**Understanding the Intersection of Race**/**Ethnicity, Socioeconomic Status, and Geographic Location: A Scoping Review of U.S. Consumer Food Purchasing**

**Chelsea R. Singleton 1,\*, Megan Winkler 2, Bailey Houghtaling 3, Oluwafikayo S. Adeyemi 1, Alexandra M. Roehll 1, JJ Pionke <sup>4</sup> and Elizabeth Anderson Steeves <sup>5</sup>**


Received: 1 September 2020; Accepted: 19 October 2020; Published: 21 October 2020

**Abstract:** Disparities in diet quality persist in the U.S. Examining consumer food purchasing can provide unique insight into the nutritional inequities documented by race/ethnicity, socioeconomic status (SES), and geographic location (i.e., urban vs. rural). There remains limited understanding of how these three factors intersect to influence consumer food purchasing. This study aimed to summarize peer-reviewed scientific studies that provided an intersectional perspective on U.S. consumer food purchasing. Thirty-four studies were examined that presented objectively measured data on purchasing outcomes of interest (e.g., fruits, vegetables, salty snacks, sugar-sweetened beverages, Healthy Eating Index, etc.). All studies were of acceptable or high quality. Only six studies (17.6%) assessed consumer food purchases at the intersection of race/ethnicity, SES, or geographic location. Other studies evaluated racial/ethnic or SES differences in food purchasing or described the food and/or beverage purchases of a targeted population (example: low-income non-Hispanic Black households). No study assessed geographic differences in food or beverage purchases or examined purchases at the intersection of all three factors. Overall, this scoping review highlights the scarcity of literature on the role of intersectionality in consumer food and beverage purchasing and provides recommendations for future studies to grow this important area of research.

**Keywords:** intersectionality; food purchasing; diet quality; race; ethnicity; socioeconomic status; urban; rural

#### **1. Introduction**

Most Americans' diets fall short of national dietary guidelines [1]. Nearly 75% of Americans consume too few fruits and vegetables, and more than 60% consume excess added sugar, saturated fat, and sodium [2]. Furthermore, most Americans' overall diet quality is rated moderate to poor [2]. Food purchasing is a critical behavior in shaping the overall nutritional quality of consumed diets [3,4]. Purchases made in full-service (e.g., supercenters, grocery stores, etc.) and limited-service (e.g., corner stores, gas stations, dollar stores, pharmacies, etc.) stores comprise upwards of 63% of an individual's total daily energy intake [5]; the remaining 37% is acquired from venues such as full-service and

fast food restaurants. Additionally, more than 60% of the sugar-sweetened beverages (SSB) and discretionary foods consumed by U.S. adults come from retail food outlets [6].

Food retailer availability, adverse dietary behaviors, and the related health consequences are not distributed equally across the U.S. population [7–10]. Significant inequities in diet and health status have been, and continue to be, documented by race/ethnicity, socioeconomic status (SES), and geographic location (i.e., urban vs. suburban vs. rural) in the U.S. [8–10]. However, health disparities are often researched and described by experts in a way that can discount the complex identities of many marginalized individuals [11–13]. Intersectionality is a theoretical framework used to describe how multiple social categories measured at the individual level (e.g., race, ethnicity, SES) reflect interlocking systems of privilege and oppression at the societal level [11]. As these realities are experienced jointly, it is important to examine how these factors work together to influence health behaviors such as dietary intake and food purchasing.

Prior reviews of food and beverage purchasing have primarily focused on evaluating interventions aimed at improving purchasing behaviors [14–19], and recently, the use of commercial food purchasing datasets to discover specific purchasing trends [3]. Studies often present information on food and beverage purchasing behaviors at the individual or household-level by racial/ethnic group or SES [3,5]. However, there continues to be a limited synthesized understanding of how the intersectional nature of these factors influences trends in consumer food purchases. Filling this gap in knowledge can inform research and practice approaches to improve food purchasing environments and behaviors among populations with a long-standing history of oppression and marginalization.

Therefore, the primary aim of this scoping review was to identify and summarize scientific studies providing an intersectional perspective on U.S. consumer food purchasing. Specifically, we were interested in assessing food and/or beverage purchasing at the intersection of race/ethnicity, SES, and geographic location as these three factors are often considered in studies of nutritional inequities across populations [8–10]. Additional aims of this review included (1) summarize key findings from studies that assessed consumer food purchasing solely by race/ethnicity, SES, or geographic location and (2) identify areas for future research that will expand the field's understanding of how the intersection of these three factors influences food and beverage purchasing. Thus, findings from this review may significantly contribute to the work of public health researchers, policy makers, and individuals in the private sector seeking to gain a better understanding of food retail, purchasing, and marketing in the U.S. and develop solutions to address nutritional inequities.

#### **2. Materials and Methods**

#### *2.1. Search Strategy and Inclusion Criteria*

In December 2019, a systematic search of the literature was conducted to identify peer-reviewed papers on U.S. consumer food and beverage purchasing. A librarian (J. P.) searched the following six databases, selected based on lead sources for peer-reviewed literature among several disciplines including public health, medicine, psychology, sociology, and economics: PubMed, Scopus, PsycINFO, CINAHL, ScoINDEX, and Business Source Ultimate. The search strategy developed by the librarian based on preliminary testing in PubMed (See Supplementary Material Part I) was translated across all remaining databases for optimum article retrieval. All citations returned by the search were extracted and imported into an open-source citation management software.

The following inclusion criteria were used: (1) published in a peer-reviewed journal, (2) published in 2000 or later (up until December 2019), (3) available in English, (4) based in the U.S., (5) employed an observational study design (e.g., cross-sectional, longitudinal, etc.), (6) analyzed objectively measured food and/or beverage purchasing data collected at any level (i.e., individual, household, or store) from full-service or limited-service stores, and (7) presented findings on purchasing by race/ethnicity, SES, geographic location, or any combination of these three factors. Studies that examined purchasing intersections (i.e., explored interaction terms or reported stratified regression models) for two or

more factors were labeled "intersectional". Studies that presented purchasing findings for a specific intersectional population (example: low-income non-Hispanic Black households living in an urban setting) were also included. These studies were labeled as "targeted". Since this review aimed to summarize observational data on consumer food purchasing, interventions, natural experiments, and policy evaluation studies were excluded. Furthermore, studies that solely analyzed self-reported food and/or beverage data were also excluded. A wide range of objectively measured purchasing data were considered including store-generated sales data, annotated receipt data, and customer intercept data. Given the large variability in food and beverage purchasing outcomes assessed by selected studies, the types of outcomes considered by the current study were narrowed to a specific list of categories (see *Data Extraction*).

#### *2.2. Study Selection*

A flow chart describing the study selection process is presented in (Figure 1). The search returned 1256 citations: PubMed (*n* = 430), Scopus (*n* = 354), PsycINFO (*n* = 140), CINAHL (*n* = 181), ScoINDEX (*n* = 28 results), and Business Source Ultimate (*n* = 123).

