Food, Emotions and Behaviour Change: Comparison
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Please note this is a comparison between Version 2 by Enzi Gong and Version 1 by Nimat Ullah.

Behaviour change techniques are considered effective means for changing behaviour, and with an increase in their use the interest in their exact working principles has also expanded.

  • desire regulation
  • emotion regulation
  • behaviour change techniques
  • mental health
  • physical health
  • BCTs

1. Overview

Behaviour change techniques are considered effective means for changing behaviour, and with an increase in their use the interest in their exact working principles has also expanded. This information is required to make informed choices about when to apply which technique. Computational models that describe human behaviour can be helpful for this. In this paper a few behaviour change techniques have been connected with a computational model of emotion and desire regulation. Simulations have been performed to illustrate the effect of the techniques. The results demonstrate the working mechanisms and feasibility of the techniques used in the model. 

2. Behaviour Change Techniques

Behaviour change techniques (BCTs) [1,2][1][2] provide a strong basis for changing one’s behaviour to promote healthy lifestyles. These techniques are used as interventions for changing specific undesired behaviours, for instance, to ensure healthy eating and physical activity [3[3][4],4], mood regulation, and the avoidance of excessive use of drugs, etc. Although the techniques are broadly applied, their exact working remains vague. However, from another perspective, some of the techniques listed as BCTs [2] are somehow already known to us. For instance, the generation and regulation of emotions has quite extensively been explained by Gross [5]. Similarly, the interplay of emotions and desires has also been thoroughly explored by various scholars, for instance [6,7,8][6][7][8]. These findings from relatively different disciplines, therefore, provide very sound ground for the amalgamation of these perspectives.
Definitions from within the behavioural sciences show the effects of certain specific interventions clearly, however, understanding how an intervention does what it does is often not so clear. On the other hand, from the social sciences such as psychology, we have an understanding of how some of the more commonly used emotion regulation strategies work. Some of these same strategies also form part of the BCTs used in this model, as given below in Table 1. For instance, the intervention called ‘regulation of negative emotions’, from behavioural science, employs the same emotion regulation strategies that are defined in psychology. Thus, to fill this gap, in this paper these two different fields have been brought together with the help of computational modelling. The model therein demonstrates the working mechanism of the BCTs used in this model and illustrates how they helps in changing unhealthy behaviours in terms of eating. Hence, as with other such models, this model is also considered a behaviour change model. Moreover, the model tries to reduce the negative impact of the interplay between different negative emotions, i.e., anxiety and food desires.
Table 1. Relevant coded interventions and their descriptions.
S. # BCTs Description
1. Information about health consequences [5.1] Provide information (e.g., written, verbal, visual) about the health consequences of performing the behaviour
2. Reduce negative emotions [11.2] Advise on ways of reducing negative emotions to facilitate the performance of the behaviour
3. Stress management [11.2] Advise on ways of reducing stress
4. Problem solving/coping planning [1.2] Analyse, or prompt the person to analyse, factors influencing the behaviour and generate or select strategies that include overcoming barriers and/or increasing facilitators (includes ‘relapse prevention’ and ‘coping planning’)
Goal setting (outcome) [1.3] Set or agree on a goal defined in terms of a positive outcome of wanted behaviour
Goal setting (behaviour) [1.1] Set or agree on a goal defined in terms of the behaviour to be achieved

