Non-communicable diseases (NCDs) such as CVD, diabetes, and cancer represent a substantial global health burden and contribute significantly to mortality, with unhealthy dietary patterns being a major risk factor(1). Excessive consumption of processed foods and insufficient intake of fruits and vegetables contribute significantly to this epidemic(Reference Afshin, Sur and Fay2). To address these dietary risk factors, front-of-pack labelling (FOPL) has been introduced by the World Health Organization (WHO) as a ‘best-buy’ NCDs prevention intervention(3), providing consumers with clear and accessible nutrition information at the point of purchase(4–Reference Cowburn and Stockley6).
Various forms of FOPL, including interpretive and non-interpretive labels have been developed to present critical nutrition information(Reference Jones, Neal and Reeve7). Interpretive labels (such as traffic light labels and warning labels) simplify nutritional information using symbols or color codes to help consumers quickly assess a product’s healthiness, while non-interpretive labels (such as Guideline Daily Amount (GDA)) present factual nutritional information without evaluative guidance(Reference Jones, Neal and Reeve7). Systematic reviews show interpretive labels are generally more effective in improving consumer understanding and influencing purchasing decisions(Reference Shrestha, Cullerton and White8–Reference Song, Brown and Tan10), though their impact in real-world settings has been variable. The Nutri-Score system, voluntarily adopted in various countries in Europe such as France, Spain, Belgium, and Germany, has boosted the purchase of foods with better nutritional quality but showed no noticeable effect on the purchase of foods with moderate, low, or unlabeled nutritional quality(Reference Dubois, Albuquerque and Allais11). The warning labels, adopted in Chile in 2016, were associated with a reduction in unhealthy food purchasing in the first phase of implementation(Reference Taillie, Bercholz and Popkin12). In contrast, non-interpretive labels like GDA have shown limited effectiveness in changing Britain consumer behavior, often due to difficulties in interpretation(Reference Grunert, Wills and Fernández-Celemín13), although they do increase consumer awareness of nutritional information(Reference Gregori, Ballali and Vögele14) among European populations.
Low- and middle-income countries-specific considerations in front-of-pack labelling research
Policy implementation is crucial yet challenging, particularly at the national level. Although the WHO has advocated for the use of FOPL to promote healthier diets since 2004(Reference Kanter, Vanderlee and Vandevijvere15), there is limited standardized guidance on how best to develop and implement FOPL, and which stakeholders to involve in its enforcement(Reference Jones, Neal and Reeve7,Reference Kanter, Vanderlee and Vandevijvere15) . From a policy perspective, significant challenges exist in managing conflicts of interest and industry interference(Reference Jones, Neal and Reeve7).
While some countries have successfully implemented FOPL systems with positive impacts on consumer behavior(Reference Croker, Packer and Russell16,17) and product reformulation(Reference Mhurchu, Eyles and Choi18,Reference Shahid, Neal and Jones19) , significant gaps remain in understanding FOPL effectiveness in low- and middle-income countries (LMIC) contexts. LMIC often face significant constraints in regulatory capacity, limited resources for monitoring and enforcement, and competing policy priorities(Reference Kanter, Vanderlee and Vandevijvere15,Reference Thow, Jones and Schneider20) . These implementation challenges require research approaches that can capture policy processes operating under resource constraints and examine how limited enforcement capacity affects industry compliance and consumer response patterns.
LMIC food markets typically have different structures, with greater presence of local and regional producers alongside multinational corporations, varying technical capacities for product reformulation, and different economic pressures. Industry response patterns in LMIC may involve distinct strategies that require context-specific research methodologies to understand compliance mechanisms and reformulation capabilities(Reference Pettigrew, Coyle and McKenzie21).
The nutrition landscape in LMIC presents unique contextual factors that affect FOPL policy effectiveness. Many LMIC are experiencing rapid nutrition transitions with simultaneous exposure to traditional and processed food systems(Reference Baker and Friel22), creating complex food environments that differ markedly from established markets in high-income countries. Additionally, there is limited evidence on how diverse socio-economic and cultural factors in LMIC influence FOPL interpretation and use(Reference Kasapila and Shaarani23), including variations in literacy levels, cultural interpretations of health information, and economic constraints on food choices.
These LMIC-specific factors collectively demonstrate why research methodologies developed in high-income countries cannot be directly extrapolated to LMIC contexts. Developing context-appropriate research approaches for FOPL policy evaluation in LMIC is therefore essential for informing effective policies in settings where most of the world’s population resides.
Knowledge gaps and research needs
Despite growing FOPL policy adoption across LMIC, there is limited understanding of the research approaches and methodologies used to evaluate these policies. While studies have examined consumer responses to FOPL, there is a paucity of research on the challenges and best practices in policy implementation processes in resource-constrained settings(Reference Kanter, Vanderlee and Vandevijvere15,Reference Thow, Jones and Schneider20) . Moreover, the theoretical frameworks, measurement tools, and analytical methods used in LMIC contexts remain poorly characterized, hindering the development of evidence-based policy recommendations appropriate for these settings. Understanding these dynamics is crucial for developing context-appropriate policies and ensuring their effectiveness in improving population health across diverse global settings.
