Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Apr 1.
Published in final edited form as: Health Psychol. 2019 Apr;38(4):334–342. doi: 10.1037/hea0000727

Delay discounting and household food purchasing decisions: the SHoPPER study

Bradley M Appelhans a,b, Christy C Tangney c, Simone A French d, Melissa M Crane a, Yamin Wang a
PMCID: PMC6430149  NIHMSID: NIHMS1018317  PMID: 30896220

Abstract

Objective:

Delay discounting is a neurocognitive trait that has been linked to poor nutritional health and obesity, but its role in specific dietary choices is unclear. This study tested whether individual differences in delay discounting are related to the healthfulness of household food purchases and reliance on non-store food sources such as restaurants.

Methods:

The food purchases of 202 primary household food shoppers were objectively documented for 14 days through a food receipt collection and analysis protocol. The nutrient content of household food purchases was derived for each participant, and the overall diet quality (Healthy Eating Index-2015) and energy density (kcal/g) of foods and beverages were calculated. The proportion of energy from non-store food sources was also derived. Delay discounting was assessed with a choice task featuring hypothetical monetary rewards.

Results:

Data were available for 12,624 foods and beverages purchased across 2,340 shopping episodes. Approximately 13% of energy was purchased from restaurants and other non-store food sources. Steeper discounting rates were associated with lower overall Healthy Eating Index-2015 scores, and a higher energy density (kcal/g) of purchased foods. Associations were attenuated but remained statistically significant when accounting for body mass index and sociodemographic variables. Discounting rates were unrelated to reliance on non-store food sources or the energy density of purchased beverages.

Conclusions:

Delay discounting is related to the healthfulness of food purchases among primary household shoppers. As food purchasing is a key antecedent of dietary intake, delay discounting may be a viable target in dietary and weight management interventions.

Keywords: Delay discounting, Diet, Fast food, Consumer behavior

INTRODUCTION

Poor dietary intake is a major contributor to obesity, heart disease, stroke, and type 2 diabetes (Schwingshackl, Bogensberger, & Hoffmann, 2018). Many U.S. children and adults consume a diet that is suboptimal for their health (Gu & Tucker, 2017; Wang et al., 2014), and diet-related chronic diseases represents a major public health challenge.

Most food consumed in modern society is purchased from food retail sources or restaurants (Poti & Popkin, 2011), and food purchasing decisions can be considered important “upstream” antecedents of dietary intake. There is moderately strong concordance between the composition of household food purchases and dietary intake in terms of fat, energy, and nutrient content (Appelhans, French, Tangney, Powell, & Wang, 2017; Eyles, Jiang, & Ni Mhurchu, 2010; Ransley et al., 2003; Ransley et al., 2001). Food purchasing decisions are important targets for chronic disease prevention as they can potentially influence dietary intake across multiple eating occasions, and for multiple individuals within a household.

The foods and beverages that an individual purchases are the outcome of a series of decisions, including whether to buy food in ready-to-eat or unprepared form, when and from which sources to obtain food, which individual food and beverage items to purchase from the available options, and in what quantity to purchase each item. Each of these decisions can potentially influence diet quality. For example, the healthfulness of purchased foods and beverages varies markedly across different types of food sources (e.g., grocery stores, restaurants, vending machines). Purchases from grocery stores and supermarkets are generally more healthful than those from convenience stores, restaurants, and vending machines (Appelhans et al., 2017; Stern, Ng, & Popkin, 2016), whereas the nutrient profiles of food purchases from various types of grocery stores (supermarkets, mass merchandisers, supercenters) are comparable (Chrisinger, Kallan, Whiteman, & Hillier, 2018; Stern, Poti, et al., 2016). Individuals who rely on supermarkets and grocery stores tend to consume more healthful and less unhealthful foods and beverages (Vankim, Erickson, & Laska, 2015).

Within a given food source, the selection of specific foods and beverages are known to be affected by economic and environmental factors. Price is inversely associated with purchasing rates across a broad range of foods and beverages (Andreyeva, Long, & Brownell, 2010; Cornelsen et al., 2015), and pricing interventions are effective for increasing purchasing of fruits, vegetables, and other healthy items (Adam & Jensen, 2016; An, 2013; Liberato, Bailie, & Brimblecombe, 2014; Thow, Downs, & Jan, 2014). Environmental factors also affect food purchasing, including product availability, prominent placement of targeted items, in-store advertising and other types of point-of-purchase signage (Foster et al., 2014; Harnack & French, 2008; Thorndike, Bright, Dimond, Fishman, & Levy, 2017). Most of what is currently known about predictors of food purchasing is limited to external (economic and environmental) factors; the role of individual differences in neurocognitive traits has been largely unexplored.

One potential neurocognitive influence on food purchasing decisions is delay discounting (DD), which refers to a preference for smaller immediate rewards over larger delayed rewards (e.g., $50 now rather than $100 in a year). Specifically, DD reflects the observation that the subjective value of a given reward diminishes at a hyperbolic rate as the latency to its receipt increases (Mazur, 1987). While DD is a general human tendency, there are large individual differences in discounting rates that are relatively stable over time and consistent across different types of reward (e.g., money, food, cigarettes) (Anokhin, Golosheykin, & Mulligan, 2015; Odum, 2011b). Thus, DD is a trait-like neurocognitive variable (Odum, 2011a). The theoretical linkage between DD and health behavior is based on a conceptualization of health behaviors as a series of intertemporal tradeoffs between immediate gratification and delayed health outcomes (Loewenstein, Read, & Baumeister, 2003). In this model, individuals who steeply discount delayed rewards overvalue immediate gratification (from junk food, cigarettes, etc.) at the expense of their long-term health. Steeper discounting has been implicated in a range of maladaptive health behaviors (Reynolds, 2006; Story, Vlaev, Seymour, Darzi, & Dolan, 2014) and is associated with tobacco and substance use (Amlung, Vedelago, Acker, Balodis, & Mackillop, 2017; Bickel, Jarmolowicz, Mueller, Koffarnus, & Gatchalian, 2012), lower uptake of preventive interventions (Bradford, 2010), and greater sexual risk taking (Jones et al., 2018).

