University of Amsterdam
Graduate School of Communication
Research Master’s program Communication Science Research Master’s thesis
Starve Your Distractions, Feed Your Focus: Effects of In-Store Distraction and Satiety on Attention Towards Healthy Food and Healthy Food Choice
Anne Vos
Student number: 10616810 Supervisor: dr. G.J. de Bruijn 01-02-2019
Abstract
In the so-called obesogenic environment, people are confronted with many external and internal influences that negatively affect the healthful dietary choices they make, which contributes to the worldwide public health problem of obesity. The supermarket is a place where people are particularly affected by these influences and often succumb to the
temptation of unhealthy foods, as hedonic food cues calling attention to palatable foods and other shopping distractions impairing self-control performance are ever-present. This study is the first to investigate how non-food-related distractions in store environments (i.e., high crowdedness and loud music) and internal temptation (i.e., a state of low satiety) influence attentional bias towards healthy food and healthy food choice in individuals who were instructed to exclusively focus attention on healthy food products while being exposed to a 360-degree virtual reality supermarket environment.
A 2 (in-store non-food distraction: low versus high) x 3 (satiety state: low, moderate, high) laboratory experiment was conducted among 158 students to examine these effects. Findings demonstrated no significant main effects of in-store non-food distraction or satiety state on attentional bias towards healthy food or healthy food choice. In addition, results indicated no significant interaction effect between non-food-related distraction and satiety on food choice. However, findings did reveal a significant interaction between in-store non-food distraction and satiety state on participants’ attentional bias towards healthy food. This interaction effect appeared to be driven by the experimental group of participants who were moderately satiated. Contrary to expectations, however, their attentional bias towards healthy food was significantly higher after exposure to a high level of in-store distraction than after exposure to a low level of distraction. This study thus highlights a significant relationship between non-food-related distraction in store environments, satiety state, and grocery shoppers’ attention for healthy foods. Recommendations how future studies can further explore the exact nature of this relationship and its apparent discrepancy with earlier studies in the field are discussed.
Starve Your Distractions, Feed Your Focus: Effects of In-Store Distraction and Satiety on Attention Towards Healthy Food and Healthy Food Choice A rapidly growing public health problem that is affecting countries worldwide in terms of prevalence and detrimental health effects is obesity. It is estimated that the number of obese adults has nearly tripled globally over the past three decades, which means that currently over half a billion adults are obese (World Health Organization, 2018). This is a worrisome trend, as obesity is a major risk factor for associated comorbid conditions such as cardiovascular disease, several cancers, and diabetes type II (World Health Organization, 2018). It is expected that if the obesity epidemic continues unabated, it will become the biggest cause of premature death in Western societies (Agha & Agha, 2017). Therefore, effective approaches to prevent and reduce obesity are urgently needed.
One characteristic of modern Western environments that is attributable to the increased prevalence of obesity, is the all-pervasive presence of readily available densely calorific foods and associated food cues. It has been established that the so-called obesogenic environment with its wide availability of inexpensive, energy dense foods and related food cues acts as a trigger for overconsumption of unhealthy food (Boyland et al., 2016; Hill, Wyatt, Reed, & Peters, 2003). Therefore, the key to counteracting the obesity problem may lie in determining effective ways to help people cope with the obesogenic environment and manage temptation from the palatable but unhealthy foods that surround them.
An environment where people often succumb to the temptation of unhealthy foods is the supermarket. According to a study by Ravensbergen, Waterlander, Kroeze, and Steenhuis (2015) about the proportion of healthy and unhealthy food promotions by Dutch
supermarkets, 70% of food promotions in stores could be categorized as unhealthy.
Marketing of food products through packaging, displays, or labels can strongly affect in-store decisions (Cohen & Babey, 2012; Wang & Lang, 2015). Furthermore, research has
demonstrated how psychological temptations in store environments in the form of hedonic food cues can non-consciously influence people to choose unhealthy palatable foods (Papies & Hamstra, 2010), particularly when people are less satiated. Tal and Wansink (2013) for instance found that less satiated shoppers purchase relatively more densely calorific foods compared to low-caloric food.
While various studies have analyzed the effects of food-related cues on food choice, less attention has been given to exploring how in-store elements of supermarkets that are not food-related impact food purchases. During a shopping trip, however, grocery shoppers are
exposed to various external non-food influences that affect self-control performance and subsequent behavior. For instance, high perceived crowding in terms of the number of shoppers that is present in a store environment at any one time has been shown to increase impulse purchasing (Mattila & Wirtz, 2008). Another atmospheric feature of supermarkets that has been related to unplanned purchasing is loud music, through its capacity to modulate shoppers’ physiological arousal (Mattila & Wirtz, 2001; Turley & Milliman, 2000). Since few studies have examined the influence of such in-store non-food distractions on grocery shoppers’ dietary choices, the current study aims to contribute to filling this gap in scientific knowledge. In addition, this study is the first to empirically investigate how in-store non-food distractions interact with internal distracting influences, such as various states of satiety.
When a grocery shopper has the intention to only purchase healthy foods during a shopping trip, this shopper first needs to be able to focus attention towards the healthy products that are available in the store and avoid unhealthy temptations. However, this is not an easy task, as unhealthy foods are more likely to automatically capture attention due to their rewarding and palatable qualities (Polivy, Herman, & Coelho, 2008). This attentional bias towards unhealthy food has been demonstrated to be especially strong when people are less satiated (Piech, Pastorino, & Zald, 2010). In a similar way, in-store non-food distractions such as high crowding or loud music could make it difficult for shoppers to focus attention on the healthy products they intended to purchase and not be tempted by unhealthy foods.
Therefore, better comprehension of the combined effects of in-store non-food distractions and internal distractions on attention for healthy and unhealthy foods in store environments is crucial in advising the development of approaches to shape behavioral change.
In brief, the goal of the current study is to investigate the effects of non-food-related distraction and satiety on grocery shoppers’ attention towards healthy food and healthy food choice in store environments. This study employed an immersive and highly realistic headset-based virtual reality (VR) supermarket environment to explore these effects. Specifically, the non-food-related distractions that were examined concern the level of crowdedness and music volume in a store environment. The central research question this study intended to answer is: How do in-store non-food distraction (i.e., crowdedness and background music volume) and internal distraction (i.e., satiety state) influence attention towards healthy food and healthy food choice in grocery shoppers who are instructed to focus exclusively on healthy products in a virtual reality supermarket environment?