This study aimed to (1) explore research approaches, theoretical frameworks, tools, and data analysis methods used in assessing FOPL policy implementation and response in LMIC, and (2) identify methodological gaps and patterns in this literature.
Conceptual framework for front-of-pack labelling policy implementation and response
Understanding FOPL effectiveness requires examining the complex interactions between policy design, implementation processes, and stakeholder responses. Drawing from Grunert and Wills(Reference Grunert and Wills24), WHO’s FOPL policy implementation guidelines(25), and the World Cancer Research Fund report on the FOPL policy implementation lessons(26), we developed a conceptual framework that examines how stakeholder dynamics, political context, and industry responses shape policy outcomes across different stages (Figure 1).
Figure 1 Conceptual framework of front-of-pack labelling (FOPL) policy implementation and response.
This framework identifies three primary actors in FOPL policy processes: government, industry, and consumers, each operating within specific implementation and response pathways. The national government undertakes the design of the FOPL policy, determining label types, format, and product coverage(25), establishes nutrient profiling criteria(Reference Kelly and Jewell27), and manages implementation approaches (voluntary or mandatory) with accompanying monitoring, evaluation, and enforcement mechanisms(25). Industry response primarily involves compliance and reformulation. Compliance requires changes in packaging design and production processes, varying by implementation approach (voluntary or mandatory) and enforcement mechanisms(25). Product reformulation may involve reducing nutrients of concern or increasing beneficial nutrients(Reference Mhurchu, Eyles and Choi18,Reference Mantilla Herrera, Crino and Erskine28–Reference Ares, Aschemann-Witzel and Curutchet30) , influenced by technical feasibility, costs, and consumer acceptance(Reference Nohlen, Bakogianni and Grammatikaki31). Furthermore, the industry may adopt other strategies such as marketing to maintain their profit margins(Reference Baker and Friel22,Reference Hawkes, Yach and Puska32) . Consumer response occurs through several potential pathways(Reference Grunert and Wills24,25) including awareness of FOPL presence, understanding of label information, and utilization through informed food choices and purchasing behavior(Reference Grunert, Wills and Fernández-Celemín13). The implementation and response of FOPL policies are also influenced by a range of contextual factors. These factors include contextual elements such as personal knowledge, food availability, public health campaigns, and socioeconomic variables, which can affect the effectiveness and impact of the FOPL policy(Reference Muzzioli, Penzavecchia and Donini33,Reference Batista, de Carvalho-Ferreira and Thimoteo da Cunha34) .
Methods
Search strategies
The method for conducting this review followed the scoping review protocol outlined by the Joanna Briggs Institute(Reference Peters, Marnie and Tricco35) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist(Reference Tricco, Lillie and Zarin36) (see online supplementary material, Supplemental Table S1). Peer-reviewed journal articles were selected from five databases: Medline, Web of Science, Scopus, Global Health and CINAHL, supported by the University of Sydney Library. A search strategy was developed in the Medline database and revised accordingly for other databases. Key search terms included ‘front-of-pack’, ‘labelling’, ‘food’, ‘nutrition’, ‘policy’, and ‘implementation’. The details of search strategy and search terms are provided in online supplementary material, Supplemental Table S2. All searches were initially conducted on 9 November 2023 and subsequently updated on 20 August 2025.
Data collection and analysis
We included only original articles and primary research studies, conducted in LMIC. The focus was on studies that investigate FOPL policies, in any label format, implemented at the national level regardless of their policy mandates and study design. To be included, studies must: (1) be peer-reviewed papers with full-text accessible, (2) be published in English between 2014 and 2025, (3) assess and analyze FOPL policy implementation and response, and (4) employ research approaches, theories, frameworks, tools, or measurements to evaluate and analyze FOPL policy implementation or responses. The policy responses were defined by the conceptual framework, and included product reformulation, consumer awareness, perception, understanding and use of FOPL. The responses were extended to purchasing and consumption behaviors, and others if relevant. Papers were excluded when focused on the impact of FOPL on health and economic outcomes, to maintain focus on methodological approaches to studying implementation and response processes rather than ultimate health impacts, which would require different methodological considerations.
All papers identified through searches in five databases were imported into Covidence software(37). Duplicate entries were removed. Title and abstract screening run by PP and JJ independently. Relevant data on FOPL policy (policy mandate, type of label, product coverage, and year of policy Implementation), study design (study type, populations, setting, tools and measurement, data source and collection, and data analysis), study outcomes (key findings), and limitations and future research mentioned in the papers were extracted by two researchers (PP and KC) into Excel. Conflicts during data screening and extraction were discussed, and a consensus was reached between the researchers. Detailed critical appraisal of the research study design was not included. Following the Joanna Briggs Institute Methodology for Scoping Reviews(Reference Peters, Godfrey and McInerney38), detailed critical appraisal of the research study design was not conducted as this is not a required component of scoping review methodology, which focuses on mapping the available evidence rather than assessing its methodological quality.
