The developers suggest that the fork can help individuals track the speed at which they eat as well as the amount that they eat. Feedback from the fork then encourages the eater to eat more slowly take fewer forkfuls (eat less?). They extrapolate further, suggesting that eating more slowly and taking fewer forkfuls will benefit weight loss, digestive problems, and other health concerns.

Does it work? In order to answer this, we need to know both if the fork is successful at altering behaviour (e.g. to eat more slowly), and also if the change in behaviour can contribute to weight loss (e.g. does eating more slowly support weight loss). The developer’s web site indicates that a prototype of the fork was used at a medical centre to track and compare eating speeds. However, I was unable to find any published results about the fork. I suspect, that like many other tracking tools, some will find it very useful, others will initially be interested but the novelty will wear off, and yet others will not find it helpful at all.

Despite the common advice to eat slowly to allow time to feel full and thus eat less during a meal, a recent systematic review found only limited evidence to suggest that there may be an association between eating quickly and higher body weight, noting in particular that no longitudinal studies had examined this question(1). A 2007 study examined the effect of eating rate on the amount of food consumed at a meal, finding that eating slowly reduced food intake at a single meal for men, but not for women(2).

Would a fork that tracks your food intake be useful to you?

More information about the fork here:

• HAPILABS
• dezeen magazine

References

Mesas A. E. et al, Selected Eating Behaviours and Excess Body Weight: A Systematic Review, Obes Rev. 2012;13(2):106-35

Martin CK, Anton SD, Walden H, Arnett C, Greenway FL, & Williamson DA (2007). Slower eating rate reduces the food intake of men, but not women: implications for behavioral weight control. Behaviour research and therapy, 45 (10), 2349-59 PMID: 17517367

Previous studies from the registry have identified common behaviours and strategies used by successful weight loss maintainers, including:

• low calorie, low fat diets
• high levels of physical activity
• self-monitoring of body weight and food intake
• eat breakfast regularly
• high dietary restraint

However, there is a large degree of variability in the behaviours of successful weight loss maintainers (for example with regards to amount of calories consumed, amount of exercise, etc…). In order to understand this variability better, the researchers sought to identify unique sub-groups of participants that share common characteristics, for example, medical and weight history, reliance on exercise and diet, and use of resources and supports.

Ogden et al categorized NWCR participants into four distinct clusters (Table 1). For additional descriptions of the four clusters, I highly recommend Dr. Arya Sharma’s thoughtful discussions here: Mark, the Golden Boy of Weight Loss, Julie, the Fitness Enthusiast, Gertrude, the Poor Eater, and Janice, the Struggler.

While the clusters may conjure up the idea of a “What’s your weight loss style” quiz found in your favourite health magazine, the authors hope that identifying these different clusters will help enable personalization of weight management strategies.  How can this information be useful to health care practitioners and patients?

Weight management strategies, despite a wide range of “packaging”, frequently focus on the simple advice to “eat less, move more” and ignore the complex interactions between various psychological, social and biological factors. For each individual, only some of these factors are relevant. Identifying these four clusters is a first step in moving beyond a one size fits all approach. However, there is still considerable variability with each cluster; can we do even better in identifying factors relevant for an individual and applying them to further personalization of care?

Translating this into practice requires strategies and tools to help determine which specific factors contributing to obesity are relevant to a particular individual. For example, a card sort tool (2) that enables individuals to select cards with statements about factors relevant to weight management (e.g. self-efficacy, disinhibition, physical activity, social and environmental factors, etc…) that best describes them. Such a tool, administered to patients seeking help with weight management would allow practitioners to tailor interventions based on the factors identified by the patients as most relevant for them. Such a tool also has the potential to examine and identify “why” individuals select certain behaviours, increasing the usefulness to the health care provider.

It is important to also consider the limitations, both of this study, and future endeavours. The study is based on data that is self-reported, and participants self-select for inclusion in the database. These individuals are unique in their own right, as successful weight loss maintenance is uncommon. Additionally, as highlighted by the data from the NWCR, there are many individuals who do not seek the help of structured programs or health care providers. Is it possible to reach these individuals too?

