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Effects of front-of-package nutrition labelling systems on objective understanding and purchase intention in Panama: results from a multi-arm parallel-group randomised controlled trial

Published online by Cambridge University Press:  16 May 2024

Fabio S Gomes*
Affiliation:
Pan-American Health Organization (PAHO), Washington DC, USA
Israel Ríos-Castillo
Affiliation:
Food and Agriculture Organization for the United Nations, Subregional Office for Mesoamerica, Panama City, Panama
Leon Ramon Leal Correa
Affiliation:
Fundación Movimiento de Alimentación Saludable, Panama City, Panama
Bethy Cruzado
Affiliation:
Fundación Movimiento de Alimentación Saludable, Panama City, Panama
Carlos Felipe Urquizar Rojas
Affiliation:
Department of Design, University of Joinville Region, Joinville, Brazil Laboratory of Information Design Systems (LabDSI), Federal University of Paraná, Curitiba, Brazil
Gastón Ares Devincenzi
Affiliation:
Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, Pando, Uruguay Centro de Investigación Básica en Psicología (CIBPsi), Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
Elka González-Madden
Affiliation:
Pan-American Health Organization (PAHO), Panama City, Panama
Jorge Victoria
Affiliation:
Pan-American Health Organization (PAHO), Panama City, Panama
*
*Corresponding author: Email [email protected]
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Abstract

Objective:

To assess the effect of different front-of-package labelling (FOPL) schemes on the objective understanding of the nutritional content and intention to purchase products, in Panama.

Design:

Single-blinded multi-arm parallel-group randomised controlled trial.

Setting:

Supermarkets across Panama. Participants were exposed to two-dimensional images of fifteen mock-up products presented at random and balanced orders. Participants assigned to the intervention groups were exposed to mock-ups featuring one FOPL scheme: black octagonal warning labels (OWL), traffic-light labelling (TFL) or guideline daily amounts (GDA). The control group was not exposed to any FOPL scheme.

Participants:

Adult supermarket shoppers (n 1200). Participants were blinded to group assignment.

Results:

A similar number of participants were randomised and analysed in each group: OWL (n 300), TFL (n 300), GDA (n 300) and control (n 300). The odds of choosing to purchase the least harmful or none of the options more often was the highest in the OWL group. Compared with the control group, these odds were two times higher in the OWL group (OR 2·13, 95 % CI 1·60, 2·84) and 57 % higher in the TFL (1·57, 1·40–2·56), with no changes in the GDA (0·97, 0·73–1·29). OWL also resulted in the highest odds for correctly identifying the least harmful option and for correctly identifying a product with excessive amounts of sugars, sodium and/or saturated fats.

Conclusions:

OWL performed best in helping shoppers to correctly identify when a product contained excessive amounts of nutrients of concern, to correctly identify the least harmful option and to decide to purchase the least harmful or none of the options, more often.

Type
Research Paper
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
© Pan American Health Organization, 2024. Published by Cambridge University Press on behalf of The Nutrition Society

Panama faces a serious public health problem due to the high prevalence of overweight, obesity and chronic non-communicable diseases (NCD)(Reference Hammond and Levine1,Reference Bloom, Cafiero and Jané-Llopis2) . According to the latest nationally representative survey, the prevalence of overweight and obesity was 71·7 % among adults, 36·6 % in schoolchildren and 13 % in pre-schoolers(3). Like in other countries worldwide, NCD are the main leading cause of death in Panama(Reference Razzaghi, Martin and Quesnel-Crooks46), including CVD, neoplasia, diabetes and hypertension(7).

Unhealthy eating has been identified as one of the main modifiable causes of overweight and obesity, and NCD (8). The expansion of unhealthy diets has been largely driven by the consumption of ultra-processed products and processed products that are energy-dense and contain excessive amounts of nutrients associated with NCD (i.e. sugars, sodium, total fat, saturated fats, and trans fats)(9,Reference Swinburn, Kraak and Allender10) .

