Effects Of Alternative Food Voucher Delivery Strategies On Nutrition Among Low-Income Adults

Nutrition assistance programs governed by the 2018 Farm Bill have come under increasing scrutiny.^1,2 Federal programs such as the Supplemental Nutrition Assistance Program (SNAP) support the more than one in seven low-income Americans who receive a cash-equivalent food subsidy.³ Nutrition assistance program reforms through the Farm Bill and attendant legislation could affect socioeconomic disparities in obesity, cardiovascular disease, and type 2 diabetes, given both the widespread participation in these programs and the elevated rate of nutrition-related chronic disease among low-income Americans.^4–6 In addition, a growing number of state and local food voucher programs are designed to supplement federal nutrition assistance programs and reach ineligible low-income populations.

Whether these nutrition assistance programs can support healthier diets by subsidizing healthier food options or restricting food options has been the subject of extensive discussion.^4,5 Some nutrition assistance programs already restrict food options. For example, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) explicitly restricts purchases to foods considered healthy, and the Food Insecurity Nutrition Incentive (FINI) Program matches payments only for fruit and vegetables.^3,7 State and local food voucher programs generally restrict eligible purchases to fruit and vegetables.³ These programs assume that providing money for specific food categories will improve dietary intake by encouraging healthier food purchasing patterns. However, most people receiving nutrition assistance also use their own money for additional food purchases, so the extent to which such assistance alters dietary intake, rather than just the source of the money used to purchase the same foods, remains unclear.

To further improve nutrition assistance programs, behavioral economists have also suggested increasing the frequency of the benefit delivery.^8,9 Program benefits delivered in a monthly lump sum may contribute to a “consumption cycle”—the tendency for low-income Americans to spend their benefits immediately after receiving their monthly payment and then have limited money for food by month’s end.^10–13 Programs that provide food benefits in smaller increments distributed throughout the month may smooth this cycle and counteract the tendency to use once-monthly funding installments for purchasing calorie-dense processed foods immediately upon receipt of funds.^8,9,14 Whether nutrition assistance programs that increase the frequency of benefit delivery could meaningfully modify dietary intake has not been assessed in a randomized trial.

Our objective was to test two policy-relevant hypotheses in a randomized, 2x2-factorial, community-based trial. First, we tested whether food vouchers redeemable only for fruit and vegetable purchases would improve both fruit and vegetable consumption and scores on the Health Eating Index (HEI)—a measure of overall nutrition quality¹⁵—more than vouchers redeemable for any SNAP-eligible food. Second, to assess the impact of varying the distribution schedule, we tested whether food vouchers redeemable only in one-week increments would improve both fruit and vegetable consumption and HEI scores more than vouchers redeemable in monthly increments.

Study Data And Methods

Trial Design

We conducted a randomized, 2x2-factorial,¹⁶ open-label trial with equal allocation to each of four intervention groups, specified below. No changes to the prespecified methods were made after trial commencement. Information on the study protocol and Consolidated Standards of Reporting Trials (CONSORT) guideline checklist¹⁷ is in online appendix exhibit 1.¹⁸

Participants

Inclusion criteria for participants were age twenty-one or older; household income less than 250 percent of the federal poverty level; regular access to a phone; English fluency sufficient to provide informed consent; residency in San Francisco, California; and willingness to be randomly assigned to a study arm. Exclusion criteria were participation in another dietary study or food voucher program, current diagnosis of cancer or congestive heart failure, or plans to move out of San Francisco within the following year. SNAP participation was not an explicit inclusion or exclusion criterion. The rationale for this was that while the trial would be relevant to SNAP, many cities and counties have food voucher programs to support low-income Americans who do not participate in SNAP, and an estimated 50 percent of SNAP-eligible Californians do not participate in the program.¹⁹ We included participants with gross monthly household incomes of less than 250 percent of poverty, rather than the SNAP eligibility threshold of 130 percent of poverty, for two reasons: Income deductions included in calculating income for SNAP eligibility result in the participation of households with real incomes often higher than 130 percent of poverty, and the high cost of living in San Francisco translates into lower purchasing power than in most US cities.²⁰ Participants were recruited through transit advertisements, website advertisements, fliers at community centers, and word of mouth. Eligible participants, based on screening, attended an in-person orientation session to verify eligibility and provide informed consent. Enrollment occurred in the period February–October 2017.

