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Impact of color-coded and warning nutrition labelling schemes: A systematic review and network meta-analysis
Abstract
Background
Suboptimal diets are a leading risk factor for death and disability. Nutrition labelling is a potential method to encourage consumers to improve dietary behaviour. This systematic review and network meta-analysis (NMA) summarises evidence on the impact of colour-coded interpretive labels and warning labels on changing consumers’ purchasing behaviour.
Methods and findings
We conducted a literature review of peer-reviewed articles published between 1 January 1990 and 24 May 2021 in PubMed, Embase via Ovid, Cochrane Central Register of Controlled Trials, and SCOPUS. Randomised controlled trials (RCTs) and quasi-experimental studies were included for the primary outcomes (measures of changes in consumers’ purchasing and consuming behaviour). A frequentist NMA method was applied to pool the results. A total of 156 studies (including 101 RCTs and 55 non-RCTs) nested in 138 articles were incorporated into the systematic review, of which 134 studies in 120 articles were eligible for meta-analysis. We found that the traffic light labelling system (TLS), nutrient warning (NW), and health warning (HW) were associated with an increased probability of selecting more healthful products (odds ratios [ORs] and 95% confidence intervals [CIs]: TLS, 1.5 [1.2, 1.87]; NW, 3.61 [2.82, 4.63]; HW, 1.65 [1.32, 2.06]). Nutri-Score (NS) and warning labels appeared effective in reducing consumers’ probability of selecting less healthful products (NS, 0.66 [0.53, 0.82]; NW,0.65 [0.54, 0.77]; HW,0.64 [0.53, 0.76]). NS and NW were associated with an increased overall healthfulness (healthfulness ratings of products purchased using models such as FSAm-NPS/HCSP) by 7.9% and 26%, respectively. TLS, NS, and NW were associated with a reduced energy (total energy: TLS, −6.5%; NS, −6%; NW, −12.9%; energy per 100 g/ml: TLS, −3%; NS, −3.5%; NW, −3.8%), sodium (total sodium/salt: TLS, −6.4%; sodium/salt per 100 g/ml: NS: −7.8%), fat (total fat: NS, −15.7%; fat per 100 g/ml: TLS: −2.6%; NS: −3.2%), and total saturated fat (TLS, −12.9%; NS: −17.1%; NW: −16.3%) content of purchases. The impact of TLS, NS, and NW on purchasing behaviour could be explained by improved understanding of the nutrition information, which further elicits negative perception towards unhealthful products or positive attitudes towards healthful foods. Comparisons across label types suggested that colour-coded labels performed better in nudging consumers towards the purchase of more healthful products (NS versus NW: 1.51 [1.08, 2.11]), while warning labels have the advantage in discouraging unhealthful purchasing behaviour (NW versus TLS: 0.81 [0.67, 0.98]; HW versus TLS: 0.8 [0.63, 1]). Study limitations included high heterogeneity and inconsistency in the comparisons across different label types, limited number of real-world studies (95% were laboratory studies), and lack of long-term impact assessments.
Conclusions
Our systematic review provided comprehensive evidence for the impact of colour-coded labels and warnings in nudging consumers’ purchasing behaviour towards more healthful products and the underlying psychological mechanism of behavioural change. Each type of label had different attributes, which should be taken into consideration when making front-of-package nutrition labelling (FOPL) policies according to local contexts. Our study supported mandatory front-of-pack labelling policies in directing consumers’ choice and encouraging the food industry to reformulate their products.
Protocol registry
PROSPERO (CRD42020161877).
Jing Song and co-workers report a systematic review and network meta-analysis assessing evidence on food labeling and purchasing decisions.
Author summary
Why was this study done?
- Interpretive front-of-package labelling is considered a cost-effective strategy to promote a more healthful diet and mitigate the burden of non communicable diseases (NCDs), and colour-coded labels and warning labels are the most adopted interpretive front-of-package labelling schemes worldwide.
- Prior to this study, evidence on the impact of each type of colour-coded labels and warning labels on modifying consumers purchasing behaviour was mixed.
- The feasibility and likely effectiveness of each label type applied in different contexts was unclear.
What did the researchers do and find?
