Study design

The study was conducted from March to May 2018. Ethical approval was granted by the Office of Research Ethics at the University of Waterloo (ORE #22494).

An experimental marketplace is an approach commonly used in the field of behavioural economics and marketing to study actual consumer behaviour, and provides the opportunity to manipulate price and other variables of interest to assess their influence on consumers’ purchases [36, 37]. Participants are provided with a sum of money, and presented with multiple products available for purchase. If the participant does not spend the entire sum of money, they are permitted to keep the remainder, along with the product they selected. In this way, participants spend real money and incur a financial cost for their purchases, leading to more realistic product selections [36, 37].

Study protocol

Participants and recruitment

Participants aged 13 years and older were recruited using convenience sampling from large shopping centres in three Canadian cities (Kitchener, Waterloo, and Toronto) within the province of Ontario. Youth are an important subpopulation to include in diet-related research due to their higher consumption of nutrients of concern and differed interactions with tax and labelling policies compared to older populations [38,39,40,41]. Research assistants were stationed at booths in high-traffic areas in the shopping centres, and approached potential participants to ask if they were interested in participating in a study on food and beverage purchasing patterns. All interested participants were asked to provide their age prior to giving written informed consent and beginning the study. Additional written informed consent from a parent or guardian was required for all participants under 16 years; if a parent or guardian was not present, the shopper was not permitted to participate. Participants completed the study at the booth with the research assistant, immediately following consent.

Purchasing tasks

The experimental purchasing tasks were delivered in the format of a 5 (FOP label condition) × 8 (tax condition) between-within group experiment. A visual depiction of the purchasing task protocol is available in Additional file 1 (Figure S1). Participants were randomly assigned to one of five FOP label conditions. Within their assigned label condition, participants completed eight consecutive purchasing tasks, which each corresponded to a different tax condition. In each of the eight purchasing tasks, participants were shown a selection of beverage or snack products on a large (62.5 × 50 cm) laminated print-out, which was designed to replicate the appearance of a grocery or convenience store shelf (Fig. 1). A new print-out was shown for each purchasing task, reflecting the appropriate label and tax condition for that purchase. In the first five purchases, participants selected from 20 different beverage products. In the last three purchases, participants selected from 20 different snack food products. The order of the tax conditions was randomized within the five beverage tasks and within the three food tasks. At the end of the survey, the program randomly selected one of the eight purchasing tasks to be the actual purchase, and the participant received the product selected with that task.

Fig. 1 Example product shelf images showing two combinations of FOP and taxation conditions: a beverages with health star rating labels and tiered SD tax, b foods with high in labels and 20% sugar tax Full size image

Prior to each of the eight purchasing tasks, research assistants emphasized the following points to each participant: (1) they had a budget of $5.00 to purchase one item, (2) the labels may be different from what they’ve seen in the past, (3) the prices may have changed since the last task, and (4) they would receive their change from the $5.00 and the actual food or beverage product from one of the eight purchases. Research assistants were instructed to not engage in discussion or answer questions about nutrition, diet, or food policies. For each task, participants made their selection on an iPad after viewing the large shelf image. Participants did not know which purchase selection they would receive (along with any change from the $5.00) until the end of the experiment and were instructed to treat all eight tasks as real purchases.

Upon completion of the eight purchasing tasks, each participant was asked “In all of the previous purchasing tasks, did you notice any nutrition labels or symbols on the front of the food and beverage packages?”, with response options “yes”, “no”, “don’t know”, or “refuse to answer”.

Experimental conditions

Five FOP label conditions were tested, including two nutrient-specific labels and two summary indicator systems. The FOP label conditions were no label (control); a high in warning system labelling foods high in sugars, sodium or saturated fats; a multiple traffic light system (MTL) for sugars, sodium and saturated fats; a health star rating label; and a five-colour nutrition grade label (Fig. 2).

Fig. 2 Images of label conditions, excluding no label (control). From top to bottom: high in, MTL, health star rating, and nutrition grade Full size image

The high in warning system was modelled after early iterations of Health Canada’s proposed FOP warning symbols for foods high in sugars, sodium and saturated fats, with nutrient thresholds based on Health Canada’s proposed guidelines [33]. The MTL system was loosely based on the UK’s voluntary traffic light labelling system [42]. To ensure comparability with the high in system, MTL labels were displayed only for sugars, sodium and saturated fats. Criteria for ‘high’, ‘medium’ and ‘low’ were based on the UK’s regulations [42]; however, in two cases in which the MTL was incongruent with the high in warning labels, the MTL was adjusted to match Health Canada high in warnings. The health star rating label design and scoring system were modeled after Australia and New Zealand’s Health Star Rating system [43]. The nutrition grade system was designed based on France’s Nutri-Score system [44]. Due to differences in criteria and scoring algorithms across the two summary indicator systems, the nutrition grade scores were adjusted to match those of the health star rating for the purposes of this study (i.e., 0.5 to 1 stars = ‘E’ nutrition grade; 1.5 to 2 stars = ‘D’; 2.5 to 3 stars = ‘C’; 3.5 to 4 stars = ‘B’; 4.5 to 5 stars = ‘A’). The FOP labels were not applied to fresh fruits or vegetables (i.e., the apple and carrots) to align with most real-world FOP nutrition labelling systems. See Additional file 1: Table S1 for details on the FOP labels assigned to all food and beverage products.