**Figure 1.** Flow chart for scoping review.

After removing duplicate citations, three reviewers (O. S. A., A. M. R., and C. R. S.) reviewed titles and abstracts among 982 unique studies. Titles and abstracts indicated that 910 studies did not meet inclusion criteria. The complete text was retrieved for citations appearing to meet inclusion criteria or were unclear (*n* = 72). Two independent reviewers (O. S. A. and A. M. R.) performed the full text review, and a third reviewer (C. R. S.) made the final decision on inclusion for any disagreements. Excluded studies were ineligible because they (1) did not present findings on food and/beverage purchases (*n* = 11), (2) used self-reported purchasing measures (*n* = 14), (3) did not present findings by one or more of the three factors of interest (*n* = 14), or (4) did not present findings on a purchasing outcome of interest (*n* = 1). Hand searching, specifically forward and backwards reference searching of

intersectional papers, was performed to find intersectional studies not captured by the search strategy resulting in the identification of one additional paper. The search was repeated in September 2020 to identify additional intersectional papers published since December 2019. Again, one paper was identified bringing the final number of studies included in this scoping review to 34.

#### *2.3. Data Extraction*

All authors extracted data from an assigned subset of included studies using a standardized data extraction tool developed by research team members (C. R. S., M. W., B. H., and E. A. S.). Specifically, data on authors, study design, study population, sample size, and detailed information on measurement methods used to capture consumer food and/or beverage purchasing as well as variable definitions for race, ethnicity, SES, and geographic location were extracted. An additional team member performed a quality assessment for each source (see *Methodological Quality Assessment*).

Given the enormous diversity in customer purchasing outcomes examined across the included studies, team members (C. R. S., M. W., B. H., or E. A. S.) extracted food-at-home customer purchasing results for a pre-specified list of product and nutrition outcomes. These particular outcomes were selected because they are often the subject of U.S.-based policy and public health interventions [3,7]: (1) fruits, (2) vegetables, (3) whole grains, (4) salty snacks, (5) desserts, sweet snacks, and candy, (6) sugar-sweetened beverages (SSBs), including regular soda, juice drinks (<100% juice), sports drinks, and energy drinks, (7) non-sugar-sweetened beverages (non-SSBs), including water, diet/zero calorie soda, 100% juice, diet/zero calorie sports drinks, and diet/zero calorie energy drinks, (8) healthy eating index (HEI), (9) total energy (i.e., kilocalories/kcals), (10) specific nutrients, including sugar; saturated fat; and sodium. We extracted results on these outcomes in any form (e.g., weekly expenditures, proportion of weekly purchases, kilocalories/person/day purchased for household, etc.) and prioritized inferential results, although descriptive results were extracted if it was the only data available. Lastly, we extracted results for any study that examined intersections or presented inferential results by race/ethnicity, SES, or geographic location. A narrative format was used to describe review results and identify similarities/differences in population purchasing trends based on intersectionality.

#### *2.4. Methodological Quality Assessment*

Risk of bias was assessed using the National Heart, Lung, and Blood Institute's (NHLBI) Quality Assessment Tool for Observational Cohort and Cross-sectional Studies [20]. The tool allowed reviewers to evaluate internal validity across 14 criteria, four of which were deemed not applicable to the studies of consumer food purchasing included in this review (items 6, 7, 10, and 12). One reviewer (O.S.A. or A. M. R.) conducted this assessment, with a second reviewer (C. R. S.) reviewing for agreement. Reviewers recorded yes, no, or cannot determine for each item regarding a study's original aim/purpose and results. Thus, quality scores represent overall quality of study designs and not necessarily the quality of purchasing results extracted. "Yes" responses were tallied and the highest score a study could receive was a 10. Although the tool was not intended for use as a scoring scheme, we identified scores between 1–4 as low, 5–7 as acceptable, and 8–10 as high quality to assist our results interpretation.

#### **3. Results**

Thirty-four studies were included in this scoping review [21–54]. Information on customer purchasing assessment methodologies used across studies is shown in (Table 1). Most studies examined both food and beverage purchasing (*n* = 29; 85.3%) and collected data at the household level (*n* = 24, 70.6%). While several studies assessed purchases from all types of stores (*n* = 24, 70.6%), seven (21.2%) and three (9.1%) studies focused solely on purchasing at limited-service and full-service stores, respectively. A variety of data sources were used across studies with most using Nielsen Consumer Panel data (*n* = 11, 33.3%) or the USDA's Food Acquisition and Purchasing Survey (FoodAPS) dataset

(*n* = 5, 20.8%). Several data collection methods were used to study purchasing including customer intercepts, receipt collection, and Universal Product Code (UPC) scanning.

Descriptive characteristics of studies are provided in (Table 2). All studies were considered acceptable or high quality according to our interpretation of papers using the NHLBI Quality Assessment Tool for Observational Cohort and Cross-sectional Studies. The majority examined purchasing using a nationally representative sample of U.S. households (*n* = 18, 52.9%). All other studies assessed purchasing locally in a specific city or regionally in the Midwest or Northwest.

Key findings are described below by intersectional attributes. Studies that presented intersectional results on consumer food and beverage purchases are described first, followed by those that studied a single attribute (i.e., examined purchasing by race/ethnicity, SES, or geographic location alone). Finally, descriptive results from studies with targeted populations are provided.

#### *3.1. Intersectional Results*

Key findings from studies that assessed consumer food and/or beverage purchases at the intersection of race/ethnicity, SES, or geographic location are in (Table 3). Details on how each study measured each purchasing outcome of interest are also provided in (Table 3). Only six studies (17.6%) examined any intersection between our three factors of interest [29,34,35,45,47,54]. All six studies examined intersections between race/ethnicity and SES by using interaction terms or stratified regression models. We focused on results with significant interaction terms or with different associative patterns in the stratified models (e.g., association between race/ethnicity and purchasing was significant in opposite directions across SES groups or the association was statistically significant for one SES group and non-significant for the other).

Three studies examined fruit and vegetable purchasing and only one identified different associations between race/ethnicity and purchasing across SES [29,34,45]. Using specific market basket items and stratifying by SES, Palmer et al. (2019) reported more purchasers than non-purchasers of canned/bottled peaches and potatoes among White higher income households (>200% FPL), whereas no significant difference in proportion of purchasers to non-purchasers was observed among White low-income households [45]. In addition, there were significantly fewer purchasers than non-purchasers of potatoes among Black higher income households, which was not observed among Black low-income households [45]. No studies examined whole grain purchasing. Three studies examined salty snacks and desserts, sweet snacks, and candy purchasing [29,34,35], with only one identifying different associations between race/ethnicity and purchasing across stratified SES models [35]. Among households not participating in the Supplemental Nutrition Assistance Program (SNAP), Grummon and Taillie (2018) identified non-Hispanic Black households (henceforth NHB) purchased less salty snacks, desserts, and sweet snacks compared to non-Hispanic White households (henceforth NHW) [35]. In addition, Hispanic households purchased less candy, desserts, and sweet snacks compared to NHW households. These race/ethnicity differences were not observed among SNAP-participating households. Three studies examined SSBs and non-SSBs, but none found significant differences across intersections [29,34,35].