Interventions

The factorial design was used to randomly assign participants to receive via mail either fruit and vegetable–only or unrestricted vouchers and either weekly or monthly vouchers. The four arms were therefore as follows: Recipients of weekly fruit and vegetable–only vouchers received four $5 vouchers every month, with each voucher valid for a specified week of the month (that is, one voucher was valid for week 1 only, another for week 2 only, and so on). These vouchers were restricted to purchases of qualified fruit and vegetables, defined as fresh or frozen fruit, vegetables, or herbs without added sugars or fats. Recipients of monthly fruit and vegetable–only vouchers received four $5 vouchers every month, each valid for the entire month. Recipients of weekly unrestricted vouchers received four $5 vouchers every month, each valid for a specified week of the month. These vouchers allowed purchases of any SNAP-eligible food. Recipients of monthly unrestricted vouchers received four $5 unrestricted vouchers every month, each valid for the entire month.

Participants in all four study arms received $20 of vouchers (four $5 vouchers) by mail each month for six months. Vouchers were redeemable at any of nineteen vendors (three grocery stores, fifteen corner stores, and one farmers market) in various neighborhoods of San Francisco that were convenient to the low-income neighborhoods where we concentrated recruitment. For reference, $20 per month constituted 2 percent of the average monthly income for participants and could purchase a sample basket of a dozen bananas, a dozen eggs, a dozen tomatoes, six yellow onions, a sixteen-ounce jar of peanut butter, a loaf of whole wheat bread, a six-pack of chicken drumsticks, a gallon of milk, a one-pound box of pasta, and a twenty-four-ounce jar of pasta sauce.²⁰ Vouchers were preprinted on check-size paper and redeemable with the participant’s signature. Vouchers were designed to have equal value, for ease of redemption by cashiers, and to have a small dollar value to minimize the need for a large voucher to be spent all at once. Start and expiration dates were stamped on each voucher according to the study arm. Vouchers were collected by the cashier when the participant checked out of a store or at the information booth at the farmers market, which enabled the participant to have the voucher’s value immediately deducted from their grocery bill (similar to SNAP benefits). Vouchers could not be redeemed for cash, and change was not provided for partially used vouchers.

Outcomes

The prespecified primary outcome was change in fruit and vegetable intake measured in cup equivalents between baseline (month 0) and month 6 of the trial, using the National Health and Nutrition Examination Survey food grouping scheme to classify food items.^21,22 Fruit and vegetable intake was assessed by prescheduled twenty-four-hour dietary recalls administered by phone by registered dietitians blinded to study allocation. Recalls were collected using the Nutrition Data System for Research software, adopting the multipass approach for rigorous collection, supplemented by visual aids. Four dietary recalls were conducted in month 0, two during week 1 and two during week 4, to capture potential fluctuations in dietary intake related to the consumption cycle before the intervention began. Wherever possible, recalls were conducted on one weekday and one weekend day. Four follow-up dietary recalls were conducted after exposure to six months of vouchers (the final month of receiving vouchers), two during week 1 and two during week 4 of month 6.

Secondary outcomes included changes from month 0 to month 6 in the scores on the Healthy Eating Index¹⁵ and the Alternate Healthy Eating Index;²³ voucher use rate (the percentage of vouchers mailed over six months that were redeemed at participating vendors, based on vouchers returned by vendors for reimbursement); change from month 0 to month 6 in food insecurity, as measured by the six-item Department of Agriculture Food Security Survey module;²⁴ responses to an ease-of-use survey at month 6 (see the appendix for survey questions);¹⁸ and change in the consumption cycle (ratio of total calorie intake in week 4 to that in week 1). The outcomes were reassessed at month 12 (six months after voucher discontinuation) to assess the maintenance of effect. For reference, the HEI score is scaled from 0 to 100, where 100 corresponds to perfect adherence to the Dietary Guidelines for Americans: 2015–2020,²⁵ and the average score among the US population is 59.²³ The Alternate Healthy Eating Index (AHEI) score is scaled from 0 to 110, based on intake levels of nine components associated with reduced chronic disease risk in observational cohort studies.²³ The HEI has a greater emphasis on lowering fats as opposed to carbohydrates than does the AHEI, although both are correlated to all-cause mortality and cardiovascular mortality—among other chronic disease outcomes.²⁶

