Advertisement
American Journal of Preventive Medicine
ACPM
APTR
Advanced searchSave search
RESEARCH ARTICLE| Volume 54, ISSUE 5, SUPPLEMENT 2, S160-S169, May 2018

Download started.

Ok

Changes in Consumer Purchases in Stores Participating in an Obesity Prevention Initiative

Open AccessDOI:https://doi.org/10.1016/j.amepre.2017.12.002
Changes in Consumer Purchases in Stores Participating in an Obesity Prevention Initiative
Previous ArticleA Community-Level Initiative to Prevent Obesity: Results From Kaiser Permanente’s Healthy Eating Active Living Zones Initiative in California
Next ArticleChanges in Nutrition Policies and Dietary Intake in Child Care Homes Participating in Healthy Eating and Active Living Initiative

      Introduction

      From 2011 to 2014, small stores in three communities participated in a community-wide obesity prevention initiative. The study aimed to determine how participation in the initiative influenced store environments and consumer purchases.

      Study design

      Pre- and post-intervention without control. Structured observations of the store environments and intercept surveys of adult shoppers at all stores, and of children at two stores, conducted at baseline and follow-up. Manager/owner interviews regarding perceived impacts of the intervention conducted at follow-up.

      Setting/participants

      Shoppers at nine small stores in three diverse, low-income communities in Northern California.

      Intervention

      The store interventions were determined locally with combinations of strategies such as product displays, healthier options, marketing and promotion, store layout, and facility improvements that were implemented to varying degrees at each site.

      Main outcome measures

      Changes in store environments and purchases of select foods and beverages.

      Results

      Stores experienced consistent, but not always significant, declines in purchases of sweets and chips and increases in purchases of fruits and vegetables at select stores. Decreases in purchases of targeted sugar-sweetened beverages were offset by increases in purchases of other sugar-sweetened beverages. Changes in store environments and promotional activities varied widely from store to store and corresponded to variations in changes in purchasing. The owners/managers perceived benefits to their bottom line and community/customer relations, but challenges were identified that may account for the varied degree of implementation.

      Conclusions

      Substantive improvements in fruit and vegetable availability and promotion were needed to achieve a measurable impact on purchases but reducing purchases of unhealthy foods, like sweets and chips, required a less consistent intensive effort. These findings suggest it may be challenging to achieve the consistent and targeted implementation of changes and ongoing promotional efforts at a large enough proportion of stores where residents shop that would be required to get measurable impacts at the community level.

      Supplement information

      This article is part of a supplement entitled Building Thriving Communities Through Comprehensive Community Health Initiatives, which is sponsored by Kaiser Permanente, Community Health.

      Introduction

      In 2014 the prevalence of obesity in the U.S. was 35% among adult men, 40% for women, and 17% among children aged 2 to 19 years.
      • Flegal K.M.
      • Kruszon-Moran D.
      • Carroll M.D.
      • Frayar C.D.
      • Ogden C.L.
      Trends in obesity among adults in the United States, 2005 to 2014.
      JAMA. 2016; 315: 2284-2291https://doi.org/10.1001/jama.2016.6458
      , 
      • Ogden C.L.
      • Carroll M.D.
      • Lawman H.G.
      • et al.
      Trends in obesity prevalence among children and adolescents in the United States, 1988–1994 through 2013–2014.
      JAMA. 2016; 315: 2292-2299https://doi.org/10.1001/jama.2016.6361
      These high rates of obesity and related diseases, such as diabetes,
      • Menke A.
      • Casagrande S.
      • Cowie C.C.
      Prevalence of diabetes in adolescents aged 12 to 19 years in the United States, 2005–2014.
      JAMA. 2016; 316: 344-345https://doi.org/10.1001/jama.2016.8544
      have prompted healthcare and community organizations to work together to improve healthy eating and physical activity through innovative policy and environmental approaches.
      • Bunnell R.
      • O’Neil D.
      • Soler R.
      • et al.
      Fifty communities putting prevention to work: accelerating chronic disease prevention through policy, systems and environmental change.
      J Community Health. 2012; 37: 1081-1090https://doi.org/10.1007/s10900-012-9542-3
      , 
      • Cheadle A.
      • Samuels S.E.
      • Rauzon S.
      • et al.
      Approaches to measuring the extent and impact of environmental change in three California community-level obesity prevention initiatives.
      Am J Public Health. 2010; 100: 2129-2136https://doi.org/10.2105/AJPH.2010.300002
      Increasing access to healthy options has become a focal point of population-based obesity prevention.
      • Booth K.M.
      • Pinkston M.M.
      • Poston W.S.
      Obesity and the built environment.
      J Am Diet Assoc. 2005; 105: 110-117https://doi.org/10.1016/j.jada.2005.02.045
      Limited access to healthy food is particularly evident in low-income communities where a predominance of convenience stores offer little in the way of healthy options.
      • Walker R.E.
      • Keane C.R.
      • Burke J.G.
      Disparities and access to healthy food in the United States: A review of food deserts literature.
      Health Place. 2010; 16: 876-884https://doi.org/10.1016/j.healthplace.2010.04.013
      , 
      • Morland K.B.
      • Evenson K.R.
      Obesity prevalence and the local food environment.
      Health Place. 2009; 15: 491-495https://doi.org/10.1016/j.healthplace.2008.09.004
      , 
      • Galvez M.P.
      • Hong L.
      • Choi E.
      • et al.
      Childhood obesity and neighborhood food-store availability in an inner-city community.