In the context of dietary intake, individuals who steeply discount delayed rewards would be expected to more frequently select highly palatable foods that provide immediate gratification but are detrimental to long-term health (Appelhans, 2009; Herman & Polivy, 2003). Several recent meta-analyses and systematic reviews support an association between steeper discounting rates and obesity status and unhealthy dietary intake (Amlung, Petker, Jackson, Balodis, & Mackillop, 2016; Barlow, Reeves, Mckee, Galea, & Stuckler, 2016; Stojek & Mackillop, 2017; Sweeney & Culcea, 2017). However, the specific dietary decisions through which discounting contributes these outcomes are unclear. Steep discounting has been associated with greater self-reported reliance on fast food sources (Garza, Ding, Owensby, & Zizza, 2016; Shuval et al., 2016) and selection of more energy-dense items at restaurants or in ready-to-eat forms (Appelhans et al., 2012), but no prior study has examined whether individual differences in DD are related to objectively documented food purchases from various sources.

The SHoPPER Study (Study of Household Purchasing Patterns, Eating and Recreation) was designed to test whether individual differences in delay discounting rates account for between person-variability in food purchasing outcomes. The SHoPPER study included a novel food purchase assessment protocol that captures objective information on food and beverage purchases from all sources over a 14-day period. This intensive protocol was used to quantify the overall diet quality, energy density, and sources of food purchases made by primary household food shoppers. It was hypothesized that steeper delay discounting rates would be associated with 1) poorer overall diet quality of purchased foods as reflected in Healthy Eating Index-2015 scores, 2) higher energy density (kcal/g) of purchased foods, and 3) a greater proportion of purchased food from non-store food sources such as fast food and carryout restaurants.

METHODS

The Study of Household Purchasing Patterns, Eating, and Recreation (SHoPPER) was a cross-sectional study of neurocognitive correlates of household food purchasing patterns. Adult residents of Chicago were recruited between 2014–2016 through printed flyers and newspaper advertisements, direct mailings, online advertisements (clinicaltrials.gov, rush.edu, craigslist.org), and word-of-mouth. Eligible individuals reported making at least 75% their household’s food purchases. Major exclusion criteria included: 1) not fluent in English, 2) not living in Chicago, 3) major food allergies, 4) religious or medical dietary restrictions during study period (e.g., religious fasts), 5) temporary or group housing or living with a roommate with whom food is shared, 6) lack of telephone access, 7) inability to walk 2 blocks unassisted, 8) serious medical conditions, 9) history of psychosis, eating disorder, or syndromal cause of obesity, 10) unwilling to let researchers into their home, and 11) conditions that would make it unsafe for researchers to visit their home (e.g., nearby criminal activity, extreme unsanitary conditions). Of 347 individuals screened, 300 (86.5%) met eligibility criteria and 209 (69.7%) ultimately enrolled. Three participants were administratively withdrawn due to scheduling conflicts that prevented completion of the 14-d assessment protocol, and two participants were withdrawn due to protocol noncompliance (n=2). All participants provided written informed consent. Study procedures conformed to principles of the Declaration of Helsinki and were approved by the Rush University Medical Center Institutional Review Board. The study was registered through clinicaltrials.gov (NCT02073643). Participants received $100 for completing study procedures ($25 per visit).

Procedures

Study procedures were carried out during four home assessment visits distributed across a 14-day period. Assessment visits were separated by either 3 or 4 days. In the first visit, participants completed measures of DD and sociodemographic variables and received training in the food purchase assessment protocol (see below). Subsequent visits primarily focused on collecting and reviewing food purchase documentation. Participation did not occur within one week of major holidays, as food purchasing patterns may differ during these times.

Food Purchase Assessment Protocol

The primary household food shopper was trained to collect and annotate food receipts for all food and beverage purchases made by themselves or any member of their household. A binder containing forms and instructions for documenting food purchases was provided. For each food shopping episode, participants were asked to complete a documentation form with the date, time, and location of each food purchase, and name of the individual(s) who made the purchases. The forms contained fields to capture the type of food source (grocery store/supermarket, fast food/carryout restaurant, full-service restaurant, cafeterias, vending machine, bar/tavern, “other”), as well as the quantity, size, price, and description of each purchased food or beverage. Participants were instructed to place matching color-coded and/or numbered stickers on the documentation form, all of the foods and beverages listed on that form, and on any store receipts for that shopping episode. Different sets of stickers were used for each shopping episode, thereby providing a link between each food receipt and the physical items purchased on that occasion. Given that food/beverage packaging contains important data on its brand, variety, and quantity/amount, participants were instructed to place the packaging of consumed items in a large paper bag with the study logo for subsequent collection by research staff. Research staff telephoned participants between visits to support compliance to the protocol.

In the second, third, and fourth home visits, research staff reviewed the food receipts and documentation forms collected by participants and queried any items requiring additional clarification. All foods and beverages purchased since the prior visit were located in the home and digitally photographed. Detailed field notes were recorded on the type/variety, preparation, and amount purchased for foods and beverages that either lacked packaging (e.g., produce, bulk items) or were purchased in ready-to-eat form. Less than 1% of ready-to-eat foods could not be accurately characterized (e.g., buffet meals that were immediately consumed) and were excluded from analysis. Gender differences in food purchasing patterns (Crane, Tangney, French, Wang, & Appelhans, 2018), and associations between the healthfulness of food purchases and dietary intake in the SHoPPER study have been published (Appelhans et al., 2017).

Measures

Delay discounting rate.

Individual differences in discounting rates for hypothetical monetary rewards were measured with a widely-used, computerized binary choice task. The task features 161 trials in which participants choose to receive either a fixed reward of $100 available after a range of delays (from 1 day to 5 years in 7 increments), or an immediate reward ranging from $0.10 to $105.00 USD in 23 increments. An “indifference point” -- the value of immediate reward that is subjectively equivalent to the larger fixed reward offered at that delay interval -- was inferred from the pattern of responses. By default, the indifference point was calculated as the midpoint between the lowest immediate reward selected, and the highest value of immediate reward at which the delayed option was chosen. For example, an individual who prefers immediate rewards of $60 or more, but not rewards of $55 or less, to a delayed reward of $100 after 90 days would have an indifference point of $57.50 at the 90-day delay interval. In cases where responses were not consistent with a single indifference point, the indifference point was defined based on the two highest consecutive values of immediate reward that was preferred to the delayed reward (which was not always the lowest value immediate reward chosen) (Mitchell, 1999). Each participant’s indifference points were plotted across the seven delay intervals, and curve-fitting software (Prism 6, Graphpad Software, Inc., La Jolla, CA, USA) was used to calculate the area under the discounting curve according to the trapezoidal geometric method described by Myerson, Green, & Warusawitharana (2001). Lower values of the DD variable reflect steeper discounting, or a greater preference for immediate gratification. To enable comparisons with prior studies, we also report the median k value and R2 of discounting rates calculated using Mazur’s (1987) hyperbolic discounting function.