SummaryofCustomerPurchasingDataAssessmentMethodologiesofIncludedStudies


*IJERPH* **2020**, *17*, 7677

**Table 2.** *Cont.*


#### *IJERPH* **2020**, *17*, 7677

**Table 2.** *Cont.*


**Table 2.** *Cont.*


#### **Table 2.** *Cont.*


**Table2.***Cont.*


#### *IJERPH* **2020**, *17*, 7677

**Table 2.** *Cont.*




cakes/pies/desserts,

juice, and water.

 candy, carbonated and sweetened drinks, 100% fruit

#### *IJERPH* **2020** , *17*, 7677




#### *IJERPH* **2020**, *17*, 7677

**Table 3.**

*Cont.*



results follow the authors' definition (e.g., some use Bonferroni correction).

results for

kilocalories/energy

 density, sugar, saturated fat, sodium, or other category was examined among food purchases and beverage purchases separately.

Underline-bold

 highlights purchasing outcomes of interest in this review.

*Underline-italics* indicates when

One study examined the quality of household food purchases using HEI [54]. However, Vadiveloo et al. (2020) reported no significant interactions between race/ethnicity and family income. Two studies examined overall kilocalories purchased [34,35], with one identifying relevant results [35]. Among SNAP households, Grummon and Taillie (2018) identified that NHB purchased significantly more kilocalories compared to NHW, which was not observed among non-SNAP households. Two studies examined sugar, saturated fat, and sodium, and Grummon and Taillie (2018) reported significant intersectional results for sodium and sugar [34,35]. Hispanics had significantly greater purchasing of sodium compared to NHW among non-SNAP households, which was not observed in SNAP households. In addition, among SNAP households, Hispanics had significantly less purchasing of sugar than NHW, though this was not observed in non-SNAP households.

Poti et al. (2016) was the only study that examined purchasing outcomes that were not part of our primary outcomes of interest across intersectional attributes [47]. They explored whether household income moderated the association between race/ethnicity and purchasing products with different degrees of processing (e.g., highly processed, minimally processed) and ready-to-eat (e.g., requires cooking, ready-to-heat). Significant interactions between race/ethnicity and SES were identified for basic-processed and requires cooking food purchases. Greater purchasing of both outcomes was observed among NHB and Hispanics compared to NHW among low-income households.

#### *3.2. Single Attribute Results*

#### 3.2.1. Race/Ethnicity

Fifteen studies (44.1%) examined purchasing outcomes across racial and/or ethnic groups [27,29,30,39,42,43,45–51,53,54]. All studies examined purchasing among NHW, 14 examined purchasing among NHB, 14 studied purchasing among Hispanic, nine examined purchasing among non-Hispanic Other (or a different author definition that collapsed multiple racial/ethnic groups), and three investigated purchasing among Asian (using the author definition). Key findings from studies that presented racial/ethnic differences in consumer food and/or beverage purchases are described in detail in Supplementary Material Part II (Table S1).

#### 3.2.2. Socioeconomic Status

We identified 19 (55.9%) studies that examined purchasing outcomes across SES categories [26,27,29–34,36,37,39,42,43,45,49–52,54]. Ten studies evaluated SES by looking across household income levels, while seven studies used federal food assistance program participation status (i.e., SNAP status), four studies used education level, one study used employment status, and one study classified food retail stores based on income of the surrounding neighborhoods. In three studies, SES was examined in more than one way (e.g., both income and education levels were assessed). Supplementary Material Part II (Table S2) present key findings from the studies that evaluated SES differences in consumer food and/or beverage purchases.

#### 3.2.3. Geographic Location

We did not identify any studies that examined differences in customer food and/or purchasing by geographic setting (i.e., urban vs. suburban vs. rural).

#### *3.3. Targeted Population Results*

#### 3.3.1. Intersectional Targeted Populations

Eleven studies (33.3%) were labeled targeted [21–26,28,38,40,41,44]. Five examined consumer food purchases among an intersectional targeted population [23,26,38,40,44]. These populations were low-income individuals or households living in an urban city [23,38,40,44] and NHBs living in an urban city [26]. All studies with a low-income urban population focused solely on limited-service store purchasing. Three studies assessed fruit and vegetable purchasing while none examined whole grain purchasing [26,40,44]. Overall, fruit and vegetable purchasing was moderate to low. Chrisinger et al. (2018) reported that 14% of total food expenditures among a small sample of NHB women were spent on fruits and vegetables [26]. Lent et al. (2014) and O'Malley et al. (2013) found that fruits and vegetables comprised 2.3% and 5% of purchases from limited-service store shoppers in low-income urban communities, respectively [40,44]. All five studies examined purchasing of salty snacks, desserts, sweet snacks, and/or candy. While Chrisinger et al. (2018) reported that these items represented only 11% of food expenditures among NHB women, the other four articles found that these items represented a large percentage of customer purchases in limited-service stores (>20%). All five studies assessed SSB purchasing; only three assessed non-sweetened beverage purchasing [23,40,44]. SSB were the items most often purchased across all studies. No study examined the quality of purchases using HEI. Only Borradaile et al. (2009) and Lent et al. (2014) examined kilocalories, saturated fat, sugar, and sodium content of purchases [40,44]. Both studies reported high volumes of each nutrient among customer purchases from limited-service stores. Key findings from studies that assessed consumer food and/beverage purchases with a targeted population are reported in (Table S3) of Supplementary Material Part III.

#### 3.3.2. Single Factor Targeted Populations

The remaining six targeted studies reported purchasing for a single factor targeted population [21,22,24,25,28,41] including low-income individuals or households [21,41] and individuals or households residing in an urban city [22,24,25,28]. Low-income targeted populations focused on participants of federal food assistance programs such as SNAP and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Key findings from studies that focused on single factor targeted populations are also presented in Supplementary Material Part III (Table S3).

#### **4. Discussion and Future Directions**

We aimed to summarize peer-reviewed scientific studies that assessed U.S. food and/or beverage purchasing at the intersection of race/ethnicity, SES, and geographic location, and recommend future approaches to expand this area of research. Food purchasing behaviors have been reviewed previously [4,14–19], although this scoping review is the first to (1) synthesize findings on food and beverage purchases by race/ethnicity, SES, and geographic location and (2) examine the intersectional nature of these factors. Our main finding is a limited number of studies published since 2000 provide an intersectional perspective on food and/or beverage purchasing across our three factors of interest, which have been consistently linked with diet and health inequities [29,34,35,45,47,54]. Thus, the vast majority of studies evaluated purchasing by a single attribute or within a specific targeted population. Below, we describe the implications of our review findings by attribute and provide future recommendations for studies seeking to contribute to this literature. A comprehensive list of future directions is provided in (Table 4).




**Table 4.** *Cont.*

Note. SES, Socioeconomic Status; NHW, non-Hispanic White; NHB, non-Hispanic Black.