Appendix text 1 contains details on the sample size power calculation, randomization, and blinding.¹⁸

Statistical Methods

The primary outcome was calculated as change in mean fruit and vegetable intake between months 0 and 6, based on dietary recalls. The secondary outcome of the HEI score was calculated using composite macros from the National Institutes of Health.¹⁵

We conducted an intention-to-treat analysis using linear mixed effects models to estimate changes in the primary and secondary outcomes between months 0 and 6, with main effects for treatment arm and assessment month and a random effect for study participant to adjust for each individual’s baseline consumption levels.²⁷

Limitations

The study had several inherent limitations. First, our results were dependent on dietary recalls, which are subject to underreporting and social biases.

Second, our trial was conducted in one city, and the results might not be fully generalizable to other localities. Relatedly, even within this one city—in which we were able to involve nineteen stores, including most of the target neighborhoods’ large supermarkets, small corner shops, and the farmers market—not all stores in which participants shopped were in our vendor network.

Third, we were limited in terms of the value and frequency of the vouchers in our study. It was not feasible to study a fuller range of either.

Finally, we recruited into the study a broader population than current SNAP recipients, and our findings might therefore have differed if the study had been implemented among SNAP recipients only.

We elaborate on some of these issues below in the context of interpreting our results.

Study Results

Participant Flow

Of 1,393 people assessed for eligibility (appendix exhibit 2),¹⁸ 211 did not meet inclusion criteria, 815 who visited the study website and met initial eligibility criteria did not attend a study orientation session to enroll, and 8 were excluded for other reasons such as failure to present for informed consent after attending a study orientation session, leaving 359 participants for random assignment to a study arm. In each arm, four to eight people were lost to follow-up or discontinued participation, leaving a total of 332 (92.5 percent) who completed at least one dietary recall at month 6 for analysis.

Baseline Data

As shown in exhibit 1, eighty-six participants were randomly assigned to receive weekly fruit and vegetable–only vouchers, ninety to receive monthly fruit and vegetable–only vouchers, ninety-two to receive weekly unrestricted vouchers, and ninety-one to receive monthly unrestricted vouchers. Baseline demographic characteristics for the overall sample depict a population of whom the majority were female, and approximately 45 percent were members of ethnic/racial minority groups. Fewer than one-third of participants had a college degree. They had very low incomes, averaging $1,180 per month, and two-thirds lived in a single-person household. The nutritional characteristics of the overall sample show that more than a quarter were SNAP participants, and about 3 percent participated in WIC. Two-thirds experienced food insecurity, defined by Department of Agriculture measures of low and very low food security. Participants averaged 1.1 cup equivalent of fruit and vegetables per day and had a mean HEI score of 56.6 at baseline.

Outcomes

The primary outcome of fruit and vegetable consumption did not improve significantly between months 0 and 6 in any study arm (exhibit 2). While fruit and vegetable consumption between months 0 and 6 increased among all groups, receiving fruit and vegetable–only vouchers did not significantly increase fruit and vegetable consumption compared with receiving an unrestricted voucher. Also, weekly vouchers did not significantly increase fruit and vegetable consumption compared with monthly vouchers (exhibit 3).

The secondary outcome of HEI score did not improve significantly between months 0 and 6 in three of the four arms, and it improved only modestly in the weekly unrestricted voucher arm (exhibits 2 and 3). Receiving fruit and vegetable–only vouchers did not significantly increase HEI scores compared with receiving unrestricted vouchers, and receiving weekly vouchers did not significantly increase HEI scores compared with receiving monthly vouchers. The AHEI score paralleled the HEI score, with no significant increases across study groups.

