A suburban food pantry in the Northeast US planned to implement the SWAP system in May 2019, providing an opportunity to conduct a pre-post comparison study. The pantry invited clients to visit once per week and choose a specific number of items within each food category based on household size. The pantry employed “client choice,” meaning that clients selected the specific items they wanted from the pantry shelves, as one would in a grocery store. The client choice model was in place both before and after SWAP implementation. For simplicity, pre-SWAP data collection is referred to as time 1 and the post-SWAP data collection as time 2. Time 1 data collection occurred in May 2019 over five days within the two weeks prior to SWAP implementation. No data were collected for two weeks following the reopening of the pantry. Time 2 data were collected in June–July 2019 over seven days, between three and five weeks after SWAP implementation.
Staff from the local food bank provided training to pantry staff and volunteers on how to rank food using SWAP. They provided materials and supplies, and made suggestions for implementing the system with the pantry layout. The pantry closed for a few days over a holiday weekend to implement SWAP. Pantry staff and volunteers ranked food according to SWAP, reorganized the placement of food on shelves based on SWAP rankings, and added SWAP shelf tags and signage. For example, the section for soup was reorganized so the “choose often” (green) soups were at eye level, and then the “choose sometimes,” and “choose rarely” (red) soups were on a shelf below. All shelves were labeled with the appropriate green, yellow, or red shelf tags. Additional nutrition education was added; for example, signage near canned vegetables suggesting rinsing canned foods to reduce the sodium content. The placement of food was also adjusted to promote healthy food. A display of snacks near the check-in counter was replaced with fresh vegetables as a behavioral “nudge” (Wilson et al. 2017).
Pantry inventory assessment
SWAP implementation may encourage pantries to increase the availability of healthy foods and decrease unhealthy foods in their inventory, thereby shifting the choices available to clients. To ensure that any changes in client food selection were not solely due to changes in the foods available at the pantry following SWAP implementation, a brief food inventory assessment was conducted over nine days (three days during time 1 and six days during time 2). The number of items available for each of the SWAP food groups (e.g., fruits and vegetables, grains, dairy, etc.) and each of the three SWAP ranks (green, yellow, and red) was rated using the following five-point 0 to 4 scale: none (no items) = 0; very little (1 to 5 items) = 1; few (6 to 15 items) = 2; several (16 to 29 items) = 3; and a lot (30 or more items) = 4.
Client data collection and measures
A convenience sample of pantry clients was invited to participate by a research team member or pantry volunteer as clients entered the pantry. After participants finished shopping, they were asked to complete a survey to collect their demographic information while members of the research team recorded the food items they selected. No identifying information was collected from participants during either time period. The client survey information included the number of adults and children in the household, age, gender, race and ethnicity, education, income, and participation in government food assistance programs.
To assess the SWAP rank, research staff scanned a food’s universal product code (UPC) using WellSCAN, a web-based application that reads the barcode and returns an item’s SWAP rank (WellSCAN). Research staff first selected the food’s category (e.g., “protein” for a can of beans), then scanned the item. WellSCAN retrieves nutrition information from a database, then calculates a SWAP ranking based on the amount of saturated fat, sodium, and sugar in the food. For example, a can of beans with 0 g of saturated fat, 460 mg of sodium, and 1 g of sugar per serving would be ranked as yellow: the saturated fat value falls below the maximum of 2 g for a green ranking in the “protein” category; the sodium value falls between 200 mg and 480 mg, ranking it yellow; and the sugar value falls below the maximum of 5 g for a green ranking. The food’s final ranking is based on the lowest value of its individual nutrient rankings (i.e., saturated fat, sodium, and sugar); therefore, the can of beans would be ranked as yellow. For a complete listing of the SWAP nutrition standards used in this pilot, see Cooksey-Stowers et al. (2020). As a note, the SWAP standards have recently been revised to align with Healthy Eating Research Nutrition Guidelines for the Charitable Food System, published in 2020 (Schwartz et al. 2020). Researchers photographed any items without UPCs (e.g., fresh produce) and used publicly available nutrition information to calculate the SWAP rank.
Because no identifying information was collected from participants, the time 1 (pre-SWAP) and time 2 (post-SWAP) clients were treated as independent samples. The demographic data from both samples were summarized in means and percentages, and tested for significant differences using independent t-tests for continuous variables and chi-square (test for homogeneity) for categorical variables.
Food pantry inventory
Regression analyses were used to assess changes from pre to post in the inventory ratings for each SWAP rank (i.e., green, yellow, and red). The models defined the inventory ratings as the outcome; SWAP rank, time, and the interaction of SWAP rank and time as predictors; and food type (e.g., dairy, grain) as a fixed effect.
Nutritional quality of client carts
The proportion of green, yellow, and red items overall, and for each food group, was calculated. The hypothesis was that green selections would increase and red selections would decrease; therefore, the proportion of green and red selections were the outcomes of interest. The proportions were examined for normality using normal quantile plots, skewness, and kurtosis. The overall proportion of clients’ selections were normally distributed; however, the proportion of clients’ green and red selections separated by food type did not meet the assumption of normality. Thus, two strategies were used. Changes in the selection of green and red items within specific food types were assessed using Wilcoxon rank-sum non-parametric tests. Changes in the proportion of green and red foods with all food types combined were assessed using two linear regression models. The independent variable was time (pre-SWAP or post-SWAP) and the dependent variables were the proportion of green items in model 1 and the proportion of red items in model 2. All models included covariates (gender, age, race, education, income, and participation in government food assistance programs).
To test whether the observed changes in client food selection were influenced by daily changes in food pantry inventory, a second set of two adjusted regression analyses was conducted that controlled for the inventory. Specifically, the daily average red or green inventory data from each of the time 1 and time 2 data collection days were matched with the appropriate client-level data from the same days. Because pantry inventory data were assessed for nine of the 12 days (pre: n = 3 and post: n = 6), the adjusted model only included clients from days with corresponding inventory data (n = 149). All data were analyzed in SAS 9.4 (SAS Institute Inc. Cary, NC) or STATA 16 (StataCorp LLC. College Station, TX) and significance was set at p < 0.05.