Deconstructing the logic of calorie labelling as an intervention
How to use reverse engineering to refine behaviour change strategies
In my last article, we explored the findings of a recent study on calorie labelling in England. The policy, which aimed to encourage healthier choices by displaying calorie information on menus, had mixed results. While it increased awareness, it didn’t lead to a measurable reduction in caloric intake. This raises an important question: why didn’t it work?
To understand why calorie labelling fell short and how it might be improved, we need to reverse engineer its logic. By unpacking the behavioural assumptions it relies on and identifying the gaps it leaves unaddressed, we can uncover broader lessons for designing behaviour change interventions. Using tools like the Behaviour Change Wheel (BCW), this article explores why calorie labelling hasn’t delivered on its promise—and how this kind of analysis can help evaluate and improve other interventions.
Breaking down the logic of the calorie labelling intervention
Calorie labelling is built on a straightforward idea: if diners know how many calories are in their meals, they will make healthier choices. On the surface, this seems logical, but a closer look reveals the assumptions that underpin it and why its impact might be limited.
The logic of calorie labelling as an intervention can be deconstructed into three key assumptions, each tied to a specific element of the COM-B framework:
Consumers can interpret and act on the information (Capability): Diners have the knowledge, skills, and mental bandwidth to process calorie information and apply it to their decisions in real-time. This presumes that all diners understand calorie information in context (e.g., daily needs) and have the cognitive resources to consider it in high-pressure or fast-paced environments.
The environment enables calorie-conscious choices (Opportunity): The dining environment supports healthier decisions by offering appealing, accessible, and affordable lower-calorie options. This assumes that external barriers—such as menu design, time constraints, or the availability of healthy meals—do not hinder calorie-conscious decisions.
Awareness leads to better choices (Reflective Motivation): By providing calorie information at the point of decision-making, diners will become more aware of the energy content of their meals and make healthier choices. This assumes that reflective, rational decision-making is the dominant driver of food choices, overlooking the impact of habits, emotions, and social influences (automatic motivation).
The intervention is built on a narrow set of assumptions that address reflective motivation through awareness while largely neglecting capability such as nutritional literacy and mental bandwidth, and contextual factors such as economic pressures, and social context. As a result, it isn’t hard to see why calorie labelling alone is unlikely to drive meaningful behaviour change without complementary interventions that address these gaps.
Why we need to be specific when diagnosing behavioural challenges
Behavioural interventions often fail because they target one aspect of behaviour while neglecting others.
Calorie labelling is a clear example: it assumes that providing calorie information will be enough to change behaviour, yet it misses key challenges in both capability and motivation. Understanding these nuances—and addressing them specifically—is critical for designing effective solutions. Psychological capability and reflective motivation illustrate how these dimensions work together but also where they can break down:
Psychological capability is about understanding. It includes skills like reading calorie labels, interpreting them in the context of daily energy needs, and recalling relevant guidelines.
Reflective motivation is about intention. It involves using that understanding to align decisions with personal goals, such as prioritising health over indulgence.
For calorie labelling to succeed, diners need both. They must be able to interpret calorie counts (capability) and be motivated to act on that information (motivation). However, reflective motivation is often overshadowed by automatic behaviours like habits and cravings, which explains why awareness alone rarely drives meaningful change.
The interplay between capability and motivation
Specific behavioural diagnosis allows us to design interventions that address not just what’s missing but why it’s missing.
Understanding where capability and motivation break down reveals why specificity matters. Imagine two diners encountering a menu with calorie labels:
Diner A lacks psychological capability. They can’t understand the calorie information, so even if they want to make a healthier choice, they’re unable to act.
Diner B lacks reflective motivation. They understand the calorie labels perfectly but choose a high-calorie option anyway because indulgence or value for money feels more important in the moment.
This distinction isn’t academic—it’s actionable. Designing effective interventions requires understanding whether the challenge lies in helping diners interpret information (capability) or strengthening their intention to act on it (motivation). Without this level of specificity, interventions risk being too narrow or addressing the wrong barriers entirely.
Behaviour is rarely driven by a single factor - for example, decisions about food, for example, are shaped by overlapping influences. Calorie labelling targets reflective motivation, but by neglecting capability gaps (like literacy or numeracy) and automatic drivers (like cravings), it falls short.