Diet quality of food purchases.

Three pre-specified outcomes were calculated through nutritional analysis of household food purchase data: 1) overall diet quality, as reflected in Healthy Eating Index-2015 scores (HEI-2015), 2) energy density of purchased foods and beverages in units of kcal/g, and 3) proportion of total energy from fast food, take-out/delivery, and full-service restaurants. The Nutrition Data System for Research (NDSR: versions 2013–2015, Nutrition Coordinating Center, University of Minnesota, MN) was used to obtain the nutrient and energy content of each purchased food or beverage. Nutrient and energy content were aggregated across all purchasing episodes; therefore, estimates of diet quality and energy density reflect the entire set of food purchases made over the 14-d assessment period. Medications, nutritional supplements, infant formulas, baby foods, and chewing gums were excluded from all calculations of nutrient and energy purchasing.

Diet quality was quantified by applying the Healthy Eating Index-2015 (HEI-2015) scoring criteria (Guenther et al., 2013; https://epi.grants.cancer.gov/hei/) to the nutrient and food group data of purchased foods and beverages. The HEI-2015 scores adherence to the Department of Health and Human Services’ 2015 Dietary Guidelines for Americans. Adherence to recommended intakes for 13 key dietary components is scored on continuous scale for each component, and the 13 component scores are then summed to obtain a total score ranging from 0–100. Higher scores reflect closer adherence to the dietary guidelines. Twelve of the thirteen dietary components are scored based on nutrient/food group densities (components per 1,000 kcal), and the thirteenth component is a ratio of monounsaturated and polyunsaturated fatty acids to saturated fatty acids. Therefore, HEI-2015 scores reflect diet quality independent from the quantity of food consumed. Energy density (kcal/g) was calculated separately for purchases foods and beverages, as beverages are much less energy dense than solid foods (Ledikwe et al., 2005). The third outcome examined was the proportion of total energy purchased from non-store sources (fast food, take-out/delivery, full-service restaurants, bars, taverns, cafeterias and vending machines) relative to food stores during the 14-day assessment period.

Anthropometrics.

Height and weight were measured in light clothing with a portable stadiometer (model 213, SECA, Hamburg, Germany) and scale (model 876, SECA, Hamburg, Germany), and body mass index (BMI) was calculated [weight (kg) / height2 (m)].

Sociodemographic variables.

The primary shopper’s age was calculated from date of birth. Gender, ethnicity/race, household income, education level, and household composition were collected via self-report. The poverty:income ratio was calculated by dividing household income by the Federal Poverty Threshold for each participants’ respective household size.

Statistical Analyses

Analyses were performed using Stata 13.1 (College Station, TX). Measures of central tendency were calculated for key sample characteristics and food purchasing variables. Variable distributions and model residuals were examined for skew and extreme values using skew indexes and normal quantile plots.

Non-systematic delay discounting data were identified based on Johnson and Bickel’s (2008) criteria: 1) the indifference point at any delay interval was greater than the indifference point at the previous delay interval by at least 20% of the value of the delayed reward, or 2) the indifference point at the final delay interval was not greater than the indifference point at the first delay interval by at least 10% of the value of the delayed reward. Data from 31 (15.4%) participants were categorized as non-systematic, a level of non-systematic responding commonly reported in the literature (Smith, Lawyer, & Swift, 2018). Findings were equivalent when analyses were conducted with and without participants with non-systematic DD data. Therefore, only those results based on systematic delay discounting data are reported.

The association between DD and the diet quality of food purchases (HEI-2015) was tested with linear regression. The same analytic approach was used to test associations between delay discounting and the energy density of food and beverage purchases. The third outcome of interest was the proportion of total energy purchased from non-store food sources. This variable was skewed and had a relatively large number of maximum (100%) values which precluded the use of transformations to normalize the distribution. For these reasons, proportion of total energy purchased from non-store food sources was converted to quintiles, and its association with DD was tested using ordered logit models. Results are reported from both unadjusted models and fully adjusted models that controlled for age, sex, BMI, poverty:income ratio, and educational attainment (in four categories). Partial eta-squared (η2) is reported as a measure of effect size, with values of η2=0.010, 0.059, and 0.138 corresponding to small, medium, and large effects.

RESULTS

Sample characteristics and food purchasing variables are summarized in Table 1, both for the overall sample and separately for those with and without systematic DD data. Those with and without non-systematic delay discounting data did not significantly differ on any demographic variables, but non-systematic responders had a higher percentage of energy purchased from non-store food sources (Mann-Whitney U test: z=−2.25, p=.02). The mean area under the delay discounting curve was 29.4 (SD=25.0). The median k value was 0.886 (interquartile range: 0.779–0.944), and a hyperbolic curve was found to be a good fit to the plot of indifference points for most participants (median R2=0.90; interquartile range: 0.84–0.95).

Table 1.

Characteristics and food purchasing outcomes of primary household food shoppers. Data are shown for the overall sample, and for those with and without systematic response patterns on the delay discounting (DD) task.

Overall Systematic DD Non-systematic DD p-value a
Sample size (n) 202 171 31
Mean (SD)
Age (y) 44.1 (13.3) 44.7 (13.5) 40.6 (11.8) .11
Body mass index (BMI; kg/m2) 31.2 (9.1) 31.5 (9.1) 29.4 (9.1) .25
Income:poverty ratio 3.7 (3.8) 3.8 (4.0) 3.1 (2.5) .34
Healthy Eating Index-2015 58.2 (15.1) 58.2 (15.1) 56.4 (16.1) .54
Energy density-food (kcal/g) 2.0 (0.6) 2.0 (0.6) 2.0 (0.6) .78
Energy density-beverages (kcal/g) 0.5 (0.5) 0.5 (0.5) 0.5 (0.4) .84
Median (interquartile range)
Non-store food purchases
 % energy 10.7 (2.5, 24.0) 10.4 (1.8, 22.0) 20.0 (7.0, 35.1) .02
 % mass 9.3 (2.1, 21.3) 8.6 (1.8, 19.1) 15.0 (3.3, 36.2) .05
n (%)
Female sex 168 (83.2) 143 (83.6) 25 (80.6) .79
Education .14
 High school or less 24 (11.9) 18 (10.5) 6 (19.4)
 Some college 69 (34.2) 61 (35.7) 8 (25.8)
 College degree 63 (31.2) 50 (29.2) 13 (41.9)
 Graduate degree 46 (22.8) 42 (24.6) 4 (12.9)
Ethnicity/race .30
 African-American 91 (45.0) 73 (42.7) 18 (58.1)
 Hispanic/Latino 23 (11.4) 21 (12.3) 2 (6.5)
 Multi-ethnic/other 27 (13.4) 22 (12.9) 5 (16.1)
 Non-Hispanic white 61 (30.2) 55 (32.2) 6 (19.4)
Open in a new tab
a

p-value reflects group difference based on t-test for normally-distributed continuous variables, Mann-Whitney U-test for non-normally distributed continuous variables, and Fisher’s exact test for categorical variables.

