Podcast available onlineat www.jneb.org Research Brief

Using a Grocery List Is Associated With a Healthier Dietand Lower BMI Among Very High-Risk AdultsTamara Dubowitz, MSc, ScD; Deborah A. Cohen, MD, MPH; Christina Y. Huang, MPH;Robin A. Beckman, MPH; Rebecca L. Collins, PhD

ABSTRACT

Objective: Examine whether use of a grocery list is associated with healthier diet and weight among fooddesert residents.Methods: Cross-sectional analysis of in-person interview data from randomly selected household foodshoppers in 2 low-income, primarily African American urban neighborhoods in Pittsburgh, PA withlimited access to healthy foods.Results: Multivariate ordinary least-square regressions conducted among 1,372 participants and control-ling for sociodemographic factors and other potential confounding variables indicated that although mostof the sample (78%) was overweight or obese, consistently using a list was associated with lower bodymassindex (based onmeasured height andweight) (adjusted multivariant coefficient¼ 0.095) and higher dietaryquality (based on the Healthy Eating Index–2005) (adjusted multivariant coefficient ¼ 0.103) (P < .05).Conclusions and Implications: Shopping with a list may be a useful tool for low-income individuals toimprove diet or decrease body mass index.KeyWords: food desert, food shopping, grocery list, dietary quality, bodymass index (J Nutr Educ Behav.2015;47:259-264.)

Accepted January 26, 2015.

INTRODUCTION

In the US today, individuals face amyriad of daily food choices and agreat deal of marketing. Shoppingthus requires calculated tradeoffsbetween taste, nutrition, price, andconvenience. This may make it partic-ularly difficult to eat nutritiously ormaintain a healthy weight.

African Americans and low-income individuals are at increasedrisk for poor diet, overweight, andobesity.1-3 It is especially difficult toeat healthfully with barriers such astargeted marketing, or residence inan area with no access to healthy,fresh foods.4-8 Living in a food desert,or a geographic area with limitedaccess to healthy foods, may cause‘‘deprivation amplification,’’9 whereby

the multiple barriers (eg, limitedaccess to healthy options, targetedmarketing) may exacerbate healthrisk because of the additional barriersfaced by residents.

Grocery shopping with a list is atool that may help people to navigatecomplicated food marketing environ-ments.10 A shopping list can functionas (1) a memory aid, (2) a guide tolimiting impulse purchases, and (3) aformal planning method that struc-tures meals and eating habits and pre-serves financial resources.11-14 Forshoppers attempting to eat a healthydiet or limit calories, attending to alist may help filter out products andpromotions that undermine thesegoals. Among low-income individ-uals, lists may be particularly effectiveat directing purchases if, after paying

for all items on the list, there are littleor no funds remaining to spend ondiscretionary items such as snackfoods and sweets.15 For food desertsresidents, lists might also optimizepurchases during trips to distant, lessfrequently visited stores.

Prior studies employing a variety ofdesigns and measures provide mixedevidence that using a list is associated

with improved dietary quality orweight.16-19 Only 1 examined a high-risk population of low-income wo-men with limited access to healthyfoods.20 In an analysis of householdsthat were part of the SupplementalNutrition Assistance Program survey,half used shopping lists pretty muchall the time and those who did weresignificantly more likely to meet daily

RAND Health, RAND Corporation, Pittsburgh, PAConflict of Interest Disclosure: The authors’ conflict of interest disclosures can be found onlinewith this article on www.jneb.org.Address for correspondence: Tamara Dubowitz, ScD, MSc, SM, RANDCorporation, 4570Fifth Ave, Ste 600, Pittsburgh, PA 15213; Phone: (412) 683-2300, ext 4400; Fax: (412) 683-2800; E-mail: dubowitz@rand.org�2015 Society for Nutrition Education and Behavior. Published by Elsevier, Inc. All rightsreserved.http://dx.doi.org/10.1016/j.jneb.2015.01.005

Using a list when shoppingcan be a useful tool to limitextraneous purchases andcounter the effects ofmarketing of unhealthyoptions.