- This network meta-analysis summarised the currently available 118 peer-reviewed studies to update knowledge of the most mainstream interpretive front-of-package nutrition labelling (FOPL) schemes.
- We found that the traffic light labelling system (TLS), Nutri-Score (NS), nutrient warning (NW), and health warning (HW) were all able to direct consumers towards more healthful purchasing behaviour.
- Colour-coded labels (TLS and NS) performed better in promoting the purchase of more healthful products, while warning labels (NW and HW) had the advantage in discouraging unhealthful purchasing behaviour.
- The difference in consumers’ behaviour could be explained by different underlying psychological mechanisms for each label.
What do these findings mean?
- We provide more comprehensive evidence to guide policy-makers in choosing the optimal front-of-package labelling policies. This evidence synthesis may inform further generalisation of mandatory front-of-package labelling schemes and help to mitigate the burden of NCDs.
- Future studies should focus on the impact of FOPLs on dietary consumption in individuals, and industrial reformulation at the population level, especially in real-world settings and over a longer time frame. This will provide crucial, robust, and comprehensive evidence to guide policy making.
Introduction
Suboptimal diets, linked to food environments that promote food and drink high in salt, sugar, and saturated fat, are a leading risk factor for death and disability worldwide, due to their relationship with non communicable diseases (NCDs) [1–6]. Nearly 8 million deaths in 2019 were attributable to dietary risk factors such as high salt intake and low wholegrain intake. To mitigate the healthcare burden resulting from NCDs, providing clear information about the nutritional profile of products is a recognised method to nudge consumers to more healthful food and drink options and exert pressure on manufacturers to carry out reformulation to improve the nutritional profile of their products [7,8]. As a minimum, many countries have mandatory nutrition tables on the back of food packaging [9], but the World Health Organization (WHO) additionally recommends front-of-pack nutrition labelling (FOPL) to promote healthful diets and help reduce NCD prevalence [10–13]. FOPL provides key nutritional information, typically including calorie, saturated fat, salt, and sugar content, in a visible format [14], and many countries have a voluntary FOPL system in place [15]. FOPL generally falls into 2 main categories—interpretive and noninterpretive. Interpretive labels present symbols, figures, or cautionary text to indicate the overall healthfulness or nutrient content of a product, such as the Nutri-Score (NS) label [16], Chilean style warning labels [17], Health Star Ratings (Australia and New Zealand), and the “traffic light” labelling system (TLS). For example, the United Kingdom’s TLS has red (high), amber (medium), or green (low) labels to indicate levels of total fat, saturated fat, sugars, and salt [18]. Noninterpretive FOPL systems, such as the Guideline Daily Amount, convey nutritional content as numbers rather than graphics, symbols, or colours, allowing consumers to create their own judgements on healthfulness.
At the time of completing this manuscript, a total of 31 countries had implemented interpretive FOPL systems, including 6 countries that had adopted mandatory warning labels on packaged foods and 3 countries that had utilised mandatory colour-coded FOPL systems [19]. Interpretive warning labels and colour-coded labels were the most adopted labels endorsed by governments. So far, real-world evidence is limited and mainly focused on the Chilean style warning label (a type of interpretative nutrient warning (NW) label). Observational studies found that Chile’s Law of Food Labelling and Advertising, which included the mandatory implementation of warning labels nationwide, resulted in lower sales of beverages high in sugars, salt, saturated fat, or energy [20] and was likely to improve understanding and utilisation, especially in children [21]. However, no data are available on changes in consumption.
