Visual Signals, Healthier Choices – Study Shows Colour-Coded Labels Influence Consumer Decisions
Key Takeaways:
- Colour-coded nutrition labels are more effective than traditional tables in guiding healthier food choices
- Red warning signals have a stronger behavioural impact than green positive cues, reflecting a “negative bias” in decision-making
- Simple visual labelling systems may support public health efforts to address obesity and poor dietary habits
The growing use of colour-coded nutrition labels
Colour coding on food packaging is becoming increasingly common, particularly as policymakers and manufacturers seek ways to guide consumers towards healthier dietary choices. A recent study conducted by researchers from SWPS University, the University of Wisconsin, and the University of Massachusetts suggests that these visual systems are significantly more effective than traditional nutritional tables.
The findings, published in Current Psychology, indicate that the effectiveness of colour-coded labels lies in how the brain processes signals of benefit and risk. Rather than requiring effortful interpretation, colour cues allow for rapid, intuitive judgements about a product’s healthfulness.
Obesity and the need for clearer nutritional guidance
According to the World Health Organization, overweight and obesity are major contributors to the development of chronic diseases. Over the past three decades, the proportion of children and adolescents in the United States who are overweight or at risk has more than tripled, reaching 37% and 34% respectively.
This trend has been driven largely by reduced physical activity and the increased consumption of foods high in fat and sugar. In response, clearer and more accessible nutritional labelling systems are being explored as tools to help people make more informed food choices.
How traffic light labelling works
One widely adopted approach is the traffic light labelling (TLL) system, originally developed in the United Kingdom. This system uses colours to indicate the levels of key nutrients such as calories, fat, saturated fat, sugar, and salt relative to recommended intake levels.
- Green indicates low levels, typically below 15% of the reference intake
- Red signals high levels, typically exceeding 25% of the reference intake
By translating numerical data into easily recognisable visual cues, the system allows consumers to assess a product’s nutritional profile at a glance.
“A picture is worth a thousand words”
The study aimed to explore the psychological mechanisms behind how people interpret these colour-coded labels.
“We decided to investigate the psychological mechanisms behind the reading of color-coded product labels. We drew on theories about verbal and visual information processing, as well as the perception of information in positive and negative contexts. We wanted to bridge a gap. Previous studies focused exclusively on consumer purchasing behavior and analyzed the extent to which color-coded labels influenced the choice of healthy food products,” says Professor Andrzej Falkowski, a business psychologist from the Institute of Psychology at SWPS University and the author of the study.
To examine this, researchers recruited 79 participants in the United States via Amazon Mechanical Turk. Participants were asked to evaluate products such as chicken noodle soup, ranch dressing, and peanut butter. These products were presented either with colour-coded nutrient indicators or with traditional text-based information.
Participants rated each product on a scale from 0 to 10, where 0 indicated “harmful” and 10 indicated “healthy”.
Faster processing, more intuitive decisions
The findings confirmed that visual information is easier for people to process than text. Colour cues are interpreted almost instantly by the brain, requiring minimal cognitive effort.
This enables individuals to make quick, instinctive judgements about whether a product is beneficial, even in time-pressured situations such as shopping. In contrast, traditional nutritional tables require more deliberate analysis, which may reduce their practical usefulness in real-world settings.
The power of red and the role of negative bias
One of the most striking findings was the disproportionately strong influence of the colour red. While green highlights positive attributes, red signals high levels of fat or sugar and prompts caution.
“This result also aligns with existing theories suggesting that negative events exert a stronger influence on behavior than positive ones. It is this ‘negative bias’ that makes color systems so effective. Red causes us to pause and reconsider a purchase,” Professor Falkowski emphasizes.
This asymmetry – where negative signals carry more weight than positive ones – was not observed with traditional labelling formats. Without clear visual cues, participants found it more difficult to distinguish between beneficial and harmful aspects of a product.