The secondary outcome of voucher use was higher in the monthly than in the weekly voucher arms (on average, 80 percent versus 68 percent; exhibit 2). Voucher use rates were 74.3 percent, on average, across arms over the six-month intervention period. Participants with fruit and vegetable–only vouchers had similar use compared to participants with unrestricted vouchers.

The secondary outcome of food insecurity improved across all groups. Participants with fruit and vegetable–only vouchers and those with unrestricted vouchers had similar improvements, and participants with weekly vouchers and those with monthly vouchers had similar improvements.

At month 6 of the study, using a scale of 0 (not easy) to 3 (very easy), participants reported fruit and vegetable–only vouchers to be easier to use compared to unrestricted vouchers (exhibit 2). Weekly vouchers did not differ compared with monthly vouchers.

In terms of the consumption cycle, dietary recalls conducted in week 1 and those conducted in week 4 at baseline revealed similar nutrition profiles (appendix exhibit 2).¹⁸ Similar results were observed in month 6 (fruit and vegetable cup equivalent intake: 1.21 in week 1 versus 1.15 in week 4; HEI score: 57.6 in week 1 versus 57.9 in week 4; average week 4 to week 1 calorie ratio: 1.03) (appendix exhibit 3).¹⁸

Outcomes were not substantially different in month 12, six months after voucher receipt ended (appendix exhibit 4).¹⁸

Discussion

There are ongoing policy debates about whether and how to leverage nutrition assistance programs to reduce the burden of chronic disease in low-income communities.²⁸ Despite vigorous discussion of the merits and ethics of various policy reforms, few proposed reforms have had rigorous empirical study. Nutrition assistance program reforms are difficult to research, as they require large-scale, randomized assessments of programs in which food can be purchased from a large number of stores.^29,30 A pilot test has examined the implementation of financial incentives built into the SNAP program in Massachusetts; it suggested that a 30-cent reward for each $1 of fruit and vegetables purchased with SNAP benefits produced a 0.24 cup equivalent per person per day increase in fruit and vegetable intake.³¹ Other studies also suggest that fruit and vegetable purchasing incentives can effectively increase the purchasing of fruit and vegetables.^32–37 Notably, a randomized trial in Minnesota found that pairing a 30 percent financial incentive for fruit and vegetable purchasing with a restriction preventing purchases of sugar-sweetened beverages, sweet baked goods, or candy produced greater reductions in calorie intake and greater improvements in HEI scores and fruit intake than either an incentive or restriction alone.³⁸ Yet essentially no data exist to inform policy makers on two other key proposed modifications to nutrition assistance programs: whether restricting added benefits to just fruit and vegetables, similar to existing voucher programs, improves fruit and vegetable intake or overall dietary quality more than unrestricted food vouchers do; and whether more frequent distribution schedules improve dietary quality by smoothing the consumption cycle characterized by purchasing calorie-dense foods soon after monthly benefit receipt and running out of funds for food by month’s end.^4,6,11,39

We performed the first randomized, community-based experiment to empirically assess both proposed policy modifications. We did not find evidence that restricting benefits to only fruit and vegetables (rather than all foods) or distributing benefits weekly (rather than monthly) significantly enhanced fruit and vegetable intake, overall dietary quality, or food insecurity.

In this study, all participants received the same total dollar value of benefits. Our study therefore stands in contrast to several previous studies that demonstrated improvements in fruit and vegetable intake through financial incentives for fruit and vegetable purchases that increased the total dollars available for foods.^31,40 For example, a fruit and vegetable–only voucher program in the United Kingdom increased fruit and vegetable consumption by 15 percent, despite having an equivalent relative purchasing power to the vouchers in our study.⁴¹ Importantly, we found no substantial difference in fruit and vegetable consumption or overall dietary quality between the group with vouchers restricted to fruit and vegetable intake and the group with unrestricted vouchers. This finding suggests that at least to some extent, participants use their own income to supplement benefit dollars for purchases of foods that are not allowed through their benefit dollars. For SNAP participants receiving approximately $4 per day for thirty days, the additional $20 voucher represented an increase of about 17 percent in assistance funds. Food insecurity was notably reduced across all study arms in our assessment.