*4.1. Understanding the Intersection of Race*/*Ethnicity, SES, and Geographic Location*

As mentioned, several studies have reported health and nutritional inequities by race/ethnicity, SES, and urban vs. rural status [8–10]. Assessing the intersectional nature of these factors may provide researchers new insight into food and beverage purchasing patterns to inform the design of policy, systems, and environmental change interventions that advance health equity [11–13]. Only six studies (17.6%) in this review examined the intersection of two attributes with all assessing race/ethnicity by SES differences [29,34,35,45,47,54]. Given the small number of studies and the inconsistency in food and beverage purchasing outcomes considered, specific patterns in purchasing could not be identified. Thus, we still have limited understanding of how measures reflecting SES moderate racial/ethnic differences in food purchasing. Future studies should examine U.S. consumer food and/or beverage purchases at the intersection of more than two factors. Since none of the intersectional studies considered geographic location, future studies should determine how urban vs. suburban vs. rural status moderates racial/ethnic and SES differences in purchasing. Moreover, since most studies included in this review (*n* = 18, 52.9%) examined purchasing using data collected from a nationally-representative sample of U.S. households, future studies could focus on providing an intersectional perspective on food and beverage purchasing at the local and regional levels, especially in the South and West regions of the country.

#### *4.2. Race*/*Ethnicity*

Several reviewed studies (*n* = 15, 44.1%) presented purchasing findings by race/ethnicity [27,29,30,39,42,43,46–51,53,54]. Despite the large number of studies conducted to date, inconsistencies exist. Across studies, we identified more consistent patterns between NHW and Hispanics regarding purchasing, with Hispanics exhibiting healthier purchasing patterns relative to NHW. For example, we found that most studies examining differences between NHW and Hispanics reported greater fruit and/or vegetable purchasing and less salty snack, dessert, and candy purchasing. Fewer consistencies were noted between NHB and NHW although several studies reported greater SSB and sugar purchasing among NHB compared to NHW. These findings align with the dietary consumption literature, which continues to highlight significant racial/ethnic differences in intake among adults and children [8,10,54–56]. Additional studies are needed to establish consistent patterns

in food and beverage purchasing by racial/ethnic group. Future studies should evaluate consumer food and/or beverage purchases across a greater variety of racial/ethnic groups (i.e., non-Hispanic Asian, Native American, Pacific Islander, etc.). Given the heterogeneous composition of all races and ethnicities, future studies could conduct robust assessments of purchasing within groups, which will permit the study of characteristics such as acculturation and nativity—two factors that are often considered in studies of diet quality [55,56]. In recent years, public health research has placed greater emphasis on socio-political factors that create racial/ethnic inequities in health such as structural and systemic racism [57]. Future studies should consider how these important social factors impact food and beverage purchasing.

#### *4.3. Socioeconomic Status*

Most studies included in this review (*n* = 19, 55.9%) examined SES differences in consumer food and/or beverage purchases [26,27,29–34,36,37,39,42,43,45,49–52,54]. These findings underscore that identifying purchasing patterns by SES continues to be a major priority in the field; included studies generally showed a lower likelihood of fruit, vegetable, and whole grain purchases and a higher likelihood for discretionary product purchases (i.e., salty snacks, sweets, and SSB) among consumers with lower incomes compared to higher incomes [58]. Low-income consumers have been described as more likely to be targeted by marketing for food items high in kilocalories, saturated fat, added sugars, and sodium in retail food outlets [59–61], and the results of this review and reviews of diet quality differences by SES align with such observations given the poor quality of food and beverage purchases observed [62]. Furthermore, qualitative evidence has found that low-income consumers are more likely than consumers with higher incomes to purchase less costly, energy-dense and nutrient-poor products amid household financial constraints [63]. Approaches are needed to assess SES differences in purchasing using intersectional theory as a guiding framework to discern opportunities for tailored policy, systems, and environmental change interventions to improve the dietary quality of populations who experience diet-related health disparities [11]. Moreover, given the increase in studies that have evaluated the public health implications of community-level factors such as economic deprivation, blight, and gentrification displacement, future studies should also consider these factors in the context of consumer food and/or beverage purchasing [64,65].

#### *4.4. Geographic Location*

No studies included in this review examined geographic differences (i.e., urban vs. suburban vs. rural) regarding consumer food and/or beverage purchasing. This is particularly concerning because rural populations experience a higher burden of major diet-related diseases than urban populations (e.g., heart disease, cancer, stroke), which represent the leading causes of death in the U.S. [66]. The idea that more food environment research specific to rural people and places is needed is not new [67,68]. Rural residents have been shown to have few opportunities for choosing food and beverage options aligned with dietary guidelines in general when compared to residents of more urban areas [69,70]. It is unknown how food environment disparities influence differences in purchasing and dietary patterns between urban, suburban, rural populations, and how multiple socio-demographic factors such as race/ethnicity and SES to influence food purchasing disparities. This requires much more focus moving forward, in order to mitigate prominent health disparities in the U.S.

#### *4.5. Targeted Populations*

We included studies that targeted a specific population in order to provide greater context to findings from studies that evaluated consumer food and/or beverage purchases by race/ethnicity, SES, or geographic location [21–26,28,38,40,41,44]. While several studies were labeled targeted (*n* = 11, 33.3%), the variety of target populations considered was limited to primarily low-income individuals and households residing in an urban setting. No targeted study described purchasing in a rural population or specific racial/ethnic group that is often understudied in this area of research: non-Hispanic Asian, Native American, etc. Thus, studies are needed to address this gap and contribute more knowledge on the food and beverage purchases of intersectional target populations that represent two or more attributes (example: low-income Hispanic families living in a rural setting).

#### *4.6. Limitations*

Several limitations should be considered alongside review results. First, like most reviews, there were limitations in the research strategy. While a trained research librarian (J.P.) guided the literature search process, we limited our search to six databases with a set combination of key words. There is the possibility that relevant studies available in other databases were not included in this review. The large variety of purchasing measures presented by included studies made it not feasible to extract all of the purchasing data. Data from included studies were extracted based upon pre-selected purchasing outcomes of interest such as food groups (fruits, vegetable, whole grains, etc.) and nutritional characteristics (HEI, kcals, etc.). Thus, some purchasing outcomes (e.g., meat, dairy products, etc.) were not examined because they fell outside the scope of our data extraction protocol. An "Other" category was included to allow for the extraction of specific results of interest (example: nutrient claims) that did not align with the pre-specified categories.

Interventions and natural experiments that aimed to modify food and/or beverage purchasing were excluded from the review. It is possible that baseline findings from these studies documented food and beverage purchasing by one or more of our factors of interest. Because this scoping review solely focused on U.S. consumer food and beverage purchasing, findings may not be generalizable to other countries. The methodological assessment tool was not designed to assess the quality of nutrition studies or studies of consumer food purchasing. As previously mentioned, quality scores reported in (Table 2) solely reflect study design and not the quality of the purchasing data presented in the paper. Finally, during the data extraction phase, statistical significance was relied on heavily to identify which results to include in this review. While this made data extraction practical for the research team, this method limits the ability to account for the magnitude of differences in the various analyses. Detailed descriptions of key findings from included studies presented in this paper and the supplemental tables allow the reader to explore consumer purchasing outcomes in more detail.