Results
Study characteristics
The database search yielded a total of 5583 records for identification. Following the removal of duplicates, 3826 unique papers were screened, of which 131 were selected for full-text review. Ultimately, 31 papers met the inclusion criteria and were included in the analysis (Figure 2). Among these, nine studies were conducted in Mexico, five were done in Brazil. Ecuador and Peru each contributed four studies. Three studies were performed in Thai context. Columbia and Malaysia had each two studies. The remaining studies originated from Iran and Sri Lanka (Table 1).
Figure 2 Covidence flow diagram of the paper screening process.
Table 1 Study characteristics
The timeline of FOPL policy implementation varies across countries. Brazil introduced its Magnifying Glass label in 2022(Reference de Alcantara, Martins and Ares39–Reference Faria, Andrade and Ruas43). Columbia adopted the warning labels as circular black label in 2021, and the label was changed into octagonal ones and added warnings for trans fats and sweeteners, and eliminated the optional positive label in 2024(Reference Arboleda44,Reference Rangel-Quinonez, Vecchio and Arenas-Estevez45) . Ecuador adopted the Traffic Light label in 2013(Reference Cordero-Ahiman, Vanegas and Fernández-Lucero46–Reference Orozco, Ochoa and Muquinche49), followed by Iran in 2016(Reference Roudsari, Abdollah Pouri Hosseini and Bonab50). Malaysia implemented two types of labels: the voluntary Healthier Choice Logo (HCL) in 2017(Reference Sulong, Ibrahim and Norrahim51) and the mandatory Energy Icon label in 2012(Reference Sulong, Salleh and Mohd Ali52). Mexico had several milestones, with a national FOPL law introduced in October 2020, though earlier studies, such as one from September 2019, reflect pre-implementation considerations. Mexico also implemented the warning labels in 2020(Reference Salgado, Pedraza and Contreras-Manzano53–Reference Sagaceta-Mejía, Tolentino-Mayo and Cruz-Casarrubias60), after the implemented the GDA labels in 2014(Reference Sagaceta-Mejía, Tolentino-Mayo and Cruz-Casarrubias60,Reference Nieto, Castillo and Alcalde-Rabanal61) . Peru’s warning labels policy was rolled out in two phases, starting in 2019–2020, with the second phase in 2021(Reference Mamani-Urrutia, Durán and Bustamante-López62–Reference Saavedra-Garcia, Taboada-Ramirez and Hernández-Vásquez65). Sri Lanka developed the Traffic Light label in 2016 for sugar-sweetened beverage and the label was expanded to solid foods in 2019(Reference Madurawala, Kiringoda and Thow66). Thailand had a voluntary label HCL in 2016(Reference Nguyen Ngoc, Ditmetharoj and Rojroongwasinkul67,Reference Nguyen Ngoc, Photi and Tangsuphoom68) which happened after a 25 % reduction in sugar, fat, and sodium (25 % SFS)(Reference Phulkerd, Sacks and Vandevijvere69) was implemented in 2009.
Interpretive labels were commonly used across all countries such as the warning labels in most Latin American countries and the HCL in Malaysia and Thailand. Non-interpretive were also observed including GDA. Most FOPL policies were government-mandated, with mandatory labels such as the Magnifying Glass in Brazil, the Traffic Light label in Ecuador, Iran and Sri Lanka, and the warning labels in Columbia, Mexico and Peru. Voluntary labels were implemented, including Malaysia’s HCL, Mexico’s Nutritional Stamp, and Thailand’s HCL and 25 % SFS.
Studies focusing on policy implementation
Research approaches
Three out of thirty-one studies primarily examined the implementation of FOPL policies, including one each study from Mexico and Sri Lanka, and Thailand. A qualitative approach focusing on secondary data analysis and semi-structured in-depth interviews was employed as main study design. Data collection tools ranged from media reports and legislative reviews to specialized product databases and interviewed guided by theory, all contributing to a detailed examination of the factors affecting the success of food labelling policies.
A qualitative secondary data analysis conducted in Mexico(Reference Crosbie, Otero Alvarez and Cao56) focused on the role of key stakeholders in implementing warning labels. This study used secondary data obtained from various online sources, including media reports, government websites, and legislative documents. The study applied the policy cycle model to explore the factors influencing both the implementation and responses to the policy. Sri Lanka’s study(Reference Madurawala, Kiringoda and Thow66) employed interviews and documentary analysis to explore how ideas, institutions, and power dynamics influence the formulation and implementation of the policy. A study in Thailand(Reference Phulkerd, Sacks and Vandevijvere69) employed a qualitative study design to identify the barriers and facilitators to implementing a 25 % SFS. This study was based on a qualitative approach using in-depth interviews among a broad range of stakeholders, such as government entities, NGOs, academics, private sectors, and multisector organizations.
These studies applied a theoretical framework to analyze FOPL policy implementation. The Mexican study applied Knill and Tosun’s policy cycle model to examine implementation standards, monitoring and enforcement mechanisms, and policy evaluation(Reference Crosbie, Otero Alvarez and Cao56). The Sri Lankan study drew upon Kingdon’s theory of agenda-setting and Campbell’s institutionalist approach to Political Economy Analysis to examine the policy development process and institutional factors influencing FOPL implementation(Reference Madurawala, Kiringoda and Thow66). The Thai study synthesized three categories of theories (classic behavioral, implementation, and adapted frameworks) to develop interview themes across four domains: policy characteristics, individual adopter characteristics, intra-organization characteristics, and environmental influences(Reference Phulkerd, Sacks and Vandevijvere69).