References

1. Ogden, L., Stroebele, N., Wyatt, H., Catenacci, V., Peters, J., Stuht, J., Wing, R., & Hill, J. (2012). Cluster Analysis of the National Weight Control Registry to Identify Distinct Subgroups Maintaining Successful Weight Loss Obesity, 20 (10), 2039-2047 DOI: 10.1038/oby.2012.79

2. Merth, T. et al. “Dealing” with complexity: Construction and analysis of a card-based communication tool for obese patients. CON: 2nd Annual Obesity Summit Poster Abstracts http://www.diabetes.ca/documents/for-professionals/CJD–May_2011–CON_Abstracts.pdf

Also recommended:

In addition to the four posts exploring the characteristics of the individuals with each of the four clusters, Dr. Arya Sharma also provides an insightful summary of the article: Why Are Some People Successful At Maintaining Weight Loss? and What Works For You May Not Work For Me.

Potential problems can arise at each of the links in the feedback loop (Figure 1). The information we receive may be inaccurate or even missing. Inaccuracies can occur when we try to measure or record the information. We, or others, may introduce bias, distortion, or selective perception. The information may not be available when needed, introducing time delays. An important example within the obesity system includes society’s emphasis on body weight and not health, providing misguided and sometimes wrong, information. Another example is that of variability and errors in self-reported measures, such as for physical activity levels and food consumption.

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

Our mental models may also themselves be barriers. These mental models or deeply beliefs, exist at multiple levels: within the individual, within communities, within policies. Unscientific reasoning or misapplication of scientific reasoning, judgemental biases, and defensive routines are pervasive within the obesity system. Blaming the individual as cause, and solutions that look no further than the inappropriate recommendation to simply “eat less and move more” are common beliefs. Challenging these models is necessary for change.

The strategies, structure and decision rules and the decisions we make are also fraught with potential challenges, in part because they are based on our mental models. In obesity, the typical decisions focus on lifestyles changes (eat less, move more! or perhaps behaviour therapy), pharmacological solutions, or surgery. These are well intentioned scripts, but typically fail to account for the complex interactions of multiple factors that contribute to obesity as well as the heterogeneity of the population; only a subset of factors are germane for a given individual. These factors also change over time. But even the “right” or “best” decision that is not plagued by a biased mental model or incorrect information isn’t guaranteed to meet with success and can be subject to implementation failure. For example, the challenge of translating interventions developed through research into practical settings.

We can only attempt to simulate and understand the real world; our models cannot fully capture the dynamic complexities, experiments cannot be controlled, again introducing the potential for errors (to quote George Box, “all models are wrong, but some are useful“). The dynamic complexities of the obesity system are reflected in the multiple factors and their interactions found in the Foresight Obesity Map. Not only are there many factors, there are multiple connections, the relationships are non-linear, they exhibit stochastic behaviour, and include both negative and positive feedback. Yet despite this complexity, the interventions we most often try to implement are reductionist, assign blame, focus on outcome rather than process and ignore our capacities.

For each of these barriers to learning, the challenge lies in shifting our current beliefs to enable implemention of strategies that embrace a complex systems approach (e.g. matching complexity to capacity, establishing networks, fostering emergence, etc…).

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

The same sentiments are echoed by Emerson, “life is a journey, not a destination”; yet when it comes to behaviour change, goal setting is often considered the gold standard. For example, weight loss is a common goal used by individuals to motivate participation in exercise or to follow healthy eating patterns. A recent study by Fishbach and Choi (1) investigates how focusing on goals influences our motivation to pursue an activity and compares this to the role of the experience of the activity itself.

The authors propose that focusing on the end result can lead to unintended consequences. They suggest that focusing on goals helps kickstart us into action, that goal setting encourages us to begin a new activity. However, they also suggest that too much emphasis on the outcome reduces motivation to continue participation in the activity over time. Instead, an emphasis on experience is more important for maintaining pursuit of the activity.

Fishbach and Choi used four different studies to test their ideas. In the first experiment, participants were asked to either focus on their goals or on the experience of a treadmill workout. Subjects that focused on their goals ran for a shorter time period than they intended too, while subjects who focused on the running experience itself ran for a slightly longer duration than intended.

The subsequent studies examined various other activities, including origami, dental flossing and yoga. Regardless of the activity, whether primarily pursued for its own enjoyment (origami), pursued for function but not normally perceived as “fun” (flossing), or something that has elements of both (yoga), thinking about the goals of the activity increased the intention to start it, but decreased enjoyment in the activity and decreased the continued pursuit of the activity over time.