In total, analyses included data from 12,624 food purchases distributed over 2,340 purchasing episodes. This corresponds to an average of 62.5 (SD=35.4) purchased items across 11.6 (SD=7.6) purchasing episodes per participant. Over the 14-day assessment period, participants purchased an average of 53,628 (SD=40,516) kcal, or 39.7 (SD=26.1) kg, of foods and beverages. The median percentages of total energy and food/beverage mass purchased from non-store food sources were 10.7% and 9.3%, respectively, and 14.9% of participants did not make any non-store food purchases during the 14-day assessment period.

Steeper discounting rates (reflecting more impulsive, short-sighted decision-making) were associated with lower overall diet quality in both unadjusted (estimate=0.17, SE=0.04, t=3.82, p<.001, η2=.08) and fully-adjusted models (estimate=0.10, SE=0.04, t=2.36, p=.02, η2=.03). The covariate-adjusted association is depicted in Figure 1.

Figure 1.

Figure 1.

Open in a new tab

Predicted association between delay discounting (area under the curve of indifference points) and the diet quality of household food purchases based on the Healthy Eating Index-2015, adjusted for demographic factors and body mass index. Higher values for delay discounting reflect a lower discounting rate, or less impulsive responding on the task.

Steeper delay discounting rates were also associated with a greater energy density of purchased foods in both the unadjusted (estimate=−0.006, SE=0.002, t=−3.52, p=.001, η2=.07) and fully-adjusted models (estimate=−0.004, SE=0.002, t=−2.29, p=.02, η2=.03; Figure 2). In contrast, DD was not significantly associated with the energy density of purchased beverages in either unadjusted (estimate=0.0001, SE=0.001, t=0.08, p=.94, η2=.00) or fully-adjusted models (estimate=0.001, SE=0.002, t=0.33, p=.74, η2=.00).

Figure 2.

Figure 2.

Open in a new tab

Predicted association between delay discounting (area under the curve of indifference points) and the energy density (kcal/g) of purchased foods, adjusted for demographic factors and BMI. Higher values for delay discounting reflect a lower discounting rate, or less impulsive responding on the task.

Analyses modeling the odds of increasing quintiles of non-store food purchases indicated no associations with delay discounting rates in unadjusted (OR=1.004, SE=0.005, p=.44) or fully-adjusted models (OR=0.996, SE=0.006, p=.52). Follow-up analyses focused on purchases from fast food/carryout restaurants only, which represented a median of 5.0% (interquartile range: 0.5, 13.5) of total purchased energy, found no association between DD and quintiles of fast food/carryout purchasing in either unadjusted (OR=0.999, SE=0.005, p=.81) or fully-adjusted models (OR=0.994, SE=0.006, p=.27).

In exploratory analyses, education and income:poverty ratio did not moderate any of the associations between DD and the food and beverage purchasing outcomes reported above.

DISCUSSION

This study examined whether DD, a neurocognitive trait previously implicated in poor nutritional health and obesity, accounts for variability in the sources and healthfulness of food purchases measured through an intensive food purchase analysis protocol. It was found that primary household food shoppers with steeper discounting rates purchased foods and beverages that were lower in overall diet quality, and foods that were higher in energy density. These associations were attenuated but remained significant when accounting for BMI and sociodemographic variables. DD was not associated with greater reliance on non-store food sources such as fast food, take-out/delivery, and full-service restaurants – which are known to be sources of less healthful food purchases (Chrisinger et al., 2018). This contrasts with two prior studies reporting associations between DD and fast food intake (Garza et al., 2016; Shuval et al., 2016), which may be attributable to reliance on self-reported (rather than objectively documented) fast food purchasing in those studies. The current findings offer a potential mechanism linking DD to dietary outcomes and obesity. Whereas previous studies have generally emphasized impulsivity immediately prior to or contemporaneous with food/beverage consumption (Appelhans et al., 2012; Ely, Howard, & Lowe, 2015; Zimmerman et al., 2017), the current findings suggest that DD influences food purchasing decisions that occur much further “upstream” of actual consumption, at the point when food is first acquired from the environment.

The statistical effect sizes (η2) for observed associations were relatively modest, but may be clinically significant. To put the findings in context, the food purchases of an individual at the 25th percentile of DD would have an HEI-2015 score that is 7.7 (4.7 adjusted) points higher than a more impulsive individual at the 75th percentile of DD. No clinical cutpoints for the Healthy Eating Index have been established, but diet quality is linearly related to overall, cardiovascular, and cancer mortality risk (Harmon et al., 2015). Differences of 5–9 points on the HEI-2015 show measurable associations with mortality (Panizza et al., 2018). The energy density of foods purchased by a shopper at the 75th percentile of DD would be 0.28 (0.19 adjusted) kcal/g higher than that for purchases made by a shopper at the 25th percentile of DD. Assuming a 2,000 kcal diet (~1kg of food daily at 2.0 kcal/g), a steep discounting shopper (75th percentile) would consume an additional 190–280 kcal/d than a shallow discounting shopper (25th percentile) at the same mass of food. Dietary energy density has a major effect on overall energy intake (Rolls, 2017; Rouhani, Haghighatdoost, Surkan, & Azadbakht, 2016), and an additional 190–280 kcal/d would meaningfully increase obesity risk (Dietz, 2012; Wang, Orleans, & Gortmaker, 2012).

The current findings suggest DD may be an important target in interventions that aim to promote healthy food purchasing patterns. Prior food purchasing interventions have primarily focused on environmental modifications, economic incentives, and nutrition education (Adam & Jensen, 2016; Escaron, Meinen, Nitzke, & Martinez-Donate, 2013; Foster et al., 2014; Liberato et al., 2014; Milliron, Woolf, & Appelhans, 2012; Thorndike et al., 2017; Thow et al., 2014). Neurocognitive variables have been largely unaddressed in food purchasing interventions. Two conceptually distinct approaches to mitigating the impact of discounting on decision making are in development. Episodic future thinking (EFT) is a cognitive technique that aims to increase the decisional weight given to future (relative to immediate) outcomes. EFT protocols typically involve repeated sessions of episodic prospection, or envisioning future experiences in detail to “pre-experience an event” (Atance & O’neill, 2001). EFT reduces discounting rates (Kaplan, Reed, & Jarmolowicz, 2016; Lin & Epstein, 2014; O’donnell, Oluyomi Daniel, & Epstein, 2017), decreases laboratory snack intake and food demand (Dassen, Jansen, Nederkoorn, & Houben, 2016; O’neill, Daniel, & Epstein, 2016; Sze, Stein, Bickel, Paluch, & Epstein, 2017), and has been pilot tested as an intervention component (Sze, Daniel, Kilanowski, Collins, & Epstein, 2015). It is possible that EFT could also lead to healthier food purchasing decisions.