All studies exhibited limitations: the secondary data analysis lacked comprehensive review of public comments and in-depth examination of governmental interests(Reference Crosbie, Otero Alvarez and Cao56,Reference Phulkerd, Sacks and Vandevijvere69) , while the Thai study was constrained by the absence of successful policy implementation examples(Reference Phulkerd, Sacks and Vandevijvere69). Additional constraints included limited industry representation, particularly from carbonated beverage manufacturers, and documentation gaps in government institutes that prevented access to relevant policy documents during initial screening stages(Reference Madurawala, Kiringoda and Thow66).
Key findings from the study on the front-of-pack labelling policy implementation
Studies from Mexico, Sri Lanka and Thailand reveal consistent FOPL implementation challenges. Mexico’s warning labels faced enforcement capacity limitations and regulatory gaps that compromised industry compliance(Reference Crosbie, Otero Alvarez and Cao56). Similarly, the Traffic Light label policies in Sri Lanka aimed at discouraging SSB consumption faced industry resistance and achieved limited effectiveness compared to taxation due to insufficient public awareness, despite Ministry of Health leadership and industry reformulation responses(Reference Madurawala, Kiringoda and Thow66). Thailand’s voluntary 25 % SFS labels encountered monitoring deficiencies and insufficient funding allocation but benefited from strong governmental support(Reference Phulkerd, Sacks and Vandevijvere69).
Studies focused on policy response
Research approaches
There were 28 studies focused on the response to the FOPL policies. Among those, eight studies focused on the response from the industry, mainly on reformulation efforts and compliance with labelling regulations(Reference Senda, Raposo and Teixeira-Lemos42,Reference Sulong, Ibrahim and Norrahim51,Reference Salgado, Pedraza and Contreras-Manzano53,Reference Mamani-Urrutia, Durán and Bustamante-López62,Reference Saavedra-Garcia, Meza-Hernandez and Diez-Canseco64,Reference Saavedra-Garcia, Taboada-Ramirez and Hernández-Vásquez65,Reference Nguyen Ngoc, Ditmetharoj and Rojroongwasinkul67,Reference Nguyen Ngoc, Photi and Tangsuphoom68) . The remaining 20 studies explored consumer responses, awareness and perception of FOPL(Reference de Alcantara, Martins and Ares39,Reference França, de Alcantara and Deliza40,Reference Arboleda44,Reference Sánchez-García, Rodríguez-Insuasti and Martí-Parreño48–Reference Roudsari, Abdollah Pouri Hosseini and Bonab50,Reference Sulong, Salleh and Mohd Ali52,Reference Arellano-Gomez, Jauregui and Nieto55,Reference Campos-Nonato, Cervantes-Armenta and Pacheco-Miranda58,Reference Nieto, Castillo and Alcalde-Rabanal61,Reference Diez-Canseco, Najarro and Cavero63) , understanding and use of FOPL(Reference Cordero-Ahiman, Vanegas and Fernández-Lucero46,Reference Orozco, Ochoa and Muquinche49,Reference Sulong, Salleh and Mohd Ali52,Reference Campos-Nonato, Cervantes-Armenta and Pacheco-Miranda58–Reference Nieto, Castillo and Alcalde-Rabanal61,Reference Diez-Canseco, Najarro and Cavero63) , and changes in purchasing behavior(Reference da Costa Soares, Pereira and Santana41,Reference Rangel-Quinonez, Vecchio and Arenas-Estevez45,Reference Sandoval, Carpio and Sanchez-Plata47,Reference Contreras-Manzano, White and Nieto54) and consumption(Reference Faria, Andrade and Ruas43,Reference Villaverde, Tolentino-Mayo and Cruz-Casarrubias57) . This distribution highlights a research emphasis on understanding the impact of FOPL policies, particularly from the perspective of consumers.
