSBs at least once per day, and 23% consumed twice or more per day. SSB consumption was higher among children 12 to 18 years vs 3 to 5 years (P<0.0001), of non-Hispanic black vs non-Hispanic white race/ethnicity (P=0.010), who were moderate fast food consumers vs never consumers (P=0.003), and those whose parents were high vs low SSB consumers (P<0.0001). Living in a non-English-speaking household (P=0.030), having a parent with a college or higher education vs less than high school (P=0.003), and having breakfast 6 to 7 days/wk vs never to 2 days/wk or less were associated with lower SSB consumption (P=0.001).
Conclusions: We identified a number of household-, parent-, and child-level predictors of SSB consumption, which varied by race, sex, and age, useful for building customized interventions targeting certain behaviors in ethnically diverse, low-income children.