Journal of Nutrition Education and Behavior � Volume 47, Number 3, 2015 259

UNDER EMBARGO UNTIL MAY 7, 2015, 12:01 AM ET

recommended dietary guidelines forcertain nutrients.20

Because of limited evidence, thisresearch builds on the opportunity toexamine a sample of low-income, pre-dominantly African American house-hold food shoppers residing in 2 urbanfood deserts, to determine the charac-teristicsofgrocery listusersandwhetherusing a list was associated with a betterdiet and a healthier weight.

METHODSParticipants and Procedures

The researchers collected data aspart of the Pittsburgh Hill/HomewoodResearch on Eating, Shopping, andHealth (PHRESH) study, a population-based longitudinal survey designedto improve understanding of the foodshopping and dietary patterns of urbanfood desert residents. The PHRESHparticipants were 1,372 adults whowere the primary food purchasers forhouseholds sampled from 2 socio-demographically similar, low-income,predominantly African Americanneighborhoods characterized by pooraccess to healthy food options suchas fresh fruit and vegetables, both ofwhich were in the Pittsburgh area.

Households were enrolled and base-line surveys were administered in sum-mer and fall, 2011 (May to December).Households were randomly selectedfrom a complete list of neighborhoodaddresses obtained from the PittsburghNeighborhood and Community Infor-mation System, which had beenmerged with Allegheny County Officeof Property Investment data to identifyresidential addresses. All residential ad-dresses were cross-referenced withpostal service data to remove vacantproperties from the sample. Stratifiedrandom sampling was applied to thecohort within the intervention neigh-borhood (the authors sampled 3concentric radii of distances to wherethe construction of a full-servicesupermarket was planned). There alsowas oversampling of households inthe intervention neighborhood. Pre-notification postcards and letters weremailed to each selected address.

Eighteen trained data collectorswho lived in the neighborhoodswent door-to-door to enroll house-holds. They were able to speak withan adult and identify the address as a

residence for 1,956 households (67%of all selected addresses). Of thosehouseholds, 1,649 were eligible (ie,the study was able to contact the pri-mary food shopper who was aged$18 years and cognitively and physi-cally able to complete the interview);1,434 (87%) agreed to do so. Of thosewho participated, 62 (4%) had largeamounts of missing data, which lefta final sample of 1,372 householdshoppers (70% of those with whomdata collectors were able to speak).

Data collectors interviewed partici-pants in their homes, entering datainto a laptop computer. The survey as-sessed sociodemographic characteris-tics including educational attainment,household income, employment sta-tus, marital status, food security, foodshopping behaviors, and a variety offactors related to food purchasing.Residents self-administered sensitivequestions (eg, income). Interviewersmeasured respondent height andweight at the conclusion of the inter-view and guided respondents througha 24-hour online dietary recall. Approx-imately 1 week later, participantsrepeated this dietary recall via tele-phone. Participants gave informedcon-sent for the study and received $25 forthe initial survey (and dietary recall),and an additional $15 for completingthe second dietary recall. All study pro-tocols were approved by the RANDHu-man Subjects Protection Committee.Analyses presented here use PHRESHbaseline data, collected before a super-market was constructed and subse-quently opened in the study areas.

Measures

Shoppingwitha listwasmeasuredwiththequestion, In general, howoften do yougogrocery shoppingwitha list of things youneed to buy? Response categories wereNever, Sometimes, Often, or Always.Based on the distribution of responsesand for ease of presentation, responseswere dichotomized to create a groupof users who responded that they al-ways use a list vs inconsistent or non-list users (all others) for the analyses.