Theoretical Framework Self-Control and Dual Processing Theories
It is well-known that even when people have the intention of sticking to a healthy eating plan, they often fail to disregard temptations of short-term gratifications that hinder goal pursuit. For example, a dieter might be invited to a birthday party where various sugary and fatty snacks are put in front of him, and subsequently find himself unable to resist the temptation of eating some, thereby compromising his long-term goal for a healthier weight or lifestyle. In these kinds of situations, we need self-control to help us resist giving in to
impulses for the purpose of long-term goal pursuit.
Dual processing theories suggest that the interaction between two distinct modes of information processing determine the success or failure of self-control: impulsive processing that is automatic, unconscious, and fast, versus reflective processing that is deliberative, conscious, and slow (Hofmann, Friese, & Wiers, 2008; Kahneman, 2011; Strack, & Deutsch, 2004). Research has demonstrated that even though the majority of day-to-day behaviors is guided by impulsive, automatic processing (e.g., Cialdini, 2008; Dijksterhuis, Smith, van Baaren, & Wigboldus, 2005), it is subject to cognitive biases and decision-making errors (Kahneman, 2011). When cognitive capacity is available, reflective processing therefore interacts with the impulsive processing system to override its automatic impulses (Rothman, Sheeran, Wood, 2009).
Moderation by the reflective processing system is required when a situation arises in which short-term gratifications conflict with long-term goals. For instance, the sight of the snacks that were presented to the dieter at the birthday party might trigger the impulsive processing system to automatically generate the impulse to indulge. Yet, if the reflective processing system can override this impulse to resist temptation and stick to the diet, then the outcome would be self-control success. However, as the reflective processing system requires the availability of sufficient cognitive capacity, it is not permanently active and does not always override the automatic responding mode of the impulsive processing system (Rothman et al., 2009). In that case, long-term goals tend to be disregarded in favor of satisfying short-term gratifications.
The Influence of Hedonic Food Cues
Research has shown that whenever people rely on their impulsive processing system, their susceptibility to heuristic influences is heightened. A heuristic is a simple inferential rule that simplifies decision-making by reducing the cognitive capacity required to formulate judgments or decisions (Cialdini, 2008). For example, Pohl, Erdfelder, Hilbig, Liebke, and Stahlberg (2013) demonstrated that impulsive processing was related to an increased use of the recognition heuristic (i.e., the tendency to believe a familiar option is the best option) in a
decision-making context. Similar findings were demonstrated in a food choice context by Salmon, Fennis, de Ridder, Adriaanse, and de Vet (2014), who found that individuals who depended on impulsive processing made more healthy food choices when a social proof heuristic that informed them about the preference of most others for the healthy option was available, compared to when no heuristic was presented.
Heuristics exist in many types and forms, and are also abundantly present in store environments in the form of hedonic food cues. Examples of hedonic cues that indicate the tempting qualities of food are its sight, smell, packaging, or the way it is displayed. Store environments are often purposefully designed with hedonic cues to influence shoppers’ purchase probability. Supermarkets, for example, have been known to use bread aroma to guide shoppers towards the bread department and increase sales of bread (de Wijk et al., 2018). Many studies have revealed that exposure to hedonic food cues in stores indeed affects food choice (e.g., Cohen & Babey, 2012; Papies & Hamstra, 2010; Wang & Lang, 2015). For example, Papies and Hamstra (2010) conducted a study in which it was demonstrated that the smell of grilled chicken in a store triggered overeating of free meat snacks in shoppers who had a dieting goal. This overeating was subsequently reduced when shoppers were first exposed to an environmental cue in the form of a poster that reminded them of dieting before entering the store.
Most food cues in supermarkets are related to unhealthy palatable foods. For example, a cross-sectional study by Ravensbergen and colleagues (2015) revealed that Dutch
supermarkets promoted unhealthy foods more heavily than healthy foods. In addition, their study showed that shoppers had to buy more unhealthy products to receive the promoted discount when the promotion concerned unhealthy foods, which further reinforced the purchasing of unhealthy products. This has also been found in a qualitative study by
Hollywood and colleagues (2013), where participants indicated that they were often guided by supermarket promotions to decide what to buy, even if that meant that they would mostly resort to unhealthy products as these are on offer more frequently. Hedonic food cues in supermarkets thus make it difficult for shoppers to do healthful grocery shopping. In-Store Non-Food Distractions
Although a lot of research has been devoted to investigating the effects of hedonic food cues in store environments, a great deal less is known about the influence of in-store characteristics that are non-food related, such as lighting, noise, or crowding, on dietary choices. However, these types of environmental influences could nevertheless distract grocery shoppers from their long-term health goal and subsequently affect their shopping
decisions. As this topic has largely been ignored in prior research examining environmental influences in grocery outlets, this study aims to contribute to the literature by exploring how in-store non-food distractions impact grocery shoppers’ food-related behaviors. Specifically, the distracting effects of crowding and background music are investigated in the present research.
Despite the lack of prior research regarding the effects of in-store environmental features on shoppers’ dietary choices specifically, a fair number of earlier empirical studies have examined atmospheric influences on other consumer behaviors in retail settings (e.g., Turley & Milliman, 2000). Particularly marketing researchers have analyzed the ways in which consumers are affected by environmental characteristics of the point of purchase. Most studies of retail environments have analyzed the impact of various atmospheric variables, such as temperature, cleanliness, and music, on a range of attitudinal and behavioral
outcomes such as store satisfaction, time spent in the store, and impulse purchasing (Turley & Milliman, 2000).
With regard to the effects of in-store crowding, Mattila and Wirtz (2008) for instance found that perceived crowding influenced unplanned buying in retail outlets. They theorized that high levels of crowding lead to an over-stimulating store environment, which reduces shoppers’ self-control performance and thereby enhances impulse buying. This mediating role of control in the effects of crowdedness on consumer behaviors has been confirmed in other studies as well (Dion, 2004; van Rompay, Galetzka, Pruyn, & Garcia, 2008). Beyond effects of crowding, Mattila and Wirtz (2001) found that background music additionally provides potential for increased unplanned purchasing in retail environments. A study by Morrison, Gan, Dubelaar and Oppewal (2011) corroborates this, with findings indicating that high volume music caused increased arousal levels in shoppers, which in turn enhanced time and money spent in the retail outlet. Research thus suggests that high levels of in-store crowding and background music negatively affect grocery shoppers’ self-control capacity, which leads to subsequent impulsive purchasing decisions.