Improved consistency in consumer judgements
The study also found that colour-coded labels led to more consistent evaluations across participants. Because the visual system clearly differentiates between risks and benefits, individuals were better able to assess products in a uniform way.
By contrast, traditional descriptors such as “low fat” can be ambiguous and open to interpretation, particularly for those with limited nutritional knowledge. Colour coding, based on universally recognised traffic signals, offers a more accessible and intuitive alternative.
Implications for public health and obesity prevention
The researchers suggest that these findings have important implications for public health policy.
“Given the ongoing global challenges of obesity and poor dietary habits, color-coded labeling represents a simple yet impactful strategy for guiding healthier consumer choices,” Falkowski says.
By enhancing the visibility and clarity of nutritional information, colour-coded systems may encourage people to select healthier options. Over time, such behavioural shifts could contribute to improvements in population health.
The authors conclude that leveraging visual attention mechanisms and simplifying complex nutritional data may be a practical and scalable approach to addressing poor dietary habits and the global rise in obesity.
CCH insights:
The results of this study supports the use colour-coded labelling system as it enables quick health-based decision-making, with minimal time or effort required. And it also revealed that we use the system more to avoid unhealthy ‘red’ foods than to actively choose healthy ‘green’ foods. These outcomes emphasise the complex range of factors that contribute to shopping behaviours and decisions about what people eat. And if we want to encourage people to eat healthily, we need to understand these factors better and consider how best to influence them.
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Children with Obesity Face Elevated Long-Term Health Risks Even with Normal Test Results, Study Finds
Key Takeaways:
- Children living with obesity can face significantly higher risks of future disease even when current clinical tests appear normal
- By early adulthood, rates of type 2 diabetes, hypertension and abnormal lipids are markedly higher compared with the general population
- Effective obesity treatment in childhood is associated with meaningful reductions in long-term health risks
Rethinking “metabolically healthy” obesity in childhood
Children living with obesity who show no immediate signs of metabolic complications may still be at substantial risk of developing serious health conditions later in life. New research from the Karolinska Institutet, published in JAMA Pediatrics, challenges the long-standing notion that some children with obesity can be considered “metabolically healthy” and therefore may not require intervention.
The findings contribute to an ongoing clinical debate about whether normal blood markers, liver function and blood pressure in childhood are sufficient indicators of long-term health.
“There has been a debate about whether children with normal blood and liver values and normal blood pressure might not need treatment for their obesity. Our study shows that this assumption is incorrect,” says Claude Marcus, professor at the Department of Clinical Science, Intervention and Technology at Karolinska Institutet.
Study design and population
The study followed just over 7,200 children aged 7–17 in Sweden who had initiated obesity treatment. Participants were tracked longitudinally up to the age of 30, allowing researchers to assess long-term health outcomes.
Children were grouped into three categories:
- Those with metabolically healthy obesity (MHO)
- Those with obesity and impaired cardiometabolic risk markers (MUO)
- A control group drawn from the general population
This design enabled a direct comparison of long-term disease risk across different metabolic profiles in childhood.
A clearly increased risk of future disease
Despite appearing clinically healthy in childhood, individuals with MHO demonstrated a substantially elevated risk of developing cardiometabolic diseases by early adulthood.
By the age of 30:
- 9 percent of individuals with MHO had developed type 2 diabetes, compared with 17 percent in the MUO group and 0.5 percent in the control group
- High blood pressure was observed in 11 percent of the MHO group, 18 percent of the MUO group and 4 percent of the general population
- Abnormal blood lipid levels were present in 5 percent of those with MHO and 13 percent of those with MUO, compared with just 1 percent among controls
These findings indicate that even in the absence of early warning signs, children living with obesity carry a significantly increased burden of future disease risk.
“Even children with obesity who show no signs of cardiometabolic impact have a clearly increased risk of future diseases. This means that normal blood pressure and the absence of abnormal blood test results are not sufficient protection against future morbidity,” says Emilia Hagman, associate professor at the same department and the study’s corresponding author.
