“Smart fork”: Useful feedback tool or dietary gimmick?

hapiforkDo you eat too quickly? Or eat too much? A novel tool, the HAPIfork, is designed to track eating habits, such as the number of forkfuls of food consumed and the speed of consumption. Sensors within the fork detect movement and allow for feedback to the eater.  Feedback can be immediate, in the form of vibrations and flashing lights triggered by eating too quickly. The fork is also paired with software or an app and measures meal duration, the number of forkfuls per minute, and the pauses between forkfuls enabling the user to track eating habits over time.

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:


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

ResearchBlogging.orgMartin 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

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What’s your weight loss “style”? The role of heterogeneity in weight management

puzzleA new perspective on data from the National Weight Control Registry (NWCR) by Ogden et al. (2012) (1) suggests that individuals who successfully maintain weight loss fall into four distinct categories, each with unique characteristics. This is an essential step towards recognizing the importance of heterogeneity for successful weight management and will hopefully help break the stereotypic weight loss advice to simply eat less and move more, allowing for development of weight management programs that are tailored to the unique needs of individuals.

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 EnthusiastGertrude, the Poor Eater, and Janice, the Struggler.

nwcr_cluster_tableWhile 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?


ResearchBlogging.org 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.

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Failed Feedback: Barriers to Behaviour Change

Why does feedback fail? As I’ve discussed previously, learning (or behaviour change) is a feedback process. According to Sterman (1), there are two important requirements that must be met in order for learning (behaviour change) to be successful. First, the overall cycle of the feedback loops must occur within an appropriate time frame in order for the information to still be relevant. Additionally, each link in the feedback loop must be successful of itself; although the feedbacks exist, they often do not function very well. Today’s post will focus on the potential barriers that can affect each component of the feedback loop as discussed by in his article “Learning in and about complex systems”. (Delays have been discussed briefly in a previous post and will explored again in future ones).

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…).

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

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Is it about the journey or the destination? Goals vs. experience in motivating behaviour change

“The road of life twists and turns and no two directions are ever the same. Yet our lessons come from the journey, not the destination.” Don Williams, Jr.

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.


1. ResearchBlogging.org 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

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