#### **5. Conclusions**

This scoping review found that few studies to date have examined consumer food and beverage purchasing in the U.S. at the intersection of race/ethnicity, SES, and geographic location, despite the large number of studies that assessed purchasing by one of these factors alone. To expand this area of research, future studies should use intersectional theory to guide efforts to evaluate consumer food and/or beverage purchasing in the U.S. at the intersection of race/ethnicity, SES, and geographic location rather than continuing to examine factors individually. Furthermore, future studies should select data collection and assessment methodologies that allow for the gathering of rich data on the relationship between intersectional identity and food purchasing [13]. For example, consumer purchasing intercepts coupled with qualitative interviews that elicit rich descriptions of factors influencing dietary purchasing decisions may be a useful approach to increase our knowledge base on the socio-political and cultural factors that create persistent inequities in food purchasing behavior, dietary intake, and health.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1660-4601/17/20/7677/s1, Supplementary Material Part I: Search Terms, Supplementary Material Part II: L Single Attribute Results, Table S1: Key Findings from Studies Examining Racial/Ethnic Differences, Table S2: Key Findings from Studies Examining Socioeconomic Differences, Supplementary Material Part III: Targeted Results, Table S3: Key Findings from Targeted Studies.

**Author Contributions:** C.R.S., E.A.S., M.W., and B.H. conceptualized the project and designed the data collection tools. J.J.P. developed the search strategy and performed the search. O.S.A. and A.M.R. performed the abstract and full text review. All authors extracted the data and synthesized the results. All authors participated in the writing of the manuscript and approved the final version for submission. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Healthy Eating Research, a national program of the Robert Wood Johnson Foundation. Support for MRW's effort was provided by the National Heart, Lung, and Blood Institute, grant number K99HL144824. BH's effort was supported by the USDA National Institute of Food and Agriculture, Hatch project 1024670. Funders had no role in review design, results, or conclusions. All authors have read and agreed to the published version of the manuscript.

**Acknowledgments:** The authors would like to acknowledge the guest editors (Alyssa Moran and Cristina Roberto), Mary Story, Kirsten Arm, and Megan Lott at Healthy Eating Research for their guidance and assistance.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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International Journal of *Environmental Research and Public Health*

## *Review* **Improving Consumption and Purchases of Healthier Foods in Retail Environments: A Systematic Review**

**Allison Karpyn 1,\*, Kathleen McCallops 1, Henry Wolgast <sup>1</sup> and Karen Glanz <sup>2</sup>**


Received: 21 August 2020; Accepted: 10 October 2020; Published: 16 October 2020

**Abstract:** This review examines current research on manipulations of U.S. food retail environments to promote healthier food purchasing and consumption. Studies reviewed use marketing strategies defined as the 4Ps (product, price, placement, promotion) to examine results based on single- and multi-component interventions by study design, outcome, and which of the "Ps" was targeted. Nine electronic databases were searched for publications from 2010 to 2019, followed by forward and backward searches. Studies were included if the intervention was initiated by a researcher or retailer, conducted in-store, and manipulated the retail environment. Of the unique 596 studies initially identified, 64 studies met inclusion criteria. Findings show that 56 studies had at least one positive effect related to healthier food consumption or purchasing. Thirty studies used single-component interventions, while 34 were multi-component. Promotion was the most commonly utilized marketing strategy, while manipulating promotion, placement, and product was the most common for multi-component interventions. Only 14 of the 64 studies were experimental and included objective outcome data. Future research should emphasize rigorous designs and objective outcomes. Research is also needed to understand individual and additive effects of multi-component interventions on sales outcomes, substitution effects of healthy food purchases, and sustainability of impacts.

**Keywords:** food access; nutrition; healthier food; dietary behaviors; review; retail food environment; dietary intake

#### **1. Introduction**

The promotion of healthy purchasing in shopping environments is a focal point of public health and research efforts aimed at reducing obesity and improving health outcomes. In the U.S., 71.2% percent of adults and 41.0% of children ages 2–19 have overweight or obesity, a condition that increases risk for cardiovascular disease, cancer, and diabetes [1,2]. Recent examination of American diets found most Americans eat more total calories, saturated fat, salt, and added sugar than they need, and do not consume enough fruits and vegetables, and whole grain products [3]. The majority of food purchasing occurs in supermarkets, which are uniquely positioned between the consumer and food purchasing decisions [4]. In addition to providing access to food, the in-store food retail environment is recognized for its influential role in dietary outcomes [5]. In-store, food retail interventions influencing the food purchasing decisions of consumers have grown in popularity over the past 10 years. This shift is in part due to the popularity of behavioral economics as a foundation by which customers may be "nudged", though indirect suggestions, toward healthier products [6,7]. Most commonly, research on in-store approaches is characterized by the 4Ps of marketing (product, price, promotion, and place) and approaches targeting consumer purchasing habits toward "better-for-you" products [8,9]. Such products are often lower-calorie, lower-sugar, lower-salt, or include more whole grains. Better-for-you products have been promoted in food retail settings to reach those at highest risk for diet-related disease [10].

Despite growing research, increasing recognition of the importance of marketing in the food retail environment and the popularizing practice of multi-component interventions, which manipulate more than one of the four Ps [11], there remain many unanswered questions about best practices for implementing effective in-store interventions. Food marketing and consumer behavior research is cross-disciplinary by nature, with outcomes published in outlets unique to industry, business, agriculture as well as public health, creating an aggregation challenge for practitioners.

This review seeks to update and build on prior reviews which terminate with studies published on or around 2010 [9,12,13] by analyzing U.S.-specific interventions occurring within the past 10 years with the goal of examining the extent to which contemporary manipulations of U.S. food retail environments (i.e., grocery and supermarket) specifically intended to promote healthier food purchasing and consumption are effective. Findings were synthesized and organized based on whether the intervention was a single-component intervention, which manipulates one of the four Ps, or a multi-component intervention, which manipulates more than one of the four Ps, and further broken down into the 4Ps of marketing and study design: Experimental, quasi-experimental, pre-experimental, and time series. An emphasis is placed on the marketing techniques utilized in study interventions in order to determine which strategies have been found to be most and least effective using different research designs and outcome measures.

#### **2. Materials and Methods**

This review used the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [14].

#### *2.1. Search Strategy*

The authors used several methods to ensure a thorough and comprehensive review of the literature on in-store marketing interventions for healthy food promotion. First, a list of inclusion criteria was created to identify papers to be included in the review sample. Second, a list of key terms was created to search for studies. Third, appropriate databases were identified for the search based on the database topics. Finally, the database search was conducted to identify inclusion articles, and forward and backward searches were conducted for each inclusion article. Below are the processes used to identify studies for this review.

#### *2.2. Inclusion Criteria*

The studies included are original empirical research published between 2010 and 2019, in English, and from the United States. Studies were researcher- or retailor-initiated, conducted inside the retail environment, and manipulated the retail environment. Evaluations could be quantitative or mixed methods and all interventions had to include at least one of the following outcomes: (1) Purchasing-related (i.e., objective store sales data, objective food purchasing data, customer receipts, and survey self-reported purchases or expenditures, store sales, or intent to purchase), and/or (2) consumption-related (i.e., food frequency questionnaire (FFQ), 24-h dietary recall, food diary, Veggie MeterTM or other biometrics, or other survey self-reported diet/consumption or intent to eat).

#### *2.3. Exclusion Criteria*

Interventions were excluded if they were implemented by an entity other than a researcher or retailer (e.g., price intervention at the wholesale level or front-of-pack labels initiated by a food company), if they did not occur inside the retail environment (e.g., restaurants, schools, mobile food trucks, online, and laboratory), or if they did not manipulate the retail environment (e.g., grocery store tours).

