In response to lack of access to healthy foods, many low-income communities are instituting local healthy corner store programs. Some stores also participate in the United States Department of Agriculture's Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and the Supplemental Nutrition Assistance Program (SNAP). This study used two assessment tools to compare the healthfulness of offerings at stores participating in local healthy store programs (upgraded stores), WIC, and/or SNAP to that of similar non-participating stores.

Based on store audits conducted in 315 New Jersey corner stores in 2014, we calculated healthy food availability scores using subsections of the Nutrition Environment Measures Survey for Corner Stores (NEMS-CS-Availability) and a short-form corner store audit tool (SCAT). We used multivariable regression to examine associations between program participation and scores on both instruments.

Adjusting for store and block group characteristics, stores participating in a local healthy store program had significantly higher SCAT scores than did non-participating stores (upgraded: M = 3.18, 95% CI 2.65–3.71; non-upgraded: M = 2.52, 95% CI 2.32–2.73); scores on the NEMS-CS-Availability did not differ (upgraded: M = 12.8, 95% CI 11.6–14.1; non-upgraded: M = 12.5, 95% CI 12.0–13.0). WIC-participating stores had significantly higher scores compared to non-participating stores on both tools. Stores participating in SNAP only (and not in WIC) scored significantly lower on both instruments compared to non-SNAP stores.

WIC-participating and non-SNAP corner stores had higher healthfulness scores on both assessment tools. Upgraded stores had higher healthfulness scores compared to non-upgraded stores on the SCAT.

Background

The transition from the home to college is a phase in which emerging adults shift toward more unhealthy eating and physical activity patterns, higher body mass indices, thus increasing risk of overweight/obesity. Currently, little is understood about how changing friendship networks shape weight gain behaviors. This paper describes the recruitment, data collection, and data analytic protocols for the SPARC (Social impact of Physical Activity and nutRition in College) study, a longitudinal examination of the mechanisms by which friends and friendship networks influence nutrition and physical activity behaviors and weight gain in the transition to college life.

Methods

The SPARC study aims to follow 1450 university freshmen from a large university over an academic year, collecting data on multiple aspects of friends and friendship networks. Integrating multiple types of data related to student lives, ecological momentary assessments (EMAs) are administered via a cell phone application, devilSPARC. EMAs collected in four 1-week periods (a total of 4 EMA waves) are integrated with linked data from web-based surveys and anthropometric measurements conducted at four times points (for a total of eight data collection periods including EMAs, separated by ~1 month). University databases will provide student card data, allowing integration of both time-dated data on food purchasing, use of physical activity venues, and geographical information system (GIS) locations of these activities relative to other students in their social networks.

Discussion

Findings are intended to guide the development of more effective interventions to enhance behaviors among college students that protect against weight gain during college.

Objective

In response to recent national efforts to increase the availability of healthy food in small stores, we sought to understand the extent to which small food stores could implement the newly published Healthy Small Store Minimum Stocking Recommendations and reflect on the new US Department of Agriculture Food and Nutrition Service's final rule for stocking of staple foods for Supplemental Nutrition Assistance Program–approved retailers.

Design

We collected qualitative and quantitative data from 57 small stores in four states (Arizona, Delaware, Minnesota, and North Carolina) that accepted Supplemental Nutrition Assistance Program but not Special Supplemental Nutrition Assistance Program for Women, Infants, and Children benefits. Data from semistructured, in-depth interviews with managers/owners were transcribed, coded, and analyzed. We collected quantitative store inventory data onsite and later performed descriptive analyses.

Results

Store interviews revealed a reluctant willingness to stock healthy food and meet new recommendations. No stores met recommended fruit and vegetable stocking, although 79% carried at least one qualifying fruit and 74% carried at least one qualifying vegetable. Few stores met requirements for other food categories (ie, whole grains and low-fat dairy) with the exception of lean proteins, where stores carrying nuts or nut butter were more likely to meet the protein recommendation. Water and 100% juice were widely available and 68% met basic healthy beverage criteria.

Conclusions

In contrast to the inventory observed, most owners believed store stock met basic recommendations. Further, findings indicate that small stores are capable of stocking healthy products; however, technical and infrastructure support, as well as incentives, would facilitate shifts from staple to healthier staple foods. Retailers may need support to understand healthier product criteria and to drive consumer demand for new products.

Disparities in healthy food access are well documented in cross-sectional studies in communities across the United States. However, longitudinal studies examining changes in food environments within various neighborhood contexts are scarce. In a sample of 142 census tracts in four low-income, high-minority cities in New Jersey, United States, we examined the availability of different types of food stores by census tract characteristics over time (2009–2017). Outlets were classified as supermarkets, small grocery stores, convenience stores, and pharmacies using multiple sources of data and a rigorous protocol. Census tracts were categorized by median household income and race/ethnicity of the population each year. Significant declines were observed in convenience store prevalence in lower- and medium-income and majority black tracts (p for trend: 0.004, 0.031, and 0.006 respectively), while a slight increase was observed in the prevalence of supermarkets in medium-income tracts (p for trend: 0.059). The decline in prevalence of convenience stores in lower-income and minority neighborhoods is likely attributable to declining incomes in these already poor communities. Compared to non-Hispanic neighborhoods, Hispanic communities had a higher prevalence of small groceries and convenience stores. This higher prevalence of smaller stores, coupled with shopping practices of Hispanic consumers, suggests that efforts to upgrade smaller stores in Hispanic communities may be more sustainable.