The Role of Executive Functioning
In order to better understand self-control capacity in situations where people are confronted with environmental distractions and automatic impulses, it is necessary to recognize the mechanisms that are involved in self-control success and resistance to
temptation. One mechanism that is important for impulse control which has been identified by self-regulation research, is executive functioning (Hofmann, Schmiechel, & Baddeley, 2012). Executive functions are cognitive processes that individuals use in the active pursuit
of long-term goals, including health-related goals such as maintaining a healthful diet. There are various types of executive functions, which can be categorized into three distinctive but related domains (Miyake et al., 2000): (i) working memory, representing the ability to hold, add, and remove information from one’s mind, (ii) inhibitory control, representing the ability to suppress dominant cognitive, affective, and behavioral impulses, and (iii) cognitive
flexibility, representing the ability to shift between tasks or mental perspectives (Appelhans, French, Pagoto, & Sherwood, 2016; Jones, Hardman, Lawrence, & Field, 2018). Recent research has demonstrated that low executive functioning is related to greater susceptibility to automatic impulses. For example, Hofmann, Friese, and Roefs (2009) showed that executive functions moderated the relationship between automatic affective reactions towards candy and subsequent candy consumption, such that automatic affective reactions had a weaker influence on candy consumption for participants who had efficient executive functioning.
Importantly, studies have shown that executive functioning capacity is amenable to change by means of cognitive training. For example, research suggests that working memory capacity can be improved by performing adaptive training tasks (Klingberg, 2010). Houben, Dassen, and Jansen (2016) tested the effectiveness of working memory training in overweight individuals, and demonstrated that working memory training effectively increased
participants’ self-regulation by shielding dieting goals from distraction by unhealthy food-related thoughts and emotions. An important element of working memory training aimed at improving control over impulses related to food and eating, is executive attention training (Hofmann et al., 2009). Executive attention is the ability to control attention to ongoing cognitive processes, and is thus essential for blocking distractions or other sources of conflict from the focus of attention (Engle, 2002). Research by Hofmann, Gschwender, Friese, Wiers, and Schmitt (2008) indicated that low executive attention capacity is related to a stronger influence of impulses on behavior.
In supermarket environments, instructing grocery shoppers to use a shopping list is an example of a task that can enhance executive attention by serving as a memory aid and a guide to resolve conflicts between healthy and unhealthy product options (Au, Marsden, Mortimer, & Lorgelly, 2013). However, to date no research has investigated whether such task-focused shopping can shield grocery shoppers from the influence of in-store non-food distractions, such as high crowdedness and loud background music. Therefore, this study examined the effects of these in-store non-food distractions on shoppers’ ability to remain task-focused in a VR supermarket environment. Specifically, the executive attention task that was utilized in this study instructs grocery shoppers to exclusively focus their attention on the
healthy foods that are available in the VR supermarket. In light of the described findings from prior research, it is expected that focusing on this task and controlling attention
exclusively to healthy foods will be more difficult in supermarket environments that contain a high level of in-store non-food distractions compared to those that contain a low amount of distractions. In addition, it is assumed that a high level of non-food-related distractions will make it harder for grocery shoppers to purchase healthy products instead of unhealthy ones. Therefore, the following hypotheses are posited:
H1. Exposure to a high level of in-store non-food distraction (i.e., crowdedness and music) will result in a lower attentional bias towards healthy food compared to exposure to a low level of in-store non-food distraction.
H2. Exposure to a high level of in-store non-food distraction (i.e., crowdedness and music) will result in a lower proportion of healthy food choice compared to exposure to a low level of in-store non-food distraction.
The Impact of Satiety
Not only external factors such as hedonic food cues or in-store non-food distractions can undermine grocery shoppers’ self-control performance – internal distractions have also been shown to impair the ability to resist unhealthy temptations. For instance, visceral states such as fatigue, thirst, and pain can negatively influence the ability to ignore automatic impulses, as they motivate people to quickly satisfy the physical needs of the body with little consideration of how their behavior may hamper long-term goal pursuit (Loewenstein, 1996).
In relation to food and eating behaviors, the visceral state of hunger, or low satiety, has often been found to impact self-regulation outcomes. Evidence exists that people who are less satiated experience a heightening of the visual attentional bias towards food-related stimuli and food seeking behavior (Raynor & Epstein, 2003; Stafford & Scheffler, 2008). In addition, researchers found that people who are less satiated have a particularly strong preference for consuming palatable, energy dense foods, as they perceive this type of
unhealthy food as more immediately rewarding (Tuorila, Kramer, & Engell, 2001; Siep et al., 2009).
The negative consequences of making decisions when less satiated have also been demonstrated in shopping environments. For instance, Tal and Wansink (2013) investigated people’s food choices in a grocery store when food-deprived, and their findings indicated that food-deprived participants chose less low-calorie products and relatively more highly caloric foods. Additionally, Nederkoorn, Guerrierie, Havermans, Roefs, and Jansen (2009) analyzed the effect of satiety on purchases in an online supermarket, and demonstrated that participants
who were both less satiated and impulsive purchased the highest total amount of calories, and particularly unhealthy snack foods.
From these findings, it seems evident that low satiety negatively impacts the ability for impulse control and thereby hinders the pursuit of long-term health-related goals. It can therefore be expected that a state of low satiety negatively affects task-focused grocery shoppers’ ability to control attention exclusively to healthy foods and make healthful food choices. Hence, it is hypothesized that:
H3. Being in a state of low satiety will result in a lower attentional bias towards healthy food compared to a state of moderate or high satiety.
H4. Being in a state of low satiety will result in a lower proportion of healthy food choice compared to a state of moderate or high satiety.
In addition, it can be expected that the internal distraction of low satiety interacts with in-store non-food distraction in its effect on attention for healthy food and healthy food choice in a supermarket environment. When grocery shoppers experience distraction from in-store non-food influences, their ability to exclusively focus attention on healthy products and make healthful food choices is likely to be even more strongly affected when being in a state of low satiety. Therefore, the following hypotheses are proposed:
H5. Exposure to a high level of in-store non-food distraction (i.e., crowdedness and music) will result in a lower attentional bias towards healthy food compared to exposure to a low level of in-store non-food distraction, and this effect will be stronger when being in a state of low satiety compared to a state of moderate or high satiety.