      Acad Pediatr. 2009; 9: 339-343https://doi.org/10.1016/j.acap.2009.05.003
      One observational study found that fresh vegetable availability was a positive predictor of vegetable intake in nearby residents.
      • Bodor J.N.
      • Rose D.
      • Farley T.A.
      • Swalm C.
      • Scott S.K.
      Neighborhood fruit and vegetable availability and consumption: the role of small food stores in an urban environment.
      Public Health Nutr. 2008; 11: 413-420https://doi.org/10.1017/S1368980007000493
      Growing evidence led the National Academy of Medicine in 2012 to call for health-promoting food and beverage retailing, including convenience stores.
      Institute of Medicine, Committee on Accelerating Progress in Obesity Prevention
      Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation.
      National Academies Press, Washington, DC2012
      Food available in corner stores is typically dominated by unhealthy snack foods and beverages high in calories, fat, added sugar, and sodium.
      • Lucan S.C.
      • Karpyn A.
      • Sherman S.
      Storing empty calories and chronic disease risk: snack-food products, nutritive content, and manufacturers in Philadelphia corner stores.
      J Urban Health. 2010; 87: 394-409https://doi.org/10.1007/s11524-010-9453-5
      , 
      • Laska M.N.
      • Borradaile K.E.
      • Tester J.
      • Foster G.D.
      • Gittelsohn J.
      Healthy food availability in small urban food stores: a comparison of four U.S. cities.
      Public Health Nutr. 2010; 13: 1031-1035https://doi.org/10.1017/S1368980009992771
      , 
      • Sharkey J.R.
      • Dean W.R.
      • Nalty C.
      Convenience stores and the marketing of foods and beverages through product assortment.
      Am J Prev Med. 2012; 43: S109-S115https://doi.org/10.1016/j.amepre.2012.05.012
      , 
      • Lent M.R.
      • Vander Veur S.
      • Mallya G.
      • et al.
      Corner store purchases made by adults, adolescents and children: items, nutritional characteristics and amount spent.
      Public Health Nutr. 2015; 18: 1706-1712https://doi.org/10.1017/S1368980014001670
      A variety of retail food interventions have been developed to increase the supply and demand for healthier items.
      • Gittelsohn J.
      • Rowan M.
      • Gadhoke P.
      Interventions in small food stores to change the food environment, improve diet, and reduce risk of chronic disease.
      Prev Chronic Dis. 2012; 9: 110015https://doi.org/10.5888/pcd9.110015
      Their impact on food purchasing and consumption has been mixed.
      • Langellier B.A.
      • Garza J.R.
      • Prelip M.L.
      • et al.
      Corner store inventories, purchases, and strategies for intervention: a review of the literature.
      Calif J Health Promot. 2013; 11: 1
      , 
      • Ortega A.N.
      • Albert S.L.
      • Chan-Golston A.M.
      • et al.
      Substantial improvements not seen in health behaviors following corner store conversions in two Latino food swamps.
      BMC Public Health. 2016; 16: 389-398https://doi.org/10.1186/s12889-016-3074-1
      , 
      • Lawman H.G.
      • Vander Veur S.
      • Mallya G.
      • et al.
      Changes in quantity, spending, and nutritional characteristics of adult, adolescent and child urban corner store purchases after an environmental intervention.
      Prev Med. 2015; 74: 81-85https://doi.org/10.1016/j.ypmed.2014.12.003
      A recent study
      • Sanchez-Flack J.C.
      • Baquero B.
      • Linnan L.A.
      • et al.
      What influences Latino grocery shopping behavior? Perspectives on the small food store environment from managers and employees in San Diego, California.
      Ecol Food Nutr. 2016; 55: 163-181https://doi.org/10.1080/03670244.2015.1112282
      of small Latino store managers concluded that interventions should focus on the physical store space (attention-grabbing displays); price; and sociocultural facets. Two recent studies
      • Caspi C.E.
      • Lenk K.
      • Pelletier J.E.
      • et al.
      Association between store food environment and customer purchases in small grocery stores, gas-marts, pharmacies and dollar stores.
      Int J Behav Nutr Phys Act. 2017; 14: 76-87https://doi.org/10.1186/s12966-017-0531-x
      , 
      • Thorndike A.N.
      • Bright O.M.
      • Dimond M.A.
      • Fishman R.
      • Levy D.E.
      Choice architecture to promote fruit and vegetable purchases by families participating in the Special Supplemental Program for Women, Infants, and Children (WIC): randomized corner store pilot study.
      Public Health Nutr. 2017; 20: 1297-1305https://doi.org/10.1017/S1368980016003074
      found that prominent placement was associated with fruit and vegetable (FV) purchasing.
      The Kaiser Permanente Community Health Initiative funds and supports policy and environmental change to promote Healthy Eating and Active Living (HEAL) interventions in under-resourced neighborhoods. In Northern California, Kaiser Permanente funded collaboratives in six low-income, diverse neighborhoods called “HEAL Zones” from 2011 to 2014.
      • Cheadle A.
      • Schwartz P.M.
      • Rauzon S.
      • et al.
      The Kaiser Permanente Community Health Initiative: overview and evaluation design.
      Am J Public Health. 2010; 100: 2111-2113https://doi.org/10.2105/AJPH.2010.300001
      Efforts at three of the six zones included small store interventions. Interventions were developed and delivered by the local HEAL team at the community level. HEAL staff worked with each store to tailor the intervention in accordance with store needs and preferences. The interventions’ designs are guided by the HEAL Zone logic model with objectives to decrease calorie consumption (sugar-sweetened beverages [SSBs], portion sizes, snacking) and increase fresh FV consumption through evidence-based environmental changes supported by promotion and programmatic strategies. The hypothesis is that the stores participating in HEAL will improve marketing and availability, leading to decreased purchases of less healthy snack foods and beverages and increased purchases of FV.