Five beverage-based sugar tax conditions (Table 1) were tested: no tax (control), a 20% ad valorem tax on SSBs (20% SSB), a 20% ad valorem tax on sugary drinks (20% SD), a tiered specific tax on SSBs (tiered SSB), and a tiered specific tax on sugary drinks (tiered SD). Beverages were categorized as SSBs if they contained added sugar, as previously defined [8]. Beverages were categorized as sugary drinks if they contained free sugar, as defined by WHO [7]. 20% SSB and 20% SD taxes were applied to beverages containing more than 5 g of added or free sugars (respectively) per 100 ml. Tiered SSB and tiered SD taxes applied a 10% price increase to beverages containing 5 to 8 g, or a 20% price increase to beverages containing more than 8 g of added or free sugars per 100 ml (modelled after the SSB tax implemented in the UK [45]). The study also tested three food-based sugar tax conditions: no tax (control), a 20% ad valorem tax on high-sugar foods (20%), and a tiered specific tax on high-sugar foods (tiered). Here, the 20% tax was assigned to all foods containing more than 10 g of total sugars per 100 g; the tiered tax applied a 10% price increase to foods containing more than 10 to 20 g of total sugars per 100 g, and a 20% price increase to foods containing more than 20 g of total sugars per 100 g. The SSB and SD tax formats were not applicable to the snack food purchases. Additional file 1 provides details on how the taxes were assigned to each product (Table S2), as well as nutrition information of all products (Table S3).

Table 1 Summary of sugar tax conditions Full size table

Sociodemographic measures

Following the purchasing tasks and using the iPad, participants provided information on their previous 7-day sugary drink consumption using a brief single-item beverage frequency measure (“During the past 7 days, how many sugary drinks did you have?”) [46]. Participants also reported their age, sex, ethnicity, education, income adequacy (“Thinking about your total monthly income, how difficult or easy is it for you to make ends meet?”), and height and weight. Self-reported height and weight were used to calculated body mass index (BMI), which was categorized into “underweight”, “normal weight”, “overweight” and “obese” using the WHO thresholds [47]. BMIs for participants 19 years of age or younger were calculated using growth charts as recommended by CDC and WHO guidelines [48, 49]. All survey items were completed after the experiment to minimize influence on participants’ behaviours in the purchasing tasks.

Remuneration

After participants had completed all survey items, the survey program randomly selected one of their eight purchasing tasks. Research assistants gave participants their actual food or beverage product and their change from the $5.00 corresponding to that purchase.

Outcome variables

Four primary outcomes were explored: grams of sugars purchased, milligrams of sodium purchased, grams of saturated fats purchased, and number of calories purchased per task. All four outcomes were measured based on the total amount of sugars, sodium, saturated fats, or calories in the entire package of the product selected in each purchasing task; all products were single-serving sized and expected to be consumed in one sitting. All four nutrient outcomes were assessed for both foods and beverages. Although sugars and calories were the principal nutrients of concern for the beverages, several presented beverages contained substantial amounts of sodium (i.e., sports drinks) and saturated fat (i.e., milks). The impacts of the sugar-based taxes on purchasing were explored for all four nutrient outcomes (including sodium and saturated fats) so as to capture any potential ‘spillover’ effects of sugar-based taxes [50]. Secondary outcomes included potential interaction effects between FOP labelling and taxes, as well as participants’ reported noticing of the FOP nutrition labels.

Analyses

Chi square tests (for categorical variables) and one-way ANOVAs (for linear variables) were used to test for sociodemographic differences between experimental conditions (FOP label format). Separate two-tailed repeated-measures ANOVAs were used to investigate the effects of labelling and tax on the amount of sugars, sodium, saturated fats, and calories purchased; foods and beverage purchases were analysed separately, resulting in a total of eight ANOVAs. Repeated-measures ANOVAs were used to account for the repeated nature of the purchasing tasks. All ANOVAs included a tax condition × label condition interaction. In the case that an ANOVA violated the assumption of sphericity [51], Greenhouse-Geisser corrections [52] were applied to the results. All statistical analyses were conducted using SPSS software (version 25.0; IBM Corp., Armonk, NY; 2017). The significance threshold was set at 0.05 for all tests. No adjustments for multiple comparisons were applied. It has been suggested that experiments based on distinct, conceptually sound a priori hypotheses and which have discrete, separate experimental arms should not apply adjustments for multiple comparisons [53,54,55]. Results should be interpreted by the strength and magnitude of the effect sizes, p-values, and confidence intervals.