H6. Exposure to a high level of in-store non-food distraction (i.e., crowdedness and music) will result in a lower proportion of healthy food choice compared to exposure to a low level of in-store non-food distraction, and this effect will be stronger when being in a state of low satiety compared to a state of moderate or high satiety.
Method Design
A 2 (in-store non-food distraction: low versus high) x 3 (satiety: low, moderate, high) experimental between-subjects design was employed, with satiety as
quasi-experimental factor. Participants
Ethical approval of the study was obtained from the local ethical committee.
Amsterdam and by handing out flyers on campus. The requirements to participate in the study were to be at least 16 years of age and to be a student at the Faculty of Social and Behavioral Sciences at the University of Amsterdam. In return for their participation, students received either 5 euros or 2 course credits. In total, 163 participants completed the experiment. Five participants were excluded from the final sample for several reasons. One participant indicated that she had recognized the supermarket in which the stimulus material was filmed, and one participant indicated that she had prior knowledge about the purpose of the study. Another three participants were found to have too many missing data points for one of the experimental tasks. To ensure these circumstances would not influence the results, these participants were excluded from the sample, leaving a final sample of 158 participants. The final sample consisted mostly of females (74.7%) with a mean age of 21.1 (SD = 3.17) and a mean body mass index (BMI) of 21.8 (SD = 2.99), which is within the normal range (i.e., 20 – 25; Garrow & Webster, 1985).
Stimulus Material
The stimulus material was a 4-minute VR video in which the viewer is walked around the aisles of a small supermarket. The videos were filmed in a store of a Dutch supermarket chain (SPAR University) that uses a grab and go principle aimed at students. Because of the “to go” character of this store, the majority of its range of products consists of food and drinks that can be consumed on the go, such as readymade meals, candy bars, canned drinks, and sliced fruit and vegetables. Both videos began at the entrance of the store. Subsequently, the 360-degree camera followed a predefined route along all the different aisles of the supermarket, meanwhile halting for a moment at each aisle to capture its shelves with products. Once the camera had been walked around the entire store to film all its products, the videos ended at the supermarket’s checkout. An overview of the store, the content of its aisles, and the route that was walked in the VR videos is included in Appendix A.
Prior to watching the VR video, participants were given an executive attention task. Participants were told they could look around the VR supermarket quite freely, but were instructed to exclusively pay close attention to the healthy food products in the supermarket while watching the video. Healthy products were defined as products that generally contain little fat and calories, such as fruit and diet soft drinks (see Appendix B for the full set of instructions).
Independent Variables
In-store non-food distraction. In-store non-food distraction concerned the extent to which non-food-related factors that made it difficult for participants to focus their attention
were present in the VR store environment, and served as a between-subjects variable. This variable was manipulated through exposure to one of the created VR videos. The variable could take on two values: low or high distraction. Two characteristics of the supermarket in the videos were systematically varied to create a low distraction and a high distraction condition: the level of crowdedness in the supermarket and the volume of the music that was played in the store. Firstly, the level of crowdedness was systematically varied across
conditions. In the low distraction condition video, there were only three shoppers present in the store, of which one left the supermarket halfway through the video. The few shoppers that were present were never standing in the same aisle as the camera. In the high distraction condition video, there were ten adult shoppers and a young child present in the store. The shoppers were often standing in the close vicinity of the camera. Secondly, the volume of the music that was played in the store was systematically varied across conditions. In the low distraction condition video, the volume level of the background music in the store was set at a low level of approximately 55 decibels. In the high distraction condition video, the volume level of the background music was set at a high level of approximately 75 decibels. The song that was played in the store, as well as the route that was walked with the camera was
identical across the two conditions, so that any effects could be fully attributed to a difference in the presence of distracting factors. Screenshots of the two videos are included in Appendix C.
Satiety. Satiety concerned the extent to which participants were in a state of being completely satisfied in terms of food and drink, and served as a quasi-experimental variable. The variable could take on three values: low, moderate, or high satiety. This variable was measured by asking participants: “How satiated are you currently feeling? By satiated we mean the feeling of being fully satisfied in your appetite.” Response options were on a continuous scale ranging from 0 to 100. The tertile split procedure was used to create the conditions of low, moderate, and high satiety. A tertile split procedure was chosen instead of a median split procedure, as trichotomization has less drawbacks compared to
dichotomization, such as a loss of information and statistical power (Gelman & Park, 2009). A score of 40 or lower on the satiety scale led to allocation to the low satiety condition (N = 54, M = 23.65, SD = 10.97), a score between 41 and 70 led to allocation to the moderate satiety condition (N = 49, M = 56.84, SD = 8.25), and a score of 71 or higher led to allocation to the high satiety condition (N = 55, M = 87.11, SD = 9.26).
Attentional bias towards healthy food. Attentional bias towards healthy food was measured by means of a visual dot probe task that was performed on a desktop computer. The main stimuli comprised 10 pairs of pictures of foods that were available in the store and shown in the VR videos, which were collected from the website of the supermarket. The pairs consisted of 10 pictures of healthy foods (e.g., banana, tomatoes, or carrots), which were paired with 10 pictures of unhealthy foods (e.g., chocolate, potato chips, or candy bar). An additional 10 pairs of non-food pictures were used as fillers to reduce habituation to food stimuli, which depicted household objects that were unrelated to food (e.g., calculator, headphones, or umbrella). Each picture pair was presented 4 times during the task.
Each trial started with a fixation cross, which was displayed for 500 milliseconds (ms) in the center of the computer screen, followed by a pair of pictures presented side by side for 1000 ms. Participants were instructed to first look at the fixation cross at the start of each trial, and then look at the two pictures. Subsequently, the two pictures disappeared and a probe stimulus (an “X”) appeared in the location of one of the preceding pictures.
Participants were instructed to respond as quickly as possible by pressing one of two keys on the computer keyboard to indicate the position of the probe: the “E” key if the probe was on the left and the “I” key if the probe was on the right. Participants had 1000 ms to press a key before the probe disappeared and the next trial began. There were 10 practice trials, followed by the main 80 trials, which consisted of 40 trials with unhealthy – healthy food picture pairs intermixed with 40 non-food filler trials in random order.