Diet was assessed with the Auto-mated Self-administered 24-Hour Die-tary Recall.21 Data collectors guidedrespondents through the recall, whichis based on a modified version of theUS Department of Agriculture’s

Automated Multiple-Pass Method.Healthy Eating Index–2005 (HEI-2005) scores were derived from theaverage of the 2 recalls unless partici-pants completed only 1 (7% of partic-ipants).22,23 The HEI-2005 includes 12components: total fruit, total vegeta-bles, total grains, milk, meat andbeans, whole fruit, dark green and or-ange vegetables and legumes, wholegrains, oils, saturated fat, sodium,and calories from solid fat, alcohol,and added sugar. The HEI can rangefrom 0 to 100, with higher scores indi-cating a higher-quality diet. AverageHEI score in the US is 57.2; for non-Hispanic black people, it is 55.0.23

Body mass index (BMI) was basedon interviewer-measured height andweight (respondents were clothedbut measured without shoes) fromMay to September, 2011. Interviewersmeasured height to the nearest eighthinch using a carpenter square (trian-gle) and an 8-ft folding wooden rulermarked in inches. Interviewersentered adjustments to the height(eg, for shoes or hair ornaments thatthe respondent chose not to remove).Weight was measured using the SecaRobusta 813 digital scale (Chino,CA), to the nearest 10th of a pound.

Age, race, gender, education,adjusted household income, maritalstatus, number of children in thehousehold, and employment weremeasured using validated items fromthe US Census/American CommunitySurvey,24 as well as the Los AngelesFamily and Neighborhood Study25

and the Project on Human Develop-ment in Chicago NeighborhoodsCommunity Survey.26 Other factorsthat might confound relationships be-tween list use and dietary quality orBMI were measured. Food securitywas assessed with the US HouseholdFood Security Module.27 As an indica-tor of nutritional knowledge, respon-dents were asked, How many servingsof fruit and vegetables are recommendedfor daily consumption? Responsesof $ 5 were accepted as correct.Similar items have been validated inmultiple studies, including one of alarge sample of low-income AfricanAmericanmen.28 Respondents also re-ported the number of people forwhom they typically purchase foodand how much they typically spendon food each week (both were open-ended). Weekly food expenditures

260 Dubowitz et al Journal of Nutrition Education and Behavior � Volume 47, Number 3, 2015

were calculated as the amount spentper person. To measure attempts tolimit calories, respondents were asked,In the past month, have you been eatingfewer calories to lose weight or keepfrom gaining weight?

Data Analysis

Descriptive statistics were calculated,including demographics, food security,and the primary predictor and out-comes (shopping with a list, BMI, andHEI). Variation in these characteristicswith respect to shopping with a listwas tested using 2-tailed t tests formeans and chi-square for proportions.Two ordinary least-square regressionmodels tested whether a dichotomousindicator of always shopping with alistwas associatedwith (1) dietary qual-ity and (2) BMI, after controlling forother factors. A second pair of modelstested whether model results wererobust to use of a continuous measureof list-use frequency. A post hoc Bonfer-roni correction was applied to eachcomparison between thosewho alwaysshopped with a list and those who didnot always shop with a list, to assesswhether associations remained statisti-cally significant. All analyses were con-ducted using SAS software (version 9.2,SAS Institute Inc, Cary, NC, 2013).

RESULTS

Themajority of the samplewas AfricanAmerican (91%) and reported a house-hold income of < $20,000/y (80%).The average participant had a BMI of30.6; the average score on the index ofdietary quality was 48.9. Most partici-pants were aged 45–75 years (57%),33% were employed, 70% were highschool graduates or had completedsome college or technical school, 74%were female, 19% were married orlivingwithapartner, and27%hadchil-dren in the household. Just under onethird (31%) of the sample reportedthat they always shopped with a list,17% said they often did so, and 26%used a list sometimes, and 26% never.

Table 1 compares characteristics offood shoppers who reported alwaysusing a shopping list with those whoreported all other categories (never,sometimes, or often). Those who re-ported always using a list had signifi-cantly higher dietary quality. They

were more likely to be female andolder, and less likely to be employedor to have low or very low foodsecurity. After the researchers appliedBonferroni correction, the marginalassociation between participants whoalways used a list and BMI was elimi-nated, as well as the associationbetween those who always used a list,knowledge of eating fruits and vegeta-bles, and trying to eat fewer calories.

After controlling for these factors,dietary quality remained significantlyhigher among residents who reportedalways using a list, by an average of1.4 points (Table 2). Adjusted multi-variate coefficient of the model was.103. Other predictors of higher die-tary quality included older age, hav-ing a college degree, knowledge ofgovernment recommendations offruit and vegetable consumption,and trying to eat fewer calories.