For these reasons, public policies are needed to promote food environments that facilitate healthier diets(911). The provision of information that can effectively encourage healthier food decisions is essential for this purpose(Reference Nutbeam12,Reference Mansfield, Wahba and De Grandpré13) . Quantitative nutrient declaration tables are difficult to find and understand for consumers, and therefore, they are seldom used for making food purchase decisions(Reference Campos, Doxey and Hammond14). Considering that people spend little time and cognitive effort when making their food purchase decisions(Reference Tversky and Kahneman15Reference Johnson and Payne20), simplified nutrition information schemes have been recommended as a measure to improve their ability to find and understand nutrition information, encouraging healthier food choices(11,21) . For these reasons, the adoption of front-of-package labelling (FOPL) has been identified as a priority globally(22,23) .

Several FOPL schemes have been developed worldwide. They differ in purpose and performance(11). Considering that almost half of deaths are caused by high blood fasting glucose, hypertension, and overweight and obesity(24), one of the main purposes sought to be met with FOPL is to allow consumers to easily and correctly identify products that are excessive in critical nutrients associated with those risk factors, including sugars, total fat, saturated fats, trans fats and sodium, and to discourage their consumption(Reference Ares, Antúnez and Curutchet25).

Panama, like other Central American countries, has put forward a proposal of a FOPL system, which has been tabled in the parliament(Reference Crosbie, Gomes and Olvera26,27) . Likewise, the Council of the Ministers of Health of Central America and Dominican Republic (COMISCA) has also tabled a proposal for a FOPL system for adoption by the Central American Integration System(28). The production of local evidence could help in informing the development and adoption of both these national and sub-regional regulatory initiatives.

This study was designed to add another piece to the existing body of evidence to help informing policy decisions in Panama, in Central America and worldwide. Considering that evidence on the effectiveness of different FOPL schemes is still emerging worldwide, the study makes a relevant contribution to the literature. It compares the octagonal warning labels (OWL) included in the proposal put forward by COMISCA(28), which was also tabled in Panamanian domestic parliament, and the guideline daily amounts (GDA) and the traffic-light labelling (TFL), which have been proposed by food industry sectors as alternatives.

This was the first study of its kind to be completed in Panama. The trial aimed at assessing the effect of these FOPL schemes on the objective understanding of the nutritional content (correctly selecting the least harmful option and correctly identifying sugars, sodium and/or saturated fats found to be in excess) and choosing to purchase the least harmful option (purchase intention) of a series of products, in Panama.

Methods

Design

A single-blinded multi-arm parallel-group randomised controlled trial was conducted among adult shoppers at supermarkets in Panama. Participants were randomly allocated at an equal rate (1/4) to the four study groups (three experimental and the control group). Participants in each group were exposed to either one of the experimental conditions or allocated to the control group.

Participants

Adult supermarket shoppers in Panama (n 1200) of 18 years old or older were included in the study, except for those visually impaired or unable to read or to give informed consent. Participants were recruited and interviewed at popular supermarkets serving customers of varying socioeconomic status, in April and May 2022.

A total of thirty-one supermarkets were included in the study, which were located in different provinces across Panama (Chiriquí, Coclé, Colón, Herrera, Los Santos, Panamá and Veraguas). Field research procedures to recruit and interview shoppers were similar to those adopted and described elsewhere(Reference White-Barrow Gomes and Eyre29).

Interventions

The preparation and presentation of the two-dimensional images of mock-up products shown to participants followed similar procedures used by White-Barrow et al. (Reference White-Barrow Gomes and Eyre29). Figure 1 illustrates one of the pages of a booklet of images shown to participants of one of the experimental groups.

Fig. 1 Example of a set of images of one category of products shown to participants with one of the FOPL schemes tested (TFL, traffic-light labelling scheme)

The mock-ups resembled characteristics of real commercial products available in the Panamanian market in terms of package and graphic design, and nutritional composition. Five sets of mock-ups were designed. Each set included three products from each of the five product categories of ultra-processed products commonly consumed (3 × 5 = 15 mock-up products). The product categories were breakfast cereal extrudates, chocolate flavoured milks, filled cookies, white breads and yogurts.

