Understanding consumer response to front-of-package labeling: Insights from a nationwide survey in Sri Lanka

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Front-of-package labelling, particularly the traffic light system, has been proposed as a public health intervention to promote healthier dietary choices. This study investigated consumer awareness, understanding, and response to the traffic light system in Sri Lanka, where non-communicable diseases account for 83% of deaths. Methods: This descriptive cross-sectional study was conducted from November 2022 to April 2023 and involved 2,569 participants from 25 districts in Sri Lanka. Multistage cluster sampling was performed to ensure the representativeness of the sample. Data were collected via a self-administered online questionnaire in Sinhala, English, and Tamil, which assessed sociodemographic factors, knowledge, attitudes, and purchasing behaviours related to the traffic light system. Ordinal regression analysis was used to identify the factors influencing adherence to traffic-light system practices. Results: The study revealed a high awareness of the traffic light system, with 89.8% of the participants recognising the labelling system. Over 80% of the participants correctly identified the colour codes (red, amber, and green) associated with high, medium, and low levels of sugar, salt, and fat. The participants expressed satisfaction with the clarity, adequacy, and helpfulness of the traffic-light system information. Ordinal regression analysis indicated that Sinhalese ethnicity, age ≥ 30 years, higher household income (> Rs. 50,000), and higher education levels were positively associated with adherence to the traffic light system best practices. Positive attitudes toward and good knowledge of the traffic light system significantly influenced purchasing behaviour. Conclusions: These findings suggest that the traffic light system is an effective tool for guiding healthier food choices among Sri Lankan consumers. However, targeted educational campaigns are needed to address knowledge gaps, particularly among older, lower-income, and less-educated populations. Continuous evaluation and refinement of the traffic light system are recommended to maintain its efficacy and relevance in promoting public health. Noncommunicable Diseases Food Labeling Consumer Behavior Figures Figure 1 Introduction Noncommunicable Diseases (NCD) account for 63% of global mortality annually, with 75% of these deaths occurring in low- and middle-income countries (LMICs) (1). In Sri Lanka, NCDs were responsible for 83% of deaths in 2018, highlighting the urgent need for interventions targeting modifiable risk factors such as unhealthy diets, physical inactivity, and tobacco use (2,3). To address this burden, Sri Lanka’s Ministry of Health aligned its National Multisectoral Action Plan for NCDs (2016–2020) with the WHO Global Action Plan, prioritising dietary reforms through reduced intake of salt, sugar, and saturated fats (2). However, suboptimal diets driven by food environments that promote ultra-processed products remain a persistent challenge (4), necessitating evidence-based strategies to reshape consumer behaviours and industry practices (5). The burden of diet-related NCDs, including cardiovascular diseases, diabetes, obesity, and certain cancers, is particularly high in South Asia (6), where shifts toward high-calorie, nutrient-poor processed foods have accelerated in recent decades (7). Recent data suggest that dietary risks alone account for nearly 8 million global deaths annually and are among the top three contributors to Disability-Adjusted Life Years (DALYs)(8). These statistics emphasise the importance of targeted nutritional interventions in public health policy. Nutrition labelling has emerged as a critical policy tool for combating diet-related, non-communicable diseases. While mandatory back-of-pack nutrition tables are widespread, the WHO advocates front-of-pack labelling (FOPL) as a more effective measure for guiding healthier choices (4). Among FOPL systems, the traffic-light labelling system (TLS), which uses colour codes (red/amber/green) to signal “high”, “medium”, or “low” levels of key nutrients, has gained traction (9). TLS not only simplifies decision-making but also incentivises product reformulation by industry actors (10). Despite its potential, evidence of the real-world impact of the TLS remains mixed. Systematic approaches highlight their efficacy in improving health perceptions and directing consumer attention (11); however, few studies have rigorously evaluated their influence on purchasing behaviour or long-term dietary shifts (9). Moreover, existing research disproportionately focuses on high-income countries, leaving critical gaps in understanding TLS implementation in LMICs (10). Sri Lanka’s 2016 TLS policy for sugar-sweetened beverages (SSBs), complemented by SSB taxation in 2017, provides a unique case study. Developed through collaboration among policymakers, nutrition experts, and civil society(12), the TLS framework was tailored to local dietary patterns and informed by global precedents, such as tobacco control (13). By 2019, TLS was expanded to include salt and fat content, with plans to extend the labelling to restaurant menus and digital platforms(12). Although these efforts reflect a progressive policy trajectory, challenges persist in rural outreach, industry compliance, and measuring long-term health outcomes(12). Furthermore, few studies in South Asia have systematically explored the sociocultural and behavioural drivers of nutrition label use, including literacy levels, consumer trust in food systems, and affordability concerns, all of which may influence label interpretation and use(14). Evidence from Sri Lanka and comparable LMIC settings is urgently needed to understand how such context-specific factors shape the success of interventions such as TLS. Research gaps related to TLS remain twofold: limited evidence on TLS’s behavioural impact in real-world settings, particularly in LMICs, and scant data on how TLS interacts with socioeconomic and cultural factors to shape dietary choices (4,9). This study addresses these gaps by analysing TLS’s effects on consumer behaviour in Sri Lanka, contributing novel insights from the Global South to inform global NCD policy frameworks. Methods We conducted a descriptive cross-sectional study in 25 districts in Sri Lanka from the 1st of November 2022 to the 30th of April 2023. Food consumers in Sri Lanka were defined as individuals over 18 years of age who had purchased food. The sample size for this study was calculated using the formula provided by Lwanga and Lemeshow (15), with a 95% confidence interval and margin of error of 5%. Based on previous studies, we assumed an expected prevalence of awareness of the traffic light labelling system (TLS) of 50%, which was a conservative estimate to ensure a sufficient sample size. A design effect of two was applied to account for homogeneity within the clusters, which increased the final calculated sample size to 2,840 participants. Adjustments were made to account for potential non-responses, further ensuring the representativeness of the sample. Multistage cluster sampling and probability proportional to size (PPS) sampling techniques were used. Sri Lanka is divided into 6918 public health midwife (PHM) areas. PHM is the smallest health administrative area in the country. A cluster was defined as a public health midwife area. A list of PHMs was obtained from the Ministry of Health, including their contact numbers. The number of PHM areas in each district was selected based on the PPS of the district’s population. The number of PHM areas (clusters) in each district was randomly selected for analysis. Thereafter, in the selected PHM area, five individuals > 18 years of age were randomly selected (random number method) by the area PHM. The contact details of the selected individuals were obtained through registers maintained by the PHM, which usually provide comprehensive contact information for residents in their areas. The PHM subsequently contacted the individual via mobile phone, and a link for recruitment was sent to the selected individual’s phone. A self-administered online questionnaire was used in Sinhala, English, and Tamil. The Google Form link was shared with each area’s Public Health Midwife (PHM), who was responsible for selecting and contacting individuals from the official registers. The selected individuals were contacted via mobile phone, and a survey link was sent to their mobile phones. In instances where participants did not have direct internet access or smartphones, the PHM assisted by facilitating access through their own mobile devices or helped participants complete the Google form during home visits or community clinics. The purpose of this approach was to enhance inclusion and minimise selection bias. Section one of the questionnaire contained questions on sociodemographic and socioeconomic factors. Section two consisted of questions related to knowledge and attitudes. Attitudes were analysed using a Likert scale. We used an ordinal regression analysis model to determine the factors influencing purchasing and TLS. The dependent variables were the minimum, moderate, and best practices. The best practices depicted the optimal practices (always and