We listed study limitations above, and we elaborate on several of them here. First, our limitation to nineteen stores may have altered participants’ purchasing patterns. Second, the amount of the voucher may have been insufficient to generate a more robust effect than we observed. However, for our results to be maximally generalizable, we chose a voucher value that corresponded to current policy discussions, existing local voucher programs, and the value thought to increase fruit and vegetable consumption sufficiently to derive clinically significant reductions in cardiovascular disease risk.^4–6 In addition, the observed voucher use rate was high, which indicates that the incentive was large enough for most participants to choose to use it in most weeks. Third, the weekly frequency may have been too inconvenient to participants, and the effect of changing frequency might have been more pronounced if we had used a twice-monthly versus a weekly distribution schedule.

Despite these limitations, our findings suggest that a food voucher program providing a limited subsidy ($20 per month) for fruit and vegetable purchases will have no greater effect on fruit and vegetable intake or overall dietary quality than a food voucher program that places only limited restrictions (consistent with current SNAP restrictions) on foods purchased with voucher funds. Our findings also suggest that distributing voucher funds weekly offers no nutritional benefit over distributing funds monthly.

ACKNOWLEDGMENTS

The research reported in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (Award No. R01HL132814). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank Lindsay Durand, Sarah Farzinkhou, Carrie McKinley, Anjanpreet Pandher, Jennifer Robinson, and Elizabeth Zanley for data collection and data management assistance.