The studies employed a range of methodologies to examine the FOPL policy responses across different countries. Quantitative approaches were predominant. These included, for example, cross-sectional surveys and questionnaires to assess understanding of FOPL(Reference Cordero-Ahiman, Vanegas and Fernández-Lucero46,Reference Orozco, Ochoa and Muquinche49,Reference Sulong, Salleh and Mohd Ali52,Reference Campos-Nonato, Cervantes-Armenta and Pacheco-Miranda58) , experimental designs comparing different label types or scenarios(Reference de Alcantara, Martins and Ares39,Reference da Costa Soares, Pereira and Santana41,Reference Arboleda44,Reference Rangel-Quinonez, Vecchio and Arenas-Estevez45,Reference Arellano-Gomez, Jauregui and Nieto55,Reference Contreras-Manzano, Jáuregui and Vargas-Meza59) , and modelling studies estimating potential impacts on consumption and health outcomes(Reference Faria, Andrade and Ruas43). Qualitative methods, including focus groups and in-depth interviews, were used in three studies to explore perceptions, attitudes, and understanding of FOPL(Reference Roudsari, Abdollah Pouri Hosseini and Bonab50,Reference Nieto, Castillo and Alcalde-Rabanal61,Reference Diez-Canseco, Najarro and Cavero63) . Data sources varied, with 16 studies collecting primary data through surveys, experiments, and direct observation of products(Reference de Alcantara, Martins and Ares39–Reference Senda, Raposo and Teixeira-Lemos42,Reference Arboleda44–Reference Cordero-Ahiman, Vanegas and Fernández-Lucero46,Reference Sánchez-García, Rodríguez-Insuasti and Martí-Parreño48–Reference Sulong, Salleh and Mohd Ali52,Reference Campos-Nonato, Cervantes-Armenta and Pacheco-Miranda58,Reference Contreras-Manzano, Jáuregui and Vargas-Meza59,Reference Nieto, Castillo and Alcalde-Rabanal61,Reference Saavedra-Garcia, Taboada-Ramirez and Hernández-Vásquez65) , while 9 studies utilized secondary data from national health surveys, national panel data, and market research databases such as Euromonitor and Kantar World Panel(Reference Faria, Andrade and Ruas43,Reference Sandoval, Carpio and Sanchez-Plata47,Reference Salgado, Pedraza and Contreras-Manzano53,Reference Arellano-Gomez, Jauregui and Nieto55,Reference Villaverde, Tolentino-Mayo and Cruz-Casarrubias57,Reference Sagaceta-Mejía, Tolentino-Mayo and Cruz-Casarrubias60,Reference Saavedra-Garcia, Meza-Hernandez and Diez-Canseco64,Reference Nguyen Ngoc, Photi and Tangsuphoom68) . The populations studied were diverse, including general adults in most of the studies and specific demographic groups such as women(Reference Sánchez-García, Rodríguez-Insuasti and Martí-Parreño48,Reference Orozco, Ochoa and Muquinche49) and children(Reference Contreras-Manzano, Jáuregui and Vargas-Meza59). Sampling methods varied from convenience and purposive sampling in smaller-scale studies to random sampling from national surveys in larger studies. This methodological diversity allowed for a comprehensive examination of FOPL systems, their implementation, and their effects on consumer behavior and product reformulation across different contexts and populations, primarily in Latin American countries (Brazil, Columbia, Ecuador, Mexico, and Peru).
Several limitations on the study design were consistently reported across these studies. Many studies acknowledged limited geographical scope(Reference Roudsari, Abdollah Pouri Hosseini and Bonab50), group of participants (only women or mothers)(Reference Orozco, Ochoa and Muquinche49,Reference Diez-Canseco, Najarro and Cavero63) , or sample sizes(Reference Arboleda44,Reference Sánchez-García, Rodríguez-Insuasti and Martí-Parreño48,Reference Contreras-Manzano, White and Nieto54,Reference Arellano-Gomez, Jauregui and Nieto55,Reference Contreras-Manzano, Jáuregui and Vargas-Meza59,Reference Diez-Canseco, Najarro and Cavero63,Reference Saavedra-Garcia, Taboada-Ramirez and Hernández-Vásquez65) , potentially affecting the generalizability of results to national populations. Additionally, the focus on urban populations limited the generalizability of the results to rural or lower-income areas, where access to labeled products and consumer understanding of labels may differ(Reference Cordero-Ahiman, Vanegas and Fernández-Lucero46,Reference Nieto, Castillo and Alcalde-Rabanal61) . Self-reported data in some studies introduced potential measurement errors(Reference Sulong, Ibrahim and Norrahim51,Reference Villaverde, Tolentino-Mayo and Cruz-Casarrubias57) . The short-term nature of some studies also limited their ability to assess the long-term impacts of FOPL implementation(Reference Campos-Nonato, Cervantes-Armenta and Pacheco-Miranda58). Another limitation was product coverage including restricted sample sizes, focus on specific product categories (e.g. carbonated soft drinks), exclusion of products not available at studied points of sale, and omission of away-from-home consumption(Reference Sandoval, Carpio and Sanchez-Plata47,Reference Saavedra-Garcia, Meza-Hernandez and Diez-Canseco64,Reference Saavedra-Garcia, Taboada-Ramirez and Hernández-Vásquez65) .