      Methods

      Each HEAL Zone recruited the participating stores based on results of a community needs assessment, store location, and willingness of the store to participate. Zone 1 worked with two small convenience stores (stores 1A and 1B); Zone 2 worked with two small markets and one convenience store (stores 2A, 2B, and 2C); and Zone 3 worked with one small market and three convenience stores (stores 3A, 3B, 3C, and 3D). Convenience stores and small markets were defined as selling foods and having less than four registers. Convenience stores were distinguished from small markets if they did not sell fresh meat.
      Observations of the store environment and consumer intercept surveys were conducted at two time points: before the intervention and 1 year later (Zones 1 and 3) or 3 years later (Zone 2). Measures were taken at the same time of year at baseline and follow-up to control for effects of seasonality. Store manager interviews were conducted at follow-up.
      The in-store environmental observation tools were chosen and administered by each Zone. Zones 2 and 3 used the Communities of Excellence in Nutrition, Physical Activity, and Obesity Prevention (CX3) Food Availability and Marketing Survey,
      • Ghirardelli A.
      • Quinn V.
      • Sugerman S.
      Reliability of a retail food store survey and development of an accompanying retail scoring system to communicate survey findings and identify vendors for healthful food and marketing initiatives.
      J Nutr Educ Behav. 2011; 43: S104-S112https://doi.org/10.1016/j.jneb.2011.03.003
      which assesses food availability, quality, and marketing and promotion. Zone 1 used a similar locally developed tool that had more detailed documentation of food and beverage availability and a broader range of topics than the CX3.
      • Ortega A.N.
      • Albert S.L.
      • Chan-Golston A.M.
      • et al.
      Substantial improvements not seen in health behaviors following corner store conversions in two Latino food swamps.
      BMC Public Health. 2016; 16: 389-398https://doi.org/10.1186/s12889-016-3074-1
      Local HEAL Zone staff also provided information about the nature of the technical assistance provided to and promotional efforts at each of the stores. Observations were conducted during 1 day at each time point.
      The intercept survey was administered by data collectors who were selected and recruited from the community and received extensive training from research staff, including in-store practice. Customers exiting each store were asked to participate in a short survey. The interviewer-administered, structured survey questionnaire included questions about demographic characteristics, shopping behaviors, and if targeted foods and beverages had been purchased. Adults were surveyed in all Zones; children were only surveyed in Zone 1. Survey incentives included either a small discount on a future purchase or an entry into a raffle for a larger prize. Surveys were conducted over the course of several days and included various times of day and days of the week. Follow-up survey time points were matched to baseline survey time points on dates, day of week, and time of day.
      One interview per store with the manager/owner was conducted by trained members of the HEAL Zone agency staff. The interview included structured response and open-ended short answer questions regarding perceived financial impacts, benefits, and challenges of the interventions.
      Changes in store environments were assessed by comparing baseline and follow-up CX3 scores based on rubrics developed by the California Department of Public Health.
      • Ghirardelli A.
      • Quinn V.
      • Sugerman S.
      Reliability of a retail food store survey and development of an accompanying retail scoring system to communicate survey findings and identify vendors for healthful food and marketing initiatives.
      J Nutr Educ Behav. 2011; 43: S104-S112https://doi.org/10.1016/j.jneb.2011.03.003
      For Zone 1, which did not use the CX3 tool, change in food availability from baseline to follow-up was assessed by comparing checklists of food and beverage items that were part of their locally developed tool.
      Descriptive statistics were generated and analyses conducted with SPSS, version 23.0. Significance of difference between baseline and follow-up sociodemographics was determined by t-test for means and chi-square for dichotomous or categorical variables. To assess the significance of difference in consumer purchases over time, logistic regression models (for multivariate analysis) and chi-square (for bivariate analysis) at the store and Zone level were conducted with time as the independent variable and number of consumers purchasing select foods and beverages (FVs, candy, cookies, pan dulce, total sweets, chips, soda, sports drinks, energy drinks, other SSBs, and total SSBs) as the dependent variables. Pan dulce refers to Mexican-style sugar-sweetened breads. The store 3D was not included in the Zone-level analyses, because no measurable intervention-related changes occurred at this store. Multivariate models were only performed when sample size was adequate (based on at least ten observations per degree of freedom). All multivariate models included age, gender, and race as covariates. Additional sociodemographic and shopping pattern variables likely to impact the outcomes were included when the significance of difference from baseline to follow-up was p≤0.15 (Table 1). Store was controlled for with dummy variables to account for fixed effects. Considering that clustering might be needed to account for store differences more robustly, analyses were conducted with generalized estimating equations, but did not appreciably affect p-values and interpretation of results. Therefore, the simpler model was used in the final analysis. Frequencies were generated for the responses to the structured-response interview questions. Responses to short open-ended questions were grouped into common categories prior to generating frequencies.
      Table 1Demographics and Shopping Behaviors of Survey Respondents at Baseline and Follow-up by HEAL Zone
      Sample characteristicsZone 1Zone 2Zone 3
      201220132011201420122013
      Total sample, N
      Sample sizes vary slightly by question because of exclusion of missing and don’t know responses, as well as skip sequences built into the survey for Community 1.
      		159163155151391418
      Age
      In Community 1, age was controlled for as a dichotomous variable, ≥18 years versus not; Communities 2 and 3 only surveyed adults, and age was controlled for as a continuous variable.
       M (SD)N/AN/A35.9 (12.7)34.4 (10.9)29.0 (12.0)*34.8 (12.7)***
       ≥18 years13 (84)***153 (94)***155 (100)151 (100)391 (100)418 (100)
      Gender: female72 (50)
      Indicates p-value between 0.1 and 0.15; these variables are included as covariates for a given Zone in addition to variables with p<0.10.
      		63 (41)
      Indicates p-value between 0.1 and 0.15; these variables are included as covariates for a given Zone in addition to variables with p<0.10.
      		71 (46)60 (44)152 (42)176 (45)
      Race/ethnicity
      In Community 1, race/ethnicity was controlled for as a dichotomous variable, African American or not; in Community 2, it was also dichotomous, Hispanic/Latino or not; in Community 3, race/ethnicity was kept categorical (four distinct categories).
       Hispanic/Latino26 (17)24 (15)120 (77)
      Indicates p-value between 0.1 and 0.15; these variables are included as covariates for a given Zone in addition to variables with p<0.10.
      		127 (84)
      Indicates p-value between 0.1 and 0.15; these variables are included as covariates for a given Zone in addition to variables with p<0.10.
      		111 (29)***170 (41)***
       African American/black114 (74)131 (81)5 (3)3 (2)107 (27)***128 (31)***
       White10 (7)5 (3)31 (20)*18 (12)*96 (25)***85 (20)***
       Asian0 (0)2 (1)N/AN/AN/AN/A
       Other7 (4)2 (1)2 (1)2 (1)76 (20)***35 (8)***
      Household size,
      These questions were removed from the survey for Community 1, in order to reduce respondent burden.
      M (SD)      		N/AN/A3.2 (1.8)***4.5 (1.6)***3.4 (1.9)***4.0 (2.1)***
      Households with children, M (SD)
      These questions were removed from the survey for Community 1, in order to reduce respondent burden.
      		N/AN/A80 (52)*94 (62)*185 (48)***240 (58)***
      Shopping frequency
      Shopping frequency was controlled for as an ordinal variable with the following response categories: More than 1 time/day, 3–6 times/week, 1–2 times/week, 2–3 times/month, 1 time/month, Almost never. p-values are shown for analyses using these original response categories.
       ≥1×/week151 (96)*149 (91)***93 (61)***106 (71)***245 (64)***335 (81)***
       1–3×/month3 (2)***5 (3)***35 (23)***26 (17)***51 (13)***52 (13)***
       <1×/month4 (3)*9 (6)***24 (16)***17 (11)***87 (23)***28 (7)***
      Total dollar amount purchased,
      These questions were removed from the survey for Community 1, in order to reduce respondent burden.
      M (SD)      		N/AN/A$13.61 ($13.19)*$17.29 ($19.88)*$6.62 ($16.27)**,
      n=386.
      		$4.73 ($8.38)**,
      n=417.
      Buys most groceries here45 (35)58 (39)58 (37)
      Indicates p-value between 0.1 and 0.15; these variables are included as covariates for a given Zone in addition to variables with p<0.10.
      		43 (29)
      Indicates p-value between 0.1 and 0.15; these variables are included as covariates for a given Zone in addition to variables with p<0.10.
      		21 (5)***49 (12)***
      Main shopper in household
      The p-values shown are for analyses using the following response categories: Yes, No, and No one person responsible (for Communities 2 and 3), and Yes versus No or Not one person responsible (for Community 1).
      		90 (68)114 (75)102 (66)104 (70)122 (44)***194 (47)***
      Distance of store to home
       <5 blocks98 (81)***117 (84)***50 (36)***69 (47)***187 (51)***250 (60)***
       6–15 blocks20 (16)*8 (6)*39 (28)***22 (15)***56 (15)***66 (16)***
       >15 blocks3 (3)***15 (11)***52 (37)***57 (39)***124 (34)***100 (24)***
      Transportation to store
      In Community 1, respondents could indicate more than one transportation method, so responses may total greater than 100%. p-values shown are for analyses using original response categories that included walk, car, bus, bike, and other.N/A, not available.
       Walk110 (71)115 (71)0 (0)***32 (22)***115 (29)**159 (38)**
       Car39 (25)39 (24)123 (80)***110 (74)***224 (57)**199 (48)**
       Other8 (4)11 (7)31 (20)***7 (5)***53 (14)**57 (13)**
      Note: Values are n (%) unless otherwise noted. Boldface indicates statistical significance (*p<0.10; **p<0.05; ***p<0.01).
      a Sample sizes vary slightly by question because of exclusion of missing and don’t know responses, as well as skip sequences built into the survey for Community 1.
      b In Community 1, age was controlled for as a dichotomous variable, ≥18 years versus not; Communities 2 and 3 only surveyed adults, and age was controlled for as a continuous variable.
      c Indicates p-value between 0.1 and 0.15; these variables are included as covariates for a given Zone in addition to variables with p<0.10.
      d In Community 1, race/ethnicity was controlled for as a dichotomous variable, African American or not; in Community 2, it was also dichotomous, Hispanic/Latino or not; in Community 3, race/ethnicity was kept categorical (four distinct categories).
      e These questions were removed from the survey for Community 1, in order to reduce respondent burden.
      f Shopping frequency was controlled for as an ordinal variable with the following response categories: More than 1 time/day, 3–6 times/week, 1–2 times/week, 2–3 times/month, 1 time/month, Almost never. p-values are shown for analyses using these original response categories.
      g n=386.
      h n=417.
      i The p-values shown are for analyses using the following response categories: Yes, No, and No one person responsible (for Communities 2 and 3), and Yes versus No or Not one person responsible (for Community 1).
      j In Community 1, respondents could indicate more than one transportation method, so responses may total greater than 100%. p-values shown are for analyses using original response categories that included walk, car, bus, bike, and other.N/A, not available.
      IRB approval was obtained from the University of California, Berkeley.

