Research reportStimulus control and affect in dietary behaviours. An intensive longitudinal study☆
Introduction
Every day, and in an abundance of situations, we find ourselves confronted by stimuli relating to food such as food items in shop displays, advertisements for food, or seeing other people eat. These stimuli are highly relevant, because it has been acknowledged that our dietary behaviours are predominately driven by environmental cues rather than by a motivation to restore energy homoeostasis; or, put another way, that we do not eat because we are hungry, but because we see something or encounter a situation that prompts us to eat (Weingarten, 1985).
This approach to understanding eating and other dietary behaviours including drinking is an example of stimulus control; it assumes that external factors (e.g., seeing others eat, seeing food in the environment) rather than internal states (hunger, thirst) influence our dietary behaviours or even make us feel hungry (Sobik, Hutchison, & Craighead, 2005). The present study is a first attempt at providing an integrative approach at describing stimulus control effects on a wide range of dietary behaviours in a non-clinical population – it examines environmental (external) and affective (internal) factors that are associated with eating (both during main meals and between meals) and drinking (both alcoholic and non-alcoholic beverages).
Stimulus control in eating behaviour is hypothesized to be driven by the automatic processing of food-related cues and/or cognitions (King, 2013, Lowe, Butryn, 2007): it is theorized that individuals can misinterpret their psychological responses to such internal and external food-related cues as signals of biological hunger and respond accordingly (Lutter & Nestler, 2009). External cues may include seeing or smelling food, seeing other people eating, food advertising, or being at a location where one has consumed food in the past; internal cues refer to psychological desires for rewarding experiences or to lessen negative mood states (e.g., eating to regulate negative affect, or ‘comfort’ eating; Parker, Parker, & Brotchie, 2006).
There is good evidence for the importance of external cues in eliciting eating behaviour. For example, Cleobury and Tapper (2014) found external cues to be the most important predictors of eating unhealthy snacks in an intensive longitudinal diary study of overweight and obese adults. Similarly, there is evidence that supports the idea that internal cues such as negative affect motivate us to eat, and it has been suggested that energy-dense foods in particular can serve the purpose of down-regulating negative affective states (Parker et al., 2006). Or, put more simply: that people eat in order to decrease negative effect. It has also been shown that experiencing negative affect increases selective attention to food-related stimuli (Hepworth, Mogg, Brignell, & Bradley, 2010), which might explain this association. However, other research suggests that the association between affect and eating might not be as straightforward, but could in fact depend on internal resources such as self-regulatory capacities (Sproesser, Strohbach, Schupp, & Renner, 2011).
Whereas there is rich literature on the role of external and internal stimuli for eating (both regular meals and snacks between meals) and drinking alcohol (more below), there is comparatively little research on the role of stimulus control for drinking non-alcoholic beverages. One study found that having coffee might be cued by situational factors such as going on a cigarette break (Lane, 1996), and there is some evidence that being exposed to an environment that has non-alcoholic beverages available increases the likelihood of having a non-alcoholic beverage (Tucker, Vuchinich, & Sobell, 1979). Further, it has been suggested that being exposed to drink-related cues such as brand logos activates neural pathways similar to those activated during reward processing, at least in more habitual soft drink consumers (Burger & Stice, 2014). This is in line with evidence from a study on adolescents that found that being in locations (e.g., school) or social situations (e.g., with friends), or being bored, which had previously been associated with soft drink consumption, can all increase the likelihood of the consumption of sweetened drinks (Grenard et al., 2013). However, more research is needed to better understand the role of situational factors in the consumption of non-alcoholic drinks.
Compared to non-alcoholic drinks, there is more research on the role of stimulus control for consuming alcoholic beverages. However, the majority of this research has been conducted in clinical populations and it is unclear whether stimuli are similar between clinical and non-clinical drinkers. Only few studies to date have explored drinking alcohol in non-clinical samples. For example, there is evidence that social drinkers (i.e., people who mainly consume alcohol in social situations) experience higher craving in social situations (Papachristou, Nederkoorn, Corstjens, & Jansen, 2012), which in turn might lead to subsequent alcohol consumption. Social cues such as interacting with friends may also play a role in promoting alcohol consumption in non-clinical populations (Aan Het Rot, Russell, Moskowitz, & Young, 2008). In addition, having alcohol readily available or being in an environment where alcohol is easily obtainable has been shown to increase the likelihood of alcohol consumption (Gruenewald, Remer, & Lascala, 2014). It has further been suggested that in non-clinical populations, alcohol might serve as mood-regulation agent, similar to the effects of calorie-dense food discussed above – i.e., experiencing negative affect makes alcohol consumption more likely, as people might drink to improve negative affect (Kassel et al, 2000, Peacock et al, 2015) or as a result of high arousal (Swendsen et al., 2000).