most of the time looking at the TLS while buying items) that influenced purchasing decisions. [Insert Fig. 1 here] The images shown in Fig. 1 were used as indicators of front-of-pack labels in Sri Lanka. Results In this study, the response rate was 90.5% (n = 2569). The analysis of the sociodemographic data yielded several significant findings. In terms of age distribution, a considerable proportion of the respondents were relatively young, with 38.3% falling within the age range of 18–29 years. Males accounted for 20.2% and females for 79.8% of the respondents. The majority were Sinhalese (80.1%), followed by Tamil (14.6%). Regarding educational level, 40.1% of respondents had a diploma, graduate degree, or a postgraduate degree. When considering household income, the majority of households (38.0%) fell into the category of "Between Rs. 30,001 and Rs. 50,000" income per month. Interestingly, regarding the respondents' level of awareness, 89.8% reported being aware of food products with a TLS (Table 1 ). Table 1 Sociodemographic characteristics of the sample and level of awareness (n = 2569) Variable Number % Age 18–29 years 983 38.3% 30–39 years 823 32.0% 40–59 years 675 26.3% 60 years or above 88 3.4% Sex Female 2051 79.8% Male 518 20.2% Ethnicity Sinhalese 2057 80.1% Tamil 375 14.6% Muslim 124 4.8% Burger 12 0.5% Other 1 0.0% Level of education Less than O/L 324 12.6% O/L passed 367 14.3% A/L passed 848 33.0% Diploma/graduate or Postgraduate 1030 40.1% Monthly Household Income* (n = 2538) Rs. 20,000 or less 296 11.7% Between Rs.20,001 and Rs.30,000 485 19.1% Between Rs.30,001 and Rs.50,000 964 38.0% Between Rs. 50,001 and Rs.150,000 652 25.7% Above Rs. 150,000 141 5.6% Ever seen the traffic light labeling system on any food item in Sri Lanka (n = 2569) Yes 2308 89.8% No 261 10.2% Ever heard of the traffic light labeling system in any food item in Sri Lanka (n = 261) Yes 63 24.1% No 198 75.9% [Insert Table 1 here] The respondents' level of knowledge clearly revealed that they had an excellent understanding of the colour codes used for TLS. An impressive 89.1% of participants correctly identified the red colour code, which denotes a high level of sugar, salt, or fat. Similarly, a significant number of participants (82.3%) correctly identified the green colour code, which denotes a low level of sugar, salt, and fat. Additionally, 83.9% of respondents correctly identified the amber (yellow) colour code, which represents a medium level of sugar, salt, and fat (Table 2 ). Overall, the data indicated that a significant majority of the respondents had a strong understanding of the colour codes of the traffic-light labelling system. Specifically, 78.2% of the participants (n = 1851) demonstrated good knowledge of colour codes, suggesting that the system effectively communicates nutritional information to a large portion of the population. Conversely, 21.8% of the respondents (n = 517) had less knowledge of TLS, indicating the need for ongoing educational efforts to enhance understanding among this group. Table 2 Level of knowledge of the traffic light labeling system (n = 2368*) High level of sugar/salt/fat of the food product n (%) Medium level of sugar/salt/fat of the food product n (%) Low level of sugar/salt/fat of the food product n (%) I do not have any idea n (%) Red color code on food packaging 2111 (89.1%) 104 (4.4%) 40 (1.7%) 113 (4.8%) Amber (Yellow) color code on food packaging 72 (3.0%) 1987 (83.9%) 165 (7.0%) 144 (6.1%) Green color code on food packaging 56 (2.4%) 225 (9.5%) 1949 (82.3%) 138 (5.8%) *missing = 201 The sociodemographic characteristics of the participants who reported not having good knowledge of TLS revealed several notable patterns. Among the 517 participants, a substantial majority (63.6%) were aged ≥ 30 years, indicating that a lack of knowledge was more prevalent in this age group. In terms of household income, 83.9% earned Rs. 50,000 or less per month, suggesting that lower income may be associated with reduced knowledge of TLS. Ethnically, most of those with limited knowledge were Sinhalese (78.9%), with non-Sinhalese individuals constituting a small percentage (21.1%). The gender distribution revealed that 83.0% of the less knowledgeable participants were female and that 17.0% of the less knowledgeable participants were male. Interestingly, 56.9% of the participants with higher educational qualifications had less knowledge (Table 3 ). Table 3 Sociodemographic characteristics of the participants without good knowledge of the traffic light labeling system (n = 517) Frequency Percentage (%) Age (n = 517) less than 30 years 188 36.4 30 years or more 329 63.6 Household Income (n = 509) Rs.50000 or less 427 83.9 More than Rs.50000 82 16.1 Ethnicity (n = 517) Non-Sinhalese 109 21.1 Sinhalese 408 78.9 Educational Level (n = 517) Below A/L 223 43.1 A/L and above 294 56.9 Gender (n = 517) Male Female 88 429 17.0 83.0 Several consumers mentioned that the information displayed on food packaging was adequate, with 37.3% mentioning that the information displayed on food packaging was "Quite a lot", whereas 21.6% mentioned it as "Somewhat", and 16.1% mentioned it as “very much.” When considering the confusion of the provided information, a substantial portion (31.7%) of participants found the information "Not at all" confusing, followed by 29.4% who found it "somewhat" confusing. Only a few participants (3.3%) said that it was “very much” confusing. Regarding the helpfulness of the information, the majority of consumers found the information on food packaging to be helpful, with 40.2% marking it as “Quite a lot” and 30.5% as “very much”, while 3.5% marked it as “Not at all". A notable proportion of participants mentioned that the information on food packaging was both clear and understandable. For clarity, 39.9%, and for understandability, 39.6% were marked as "Quite a lot.” Forty-five percent of the participants mentioned that the information was “Not at all” misleading (Table 4 ). Table 4 Level of information provided by the traffic light labeling system (n = 2371*) Not at all n (%) A little bit n (%) Somewhat n (%) Quite a lot n (%) Very much n (%) Adequacy of information displayed 179(7.5%) 416(17.5%) 511(21.6%) 884(37.3%) 381(16.1%) Whether the information provided is confusing 751(31.7%) 561(23.7%) 698(29.4%) 283(11.9%) 78(3.3%) Helpfulness of information 82(3.5%) 206(8.7%) 406(17.1%) 954(40.2%) 723(30.5%) Clarity of information 110(4.6%) 338(14.3%) 487(20.5%) 945(39.9%) 491(20.7%) Understandability of information 90(3.8%) 334(14.1%) 426(18.0%) 939(39.6%) 582(24.5%) Whether the information is misleading 1067(45.0%) 417(17.6%) 662(27.9%) 152(6.4%) 73(3.1%) * missing = 198 [Insert Table 4 here] Those who were Sinhalese (aOR = 2.52, 95% CI = 2.04, 3.11), aged ≥ 30 years (aOR = 1.68, 95% CI = 1.43, 1.98), had a household income ˃ Rs.50000 (aOR = 1.21, 95% CI = 1.01, 1.45), and had an education level of A/L or above (aOR = 1.41, 95% CI = 1.16, 1.72) had greater odds of adhering to best practices when purchasing the product. Furthermore, individuals with positive overall attitudes toward TLS (aOR = 2.54, 95% CI = 1.95, 3.32) and good knowledge of TLS (aOR = 1.87, 95% CI = 1.54, 2.28) had greater odds of adhering to best practices while purchasing (Table 5 ). The ordinal regression model demonstrated excellent global fit, as evidenced by a highly significant likelihood ratio test [χ²(7) = 277, *p* < .001], confirming the collective explanatory power of all the predictors. Table 5 Ordinal regression analysis of factors influencing traffic light labeling system (TLS) practices (order of the dependent variable; minimum, moderate and best practices) Adjusted Odds ratio 95% Confidence Interval p value Lower Bound Upper Bound Non-Sinhalese Reference category Sinhalese 2.52 2.04 3.11 < 0.001 Female Reference category Male 1.00 0.819 1.23 0.976 Age ˂30 years Reference category Age ≥ 30 Years 1.68 1.43 1.98 < 0.001 Household income ≤ Rs.50000 Reference category Household income ˃Rs.50000 1.21 1.01 1.45 0.035 Education below A/L Reference category Education A/L or above 1.41 1.16 1.72 < 0.001 Negative overall attitudes on TLS Reference category Positive overall attitudes on TLS 2.54 1.95 3.32 < 0.001 Not having good knowledge on TLS Reference category Having good knowledge on TLS 1.87 1.54 2.28 < 0.001 [Insert Table 5 here] Discussion This study offers critical insights into the behavioural and attitudinal impacts of the Sri Lankan traffic light labelling system on food purchasing decisions, addressing gaps in the evidence from low- and middle-income countries. Demographic representativeness and implications The high response rate (90.5%, n = 2569) highlights robust engagement, although the sample is skewed toward younger (38.3% aged 18–29), female (79.8%), and Sinhalese (80.1%) participants with higher education levels (40.1% holding diplomas or degrees) (16). This contrasts with the general population of Sri Lanka (51% female, 14% aged 20–29 years) (16), suggesting a potential selection bias. Despite this, the findings illuminate TLSs’ reach across urban and educated demographics while highlighting accessibility gaps in rural and lower-income groups. The potential barriers to TLS usage in rural and low-income communities include limited nutritional literacy, economic constraints influencing food choices, reduced exposure to health promotion