In contrast to EFT, commitment strategies circumvent impulsive decision making by allowing an individual to constrain their future choices to healthy options (Rachlin, 2000). The premise underlying commitment strategies is that decisions made in advance, for one’s “future self,” will be less susceptible to discounting than choices made right now for the “current self”. Consistent with this notion, ordering food well in advance of consumption leads to healthier choices (Milkman, Rogers, & Bazerman, 2010; Stites et al., 2015). One category of commitment strategies is financial contracting, which involves agreements in which individuals stand to forfeit their own money unless they succeed in meeting a specific goal (Halpern, Asch, & Volpp, 2012). Financial contracting has been effective for promoting weight loss (Forster, Jeffery, Sullivan, & Snell, 1985; Jeffery, Bjornson-Benson, Rosenthal, Lindquist, & Johnson, 1984; John et al., 2011). In the only prior study of financial contracting in the context of food purchasing, households were given the chance to opt in to a binding commitment to increase their purchasing of healthy foods in order to earn a grocery discount. Households that accepted the commitment contract made more healthy grocery purchases than households who still received the same grocery discount without commitment (Schwartz et al., 2014). To the extent that they attenuate the influence of DD on food purchasing decisions, interventions such as EFT and commitment strategies could potentially lead to improvements in the home food environment and dietary intake. However, based on the present pattern of findings, such interventions may be less effective for reducing intake of fast food or restaurant meals.

A key strength of this study was its intensive protocol for objectively documenting household food purchases. This protocol was previously shown to yield estimates with moderate agreement with dietary intake, and to be unrelated to social desirability bias (Appelhans et al., 2017). To reduce assessment burden and resources, future research on food purchasing decisions could utilize semi-automated methods that rely only barcode scanning such as those used in ongoing consumer panels (e.g., Food APS, Nielsen HomeScan). However, these methods may be less reliable for capturing non-store purchases and non-packaged foods (French, Shimotsu, Wall, & Gerlach, 2008; Hu, Gremel, Kirlin, & West, 2017). This study included a diverse community sample, but was primarily composed of urban, female shoppers who were responsible for making the majority of food purchases for their households. Women make the majority of food purchases in 70–85% of U.S. households (French et al., 2017; Harnack, Story, Martinson, Neumark-Sztainer, & Stang, 1998; Hillier, Smith, Whiteman, & Chrisinger, 2017), but it is possible that observed associations between DD and food purchasing decision may not generalize to other household members. While this is one of the larger studies of delay discounting, the current sample did not afford adequate statistical power to detect small effects or perform stratified analyses. There were also relatively few observations of food purchasing episodes from individual non-store food sources, which necessitated analyzing them in aggregate. This study examined a limited number of potential influences on food purchasing patterns, and others such as executive function or attitudes towards healthy eating warrant further study. Finally, this study did not obtain data on potentially important environmental influences on purchasing decisions, including specific characteristics and products offered at each food store, and shoppers’ geographic proximity to various food sources. These factors may influence purchasing decisions in interaction with DD and should be considered in future research.

Conclusions

Steeper DD was associated with lower overall diet quality and higher energy density of food purchases made by primary household food shoppers, which suggests that DD may be a viable target in dietary and weight management interventions.

Acknowledgements.

We are grateful for assistance from Tamara Olinger, Elizabeth Avery-Mamer, Vernon Cail, Jessica Rusch, Olivia Moss, Leah Cerwinske, Michelle Li, Kelly Nemec, Caitlyn Busche, Marieli Guzman, David Mata, Hong Li, Christine Sharp, and Leila Shinn.

Funding. This study was supported by National Heart, Lung, and Blood Institute (NIH) grant R01HL117804. The content is solely the responsibility of the authors and does not necessarily reflect the official views of the NIH.

Footnotes

Competing interests. The authors have no competing interests to disclose.