In this adjusted model, the associa-tion between BMI and always using ashopping list became significant(Table 3). Adjusted multivariate coeffi-cient of the model was .095. List shop-ping was associated with a lower BMIof 1 unit, equivalent to weighing anaverage of 5 fewer pounds for a personwhose height is 5050 0 (1.65 m). Beingmale was associated with a lower BMI.Having children in the household,knowing government-recommendedfruit and vegetable servings, and tryingto eat fewer calories were associatedwith a higher BMI.

Independent associations betweenlist use and dietary quality, and be-tween list use and BMI were alsotested using the ordinal version ofthe list-use variable (ie, never, some-times, usually, always) in regressionequations otherwise identical to thosereported in Tables 2 and 3. Bothassociations remained significant:dietary quality b ¼ 0.59 (standarderror, .025), P ¼ .02; BMI b ¼ –0.35(standard error, .17), P ¼ .04.

DISCUSSION

Among this predominantly low-income African American sample offood desert residents, most reportedthat they did not always shop with alist, but those who did had better die-tary quality and lower BMI. Indeed,there appears to be a relationship be-tween list use and these factors such

that individuals who reported alwaysshopping with a list had slightly bet-ter dietary quality and slightly lowerweight status.

The frequency of list use observedappears to be somewhat lower thanthat obtained by Hersey et al,20 whofound that 50% of National FoodStamp Program Survey participantsused a shopping list pretty much everytime and 25% of Expanded Food andNutrition Education Program Evalua-tion/Reporting System participantsalmost always used a shopping list,whereas the current study foundthat 17% often and 31% always useda list. Direct comparison is difficultbecause of the rough, differentresponse categories used acrossstudies.

Associations between list use andnutrition obtained here are consistentwith others. A similar study foundthat low-income shopping list userswere more likely to meet Recommen-ded Dietary Allowances for a few keynutrients. The current work extendsthese findings and those of other priorstudies16,17 to the very high-risk,high-priority population of low-income, African American US fooddesert residents. This study also ex-pands these prior studies to show as-sociations between list use and BMI.

The exact mechanism throughwhich lists are associated with a lowerweight and better diet is speculative,but it is possible that a shopping listacts as a shield against the availabilityof unhealthy foods and may limitimpulsive choices. People experiencediminished self-control as the numberor difficulty of decisions made in-creases.29,30 Shopping with apredetermined list of items spreadsthe number of decisions made overdifferent conditions (eg, at home,where the list is constructed; at themarket when shopping), conservingself-regulatory energy. Such an aidmay be even more important forlow-income individuals because theyhave less discretionary spending andneed tomakemore complicated trade-offs between price and nutrition. Thiswould be consistent with a recentUnited Kingdom–based qualitativestudy that explored how 26 residentsof a low-income area made food shop-ping decisions. Shoppers who used anitem-by-item or restricted and budg-eted approach, including behaviors

Journal of Nutrition Education and Behavior � Volume 47, Number 3, 2015 Dubowitz et al 261

such as shopping with a list or pre-planning purchases, relied less on in-stores cues and were more successfulat constraining their food choices to

match their health and/or financialvalues.15

It is tempting to conclude that us-ing grocery lists leads to healthier

eating and lower weight, but thecross-sectional nature of the dataand analysis in this study does notallow for casual inference. It is

Table 2. Predicting Dietary Quality With Shopping List Use Among Low-Income, Predominantly African-American Shoppers:Ordinary Least-Square Regression Model of Associations Between Sociodemographic Characteristics, ShoppingWith a List, and Dietary Quality (n ¼ 1,344)