initiatives, and limited availability of TLS-labelled products in rural markets (17,18). Furthermore, cultural dietary practices and targeted food marketing strategies in low-income areas may contribute to deprioritising nutritional labelling (18). These considerations offer a more comprehensive understanding of the factors underlying uneven awareness and adoption of TLS. However, the disproportionately higher representation of educated, urban, and female study respondents may reflect a population that is already predisposed to health literacy and behavioural responsiveness. This suggests that while TLS is effective among such groups, it may not achieve similar results among less literate or underserved populations without supportive interventions. Thus, the study's findings should be interpreted in the context of this demographic skew, especially when generalising the behavioural impact of TLS across the entire population. Awareness and comprehension of TLSs Approximately 90% of the participants reported being familiar with TLSs, and over 80% accurately interpreted colour-coded nutrient thresholds. This aligns with evidence that TLSs simplify nutritional decision-making by reducing cognitive load, as demonstrated in previous eye-tracking studies (11). Notably, the substitution of red-labelled items with healthier alternatives is correlated with reduced caloric, fat, and salt intakes, reinforcing the potential of TLSs for population-level dietary improvements (10). The capacity to accurately identify and understand the red, amber, and green codes indicates not merely superficial knowledge but a profound understanding of nutrient risk gradation. This suggests that visual labelling can address deficiencies in challenging nutritional literacy if the labels are contextually applicable and consistently applied. Furthermore, colour-coded nutrition labelling on traffic lights helps consumers with limited self-discipline in selecting healthier food options(19). The significance of this finding lies in its prospective applicability to other low- and middle-income countries, where text-dense labelling may prove less useful owing to literacy constraints. User satisfaction and usability The participants expressed high satisfaction with TLS’s clarity and utility of the TLS: 37.3% rated information adequacy as “Quite a lot”, whereas 45% found labels “Not at all” misleading. These results mirror global findings, such as Hieke and Wilczynski’s report of high TLS understandability (5.9/7) (20) and Seward et al.’s observation that 59% of consumers found TLS helpful (21). However, Khalid’s caution against misleading labels underlines the need for standardised, culturally tailored TLS frameworks to ensure accuracy (22). The Traffic Light Labelling system in Sri Lanka has demonstrated its potential as an effective behavioural nudge for consumers. A survey revealed that most Sri Lankan consumers have a satisfactory understanding of nutrition labels, including the TLS (23). Moreover, the finding that nearly one-third of consumers rated the sufficiency of labeling as considerable illustrates TLS's practical applicability beyond mere theoretical understanding. The data indicate that TLS may affect both awareness and actual behavioural change, especially when accompanied by product reformulation and focused messaging. Determinants of TLS adherence Ordinal regression identified Sinhalese ethnicity, age ≥ 30 years, household income ≥ Rs.50,000, and higher education level as predictors of TLS adherence. This finding suggests that socioeconomic and cultural factors mediate the effectiveness of labels. Among the 517 participants with limited TLS understanding, barriers included restricted access to nutrition education (especially in rural areas), reliance on unlabelled traditional diets, and cognitive challenges among older populations. Targeted interventions addressing these disparities could enhance equity in TLS adoption, as evidenced by studies linking nutritional literacy to purchasing behaviour (21,24). The comparatively lower adherence among non-Sinhalese populations, despite similar or increased health requirements, necessitates culturally sensitive interventions that respect linguistic diversity and regional dietary practices. This includes developing communication that appeals to ethnic minorities and customising educational materials to accommodate diverse cognitive and linguistic capabilities. Moreover, the absence of substantial gender disparity in TLS adherence, despite the majority of women in the sample, may indicate women's established role in household food purchasing. This implies that enhancing women's literacy regarding food labelling could yield significant improvements in household dietary behaviours (25). Nevertheless, certain studies have indicated that consumer behaviour varies across different demographic groups and that there is an absence of a consistent correlation between nutritional quality and consumer behaviour (9,26). Strengths and limitations The strengths of this study include its large sample size, multilingual data collection (Sinhala, English, and Tamil), and multistage cluster sampling to improve representativeness. The use of visual TLS simulations in questionnaires enhanced ecological validity by mimicking real-world shopping experiences. However, its cross-sectional design limits causal inference. Additionally, reliance on mobile-based recruitment and online data collection may have excluded individuals with limited digital access or low digital literacy, potentially introducing participation bias. Despite the assistance provided by PHMs, digital exclusion may have a disproportionate effect on individuals with a lower level of education or socioeconomic status. Furthermore, the possibility of self-selection bias, such as a greater likelihood of participation by health-conscious individuals, and recall bias may limit the generalisability of the findings to the broader population. Although multistage cluster sampling and probability proportional to size (PPS) techniques were employed, sample weighting was not applied. Therefore, the findings may not fully reflect the demographic distribution of the national population, which may limit the generalisability of the results. Future studies aiming for population-level inference should consider applying post-stratification weighting based on national census data. Conclusions and recommendations This study demonstrates that the Sri Lankan traffic light labelling system significantly shapes food purchasing behaviour, with high baseline awareness and understanding among consumers. The participants perceived the TLS as clear, adequate, and actionable, particularly in terms of urban and educational demographics. However, adherence to the TLS is influenced by socioeconomic and cultural factors. These findings emphasise TLS’s potential as a public health tool in LMICs, while highlighting disparities in access and understanding among rural, lower-income, and less-educated populations. To maximise the impact of TLS in Sri Lanka, targeted interventions are required to address existing gaps and enhance equity. Promotional campaigns should be expanded to rural and marginalised communities, leveraging high urban awareness. These initiatives could include community-driven education programs delivered in collaboration with local leaders and schools to improve nutritional literacy and TLS comprehension. Simplified messaging using visual aids and regional languages can help overcome literacy barriers, particularly for older adults and low-income households. Additionally, investing in longitudinal and equity-focused research is essential to understand the long-term impact of TLS. Periodic evaluations can help track behavioural trends, dietary shifts, and non-communicable disease outcomes, while also identifying how cultural practices, subsistence diets, and digital divides influence TLS effectiveness. Abbreviations NCD Non-communicable diseases TLS Traffic Light System LMIC Low and middle-income countries WHO World Health Organization FOPL Front-of-Pack Labelling SSB Sugar-Sweetened Beverages PPS Probability Proportional to the Size PHM Public Health Midwife Declarations Ethics approval and consent to participate: This study was conducted in accordance with the guidelines of the Declaration of Helsinki, and all procedures involving the study participants were approved by the Ethics Review Committee of the Postgraduate Institute of Medicine, University of Colombo (ERC/PGIM/2022/169). Informed consent was obtained from all the participants. Consent for publication Not applicable Availability of data and materials The datasets used in the current study are available from the corresponding author upon reasonable request. Conflict of interest None declared. Funding No funding was received from any institution or department for this study. Author contributions: Conceptualization, M.S.D.W; Methodology, M.S.D.W, N.A.K.A.I.N, U.G.K, B.M.I.G and W.M.P.C.W; Software, W.M.P.C.W, N.A.K.A.I.N, U.G.K and M.S.D.W; Validation, M.S.D.W, N.A.K.A.I.N, B.M.I.G, V.C.N.V and K.L.K.M; Formal analysis, M.S.D.W, N.A.K.A.I.N, U.G.K W.M.P.C.W ,V.C.N.V and K.L.K.M; Investigation, N.A.K.A.I.N, U.G.K, B.M.I.G, W.M.P.C.W, A.M.A.A.P.A and R.B; Resources, N.A.K.A.I.N, U.G.K, B.M.I.G, W.M.P.C.W, V.C.N.V, K.L.K.M, A.M.A.A.P.A and R.B; Data curation, N.A.K.A.I.N, U.G.K and R.B; Writing – original draft, M.S.D.W; Writing – review & editing, M.S.D.W, N.A.K.A.I.N, U.G.K, B.M.I.G and W.M.P.C.W; Supervision, M.S.D.W, A.M.A.A.P.A and R.B ;Visualization: N.A.K.A.I.N, U.G.K, W.M.P.C.W, A.M.A.A.P.A and R.B; Project administration, M.S.D.W and R.B. All authors have read and agreed to the published version of this manuscript. Acknowledgments The authors acknowledge the public health staff for their support in the conduct of this study. 