Mental Models of Obesity

Previously I suggested that in order for health behaviour change to be successful, we not only need to alter the decisions we make, but that we also need to modify the habits, routines, interpretations, and attributions that we base these decisions on. In other words, our mental models about obesity can act as a barrier to success in weight management, as based on Sterman’s description of double loop learning (1). So, just what are our most deeply held beliefs about obesity and weight management?

A 2001 study by Ogden et. al. addresses this very question (2). They administered questionnaires to both patients and general practitioners in order to gain insight into how each group viewed: a) the causes of obesity, b) the consequences of obesity, and c) treatment options. Their results highlight some differences as well as some similarities between the groups’ perspectives (Figure 1), which may have implications for the clinical management of obesity.

General practitioners were more likely to attribute the cause of weight gain to patient behaviours such as overeating, lack of exercise and poor diet. Although patients also attributed weight gain to individual behaviour, they were more likely to identify other factors as significant contributors, including slow metabolism, gland or hormone problems, and stress, as compared to the physicians. The two groups also differed in what they perceived to be the most important consequence of obesity. While physicians ranked development of diabetes as the top concern, patients were more concerned with difficulty getting work. Perspectives on treatment methods were similar for both groups. Physicians however again placed the responsibility on the patients, while patients identified a need for help from their general practitioner or other health care providers.

Figure 1: Mental Models of Obesity (adapted from Ogden et. al.)

The study is limited by the use of a survey to determine the factors that subjects identify as shaping their beliefs about obesity, its causes and consequences and options for treatment. The survey format used only allowed those factors initially identified by the researchers and included in the survey to be identified by the study participant. It is possible that other factors may also help shape mental models of obesity.

As Ogden et al discuss, these discrepancies in mental models about obesity have implications for the communication between a physician and a patient, as well as for the success of interventions (2). Do these differences contribute to the poor success rates of primary care intervention for obesity? And regardless of the effect on communication between physicians and patients, do these perceptions about obesity themselves create barriers to learning and behaviour change?

The next questions to explore include identifying the existing feedback loops that shape these mental models and looking for ways in which we can modify them or add new feedback loops.

1. Sterman, J. D. (1994). Learning in and about complex systems. System Dynamics Review, 10(2-3), 291-330. doi:10.1002/sdr.4260100214

ResearchBlogging.org 2. Ogden J, Bandara I, Cohen H, Farmer D, Hardie J, Minas H, Moore J, Qureshi S, Walter F, & Whitehead MA (2001). General practitioners’ and patients’ models of obesity: whose problem is it? Patient education and counseling, 44 (3), 227-33 PMID: 11553423

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Cake or Fruit: Non-linearity in Decision-Making

Previously I’ve discussed the work of Resnicow and Vaughn (1), who suggest that traditional models of behaviour change fail to embrace the complexity of human behaviour. They call for a new paradigm to conceptualize behaviour change using a complex systems lens. As highlighted before, most traditional theories view behaviour change as linear and deterministic, they often assume homogeneity of the target population, feedback is often missing, and although they acknowledge multiple contributing factors, the interdependency of these factors is ignored.

Figure 1

Let’s explore the non-linearity of behaviour a little more closely, focusing on one characteristic of complexity.  Fitting a linear, deterministic model to human behaviour fails to address the complexity inherent in behaviour change. Consequences of this may limit the potential for success in interventions based on these models. In a system that is linear, x varies in proportion to y, as is illustrated in Figure 1.

In a dynamic system, this relationship is not linear – there are multiple possibilities. One example relevant to behaviour change is that of human decision making and how it varies over time. Dan Ariely discusses how time affects the decisions we make about healthy eating(2). For example, many of us decide that we want to eat healthy foods more often, such as choosing fresh fruit for dessert instead of chocolate cake. Yet we struggle to uphold these choices over time.

Before a meal, we have a stronger preference for fresh fruit over chocolate cake: fruit is healthier and the higher calories of a decadent dessert may contribute to unwanted weight gain. But our preferences change over time (Figure 2). When the time to have dessert arrives, our preference for cake increases, perhaps driven by hedonistic desires, and surpasses the reward value of the fruit. Later, after the meal is over, our preferences may change again. The cake no longer seems as appealing, perhaps we even regret the choice or feel guilt. As its reward value decreases over time, fruit again becomes preferable.

Figure 2: Non-linearity in human behaviour – how decisions vary with time (Adapted from Ariely, D. (2001). A timely account of the role of duration in decision making. Acta Psychologica, 108(2), 187-207)

This variability in our decision making over time can be applied to many other examples, such as evening plans to go for a walk versus watching TV. Even if we try to be more logical about decisions, by weighing the costs and benefits of the different options, the importance we place on the various costs and benefits also changes with time.

These examples clearly illustrate how human behaviour change does not fit a linear model, and highlight the need for a complex systems approach as called for by Resnicow and Vaughn. In future posts I’ll explore what this complex systems approach might look like, please stay tuned!

1. Resnicow K, & Vaughan R (2006). A chaotic view of behavior change: a quantum leap for health promotion. The international journal of behavioral nutrition and physical activity, 3 PMID: 16968551

ResearchBlogging.org 2. Ariely D, & Zakay D (2001). A timely account of the role of duration in decision making. Acta psychologica, 108 (2), 187-207 PMID: 11569762 doi:10.1016/S0001-6918(01)00034-8

Photo Credit: Strawberry Sacrilege by Martin Newman.