The same fifteen mock-up products were used in each group; the only difference across groups was the FOPL scheme they were featuring. Participants were randomly allocated to one of four experimental groups: OWL, TFL, GDA or no FOP label (control group). Mock-ups shown to participants featured solely the scheme they were allocated to.

The application of TFL followed the specifications developed by the UK Department of Health, the Food Standards Agency and devolved administrations in Scotland, Northern Ireland and Wales in collaboration with the British Retail Consortium(30). For the GDA, the specifications proposed by the industry for adoption in the Central American Integration System were used(28). The serving sizes featured on TFL and GDA labels resembled the ones found in real products (see online supplementary material, Table S1). The specifications used for the application of black OWL followed the standard proposed by COMISCA(28). For consistency, thresholds used to define the ‘high/excess’ content of sugars, total fat, saturated fats or sodium were the same for all FOPL systems, when such category applied (i.e. OWL and TFL), and the Pan American Health Organization criteria included in the COMISCA proposed standard were the one used(31). See the nutritional composition of products in the supplementary material (see online supplementary material, Table S1). All sets of mock-up products were identical except for the FOPL icons featured. Figure 2 illustrates one of the mock-up products with the FOPL schemes applied.

Fig. 2 Example of a product with the different front-of-package labelling (FOPL) schemes applied. (A) No FOPL (control condition), (B) guideline daily amounts (GDA), (C) traffic-light labelling scheme (TFL), (D) octagonal warning label (OWL)

The order of the questions asked aimed at reducing potential response bias and followed the procedures used and described in greater detail elsewhere(Reference White-Barrow Gomes and Eyre29). In summary, first, participants were asked to indicate which product they would buy in each category and, in a second task, which product was the least harmful to health. For the last task, they were asked whether products had an amount of sugars, sodium, total fat, saturated fat, trans fat or none of these nutrients, which was higher than the recommended for a healthy diet.

Outcomes

The contribution of the different FOPL schemes to improving the decision of participants to buy the least harmful option more often, the selection of the least harmful option more often and the correct identification of sugars, sodium, total fat, trans fats and/or saturated fats found to be in excess in the products more often are the primary outcomes of this study.

The metrics used to assess the outcomes have been described in greater detail elsewhere(Reference White-Barrow Gomes and Eyre29). These included the frequency with which participants responded they would buy the least harmful option or none of the options, the frequency with which participants made a correct identification of the least harmful option, and the number of correct answers and the proportion of participants with zero, one, two, three, four or five correct answers when responding whether the products contained amounts of sugars, sodium, total fat, trans fats and/or saturated fats found to be higher than the recommended for a healthy diet.

Sample size

The sample size was estimated based on calculations to detect a difference between two proportions. The most conservative criterion was used, assuming that the proportion of participants who correctly identify products with nutrient above nutritional recommendations for the control condition would be 50 %. The number of participants needed to detect an absolute increase of 12 % (which is smaller than what has been previously reported(Reference Arrúa, Machín and Curutchet32,Reference Acton, Rynard and Adams33) ) with a confidence level of 95 % and a power level of 80 % was estimated in 265 participants in each of the experimental groups (comparisons and control) (n = 265 × 4 = 1060). The total resulting sample size used was of 1200 participants.

Randomisation

Shoppers were selected using quota sampling to meet a composition of age, gender and educational level within each group that resembles the one found for the population of Panama. A similar number of participants were randomly allocated to one of the three intervention groups or the control group: OWL (n 300), TFL (n 300), GDA (n 300) and the control group which was not exposed to any FOPL scheme (n 300). The randomisation of the experimental conditions and groups and single-blinding procedures were similar to those used and described in greater detail elsewhere(Reference White-Barrow Gomes and Eyre29).

Statistical analyses

Descriptive statistics on the sample included proportions (for categorical variables), means (for numeric variables) and their respective 95 % CI. Ordered logistic regression models were used to estimate the OR of FOPL schemes improving, more often, the frequency with which consumers would choose to purchase the least harmful products or none of the products, the correct identification of the least harmful options of products and the number of times participants correctly identified when products contained excessive amounts of sugars, total fat, saturated fats, trans fats and/or sodium.