What we can learn from reverse engineering interventions
Reverse engineering calorie labelling reveals critical lessons about why it falls short and how behaviour change interventions can be designed more effectively.
Interventions need layers. Calorie labelling highlights how targeting one domain of behaviour is usually not enough. For example:
If diners lack the ability to interpret calorie information (capability), awareness won’t drive change.
If dining environments favour indulgent, high-calorie meals (opportunity), reflective motivation is unlikely to override those influences.
Layered interventions address these gaps by combining strategies. For calorie labelling, this might mean e.g. pairing awareness with defaults, menu restructuring, or financial incentives that guide healthier choices without relying solely on rational decision-making.
Social context matters. Eating is rarely an isolated activity. It is often social, shaped by norms, peer influence, and the dynamics of the occasion. Interventions that neglect social dynamics risk being ineffective. To address this, strategies like social norm messaging or encouraging healthier group choices can complement calorie labelling.
Beware of "knowledge bias." Knowledge-based interventions often assume that awareness leads directly to action, but behaviour change is rarely that linear. Awareness is a starting point, not an endpoint. Calorie labelling might increase diners’ understanding of meal content, but reflective motivation is often overridden by habits, cravings, or convenience. Without addressing these automatic behaviours, interventions like calorie labelling will struggle to deliver measurable change.
Why reverse engineering matters in behaviour change
Reverse engineering interventions like calorie labelling helps us move beyond surface-level evaluations of effectiveness.
Behavioural interventions are rarely simply "effective" or "ineffective." By unpacking the assumptions and barriers behind interventions, we can reveal insights that are often missed in traditional evaluations.
Reverse engineering helps us explore the nuances, uncovering why an intervention may work well for some groups but not others. For calorie labelling, this approach reveals how it addresses reflective motivation but leaves gaps in capability and opportunity. Instead of dismissing calorie labelling outright, reverse engineering positions it as a starting point that needs complementary strategies to succeed.
Reverse engineering also highlights gaps that future studies can address, improving the design of interventions and the questions researchers ask. For example, calorie labelling studies might benefit from examining how social contexts, menu structures, or economic constraints influence diners’ choices. This approach ensures that research doesn’t just measure outcomes but also considers the mechanisms driving behaviour change.
In general, reverse engineering can be applied to any intervention to evaluate not just its outcomes, but the assumptions it relies on and the barriers it may overlook. Whether you’re analysing health campaigns, environmental nudges, or workplace incentives, reverse engineering provides a framework for understanding why interventions succeed or fail.
Key lessons and takeaways
Behaviour change interventions are rarely straightforward. Their effectiveness depends on complex interactions between capability, opportunity, and motivation—factors that vary across contexts, populations, and individual circumstances. To design interventions that work in the real world, we must go beyond asking "does this work?" and instead ask "why does this work—or not?"
Reverse engineering offers a critical tool for behavioural science. By analysing the assumptions behind interventions and identifying overlooked barriers, it allows us to move past binary judgements of success or failure. This approach shifts the focus from outcomes alone to the mechanisms that drive behaviour, revealing opportunities for improvement that might otherwise be missed.
Behaviour change requires layered, systemic approaches. Addressing a single domain—like awareness or motivation—is rarely enough. Instead, effective strategies engage with the broader behavioural ecosystem, tackling capability, opportunity, and motivation in tandem. This layered approach ensures that interventions not only inform people but also create environments that support healthier decisions and align with people’s real-world contexts.
This is not just about individual interventions—it’s a mindset shift. Reverse engineering reminds us that behaviour change is a process, not a one-size-fits-all solution. By embracing nuance and understanding the interplay of behavioural drivers, we can design interventions that are more robust, flexible, and tailored to diverse populations.
Reverse engineering helps us move beyond surface-level evaluations of interventions. By identifying assumptions and overlooked barriers, it reveals opportunities to refine strategies and address real drivers of behaviour. This approach isn’t limited to calorie labelling—it’s a practical tool for designing interventions that work in diverse, real-world contexts.
The study referred to in this post:
Polden, M., Jones, A., Essman, M., Adams, J., Bishop, T. R., Burgoine, T., ... & Robinson, E. (2024). Evaluating the association between the introduction of mandatory calorie labelling and energy consumed using observational data from the out-of-home food sector in England. Nature Human Behaviour, 1-10. (Open access)