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Hieke S, Wilczynski P. Colour Me In – an empirical study on consumer responses to the traffic light signposting system in nutrition labelling. Public Health Nutr. 2012 May;15(5):773–82. Seward MW, Block JP, Chatterjee A. A Traffic-Light Label Intervention and Dietary Choices in College Cafeterias. Am J Public Health. 2016 Oct;106(10):1808–14. Khalid SMN. Food Labeling Regulations in South Asian Association for Regional Cooperation (SAARC) Countries: Benefits, Challenges and Implications. Turkish Journal of Agriculture - Food Science and Technology. 2014 Dec;3(4):196. Perera T, Subasinghe GT, Pathirana T. Consumer Knowledge, Perceptions, Attitudes and Practices on the Use of Nutrition Labeling including Traffic Light Labeling (TLL) System in Sri Lanka. Current Developments in Nutrition. 2022 Jun;6:855. Martinez OD, Roberto CA, Kim JH, Schwartz MB, Brownell KD. A Survey of undergraduate student perceptions and use of nutrition information labels in a university dining hall. Health Education Journal. 2013 May;72(3):319–25. Byrd SR, Gelber RH. Effect of dapsone on haemoglobin concentration in patients with leprosy. Lepr Rev. 1991 Jun;62(2):171–8. Roudsari AH, Abdollah Pouri Hosseini SF, Bonab AM, Zahedi-rad M, Nasrabadi FM, Zargaraan A. Consumers’ perception of nutritional facts table and nutritional traffic light in food products’ labelling: A qualitative study. Health Planning & Management. 2021 May;36(3):628–42. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6004275","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475889794,"identity":"74b16f81-c695-4646-959e-74469fdc4ff1","order_by":0,"name":"Millawage Supun Dilara Wijesinghe","email":"","orcid":"","institution":"Health Promotion Bureau","correspondingAuthor":false,"prefix":"","firstName":"Millawage","middleName":"Supun Dilara","lastName":"Wijesinghe","suffix":""},{"id":475889795,"identity":"c03c1b12-92c3-48b5-ae55-a36c3cf24116","order_by":1,"name":"Nissanka Achchi Kankanamalage Ayoma Iroshanee 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Lanka.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6004275/v1/109c5c6f29420fee83147119.png"},{"id":95040434,"identity":"b3e1ebb4-9494-42f9-84c3-98f6ee1f226d","added_by":"auto","created_at":"2025-11-03 16:08:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2578118,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6004275/v1/80fc1d4a-6333-485e-a4f6-04b595886d7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding consumer response to front-of-package labeling: Insights from a nationwide survey in Sri Lanka","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNoncommunicable Diseases (NCD) account for 63% of global mortality annually, with 75% of these deaths occurring in low- and middle-income countries (LMICs) (1). In Sri Lanka, NCDs were responsible for 83% of deaths in 2018, highlighting the urgent need for interventions targeting modifiable risk factors such as unhealthy diets, physical inactivity, and tobacco use (2,3). To address this burden, Sri Lanka\u0026rsquo;s Ministry of Health aligned its National Multisectoral Action Plan for NCDs (2016\u0026ndash;2020) with the WHO Global Action Plan, prioritising dietary reforms through reduced intake of salt, sugar, and saturated fats (2). However, suboptimal diets driven by food environments that promote ultra-processed products remain a persistent challenge (4), necessitating evidence-based strategies to reshape consumer behaviours and industry practices (5).\u003c/p\u003e \u003cp\u003eThe burden of diet-related NCDs, including cardiovascular diseases, diabetes, obesity, and certain cancers, is particularly high in South Asia (6), where shifts toward high-calorie, nutrient-poor processed foods have accelerated in recent decades (7). Recent data suggest that dietary risks alone account for nearly 8\u0026nbsp;million global deaths annually and are among the top three contributors to Disability-Adjusted Life Years (DALYs)(8). These statistics emphasise the importance of targeted nutritional interventions in public health policy.\u003c/p\u003e \u003cp\u003eNutrition labelling has emerged as a critical policy tool for combating diet-related, non-communicable diseases. While mandatory back-of-pack nutrition tables are widespread, the WHO advocates front-of-pack labelling (FOPL) as a more effective measure for guiding healthier choices (4). Among FOPL systems, the traffic-light labelling system (TLS), which uses colour codes (red/amber/green) to signal \u0026ldquo;high\u0026rdquo;, \u0026ldquo;medium\u0026rdquo;, or \u0026ldquo;low\u0026rdquo; levels of key nutrients, has gained traction (9). TLS not only simplifies decision-making but also incentivises product reformulation by industry actors (10). Despite its potential, evidence of the real-world impact of the TLS remains mixed. Systematic approaches highlight their efficacy in improving health perceptions and directing consumer attention (11); however, few studies have rigorously evaluated their influence on purchasing behaviour or long-term dietary shifts (9). Moreover, existing research disproportionately focuses on high-income countries, leaving critical gaps in understanding TLS implementation in LMICs (10).\u003c/p\u003e \u003cp\u003eSri Lanka\u0026rsquo;s 2016 TLS policy for sugar-sweetened beverages (SSBs), complemented by SSB taxation in 2017, provides a unique case study. Developed through collaboration among policymakers, nutrition experts, and civil society(12), the TLS framework was tailored to local dietary patterns and informed by global precedents, such as tobacco control (13). By 2019, TLS was expanded to include salt and fat content, with plans to extend the labelling to restaurant menus and digital platforms(12). Although these efforts reflect a progressive policy trajectory, challenges persist in rural outreach, industry compliance, and measuring long-term health outcomes(12).\u003c/p\u003e \u003cp\u003eFurthermore, few studies in South Asia have systematically explored the sociocultural and behavioural drivers of nutrition label use, including literacy levels, consumer trust in food systems, and affordability concerns, all of which may influence label interpretation and use(14). Evidence from Sri Lanka and comparable LMIC settings is urgently needed to understand how such context-specific factors shape the success of interventions such as TLS.\u003c/p\u003e \u003cp\u003eResearch gaps related to TLS remain twofold: limited evidence on TLS\u0026rsquo;s behavioural impact in real-world settings, particularly in LMICs, and scant data on how TLS interacts with socioeconomic and cultural factors to shape dietary choices (4,9). This study addresses these gaps by analysing TLS\u0026rsquo;s effects on consumer behaviour in Sri Lanka, contributing novel insights from the Global South to inform global NCD policy frameworks.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a descriptive cross-sectional study in 25 districts in Sri Lanka from the 1st of November 2022 to the 30th of April 2023. Food consumers in Sri Lanka were defined as individuals over 18 years of age who had purchased food. The sample size for this study was calculated using the formula provided by Lwanga and Lemeshow (15), with a 95% confidence interval and margin of error of 5%. Based on previous studies, we assumed an expected prevalence of awareness of the traffic light labelling system (TLS) of 50%, which was a conservative estimate to ensure a sufficient sample size. A design effect of two was applied to account for homogeneity within the clusters, which increased the final calculated sample size to 2,840 participants. Adjustments were made to account for potential non-responses, further ensuring the representativeness of the sample. Multistage cluster sampling and probability proportional to size (PPS) sampling techniques were used. Sri Lanka is divided into 6918 public health midwife (PHM) areas. PHM is the smallest health administrative area in the country. A cluster was defined as a public health midwife area. A list of PHMs was obtained from the Ministry of Health, including their contact numbers. The number of PHM areas in each district was selected based on the PPS of the district\u0026rsquo;s population. The number of PHM areas (clusters) in each district was randomly selected for analysis. Thereafter, in the selected PHM area, five individuals\u0026thinsp;\u0026gt;\u0026thinsp;18 years of age were randomly selected (random number method) by the area PHM. The contact details of the selected individuals were obtained through registers maintained by the PHM, which usually provide comprehensive contact information for residents in their areas. The PHM subsequently contacted the individual via mobile phone, and a link for recruitment was sent to the selected individual\u0026rsquo;s phone.