The dot probe task was used to compare the mean reaction times (RTs) of responding to a probe replacing a healthy food picture versus a probe replacing an unhealthy food picture. Data from practice and filler trials were not used in the analyses. Three participants were excluded from the analyses, as they had exceptionally high rates of missing data (>50%) due to errors. For the remaining participants, trials in which they had given incorrect
responses were not used in mean RT calculations (2.6% of data). In addition, trials with RTs of less than 100 ms were removed, as these indicate that a participant had pressed a key before the onset of the probe (less than 0.5% of data). Attentional bias scores were calculated for each participant by subtracting the mean RT for probes replacing unhealthy food pictures from the mean RT for probes replacing healthy food pictures, following previous work (Brignell, Griffiths, Bradley, & Mogg, 2009; Castellanos et al., 2009; Hou et al., 2011). Therefore, an attentional bias towards healthy food is indicated by a positive bias score.
Healthy food choice. Healthy food choice was measured by means of a booklet in which participants were instructed to prepare a shopping list. The booklet was printed in
color and included a set of instructions on the front page. Participants were instructed to imagine that they had a busy working day ahead of them for which they already had
breakfast, lunch, and dinner, but no snacks. Their task was to complete a shopping list with snacks to eat during this day by ticking snacks that were depicted on the next pages of the booklet. Participants were explicitly asked to choose snacks as they would also do in real life. To ensure that participants would choose snacks that they would genuinely eat in real life, they were informed that interested participants could join a raffle to win the products
specified on their shopping list. The next pages of the booklet included pictures of foods that were available in the supermarket and shown in the VR video, which were collected from the website of the supermarket. These foods were selected from three different categories of healthy and unhealthy snack foods: fruit and vegetables, chocolates, and potato chips. The order in which pictures of the different snack categories were presented in the booklets was randomized. The final page of the booklet contained two questions asking participants for the total cost of their chosen snacks, and their email address in case they would like to join the raffle.
The completed booklets were coded by counting the total number of snacks that participants had chosen and tallying how many of these were healthy and unhealthy snacks. Subsequently, the proportion of healthy food choice was calculated by dividing the number of chosen healthy snacks by the total number of chosen snacks, and multiplying this value by 100 in order to reach a percentage.
Control variables. Demographic control variables that were included measured gender, age, length, and weight. BMI was calculated using the formula [weight] kg / [height] m2 (Garrow & Webster, 1985) from self-reported data.
Manipulation Checks
A manipulation check was carried out to ensure that participants had processed the stimulus material as intended. To check whether the level of crowdedness and the volume of the music in the VR supermarket video were perceived to be either low or high, participants were asked to what extent they agreed with these statements: “I thought it was busy in the supermarket” and “The volume of the music in the supermarket was loud”. Response options were on a 7-point scale (1 = “strongly disagree”, 7 = “strongly agree”).
Procedure
The study was conducted at the University of Amsterdam in several lab cubicles that only contained a desk with a computer and an office chair. Upon arrival at the laboratory, participants were randomly assigned to a cubicle related to one of the distraction conditions.
Participants provided informed consent and then completed the first part of a questionnaire with measures for demographics, satiety, and filler questions regarding how often they go grocery shopping, how much they usually spend, and how often they buy various foods. Subsequently, participants were handed instructions for the executive attention task related to watching the VR video (see Appendix B). Once participants had read the instructions, the experimenter handed them a VR headset with built-in headphones and an attached
smartphone. The experimenter assisted participants with calibrating the headset and started the video once the image was in focus. Once the video had finished playing, the experimenter collected the headset and handed participants the material for the next task. It was
randomized whether participants first completed the dot probe task or the shopping list booklet. For the dot probe task, participants were handed instructions and once they had read these, the task was started by the experimenter. For the shopping list task, participants were handed a booklet and a pen to complete it. After both tasks were administered, participants completed the second part of the questionnaire with measures for the manipulation checks. Finally, participants were debriefed and thanked for their participation.
Results Manipulation Checks
Results of a one-way multivariate analysis of variance (MANOVA) with in-store non-food distraction as the independent variable and the perceived level of crowdedness and the perceived level of music volume in the VR supermarket video as the dependent variables showed a significant difference in the intended direction between the scores in the low distraction and the high distraction condition. Participants considered the supermarket significantly more crowded in the high distraction condition (M = 4.16, SD = 1.44) than in the low distraction condition (M = 1.91, SD = 1.08), F(1,156) = 124.51, p < .001, ηp2 = .44. Furthermore, participants considered the music in the high distraction condition significantly louder (M = 3.58, SD = 1.52) than in the low distraction condition (M = 2.52, SD = 1.27),
F(1,156) = 22.85, p < .001, ηp2 = .13. Randomization Checks
A chi-square test and a two-way MANOVA showed that the experimental groups did not significantly differ with respect to gender (2
(2) = 3.33, p = .189), age (F(2, 152) = 0.30,
p = .744, ηp2 = .00), and BMI (F(2, 152) = 0.22, p = .802, ηp2 = .00). Hence, randomization was successful in the experiment and no control variables were included as covariates in the subsequent analysis.
Hypothesis Tests
To test hypotheses 1 through 6, a two-way MANOVA was conducted with the between-subjects factors in-store non-food distraction (i.e., low, high) and satiety (i.e., low, moderate, high) as the independent variables, and the attentional bias towards healthy food and the proportion of healthy food choice as the dependent variables. The results of the analysis are discussed in the following paragraphs.
In-Store Non-Food Distraction Effects
The first hypothesis stated that exposure to a high level of in-store non-food
distraction will lead to a lower attentional bias towards healthy food compared to exposure to a low level of in-store non-food distraction. Results indicated that the main effect of in-store non-food distraction on attentional bias towards healthy food was not significant, F(1, 152) = 0.02, p = .898, ηp2 = .00. This means that there was no empirical support for H1.
The second hypothesis stated that exposure to a high level of in-store non-food distraction will lead to a lower proportion of healthy food choice compared to exposure to a low level of in-store non-food distraction. The findings showed no significant main effect of in-store non-food distraction on proportion of healthy food choice, F(1, 152) = 1.63, p = .203, ηp2 = .01. Therefore, H2 had to be rejected.
Satiety Effects
In the third hypothesis, it was proposed that being in a state of low satiety will lead to a lower attentional bias towards healthy food compared to being in a state of moderate or high satiety. The results showed that the main effect of satiety on attentional bias towards healthy food was not significant, F(2, 152) = 0.55 p = .576, ηp2 = .01. Therefore, the results did not support H3.