Previous studies have examined stimulus control on specific dietary behaviours, but to date, no study has examined the role of external and internal stimuli on a comprehensive set of dietary behaviours. Furthermore, only few studies on stimulus control and eating to date have broken down eating into main meals and snacking, the latter being the type of eating arguably most likely to be affected by situational variables, given that it can be viewed as being more discretionary (Cleobury & Tapper, 2014). In this study, we differentiate between eating during main meal periods (i.e., breakfast, lunch, dinner) and snacking (defined as spontaneous additions to the diet; Nielsen, Siega-Riz, & Popkin, 2002). Snacks are typically higher in energy and lower in nutrient content than meals eaten during main meal times (Gearhardt, Grilo, Dileone, Brownell, & Potenza, 2011). Increased snacking frequency has been associated with obesity (Miller, Benelam, Stanner, & Buttriss, 2013), and snack-dominated meal patterns seem to lead to higher intakes of energy, alcohol, sugars, and sucrose, and lower intake of micronutrients (Ovaskainen et al., 2006).
Previous studies on stimulus control and eating behaviour also tended to rely on retrospective assessments such as food frequency questionnaires (Flint, Cummins, & Matthews, 2013), clinical interviews (Lowe et al., 2009), written food diaries (O'Connor et al, 2008, Verhoeven et al, 2012), or laboratory experimentation and/or observation (e.g., Werthmann et al., 2011), but it has been argued that such methods lead to under-reporting of food intake, particularly snacks (Heitmann & Lissner, 1995). Ecological momentary assessment (EMA; Shiffman, Stone, & Hufford, 2008) procedures – where participants record events in real-time as they go about their day-to-day life – allow researchers to study behaviours in more detail, in real-world settings, and close to real-time. EMA methods to assess food intake have been used previously in eating-disordered populations (Norton, Wonderlich, Myers, Mitchell, & Crosby, 2003), but only few studies so far have used EMA in non-clinical populations (Grenard et al, 2013, Hofmann et al, 2013, Thomas et al, 2011). In this study, we aim to examine dietary behaviours close to real-time and within the environment in which the behaviours are performed, thus providing a more ecologically valid approach to examining stimulus control of eating and drinking including a wider range of dietary behaviours than previous studies. Further, as previous work has been mainly conducted in clinical samples, our study targeted a non-clinical sample from the general population.
Section snippets
Method
We employed EMA methods to study eating patterns in a community sample. Participants carried a programmable electronic device throughout the day and logged episodes of eating and drinking as well as responded to randomly-timed non-eating/non-drinking prompts. This allows comparing the presence and intensity of a range of internal and external stimuli between consumption logs and random prompts (Shiffman et al., 2014). With training, participants can be highly compliant with such procedures (
Results
Overall, there were 638 participant days of monitoring. Consistent with previous EMA studies (see Schüz et al., 2014), we excluded from analysis individual days where poor compliance was observed (defined as answering <50% of random prompts; 5.80% of monitored days). This left an evaluable sample of 601 participant-days of observation (mean of 11.91 days per person, SD = 2.62). Participants completed an average of 13.78 hours (SD = 12.75) of monitoring per day. A total of 2057 random prompts
Discussion
This study examined stimulus control and individual dietary behaviour over a period of ∼10 days in 53 non-clinical individuals from the general population using EMA methods to assess real-time associations between situational antecedents, affect, as well as arousal – ‘cues’ – and eating or drinking. The study supports a significant role of the availability of foodstuffs in the environment for eating. It also supports the role of social cues and suggests that observing others eating is
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2021, Journal of Nutrition Education and BehaviorCitation Excerpt :Environmental cues include seeing and smelling food, food advertisements, watching others eat, or being in a location where food has been eaten.6 In a nonclinical sample of Australian adults, food availability and observing others eat were the strongest predictors of food consumption.6 Consistent with SCT, when food is readily available and visible, people are more likely to incorporate them into their meals.9
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2020, AppetiteCitation Excerpt :The present study examined BMI differences in stimulus control and eating behaviour over the duration of ~14 days in a community sample through the use of EMA. All domains assessing internal and external cues to eating could accurately differentiate between eating and non-eating instances, providing further support for the role of stimulus control in influencing eating behaviour (Elliston et al., 2016; Schüz et al., 2015; Schüz, Papadakis, & Ferguson, 2018). The domain which could distinguish between eating and non-eating instances with the greatest accuracy was having food easily available.
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Acknowledgements: This study was funded through an internal University of Tasmania grant to Stuart G Ferguson.