Variable b Coefficient Standard Error 95% Confidence Intervals 2-Tailed P

Intercept 39.54 1.35 36.88–42.19 < .001**

Always shops with list 1.42 0.62 0.20–2.64 .022*

Age 0.11 0.02 0.08–0.15 < .001**

Male �0.38 0.67 �1.70–0.93 .569

Adjusted household income/1,000 0.05 0.02 0.00–0.10 .036*

College degree 3.81 0.82 2.21–5.42 < .001**

Married 0.14 0.73 �1.29–1.57 .851

Any children in household 0.23 0.79 �1.32–1.77 .775

Food-insecure (low/very low security) �0.45 0.63 �1.69–0.78 .472

Know $ servings/d 2.76 0.71 1.36–4.16 < .001**

Trying to eat fewer calories 3.18 0.60 2.00–4.37 < .001**

*P < .05, **P < .005.Notes: The authors employed an ordinary least-square regression model in which dietary quality (Healthy Eating Index) was thedependent variable, always shopping with a list was the independent variable of interest, and covariates included age, sex,adjusted household income, college degree, being married, having children in the household, food insecurity, knowledge offruit and vegetable serving recommendations, and report of trying to eat fewer calories per day. Adjusted multivariate coeffi-cient ¼ .103.

Table 1. Sociodemographic Characteristics of People Who Do or Do Not Always Shop With a List, and Their Comparison,Employing 2-Tailed t and Chi-Square Tests

CharacteristicAlways Shops With

a List (n ¼ 424)Does Not Always ShopWith a List (n ¼ 948) P

Mean age, y 57.0 52.4 < .001*

Female 80.0% 71.4% .001*

African American 88.6% 91.4% .252

College degree 16.3% 14.9% .506

Adjusted household income (USD) $13,144 $13,518 .626

Employed 27.3% 35.4% .003*

Families with children 25.2% 27.9% .314

Married or living with partner 19.4% 18.2% .609

Weekly food expenditures per person feeding (USD) $37.50 $36.60 .580

Mean body mass index (kg/m2) 30.1 30.8 .084

Mean Healthy Eating Index score 50.3 48.2 .001*

Food-insecure 26.0% 34.3% .002*

Knowledge of eating $ 5 servings of fruits/vegetables per day 24.5% 19.4% .035

Trying to eat fewer calories in past month 38.4% 32.7% .039

*Significant after applying Bonferroni correction for the 14 tests conducted, resulting in a cutoff of P < .004 for significance (ie,P < .05/14 ¼ P < .004).Note: For each variable, the researchers looked at the mean for participants who always shop with a list and those who do notalways shop with a list. They then tested for statistically significant differences using 2-tailed t tests for means and chi-squarefor proportions.

262 Dubowitz et al Journal of Nutrition Education and Behavior � Volume 47, Number 3, 2015

equally likely, if not more so, that in-dividuals with healthier eating habitsand healthier weights more oftenchoose to shop with lists. Using alist may reflect conscientious person-ality,31 a trait that also might lead togreater attention to nutrition and ahealthy weight. Results also cannotbe generalized to higher-incomehouseholds or to people who do notlive in a food desert. Finally, if listsfunction differently in different sea-sons, results may not generalize toJanuary through April, because datawere not collected in these months.However, results did not changewhen season was controlled for inancillary models. Future researchshould also address the use of shop-ping lists in higher-income house-holds and those not living in fooddeserts.

IMPLICATIONS FORRESEARCH ANDPRACTICE

More frequent use of a shopping listwas associated with a better-qualitydiet and slightly lower weight amonghigh-risk, low-income individualsliving in a food desert. Furtherresearch is needed to address whetherlists exert a causal influence, but theexistence of these associations in apopulation much in need of effectiveinterventions is promising. A shop-ping list may serve as a useful, easilyimplemented, and practically no-cost tool to support food purchasingconsistent with healthier eating andhealthier weight.

ACKNOWLEDGMENTS

This work was supported by the Na-tional Institutes of Health, grantCA149105 R01, ‘‘Does a New Super-market Improve Dietary Behaviors ofLow-Income African Americans?’’The authors acknowledge the incred-ible work of their community-baseddata collection team and their fieldcoordinator, La’Vette Wagner, as wellas the project management of Eliza-

beth Steiner and administrative assis-tance of Stephanie Lonsinger.