Based on the health communication theory and previous similar studies [22–26] (Fig 1), visual attention to labels is a prerequisite for the perception and understanding of FOPLs, but the mechanisms linking label interpretation and behavioural changes differ across types of FOPLs. Warning labels may elicit negative perception of unhealthful foods (e.g., perception of severe risk, lower grade of healthfulness, lower level or frequency of recommended consumption for unhealthful products) in the process of changing food choice. However, interpretative colour-coded labels (e.g., TLS) tend to modify purchasing behaviour by increasing the perception of healthfulness for healthier food options. The effects of FOPLs are also modified by study population demographics, knowledge of nutrition labels, frequency of grocery shopping, familiarity with the brand, level of weight consciousness and health status, product categories, and characteristics of labels [23–28]. For example, women and people with special diet needs or higher perceived nutrition knowledge are more likely to look at FOPLs; the provision of serving size information or percentage of recommended intake may add to consumers’ difficulty in understanding FOPLs. Due to the abovementioned reasons, experimental evidence regarding the effectiveness of FOPL in modifying consumers’ purchasing or consuming behaviour was mixed [14,23–40]. Meta-analyses showed that interpretative colour-coded FOPLs (e.g., TLS) significantly increased consumers’ selection of more healthful options from a range of products, as well as decreased calorie and salt content of food purchased [31,41], but no synthesised results for warning labels were available. Experimental studies also provided some insights into the underlying mechanisms of how colour-coded and warning labels change consumers’ behaviour, suggesting that warning labels effectively elicited negative emotions and raised health awareness, which led to the ultimate modifications in food choice; on the other hand, colour-coded labels (e.g., TLS) were indicated to have a favourable effect on increasing consumers’ preference for healthier foods [25,27,28,31].
Attention to labels is a prerequisite for understanding of labels and forming perception towards labels (amber box), but the mechanisms connecting changes in label understanding and perception with behavioural changes differ across FOPL types. Warning labels (e.g., NW and HW) elicit negative perception of food products (e.g., perception of severe risk, negative emotion) in motivating changes in food choice, while interpretive colour-coded labels (e.g., TLS and NS) improve the perception of healthfulness for healthier food options (red box). Demographic characteristics, knowledge of labels, nutrition and health, food categories, and experiment settings also modify the effects of FOPLs throughout the mechanism (blue box). FOPL, front-of-package nutrition labelling; HW, health warning; NS, Nutri-Score; NW, nutrient warning; TLS, traffic light labelling system.
To summarise the existing findings and provide evidence for policy-makers in the proposed implementation or modification of food labelling policies, we aim to assess the impact of colour-coded and warning labels, the most studied and promising labels, on changes in both intended and actual purchasing and consumption behaviour. We also aim to gain insight into the underlying psychological mechanism based on the health communication theory to explore the heterogeneity across label types [22–26].
Methods
The protocol of this systematic review was registered on PROSPERO (CRD42020161877).
Search strategy
The systematic review was conducted and reported in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Network Meta-analysis guideline (S1 PRISMA Checklist). We searched 4 databases (PubMed, Embase via Ovid, Cochrane Central Register of Controlled Trials, and SCOPUS) initially on 26 November 2019 using the following search strategy: (“food and beverages” OR “food” OR “drink” OR “beverage” OR “meal” OR “nutrition” OR “menu” OR “restaurant”) AND (“warning” OR “traffic light” OR “Wheel of health” OR “colored GDA” OR “coloured GDA” OR “5 CNL” OR “Color Nutrition” OR “Nutriscore” OR “Nutri-score” OR “simplified nutrition labelling system” OR “SENS” OR “colour-coded” OR “color-coded” OR “colour coding” OR “color coding” OR “red label” OR “green label” OR “Evolved Nutrition Label”). There was no restriction on language. The search was updated on 24 May 2021 to capture the latest publications up to the submission of manuscript. Additional articles were identified by reviewing the bibliographic reference of identified articles. Articles of all types published from 1 January 1990 were included at this stage.
Inclusion and exclusion criteria, data extraction, and bias assessment
Original studies were selected based on the population, intervention, comparator, outcome, and study design (PICOS) framework (Table 1). All randomised controlled trials (RCTs) and quasi-experimental studies assessing 4 types of interpretative FOPLs (TLS, NS, NW, and HW) were included. In addition, cross-sectional and cohort studies were included in the assessment of secondary outcomes (see “Measurement of outcomes”). We followed the criteria of a previous meta-analysis, which set a control group by merging both Nutrition Facts table (NFt, also known as Nutrition Information Panel in some countries) and no-label condition into the control group in the main analyses, so as to increase the statistical power and precision of network meta-analysis (NMA) [25,42]. Studies that featured other interpretive or noninterpretive FOPLs (e.g., Guideline Daily Amount, Health Star Rating) as the reference group were not eligible as they differed too much from the control group specified (i.e., back-of-package labels and no-label control). Reviews, study protocols, and conference abstracts were also excluded.