#### *2.4. Search Terms and Databases*

Nine databases (i.e., Academic OneFile, Business Source Premier, CAB Abstracts, Communication and Mass Media Complete, Family and Society Studies Worldwide, PsycINFO, PubMed, Sociological Abstracts, and Web of Science) from a variety of sectors (i.e., agriculture, business, communication, health, and psychology) were searched. Key terms were constructed based on three concepts: (1) Healthier food, (2) study design, and (3) setting. A variety of search terms were used to ensure articles would be included with nuanced differences in terms (e.g., healthy food vs. better-for-you) across sectors. The following key terms were used in all databases:


#### *2.5. Procedure of Article Search*

RefWorks database was used to organize all articles. The searches were conducted by two authors and yielded 1231 studies (see Figure 1). After excluding 635 duplicate articles, two authors reviewed each full-text article to determine eligibility and excluded 548 studies. This review yielded 42 articles that met all inclusion criteria. Then, citation and bibliography searches were conducted with all 42 articles identifying an additional 22 articles for a final total of 64 articles (see Table 1).

After removing duplicates, two reviewers independently screened the title, abstract, and full text of the remaining 596 articles. Reviewers discussed any differences and consulted a third reviewer, when necessary, and a consensus was reached. One reviewer conducted forward and backward searches of the included articles. Titles and then full texts were reviewed to assess eligibility. Articles were abstracted and coded independently with two coders; discrepancies were discussed until a consensus was reached. Article abstractions included participants, study design, intervention description, 4 Ps, intervention setting, duration of intervention, data collection methods, outcome variables, and key findings. Our research reviewed studies and categorized them according to the 4 Ps: Product, price, promotion, and/or placement. Examples of interventions that were classified as product included determining how many and how much variety of a product to stock. Interventions that examined price included strategies such as price reductions and coupons. Furthermore, examples of interventions classified as promotion included shelf labels, recipe cards, and taste tests, and examples of placement strategies included altering the in-store location of products, such as moving to an endcap or to eye level. Our review included an examination for biases, with a focus on research design (eliminating confounders) and measures (i.e., self-report vs. objective data). Bias was assessed using the principles laid out in the Cochrane risk of bias tool [15].


**Table 1.** Study design characteristics for inclusion articles.


**Table 1.** *Cont.*

<sup>1</sup> Percentages do not add up to 100 because multiple intervention settings were used in some studies; <sup>2</sup> One intervention had two study designs; <sup>3</sup> Percentages do not add up to 100 because multiple outcomes were used in some studies; <sup>4</sup> Two interventions have different follow-up periods for difference stores.

**Figure 1.** Article inclusion flow chart.

#### **3. Results**

#### *3.1. Features of Included Articles*

The primary intervention sites varied in terms of store size and included supermarkets (43.8%), corner stores (31.3%), grocery stores (26.6%), and/or convenience stores (9.4%) (see Table 1). Experimental designs accounted for about one-third (35.4%) of available studies, while the remaining were pre-experimental (33.8%), quasi-experimental (27.7%), or time series (3.1%). The most frequently used objective outcome data were store sales data (46.9%), while self-reported purchasing or expenditures was the most frequently used self-report measure (40.6%). Intervention length varied from 22 min to 3.5 years. Most studies (89%) incorporated promotion as a key component of the intervention, although efforts to address product (34%) and placement (31%) were also prominent. Relatively few interventions focused on price (16%). A total of 56 of 64 studies (87.5%) had at least one positive effect. When considering only objective measures of sales and more rigorous methods of determining dietary intake (i.e., 24 h recalls or biometric data), 100% (14 out of 14) had at least one positive effect.

#### *3.2. Single- and Multi-Component Interventions*

Thirty interventions were classified as single-component interventions because they only manipulated one of the four Ps, while 34 interventions were classified as multi-component. Over the past 10 years, the number of single- and multi-component interventions have both slightly increased (see Figure 2).

**Figure 2.** Number of single- and multi-component interventions by year.

#### *3.3. Single-Component Interventions*

Among the 30 single-component interventions, 27 had at least one positive effect on improving consumption and purchasing of healthier foods. Promotion was the most commonly utilized marketing P and was the focus of 23 studies. Overall, 1 study had mixed effects (positive + negative) [16], 5 had mixed effects (positive + null) [17–21], 8 had mixed effects (positive + null + negative) [22–29], 1 had negative effects [30], 2 had null effects [31,32], and 13 had positive effects [33–45], (see Table 2).



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**Table 2.** *Cont.*


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**Table** 

**2.** *Cont.*


**Table2.***Cont.*

#### *IJERPH* **2020**, *17*, 7524


**Table 2.** *Cont.*

EXP = experimental; QE = quasi-experiment; PE = pre-experiment; Mixedˆ = positive + negative; Mixedˆˆ = positive + null; Mixedˆˆˆ = positive + null + negative; 'P' indicates thatintervention utilized this marketing approach.

#### *IJERPH* **2020** , *17*, 7524

#### 3.3.1. Product

Of the 30 single-component interventions, only one intervention manipulated product [40]. The study utilized a pre-experimental design and found positive effects on produce sales after increasing stocking and availability of fresh produce [40].

#### 3.3.2. Placement

One study implemented a placement-only intervention [23]. This experimental study had mixed effects (positive + null + negative). Positive effects were found such that featuring healthy products in aisle endcaps increased sales of these healthy products. However, when healthy products and indulgent products were featured together in aisle endcaps, sales of indulgent products increased while healthy products did not increase [23].

#### 3.3.3. Price

Three studies implemented price-only interventions [18,29,33]. One study had mixed effects (positive + null + negative) [29], while another had mixed effects (positive + null) [18] and one study had only positive effects [33]. Both experimental studies provided a 50% discount for fruits and vegetables [18,33] and found that customers who received the discount purchased significantly more fruits and vegetables than customers who did not receive the discount [18,33]. However, one study also found no sustained effect on participants' spending on fruits and vegetables from baseline to follow-up period [18]. In addition, one study used a pre-experimental design [29].

#### 3.3.4. Promotion

Twenty-three studies used a promotion strategy [17,19–22,24–28,30–32,34,36–39,41–45] as the sole intervention approach. Ten promotion interventions had positive effects [34,36–39,41–45], four reported mixed effects (positive+null) [17,19–21], six reported mixed effects (positive+null+negative) [22,24–28], two reported null effects [31,32], and one reported negative effects [30].

Four studies used experimental designs [17,18,22,34]. The interventions focused on nutrition shelf labeling [18], food samples [34], nutrition education [17], and a smartphone app [24]. One study found positive effects on fruit and vegetables purchases [34], while another study found mixed effects (positive + null) on food purchasing (e.g., positive effects on servings of fruit and no effect on servings of vegetables) [17]. Two studies found mixed effects (positive + null + negative) for the change in consumption and purchases of products authors classified as healthier (e.g., fruits, vegetables, and whole grains) as compared to products identified as less healthy (e.g., higher calorie products and sweets) [24] and for change in the sale of popcorn using different nutrition shelf labels [22]. One example of a study with largely positive results used a combination of shelf labels (e.g., "healthier option," "low sodium") in combination with education about the labels [17]. Positive effects were found such that customers purchased more servings of fruits and dark-green/bright-yellow vegetables. However, there were no significant differences between the groups on saturated fat, trans fat, and servings of vegetables [17].