REFERENCES

1. Basiotis PP, Lino M, Anand R. Reportcard on the diet quality of AfricanAmericans. Fam Econ Nutr Rev. 1998;11:61-63.

2. Flegal KM, Carroll MD, Kit BK,Ogden CL. Prevalence of obesity andtrends in the distribution of body massindex among US adults—1999-2010.JAMA. 2012;307:491-497.

3. Guenther PM, Juan WY, Lino M,HizaHA, FungweT, LucasR.DietQual-ity of Low-Income and Higher Income Amer-icans in 2003-04 asMeasured by the HealthyEating Index–2005. Washington, DC:Center for Nutrition Policy and Promo-tion, US Dept of Agriculture; 2008.

4. Airhihenbuwa CO, Kumanyika S,Agurs TD, Lowe A, Saunders D,Morssink CB. Cultural aspects of Afri-can American eating patterns. EthnHealth. 1996;1:245-260.

5. Lucan SC, Barg FK, Long JA. Pro-moters and barriers to fruit, vegetable,and fast-food consumption among ur-ban, low-income African Americans—a qualitative approach. Am J PublicHealth. 2010;100:631-635.

Table 3. Predicting Body Mass Index With Shopping List Use Among Low-Income, Predominantly African American Shoppers:Ordinary Least-Square Regression Model of Associations Between Sociodemographic Characteristics, Shopping Witha List, and Body Mass Index (n ¼ 1,344)

Variable b Coefficient Standard Error 95% Confidence Intervals 2-Tailed P

Intercept 29.73 0.94 27.89–31.57 < .001**

Always shops with list �0.99 0.43 �1.83 to –0.14 .022*

Imputed: age �0.004 0.01 �0.03–0.02 .780

Male �2.01 0.46 �2.92 to –1.10 < .001**

Adjusted household income/1,000 �0.01 0.02 �0.05–0.02 .474

Education �1.38 0.57 �2.50 to –0.26 .016*

Married 0.38 0.50 �0.61–1.37 .448

Any children in household 2.01 0.55 0.93–3.08 < .001**

Food-insecure (low/very low security) 0.50 0.43 �0.35–1.36 .249

Know $ 5 servings/day 1.13 0.50 0.15–2.10 .024*

Have been eating fewer calories 3.63 0.42 2.81–4.46 < .001**

*P < .05, **P < .005.Notes: The researchers employed an ordinary least-square regression model in which body mass index was the depen-dent variable, always shopping with a list was the independent variable of interest, and covariates included age, sex,adjusted household income, college degree, being married, having children in the household, food insecurity, knowledgeof fruit and vegetable serving recommendations, and report of trying to eat fewer calories per day. Adjusted multivariatecoefficient ¼ .095.

A shopping list may be auseful tool to supporthealthy eating and weightamong low-incomeindividuals living in a fooddesert.

Journal of Nutrition Education and Behavior � Volume 47, Number 3, 2015 Dubowitz et al 263

6. Lucan SC, Barg FK, Karasz A,Palmer CS, Long JA. Perceived influ-ences on diet among urban, low-income African Americans.Am JHealthBehav. 2012;36:700-710.

7. Yancey AK, Cole BL, Brown R, et al.A cross-sectional prevalence study ofethnically targeted and general audienceoutdoor obesity-related advertising.Milbank Q. 2009;87:155-184.

8. Grier SA, Kumanyika SK. The contextfor choice: health implications of tar-geted food and beverage marketing toAfrican Americans. Am J Public Health.2008;98:1616-1629.

9. Macintyre S, Macdonald L, Ellaway A.Do poorer people have poorer access tolocal resources and facilities? The distri-bution of local resources by area depri-vation in Glasgow, Scotland. Soc SciMed. 2008;67:900-914.

10. Au N, Marsden G, Mortimer D,Lorgelly PK. The cost-effectiveness ofshopping to a predetermined grocerylist to reduce overweight and obesity.Nutr Diabetes. 2013;3:e77. http://dx.doi.org/10.1038/nutd.2013.18.

11. Beneke WM, Davis CH, VanderTuig JG. Effects of a behavioralweight-loss program food purchases:instructions to shop with a list. Int JObes. 1988;12:335-342.