 

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Learning is a feedback process

Learning depends on feedback. This is not a surprising statement, and is a common theme within many different academic disciplines, including Systems Thinking (as discussed by Sterman, Meadows, Forrester and others). But despite this consensus, learning, in the sense of shifting our beliefs about something, is very hard to do. And feedback loops are not as easy to understand as we often believe them to be. What follows is my attempt to explore John D. Sterman’s perspective on learning and feedback and apply it to health behaviour change in the context of obesity.

First let’s look at a simple feedback loop and learning process. We receive information about something, and based on this, we make a decision. The actions we take based on that decision influence various outcomes and we get new and updated information that allows us to make additional decisions and take further actions (Figure 1). This is sometimes referred to as single loop learning. We modify our decisions based on the information we receive, and the information we receive is in part determined by the actions we take.

Figure 1: Single Loop Learning. Adapted from Sterman, J.D. Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. Boston. 2000

Using weight management as an example, the decisions made might include restricting food intake or exercising more in order to produce a change in body weight. The information about the “real world” then comes from stepping on the scale to assess if there is a change in body weight.

In this example, the decisions we make are consistent, each time based on our preconceptions of how the real world works. These preconceptions, or mental models, provide us with the rules or guidelines for the decisions we make. They include habits and routines, interpretations, and attributions. In single loop learning, our mental models are not affected by the feedback we receive. Our understanding of how things work, our goals, and values are not influenced. Is this single loop learning enough to change health behaviour? Are the mental models that we have appropriate for supporting the change we hope to see?

What would happen if we changed the rules that governed how we made decisions? This requires a second feedback loop, creating what is referred to as double loop learning (Figure 2). In this example, information we receive can not only affect our decisions, but can alter the guidelines that we use in making them.

Figure 2: Double Loop Learning. Adapted from Sterman, J.D. Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. Boston. 2000

I’ll revisit the ideas and questions I’ve raised here in future posts; to discuss what the current mental models of obesity are, how they affect “learning” (or behaviour change), and explore potential strategies to change our mental models.

References:
ResearchBlogging.org 1. Sterman, John D. (1994). Learning in and about complex systems. System Dynamics Review, 43 (2-3), 239-330 DOI: 10.1002/sdr.4260100214

2. Sterman, J.D. Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. Boston. 2000

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Improving healthy food & beverage choices through choice architecture

In my previous blog post, I discussed a study by Thorndike et al that looked at how both labeling healthy and unhealthy food choices with colour codes affected the purchase of healthy foods and beverages (1). The previous post focused on the “traffic light” labeling program, which created a new feedback loop that simplified the complexity of making healthy decision. Today I’ll look at the second phase of their experiment – choice architecture. Strategies that alter our environment to “nudge” people towards a desired choice can also serve to reduce the complexity of decision making. The results presented by Thorndike et al provide evidence to suggest that the simple rearrangement of the location of healthy food choices within a cafeteria can contribute to an increase consumer purchases of these options (1).

Choice architecture refers to how our environment is designed to influence the choices we make. In the study by Thorndike et al, healthy items (those with a green coded label) were placed at eye level in coolers throughout the cafeteria. Additionally, bottled water was placed in displays in new locations – all with the goal of increasing visibility. Is it simply enough to make a healthy choice more visible to get people to choose it? The study found that yes, at least for beverages, placing healthy choices more prominently throughout the cafeteria led to an increase in sales of those items. Bottled water sales increased 25.8% during the second phase of the study.

According to Richard Thaler (2), there are three basic principles of choice architecture. First, establish desirable defaults. Defaults refer to the the path of least resistance. Next expect errors; or design with the acknowledgement that humans make mistakes. And third, give feedback.

How can we leverage these principles to effectively nudge people towards healthier behaviours? Some interventions, such as studied by Thorndike et al have begun by modifying the defaults. The Smarter Lunchrooms Movement is another initiative with similar goals in schools across the US. Reducing the size of bowls in the lunch room, an example of changing the default, led to students serving smaller portions of cereal at breakfast. Similarly, putting chocolate milk behind the plain milk increases the difficulty of getting chocolate milk and led to an increase in the amount of plain milk sold over chocolate milk. Moving baskets of fresh fruit next to the cashiers in school cafeteria also led to an increase in their sales(3).

Can the concepts of nudges be taken beyond the school lunch room? In an upcoming post I’ll take a look at the UK Behavioural Insights Teams and the opposing views on its potential for success.

References:

ResearchBlogging.org 1. Thorndike, A., Sonnenberg, L., Riis, J., Barraclough, S., & Levy, D. (2012). A 2-Phase Labeling and Choice Architecture Intervention to Improve Healthy Food and Beverage Choices American Journal of Public Health DOI: 10.2105/AJPH.2011.300391

2. Thaler, R. and C. Sunstein. (2008) Nudge: Improving Decisions about Health, Wealth and Happiness. Yale University Press. New Haven and London.

3.  Just, D.R. and B. Wansink (2009) Smarter Lunchrooms: Using Behavioral Economics to Improve Meal Selection. Choices 24(3)

Related readings:

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