Table 1
| Criteria | Description |
|---|---|
| Population | General population |
| Intervention | Inclusion: |
| 1. All colour-coded labels and warning front-of-package labels: TLS, NS, NW, and HW; | |
| 2. For each of TLS, NW, and HW, different formats were also separately included into analysis: summary TLS versus nutrient-specific TLS, negative message framing versus positive message framing, and textual warning versus textual + nontextual warning (e.g., picture of tooth decay, stop sign). | |
| Exclusion: | |
| 1. Other colour-coded labels (e.g., Wheel of health, ENL, and SENS) were not included because they had been scrapped or no longer actively developed due to political reasons or lack of public popularity. | |
| 2. Studies that combined effects of nutrition labels of included types and excluded types (e.g., TLS + HSR). | |
| 3. Studies investigated the combined effect of nutrition labelling and other intervention (e.g., TLS + food placement). | |
| 4. Studies that assessed only the effect of nutritional education using traffic light label as educational tools were not included. | |
| 5. Studies that applied colour-coding schemes (traffic light label score, NS) only to categorise the healthfulness of products were excluded. | |
| Comparator | 1. Absence of nutritional information (no-label) |
| 2. Only NFt present | |
| Outcome | All measured outcomes were included based on the health communication model. |
| Study design | 1. RCTs, including randomised controlled crossover trials and clustered randomised trials |
| 2. Quasi-experimental studies: | |
| • Nonequivalent control group design | |
| • Interrupted time series design | |
| • One-group prepost design | |
| • Choice-based conjoint studies | |
| 3. Cross-sectional studies evaluating at least 2 intervention labels, or 1 intervention labels and control condition (no-label exposure or NFt): only eligible for the secondary outcomes. |
ENL, evolved nutrition label; FOPL, front-of-package nutrition labelling; HSR, Health Star Rating; HW, health warning; NFt, Nutrition Facts table; NS, Nutri-Score; NW, nutrient warning; PICOS, population, intervention, comparator, outcome, and study design; RCT, randomised controlled trial; TLS, traffic light labelling system.
To select eligible studies, titles, abstracts, and main texts were reviewed based on the eligibility criteria. The bibliographic references of the eligible articles were also reviewed to identify additional articles missed out by the database search strategy. Data extraction was carried out using an extraction spreadsheet, consisting of the following variables: publication year, language, country of study, study design, sample size, setting (real-world setting that utilises the sales data before and after the implementation of the actual labelling intervention in retail outlets, or controlled laboratory settings based on a virtual food environment), race, mean age or age range, percentage of female, education, income, occupation, socioeconomics, investigated food types, number of food types (single or multiple), intervention condition, control condition, access to NFt for both intervention and control groups, time pressure, outcome, measure of outcome, and effect estimate.
Bias was assessed using the revised Cochrane risk-of-bias tool for randomised trials (ROB 2) for RCTs [43], Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) for quasi-experimental studies [44], and National Heart, Lung, and Blood Institute (NHLBI) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies [45] for cross-sectional studies. The overall risk-of-bias judgement for each study was summarised as low risk of bias if the study received the assessment of “low risk” for all domains, and high risk of bias if the study was judged to be at high risk in at least one domain. Studies with insufficient information to assess the risk of bias for some domains was categorised into “some concerns” (S1 Text and S10–S12 Tables).
Inclusion and exclusion, data extraction, and risk of bias were first assessed by 2 independent reviewers (JS and MB). A total of 16 discrepancies (13.6%) was found and were referred to a third reviewer (MT).