Models were adjusted for age, gender, education level, and reported NCD and related risk factors. Subset analyses for each single category of products were also conducted using logistic regression with logit link function.

All tests were two-sided, and we considered P ≤ 0·05 to be statistically significant. The analyses were conducted in R language and environment for statistical computing version 4.0.1(34).

Results

As shown in Table 1, most of the respondents were under 50 years of age, women and who reached the secondary level of education or lower. In addition, the most commonly reported NCD condition and risk factor was hypertension, followed by high cholesterol, overweight and obesity, diabetes and heart disease.

Table 1 Sociodemographic characteristics and reported non-communicable disease conditions and related risk factors of the sample

GDA: guideline daily amounts; TFL: traffic-light system; OWL: octagonal warning labels.

When compared with the control, the chances of participants choosing to purchase the least harmful option or none of them more often doubled when they were exposed to the OWL (OR 2·13, 95 % CI 1·60, 2·84). The TFL (OR 1·57, 95 % CI 1·17, 2·10) performed significantly better than the control and the GDA and significantly worse than the OWL, and the GDA (0·97, 95 % CI 0·73, 1·29) was inefficacious in improving such odds compared with the control condition (Table 2).

Table 2 Effect of different FOPL schemes on the objective understanding of the nutritional content, harmfulness perception and intention to purchase products, in Panama, compared with the control condition. Values are odds ratios (95 % CI)

GDA: guideline daily amounts; TFL: traffic-light system; OWL: octagonal warning labels.

a,b,c Different superscript letters within a row in the comparison between columns indicate significant differences between the effects of FOPL schemes (P ≤ 0.05).

Estimates for sets of products were obtained using ordered logistic regression models and estimates for single categories of products were obtained using logistic regression models with link function binomial logit. All estimates were adjusted for adjusted for age, gender and education level.

* Significantly different from the control condition. Also highlighted in bold (P ≤ 0.05).

The OWL also practically doubled the odds of participants choosing to purchase the least harmful option more often compared with the control (OR 1·94, 95 % CI 1·31, 2·88). As shown in Table 2, all the other systems were inefficacious in improving such odds (GDA, 1·05, 95 % CI 0·71, 1·54; TFL, 1·43, 95 % CI 0·97, 2·10).

The effects exerted by the OWL on the intention to purchase the least harmful option or none of the options were similar for almost all product categories when they were analysed separately. The TFL was only able to improve the intention to purchase the least harmful or none of the options for two categories (yogurts and breakfast cereals), whereas the GDA was inefficacious for all categories. As a matter of fact, the GDA performed worse than the control in terms of encouraging consumers to choose to buy the least harmful or none of the options (Table 2).

The odds of participants correctly identifying the least harmful option more often was the highest and increased by more than sevenfold compared with the control when they were exposed to the OWL (OR 7·51, 95 % CI 5·52, 10·27). It was 2·7 times higher for participants in the TFL group (2·74, 95 % CI 2·03, 3·71), whereas the GDA (0·81, 95 % CI 0·61, 1·08) was inefficacious in improving such odds, compared with the control (Table 2).

When analysing the results separately by product category, the OWL again performed best in improving the capacity of participants to identify the least harmful option for all product categories. The TFL performed significantly worse than the OWL and significantly better than the GDA for all product categories. The GDA was not able to help participants completing this task correctly when applied to flavoured milks and to white breads. In addition, it worsened the capacity of participants correctly completing this task in yogurts and filled cookies (Table 2).

The chances of participants correctly identifying when a product contained excessive amounts of critical nutrients (sodium, sugars and saturated fats) more often were also the highest when they were exposed to the OWL (16·32, 95 % CI 11·70, 22·89), followed by the TFL (3·71, 95 % CI 2·72, 5·06), whereas the GDA (1·08, 95 % CI 0·78, 1·48) was inefficacious (Table 2). The Wald statistics for homogeneity also confirms the superiority of OWL in improving the capacity of participants to correctly identify products with excessive amounts of critical nutrients (Fig. 3). When analysing these results separately by product category, the OWL again performed best for all product categories. Again, the TFL performed significantly worse than the OWL and significantly better than the GDA for all product categories. The GDA was not able to help participants completing this task correctly when applied to yogurts, filled cookies and white breads. In addition, it worsened the capacity of participants correctly completing this task for flavoured milks (Table 2).