Responding towards front-of-pack labelling policy
Industry response to FOPL policies was evident in compliance of FOPL policy, product reformulation, product uptake and nutrition composition, and marketing strategies(Reference Senda, Raposo and Teixeira-Lemos42,Reference Sulong, Ibrahim and Norrahim51,Reference Salgado, Pedraza and Contreras-Manzano53,Reference Mamani-Urrutia, Durán and Bustamante-López62,Reference Saavedra-Garcia, Meza-Hernandez and Diez-Canseco64,Reference Saavedra-Garcia, Taboada-Ramirez and Hernández-Vásquez65,Reference Nguyen Ngoc, Ditmetharoj and Rojroongwasinkul67,Reference Nguyen Ngoc, Photi and Tangsuphoom68) . For example, a study in Brazil showed 541 out of 2145 products displayed warnings label with significant variation across food categories(Reference Senda, Raposo and Teixeira-Lemos42). In Mexico, all food groups reduced their nutrients, with the most significant decreases in products exceeding cutoffs for sodium (up to –63·1 percentage points), saturated fat (up to –26·3 percentage points), and non-caloric sweeteners (up to –29·0 percentage points)(Reference Salgado, Pedraza and Contreras-Manzano53). Product reformulation mainly occurred post-implementation rather than proactively(Reference Salgado, Pedraza and Contreras-Manzano53). Peruvian study showed significant nutrient reductions following the warning labels implementation, with total decreases of 3·4 % in calories, 14 % in sodium, 36·7 % in sugar, and 9·2 % in saturated fats(Reference Mamani-Urrutia, Durán and Bustamante-López62). In Thailand, a longitudinal study showed a gradual increase in HCL product uptake over five years, with the logo appearing on 10·7 % of total products and 39·5 % of eligible products, though only 19 % of manufacturers (primarily SMEs) launched healthier products with the logo(Reference Nguyen Ngoc, Photi and Tangsuphoom68). Beverages carrying the HCL exhibited significantly better healthfulness compared to those without the label(Reference Nguyen Ngoc, Ditmetharoj and Rojroongwasinkul67).
Consumer response to FOPL was multifaceted, encompassing awareness, understanding, and behavioral changes. Awareness of FOPL was generally high across studies, ranging from 85 % in Malaysia(Reference Sulong, Salleh and Mohd Ali52) to 97 % in Ecuador(Reference Cordero-Ahiman, Vanegas and Fernández-Lucero46). However, awareness varied significantly among different population groups, with one study in Ecuador reporting that 84·3 % of indigenous women were unaware of the labeling system compared to 46 % of mestiza women(Reference Orozco, Ochoa and Muquinche49). Furthermore, warning labels consistently demonstrated superior comprehension compared to GDA labels(Reference Campos-Nonato, Cervantes-Armenta and Pacheco-Miranda58).
The impact of FOPL on purchasing behavior and consumption showed mixed results. Studies in Brazil, Columbia and Mexico reported a reduction on purchasing high-energy and high-sugar products with the benefit from interpretive labels (magnifying glass and warning labels)(Reference da Costa Soares, Pereira and Santana41,Reference Rangel-Quinonez, Vecchio and Arenas-Estevez45,Reference Contreras-Manzano, White and Nieto54) . Additionally, modeling studies in Brazil and Mexico projected significant potential reductions in energy and nutrient intake, including a 54·1 % decrease in added sugar consumption(Reference Faria, Andrade and Ruas43,Reference Villaverde, Tolentino-Mayo and Cruz-Casarrubias57) . However, no definitive evidence was found that Traffic Light Nutritional Labeling reduced purchases of carbonated soft drinks in Ecuador(Reference Sandoval, Carpio and Sanchez-Plata47).
The review found that responses to FOPL varied significantly across different population groups. Children showed distinct patterns of label comprehension, with a study in Mexico finding that warning labels led to a higher percentage of children correctly identifying the healthiest and least healthy options compared to Nutrient Fact panels(Reference Contreras-Manzano, Jáuregui and Vargas-Meza59). Among adults, those with NCDs showed better understanding of warning labels compared to GDA labels(Reference Sagaceta-Mejía, Tolentino-Mayo and Cruz-Casarrubias60). Socioeconomic status also influenced responses, with higher-status households in Ecuador tending to purchase less high-sugar and more low- and non-sugar soft drinks(Reference Sandoval, Carpio and Sanchez-Plata47).
Cross-cutting factors influencing implementation and response
Several factors influence FOPL policy effectiveness across personal, environmental, and policy levels. At the personal level, socio-demographic characteristics emerge as significant determinants. Age plays an influential role, with children showing distinct patterns of label comprehension(Reference Contreras-Manzano, Jáuregui and Vargas-Meza59), while educational levels positively correlate with better understanding and use of labels(Reference Rangel-Quinonez, Vecchio and Arenas-Estevez45,Reference Campos-Nonato, Cervantes-Armenta and Pacheco-Miranda58,Reference Sagaceta-Mejía, Tolentino-Mayo and Cruz-Casarrubias60) . Socioeconomic status is another crucial factor, with higher-status households more likely to purchase lower-sugar products in response to labeling(Reference Sandoval, Carpio and Sanchez-Plata47). Health status also impacts FOPL effectiveness, as individuals with NCDs demonstrate improved comprehension of warning labels compared to other label types(Reference Sagaceta-Mejía, Tolentino-Mayo and Cruz-Casarrubias60). Emotional responses, though less studied, influence consumer perceptions, as seen in Ecuador where red labels in traffic light systems elicited more fear and guilt, particularly among lower-income consumers(Reference Sánchez-García, Rodríguez-Insuasti and Martí-Parreño48).