NOTES

• 1 D’Angelo K, Libal K, Seymour N, Hamel R. After the “Great Recession”: excluding “able-bodied” adults from food entitlements in the United States. J Policy Pract. 2017;16(4):452–71. Crossref, Google Scholar
• 2 Manning L. An interview with outgoing Secretary of Agriculture Tom Vilsack: reflections on his legacy and challenges facing a new era in American agriculture policy. J Food Law Policy. 2017;13(1):162–80. Google Scholar
• 3 Ziliak JP. Why are so many Americans on food stamps? The role of the economy, policy, and demographics. Chap. 3 in: Bartfeld J, Gundersen C, Smeeding TM, Ziliak JP, editors. SNAP matters: how food stamps affect health and well-being. Stanford (CA): Stanford University Press; 2016. p. 18–48. Google Scholar
• 4 Basu S, Seligman H, Bhattacharya J. Nutritional policy changes in the Supplemental Nutrition Assistance Program: a microsimulation and cost-effectiveness analysis. Med Decis Making. 2013;33(7):937–48. Crossref, Medline, Google Scholar
• 5 Basu S, Seligman HK, Gardner C, Bhattacharya J. Ending SNAP subsidies for sugar-sweetened beverages could reduce obesity and type 2 diabetes. Health Aff (Millwood). 2014;33(6):1032–9. Go to the article, Google Scholar
• 6 Choi SE, Seligman H, Basu S. Cost effectiveness of subsidizing fruit and vegetable purchases through the Supplemental Nutrition Assistance Program. Am J Prev Med. 2017;52(5):e147–55. Crossref, Medline, Google Scholar
• 7 Department of Agriculture. Food Insecurity Nutrition Incentive (FINI) Grant Program [Internet]. Washington (DC): USDA; [cited 2019 Feb 19]. Available from: https://nifa.usda.gov/program/food-insecurity-nutrition-incentive-fini-grant-program Google Scholar
• 8 Shapiro JM. Is there a daily discount rate? Evidence from the food stamp nutrition cycle. J Public Econ. 2005;89(2–3):303–25. Crossref, Google Scholar
• 9 White JS, Basu S. Does the benefits schedule of cash assistance programs affect the purchase of temptation goods? Evidence from Peru. J Health Econ. 2016;46:70–89. Crossref, Medline, Google Scholar
• 10 Todd JE. Revisiting the Supplemental Nutrition Assistance Program cycle of food intake: investigating heterogeneity, diet quality, and a large boost in benefit amounts. Appl Econ Perspect Policy. 2015;37(3):437–58. Crossref, Google Scholar
• 11 Durward C, Zimmerman G, Savoie MR. The food stamp cycle negatively impacts Supplemental Nutrition Assistance Program (SNAP) participants’ ability to make use of fruit and vegetable incentives. J Nutr Educ Behav. 2016;48(7):S142. Crossref, Google Scholar
• 12 Kharmats AY, Jones-Smith JC, Cheah YS, Budd N, Flamm L, Cuccia Aet al. Relation between the Supplemental Nutritional Assistance Program cycle and dietary quality in low-income African Americans in Baltimore, Maryland. Am J Clin Nutr. 2014;99(5):1006–14. Crossref, Medline, Google Scholar
• 13 Wilde PE, Ranney CK. The monthly food stamp cycle: shopping frequency and food intake decisions in an endogenous switching regression framework. Am J Agric Econ. 2000;82(1):200–13. Crossref, Google Scholar
• 14 Hastings J, Washington E. The first of the month effect: consumer behavior and store responses. Am Econ J Econ Policy. 2010;2(2):142–62. Crossref, Google Scholar
• 15 National Cancer Institute. Epidemiology and Genomics Research Program: developing the Healthy Eating Index: HEI-2015 [Internet]. Bethesda (MD): NCI; 2018 [last updated 2018 Feb 12; cited 2019 Feb 19]. Available from: https://epi.grants.cancer.gov/hei/developing.html#2015 Google Scholar
• 16 Collins LM, Dziak JJ, Kugler KC, Trail JB. Factorial experiments: efficient tools for evaluation of intervention components. Am J Prev Med. 2014;47(4):498–504. Crossref, Medline, Google Scholar
• 17 Moher D, Hopewell S, Schultz KF, Montori V, Gøtzsche PC, Devereaux PJet al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c869. Crossref, Medline, Google Scholar
• 18 To access the appendix, click on the Details tab of the article online.
• 19 Department of Agriculture, Food and Nutrition Service. Supplemental Nutrition Assistance Program (SNAP): national level annual summary: participation and costs, 1969–2018 [Internet]. Alexandria (VA): FNS; [last updated 2019 Feb 8; cited 2019 Feb 19]. Available for download from: https://www.fns.usda.gov/pd/supplemental-nutrition-assistance-program-snap Google Scholar
• 20 Basu S, Wimer C, Seligman H. Moderation of the relation of county-level cost of living to nutrition by the Supplemental Nutrition Assistance Program. Am J Public Health. 2016;106(11):2064–70. Crossref, Medline, Google Scholar
• 21 Department of Agriculture, National Agricultural Library. Food and Nutrient Database for Dietary Studies (FNDDS) [Internet]. Beltsville (MD): USDA; [cited 2019 Feb 19]. Available from: https://data.nal.usda.gov/dataset/food-and-nutrient-database-dietary-studies-fndds Google Scholar