Trial Registration: ClinicalTrials.gov identifier: NCT02073643

REFERENCES

  1. Adam A, & Jensen JD (2016). What is the effectiveness of obesity related interventions at retail grocery stores and supermarkets? -a systematic review. BMC Public Health, 16(1), 1247. doi: 10.1186/s12889-016-3985-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Amlung M, Petker T, Jackson J, Balodis I, & MacKillop J (2016). Steep discounting of delayed monetary and food rewards in obesity: a meta-analysis. Psychological Medicine, 46(11), 2423–2434. doi: 10.1017/s0033291716000866 [DOI] [PubMed] [Google Scholar]
  3. Amlung M, Vedelago L, Acker J, Balodis I, & MacKillop J (2017). Steep delay discounting and addictive behavior: a meta-analysis of continuous associations. Addiction, 112(1), 51–62. doi: 10.1111/add.13535 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. An R (2013). Effectiveness of subsidies in promoting healthy food purchases and consumption: a review of field experiments. Public Health Nutrition, 16(7), 1215–1228. doi: 10.1017/s1368980012004715 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Andreyeva T, Long MW, & Brownell KD (2010). The impact of food prices on consumption: a systematic review of research on the price elasticity of demand for food. American Journal of Public Health, 100(2), 216–222. doi: 10.2105/ajph.2008.151415 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Anokhin AP, Golosheykin S, & Mulligan RC (2015). Long-term test-retest reliability of delayed reward discounting in adolescents. Behavioural Processes, 111, 55–59. doi: 10.1016/j.beproc.2014.11.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Appelhans BM (2009). Neurobehavioral inhibition of reward-driven feeding: implications for dieting and obesity. Obesity, 17(4), 640–647. doi: 10.1038/oby.2008.638 [DOI] [PubMed] [Google Scholar]
  8. Appelhans BM, French SA, Tangney CC, Powell LM, & Wang Y (2017). To what extent do food purchases reflect shoppers’ diet quality and nutrient intake? International Journal of Behavioral Nutrition and Physical Activity, 14(1), 46. doi: 10.1186/s12966-017-0502-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Appelhans BM, Waring ME, Schneider KL, Pagoto SL, DeBiasse MA, Whited MC, & Lynch EB (2012). Delay discounting and intake of ready-to-eat and away- from home foods in overweight and obese women. Appetite, 59(2), 576–584. doi: 10.1016/j.appet.2012.07.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Atance CM, & O’Neill DK (2001). Episodic future thinking. Trends in Cognitive Science, 5(12), 533–539. [DOI] [PubMed] [Google Scholar]
  11. Barlow P, Reeves A, McKee M, Galea G, & Stuckler D (2016). Unhealthy diets, obesity and time discounting: a systematic literature review and network analysis. Obesity Reviews, 17(9), 810–819. doi: 10.1111/obr.12431 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bickel WK, Jarmolowicz DP, Mueller ET, Koffarnus MN, & Gatchalian KM (2012). Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other disease-related vulnerabilities: Emerging evidence. Pharmacology & Therapeutics, 134(3), 287–297. doi: 10.1016/j.pharmthera.2012.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Bradford WD (2010). The association between individual time preferences and health maintenance habits. Medical Decision Making, 30(1), 99–112. doi: 10.1177/0272989X09342276 [DOI] [PubMed] [Google Scholar]
  14. Chrisinger BW, Kallan MJ, Whiteman ED, & Hillier A (2018). Where do U.S. households purchase healthy foods? An analysis of food-at-home purchases across different types of retailers in a nationally representative dataset. Preventive Medicine, 112, 15–22. doi: 10.1016/j.ypmed.2018.03.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cornelsen L, Green R, Turner R, Dangour AD, Shankar B, Mazzocchi M, & Smith RD (2015). What happens to patterns of food consumption when food prices change? Evidence from a systematic review and meta-analysis of food price elasticities globally. Health Economics, 24(12), 1548–1559. doi: 10.1002/hec.3107 [DOI] [PubMed] [Google Scholar]
  16. Crane MM, Tangney CC, French SA, Wang Y, & Appelhans BM (2018). Gender comparison of the diet quality and sources of food purchases made by urban primary household food purchasers. Journal of Nutrition Education and Behavior. doi: 10.1016/j.jneb.2018.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Dassen FCM, Jansen A, Nederkoorn C, & Houben K (2016). Focus on the future: Episodic future thinking reduces discount rate and snacking. Appetite, 96, 327–332. doi: 10.1016/j.appet.2015.09.032 [DOI] [PubMed] [Google Scholar]
  18. Dietz WH (2012). Implications of the energy gap for the prevention and treatment of childhood obesity. American Journal of Preventive Medicine, 42(5), 560–561. doi: 10.1016/j.amepre.2012.02.001 [DOI] [PubMed] [Google Scholar]
  19. Ely AV, Howard J, & Lowe MR (2015). Delayed discounting and hedonic hunger in the prediction of lab-based eating behavior. Eating Behaviors, 19, 72–75. doi: 10.1016/j.eatbeh.2015.06.015 [DOI] [PubMed] [Google Scholar]
  20. Escaron AL, Meinen AM, Nitzke SA, & Martinez-Donate AP (2013). Supermarket and grocery store-based interventions to promote healthful food choices and eating practices: a systematic review. Preventing Chronic Disease, 10, E50. doi: 10.5888/pcd10.120156 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Eyles H, Jiang Y, & Ni Mhurchu C (2010). Use of household supermarket sales data to estimate nutrient intakes: a comparison with repeat 24-hour dietary recalls. Journal of the American Dietetic Association, 110(1), 106–110. doi: 10.1016/j.jada.2009.10.005 [DOI] [PubMed] [Google Scholar]
  22. Forster JL, Jeffery RW, Sullivan S, & Snell MK (1985). A work-site weight control program using financial incentives collected through payroll deduction. Journal of Occupational Medicine, 27(11), 804–808. [DOI] [PubMed] [Google Scholar]
  23. Foster GD, Karpyn A, Wojtanowski AC, Davis E, Weiss S, Brensinger C, … Glanz K (2014). Placement and promotion strategies to increase sales of healthier products in supermarkets in low-income, ethnically diverse neighborhoods: a randomized controlled trial. American Journal of Clinical Nutrition, 99(6), 1359–1368. doi: 10.3945/ajcn.113.075572 [DOI] [PubMed] [Google Scholar]
  24. French SA, Rydell SA, Mitchell NR, Michael Oakes J, Elbel B, & Harnack L (2017). Financial incentives and purchase restrictions in a food benefit program affect the types of foods and beverages purchased: results from a randomized trial. International Journal of Behavioral Nutrition and Physical Activity, 14, 127. doi: 10.1186/s12966-017-0585-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. French SA, Shimotsu ST, Wall M, & Gerlach AF (2008). Capturing the spectrum of household food and beverage purchasing behavior: a review. Journal of the American Dietetic Association, 108(12), 2051–2058. doi: 10.1016/j.jada.2008.09.001 [DOI] [PubMed] [Google Scholar]