      Results

      The survey response rates at baseline and follow-up were 38% and 55% in Zone 1, 67% and 75% in Zone 2, and 68% and 59% in Zone 3, respectively. The sample consisted primarily of adults and similar proportions of males and females (Table 1). Average household size varied from 3.2 to 4.5 people (across time periods and zones), and a little more than half of the households included children.
      In Zone 1, respondents were predominately African American, most lived near the stores, and traveled there on foot. In Zone 2, respondents were predominately Latino, less than half lived within five blocks of the store, and most traveled there by car (Table 1). In Zone 3, race/ethnicity was more mixed, a little more than half lived within five blocks of the store, and about half traveled there by car.
      About one third of respondents in HEAL Zones 1 and 2 purchased most of their groceries at the store where they were surveyed, whereas only 5%–12% did in HEAL Zone 3 (Table 1). Amount spent per transaction was greater at the small markets than at the convenience stores.
      The two stores in Zone 1 were completely remodeled, including new shelving, produce bins, and refrigeration with layout designed to encourage healthy selections (Table 2). Promotional activities including taste testing, free samples, grand re-opening events, and a reduction in ads for unhealthy foods. Outreach in the community included coupons, posters, door hangers, sandwich boards, school events, and media coverage. Extensive technical assistance was provided regarding how to promote healthy foods and implement their new business model. From baseline to follow-up, FV availability increased from four to seven canned/frozen options and from zero to 19 fresh options on average per store (Table 2). Availability of other healthy foods also increased.
      Table 2Summary of Intervention Components (Environmental Changes and Promotional Activities)
      Category of change madeCX3 scores (baseline - follow-up by store) and description of changes made
      Zone 1Zone 2Zone 3
      Healthy food availability
       FV variety/quality (max points: 40)
      • CX3: N/A Baseline: 4 canned/frozen and no fresh per store
      • Follow-up: 7 canned/frozen and 19 fresh per store
      • A: 38–40
      • B: 34–36
      • C: 36–27
      • A: 20–14
      • B: 10–20
      • C: 0–0
      • D: 0–0
       FV prices (max points: 10)N/A
      • A: 8–10
      • B: 4–5
      • C: 0–1
      • A: 0–0
      • B: 0–0
      • C: N/A
      • D: N/A
       Other healthy foods (max points 10)
      • CX3: N/A
      • Added or increased lower-fat milks, whole grains, healthier snacks
      • A: 8–10
      • B: 7–7
      • C: 7–7
      • ↑water, lower fat milks, healthy snacks at check out
      • A: 8–7
      • B: 5–5
      • C: 5–7
      • D: 7–7
      • Minimal changes
      Promotional activities
       CX3 marketing and promotion scoreN/A
      • A: 8.5–13.5
      • B: 5–12
      • C: 2.5–9
      • A: 4–4.5
      • B: 3–7
      • C: 3–3
      • D: 4–4.5
       Interior store design/layoutTotal make-over: new shelving, produce bins, refrigerationRefurbished healthy snack zoneMinimal
       Exterior improvementsYes, unspecifiedFaçade improvements at 2 storesMurals and landscaping
       In-store promotionComplete reorganization of placement; posted signs to encourage healthy selection; taste testing; free samples; grand re-opening eventsHarvest of the Month promotional materials; food demos; dairy and meat promotion; product placementSignage, posters, window clings, recipe cards, food demos, product placement
       Community outreachCoupons, posters, door hangers to local businesses; sandwich boards; school event; local media coverageMail marketing; local media coverageNothing specific to the stores
       Technical assistance providedBusiness development: procurement, pricing, merchandising, financing; promotion and marketing: materials and trainingTool kits; marketing materials; training on produce handling, storage, marketing, and promotionTechnical assistance to store managers to support healthy changes
      CX3, Communities of Excellence in Nutrition, Physical Activity, and Obesity Prevention Food Availability and Marketing Survey; FV, fruits and vegetables; N/A, not applicable or not available.
      In Zone 1, there was a statistically significant increase in FV purchases from 1% to 6% of intercepted shoppers (Table 3). Multivariate analysis was not performed because of limited sample size. The manager of store 1A reported that according to sales records, sales of fresh FVs increased from zero at baseline to 72 units (pieces, pounds, or bunches depending on the item) per day at follow-up (data not shown). The proportion of intercepted shoppers that purchased sweets and chips declined, but the reduction was only significant for sweets (p=0.036 with bivariate and p=0.066 with multivariate analysis; Table 3). A nonsignificant reduction in soda purchases was offset by a significant increase in other SSBs, resulting in no significant change in SSB purchases.
      Table 3Number and Percent of Survey Respondents Who Purchased Select Items at Baseline and Follow-up
      Zone, store, and dateFoods (% of total respondents)Beverages (% of those who purchased any beverage)
      nFruit and vegetableSweets
      Zone 1: sweets=candies, cookies, pastries, and baked goods; Zones 2–3: sweets=candies, cookies, and pan dulce.
      		ChipsnSodaSports drinksEnergy drinks
      Zones 1 and 2 did not collect data that distinguished energy drinks; rather they are included as part of “Other SSBs.”
      		Other SSBsAll SSBs
      Zone 1
       Store A+B
        20121591 (1)53 (33)52 (33)9059 (66)1 (1)N/A12 (13)72 (80)
        20131639 (6)37 (23)43 (26)10859 (55)3 (3)N/A37 (34)94 (88)
        p-value
      Unadjusted (bivariate) model.
      		0.0200.0360.2210.1460.627N/A0.0010.169
        p-value
      Zone 3–level data were examined without store 3D because no measurable intervention-related changes occurred at that store; store D therefore serves as a self-selected comparison site.
      		N/A0.0660.4450.107N/AN/A0.0010.102
      Zone 2
       Store A+B+C
        201115548 (31)60 (39)47 (30)9351 (55)10 (11)N/A10 (11)66 (71)
        201415155 (36)40 (27)30 (20)9733 (34)18 (19)N/A41 (42)76 (78)
        p-value
      Unadjusted (bivariate) model.
      		0.3140.0230.0360.0040.133N/A<0.0010.243
        p-value
      Community-level multivariate model with the following covariates: Zone 1: age, gender, race, and shopping frequency, and store fixed effects; Zone 2: race, shopping frequency, total amount spent, household size, presence of children in home, and store fixed effects; analyses with “all SSBs” combined, also adjusted for age and gender; Zone 3: age, gender, race, average household size, presence of children in home, shopping frequency, whether respondent was main shopper for household, total amount spent, and store fixed effects. N/A, not applicable or not available; SSB, sugar-sweetened beverage.
      		0.4130.0700.1360.0190.151N/A<0.0010.570
      Zone 3
       Store A+B+C
      Community-level multivariate model with the following covariates: Zone 1: age, gender, race, and shopping frequency, and store fixed effects; Zone 2: race, shopping frequency, total amount spent, household size, presence of children in home, and store fixed effects; analyses with “all SSBs” combined, also adjusted for age and gender; Zone 3: age, gender, race, average household size, presence of children in home, shopping frequency, whether respondent was main shopper for household, total amount spent, and store fixed effects. N/A, not applicable or not available; SSB, sugar-sweetened beverage.
        201239114 (4)81 (27)114 (29)262128 (50)36 (14)51 (20)28 (11)206 (79)
        20134185 (1)46 (15)35 (8)257129 (50)34 (13)10 (10)42 (16)196 (76)
        p-value
      Unadjusted (bivariate) model.
      		0.0860.001<0.0010.7920.898<0.0010.0720.401
        p-value
      Community-level multivariate model with the following covariates: Zone 1: age, gender, race, and shopping frequency, and store fixed effects; Zone 2: race, shopping frequency, total amount spent, household size, presence of children in home, and store fixed effects; analyses with “all SSBs” combined, also adjusted for age and gender; Zone 3: age, gender, race, average household size, presence of children in home, shopping frequency, whether respondent was main shopper for household, total amount spent, and store fixed effects. N/A, not applicable or not available; SSB, sugar-sweetened beverage.
      		0.3860.019<0.0010.8660.307<0.0010.0540.520