3. The Interplay and Regulation of Emotions and Desires

Emotions are considered as drivers of action, [9] however, at the same time, emotions themselves can also be result of other actions, for instance, binging [10] can cause an emotional response. Similarly, binging, or in other words overeating, can be a trigger as well as a response to emotions [11] which can be considered as an impediment between the optimal response to the environment and emotions [8]. This interplay between emotions and desires [12] can have very negative psychological as well as physical health [13,14][13][14] consequences as both can prove triggers for one another [10,12,15][10][12][15] and form a continuous cycle. To avoid such a cycle [12[12][16],16], a person must have the ability to regulate their emotions and desires and do not let emotions or desires influence each other. Various strategies can be used for the regulation of emotions and desires, however the adaptivity of these strategies is purely dependent on the context in which they are employed [17,18][17][18]. In the model developed in this paper (explained in Section 5), reappraisal, situation modification, and problem solving have been used for the regulation of the negative emotions because of the way they deal with negative emotions and also because these strategies are more adaptive as compared to other similar strategies. For instance, reappraisal is considered quite an adaptive strategy [8] and remains so even if the intensity of the negative emotions/desires is low. This is because the reappraisal of low-intensity emotions requires the activation of fewer brain parts as compared to high-intensity emotions [19,20][19][20]. Therefore, the model employs reappraisal only for low-intensity negative emotions. Similarly, situation modification is considered a better option in a situation that induces a high intensity of negative emotions or desires [20]. The third strategy used in the model in this paper is problem-solving. This strategy is also considered an adaptive strategy as it suggests the solving of the problem that is causing the negative emotions [21]. Therefore, both of these strategies are activated only for the hinderance of high-intensity negative emotions whilst considering their efficacy in their respective situations. The question remains, however, as to how best to use these techniques/strategies. An answer to this question can be found within behavioural sciences.

4. Behaviour Change Interventions

According to [22] behaviour itself refers to ‘anything a person does in response to internal or external events. Actions may be overt (motor or verbal) and directly measurable, or covert (e.g., physiological responses) and only indirectly measurable; behaviours are physical events that occur in the body and are controlled by the brain’. BCTs, on the other hand, are the smallest components that are thought to bring the proposed change in one’s behaviour, either alone or in combination with other BCTs [23], and behaviour change intervention refers to the application of these BCTs. Behaviour is also considered an outcome of an intervention [24]. A universally agreed-upon list of 93 distinct BCTs have been developed as taxonomy v1 in [2], where each of which, alone or in combination with others, can be used as an intervention. However, as the underlying mechanisms are not always clear, it is sometimes difficult to decide which BCTs to use as interventions under specific circumstances. Similarly, developing an intervention itself is a very complex process as it can have many BCTs and the definition of its active, effective components is a challenge [2]. In recent years, some scholars have identified interventions for some behaviours, for instance, [3] shortlist interventions for increasing physical activity and healthy eating, [25] for smoking cessation, [26] for safe drinking, and [27] for the prevention of sexually transmitted infections, etc. The purpose of this discussion is to highlight the importance of the development of well-specified interventions [2]. Although it is a difficult task to specify which BCT or combination of BCTs will work the best, for whom, and in which specific situation, the suitability of the interventions used in this paper has been decided on the basis of their definitions as stated in [2,4][2][4] and used in different studies regarding similar behavioural goals, as mentioned above. Table 1 below provides a list of the BCTs used as interventions in the model in this paper and their descriptions.

5. Conclusions

This paper introduces a computational network model which brings findings and techniques from different disciplines together into a single network-oriented temporal-causal model. Unhealthy emotions, emotional eating, and food desires and their adverse consequences, on the one hand, are proven facts in social, cognitive, and behavioural sciences. BCTs, on the other hand, are expected to help in changing any such unwanted/unhealthy behaviour. This endeavour has, therefore, illustrated how the BCTs could work in terms of desire and emotion regulation, as interventions for avoiding unhealthy negative behaviours. The working and expected results of these interventions have been shown through simulation results. The simulations give a comparative picture of the expected results with and without certain interventions. The novelty of the model lies in the fact that it applies BCTs to negative emotions such as food desire and anxiety, simultaneously, in a computational model for the first time, where the model not only helps in disintegrating the interplay between these two different kinds of emotions but also assists in overcoming their consequences. This, apart from helping in understanding the negative outcomes of the interactions of these two emotions, also helps in avoiding their negative impact within our daily lives. Moreover, the understanding of this mechanism also creates solid ground for digitizing interventions for these types of negative emotions and adapting the same in a clinical setup for in-depth personalized analysis against real-time data. Therefore, in future, the authors aim at developing a system in which certain BCTs are recommended on the basis of the computational model presented in this paper. This potential system will help guide users to the most suitable BCT depending on their condition. The same can further be made intelligent by employing AI techniques which will not only make this system personalized but also adaptive for validation against real-time data.

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