The results from these experiments suggest that reframing how we support the maintenance of health behaviours may benefit successful behaviour change. Goal setting should not be ignored, but used where it is helpful – in initiating the behaviour change. However, encouraging continued participation in a new health behaviour over time should focus on the pleasure of the experience instead of the goal.

These conclusions fit nicely with two ideas that I believe are important. The first is Dr. Yoni Freedhoff’s philosophy that “the only goal that’s fair to set is to live the healthiest life that you can enjoy“. Secondly, from a complex systems perspective, feedback is likely to be more effective if process based (again, a focus on the experience), rather than outcome based. It’s the pleasure of the experience that keeps us coming back, to do it again and again.

References

1. Fishbach, A., & Choi, J. (2012). When thinking about goals undermines goal pursuit Organizational Behavior and Human Decision Processes, 118 (2), 99-107 DOI: 10.1016/j.obhdp.2012.02.003

Photo: White and orange blur by Martin Newman, August 2012

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

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

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

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.

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:
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

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:

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:

• Behavioural Economics – a way to fight Big Food from Verdant Nation
• Google’s Impressive Healthy Food Program from Marion Nestle on Food Politics
• Opting In versus Opting Out by Richard Thaler

Here are this week’s favourites:

• Ever suffered writer’s block? There’s a number of nifty tools to help keep you motivated and on track to meet your writing deadlines, including one called Write or Die. If you’re prone to procrastinate, this might be for you – allowing you to set a goal (number of words to write), time limit and penalty (ranging from changing screen colour, an alarm sounding, or deleting the text you’ve just written!). Me, I prefer the idea of Written? Kitten! where typing rewards you with a picture of a cute kitten, how’s that for dangling a carrot? (Hat tip to @TravisSaunders for the link to whizbang’s post on writing tools from #scio12)
• BJ Fogg of STandford University has developed a new model to help understand behaviour change. The basic premise of the model is that three elements, motivation, ability and trigger, must occur simultaneously in order to elicit a behaviour. His research at the Persuasive Technology Lab includes projects focussed on exploring ways for computing systems to assist in changing human behavior.

Image snow day! from cory schmitz on flickr

In a large hospital cafeteria, researchers implemented a two stage experiment. In the first stage, they introduced nutrition labels that were colour coded red, yellow or green to help consumers identify the healthfulness of foods and beverages. In the second stage, the locations of foods and beverages were changed in order to increase the visibility of the healthy food and beverage choices (colour coded green).

Implementation of the colour coded labels (Phase 1) led to a significant decrease in sales of all red items from baseline measurements with sales of beverages in this category decreasing the most. At the same time, sales of green items increased, again with the greatest increase seen for beverages. Changing the visibility of green items in Phase 2 led to a further decrease in the sales of red items. However, only green beverages saw an additional increase in sales due to the rearranged product placement in the cafeteria.

As I’ve blogged about previously, feedback is important to consider in promoting behaviour change. The first phase of this study is an example of creating a new feedback loop to support individuals in making healthy behaviour changes. Menu labeling at the point of purchase is not a new idea though. Policies requiring restaurants to display calories for all menu items are popular public health interventions. However, research examining the success of these interventions in changing food choices has shown mixed results (2). Reading and interpeting nutrition labels is confusing and complex, requiring the consumer to understand kilocalories, serving sizes, and energy requirements. Additionally, the caloric value of a food or beverage is not the only determinant of its healthfulness.

The simplified colour coding used in the study by Thorndike reduces the complexity associated with identifying healthy food and beverage choices and was not determined by caloric value alone. The intervention’s success suggests that similar programs may be able to further improve the effectiveness of other menu labeling policies. However, an important challenge to this simplified approach of colour-coding healthful and less healthful foods will be reaching a consensus on the criteria by which foods and beverages are categorized.

I’ll explore choice architecture, and the effects of changing where healthy foods and beverages were positioned in an upcoming post.

References

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. Robert Wood Johnson Foundation. (2009) Menu Labeling: Does Providing Nutrition Information at the Point of Purchase Affect Consumer Behavior? A Research Synthesis, June 2009. http://www.rwjf.org/files/research/20090630hermenulabeling.pdf

Hat tip to Carrie Matteson for suggesting the article.

For additional information, please also see:

• powerpoint presentation by the authors available in pdf format 
• legal and policy resources on menu labeling from the CDC

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