\u003c/p\u003e \u003cp\u003eA self-administered online questionnaire was used in Sinhala, English, and Tamil. The Google Form link was shared with each area\u0026rsquo;s Public Health Midwife (PHM), who was responsible for selecting and contacting individuals from the official registers. The selected individuals were contacted via mobile phone, and a survey link was sent to their mobile phones. In instances where participants did not have direct internet access or smartphones, the PHM assisted by facilitating access through their own mobile devices or helped participants complete the Google form during home visits or community clinics. The purpose of this approach was to enhance inclusion and minimise selection bias. Section one of the questionnaire contained questions on sociodemographic and socioeconomic factors. Section two consisted of questions related to knowledge and attitudes. Attitudes were analysed using a Likert scale. We used an ordinal regression analysis model to determine the factors influencing purchasing and TLS. The dependent variables were the minimum, moderate, and best practices. The best practices depicted the optimal practices (always and most of the time looking at the TLS while buying items) that influenced purchasing decisions.\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;1 here]\u003c/p\u003e \u003cp\u003eThe images shown in Fig.\u0026nbsp;1 were used as indicators of front-of-pack labels in Sri Lanka.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, the response rate was 90.5% (n\u0026thinsp;=\u0026thinsp;2569). The analysis of the sociodemographic data yielded several significant findings. In terms of age distribution, a considerable proportion of the respondents were relatively young, with 38.3% falling within the age range of 18\u0026ndash;29 years. Males accounted for 20.2% and females for 79.8% of the respondents. The majority were Sinhalese (80.1%), followed by Tamil (14.6%). Regarding educational level, 40.1% of respondents had a diploma, graduate degree, or a postgraduate degree. When considering household income, the majority of households (38.0%) fell into the category of \"Between Rs. 30,001 and Rs. 50,000\" income per month. Interestingly, regarding the respondents' level of awareness, 89.8% reported being aware of food products with a TLS (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the sample and level of awareness (n\u0026thinsp;=\u0026thinsp;2569)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60 years or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinhalese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTamil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than O/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO/L passed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/L passed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma/graduate or Postgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly Household Income* (n\u0026thinsp;=\u0026thinsp;2538)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRs. 20,000 or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Rs.20,001 and Rs.30,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Rs.30,001 and Rs.50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Rs. 50,001 and Rs.150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove Rs. 150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver seen the traffic light labeling system\u0026nbsp;on any food item\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ein Sri Lanka (n\u0026thinsp;=\u0026thinsp;2569)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver heard of the traffic light labeling\u0026nbsp;system\u0026nbsp;in any food item\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ein Sri Lanka (n\u0026thinsp;=\u0026thinsp;261)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe respondents' level of knowledge clearly revealed that they had an excellent understanding of the colour codes used for TLS. An impressive 89.1% of participants correctly identified the red colour code, which denotes a high level of sugar, salt, or fat. Similarly, a significant number of participants (82.3%) correctly identified the green colour code, which denotes a low level of sugar, salt, and fat. Additionally, 83.9% of respondents correctly identified the amber (yellow) colour code, which represents a medium level of sugar, salt, and fat (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the data indicated that a significant majority of the respondents had a strong understanding of the colour codes of the traffic-light labelling system. Specifically, 78.2% of the participants (n\u0026thinsp;=\u0026thinsp;1851) demonstrated good knowledge of colour codes, suggesting that the system effectively communicates nutritional information to a large portion of the population. Conversely, 21.8% of the respondents (n\u0026thinsp;=\u0026thinsp;517) had less knowledge of TLS, indicating the need for ongoing educational efforts to enhance understanding among this group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLevel of knowledge of the traffic light labeling system (n\u0026thinsp;=\u0026thinsp;2368*)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh level of sugar/salt/fat of the food product\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium level of sugar/salt/fat of the food product\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow level of sugar/salt/fat of the food product\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI do not have any idea\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed color code on food packaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2111 (89.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmber (Yellow) color code on food packaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1987 (83.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen color code on food packaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1949 (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*missing\u0026thinsp;=\u0026thinsp;201\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe sociodemographic characteristics of the participants who reported not having good knowledge of TLS revealed several notable patterns. Among the 517 participants, a substantial majority (63.6%) were aged\u0026thinsp;\u0026ge;\u0026thinsp;30 years, indicating that a lack of knowledge was more prevalent in this age group. In terms of household income, 83.9% earned Rs. 50,000 or less per month, suggesting that lower income may be associated with reduced knowledge of TLS. Ethnically, most of those with limited knowledge were Sinhalese (78.9%), with non-Sinhalese individuals constituting a small percentage (21.1%). The gender distribution revealed that 83.0% of the less knowledgeable participants were female and that 17.0% of the less knowledgeable participants were male. Interestingly, 56.9% of the participants with higher educational qualifications had less knowledge (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the participants without good knowledge of the traffic light labeling system (n\u0026thinsp;=\u0026thinsp;517)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge (n\u0026thinsp;=\u0026thinsp;517)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eless than 30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30 years or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Income (n\u0026thinsp;=\u0026thinsp;509)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRs.50000 or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than Rs.50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity (n\u0026thinsp;=\u0026thinsp;517)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Sinhalese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinhalese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Level (n\u0026thinsp;=\u0026thinsp;517)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow A/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/L and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (n\u0026thinsp;=\u0026thinsp;517)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003cp\u003e429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003cp\u003e83.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeveral consumers mentioned that the information displayed on food packaging was adequate, with 37.3% mentioning that the information displayed on food packaging was \"Quite a lot\", whereas 21.6% mentioned it as \"Somewhat\", and 16.1% mentioned it as \u0026ldquo;very much.\u0026rdquo; When considering the confusion of the provided information, a substantial portion (31.7%) of participants found the information \"Not at all\" confusing, followed by 29.4% who found it \"somewhat\" confusing. Only a few participants (3.3%) said that it was \u0026ldquo;very much\u0026rdquo; confusing. Regarding the helpfulness of the information, the majority of consumers found the information on food packaging to be helpful, with 40.2% marking it as \u0026ldquo;Quite a lot\u0026rdquo; and 30.5% as \u0026ldquo;very much\u0026rdquo;, while 3.5% marked it as \u0026ldquo;Not at all\". A notable proportion of participants mentioned that the information on food packaging was both clear and understandable. For clarity, 39.9%, and for understandability, 39.6% were marked as \"Quite a lot.