The role of early treatment
All children included in the study received structured support aimed at improving lifestyle habits. Researchers examined whether treatment response during childhood influenced long-term outcomes.
A strong response to treatment was associated with a reduced risk of developing all studied conditions – including type 2 diabetes, hypertension and dyslipidaemia. Notably, this protective effect was observed in both MHO and MUO groups.
This suggests that early intervention has meaningful and lasting clinical benefits, regardless of a child’s initial metabolic profile.
“Our results suggest that all children with obesity need treatment, even if they appear completely healthy upon examination,” says Claude Marcus.
Data sources and funding
The study drew on data from Sweden’s national quality registry BORIS, alongside several national health data registries.
Funding was provided by multiple organisations, including the Center for Innovative Medicine, the Ollie and Elof Ericsson Foundation and the Freemason Foundation for Children’s Welfare.
Several researchers reported receiving compensation from companies unrelated to this work. A full list of potential conflicts of interest is available in the original scientific publication.
Implications for clinical practice
The findings underscore the limitations of relying solely on current metabolic markers when assessing risk in children living with obesity. Even in the absence of immediate clinical abnormalities, long-term risks remain significant.
For clinicians, this supports a more proactive and inclusive approach to obesity management in paediatric populations – one that does not defer intervention based on apparently normal test results, but instead recognises obesity itself as a key driver of future health risk.
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Genetic Risk Scores Offer Improved Prediction of Obesity, Type 2 Diabetes and Long-Term Health Outcomes
Key Takeaways:
- A new polygenic risk score integrates genetic data from over 8.5 million people to better predict obesity and type 2 diabetes risk
- The model goes beyond traditional measures such as body mass index by incorporating multiple aspects of metabolic function
- Individuals with higher genetic risk were more likely to develop complications and require interventions such as GLP-1 therapy or bariatric surgery
A more comprehensive approach to metabolic risk
Obesity and type 2 diabetes are complex metabolic conditions influenced by a combination of environmental, behavioural and genetic factors. While traditional clinical measures such as body mass index have long been used to assess risk, they do not fully capture the biological complexity underlying these conditions.
In a new study published in Cell Metabolism, researchers from Mass General Brigham have developed an advanced polygenic risk score designed to improve prediction of both obesity and type 2 diabetes, as well as their long-term health consequences. Polygenic risk scores work by aggregating the effects of many genetic variants across the genome, providing an estimate of an individual’s predisposition to developing a given condition.
“Our intention was to not only capture the risk of being diagnosed with obesity or diabetes, but also to better predict health consequences across the life course by integrating many aspects of metabolic function,” said co-first author Min Seo Kim, MD, MSc. “In the future, this genomic approach could complement established clinical risk factors to inform patient care and preventative strategies.”
Building a next-generation polygenic risk score
The research team constructed two distinct metabolic risk scores – one optimised for obesity and another for type 2 diabetes. Unlike conventional models, these scores incorporate genetic signals linked to 20 different traits associated with metabolic health. These include factors such as fat distribution, insulin regulation and glucose control.
To build these models, the investigators drew on genome-wide association studies conducted across some of the largest biobank datasets globally, encompassing more than 8.5 million individuals. This scale allowed the researchers to capture a broad and diverse range of genetic influences.
Importantly, the model moves beyond reliance on body mass index alone, reflecting a growing recognition that metabolic health cannot be fully understood through weight-based measures in isolation.
Predicting disease progression and clinical outcomes
Beyond predicting the likelihood of developing obesity or type 2 diabetes, the new polygenic risk scores demonstrated the ability to forecast downstream health outcomes.
The researchers found that individuals identified as high risk were more likely to go on to develop complications such as cardiovascular disease and stroke. Even among people who were initially healthy, those with a high genetic risk score were approximately twice as likely to require clinical interventions over time.
Specifically, individuals with higher polygenic risk scores were about twice as likely to receive GLP-1 receptor agonist medications or undergo bariatric surgery compared with those with average risk scores, over a median follow-up period of 5.5 years.