• 22 Department of Agriculture, National Agricultural Library. USDA Food Patterns Equivalents Database (FPED) [Internet]. Beltsville (MD): USDA; [cited 2019 Feb 19]. Available from: https://data.nal.usda.gov/dataset/food-patterns-equivalents-database-fped Google Scholar
• 23 McCullough ML, Feskanich D, Stampfer MJ, Giovannucci EL, Rimm EB, Hu FBet al. Diet quality and major chronic disease risk in men and women: moving toward improved dietary guidance. Am J Clin Nutr. 2002;76(6):1261–71. Crossref, Medline, Google Scholar
• 24 Blumberg SJ, Bialostosky K, Hamilton WL, Briefel RR. The effectiveness of a short form of the Household Food Security Scale. Am J Public Health. 1999;89(8):1231–4. Crossref, Medline, Google Scholar
• 25 Department of Health and Human Services, Department of Agriculture. Dietary guidelines for Americans: 2015–2020. 8th ed. New York (NY): Skyhorse Publishing; 2017. p. 122. Google Scholar
• 26 Schwingshackl L, Bogensberger B, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension score, and health outcomes: an updated systematic review and meta-analysis of cohort studies. J Acad Nutr Diet. 2018;118(1):74–100. Crossref, Medline, Google Scholar
• 27 Detry MA, Ma Y. Analyzing repeated measurements using mixed models. JAMA. 2016;315(4):407–8. Crossref, Medline, Google Scholar
• 28 Seligman HK, Basu S. In an unhealthy food system, what role should SNAP play? PLoS Med. 2018;15(10): e1002662. Crossref, Medline, Google Scholar
• 29 Ioannidis JP. We need more randomized trials in nutrition—preferably large, long-term, and with negative results. Am J Clin Nutr. 2016;103(6):1385–6. Crossref, Medline, Google Scholar
• 30 Trepanowski JF, Ioannidis JPA. Perspective: limiting dependence on nonrandomized studies and improving randomized trials in human nutrition research: why and how. Adv Nutr. 2018;9(4):367–77. Crossref, Medline, Google Scholar
• 31 Olsho LE, Klerman JA, Wilde PE, Bartlett S. Financial incentives increase fruit and vegetable intake among Supplemental Nutrition Assistance Program participants: a randomized controlled trial of the USDA Healthy Incentives Pilot. Am J Clin Nutr. 2016;104(2):423–35. Crossref, Medline, Google Scholar
• 32 Polacsek M, Moran A, Thorndike AN, Boulos R, Franckle RL, Greene JCet al. A supermarket double-dollar incentive program increases purchases of fresh fruits and vegetables among low-income families with children: the Healthy Double Study. J Nutr Educ Behav. 2018;50(3):217–28. Crossref, Medline, Google Scholar
• 33 Olstad DL, Crawford DA, Abbott G, McNaughton SA, Le HN, Ni Mhurchu Cet al. The impact of financial incentives on participants’ food purchasing patterns in a supermarket-based randomized controlled trial. Int J Behav Nutr Phys Act. 2017;14(1):115. Crossref, Medline, Google Scholar
• 34 Wilde P, Klerman JA, Olsho LEW, Bartlett S. Explaining the impact of USDA’s Healthy Incentives Pilot on different spending outcomes. Appl Econ Perspect Policy. 2016;38(4):655–72. Crossref, Google Scholar
• 35 Anderson JV, Bybee DI, Brown RM, McLean DF, Garcia EM, Breer MLet al. 5 a day fruit and vegetable intervention improves consumption in a low income population. J Am Diet Assoc. 2001;101(2):195–202. Crossref, Medline, Google Scholar
• 36 Steele-Adjognon M, Weatherspoon D. Double Up Food Bucks program effects on SNAP recipients’ fruit and vegetable purchases. BMC Public Health. 2017;17(1):946. Crossref, Medline, Google Scholar
• 37 Baronberg S, Dunn L, Nonas C, Dannefer R, Sacks R. The impact of New York City’s Health Bucks Program on electronic benefit transfer spending at farmers markets, 2006–2009. Prev Chronic Dis. 2013;10:E163. Crossref, Medline, Google Scholar
• 38 Harnack L, Oakes JM, Elbel B, Beatty T, Rydell S, French S. Effects of subsidies and prohibitions on nutrition in a food benefit program: a randomized clinical trial. JAMA Intern Med. 2016;176(11):1610–8. Crossref, Medline, Google Scholar
• 39 Schenck-Fontaine A, Gassman-Pines A. Poster paper: the monthly food stamp cycle: how does SNAP policy design affect food insecurity? Paper presented at: 37th Annual Fall Research Conference of the Association for Public Policy Analysis and Management; 2015 Nov 13; Miami, FL. Google Scholar
• 40 Phipps EJ, Braitman LE, Stites SD, Singletary SB, Wallace SL, Hunt Let al. Impact of a rewards-based incentive program on promoting fruit and vegetable purchases. Am J Public Health. 2015;105(1):166–72. Crossref, Medline, Google Scholar
• 41 Griffith R, von Hinke S, Smith S. Getting a healthy start: the effectiveness of targeted benefits for improving dietary choices. J Health Econ. 2018;58:176–87. Crossref, Medline, Google Scholar