  26. Garza KB, Ding M, Owensby JK, & Zizza CA (2016). Impulsivity and fast-food consumption: a cross-sectional study among working adults. Journal of the Academy of Nutrition and Dietetics, 116(1), 61–68. doi: 10.1016/j.jand.2015.05.003 [DOI] [PubMed] [Google Scholar]
  27. Gu X, & Tucker KL (2017). Dietary quality of the US child and adolescent population: trends from 1999 to 2012 and associations with the use of federal nutrition assistance programs. American Journal of Clinical Nutrition, 105(1), 194–202. doi: 10.3945/ajcn.116.135095 [DOI] [PubMed] [Google Scholar]
  28. Guenther PM, Casavale KO, Reedy J, Kirkpatrick SI, Hiza HA, Kuczynski KJ, … Krebs-Smith SM (2013). Update of the Healthy Eating Index: HEI-2010. Journal of the Academy of Nutrition and Dietetics, 113(4), 569–580. doi: 10.1016/j.jand.2012.12.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Halpern SD, Asch DA, & Volpp KG (2012). Commitment contracts as a way to health. British Medical Journal, 344, e522. doi: 10.1136/bmj.e522 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Harmon BE, Boushey CJ, Shvetsov YB, Ettienne R, Reedy J, Wilkens LR, … Kolonel LN (2015). Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. American Journal of Clinical Nutrition, 101(3), 587–597. doi: 10.3945/ajcn.114.090688 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Harnack L, Story M, Martinson B, Neumark-Sztainer D, & Stang J (1998). Guess who’s cooking? The role of men in meal planning, shopping, and preparation in US families. Journal of the American Dietetic Association, 98(9), 995–1000. doi: 10.1016/S0002-8223(98)00228-4 [DOI] [PubMed] [Google Scholar]
  32. Harnack LJ, & French SA (2008). Effect of point-of-purchase calorie labeling on restaurant and cafeteria food choices: a review of the literature. International Journal of Behavioral Nutrition and Physical Activity, 5, 51. doi: 10.1186/1479-5868-5-51 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Herman CP, & Polivy J (2003). Dieting as an exercise in behavioral economics In Loewenstein G, Read D, & Baumeister RF (Eds.), Time and decision: economic and psychological perspectives on intertemporal choice (pp. 459–489): New York: Russell Sage Foundation, c2003. [Google Scholar]
  34. Hillier A, Smith TE, Whiteman ED, & Chrisinger BW (2017). Discrete choice model of food store trips using national household Food Acquisition and Purchase Survey (FoodAPS). International Journal of Environmental Research and Public Health, 14(10). doi: 10.3390/ijerph14101133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hu M, Gremel GW, Kirlin JA, & West BT (2017). Nonresponse and underreporting errors increase over the data collection week based on paradata from the national household Food Acquisition and Purchase Survey. Journal of Nutrition, 147(5), 964–975. doi: 10.3945/jn.116.240697 [DOI] [PubMed] [Google Scholar]
  36. Jeffery RW, Bjornson-Benson WM, Rosenthal BS, Lindquist RA, & Johnson SL (1984). Behavioral treatment of obesity with monetary contracting: two-year follow-up. Addictive Behaviors, 9(3), 311–313. [DOI] [PubMed] [Google Scholar]
  37. John LK, Loewenstein G, Troxel AB, Norton L, Fassbender JE, & Volpp KG (2011). Financial incentives for extended weight loss: a randomized, controlled trial. Journal of General Internal Medicine, 26(6), 621–626. doi: 10.1007/s11606-010-1628-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Johnson MW, & Bickel WK (2008). An algorithm for identifying nonsystematic delay discounting data. Experimental and Clinical Psychopharmacology, 16(3), 264–274. doi: 10.1037/1064-1297.16.3.264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Jones J, Guest JL, Sullivan PS, Sales JM, Jenness SM, & Kramer MR (2018). The association between monetary and sexual delay discounting and risky sexual behavior in an online sample of men who have sex with men. AIDS Care, 1–9. doi: 10.1080/09540121.2018.1427851 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kaplan BA, Reed DD, & Jarmolowicz DP (2016). Effects of episodic future thinking on discounting: Personalized age-progressed pictures improve risky long-term health decisions. Journal of Applied Behavior Analysis, 49(1), 148–169. doi: 10.1002/jaba.277 [DOI] [PubMed] [Google Scholar]
  41. Ledikwe JH, Blanck HM, Khan LK, Serdula MK, Seymour JD, Tohill BC, & Rolls BJ (2005). Dietary energy density determined by eight calculation methods in a nationally representative United States population. Journal of Nutrition, 135(2), 273–278. [DOI] [PubMed] [Google Scholar]
  42. Liberato SC, Bailie R, & Brimblecombe J (2014). Nutrition interventions at point-of-sale to encourage healthier food purchasing: a systematic review. BMC Public Health, 14, 919. doi: 10.1186/1471-2458-14-919 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lin H, & Epstein LH (2014). Living in the moment: effects of time perspective and emotional valence of episodic thinking on delay discounting. Behavioral Neuroscience, 128(1), 12–19. doi: 10.1037/a0035705 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Loewenstein G, Read D, & Baumeister RF (2003). Time and decision: economic and psychological perspectives on intertemporal choice: New York: Russell Sage Foundation, c2003. [Google Scholar]
  45. Mazur JE (1987). An adjusting procedure for studying delayed reinforcement In Commons ML, Mazur JE, Nevin JA, & Rachlin H (Eds.), Quantitative Analysis of Behavior, vol. 5. The Effects of Delay and of Intervening Events on Reinforcement Value. (Vol. 5, pp. 55–73). New Jersey: Erlbaum. [Google Scholar]
  46. Milkman KL, Rogers T, & Bazerman MH (2010). I’ll have the ice cream soon and the vegetables later: A study of online grocery purchases and order lead time. Marketing Letters, 21(1), 17–35. doi: 10.1007/s11002-009-9087-0 [DOI] [Google Scholar]
  47. Milliron BJ, Woolf K, & Appelhans BM (2012). A point-of-purchase intervention featuring in-person supermarket education impacts healthy food purchases. Journal of Nutrition Education and Behavior, 44, 225–232. doi: 10.1016/j.jneb.2011.05.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mitchell SH (1999). Measures of impulsivity in cigarette smokers and non-smokers. Psychopharmacology, 146(4), 455–464. [DOI] [PubMed] [Google Scholar]