From an industry and policy perspective, company size may affect the adoption of healthier product logos, with small and medium enterprises being more likely to launch new products with such logos(Reference Sulong, Ibrahim and Norrahim51). The design and implementation approach of FOPL policies themselves influence outcomes, with mandatory approaches generally yielding higher compliance and impact compared to voluntary schemes(Reference Phulkerd, Sacks and Vandevijvere69). These findings underscore the complex interplay of personal factors (socio-demographics, health status, emotions), industry characteristics, and policy-level factors in shaping FOPL effectiveness.
Discussion
This review reveals that FOPL research in LMIC predominantly focuses on policy responses (28 studies) rather than implementation processes (3 studies), highlighting a significant research gap. Among those response studies, there is a notable skew toward consumer responses, with relatively less focus on industry responses. The scarcity of industry-focused studies in FOPL research represents a notable gap in the literature. This includes, for example, an examining of industry compliance mechanisms, challenges, and interference in FOPL policy development in resource-constrained LMIC settings, where industry has been found to actively influence policies through various strategies including direct opposition to mandatory schemes, proposing alternative labeling systems, and questioning the evidence base(Reference Pettigrew, Coyle and McKenzie21). Understanding these industry dynamics is crucial for designing effective and feasible FOPL policies that can achieve public health objectives while considering industry capabilities.
The implementation of FOPL policies has catalyzed a complex interplay between government regulations and industry responses. Our review reveals distinct patterns between mandatory and voluntary policy approaches. Mandatory approaches demonstrate higher compliance and greater potential impact compared to voluntary schemes(Reference Rebolledo, Ferrer-Rosende and Reyes70), as evidenced by formal compliance through product reformulation under mandatory schemes, such as Peru’s reduction in sugar content(Reference Saavedra-Garcia, Meza-Hernandez and Diez-Canseco64). In contrast, voluntary approaches allow industry greater autonomy in their responses, often resulting in strategic adaptations like increased marketing efforts rather than substantive product changes(Reference Saavedra-Garcia, Taboada-Ramirez and Hernández-Vásquez65). This dynamic illustrates the fundamental relationship between regulatory policy and industry behavior, where industry actively advocates for voluntary approaches while influencing enforcement strategies through arguments about market efficiency and economic feasibility(Reference Mialon, Corvalan and Cediel71). The clearer compliance patterns observed under mandatory schemes, compared to voluntary approaches, underscore how different policy approaches fundamentally shape industry responses and ultimately affect policy effectiveness(Reference Jones, Neal and Reeve7).
FOPL policy responses are observed through product reformulation, which presents both opportunities and limitations. On the positive side, reformulation can create population-wide reductions in nutrient intake without requiring individual behavior change(Reference Winkler72). Studies of reformulation programs have shown modest but meaningful reductions in population fat, salt, and sugar consumption when changes are implemented systematically across product categories(Reference Mhurchu, Eyles and Choi18,Reference Essman, Taillie and Frank73–Reference Yusta-Boyo, González and García-Solano75) . However, several limitations emerge. The nutritional quality of reformulated products often remains questionable, particularly when artificial sweeteners or salt substitutes are used(Reference Scrinis and Monteiro76). Further, reformulation may create a ‘health halo’ effect(Reference Chandon and Wansink77), potentially encouraging increased consumption of relatively unhealthy products despite marginal improvements(Reference Hawley, Roberto and Bragg78).
Our review reveals a significant imbalance in the focus of FOPL research, with most studies (28 out of 31) concentrating on responses to policies. This pattern may reflect several underlying factors, particularly in the LMIC context. First, measuring responses, particularly among the consumer group, often requires less complex study designs and can be accomplished through straightforward quantitative methods such as surveys and experimental studies, as seen in Ecuador(Reference Cordero-Ahiman, Vanegas and Fernández-Lucero46,Reference Orozco, Ochoa and Muquinche49) and Malaysia(Reference Sulong, Salleh and Mohd Ali52). In contrast, studying implementation processes is complex and requires more resource-intensive approaches, including stakeholder interviews, policy document analysis, and long-term monitoring(Reference Kraft and Furlong79), as demonstrated in the Thai study examining implementation barriers(Reference Phulkerd, Sacks and Vandevijvere69). Second, accessing implementation data may be more challenging as it often requires engagement with government stakeholders and industry actors(Reference Kraft and Furlong79). Additionally, while consumer response studies can be conducted relatively quickly after policy introduction, meaningful implementation research requires policies to be well-established(Reference Kanter, Vanderlee and Vandevijvere15), which may explain the limited implementation studies given the relatively recent adoption of FOPL policies in many LMIC.
The geographical imbalance of included studies limits our policy implication to other LMIC. 24 of 31 studies conducted in Latin American countries (Brazil, Colombia, Ecuador, Mexico, and Peru), while other LMIC regions remain severely underrepresented. Only seven studies originated from other regions: three from Thailand, two from Malaysia, and one each from Iran and Sri Lanka. This geographical bias limits our ability to make broad generalizations(Reference Henry Wai-Chung80) about FOPL policy implementation and response across all LMIC, as our findings predominantly reflect Latin American experiences with mandatory warning labels and traffic light systems(Reference Crosbie, Gomes and Olvera81). The limited representation from Africa, South Asia, and most of Southeast Asia represents a critical evidence gap, as these regions may face different regulatory contexts, food market structures, industry dynamics, and cultural factors that influence FOPL effectiveness(Reference Pettigrew, Coyle and McKenzie21,Reference Champagne, Arora and ElSayed82,Reference Temple83) . Consequently, our conclusions should be interpreted primarily as insights into Latin American FOPL policy experiences rather than universal LMIC patterns.
