Maybe it’s just me, but the food labels communicating the  amount of calories on the back of food packages don't always speak to me. Don’t get me wrong, when I see a label stating “840 kcal” on the back of my nicely smelling pizza, it does hurt a bit. But, anything less than those extremes is hard to grasp for me, and the lazy person I am, I tend to skip this information instead of going online to find out what it exactly means. 

Turns out I am not the only one. And that is exactly why researchers have started searching for alternatives that are able to nudge healthier food-consumption behavior. One of the alternatives that has been proven to be very effective is the exercise-data-based food labels, presenting calorie-equivalent exercise data. In other words, instead of just stating “300 kcal” those labels indicate how much activity is needed to burn those calories, by stating something like  “5KMs of walking needed to burn off calories”. 

But guess what: the precision of these numbers can influence people’s behavior quite drastically. In this blog, we will dive into the literature on the precision effect to find out how we can craft these exercise-data-based labels to make them more effective.

The Precision Effect Explained

The precision effect is a common theme within the psychological literature. Existing research has found that the preciseness or roundedness (8.00 vs. 7.93) of data can affect not only the persuasiveness of information, but also product satisfaction and willingness to pay. 

But it turns out that the precision effect can nudge us into behaving more healthily. This has everything to do with our expectations and discrepancies between these expectations and our experiences. 

More specifically, we humans have expectations regarding the usual format for presenting information based on prior knowledge stored in memory. Violations of such formats not only catches our attention, but can impact our judgements because of perceptions of discrepancy.

Let’s illustrate that with an example. What would you expect the price to be of oil in dollars per gallon? You've probably come up with a three-digit number. On the contrary, if I ask you what the price of a specific house would be, you would probably round it to the nearest thousand, such as 458.000 instead of 458.253. 

In similar terms, exercise-data-based food labels are often expressed in rounded (integer) formats than in precise (decimal) formats. As such, when a label does use a precise format,  this may cause a salient discrepancy compared with our expectations. 

How Discrepancy Attribution Makes us Aversive to Unhealthy Food

When labels are presented in an unexpected manner, such as in a precise format, we feel that there is a discrepancy between our experience and our expectation. But how does this discrepancy affect our behavior?

The concept of discrepancy attribution suggests that, in order to explain such atypical situations, perceived discrepancies may trigger an attribution process, leading us to rely on the most salient contextual heuristic. It was, for example, found that people infer caviar with an unusually precise weight as rare and thus more expensive than with a regular weight presentation. 

This also works the other way around. When prices are unexpectedly easy to process, we may attribute this to liking the product, which can increase our preference toward the product.

Translating this to food intake, a simple heuristic that is often present when selecting food involves the “affect-as-information” principle. This principle suggests that emotions that are experienced at the time of purchase decisions can directly affect our decision. As unhealthy foods offer us, besides immediate enjoyment, also excessive calories, people’s default emotions regarding the consumption of unhealthy foods are often negative.

As a result, the discrepancy generated by the precise exercise data may be attributed to enhance negative emotions, making us less likely to choose unhealthy foods. 

In a series of experiments, Zhou and Zhu (2022) found that this indeed is the case. They show that precise exercise-data-based food labels lower consumption intention by increasing negative emotions regarding the products. In one of their studies, they asked participants to complete a task and they thereafter let them choose between an apple and a chocolate as reward for completing the task. These two foods were presented on a table, each with food labels containing exercise data and calorie content.

And that is when it became interesting.

When the food label was presented in a precise format (“0.92 km” and “1.89 km”) , 20.5% of the participants selected the chocolate. But, from the participants that saw the label presented in a rounded format (“1 km” and “2 km”), 48,5% selected the chocolate. In other studies, the researchers showed that this has everything to do with the negative emotions experienced towards unhealthy food. Not surprisingly then, the precision effect only holds for unhealthy foods. 

To conclude, the way we visualize food labels can drastically impact our behavior. Policy makers as well as brands could use this information to promote healthy lifestyles and increase consumer welfare. 

Key Take-Aways

• The way food labels are formatted affects our food-consumption behavior. 
• Exercise-data-based food labels nudge us toward healthier choices as compared to calorie content labels. 
• The precision of this exercise data further influences our behavior. Precise labels decrease our preference for unhealthy foods and increase our preference for healthy foods.
• This effect occurs because of the fact that we expect exercise data to be rounded, and so when it is presented in a precise format, there exists a discrepancy between what we expect and what we experience.
• This discrepancy, in turn, increases the negative emotions associated with unhealthy food, making them less attractive. 
• By doing so, precise food labels can nudge us toward choosing healthier options.