\u0026rdquo; Forty-five percent of the participants mentioned that the information was \u0026ldquo;Not at all\u0026rdquo; misleading (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLevel of information provided by the traffic light labeling system (n\u0026thinsp;=\u0026thinsp;2371*)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot at all n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA little bit\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSomewhat n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuite a lot n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery much n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequacy of information displayed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179(7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e416(17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e511(21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e884(37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e381(16.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether the information provided is confusing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e751(31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e561(23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e698(29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283(11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78(3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHelpfulness of information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206(8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e406(17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e954(40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e723(30.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClarity of information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e487(20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e945(39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e491(20.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstandability of information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334(14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e426(18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e939(39.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e582(24.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether the information is misleading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1067(45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e417(17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e662(27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152(6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73(3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* missing\u0026thinsp;=\u0026thinsp;198\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThose who were Sinhalese (aOR\u0026thinsp;=\u0026thinsp;2.52, 95% CI\u0026thinsp;=\u0026thinsp;2.04, 3.11), aged\u0026thinsp;\u0026ge;\u0026thinsp;30 years (aOR\u0026thinsp;=\u0026thinsp;1.68, 95% CI\u0026thinsp;=\u0026thinsp;1.43, 1.98), had a household income ˃ Rs.50000 (aOR\u0026thinsp;=\u0026thinsp;1.21, 95% CI\u0026thinsp;=\u0026thinsp;1.01, 1.45), and had an education level of A/L or above (aOR\u0026thinsp;=\u0026thinsp;1.41, 95% CI\u0026thinsp;=\u0026thinsp;1.16, 1.72) had greater odds of adhering to best practices when purchasing the product. Furthermore, individuals with positive overall attitudes toward TLS (aOR\u0026thinsp;=\u0026thinsp;2.54, 95% CI\u0026thinsp;=\u0026thinsp;1.95, 3.32) and good knowledge of TLS (aOR\u0026thinsp;=\u0026thinsp;1.87, 95% CI\u0026thinsp;=\u0026thinsp;1.54, 2.28) had greater odds of adhering to best practices while purchasing (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The ordinal regression model demonstrated excellent global fit, as evidenced by a highly significant likelihood ratio test [χ\u0026sup2;(7)\u0026thinsp;=\u0026thinsp;277, *p* \u0026lt; .001], confirming the collective explanatory power of all the predictors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOrdinal regression analysis of factors influencing traffic light labeling system (TLS) practices (order of the dependent variable; minimum, moderate and best practices)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjusted Odds ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Sinhalese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinhalese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge ˂30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;30 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\u0026thinsp;\u0026le;\u0026thinsp;Rs.50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income ˃Rs.50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation below A/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation A/L or\u003c/p\u003e \u003cp\u003eabove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative overall attitudes on TLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive overall\u003c/p\u003e \u003cp\u003eattitudes on TLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot having good knowledge on TLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReference category\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving good knowledge on TLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers critical insights into the behavioural and attitudinal impacts of the Sri Lankan traffic light labelling system on food purchasing decisions, addressing gaps in the evidence from low- and middle-income countries.\u003c/p\u003e\n\u003ch3\u003eDemographic representativeness and implications\u003c/h3\u003e\n\u003cp\u003eThe high response rate (90.5%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2569) highlights robust engagement, although the sample is skewed toward younger (38.3% aged 18\u0026ndash;29), female (79.8%), and Sinhalese (80.1%) participants with higher education levels (40.1% holding diplomas or degrees) (16). This contrasts with the general population of Sri Lanka (51% female, 14% aged 20\u0026ndash;29 years) (16), suggesting a potential selection bias. Despite this, the findings illuminate TLSs\u0026rsquo; reach across urban and educated demographics while highlighting accessibility gaps in rural and lower-income groups.\u003c/p\u003e \u003cp\u003eThe potential barriers to TLS usage in rural and low-income communities include limited nutritional literacy, economic constraints influencing food choices, reduced exposure to health promotion initiatives, and limited availability of TLS-labelled products in rural markets (17,18). Furthermore, cultural dietary practices and targeted food marketing strategies in low-income areas may contribute to deprioritising nutritional labelling (18). These considerations offer a more comprehensive understanding of the factors underlying uneven awareness and adoption of TLS.\u003c/p\u003e \u003cp\u003eHowever, the disproportionately higher representation of educated, urban, and female study respondents may reflect a population that is already predisposed to health literacy and behavioural responsiveness. This suggests that while TLS is effective among such groups, it may not achieve similar results among less literate or underserved populations without supportive interventions. Thus, the study's findings should be interpreted in the context of this demographic skew, especially when generalising the behavioural impact of TLS across the entire population.\u003c/p\u003e\n\u003ch3\u003eAwareness and comprehension of TLSs\u003c/h3\u003e\n\u003cp\u003eApproximately 90% of the participants reported being familiar with TLSs, and over 80% accurately interpreted colour-coded nutrient thresholds. This aligns with evidence that TLSs simplify nutritional decision-making by reducing cognitive load, as demonstrated in previous eye-tracking studies (11). Notably, the substitution of red-labelled items with healthier alternatives is correlated with reduced caloric, fat, and salt intakes, reinforcing the potential of TLSs for population-level dietary improvements (10).\u003c/p\u003e \u003cp\u003eThe capacity to accurately identify and understand the red, amber, and green codes indicates not merely superficial knowledge but a profound understanding of nutrient risk gradation. This suggests that visual labelling can address deficiencies in challenging nutritional literacy if the labels are contextually applicable and consistently applied. Furthermore, colour-coded nutrition labelling on traffic lights helps consumers with limited self-discipline in selecting healthier food options(19). The significance of this finding lies in its prospective applicability to other low- and middle-income countries, where text-dense labelling may prove less useful owing to literacy constraints.\u003c/p\u003e\n\u003ch3\u003eUser satisfaction and usability\u003c/h3\u003e\n\u003cp\u003eThe participants expressed high satisfaction with TLS\u0026rsquo;s clarity and utility of the TLS: 37.3% rated information adequacy as \u0026ldquo;Quite a lot\u0026rdquo;, whereas 45% found labels \u0026ldquo;Not at all\u0026rdquo; misleading. These results mirror global findings, such as Hieke and Wilczynski\u0026rsquo;s report of high TLS understandability (5.9/7) (20) and Seward et al.\u0026rsquo;s observation that 59% of consumers found TLS helpful (21). However, Khalid\u0026rsquo;s caution against misleading labels underlines the need for standardised, culturally tailored TLS frameworks to ensure accuracy (22).