  49. Myerson J, Green L, & Warusawitharana M (2001). Area under the curve as a measure of discounting. Journal of the Experimental Analysis of Behavior, 76(2), 235–243. doi: 10.1901/jeab.2001.76-235 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. O’Donnell S, Oluyomi Daniel T, & Epstein LH (2017). Does goal relevant episodic future thinking amplify the effect on delay discounting? Consciousness and Cognition, 51, 10–16. doi: 10.1016/j.concog.2017.02.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. O’Neill J, Daniel TO, & Epstein LH (2016). Episodic future thinking reduces eating in a food court. Eating Behaviors, 20, 9–13. doi: 10.1016/j.eatbeh.2015.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Odum AL (2011a). Delay discounting: I’m a k, you’re a k. Journal of the Experimental Analysis of Behavior, 96(3), 427–439. doi: 10.1901/jeab.2011.96-423 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Odum AL (2011b). Delay discounting: trait variable? Behavioural Processes, 87(1), 1–9. doi: 10.1016/j.beproc.2011.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Panizza CE, Shvetsov YB, Harmon BE, Wilkens LR, Le Marchand L, Haiman C, … Boushey CJ (2018). Testing the predictive validity of the Healthy Eating Index-2015 in the Multiethnic Cohort: Is the score associated with a reduced risk of all-cause and cause-specific mortality? Nutrients, 10(4). doi: 10.3390/nu10040452 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Poti JM, & Popkin BM (2011). Trends in energy intake among US children by eating location and food source, 1977–2006. Journal of the Americamn Dietetic Association, 111(8), 1156–1164. doi: 10.1016/j.jada.2011.05.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Rachlin H (2000). The Science of Self-Control. Cambridge, Massachusetts: Harvard University Press. [Google Scholar]
  57. Ransley JK, Donnelly JK, Botham H, Khara TN, Greenwood DC, & Cade JE (2003). Use of supermarket receipts to estimate energy and fat content of food purchased by lean and overweight families. Appetite, 41(2), 141–148. [DOI] [PubMed] [Google Scholar]
  58. Ransley JK, Donnelly JK, Khara TN, Botham H, Arnot H, Greenwood DC, & Cade JE (2001). The use of supermarket till receipts to determine the fat and energy intake in a UK population. Public Health Nutrition, 4(6), 1279–1286. [DOI] [PubMed] [Google Scholar]
  59. Reynolds B (2006). A review of delay-discounting research with humans: relations to drug use and gambling. Behavioural Pharmacology, 17(8), 651–667. [DOI] [PubMed] [Google Scholar]
  60. Rolls BJ (2017). Dietary energy density: Applying behavioural science to weight management. Nutition Bulletin, 42(3), 246–253. doi: 10.1111/nbu.12280 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Rouhani MH, Haghighatdoost F, Surkan PJ, & Azadbakht L (2016). Associations between dietary energy density and obesity: A systematic review and meta-analysis of observational studies. Nutrition, 32(10), 1037–1047. doi: 10.1016/j.nut.2016.03.017 [DOI] [PubMed] [Google Scholar]
  62. Schwartz J, Mochon D, Wyper L, Maroba J, Patel D, & Ariely D (2014). Healthier by precommitment. Psychological Science, 25(2), 538–546. doi: 10.1177/0956797613510950 [DOI] [PubMed] [Google Scholar]
  63. Schwingshackl L, Bogensberger B, & Hoffmann G (2018). 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 metaanalysis of cohort studies. Journal of the Academy of Nutrition and Dietetics, 118(1), 74–100.e111. doi: 10.1016/j.jand.2017.08.024 [DOI] [PubMed] [Google Scholar]
  64. Shuval K, Stoklosa M, Pachucki MC, Yaroch AL, Drope J, & Harding M (2016). Economic preferences and fast food consumption in US adults: Insights from behavioral economics. Preventive Medicine, 93, 204–210. doi: 10.1016/j.ypmed.2016.10.016 [DOI] [PubMed] [Google Scholar]
  65. Smith KR, Lawyer SR, & Swift JK (2018). A meta-analysis of nonsystematic responding in delay and probability reward discounting. Experimental and Clinical Psychopharmacology, 26(1), 94–107. doi: 10.1037/pha0000167 [DOI] [PubMed] [Google Scholar]
  66. Stern D, Ng SW, & Popkin BM (2016). The nutrient content of U.S. household food purchases by store type. American Journal of Preventive Medicine, 50(2), 180–190. doi: 10.1016/j.amepre.2015.07.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Stern D, Poti JM, Ng SW, Robinson WR, Gordon-Larsen P, & Popkin BM (2016). Where people shop is not associated with the nutrient quality of packaged foods for any racial-ethnic group in the United States. American Journal of Clinical Nutrition, 103(4), 1125–1134. doi: 10.3945/ajcn.115.121806 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Stites SD, Singletary SB, Menasha A, Cooblall C, Hantula D, Axelrod S, … Phipps EJ (2015). Pre-ordering lunch at work. Results of the what to eat for lunch study. Appetite, 84, 88–97. doi: 10.1016/j.appet.2014.10.005 [DOI] [PubMed] [Google Scholar]
  69. Stojek MMK, & MacKillop J (2017). Relative reinforcing value of food and delayed reward discounting in obesity and disordered eating: A systematic review. Clinical Psychology Review, 55, 1–11. doi: 10.1016/j.cpr.2017.04.007 [DOI] [PubMed] [Google Scholar]
  70. Story GW, Vlaev I, Seymour B, Darzi A, & Dolan RJ (2014). Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective. Frontiers in Behavioral Neuroscience, 8, 76. doi: 10.3389/fnbeh.2014.00076 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Sweeney AM, & Culcea I (2017). Does a future-oriented temporal perspective relate to body mass index, eating, and exercise? A meta-analysis. Appetite, 112, 272–285. doi: 10.1016/j.appet.2017.02.006 [DOI] [PubMed] [Google Scholar]
  72. Sze YY, Daniel TO, Kilanowski CK, Collins RL, & Epstein LH (2015). Web-based and mobile delivery of an episodic future thinking intervention for overweight and obese families: a feasibility study. Journal of Medical Internet Research, 3(4), e97. doi: 10.2196/mhealth.4603 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Sze YY, Stein JS, Bickel WK, Paluch RA, & Epstein LH (2017). Bleak present, bright future: online episodic future thinking, scarcity, delay discounting, and food demand. Clinical Psychological Science, 5(4), 683–697. doi: 10.1177/2167702617696511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Thorndike AN, Bright OM, Dimond MA, Fishman R, & Levy DE (2017). 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 Nutrition, 20(7), 1297–1305. doi: 10.1017/s1368980016003074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Thow AM, Downs S, & Jan S (2014). A systematic review of the effectiveness of food taxes and subsidies to improve diets: understanding the recent evidence. Nutrition Reviews, 72(9), 551–565. doi: 10.1111/nure.12123 [DOI] [PubMed] [Google Scholar]
  76. VanKim NA, Erickson DJ, & Laska MN (2015). Food shopping profiles and their association with dietary patterns: a latent class analysis. Journal of the Academy of Nutrition and Dietetics, 115(7), 1109–1116. doi: 10.1016/j.jand.2014.12.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Wang DD, Leung CW, Li Y, Ding EL, Chiuve SE, Hu FB, & Willett WC (2014). Trends in dietary quality among adults in the United States, 1999 through 2010. JAMA Internal Medicine, 174(10), 1587–1595. doi: 10.1001/jamainternmed.2014.3422 [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Wang YC, Orleans CT, & Gortmaker SL (2012). Reaching the healthy people goals for reducing childhood obesity: closing the energy gap. American Journal of Preventive Medicine, 42(5), 437–444. doi: 10.1016/j.amepre.2012.01.018 [DOI] [PubMed] [Google Scholar]
  79. Zimmerman AR, Ferriday D, Davies SR, Martin AA, Rogers PJ, Mason A, & Brunstrom JM (2017). “What time is my next meal?” delay-discounting individuals choose smaller portions under conditions of uncertainty. Appetite, 116, 284–290. doi: 10.1016/j.appet.2017.05.01 [DOI] [PubMed] [Google Scholar]

RESOURCES