The fourth hypothesis stated that being in a state of low satiety will lead to a lower proportion of healthy food choice compared to being in a state of moderate or high satiety. Findings indicated that the main effect of satiety on proportion of healthy food choice was not significant, F(2, 152) = 1.10, p = .337, ηp2 = .01. This means that there was no empirical support for H4.
Interaction Effects
In the fifth hypothesis, it was posited that exposure to a high level of in-store non-food distraction will lead to a lower attentional bias towards healthy non-food compared to exposure to a low level of in-store non-food distraction, and that this effect will be stronger when being in a state of low satiety compared to a state of moderate or high satiety. The results demonstrated that the interaction effect between in-store non-food distraction and
satiety on attentional bias towards healthy food was significant, F(2, 152) = 4.52, p = .012, ηp2 = .06, d = .03. However, the mean values were not as expected (see Figure 1). Figure 1 shows that, in line with expectations, the attentional bias score towards healthy food from participants who were least satiated was lower in the high distraction (M= 7.43, SD = 19.03) compared to the low distraction condition (M= 9.39, SD = 28.54), although a post hoc least significant difference (LSD) test showed that this difference was not significant in a pair-wise comparison (p = .762). Contrary to what was expected, the difference in attentional bias scores between the high and low distraction condition was greater for participants who were in a state of high satiety (Mhigh = 2.74, SD = 14.04; Mlow = 15.19, SD = 19.40). However, a post hoc LSD test indicated that this difference was only marginally significant in a pair-wise comparison (p = .056). Furthermore, Figure 1 shows that an opposite effect was found for participants who were moderately satiated. The attentional bias score towards healthy food from moderately satiated participants was higher in the high distraction condition (M = 20.97,
SD = 37.32) compared to the low distraction condition (M = 5.08, SD = 23.08). A post hoc
LSD test showed that this difference was significant (p = .023). As these findings do not confirm expectations, H5 had to be rejected.
Figure 1. Mean attentional bias scores towards healthy food (ms) grouped by in-store
non-food distraction and satiety conditions.
Hypothesis six stated that exposure to a high level of in-store non-food distraction will lead to a lower proportion of healthy food choice compared to exposure to a low level of in-store non-food distraction, and that this effect will be stronger when being in a state of low satiety compared to a state of moderate or high satiety. However, results indicated that the interaction effect between in-store non-food distraction and satiety on proportion of healthy food choice was not significant, F(2, 152) = 0.19, p = .826, ηp2 = .00. Therefore, there was no empirical support for H6.
Conclusion and Discussion
This study aimed to contribute to the literature by examining the effects of in-store non-food distraction and satiety on grocery shoppers’ attentional bias towards healthy food and healthy food choice. A strength of this study is that it was one of the first to investigate these processes by utilizing an immersive VR supermarket environment. Although findings did not confirm the expected effects, which resulted in the rejection of all hypotheses, this study did reveal a significant interaction effect between non-food-related distraction in store environments (i.e., crowdedness and music) and satiety state on grocery shoppers’ attention towards healthy food. These findings therefore provide several theoretical implications.
Firstly, findings did not demonstrate a significant main effect of non-food-related distraction on attentional bias towards healthy food and healthy food choice. Hence, the expectation that these non-food related distractions negatively influence grocery shoppers’ attentional bias towards healthy food and healthy food choice was not confirmed. An explanation for these results might be found in a limitation of the current study. Although manipulation checks demonstrated that the perceived level of crowdedness and music volume were significantly higher in the high distraction condition compared to the low distraction condition, it is possible that these non-food-related distractions were not perceived as
distracting by all participants. For instance, it has been suggested that tolerance for crowding can moderate the effects of perceived crowdedness on consumer behavior (Eroglu, Machleit, & Barr, 2005). Similarly, differences in personality traits such as extraversion appear to interact with the distractibility of music when individuals are performing a task (Furnham & Strbac, 1997). As high levels of crowding and music can thus be more distracting for some than for others, future studies that examine the effects of these in-store variables should investigate these issues more explicitly.
Another limitation of this study which may explain the absence of significant effects of in-store non-food distraction on healthy food choice is the way in which food choice was measured. Since food choice was measured by means of a pencil-and-paper task after participants had finished watching the VR supermarket video, the in-store distractions were no longer present when participants had to choose their preferred snacks. Hence, participants were not hindered by the distractions at the time of choosing between healthy and unhealthy foods. Future studies should therefore measure food choice while participants are exposed to in-store distractions, for example by utilizing a VR supermarket environment in which individuals can pick up and purchase groceries (e.g., Siegrist et al., 2018).
Secondly, results revealed that satiety did not significantly affect attentional bias towards healthy food and healthy food choice. This means that, contrary to the predictions, the state of low satiety did not result in a lower attentional bias towards healthy food or a lower proportion of healthy food choice compared to a state of moderate or high satiety. A limitation of the study with regard to the food choice measure might explain this finding. Participants could select snacks from three food categories during the food choice task, which means that participants were restricted in their food choices. Since the utilized measure did not include all the snack foods that were shown in the VR store or that would typically be available in a real supermarket, it may have seemed artificial and difficult for some participants to select snacks as they would also do in real life. Future studies should avoid these issues by using a more realistic food choice task, for instance by letting participants select food products from a supermarket’s online store or in a VR supermarket environment that allows them to purchase groceries.
An additional explanation for the absence of significant effects in terms of healthy food choice is that the current study measured the proportion of selected healthy snacks as compared to the total number of selected snacks, without considering the prices of the
selected foods. However, it is conceivable that in-store non-food distraction and satiety affect the amount of money that grocery shoppers spend on healthy and unhealthy foods. For example, Romal and Kaplan (1995) showed that individuals with high self-control capacity generally spend less money than those with lower self-control capacity. As this has not been investigated before, future research should consider the possible link between distracting influences in store environments, self-control performance, and spending behavior with regard to healthy and unhealthy food purchases.
Thirdly, although findings did not demonstrate the expected interaction effect between non-food-related distraction and satiety on healthy food choice, a significant interaction was
found between non-food-related distraction and satiety on attentional bias towards healthy food. Contrary to expectations, however, the proposed effect was stronger for highly satiated participants than for those who were least satiated. Moreover, results showed an opposite effect for participants who were moderately satiated. Their attentional bias towards healthy food was significantly higher in the high distraction condition compared to the low
distraction condition. A possible explanation for these findings is that participants who were least satiated had more attention for all food stimuli, both healthy and unhealthy, and
therefore showed a weaker attentional bias towards specifically the healthy food category (e.g., Piech et al., 2010). It would therefore be interesting for future studies to investigate to what extent low satiety increases attentional bias towards food stimuli in general.