\u003c/p\u003e \u003cp\u003eThe Traffic Light Labelling system in Sri Lanka has demonstrated its potential as an effective behavioural nudge for consumers. A survey revealed that most Sri Lankan consumers have a satisfactory understanding of nutrition labels, including the TLS (23). Moreover, the finding that nearly one-third of consumers rated the sufficiency of labeling as considerable illustrates TLS's practical applicability beyond mere theoretical understanding. The data indicate that TLS may affect both awareness and actual behavioural change, especially when accompanied by product reformulation and focused messaging.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDeterminants of TLS adherence\u003c/h2\u003e \u003cp\u003eOrdinal regression identified Sinhalese ethnicity, age\u0026thinsp;\u0026ge;\u0026thinsp;30 years, household income\u0026thinsp;\u0026ge;\u0026thinsp;Rs.50,000, and higher education level as predictors of TLS adherence. This finding suggests that socioeconomic and cultural factors mediate the effectiveness of labels. Among the 517 participants with limited TLS understanding, barriers included restricted access to nutrition education (especially in rural areas), reliance on unlabelled traditional diets, and cognitive challenges among older populations. Targeted interventions addressing these disparities could enhance equity in TLS adoption, as evidenced by studies linking nutritional literacy to purchasing behaviour (21,24).\u003c/p\u003e \u003cp\u003eThe comparatively lower adherence among non-Sinhalese populations, despite similar or increased health requirements, necessitates culturally sensitive interventions that respect linguistic diversity and regional dietary practices. This includes developing communication that appeals to ethnic minorities and customising educational materials to accommodate diverse cognitive and linguistic capabilities. Moreover, the absence of substantial gender disparity in TLS adherence, despite the majority of women in the sample, may indicate women's established role in household food purchasing. This implies that enhancing women's literacy regarding food labelling could yield significant improvements in household dietary behaviours (25).\u003c/p\u003e \u003cp\u003eNevertheless, certain studies have indicated that consumer behaviour varies across different demographic groups and that there is an absence of a consistent correlation between nutritional quality and consumer behaviour (9,26).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStrengths and limitations\u003c/h3\u003e\n\u003cp\u003eThe strengths of this study include its large sample size, multilingual data collection (Sinhala, English, and Tamil), and multistage cluster sampling to improve representativeness. The use of visual TLS simulations in questionnaires enhanced ecological validity by mimicking real-world shopping experiences. However, its cross-sectional design limits causal inference. Additionally, reliance on mobile-based recruitment and online data collection may have excluded individuals with limited digital access or low digital literacy, potentially introducing participation bias. Despite the assistance provided by PHMs, digital exclusion may have a disproportionate effect on individuals with a lower level of education or socioeconomic status. Furthermore, the possibility of self-selection bias, such as a greater likelihood of participation by health-conscious individuals, and recall bias may limit the generalisability of the findings to the broader population.\u003c/p\u003e \u003cp\u003eAlthough multistage cluster sampling and probability proportional to size (PPS) techniques were employed, sample weighting was not applied. Therefore, the findings may not fully reflect the demographic distribution of the national population, which may limit the generalisability of the results. Future studies aiming for population-level inference should consider applying post-stratification weighting based on national census data.\u003c/p\u003e"},{"header":"Conclusions and recommendations","content":"\u003cp\u003eThis study demonstrates that the Sri Lankan traffic light labelling system significantly shapes food purchasing behaviour, with high baseline awareness and understanding among consumers. The participants perceived the TLS as clear, adequate, and actionable, particularly in terms of urban and educational demographics. However, adherence to the TLS is influenced by socioeconomic and cultural factors. These findings emphasise TLS\u0026rsquo;s potential as a public health tool in LMICs, while highlighting disparities in access and understanding among rural, lower-income, and less-educated populations.\u003c/p\u003e \u003cp\u003eTo maximise the impact of TLS in Sri Lanka, targeted interventions are required to address existing gaps and enhance equity. Promotional campaigns should be expanded to rural and marginalised communities, leveraging high urban awareness. These initiatives could include community-driven education programs delivered in collaboration with local leaders and schools to improve nutritional literacy and TLS comprehension. Simplified messaging using visual aids and regional languages can help overcome literacy barriers, particularly for older adults and low-income households.\u003c/p\u003e \u003cp\u003eAdditionally, investing in longitudinal and equity-focused research is essential to understand the long-term impact of TLS. Periodic evaluations can help track behavioural trends, dietary shifts, and non-communicable disease outcomes, while also identifying how cultural practices, subsistence diets, and digital divides influence TLS effectiveness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-communicable diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTraffic Light System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow and middle-income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOPL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFront-of-Pack Labelling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSugar-Sweetened Beverages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProbability Proportional to the Size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePublic Health Midwife\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the guidelines of the Declaration of Helsinki, and all procedures involving the study participants were approved by the Ethics Review Committee of the Postgraduate Institute of Medicine, University of Colombo (ERC/PGIM/2022/169).\u0026nbsp;Informed consent was obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in the current study are available from the corresponding author upon\u003c/p\u003e\n\u003cp\u003ereasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;None declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received from any institution or department for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, M.S.D.W; Methodology, M.S.D.W, N.A.K.A.I.N, U.G.K, B.M.I.G and W.M.P.C.W; Software, W.M.P.C.W, N.A.K.A.I.N, U.G.K and M.S.D.W; Validation, M.S.D.W, N.A.K.A.I.N, B.M.I.G, V.C.N.V and K.L.K.M; Formal analysis, M.S.D.W, N.A.K.A.I.N, U.G.K W.M.P.C.W ,V.C.N.V and K.L.K.M; Investigation, N.A.K.A.I.N, U.G.K, B.M.I.G, W.M.P.C.W, A.M.A.A.P.A and R.B; Resources, N.A.K.A.I.N, U.G.K, B.M.I.G, W.M.P.C.W, V.C.N.V, K.L.K.M, A.M.A.A.P.A and R.B; Data curation, N.A.K.A.I.N, U.G.K and R.B; Writing – original draft, M.S.D.W; Writing – review \u0026amp; editing, M.S.D.W, N.A.K.A.I.N, U.G.K, B.M.I.G and W.M.P.C.W; Supervision, M.S.D.W, A.M.A.A.P.A and R.B ;Visualization: N.A.K.A.I.N, U.G.K, W.M.P.C.W, A.M.A.A.P.A and R.B; Project administration, M.S.D.W and R.B. All authors have read and agreed to the published version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the public health staff for their support in the conduct of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEdiriweera DS, Karunapema P, Pathmeswaran A, Arnold M. Increase in premature mortality due to non-communicable diseases in Sri Lanka during the first decade of the twenty-first century. BMC Public Health. 2018 Dec;18(1):584.\u003c/li\u003e\n\u003cli\u003eJapan International Cooperation Agency (JICA). Data Collection Survey on NCDs prevention / treatment in Sri Lanka [Internet]. 2022. Available from: https://openjicareport.jica.go.jp/pdf/12370409.pdf\u003c/li\u003e\n\u003cli\u003eMichael Engelgau, Kyoko Okamoto, Kumari Vinodhani Navaratne, Sundararajan Gopalan. PREVENTION AND CONTROL OF SELECTED CHRONIC NCDS IN SRI LANKA: Policy Options and Action. Health, Nutrition, and Population Family (HNP) of the World Bank\u0026rsquo;s Human Development Network; 2010.\u003c/li\u003e\n\u003cli\u003eSong J, Brown MK, Tan M, MacGregor GA, Webster J, Campbell NRC, et al. Impact of color-coded and warning nutrition labelling schemes: A systematic review and network meta-analysis. Ares G, editor. PLoS Med. 2021 Oct 5;18(10):e1003765.\u003c/li\u003e\n\u003cli\u003eKhalid SMN. Food Labeling Regulations in South Asian Association for Regional Cooperation (SAARC) Countries: Benefits, Challenges and Implications. Turkish JAF SciTech. 2014 Dec 23;3(4):196.\u003c/li\u003e\n\u003cli\u003eSiegel KR, Patel SA, Ali MK. Non-communicable diseases in South Asia: contemporary perspectives. British Medical Bulletin. 