Fig. 3 Proportional distribution of the number of times participants correctly identified the presence of critical nutrients in excess in products, by experimental groups. Wald (H): Wald statistics for homogeneity indicating proportional distributions differ significantly. GDA: guideline daily amounts; TFL: traffic-light labelling scheme; OWL: octagonal warning label

Discussion

The study found that the OWL outperformed the GDA and TFL in helping consumers correctly identifying the least harmful option and the presence in products of critical nutrients in excess and choosing to purchase the least harmful option or none of the options in Panama, regardless of the populations’ age, gender and education.

Similarly, to what this study has found, previous studies have also reported that warning labels perform better than GDA and TFL in helping consumers make healthier decisions. Some of the reasons include the fact that OWL are easier and quicker to find on the labels and to understand due to their simplicity and higher salience from the usual colourful background of processed and ultra-processed packaged products(Reference White-Barrow Gomes and Eyre29,Reference Arrúa, Machín and Curutchet32,Reference Acton, Rynard and Adams33,Reference Cabrera, Machín and Arrúa35Reference Goodman, Vanderlee and Acton37) . The TFL classifies the content of target nutrient content into low/medium/high, and this information is expected to require more time and cognitive effort to interpret compared with the OWL, as reported in previous studies(Reference Arrúa, Machín and Curutchet32,Reference Cabrera, Machín and Arrúa35) . The use of green colour found in systems such as the TFL may drive consumers to misperceive a product as healthier and undesirably raise their appetite for such products, which may explain their lower effect on improving understanding of nutritional information and reducing purchase intention for products high in nutrients associated with NCD (Reference Cabrera, Machín and Arrúa35,Reference Schuldt38Reference Hock, Acton and Jáuregui43) . In addition, the use of red colour, also found in such systems, although intended to communicate a higher harmfulness level, may trigger an opposite effect in some products. Lemos et al. (2020) have shown, using objective measures of brain activities, that the red colour triggers a positive emotional motivation towards sweet ultra-processed products(Reference Lemos, Almo and Campagnoli44).

Two studies differed from some of our results(Reference Machín, Aschemann-Witzel and Curutchet45,Reference Bandeira, Pedroso and Toral46) . One study using a virtual supermarket simulator found the nutritional composition of the simulated shopping carts was similar to the OWL and TFL groups(Reference Machín, Aschemann-Witzel and Curutchet45). In another study, Bandeira et al. (2021) reported that the OWL performed better than the TFL in improving the understanding about nutritional content of products, but differences regarding the intention to purchase products were not significant(Reference Bandeira, Pedroso and Toral46).

Reviews and meta-analyses have documented that warning labels are more consistently successful and perform best in reducing purchase intention of unhealthy products(Reference Croker, Packer and Russell47,Reference Ikonen, Sotgiu and Aydinli48) . In this sense, a real-life interrupted time series study conducted in Chile has shown warning labels contributed to reductions in the purchase of products high in calories, sodium, saturated fats and sugars(Reference Taillie, Bercholz and Popkin49).

One of the major strengths of this study is the robust multi-arm parallel-group randomised controlled design which allows the results to be attributed to FOPL schemes and avoids differential carry-over effects that are more likely to happen in within-subject studies. The exposure of participants to two-dimensional mock-up products with different FOPL schemes was standardised to match real products’ label sizes and sizes and proportions of FOPL, approaching real-life conditions, also strengthened its external validity. However, it should be noted that the study was conducted using fictitious brands, products were not associated with price information and participants did not purchase the products, which has strengthened the attribution of the effects to the FOPL schemes but, conversely, limited the analysis of the relative importance of these other factors.