       Store D
        2012890 (0)7 (8)15 (17)7335 (45)13 (18)6 (8)9 (12)59 (81)
        20131160 (0)64 (55)77 (66)10778 (73)3 (3)16 (15)4 (4)94 (88)
        p-value
      Unadjusted (bivariate) model.
      		N/A<0.0010.001<0.0010.0010.2470.0390.139
        p-value
      Community-level multivariate model with the following covariates: Zone 1: age, gender, race, and shopping frequency, and store fixed effects; Zone 2: race, shopping frequency, total amount spent, household size, presence of children in home, and store fixed effects; analyses with “all SSBs” combined, also adjusted for age and gender; Zone 3: age, gender, race, average household size, presence of children in home, shopping frequency, whether respondent was main shopper for household, total amount spent, and store fixed effects. N/A, not applicable or not available; SSB, sugar-sweetened beverage.
      		N/A<0.001<0.0010.0140.0400.0580.0730.500
      Note: Boldface indicates statistical significance (p<0.05); italics indicate change in the unintended direction.
      a Zone 1: sweets=candies, cookies, pastries, and baked goods; Zones 2–3: sweets=candies, cookies, and pan dulce.
      b Zones 1 and 2 did not collect data that distinguished energy drinks; rather they are included as part of “Other SSBs.”
      c Unadjusted (bivariate) model.
      d Zone 3–level data were examined without store 3D because no measurable intervention-related changes occurred at that store; store D therefore serves as a self-selected comparison site.
      e Community-level multivariate model with the following covariates: Zone 1: age, gender, race, and shopping frequency, and store fixed effects; Zone 2: race, shopping frequency, total amount spent, household size, presence of children in home, and store fixed effects; analyses with “all SSBs” combined, also adjusted for age and gender; Zone 3: age, gender, race, average household size, presence of children in home, shopping frequency, whether respondent was main shopper for household, total amount spent, and store fixed effects.N/A, not applicable or not available; SSB, sugar-sweetened beverage.
      Changes in Zone 2 were less extensive. Smaller changes in product placement were made to encourage healthy selections, with main emphasis on creating a healthy snack zone (Table 2). They used Harvest of the Month materials to promote FV and conducted food demos and dairy and meat promotions. Community outreach included mail marketing and local media coverage. Technical assistance focused on marketing healthy options and produce handling and storage. CX3 scores indicated an improvement in marketing and promotion at all three stores, a reduction in ads for unhealthy foods at store 2C, and an excellent variety and quality of produce at baseline at all stores that improved only slightly at stores 2A and B and declined at store 2C (Table 2). Reports indicated an increase in availability of low-fat milks, water, and healthy snacks at checkout; however, CX3 scores only detected an increase in healthy options at store 2A.
      In Zone 2, frequency of purchases of FV only increased significantly at store B and did not change significantly at the Zone level (Table 3). Purchases of sweets and chips declined at the Zone level, but these declines were only significant with bivariate (p=0.023 and p=0.036), yet not significant with multivariate, analysis (p=0.07 and p=0.136). At the store level (data not shown) decreases in purchases of sweets or chips were significant at the two stores (B and C) that had the largest increases in promotion and marketing scores. A significant reduction in soda purchases was offset by a significant increase in other SSBs, resulting in no significant change in SSB purchases in Zone 2 (Table 3).
      Promotion at the four stores in Zone 3 including signage, posters, window clings, recipe cards, food demos and product placement to encourage healthy selections (Table 2). No major changes in store lay out were made. Exterior improvements included new landscaping and murals. Technical assistance and materials focused on promoting healthy selections. There were decreases in in-store ads for unhealthy foods. This zone focused on reducing intake of energy drinks. CX3 indicated that marketing and promotion scores started low and only improved appreciably at store B, only store B increased FV variety/quality, stores C and D offered no FV at either time point, and other healthy options only increased at store 3C (Table 2). The only measured change at store D was a half-point improvement in exterior conditions.
      In Zone 3, purchases of FV were infrequent at baseline and decreased slightly over time (Table 3). Frequency of sweets and chip purchases decreased significantly at the stores A, B, and C combined and increased significantly at store D (Table 3). At stores A, B, and C combined and at store D there were significant decreases in the frequency of energy drink purchases; however, those decreases were offset by increases in other types of SSBs, resulting in no change in overall purchases of SSBs.
      From the survey with store managers, almost half of the managers reported that their overall sales and profits increased, and the rest believed that sales and profits did not change over the course of the intervention (Table 4). All except one thought that sales of healthy items had increased and most thought this helped improve the bottom line. They attributed the increase in sales or profits to the increased variety of healthy options, store redesign, and increased community awareness. They reported that staffing levels had not been affected. Challenges encountered included primarily the time and costs involved in stocking and promoting healthy items as well as issues of perishability and lower profit margins of some healthy items (Table 4). Despite these challenges, managers were overwhelmingly positive about the intervention. They felt that the program had not only increased sales but also improved the stores’ image and their relationships with customers and the community.
      Table 4Store Managers’ Perceived Impact of Participating in Intervention
      Manager perceptionsNumber (percent) of stores
      Change in overall sales, n=9
       Increased4 (44)
       Stayed about the same5 (56)
       Decreased0 (0)
      Change in overall profits, n=9
       Increased4 (44)
       Stayed about the same5 (56)
       Decreased0 (0)
      Change in overall staffing level, n=6
      This question was not asked in interviews in Zone 2.
       Increased0 (0)
       Stayed about the same5 (83)
       Decreased1 (17)
      Change in sales of healthy items, n=9
       Increased8 (89)
       Stayed about the same1 (11)
       Decreased0 (0)
      Impact of healthy items on bottom line, n=9
       Improved it a lot2 (22)
       Improved it a little4 (45)
       No impact2 (22)
       Hurt it a little0 (0)
       Hurt it a lot0 (0)
       Not sure1 (11)
      Impact of healthy items on staffing, n=9
       Increased0 (0)
       No change9 (0)
       Decreased0 (0)
       Not sure0 (0)
      Reasons for overall increase in sales and/or profits, n=4
      This question was only asked if respondents indicated there was a change in overall sales or profits. WIC, Special Supplemental Nutrition Program for Women, Infants, and Children.
       Offering more (quantity or variety) healthy foods4 (100)
       Increased community awareness of store selection2 (50)
       Store re-design1 (25)
       Improved economy/more customers1 (25)
      Benefits of participating in intervention, n=9
       Customers appreciate having convenient healthy options7 (78)
       Increased business/sales3 (33)
       Good for the community/“right thing to do”3 (33)
       Improves store ambiance2 (22)
       Able to sell healthy foods to WIC customers2 (22)
       Good public relations/having store promoted1 (11)
      Challenges of offering healthy foods, n=9
       Need funding for store re-design and/or promotion2 (22)
       Figuring out space/placement of new products2 (22)
       Promoting the changes/increasing community awareness2 (22)
       Challenging to source and/or offer more healthy items2 (22)
       Too much competition from unhealthy products2 (22)
       Healthy products have lower profit margin2 (22)
       Healthy products are less shelf-stable1 (11)
       Takes a lot of time to maintain1 (11)
      a This question was not asked in interviews in Zone 2.
      b This question was only asked if respondents indicated there was a change in overall sales or profits.WIC, Special Supplemental Nutrition Program for Women, Infants, and Children.