12. Bazerman MH, Tenbrunsel AE,Wade-Benzoni K. Negotiating withyourself and losing: making deci-sions with competing internal pref-erences. Acad Manage Rev. 1998;23:225-241.

13. Thaler RH, Sunstein CR. Nudge:Improving Decisions About Health,Wealth, and Happiness. New Haven,CT: Yale University Press; 2008.

14. Rottenstreich Y, Sood S, Brenner L.Feeling and thinking in memory-based versus stimulus-based choices. JConsum Res. 2007;33:461-469.

15. Thompson C, Cummins S, Brown T,Kyle R. Understanding interactionswith the food environment: an explora-tion of supermarket food shopping rou-tines in deprived neighbourhoods.Health Place. 2013;19:116-123.

16. Crawford D, Ball K, Mishra G,Salmon J, Timperio A. Which food-related behaviours are associated withhealthier intakes of fruits and vegetablesamong women? Public Health Nutr.2007;10:256-265.

17. Perez-Cueto FJA, Verbeke W, deBarcellos MD, et al. Food-related life-styles and their association to obesityin five European countries. Appetite.2010;54:156-162.

18. Qi BB, Dennis KE. The adoption ofeating behaviors conducive to weightloss. Eat Behav. 2000;1:23-31.

19. Beneke WM, Davis CH. Relationshipof hunger, use of a shopping list andobesity to food purchases. Int J Obes.1985;9:391-399.

20. Hersey J, Anliker J,Miller C, et al. Foodshopping practices are associated withdietary quality in low-income house-holds. J Nutr Educ. 2001;33:S16-S26.

21. Subar AF, Kirkpatrick SI, Mittl B,et al. The automated self-administered 24-hour dietary recall(ASA24): a resource for researchers,clinicians, and educators from the Na-tional Cancer Institute. J Acad NutrDiet. 2012;112:1134-1137.

22. Guenther PM, Reedy J, Krebs-Smith SM. Development of theHealthy Eating Index–2005. J Am DietAssoc. 2008;108:1896-1901.

23. Ervin RB. Healthy Eating Index–2005Total and Component Scores for AdultsAged 20 and Over: National Health andNutrition Examination Survey, 2003–2004. Washington, DC: US Dept ofHealth and Human Services, Centersfor Disease Control and Prevention,

National Center for Health Statistics;2011.

24. US Bureau of the Census. AmericanCommunity Survey: documentation.http://www.census.gov/acs/www/data_documentation/documentation_main/. Accessed February 27, 2015.

25. Sastry N, Ghosh-Dastidar B, Adams JL,Pebley AR. The Design of a MultilevelSurvey of Children, Families, and Com-munities: The Los Angeles Family andNeighborhood Survey. Santa Monica,CA: RAND Corporation; 2006.

26. Inter-university Consortium for Politi-cal and Social Research. Project onHuman Development in ChicagoNeighborhoods. http://www.icpsr.umich.edu/icpsrweb/PHDCN/instruments.jsp#community. Accessed February27, 2015.

27. EconomicResearchService,USDepart-ment of Agriculture. U.S. HouseholdFood Security Survey Module: three-stage design, with screeners. www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-us/survey-tools.aspx#household. Accessed February 27,2015.

28. Wolf RL, Lepore SJ, Vandergrift JL,et al. Knowledge, barriers, and stageof change as correlates of fruit and vege-table consumption among urban andmostly immigrant black men. J AmDiet Assoc. 2008;108:1315-1322.

29. Baumeister RF, Bratslavsky E,Muraven M, Tice DM. Ego depletion:is the active self a limited resource? JPers Soc Psychol. 1998;74:1252-1265.

30. Spears D. Economic decision-makingin poverty depletes behavioral control.B E J Econom Anal Policy. 2011:11.

31. Bogg T, Roberts BW. Conscientious-ness and health-related behaviors: ameta-analysis of the leading behavioralcontributors to mortality. Psychol Bull.2004;130:887-919.

264 Dubowitz et al Journal of Nutrition Education and Behavior � Volume 47, Number 3, 2015