The findings of this study indicate that among the FOPL options under discussion and consideration in Panama and Central America, the OWL included in the COMISCA proposal(28) to be adopted by Member States of the Central American Integration System and in bills to be adopted by the Panamanian parliament is the most effective option in meeting the regulatory objective of helping the population to correctly identify the least harmful option and the presence of excessive amounts of critical nutrients and to choose to purchase the least harmful product more often. Once the system is implemented in Central American countries, future research to evaluate the impact on actual purchases of products and on dietary changes will be needed to keep track of the changes expected to be exerted by OWL in the short- and mid-terms. Efforts to safeguard the policy space from ultra-processed food industry’s known attempts to shape food and nutrition policies in their favour and jeopardising public health are also paramount.

Acknowledgements

We would like to thank Danilo Perez (Executive Director of the Centro para la Defensa del Consumidor de El Salvador) for his feedback on the research protocol.

Financial support

This work was supported by Resolve to Save Lives and by the Subregional Office for Mesoamerica of the Food and Agriculture Organization of the United Nations.

Conflict of interest

None.

Authorship

All authors provided substantial contributions to the conception and design of the study. LRLC and BC led the data acquisition. FSG performed the statistical analyses and provided a preliminary interpretation of the findings. FSG and IRC drafted the first version of the paper, and all authors revised it critically for important intellectual content. All authors approved the final version to be published. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. FSG is a staff member of the Pan American Health Organization. Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the Pan American Health Organization.

Ethics of human subject participation

This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the Institutional Ethics Committee of the Institute of Nutrition of Central America and Panama, by the Bioethics Committee of the University of Panama and by the Pan American Health Organization Ethics Review Committee. Written informed consent was obtained from all subjects.

Supplementary material

For supplementary material accompanying this paper, visit https://doi.org/10.1017/S1368980024001009.