      Discussion

      During the HEAL store interventions, reduction in purchases of unhealthy food was more consistent than increases in purchases of healthy ones. The proportion of respondents that purchased sweets and chips decreased in every zone and at every store, except at store 3D where essentially no intervention-related changes occurred. However, these decreases were only significant with multivariate analysis in Zone 3. Because the intervention-related changes in Zone 3 were not measurably more robust than in the other zones, these findings may be explained by the larger sample size for Zone 3. Most of the changes in Zone 3 were limited to marketing and promotion rather than availability, suggesting that changes in promotion and marketing (which were common to all three Zones) may be responsible for the observed reduction in purchases of these less healthy foods.
      Although all of the stores promoted FV, very few of the stores made substantive changes in the availability of FV. The stores in Zone 2 already had an excellent selection of FV at baseline, so there was little room for improvement. Only one of the stores in Zone 3 improved the availability of FV. Therefore, it is not surprising that only Zone 1 (and one store in Zone 2) where improvements in FV availability were the greatest had significant increases in FV purchases. These findings suggest that improvements in promotion and marketing alone (without increasing availability) are insufficient to increase FV purchases.
      Although the three HEAL Zones were successful in reducing purchases of specific targeted SSBs, that effect was counteracted by increases in purchases of other SSBs resulting in no overall reductions in SSB purchases. This was particularly evident in Zone 3 where a focus on energy drinks led to significant reductions in those beverages but increases in other SSBs. The switch from sodas to other SSBs at some of the other HEAL Zone stores may also be because of the misconception, often reinforced by commercial marketing strategies, that some SSBs are healthier than others. This suggests the importance of focusing on all SSBs to achieve overall reduction in added sugar and calories. The lack of increase in purchases of healthy beverage alternatives may indicate these options are less appealing to shoppers.
      The store managers viewed the interventions as having benefits for their bottom line even when the surveys indicated no or minimal increases in purchases of targeted healthy options. This suggests the extent of the effect on purchases may be greater than estimated with this sample, possibly because of lack of sensitivity in the measures or failure to measure options for which purchasing increased. The interview results suggest that owners/managers were very satisfied with the program. However, some of the challenges that they identified, such as time constraints, competition from unhealthy products, less stable shelf life, and lower profit margins are ongoing in nature and therefore could affect their ability to sustain the program. Longer-term studies are needed.
      Many studies have examined the impact of in-store interventions on FV intake
      • Ortega A.N.
      • Albert S.L.
      • Chan-Golston A.M.
      • et al.
      Substantial improvements not seen in health behaviors following corner store conversions in two Latino food swamps.
      BMC Public Health. 2016; 16: 389-398https://doi.org/10.1186/s12889-016-3074-1
      , 
      • Surkan P.J.
      • Tabrizi M.J.
      • Lee R.M.
      • Palmer A.M.
      • Frick K.D.
      Eat Right–Live Well! Supermarket intervention impact on sales of healthy foods in a low-income neighborhood.
      J Nutr Educ Behav. 2016; 48: 112-121https://doi.org/10.1016/j.jneb.2015.09.004
      , 
      • Ayala G.X.
      • Baquero B.
      • Laraia B.A.
      • Ji M.
      • Linnan L.
      Efficacy of a store-based environmental change intervention compared with a delayed treatment control condition on store customers’ intake of fruits and vegetables.
      Public Health Nutr. 2013; 16: 1953-1960https://doi.org/10.1017/S1368980013000955
      , 
      • Payne C.R.
      • Niculescu M.
      • Just D.R.
      • Kelly M.P.
      Shopper marketing nutrition interventions.
      Physiol Behav. 2014; 136: 111-120https://doi.org/10.1016/j.physbeh.2014.03.029
      , 
      • Ogawa Y.
      • Tanabe N.
      • Honda A.
      • et al.
      Point-of-purchase health information encourages customers to purchase vegetables: objective analysis by using a point-of-sales system.
      Environ Health Prev Med. 2011; 16: 239-246https://doi.org/10.1007/s12199-010-0192-8
      , 
      • Achabal D.D.
      • McIntyre S.H.
      • Bell C.H.
      • Tucker N.
      The effect of nutrition POP signs on consumer attitudes and behavior.
      J Retail. 1987; 63: 9-24
      , 
      • Ernst N.D.
      • Wu M.
      • Frommer P.
      • et al.
      Nutrition education at the point of purchase: the foods for health project evaluated.
      Prev Med. 1986; 15: 60-73https://doi.org/10.1016/0091-7435(86)90036-8
      , 
      • Milliron B.J.
      • Woolf K.
      • Appelhans B.M.
      A point-of-purchase intervention featuring in-person supermarket education affects healthful food purchases.
      J Nutr Educ Behav. 2012; 44: 225-232https://doi.org/10.1016/j.jneb.2011.05.016
      , 
      • Connell D.
      • Goldberg J.P.
      • Folta S.C.
      An intervention to increase fruit and vegetable consumption using audio communications: in-store public service announcements and audiotapes.
      J Health Commun. 2001; 6: 31-43https://doi.org/10.1080/10810730150501396
      ; consistent with this study, impacts on purchases and consumption have been mixed. Fewer published studies have examined the impact of in-store interventions on purchases or consumption of SSBs
      • Surkan P.J.
      • Tabrizi M.J.
      • Lee R.M.
      • Palmer A.M.
      • Frick K.D.
      Eat Right–Live Well! Supermarket intervention impact on sales of healthy foods in a low-income neighborhood.
      J Nutr Educ Behav. 2016; 48: 112-121https://doi.org/10.1016/j.jneb.2015.09.004
      , 
      • Ogawa Y.
      • Tanabe N.
      • Honda A.
      • et al.
      Point-of-purchase health information encourages customers to purchase vegetables: objective analysis by using a point-of-sales system.
      Environ Health Prev Med. 2011; 16: 239-246https://doi.org/10.1007/s12199-010-0192-8
      , 
      • Milliron B.J.
      • Woolf K.
      • Appelhans B.M.
      A point-of-purchase intervention featuring in-person supermarket education affects healthful food purchases.
      J Nutr Educ Behav. 2012; 44: 225-232https://doi.org/10.1016/j.jneb.2011.05.016
      , 
      • Foster G.D.
      • Karpyn A.
      • Wojtanowski A.C.
      • et al.
      Placement and promotion strategies to increase sales of healthier products in supermarkets in low-income, ethnically diverse neighborhoods: a randomized controlled trial.
      Am J Clin Nutr. 2014; 99: 1359-1368https://doi.org/10.3945/ajcn.113.075572
      or sweets.
      • Surkan P.J.
      • Tabrizi M.J.
      • Lee R.M.
      • Palmer A.M.
      • Frick K.D.
      Eat Right–Live Well! Supermarket intervention impact on sales of healthy foods in a low-income neighborhood.
      J Nutr Educ Behav. 2016; 48: 112-121https://doi.org/10.1016/j.jneb.2015.09.004
      Similar to this study’s findings, interventions have been successful in reducing purchases of sweets, but impact on SSB purchases has been more mixed and implementation of changes in the store are inconsistent for any given study. Although most studies have not examined the relationship between intervention intensity and outcomes, one study did find that the intensity of exposure was related to impact on consumer purchases,
      • Gittelsohn J.
      • Kim E.M.
      • He S.
      • Pardilla M.
      A food store–based environmental intervention is associated with reduced BMI and improved psychosocial factors and food-related behaviors on the Navajo nation.
      J Nutr. 2013; 143: 1494-1500https://doi.org/10.3945/jn.112.165266
      confirming the importance of this study’s findings relating outcomes to the strength of the interventions as they are actually implemented.

