The impact of financial incentives on participants’ food purchasing patterns in a supermarket-based randomized controlled trial

Participants and setting

Full methodological details of the SHELf RCT have been previously described [7, 13], and are briefly summarized here (Current Controlled Trials Registration ISRCTN39432901). The study was conducted amongst women living in one relatively advantaged, and one relatively disadvantaged neighborhood within Melbourne, Australia. These two neighborhoods were randomly selected from among those in the top (advantaged) and bottom (disadvantaged) tertiles of the Australian Bureau of Statistics’ Socioeconomic Index for Areas [14] that were serviced by a Coles® supermarket (the second largest grocery chain in Australia) and located within 25 km of Deakin University. Two stores from within these neighborhoods were purposively selected for the study, and women who shopped at least once every 2 weeks within either of these target stores or any other Coles supermarket within a 5 km radius comprised the sampling pool. A random sample of 3000 eligible women received a mailout of a study recruitment package to their home address. A media release targeting local newspapers was also undertaken in the catchment areas to encourage additional participation. To be eligible to participate women had to shop regularly within Coles supermarkets (at least once every 2 weeks), hold or be willing to obtain a Coles store loyalty (FlyBuys) card, be between 18 and 60 years of age, the main household food shopper, able to speak, read and write in English, willing to have their sales data collected and analyzed, and be the only woman in their household enrolled in the study.

The study was conducted between May, 2011 and November, 2012. All procedures involving human subjects were approved by the Deakin University Faculty of Health Human Ethics Advisory Group (approval HEAG-H 12/10) and adhered to the principles of the Helsinki Declaration. All women provided written, informed consent prior to participating.

Intervention

Following completion of baseline surveys, participants were randomly allocated into one of four groups according to a computer-generated blocked randomization sequence produced and implemented by an independent statistician with a 1:1:1:1 participant allocation ratio: 1) Control (n = 161), 2) Price reductions (n = 161), 3) Skill-building (n = 160), or 4) Combined price reductions and skill-building (n = 160). Allocation concealment was ensured via the secure storage of the randomization sequence separately from the participant database, which was only accessible to the data manager and statistician. Women enrolled in the price reduction arm received 20% discounts on all FV (including all varieties of fresh, canned, dried and frozen FV, excluding fruit juice, frozen french Fries and fried potatoes), and on low-energy carbonated beverages and water. The skill-building group received a set of eight mailed skill-building newsletters including information, activities and recipes to promote purchase and consumption of FV and healthier beverages. Individuals in the combined group received 20% price discounts on FV and healthier beverages, along with all skill-building resources. The interventions took place over a 3-month period, with a further 6-month no-intervention follow-up phase. Women in the control group did not receive any interventions during the study period, and were asked to maintain their usual shopping habits.

Data collection

Participants completed self-report surveys at baseline (T1), post-intervention (T2, 3 months) and 6-months post-intervention (T3, 9 months). At baseline, participants reported sociodemographic characteristics including highest educational qualification, marital status and the number of children living in the home. To assess proportionate food purchasing, at all three time points participants reported the estimated proportion of their total groceries (How much of your total food and groceries are bought from Coles supermarkets?) and of their FV (How much of your fruit and vegetables are bought from Coles supermarkets?) they purchased at Coles (response options: less than a quarter, between a quarter and a half, between half and three quarters, more than three quarters, all of them). Changes in proportionate food purchasing from T1 to T2, and from T2 to T3 were estimated on the basis of these responses. For example, participants who reported purchasing 25-50% of their FV at Coles at T1 and 50-75% of their FV at Coles at T2 were deemed to have increased proportionate FV purchasing at Coles from T1 to T2.

Statistical analyses

Fisher’s exact (for small expected cell sizes) and χ² tests assessed differences among intervention groups in self-reported proportionate food purchasing at each time point. Multinomial logistic regression with robust standard errors was used to assess differences among intervention groups in change (classified as: increase, decrease, no change) in self-reported proportionate food purchasing from T1 to T2 and from T2 to T3. Data from all participants who completed any portion of the T1 (n = 642; 100% of those randomized), T2 (n = 619; 96% of those randomized) or T3 (n = 606; 94% of those randomized) surveys were included in these analyses, thus the sample size differed for each analysis.

All statistical tests were two-sided and the significance level was set at p < 0.05. Stata software (version 13; StataCorp LP, TX) was used for all statistical analyses.

Descriptive characteristics

Change in proportionate food purchasing

Study limitations and strengths

The current findings derive from self-reported data, however they are consistent with economic theories of human behavior, and are supported by regression analyses demonstrating directional correspondence between change in reported proportional, and objectively quantified FV purchasing (data not shown), as well as by qualitative comments provided during a prior process evaluation [12]. Moreover, identical questions were asked at all three time points, there was little missing data, and there is no reason to expect differential reporting of proportionate food purchasing over time.

Implications

The challenges associated with quantifying intervention-associated change in food purchasing described herein may be difficult to address given that modern environments offer ubiquitous access to food, and that incentives to alter food purchasing behaviors are inherent within the design of some supermarket-based interventions. The degree to which these and other potential biases arise will likely vary across studies according to factors such as the specific nature of each intervention, study location, and the participant population. Given that economic incentives are increasingly being deployed in an attempt to improve lifestyle behaviors, the current findings may also have broader relevance to other types of studies.

It is not possible to quantify to what extent shifts in food purchasing affected outcomes in the SHELf study. However, correspondence between purchasing and intake measures within the price reduction group provides reassurance that study-associated increases in fruit purchasing were not solely the result of displacement of proportionate fruit purchasing into study supermarkets. Moreover, that measured fruit purchasing increased in the combined group, a group in which significant displacement of proportionate FV purchasing was not reported is also instructive, as are qualitative comments from participants indicating that price discounts led them to purchase more FV overall. Authors of similar supermarket-based price interventions have employed sensitivity analyses [8], statistical adjustment [10], triangulation of measures [10, 11], and participant pledges to shop exclusively within study supermarkets [11] to avoid potential biases associated with displacement of food purchasing. The effectiveness of such strategies remains uncertain, however.

Study findings suggest that measurement of total food purchasing may be essential. These data could be obtained by asking participants to scan the bar codes of packaged foods brought into the home (e.g. analogous to the Neilsen Homescan Panel [18]) and to photograph unpackaged items, or by supplementing electronic purchasing data from study supermarkets with receipts from foods purchased elsewhere. However, just as individuals underreport food intake, households may also underreport their supermarket shopping [19, 20]. These additional measures might also deter study participation due to increased participant burden, although in one study participants deemed collection of food receipts a useful tool in helping them to purchase healthier foods [21]. Development of new, technology-based integrated data collection methods may simultaneously improve the validity of the data collected, while reducing participant burden.