References

Hammond, R & Levine, R (2010) The economic impact of obesity in the United States. Diabetes, Metab Syndr Obes Targets Ther 3, 285295.Google Scholar
Bloom, DE, Cafiero, ET, Jané-Llopis, E et al. (2011) The Global Economic Burden of Noncommunicable Diseases. Geneva: World Economic Forum.Google Scholar
Instituto Conmemorativo Gorgas de Estudios de la Salud (2010) Sistema de información de la Encuesta Nacional de Salud de Panamá (ENSPA) 2019–2023. Panama: Departamento de Investigación y Evaluación de Tecnología Sanitaria, Ministerio de Salud.Google Scholar
Razzaghi, H, Martin, DN, Quesnel-Crooks, S et al. (2019) 10-year trends in noncommunicable disease mortality in the Caribbean region. Rev Panam Salud Pública 43, e37.Google Scholar
World Health Organization (2022) Noncommunicable diseases. Fact sheet; available at https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (accessed August 2023).Google Scholar
NCD Countdown 2030 collaborators (2018) NCD countdown 2030: worldwide trends in non-communicable disease mortality and progress towards sustainable development goal target 3.4. Lancet 392, 10721088.Google Scholar
Instituto Nacional de Estadística y Censo (INEC) (2020) Defunciones y Tasa de Mortalidad de las Cinco Principales Causas de Muerte, Por Sexo, Según Provincia, Comarca Indígena de Residencia y Causa: año 2020. Panamá: INEC.Google Scholar
GBD 2019 Risk Factors Collaborators (2020) Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 12231249.Google Scholar
Pan American Health Organization (2019) Ultra-Processed Food and Drink Products in Latin America: Sales, Sources, Nutrient Profiles, and Policy Implications. Washington, D.C.: PAHO.Google Scholar
Swinburn, BA, Kraak, VI, Allender, S et al. (2019) The global syndemic of obesity, undernutrition, and climate change: the Lancet Commission report. Lancet 393, 791846.Google Scholar
Pan American Health Organization (2020) Front-of-Package Labeling as a Policy Tool for the Prevention of Noncommunicable Diseases in the Americas. Washington, D.C.: PAHO.Google Scholar
Nutbeam, D (2000) Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promot Int 15, 259267.Google Scholar
Mansfield, E, Wahba, R & De Grandpré, E (2020) Integrating a health literacy lens into nutrition labelling policy in Canada. Int J Environ Res Public Health 17, 4130.Google Scholar
Campos, S, Doxey, J & Hammond, D (2011) Nutrition labels on pre-packaged foods: a systematic review. Public Health Nutr 14, 14961506.Google Scholar
Tversky, A & Kahneman, D (1974) Judgment under uncertainty: heuristics and biases. Science 185, 11241131.Google Scholar
Hoyer, WD (1984) An examination of consumer decision making for a common repeat purchase product. J Consum Res 11, 822829.Google Scholar
Knutson, B, Rick, S, Wimmer, GE et al. (2007) Neural predictors of purchases. Neuron 53, 147156.Google Scholar
Olshavsky, RW & Granbois, DH (1979) Consumer decision making—fact or fiction?. J Consum Res 6, 93100.Google Scholar
Wright, PL (1975) Consumer choice strategies: simplifying vs. optimizing. J Mark Res 11, 6067.Google Scholar
Johnson, EJ & Payne, JW (1985) Effort and accuracy in choice. Manage Sci 31, 395414.Google Scholar
World Health Organization (WHO) (2019) Guiding Principles and Framework Manual for Front-of-Pack Labelling for Promoting Healthy Diet. Geneva: WHO.Google Scholar
World Health Organization (WHO) (2017) Tackling NCDs: ‘Best Buys’ and Other Recommended Interventions for the Prevention and Control of Noncommunicable Diseases. Geneva: WHO.Google Scholar
World Health Organization (WHO) (2023) Political Declaration of the Third High-Level Meeting of the General Assembly on the Prevention and Control of Non-Communicable Diseases, and Mental Health: Acceleration Plan to Support Member States in Implementing the Recommendations for the Prevention and Management of Obesity Over the Life Course. Geneva: WHO.Google Scholar
Institute for Health Metrics and Evaluation (2023) Global burden of diseases 2019. Available at https://vizhub.healthdata.org/gbd-results (accessed February 2024).Google Scholar
Ares, G, Antúnez, L, Curutchet, MR et al. (2023) Warning labels as a policy tool to encourage healthier eating habits. Curr Opin Food Sci 51, 101011.Google Scholar
Crosbie, E, Gomes, FS, Olvera, J et al. (2022) A policy study on front–of–pack nutrition labeling in the Americas: emerging developments and outcomes. The Lancet Regional Health –Americas 18, 100400.Google Scholar
Asamblea Nacional de Diputados de Panamá (2019) Anteproyecto de Ley 265 que Establece un Sistema de Etiquetado Frontal de Advertencia Nutricional en Panamá a Través de Sellos Octagonales con Leyenda alto en. Panama City: Asamblea Nacional de Diputados de Panamá.Google Scholar
Consejo de Ministros de Salud de Centroamérica y República Dominicana (COMISCA) (2019) Resolución COMISCA 20–2019. Relativa al Reglamento Técnico Centroamericano de Etiquetado Frontal de Advertencia Nutricional (RTCA-EFAN). San Salvador: COMISCA.Google Scholar
White-Barrow Gomes, FS, Eyre, S et al. (2023) Effects of front-of-package nutrition labelling systems on understanding and purchase intention in Jamaica: results from a multiarm randomised controlled trial. BMJ Open 13, e065620.Google Scholar