These findings suggest that genetic profiling could help identify people at risk earlier in the disease trajectory, potentially enabling more proactive and targeted care.
Improved performance across diverse populations
A notable strength of the study lies in its use of multi-ancestry genetic data. By incorporating genome-wide association studies from a wide range of populations, including African, East Asian, South Asian and Middle Eastern groups, the researchers were able to develop risk scores that performed better across diverse populations than earlier models.
Historically, many genetic prediction tools have been less accurate in non-European populations due to limited representation in genomic datasets. This study represents a step towards addressing that imbalance and improving equity in precision medicine.
Towards more personalised prevention and treatment
The research team emphasises that this work is part of a broader effort to refine understanding of the genetic subtypes of obesity and type 2 diabetes. Improved classification of these conditions could support more precise patient stratification in clinical trials and, ultimately, more tailored interventions in routine care.
“We want clinicians to be able to think about metabolic conditions in terms beyond body mass index, with a focus more broadly on underlying genetic susceptibility,” said co-senior author Akl Fahed, MD, MPH, of the Cardiovascular Research Center at Massachusetts General Hospital and an interventional cardiologist with the Mass General Brigham Heart and Vascular Institute. “Early identification of people who are likely to have a worse trajectory of poor metabolic health, before they even develop these conditions, can help us improve prevention and clinical interventions. That is how we can cure disease, and that is the bold mission that we are after.”
Implications for clinical practice
While further validation and implementation work will be required, the findings highlight the potential role of genomic tools in enhancing current approaches to metabolic disease prevention and management. By complementing existing clinical risk factors, polygenic risk scores could support earlier identification of people at risk and enable more personalised, proactive care pathways.
As healthcare systems increasingly move towards precision medicine, integrating genetic insights with clinical decision-making may become an important step in improving outcomes for people living with obesity and type 2 diabetes.
CCH insights:
This is exciting research, and a big step towards precision obesity prevention, as it gives us an individual risk score for obesity and diabetes for each patient. However, it is only half the story – ideally we’d also like to be able to determine what type of interventions will work best for each individual (in terms of diet, lifestyle and medicine) in order to optimise their chances of good metabolic health and achieving a healthy weight. Hopefully the ability to do this is not too far away.
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Green Tea Shows Significant Benefits for Glucose Metabolism and Muscle Health in Mice with Obesity
Key Takeaways:
- Green tea extract significantly improved insulin sensitivity, glucose tolerance, and muscle preservation in mice with obesity, even when they continued consuming a high-calorie diet.
- The study controlled for temperature effects, providing clearer evidence that green tea’s metabolic benefits are independent of cold-induced energy expenditure.
- Findings suggest a potential role for green tea as a safe, accessible adjunct to obesity treatment in humans, though exact dosing for people remains to be established.
Ancient beverage, modern research
Green tea, long valued for its medicinal and antioxidant properties, continues to attract scientific interest for its impact on metabolic diseases such as obesity and type 2 diabetes. A recent study led by Professor Rosemari Otton from the Interdisciplinary Graduate Programme in Health Sciences at Cruzeiro do Sul University in São Paulo, Brazil, offers new insights into how green tea affects metabolism.
Otton, who has dedicated over 15 years to the study of green tea, explained that her initial curiosity stemmed from the popular belief that green tea promotes weight loss. The findings of her latest research, published in Cell Biochemistry & Function, reinforce the potential of green tea as a therapeutic adjunct in managing obesity.
Study design and green tea administration
The research team first fed mice a high-calorie diet for four weeks, including both a fat-rich diet and a “cafeteria diet” to replicate a Western-style eating pattern. “We give them chocolate, filled cookies, dulce de leche, condensed milk… In other words, the same type of food that many people consume on a daily basis,” said Otton.
After this induction phase, the mice continued on the high-calorie diet for 12 weeks, with some receiving a standardised green tea extract at 500mg per kilogram of body weight via intragastric gavage. This method ensured precise dosing. “If we put it in water, for example, we’d have no way of knowing how much the animal actually ingested,” Otton explained.