      Limitations

      The main limitation of this study is lack of a control group, which limits the ability to attribute findings to the intervention. However, these conclusions are strengthened by the observation that changes in purchases correspond over time to measured changes in the stores. Small sample sizes limited the ability to detect small but meaningful changes in purchases. Consumer intercept interviews are subject to bias if the characteristics of consumers who refuse are different from those who agree to participate or if respondents inaccurately report purchases because of social desirability. Immediate recall of purchases in hand and the low refusal rate helped minimize these biases. Change over time would still be valid unless these biases differed greatly between the two time points. Finally, changes in purchasing do not necessarily lead to changes in dietary intake. Despite these limitations, these findings add to a growing body of literature about the nature of changes stores are willing to make and the types of changes that are needed to influence consumer behavior.

      Conclusions

      Outcomes of the HEAL intervention on store purchases were mixed. The outcomes reflect the extent and nature of the intervention. Substantive improvements in FV availability and promotion were needed to achieve a measurable impact on purchases, but reducing purchases of unhealthy foods, like sweets and chips, required a less consistent intensive effort. Reductions in some targeted SSBs were offset by increases in others. Despite unanimous enthusiasm by store owners/managers, these findings suggest it may be challenging to achieve the consistent and targeted implementation of changes and ongoing promotional efforts at a large enough proportion of stores where residents shop that would be required to get measurable impacts at the community level.

      Acknowledgments

      The content is solely the responsibility of the authors and does not necessarily represent the official views of Kaiser Permanente. Kaiser Permanente was the sole source of funding for this study, and Kaiser Permanente staff were involved to varying degrees in all aspects of the study. The authors acknowledge and thank Kaiser Permanente for financial support. GWL contributed to study design, provided overall study leadership, and drafted the manuscript. JK contributed to study design, data collection, analysis, and interpretation of findings. EK conducted data analyses and contributed to interpretation of findings. SR contributed to design and interpretation of findings. AT, GC, CC, EG, and DR contributed to design, supervision of data collection, and interpretation of findings. KB and DW contributed to design and provided project leadership and guidance. AC provided overall study leadership, supervision of data analysis, and interpretation of findings. All authors critically reviewed and approved the manuscript. The article contents in part have been presented at professional meetings.
      No financial disclosures were reported by the authors of this paper.

      Supplement Note

      This article is part of a supplement entitled Building Thriving Communities Through Comprehensive Community Health Initiatives: Evaluations from 10 Years of Kaiser Permanente's Community Health Initiative to Promote Healthy Eating and Active Living, which is sponsored by Kaiser Permanente, Community Health.