Eight studies focused only on promotion, utilizing quasi-experimental designs [25,26,30,31,36–39]. Of these, four studies tested shelf labels [30,36,37,39], one examined the effectiveness of nutrition information labeling [26], one utilized a mass media campaign [25], one tested food demonstrations [38], and one examined the ability of increased stocking and promotions to sell healthy items [31]. Three found positive effects on purchases of healthier products [36,37,39] and one found positive effects regarding self-reported fruit and vegetable consumption [38]. One study had null effects on healthy food purchases and consumption, using both self-report measures and a skin carotenoid test [31]. Another study found negative effects on the demand for healthy popcorn [30]. Two studies found mixed effects (positive + null + negative) on sales of milk and the influence of caloric information on purchases [25,26].

Pre-experimental study designs reflected the majority of single-component intervention studies employing promotion [19–21,27,28,32,41–45]. Five implemented shelf labels [19,21,27,28,41], two used mass media social marketing campaigns [32,45], two implemented podcasts [20,42], one used taste tests [43], and one used a smartphone app [44].

#### 3.3.5. Other

In addition to the 4Ps, two studies did not fit into the standard 4P framework and therefore were classified as "other" [16,35]. Both utilized experimental study designs. One examined the effects of ambient music [16] and the other study analyzed effects of ambient scents [35]. Findings showed mixed effects (positive + negative) as lower volume music increased healthier purchasing patterns and higher volume music increased unhealthier purchases [16]. Additionally, findings showed positive effects when using an in-store indulgent scent (i.e., chocolate chip cookies), which led to increased purchasing of healthier foods, and decreased purchasing of unhealthy foods [35].

#### *3.4. Multi-Component Interventions*

Out of the 34multi-componentinterventions, 13interventionsincluded two Ps [46–58], 20interventions included three Ps [59–78], and one intervention included all four Ps [79]. All of the multicomponent interventions included promotion. Overall, 8 had positive effects [57,58,65,66,71,73,75,76], 2 studies had mixed effects (positive + negative) [68,78], 13 had mixed effects (positive + null) [46,47,49,51–53,56,59–62,64,72], 4 had mixed effects, (positive + null + negative) [50,55,63,77], and 7 had null effects [48,54,67,69,70,74,79] (see Table 3).



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**Table**

**3.**

*Cont.*




#### *IJERPH* **2020**, *17*, 7524



137

intervention utilized this marketing approach.

#### *IJERPH* **2020**, *17*, 7524

#### 3.4.1. Interventions Including 2 Ps

#### Promotion and Placement

Seven studies examined the impact of interventions that used both promotion and placement strategies [46,53–58]. Two studies found positive effects [57,58], one found null effects [54], one found mixed (positive + null + negative) [55], and three found mixed effects (positive + null) [46,53,56].

An experimental design was used in only one study [46]. The intervention included a food marketing campaign (inclusive of food demonstrations, recipe cards, and an audio novella) featuring fruit and vegetable characters in tiendas [46]. Positive effects were found on daily fruit and vegetable intake but not variety [46].

Three studies employed quasi-experimental designs [53,54,58]. One intervention manipulated the in-store location of produce (i.e., moving pre-packaged produce near checkout lines), added shelf labels, and distributed recipe cards [58]. Another intervention focused on the effects of promoting meal bundles through in-store displays [54], while another examined the effects of pre-packaged produce packs moved to aisle endcaps packages [53]. One study found that shoppers who were exposed to the intervention were more likely to purchase produce [58], and another found that moving the pre-packaged produce near checkout lines increased healthy purchasing [53]. However, displaying meal bundles was ineffective in increasing healthy item sales [54]. One study used a pre-experimental design [57].

Two studies with time series designs addressed the effects of using behavioral nudges [56] and implementing a healthy food kiosk coupled with food sampling [55]. Results showed positive effects for healthy food sales when multiple behavioral nudges were implemented simultaneously [56] and when food sampling was combined with featured food kiosks [55]. Null and negative effects were found for healthy item sales when intervention tactics were isolated as well as among certain foods [55,56].

#### Promotion and Product

Five studies examined the impact of promotion and product interventions [47–51]. Three studies had mixed effects (positive+null) [47,49,51], one study had mixed effects (positive+null+negative) [50], and one had null effects [48].

All five studies utilized an experimental design [51,59–62] and included components related to increased stock of healthier items. Promotional strategies varied: One incorporated food demonstrations [47], one used social marketing campaigns [48], and all five used point-of-purchase promotions (e.g., taste testing, shelf labels, educational displays, food samples, and signage) [47–51]. All studies found at least one null effect on healthy food consumption and purchasing [47–51]. However, positive effects were shown in four of five studies as participants' intent to purchase healthier foods increased with exposure to the interventions [47,49–51].

#### Promotion and Price

Only one study examined the effects of promotion and price and it used an experimental design [52]. The intervention examined the effects of healthy food consumption education and coupons with mixed effects (positive + null) on healthier purchases. Combining education and coupons was the most effective intervention group for increasing healthier purchases while null effects were largely observed for education and coupon only groups [52].

#### 3.4.2. Interventions Including 3 Ps

Promotion, Product, and Placement

Fifteen studies implemented interventions with promotion, product, and placement strategies [59–63,65,67–69,71,73,74,76–78]. Out of the 15, 2 had mixed effects (positive+negative) [68,78], 4 had mixed effects (positive + null) [59–62], 2 had mixed effects (positive + negative + null) [63,77], 3 had null effects [67,69,74], and 4 had positive effects [65,71,73,76].

Five studies were experimental [59–63]. The interventions included adding point-of-purchase promotions, changing the store structure and environment (e.g., adding a buffet bar or refrigerator, grouping products in a display), and altering the in-store location of products (e.g., multiple facings, prime placement, secondary placement, checkout aisle end-caps), and increased stocking of healthier products [59–63]. All five studies found mixed effects for improving the purchasing and consumption of healthy food. For example, Foster and colleagues (2014) implemented an intervention to increase the purchases of specific healthier foods through shelf tagging promotions and by altering the shelf placement of products [59]. In intervention stores, sales of 2% milk, whole milk, two targeted cereals, and one of three promoted frozen meals remained the same, while sales of skim milk, 1%, and two out of three frozen meals increased [59].

Four studies utilized quasi-experimental designs [65,67–69]. Two studies added point-of-purchase promotions, changed store structure and environment, altered in-store location, and increased stock of fresh produce [67,69]. Another study introduced healthier products to checkout lanes and added point-of-purchase promotions [68], and another changed store structure, increased media coverage about healthier choices, and offered in-store education sessions. Two studies found null effects on consumption and purchasing of fruits and vegetables [67,69], one found mixed effects (positive + negative) on consumer purchasing of healthy foods in healthy vs. standard checkout lanes [68], and one found positive effects of store owners' perceptions of changes in sales of promoted healthy foods [65]. Of these four quasi-experimental studies, two interventions were Proyecto MercadoFRESCO [67,69]. Both studies found null effects, such that there were no significant differences in consumption of and dollars spent on fruit and vegetables [67,69].