Department of Health (2016) Food Standards Agency. Guide to creating a front of pack (FOP) nutrition label for pre-packed products sold through retail outlets. London: UK-FSA.Google Scholar
Pan American Health Organization (2016) Pan American Health Organization nutrient profile model. Washington, DC: Pan American Health Organization.Google Scholar
Arrúa, A, Machín, L, Curutchet, MR et al. (2017) Warnings as a directive front-of-pack nutrition labelling scheme: comparison with the guideline daily amount and traffic-light systems. Public Health Nutr 20, 23082317.Google Scholar
Acton, RB, Rynard, VL, Adams, J et al. (2023) Awareness, use and understanding of nutrition labels among adults from five countries: findings from the 2018–2020 international food policy study. Appetite 180, 106311.Google Scholar
R Core Team (2021) R: A Language and Environment for Statistical Computing. Vienna: R foundation for statistical computing.Google Scholar
Cabrera, M, Machín, L, Arrúa, A et al. (2017) Nutrition warnings as front-of-pack labels: influence of design features on healthfulness perception and attentional capture. Public Health Nutr 20, 33603371.Google Scholar
Deliza, R, de Alcántara, M, Pereira, R et al. (2019) How do different warning signs compare with the guideline daily amount and traffic-light system?. Food Qual Preference 80, 103821.Google Scholar
Goodman, S, Vanderlee, L, Acton, R et al. (2018) The impact of front-of-package label design on consumer understanding of nutrient amounts. Nutrients 10, 1624.Google Scholar
Schuldt, JP (2013) Does green mean healthy? Nutrition label color affects perceptions of healthfulness. Health Commun 28, 814821.Google Scholar
Huang, L & Lu, J (2016) The impact of package color and the nutrition content labels on the perception of food healthiness and purchase intention. Journal of Food Products Marketing 22, 191218.Google Scholar
Nyilasy, G, Lei, J, Nagpal, A et al. (2016) Color correct: the interactive effects of food label nutrition coloring schemes and food category healthiness on health perceptions. Public Health Nutr 19, 21222127.Google Scholar
Machín, L, Aschemann-Witzel, J, Curutchet, MR et al. (2018) Traffic light system can increase healthfulness perception: implications for policy making. J Nutr Educ Behav 50, 668674.Google Scholar
Spence, C (2015) On the psychological impact of food colour. Flavour 4, 21.Google Scholar
Hock, K, Acton, RB, Jáuregui, A et al. (2021) Experimental study of front-of-package nutrition labels’ efficacy on perceived healthfulness of sugar-sweetened beverages among youth in six countries. Prev Med Rep 24, 101577.Google Scholar
Lemos, TC, Almo, A, Campagnoli, RR et al. (2020) A red code triggers an unintended approach motivation toward sweet ultra-processed foods: possible implications for front-of-pack labels. Food Qual Preference 79, 103784.Google Scholar
Machín, L, Aschemann-Witzel, J, Curutchet, MR et al. (2018) Does front-of-pack nutrition information improve consumer ability to make healthful choices? Performance of warnings and the traffic light system in a simulated shopping experiment. Appetite 121, 5562.Google Scholar
Bandeira, LM, Pedroso, J, Toral, N et al. (2021) Performance and perception on front-of-package nutritional labeling models in Brazil. Rev Saude Publica 55, 19.Google Scholar
Croker, H, Packer, J, Russell, SJ et al. (2020) Front of pack nutritional labelling schemes: a systematic review and meta-analysis of recent evidence relating to objectively measured consumption and purchasing. J Hum Nutr Diet 33, 518537.Google Scholar
Ikonen, I, Sotgiu, F, Aydinli, A et al. (2020) Consumer effects of front-of-package nutrition labeling: an interdisciplinary meta-analysis. J of the Acad Mark Sci 48, 360383.Google Scholar
Taillie, LS, Bercholz, M, Popkin, B et al. (2021) Changes in food purchases after the Chilean policies on food labelling, marketing, and sales in schools: a before and after study. Lancet Planet Health 5, e52633.Google Scholar
Figure 0

Fig. 1 Example of a set of images of one category of products shown to participants with one of the FOPL schemes tested (TFL, traffic-light labelling scheme)

Figure 1

Fig. 2 Example of a product with the different front-of-package labelling (FOPL) schemes applied. (A) No FOPL (control condition), (B) guideline daily amounts (GDA), (C) traffic-light labelling scheme (TFL), (D) octagonal warning label (OWL)

Figure 2

Table 1 Sociodemographic characteristics and reported non-communicable disease conditions and related risk factors of the sample

Figure 3

Table 2 Effect of different FOPL schemes on the objective understanding of the nutritional content, harmfulness perception and intention to purchase products, in Panama, compared with the control condition. Values are odds ratios (95 % CI)

Figure 4

Fig. 3 Proportional distribution of the number of times participants correctly identified the presence of critical nutrients in excess in products, by experimental groups. Wald (H): Wald statistics for homogeneity indicating proportional distributions differ significantly. GDA: guideline daily amounts; TFL: traffic-light labelling scheme; OWL: octagonal warning label

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