      References

        • Flegal K.M.
        • Kruszon-Moran D.
        • Carroll M.D.
        • Frayar C.D.
        • Ogden C.L.
        Trends in obesity among adults in the United States, 2005 to 2014.
        JAMA. 2016; 315: 2284-2291https://doi.org/10.1001/jama.2016.6458
        View in Article
        • Ogden C.L.
        • Carroll M.D.
        • Lawman H.G.
        • et al.
        Trends in obesity prevalence among children and adolescents in the United States, 1988–1994 through 2013–2014.
        JAMA. 2016; 315: 2292-2299https://doi.org/10.1001/jama.2016.6361
        View in Article
        • Menke A.
        • Casagrande S.
        • Cowie C.C.
        Prevalence of diabetes in adolescents aged 12 to 19 years in the United States, 2005–2014.
        JAMA. 2016; 316: 344-345https://doi.org/10.1001/jama.2016.8544
        View in Article
        • Bunnell R.
        • O’Neil D.
        • Soler R.
        • et al.
        Fifty communities putting prevention to work: accelerating chronic disease prevention through policy, systems and environmental change.
        J Community Health. 2012; 37: 1081-1090https://doi.org/10.1007/s10900-012-9542-3
        View in Article
        • Cheadle A.
        • Samuels S.E.
        • Rauzon S.
        • et al.
        Approaches to measuring the extent and impact of environmental change in three California community-level obesity prevention initiatives.
        Am J Public Health. 2010; 100: 2129-2136https://doi.org/10.2105/AJPH.2010.300002
        View in Article
        • Booth K.M.
        • Pinkston M.M.
        • Poston W.S.
        Obesity and the built environment.
        J Am Diet Assoc. 2005; 105: 110-117https://doi.org/10.1016/j.jada.2005.02.045
        View in Article
        • Walker R.E.
        • Keane C.R.
        • Burke J.G.
        Disparities and access to healthy food in the United States: A review of food deserts literature.
        Health Place. 2010; 16: 876-884https://doi.org/10.1016/j.healthplace.2010.04.013
        View in Article
        • Morland K.B.
        • Evenson K.R.
        Obesity prevalence and the local food environment.
        Health Place. 2009; 15: 491-495https://doi.org/10.1016/j.healthplace.2008.09.004
        View in Article
        • Galvez M.P.
        • Hong L.
        • Choi E.
        • et al.
        Childhood obesity and neighborhood food-store availability in an inner-city community.
        Acad Pediatr. 2009; 9: 339-343https://doi.org/10.1016/j.acap.2009.05.003
        View in Article
        • Bodor J.N.
        • Rose D.
        • Farley T.A.
        • Swalm C.
        • Scott S.K.
        Neighborhood fruit and vegetable availability and consumption: the role of small food stores in an urban environment.
        Public Health Nutr. 2008; 11: 413-420https://doi.org/10.1017/S1368980007000493
        View in Article
        • Institute of Medicine, Committee on Accelerating Progress in Obesity Prevention
        Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation.
        National Academies Press, Washington, DC2012
        View in Article
        • Lucan S.C.
        • Karpyn A.
        • Sherman S.
        Storing empty calories and chronic disease risk: snack-food products, nutritive content, and manufacturers in Philadelphia corner stores.
        J Urban Health. 2010; 87: 394-409https://doi.org/10.1007/s11524-010-9453-5
        View in Article
        • Laska M.N.
        • Borradaile K.E.
        • Tester J.
        • Foster G.D.
        • Gittelsohn J.
        Healthy food availability in small urban food stores: a comparison of four U.S. cities.
        Public Health Nutr. 2010; 13: 1031-1035https://doi.org/10.1017/S1368980009992771
        View in Article
        • Sharkey J.R.
        • Dean W.R.
        • Nalty C.
        Convenience stores and the marketing of foods and beverages through product assortment.
        Am J Prev Med. 2012; 43: S109-S115https://doi.org/10.1016/j.amepre.2012.05.012
        View in Article
        • Lent M.R.
        • Vander Veur S.
        • Mallya G.
        • et al.
        Corner store purchases made by adults, adolescents and children: items, nutritional characteristics and amount spent.
        Public Health Nutr. 2015; 18: 1706-1712https://doi.org/10.1017/S1368980014001670
        View in Article
        • Gittelsohn J.
        • Rowan M.
        • Gadhoke P.
        Interventions in small food stores to change the food environment, improve diet, and reduce risk of chronic disease.
        Prev Chronic Dis. 2012; 9: 110015https://doi.org/10.5888/pcd9.110015
        View in Article
        • Langellier B.A.
        • Garza J.R.
        • Prelip M.L.
        • et al.
        Corner store inventories, purchases, and strategies for intervention: a review of the literature.
        Calif J Health Promot. 2013; 11: 1
        View in Article
        • Ortega A.N.
        • Albert S.L.
        • Chan-Golston A.M.
        • et al.
        Substantial improvements not seen in health behaviors following corner store conversions in two Latino food swamps.
        BMC Public Health. 2016; 16: 389-398https://doi.org/10.1186/s12889-016-3074-1
        View in Article
        • Lawman H.G.
        • Vander Veur S.
        • Mallya G.
        • et al.
        Changes in quantity, spending, and nutritional characteristics of adult, adolescent and child urban corner store purchases after an environmental intervention.
        Prev Med. 2015; 74: 81-85https://doi.org/10.1016/j.ypmed.2014.12.003
        View in Article
        • Sanchez-Flack J.C.
        • Baquero B.
        • Linnan L.A.
        • et al.
        What influences Latino grocery shopping behavior? Perspectives on the small food store environment from managers and employees in San Diego, California.
        Ecol Food Nutr. 2016; 55: 163-181https://doi.org/10.1080/03670244.2015.1112282
        View in Article
        • Caspi C.E.
        • Lenk K.
        • Pelletier J.E.
        • et al.
        Association between store food environment and customer purchases in small grocery stores, gas-marts, pharmacies and dollar stores.
        Int J Behav Nutr Phys Act. 2017; 14: 76-87https://doi.org/10.1186/s12966-017-0531-x
        View in Article
        • Thorndike A.N.
        • Bright O.M.
        • Dimond M.A.
        • Fishman R.
        • Levy D.E.
        Choice architecture to promote fruit and vegetable purchases by families participating in the Special Supplemental Program for Women, Infants, and Children (WIC): randomized corner store pilot study.
        Public Health Nutr. 2017; 20: 1297-1305https://doi.org/10.1017/S1368980016003074
        View in Article
        • Cheadle A.
        • Schwartz P.M.
        • Rauzon S.
        • et al.
        The Kaiser Permanente Community Health Initiative: overview and evaluation design.
        Am J Public Health. 2010; 100: 2111-2113https://doi.org/10.2105/AJPH.2010.300001
        View in Article
        • Ghirardelli A.
        • Quinn V.
        • Sugerman S.
        Reliability of a retail food store survey and development of an accompanying retail scoring system to communicate survey findings and identify vendors for healthful food and marketing initiatives.
        J Nutr Educ Behav. 2011; 43: S104-S112https://doi.org/10.1016/j.jneb.2011.03.003
        View in Article
        • Surkan P.J.
        • Tabrizi M.J.
        • Lee R.M.
        • Palmer A.M.
        • Frick K.D.
        Eat Right–Live Well! Supermarket intervention impact on sales of healthy foods in a low-income neighborhood.
        J Nutr Educ Behav. 2016; 48: 112-121https://doi.org/10.1016/j.jneb.2015.09.004
        View in Article
        • Ayala G.X.
        • Baquero B.
        • Laraia B.A.
        • Ji M.
        • Linnan L.
        Efficacy of a store-based environmental change intervention compared with a delayed treatment control condition on store customers’ intake of fruits and vegetables.
        Public Health Nutr. 2013; 16: 1953-1960https://doi.org/10.1017/S1368980013000955
        View in Article
        • Payne C.R.
        • Niculescu M.
        • Just D.R.
        • Kelly M.P.
        Shopper marketing nutrition interventions.
        Physiol Behav. 2014; 136: 111-120https://doi.org/10.1016/j.physbeh.2014.03.029
        View in Article
        • Ogawa Y.
        • Tanabe N.
        • Honda A.
        • et al.
        Point-of-purchase health information encourages customers to purchase vegetables: objective analysis by using a point-of-sales system.
        Environ Health Prev Med. 2011; 16: 239-246https://doi.org/10.1007/s12199-010-0192-8
        View in Article
        • Achabal D.D.
        • McIntyre S.H.
        • Bell C.H.
        • Tucker N.
        The effect of nutrition POP signs on consumer attitudes and behavior.
        J Retail. 1987; 63: 9-24
        View in Article
        • Ernst N.D.
        • Wu M.
        • Frommer P.
        • et al.
        Nutrition education at the point of purchase: the foods for health project evaluated.
        Prev Med. 1986; 15: 60-73https://doi.org/10.1016/0091-7435(86)90036-8
        View in Article
        • Milliron B.J.
        • Woolf K.
        • Appelhans B.M.
        A point-of-purchase intervention featuring in-person supermarket education affects healthful food purchases.
        J Nutr Educ Behav. 2012; 44: 225-232https://doi.org/10.1016/j.jneb.2011.05.016
        View in Article
        • Connell D.
        • Goldberg J.P.
        • Folta S.C.
        An intervention to increase fruit and vegetable consumption using audio communications: in-store public service announcements and audiotapes.
        J Health Commun. 2001; 6: 31-43https://doi.org/10.1080/10810730150501396
        View in Article
        • Foster G.D.
        • Karpyn A.
        • Wojtanowski A.C.
        • et al.
        Placement and promotion strategies to increase sales of healthier products in supermarkets in low-income, ethnically diverse neighborhoods: a randomized controlled trial.
        Am J Clin Nutr. 2014; 99: 1359-1368https://doi.org/10.3945/ajcn.113.075572
        View in Article
        • Gittelsohn J.
        • Kim E.M.
        • He S.
        • Pardilla M.
        A food store–based environmental intervention is associated with reduced BMI and improved psychosocial factors and food-related behaviors on the Navajo nation.
        J Nutr. 2013; 143: 1494-1500https://doi.org/10.3945/jn.112.165266
        View in Article

      Article info

      Identification

      DOI: https://doi.org/10.1016/j.amepre.2017.12.002

      Copyright

      © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc.

      User license

      Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0) |
      How you can reuse Information Icon
      Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0)

      Permitted

      For non-commercial purposes:

      • Read, print & download
      • Redistribute or republish the final article
      • Text & data mine
      • Translate the article (private use only, not for distribution)
      • Reuse portions or extracts from the article in other works

      Not Permitted

      • Sell or re-use for commercial purposes
      • Distribute translations or adaptations of the article

      Elsevier's open access license policy

      ScienceDirect

      Access this article on ScienceDirect

      The content on this site is intended for healthcare professionals.


      We use cookies to help provide and enhance our service and tailor content. To update your cookie settings, please visit the Cookie Preference Center for this site.
      Copyright © 2023 Elsevier Inc. except certain content provided by third parties.


      RELX