This study aims to examine children’s fruit, vegetable, and added sugar consumption relative to the Dietary Guidelines for Americans and the American Heart Association’s recommendations, as well as to compare children’s reported consumption with parental perception of the child’s overall diet quality. Data were drawn from 2 independent, cross sectional panels (2009–10 and 2014–15) of the New Jersey Child Health Study. The analytical sample included 2229 households located in five New Jersey cities. Daily consumption of fruit (cups), vegetables (cups), and added sugars (teaspoons) for all children (3–18 years old) were based on parent reports. Multivariate linear regression analyses estimated children’s adjusted fruit, vegetable, and added sugar consumption across parents’ perception categories (Disagree; Somewhat Agree; and Strongly Agree that their child eats healthy). Although only a small proportion of children meet recommendations, the majority of parents strongly agreed that their child ate healthy. Nonetheless, significant differences, in the expected direction, were observed in vegetable and fruit consumption (but not sugar) across parental perceptional categories for most age/sex groups. Dietary interventions tailored to parents should include specific quantity and serving-size information for fruit and vegetable recommendations, based on their child’s age/sex, and highlight sources of added sugar and their sugar content.

School food and physical activity (PA) environments can influence children’s dietary and physical activity behaviors. However, evidence on whether school environment is associated with students’ weight status is less definitive. In this study, we examined the association between students’ body mass index (BMI) and measures of school food and PA environments. We calculated BMI from nurse-measured data collected on 19,188 6–19-year-old students from 90 public schools in four low-income cities in New Jersey in 2015–2016. Based on a questionnaire administered to school nurses, we constructed 6 food and 3 PA indices capturing the healthfulness of key dimensions in the school food and PA environment domains. Multilevel linear models, stratified by school level (elementary and secondary), examined the association between BMI z-scores and indices of the school environment. The food and PA domains were modeled separately and then combined. Joint significance of indices within each domain was tested. Analyses were conducted in 2019–2020. In the combined model for elementary schools, indices in both the food and PA domains were jointly significant (p = 0.005 and p < 0.001, respectively). With regard to specific indices in the model, students’ BMI z-score was 0.03 units lower for each additional outdoor PA facility (95% CI [−0.06, −0.00]; p = 0.036). Similarly, for secondary schools, both the food and PA domains were jointly significant (p = 0.004 and p = 0.020, respectively). Each additional unhealthy item in vending machines was associated with a 0.12 unit increase in BMI z-score (95% CI [+0.00, 0.23]; p = 0.042). Overall, healthier food and PA environments were associated with lower student BMI.

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.

The onset of the COVID-19 pandemic in March 2020 and the resulting closures of schools, businesses, and restaurants led to a massive economic disruption in Arizona. The unemployment rate at its peak reached 14.2% (April 2020) - a level even higher than during the great recession of 2008. High unemployment rates, coupled with a breakdown of local and national food supply chains, led to a remarkable increase in food insecurity rates among Arizona households. More than a year later, as vaccines became widely available and restrictions were lifted, schools and business began to reopen, and most activities slowly returned to pre-pandemic standards. The effects of the pandemic on food insecurity and food-related behaviors, however, might have long-lasting effects. This brief describes levels of food insecurity, food assistance program participation, job disruption, and food related behaviors among 814 households in Arizona, in the 12 months preceding the pandemic (March 2019 – March 2020) and approximately one year after the onset of the COVID-19 pandemic ( January 2021 –April 2021). Data collection took place between April and May 2021.

Introduction: In-store promotion of food products leads to more frequent purchases. Product promotion can vary by store characteristics. We compared marketing strategies used by grocery stores to promote fruit and vegetables (FV) in neighborhoods with varying socio-economic and racial/ethnic characteristics.<br/><br/>Methods: Data was collected from a random sample of 12 large grocery stores from the same national chain located within a 15-mile radius of Downtown Phoenix. Store zip-code level median household income was used to classify stores as located in lower (<$50,000) or higher (>$50,000) income areas. Stores located in neighborhoods with more than 50% Hispanic population were classified as majority Hispanic serving. The ProPromo tool was adapted to document the presence and promotion of FV at 8 distinct locations throughout each store. Types of promotion strategies documented included displays, price promotions, size, or themes.<br/><br/>Results: FV were present at the entrance, islands, checkouts, and produce section; while fruits were promoted in all of these locations, vegetables were promotion in fewer locations. All stores used size and price promotion to promote FV; display was used to promote vegetables in 2 stores and fruits in all stores. On average stores promoted 32 fruits and 38 vegetables. Stores serving higher and lower income areas promoted similar numbers of FV. However, stores in Hispanic majority neighborhoods promoted fewer FV (66) in comparison to those in Hispanic minority areas (73).<br/><br/>Conclusion: Fruit and vegetable promotion disparity associated with neighborhood demographics may contribute to disparities in fruit and vegetable consumption.