Furthermore, a limitation of this study that may have contributed to these unexpected findings is that attentional bias was measured indirectly with a dot probe task, without
simultaneous exposure to distractions. Future research should therefore expose participants to in-store distractions and measure attention for healthy and unhealthy foods at the same time to further explicate these effects.
To conclude, the results from this study are important from a societal standpoint, as the investigated processes play an influential role with regard to the treatment of the public health problem of obesity. The findings highlighted both theoretical and practical challenges with untangling the effects of non-food related distraction and low satiety on attentional bias towards healthy food and healthy food choice in store environments. As the current results appear to fall out of line with earlier studies in the field, further research is needed.
Nevertheless, the demonstrated significant interaction effect between in-store non-food distraction and satiety on grocery shoppers’ attention towards healthy food seems to indicate that these variables do show a relationship that is important to explore in future research. Future studies should therefore consider the described limitations and recommendations in order to move this field forward, and to develop effective approaches that can help people to make healthful dietary choices for a healthier life.
References
Agha, M., & Agha, R. (2017). The rising prevalence of obesity: part A: Impact on public health. International Journal of Surgery Oncology, 2(7), 1-6. doi:
10.1097/IJ9.0000000000000017
Appelhans, B. M., French, S. A., Pagoto, S. L., & Sherwood, N. E. (2016). Managing temptation in obesity treatment: A neurobehavioral model of intervention strategies.
Appetite, 96, 268-279. http://dx.doi.org/10.1016/j.appet.2015.09.035
Au, N., Marsden, G., Mortimer, D., & Lorgelly, P. K. (2013). The cost-effectiveness of shopping to a predetermined grocery list to reduce overweight and obesity. Nutrition
& Diabetes, 3(6), 1-5. doi: 10.1038/nutd.2013.18
Boyland, E. J., Nolan, S., Kelly, B., Tudur-Smith, C., Jones, A., Halford, J. C. G., et al. (2016). Advertising as a cue to consume: A systematic review and meta-analysis of the effects of acute exposure to unhealthy food and nonalcoholic beverage advertising on intake in children and adults. American Journal of Clinical Nutrition, 103, 519-533. doi: 10.3945/ajcn.115.120022
Brignell, C., Griffiths, T., Bradley, B., & Mogg, K. (2009). Attentional and approach biases for pictorial food cues. Influence of external eating. Appetite, 52(2), 299-306. doi: 10.1016/j.appet.2008.10.007
Castellanos, E. H., Charboneau, E., Dietrich, M. S., Park, S., Bradley, B. P., Mogg, K., & Cowan, R. L. (2009). Obese adults have visual attention bias for food cue images: Evidence for altered reward system function. International Journal of Obesity, 33(9), 1063-1073. doi: 10.1038/ijo.2009.138
Cialdini, R. B. (2008). Influence: Science and practice (5th edition). Boston, MA: Pearson Education.
Cohen, D. A., & Babey, S. H. (2012). Contextual influences on eating behaviours: heuristic processing and dietary choices. Obesity Reviews, 13(9), 766-779. doi: 10.1111/j.1467-789X.2012.01001.x
Dijksterhuis, A., Smith, P. K., Van Baaren, R. B., & Wigboldus, D. H. (2005). The unconscious consumer: Effects of environment on consumer behavior. Journal of
Consumer Psychology, 15(3), 193-202. doi: 10.1207/s15327663jcp1503_3
Dion, D. (2004). Personal control and coping with retail crowding. International Journal of
Service Industry Management, 15(3), 250-263. doi: 10.1108/09564230410540926
Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in
Eroglu, Machleit, & Barr. (2005). Perceived retail crowding and shopping satisfaction: The role of shopping values. Journal of Business Research, 58(8), 1146-1153. doi: 10.1016/j.jbusres.2004.01.005
Furnham, A., & Strbac, L. (2002). Music is as distracting as noise: The differential distraction of background music and noise on the cognitive test performance of introverts and extraverts. Ergonomics, 45(3), 203-217. doi:
10.1080/00140130210121932
Garrow, J. S., & Webster, J. (1985). Quetelet's index (W/H2) as a measure of fatness.
International Journal of Obesity, 9(2), 147-153.
Gelman, A., & Park, D. (2009). Splitting a Predictor at the Upper Quarter or Third and the Lower Quarter or Third. The American Statistician, 63(1), 1-8. doi:
10.1198/tast.2009.0001
Hill, J. O., Wyatt, H. R., Reed, G. W., & Peters, J. C. (2003). Obesity and the environment: where do we go from here? Science, 299(5608), 853-855. doi:
10.1126/science.1079857
Hofmann, W., Friese, M., & Roefs, A. (2009). Three ways to resist temptation: The independent contributions of executive attention, inhibitory control, and affect regulation to the impulse control of eating behavior. Journal of Experimental Social
Psychology, 45(2), 431-435. doi: 10.1016/j.jesp.2008.09.013
Hofmann, W., Friese, M., & Wiers, R. W. (2008). Impulsive versus reflective influences on health behavior: A theoretical framework and empirical review. Health Psychology
Review, 2(2), 111-137. doi: 10.1080/17437190802617668
Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-regulatory behavior: toward an individual differences perspective on behavior determination by automatic versus controlled processes.
Journal of Personality and Social Psychology, 95(4), 962. doi: 10.1037/a0012705
Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16(3), 174-180. doi:
10.1016/j.tics.2012.01.006
Hollywood, Cuskelly, O’Brien, McConnon, Barnett, Raats, & Dean. (2013). Healthful grocery shopping. Perceptions and barriers. Appetite, 70, 119-126. doi:
Hou, Mogg, Bradley, Moss-Morris, Peveler, & Roefs. (2011). External eating, impulsivity and attentional bias to food cues. Appetite, 56(2), 424-427. doi:
10.1016/j.appet.2011.01.019
Houben, K., Dassen, F. C., & Jansen, A. (2016). Taking control: Working memory training in overweight individuals increases self-regulation of food intake. Appetite, 105, 567-574. doi: 10.1016/j.appet.2016.06.029
Jones, A., Hardman, C. A., Lawrence, N., & Field, M. (2018). Cognitive training as a potential treatment for overweight and obesity: A critical review of the evidence.