Research implications
The patterns identified in this review highlight a fundamental mismatch between research priorities and policy needs in FOPL implementation. While most studies focus on measuring consumer and industry responses to existing policies, there is insufficient understanding of the implementation processes that determine whether policies achieve their intended effects. This research-practice gap is particularly problematic in LMIC contexts, where resource constraints, regulatory capacity limitations, and diverse stakeholder interests create complex implementation challenges that differ markedly from high-income country experiences. The geographical concentration of evidence in Latin America, combined with the predominance of response-focused studies, suggests that current research approaches may not adequately capture the full spectrum of FOPL policy experiences across diverse LMIC contexts.
These findings underscore the importance of ongoing monitoring and adaptive regulation in FOPL policy implementation. Policymakers should anticipate and address potential industry workarounds to maintain the long-term effectiveness of FOPL policies(Reference Reyes, Smith Taillie and Popkin84). Regulatory frameworks need to be both stringent and flexible, ensuring industry actions align with public health objectives while allowing for a responsive approach to emerging strategies(Reference Jones, Neal and Reeve7).
Study limitations
The literature search was restricted to English-language peer-reviewed publications, which may have excluded relevant studies from non-English speaking LMIC and valuable implementation insights from government reports and policy documents in grey literature. The timeframe of included studies (2014–2025) may not fully capture complete policy cycles and long-term impacts, particularly for policies adopted early in this period. Additionally, focusing exclusively on LMIC may have missed valuable insights from high-income countries’ FOPL experiences.
Future research recommendations
To advance FOPL policy effectiveness in LMIC, future research must prioritize several key areas. Most urgently, implementation science approaches should be expanded to balance between response and implementation studies, with particular focus on understanding policy processes in resource-constrained settings. Research programs should systematically investigate industry compliance mechanisms across diverse LMIC market structures, examining how multinational corporations and local producers respond differently to FOPL regulations. Equity-focused studies are essential to address the substantial variations in FOPL effectiveness across population groups, as demonstrated by the 84 % v. 46 % awareness gaps between indigenous and non-indigenous women in Ecuador(Reference Orozco, Ochoa and Muquinche49). Methodological innovations should include longitudinal designs, representative sampling strategies, and mixed-methods approaches that can capture both immediate responses and long-term policy impacts. Finally, as our review included only English publications, future reviews could benefit from AI-based translation tools to include studies published in other languages, particularly Spanish and Portuguese given the concentration of FOPL policies in Latin America.
Conclusions
This scoping review reveals a critical research imbalance in FOPL policy evaluation in LMIC, with response-focused studies vastly outnumbering implementation research, and evidence heavily concentrated in Latin America. While findings demonstrate that mandatory FOPL approaches consistently outperform voluntary schemes in achieving compliance and consumer impact, persistent implementation challenges including inadequate monitoring systems, limited regulatory capacity, and industry resistance remain poorly understood due to insufficient implementation science research. The pronounced geographical bias limits generalizability across diverse LMIC contexts, where varying regulatory frameworks, market structures, and cultural factors may significantly influence policy outcomes.
Future research must prioritize implementation science approaches to balance the current portfolio and provide practical guidance for policymakers in resource-constrained settings. Critical needs include geographically diverse studies across underrepresented regions, particularly Africa, South Asia, and Southeast Asia; longitudinal designs to capture policy evolution and sustained impacts; and interdisciplinary research examining how international trade agreements influence FOPL policy adoption. Only through comprehensive, equity-focused research that addresses both methodological and geographical gaps can the full potential of FOPL policies be realized in improving population health across LMIC.
Acknowledgements
We gratefully acknowledge the expertise of Kanchana Ekanayake from the University of Sydney Library, whose guidance in developing comprehensive search strategies and optimizing database-specific terminology significantly enhanced the rigor of our systematic search methodology.
Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors. This research was conducted as part of P.P.’s doctoral studies at the University of Sydney, supported by the University of Sydney International Tuition Fee Scholarship, the Faculty of Medicine and Health Research Centres Stipend Scholarship, and the Faculty of Medicine and Health Tuition Fee Scholarship during her PhD program. The scholarship providers had no role in the study design, data collection and analysis, publication decision, or manuscript preparation.
Conflict of interest
There are no conflicts of interest
Authorship
P.P., P.F., A.M.T., and S.P. contributed to the design and conceptualization of the study. P.P., K.C., and J.J. contributed to data curation. P.P. contributed to writing the original draft, reviewing and editing. P.F., A.M.T., and S.P. contributed to supervision, reviewing, and editing. All authors have reviewed and approved the final version of the manuscript.
Supplementary material
For supplementary material accompanying this paper visit https://doi.org/10.1017/S136898002510150X
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