Six studies in this category used a pre-experimental [71,73,74,76–78] design. Similar to previous studies, strategies added point-of-purchase promotions, changed store structure and environment, altered in-store location, and increased stock of fresh produce [71,74,76–78]; one study implemented these strategies and paired urban farms with corner stores such that corner stores sold products obtained from urban farms [73]. Three studies found positive effects on purchases, sales, consumption, and intent to purchase healthy food [71,73,76].

#### Promotion, Product, and Price

Three studies utilized promotion, product, and price marketing strategies [64,66,72]. One study found positive effects [66] and two found mixed effects (positive + null) [64,72].

Of the three studies, two studies used a quasi-experimental design [64,66]. Both were multifaceted interventions that included increased stocking of healthy foods, point-of-purchase promotions, and price reductions/incentive cards [64,66]. One of the studies found when shelf labels were consistently used (high fidelity), positive effects on sales of the promoted, healthy items were found [66]. The second quasi-experimental study found mixed effects (positive + null): shelf labels on healthy items led to participants purchasing more promoted foods but did not change consumption. However, the study authors did not observe changes in healthy food consumption. Finally, one study used a pre-experimental design [72], with mixed results.

#### Promotion, Placement, and Price

Two studies examined the effects of promotion, placement, and price strategies [70,75]. One study found null effects [70] and the other found positive effects [75]. Both studies used similar interventions, Plate It Up Kentucky Proud [75] and Plate It Up [70], which added point-of-purchase promotions, altered product placement, and offered coupons and discounts [70,75].

One study used a quasi-experimental design [70]. The results showed null effects on fruit and vegetable consumption. The study authors found no difference in the percent of food purchasing dollars spent on fruits and vegetables between control and intervention groups [70]. In addition, Liu and colleagues (2017) used a pre-experimental design [75] and found that recipe cards had a positive effect on customers' purchases of recipe ingredients and increased consumption of fruits and vegetables [75].

#### 3.4.3. Intervention Including 4 Ps

Finally, only one study utilized all four Ps [79]. The study used an experimental, participatory design and found null effects for fruit and vegetable consumption. However, there was a significant decrease in the consumption of some unhealthy foods (e.g., chips) [79]. The intervention increased stocking of healthy foods, altered the in-store environment, added point-of-purchase promotions, and included discounts [79].

#### **4. Discussion**

This review, which examined the scope and impact of in-store marketing strategies related to healthy food sales, purchasing, and measures of diet, yields several important conclusions. One key finding of this recent review of literature is that both single- and multi-component interventions have become equally common focal points of research. Approaches provide evidence that increasing access to healthy food products in stores, particularly while utilizing promotion strategies, increases healthy food sales, purchasing, or improves dietary outcomes. While prior reviews found that positive outcomes were more common in studies utilizing multiple Ps [12,13], ours found more parity, even when considering the level of rigor applied to research designs and outcome measures. Overall, positive results were found in 27 of 30 single-component interventions as compared to 29 of 34 multi-component interventions, despite that multi-component interventions reported results related to a higher quantity of outcome measures.

Promotion efforts, including shelf labels, call out messages, and sampling products, continue to show promise as an important mechanism to improve purchasing. In-store promotion interventions are increasingly common, often with positive effects, either in combination with other approaches, or used alone. Previous reviews have found that older interventions, specifically those prior to 2008, were more likely to manipulate promotion, most often in single-component interventions [9,11]. In the more recent studies examined in this review, promotional interventions were frequently paired with placement and product strategies in multi-component interventions, for example including the coupling of a shelf labeling intervention with an end of aisle display, yielding positive effects.

Prior literature has identified multi-component interventions' added complexities in deciphering effects of its individual components [4,11]. There are two reasons for this complexity. One is the layered nature of multi-component interventions which by definition result in activities such as taste-testing, coupled with an end-cap placement and a shelf tagging, which make it difficult to decipher how components work together or separately to influence purchasing. It is possible for example that similar effects could be seen from just a single-component intervention, rather than multiple, though such impacts are difficult to decipher. Future multi-component interventions should consider alternative research designs where elements of the intervention are incorporated at different times and in different combinations, and then removed and then incorporated again in order to understand collective and individual effects, such a 2 × 2 factorial design or an ABA design [80].

#### *Limitations and Future Directions*

Of 64 studies reviewed, 24 in total (38%) were conducted without a control or comparison group. Only 14 of the 64 studies were experimental and included objective outcome measure data. The lack of a control group in more than one-third of studies displays the limitations of food environment research. Studies conducted with control groups, using store sales outcome data, and using rigorous dietary outcome measures are needed. Further research is also needed to better understand the individual and additive effects of multi-component interventions on outcomes like product sales.

The literature is limited in its ability to capture the extent to which increased healthy food sales results in overall less healthy food purchases. While several studies examine interventions in terms of specific product substitutions, for example by testing whether promoting a healthier item in a category results in changed sales in that product and a less healthy alternative (e.g., replacing higher fat popcorn with low-fat popcorn), few studies examine how targeted product sales relate to sales in other product categories (i.e., a spillover effect; e.g., increase in fruit sales associated with increase in low-fat dairy sales). Future research is needed to understand how increases in healthy food purchases do or do not serve to substitute for less healthy foods.

In addition to better understanding the marketing mechanisms that work best to shift purchasing, future research should examine the extent to which interventions yield sustained effects. Our review found that less than 20% of studies examined impacts beyond three months and only 4.5% considered impacts beyond one year.

It is unclear how the current COVID-19 context will continue to impact in-person food sales as compared to online sales and the extent to which product promotion and placement strategies can, or will, translate into online environments. Future work should seek to better understand how online food purchasing environments, including virtual supermarkets and real-world e-commerce platforms, can incorporate the four Ps to increase access to affordable foods.

#### **5. Conclusions**

Efforts to improve consumption and purchases of healthier foods in retail environments are diverse, even within the framework of the 4Ps. Considering these marketing strategies, this review found that promotion was the most commonly utilized strategy for single-component interventions, and manipulating promotion, placement, and product was the most common strategy used for multi-component intervention. In addition, interventions included in the review often employed pre-experimental or quasi-experimental research designs and relied more on self-report data rather than objective data. New research should implement interventions using rigorous designs and objective outcomes in order to advance the field. Further, given the large proportion of studies that implemented multi-component interventions, research is also needed to understand the individual and additive effects of approaches that use more than one of the 4Ps on objective sales outcomes, substitution effects of healthy food purchases, and the sustainability of impacts.

**Author Contributions:** A.K. contributed to the study concept and design. K.M. was responsible for screening. K.M. and H.W. extracted and coded the data, analyzed the data, and drafted the manuscript. A.K. critically reviewed all drafts. A.K. and K.G. approved the final version submitted for publication. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Institute of Health and Johns Hopkins Center for a Livable Future. Publication fees were supported by Healthy Eating Research, a national program of the Robert Wood Johnson Foundation.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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International Journal of *Environmental Research and Public Health*

*Review*