For humans, this dose would equate to approximately 3 grams of green tea daily — roughly three cups. However, Otton cautioned that not all commercial products meet required standards:
“Ready-made tea bags do not always guarantee the quantity or quality of the compounds. The ideal for consumption would be to use standardised green tea extract, like those found in compounding pharmacies. This is a concentrated way of using the plant, with a guarantee of the presence of flavonoids, which are the health-beneficial compounds present in the green tea plant.”
Controlled conditions for reliable results
A distinctive feature of the study was the use of a thermoneutral environment at 28°C, eliminating confounding effects caused by chronic cold exposure. Mice are typically kept at around 22°C in animal facilities, a temperature that triggers energy expenditure to maintain body heat.
“Excessive cold activates compensatory regulatory mechanisms in the animals’ bodies, causing them to expend more energy to stay warm. This can mask the real effects of any substance,” Otton explained. “By maintaining thermoneutrality, we were able to see the effects of green tea in a ‘clean’ way, without environmental interference.”
A previous study published in European Journal of Nutrition (August 2022) found that obese mice treated with green tea lost up to 30% of their body weight — a reduction Otton described as highly significant:
“If a person loses 5% to 10% of their body weight, that’s already a lot. So this result in animals is very significant.”
Preservation of muscle health
One of the most striking findings of the latest study was the preservation of muscle fibre morphology. Obesity often leads to a reduction in muscle fibre diameter, but green tea helped maintain muscle structure.
“One way to assess muscle function is to look at fibre diameter. If it increases, we have more active muscle components. Green tea managed to maintain this diameter, showing that it protects muscle against the harmful effects of obesity,” Otton said.
Genetic and metabolic insights
The study also explored gene expression related to glucose metabolism. Green tea treatment enhanced the expression of genes such as Insr, Irs1, Glut4, Hk1, and Pi3k, all of which play a role in glucose uptake and utilisation in muscle tissue. Additionally, the activity of lactate dehydrogenase (LDH), an enzyme vital for glucose metabolism, was restored.
Otton noted that green tea appeared to have a selective effect:
“It makes obese animals lose weight but keeps lean animals at a balanced weight. This shows that the tea seems to need an environment with excess nutrients to act, which supports the hypothesis that it acts directly on fat cells.”
Synergy of bioactive compounds
Green tea contains dozens of bioactive compounds, and attempts to isolate them have proven less effective than using the whole extract.
“We’ve tried to separate these compounds and study their effects individually, but the whole extract is always more effective. There’s a synergy between the compounds that we can’t reproduce when they’re isolated,” Otton explained.
One mechanism under investigation involves adiponectin, a protein secreted by fat cells that regulates inflammation and metabolism. In mice genetically modified to lack adiponectin, green tea showed no effect — pointing to adiponectin as a key mediator of its benefits.
Translating findings to human health
Despite these promising findings, Otton cautioned that safe and effective doses for humans have yet to be established, owing to variability in extracts and individual responses. She stressed the importance of long-term, habitual consumption rather than expecting rapid results:
“The ideal is chronic consumption, as we see in Asian countries. In Japan, for example, people consume green tea every day, throughout their lives, and obesity rates are low. But this is different from drinking tea for five months and expecting a miraculous weight loss effect.”
Otton also emphasised the importance of accessible and safe treatment options:
“The idea is to have safe, natural, effective, and high-quality compounds. The Camellia sinensis plant offers this. We’re still studying all the compounds involved, but there’s no doubt that green tea, as a plant matrix rich in flavonoids, has important therapeutic potential.”
The road ahead
Finally, Otton underscored the need for caution when translating animal research to clinical practice:
“What we see in animals doesn’t always reproduce in humans. But if we want to make this translation to real life, we need to think about all the details, such as ambient temperature. It’s these precautions that increase the validity of our data. We’re far from having all the answers, but we’re getting closer and closer.”
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