2014 Sep 1;111(1):31\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eAnurag Mehta, Sumitabh Singh, Anum Saeed, Dhruv Mahtta, Vera A Bittner, Laurence Sperling, et al. Pathophysiological Mechanisms Underlying Excess Risk for Diabetes and Cardiovascular Disease in South Asians: The Perfect Storm. 2021;\u003c/li\u003e\n\u003cli\u003eEchouffo-Tcheugui JB, Ahima RS. Does diet quality or nutrient quantity contribute more to health? Journal of Clinical Investigation. 2019 Aug 26;129(10):3969\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eSacks G, Tikellis K, Millar L, Swinburn B. Impact of \u0026lsquo;traffic-light\u0026rsquo; nutrition information on online food purchases in Australia. Australian and New Zealand Journal of Public Health. 2011 Apr;35(2):122\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eEmrich TE, Qi Y, Lou WY, L\u0026rsquo;Abbe MR. Traffic-light labels could reduce population intakes of calories, total fat, saturated fat, and sodium. Simon SA, editor. PLoS ONE. 2017 Feb 9;12(2):e0171188.\u003c/li\u003e\n\u003cli\u003eJones G, Richardson M. An objective examination of consumer perception of nutrition information based on healthiness ratings and eye movements. Public Health Nutr. 2007 Mar;10(3):238\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eS.A.C Madhusanka, K.K.H.M Rathnayake, R.P Mahaliyanaarachchi. Impact of Traffic Light Food Labelling on Consumer Awareness of Health and Healthy Choices of the Pointof-Purchase. In 2021 [cited 2025 Jun 18]. p. 1\u0026ndash;14. Available from: https://i-conferences.com/publications/Agrofood_2021/Agrofood_2021_1001_Chamara_Madusanka.pdf\u003c/li\u003e\n\u003cli\u003eMadurawala S, Kiringoda K, Thow AM, Arunatilake N. Fiscal policies and regulations for healthy diets in Sri Lanka: an analysis of the political economy of taxation and traffic light labelling for sugar-sweetened beverages. Global Health Action. 2023 Dec 31;16(1):2280339.\u003c/li\u003e\n\u003cli\u003eMandle J, Tugendhaft A, Michalow J, Hofman K. Nutrition labelling: a review of research on consumer and industry response in the global South. Global Health Action. 2015 Dec;8(1):25912.\u003c/li\u003e\n\u003cli\u003eS.K. Lawanga, S. Lemeshow. Sample size determination in health studies. World Health Organization, Geneva. 1991.\u003c/li\u003e\n\u003cli\u003eDepartment of Census and Statistics Sri Lanka. Population Characteristics. Area and Population [Internet]. 2023 [cited 2025 Jun 18]. Available from: https://www.statistics.gov.lk/\u003c/li\u003e\n\u003cli\u003eSullivan AD. \u003cem\u003eDetermining How Low-Income Food Shoppers\u003c/em\u003e Perceive, Understand, and Use Food Labels. Canadian Journal of Dietetic Practice and Research. 2003 Mar;64(1):25\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eSignal L, Lanumata T, Robinson JA, Tavila A, Wilton J, Mhurchu CN. Perceptions of New Zealand nutrition labels by Māori, Pacific and low-income shoppers. Public Health Nutr. 2008 Jul;11(7):706\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eKoenigstorfer J, Groeppel-Klein A, Kamm F. Healthful Food Decision Making in Response to Traffic Light Color-Coded Nutrition Labeling. Journal of Public Policy \u0026amp; Marketing. 2014 Apr;33(1):65\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eHieke S, Wilczynski P. Colour Me In \u0026ndash; an empirical study on consumer responses to the traffic light signposting system in nutrition labelling. Public Health Nutr. 2012 May;15(5):773\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eSeward MW, Block JP, Chatterjee A. A Traffic-Light Label Intervention and Dietary Choices in College Cafeterias. Am J Public Health. 2016 Oct;106(10):1808\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eKhalid SMN. Food Labeling Regulations in South Asian Association for Regional Cooperation (SAARC) Countries: Benefits, Challenges and Implications. Turkish Journal of Agriculture - Food Science and Technology. 2014 Dec;3(4):196.\u003c/li\u003e\n\u003cli\u003ePerera T, Subasinghe GT, Pathirana T. Consumer Knowledge, Perceptions, Attitudes and Practices on the Use of Nutrition Labeling including Traffic Light Labeling (TLL) System in Sri Lanka. Current Developments in Nutrition. 2022 Jun;6:855.\u003c/li\u003e\n\u003cli\u003eMartinez OD, Roberto CA, Kim JH, Schwartz MB, Brownell KD. A Survey of undergraduate student perceptions and use of nutrition information labels in a university dining hall. Health Education Journal. 2013 May;72(3):319\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eByrd SR, Gelber RH. Effect of dapsone on haemoglobin concentration in patients with leprosy. Lepr Rev. 1991 Jun;62(2):171\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eRoudsari AH, Abdollah Pouri Hosseini SF, Bonab AM, Zahedi-rad M, Nasrabadi FM, Zargaraan A. Consumers\u0026rsquo; perception of nutritional facts table and nutritional traffic light in food products\u0026rsquo; labelling: A qualitative study. Health Planning \u0026amp; Management. 2021 May;36(3):628\u0026ndash;42.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Noncommunicable Diseases, Food Labeling, Consumer Behavior","lastPublishedDoi":"10.21203/rs.3.rs-6004275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6004275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eNon-communicable diseases are a leading cause of mortality globally, with unhealthy diets being a significant risk factor. Front-of-package labelling, particularly the traffic light system, has been proposed as a public health intervention to promote healthier dietary choices. This study investigated consumer awareness, understanding, and response to the traffic light system in Sri Lanka, where non-communicable diseases account for 83% of deaths.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis descriptive cross-sectional study was conducted from November 2022 to April 2023 and involved 2,569 participants from 25 districts in Sri Lanka. Multistage cluster sampling was performed to ensure the representativeness of the sample. Data were collected via a self-administered online questionnaire in Sinhala, English, and Tamil, which assessed sociodemographic factors, knowledge, attitudes, and purchasing behaviours related to the traffic light system. Ordinal regression analysis was used to identify the factors influencing adherence to traffic-light system practices.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe study revealed a high awareness of the traffic light system, with 89.8% of the participants recognising the labelling system. Over 80% of the participants correctly identified the colour codes (red, amber, and green) associated with high, medium, and low levels of sugar, salt, and fat. The participants expressed satisfaction with the clarity, adequacy, and helpfulness of the traffic-light system information. Ordinal regression analysis indicated that Sinhalese ethnicity, age\u0026thinsp;\u0026ge;\u0026thinsp;30 years, higher household income (\u0026gt;\u0026thinsp;Rs. 50,000), and higher education levels were positively associated with adherence to the traffic light system best practices. Positive attitudes toward and good knowledge of the traffic light system significantly influenced purchasing behaviour.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThese findings suggest that the traffic light system is an effective tool for guiding healthier food choices among Sri Lankan consumers. However, targeted educational campaigns are needed to address knowledge gaps, particularly among older, lower-income, and less-educated populations. Continuous evaluation and refinement of the traffic light system are recommended to maintain its efficacy and relevance in promoting public health.\u003c/p\u003e","manuscriptTitle":"Understanding consumer response to front-of-package labeling: Insights from a nationwide survey in Sri Lanka","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:21:41","doi":"10.21203/rs.3.rs-6004275/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-04T05:07:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T11:57:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250320797539561746943246286132665707584","date":"2025-07-01T05:34:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T06:07:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241724818926988564809035410354654269326","date":"2025-06-27T04:32:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308252705534985196405236305013537674092","date":"2025-06-26T20:16:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T16:51:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T06:41:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-06-23T07:57:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5699c3d0-c915-4c57-9ff9-54adc3d1e64c","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:04:28+00:00","versionOfRecord":{"articleIdentity":"rs-6004275","link":"https://doi.org/10.1186/s12889-025-24994-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-10-31 15:57:04","publishedOnDateReadable":"October 31st, 2025"},"versionCreatedAt":"2025-06-29 14:21:41","video":"","vorDoi":"10.1186/s12889-025-24994-1","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24994-1","workflowStages":[]},"version":"v1","identity":"rs-6004275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6004275","identity":"rs-6004275","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}