Appetite, 124, 50-67. doi: 10.1016/j.appet.2017.05.032
Klingberg, T. (2010). Training and plasticity of working memory. Trends in Cognitive
Sciences, 14(7), 317-324. doi: 10.1016/j.tics.2010.05.002
Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational
Behavior and Human Decision Processes, 65(3), 272-292. doi:
10.1006/obhd.1996.0028
Mattila, & Wirtz. (2001). Congruency of scent and music as a driver of in-store evaluations and behavior. Journal of Retailing, 77(2), 273-289. doi:
10.1016/S0022-4359(01)00042-2
Mattila, A., & Wirtz, J. (2008). The role of store environmental stimulation and social factors on impulse purchasing. Journal of Services Marketing, 22(7), 562-567. doi:
10.1108/08876040810909686
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49-100.
Morrison, Gan, Dubelaar, & Oppewal. (2011). In-store music and aroma influences on shopper behavior and satisfaction. Journal of Business Research, 64(6), 558-564. doi: 10.1016/j.jbusres.2010.06.006
Nederkoorn, C., Guerrieri, R., Havermans, R. C., Roefs, A., & Jansen, A. (2009). The interactive effect of hunger and impulsivity on food intake and purchase in a virtual supermarket. International Journal of Obesity, 33(8), 905-912. doi:
10.1038/ijo.2009.98
Papies, E. K., & Hamstra, P. (2010). Goal priming and eating behavior: enhancing self-regulation by environmental cues. Health Psychology, 29(4), 384-388. doi: 10.1037/a0019877
Piech, R. M., Pastorino, M. T., & Zald, D. H. (2010). All I saw was the cake. Hunger effects on attentional capture by visual food cues. Appetite, 54(3), 579-582. doi:
10.1016/j.appet.2009.11.003
Pohl, R. F., Erdfelder, E., Hilbig, B. E., Liebke, L., & Stahlberg, D. (2013). Effort reduction after self-control depletion: The role of cognitive resources in use of simple heuristics.
Journal of Cognitive Psychology, 25(3), 267-276. doi:
10.1080/20445911.2012.758101
Polivy, Herman, & Coelho. (2008). Caloric restriction in the presence of attractive food cues: External cues, eating, and weight. Physiology & Behavior, 94(5), 729-733. doi: 10.1016/j.physbeh.2008.04.010
Ravensbergen, E., Waterlander, W., Kroeze, W., & Steenhuis, I. (2015). Healthy or Unhealthy on Sale? A cross-sectional study on the proportion of healthy and unhealthy foods promoted through flyer advertising by supermarkets in the Netherlands. BMC Public Health, 15(1), 1-10. doi: 10.1186/s12889-015-1748-8 Raynor, H. A., & Epstein, L. H. (2003). The relative-reinforcing value of food under
differing levels of food deprivation and restriction. Appetite, 40(1), 15-24. doi: 10.1016/S0195-6663(02)00161-7
van Rompay, T., Galetzka, M., Pruyn, A., & Garcia, J. (2008). Human and spatial dimensions of retail density: Revisiting the role of perceived control. Psychology and Marketing,
25(4), 319-335. doi: 10.1002/mar.20211
Rothman, A., Sheeran, J., & Wood, P. (2009). Reflective and automatic processes in the initiation and maintenance of dietary change. Annals of Behavioral Medicine, 38(1), 4-17. doi: 10.1007/s12160-009-9118-3
Siegrist, M. Y., Ung, C., Marinello, M., Hartmann, C., Menozzi, M., Zank, M., & Kunz, A. (2018). Consumers' food selection behaviors in three-dimensional (3D) virtual reality.
Food Research International. Advance online publication. doi:
10.1016/j.foodres.2018.02.033
Siep, N., Roefs, A., Roebroeck, A., Havermans, R., Bonte, M. L., & Jansen, A. (2009). Hunger is the best spice: an fMRI study of the effects of attention, hunger and calorie content on food reward processing in the amygdala and orbitofrontal cortex.
Behavioural Brain Research, 198(1), 149-158. doi: 10.1016/j.bbr.2008.10.035
Stafford, L. D., & Scheffler, G. (2008). Hunger inhibits negative associations to food but not auditory biases in attention. Appetite, 51(3), 731-734. doi:
Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior.
Personality and Social Psychology Review, 8(3), 220-247. doi:
10.1207/s15327957pspr0803_1
Tal, A., & Wansink, B. (2013). Fattening fasting: hungry grocery shoppers buy more calories, not more food. JAMA Internal Medicine, 173(12), 1146-1148. doi: 10.1001/jamainternmed.2013.650
Tuorila, H., Kramer, F. M., & Engell, D. (2001). The choice of fat-free vs. regular-fat fudge: the effects of liking for the alternative and the restraint status. Appetite, 37(1), 27-32. doi: 10.1006/appe.2001.0410
Turley, L., & Milliman, R. (2000). Atmospheric effects on shopping behavior: A review of the experimental evidence. Journal of Business Research, 49(2), 193-211.
Wang, S. C., & Lang, M. (2015). The effects of special displays on shopping behavior.
Journal of Retailing and Consumer Services, 23, 125-132. doi:
10.1016/j.jretconser.2014.12.009
de Wijk, Smeets, Polet, Holthuysen, Zoon, & Vingerhoeds. (2018). Aroma effects on food choice task behavior and brain responses to bakery food product cues. Food Quality
and Preference, 68, 304-314. doi: 10.1016/j.foodqual.2018.03.015
World Health Organization. (2018). Obesity and Overweight. Retrieved from https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
Appendix A
Appendix B
Instructions for the Executive Attention Task
Instructions VR supermarket
In a minute you will get to watch a short video in which a 360-degree camera is walked around a small supermarket. The video starts at the entrance of the supermarket, and then continues by walking alongside a number of shelves. These shelves contain products that you can buy in most small supermarkets.
You can look around the supermarket quite freely, but we would like to ask you to pay close attention to healthy food products. These are products that generally contain little fat and calories, such as fruit and diet/light soft drinks.
Before the video starts, we will calibrate the VR headset so that the image of the screen in front of your eyes is in focus for you. If you think the screen is in focus, please let us know: we can then start the video for you.
Appendix C
Screenshots of the VR Videos
Figure 1. Screenshot of the low in-store non-food distraction VR video.
Figure 2. Screenshot of the high in-store non-food distraction VR video.