Measurement of outcomes
Based on the theory conceptualised in Fig 1, we grouped the outcome measures into 3 categories: (1) changes in consumers’ purchasing and consumption behaviour; (2) consumers’ perception and attitudes towards products; and (3) consumers’ attention, understanding, and perception of colour-coded and warning labels (S1 Table). The primary outcomes of our systematic review were measures regarding the changes in consumers’ purchasing and consuming behaviour, which contain the probability of choosing less healthful or more healthful products, self-reported ratings of purchase intention, overall healthfulness of products purchased, and energy and nutrient (salt/sodium, sugar, fat, and saturated fat) content of products purchased/consumed. The healthful and unhealthful products were grouped based on the levels of salt/sodium, sugar, saturated fat, and calories indicated by the front-of-package labelling systems. Products with warning texts, symbols, or colours (e.g., red in multiple traffic light label) indicating high levels of sugar, salt, saturated fat, or calorie content per 100 g or per 100 ml (per 100 g/ml) relative to reference intake, or overall low healthfulness (e.g. red in single traffic light, or orange and red [D and E] in NS), were defined as unhealthful. A list of measures was included in the other 2 secondary outcome domains (S1 Table). Data on the comparisons between intervention labels and control group or pairwise comparisons between any 2 types of intervention labels (e.g., TLS versus NW) were collected. Depending on the results reported in the original studies, we calculated odds ratios (ORs) and 95% confidence intervals (95% CIs) as the summary estimates for categorical outcomes, and relative mean differences (RMDs, the percentage of change comparing intervention and control group) plus standard errors (SEs) for continuous outcomes [46] (S1 Text and S1 Table).
Data synthesis and network meta-analysis
As multiple nutrition labels were evaluated, and several multiarmed trials were included in our analysis, we used the frequentist NMA method to synthesise studies and make both direct (observed) and indirect (unobserved) comparisons of multiple interventions [47,48]. Random-effect models were fitted in the NMA as we assumed the heterogeneity in our network model was high. Cochran’s Q-statistic and Higgin’s and Thompson’s I2 were applied to assess the degree of total heterogeneity in the network model, which was further divided into within- (conventional between-study heterogeneity) and between-design (overall inconsistency) variations, respectively. To test the transitivity and consistency assumptions underlying NMA, we further calculated the Q-statistic in a full design-by-treatment intervention random-effect inconsistency model [49]. When there was inconsistency between the effect estimate of direct and indirect comparisons, we took the direct effect estimate into consideration instead. We also applied the method of separate indirect from direct evidence (SIDE), generating the proportion of direct evidence for each comparison (local inconsistency) [50]. A detailed explanation of the NMA can be found in the S1 Text.
In addition to the analysis of 4 main label types (TLS, NS, NW, and HW) for each outcome measure, we also synthesised the evidence grouped by labelling formats and framing. TLS was further divided into summary TLS and detailed TLS (summary TLS was defined as the single TLS indicating the overall nutritional quality as good, medium, and poor using predefined algorithms; detailed TLS included information on individual nutrients). NW and HW were further categorised into textual or nontextual NW/HW (nontextual NW/HW included graphs and symbols that alert consumers to the high levels of nutrient content or health risks associated with high nutrient content). In addition, HW was also divided into negative or positive, depending on the framing of the warning (positive framing highlighted the health benefits resulting from lowered consumption of a nutrient; negative emphasised the harm associated with excessive consumption of a nutrient). To explore the presence of effect modifiers leading to overall inconsistency between direct and indirect comparisons, subgroup analyses by sex composition (primarily female: female >60%; primary male: female <40%; otherwise equally distributed), age group (primary adults: >70% aged 18 or older; primary children and adolescent: >70% aged less than 18; otherwise general population), setting (real-world or laboratory setting), types of products investigated (single/multiple), and display NFt both in intervention were conducted if at least 2 studies were available for quantitative synthesis in every stratum for each primary outcome measure. Sensitivity analyses of the primary outcomes were conducted by (1) evaluating only RCTs and (2) removing studies using NFt control. We did not perform meta-regression adjusting the effect modifier variables, given that there were a low number of studies available for most outcomes.
A comparison-adjusted funnel was plotted to assess the risk of publication bias under the priori hypothesis that studies identifying the superiority of newly developed labels to an existing labelling system tend to be published in higher frequency [50]. The funnel plot was only created when there were at least 10 studies available for each outcome, as recommended by Cochrane [51]. The results were considered statistically significant when p < 0.05.
All the statistical analyses were implemented in R v3.5.1. The NMA was conducted using the netmeta v1.2–1 R package [48].
Results
Study characteristics
Using the search strategy mentioned above, we obtained 15,058 records from the 4 databases. After removing duplicate articles and excluding ineligible articles based on the title, abstract, and main text, and identifying studies in the bibliographic references of the eligible articles, 156 studies nested in 138 articles remained (S2 Table), of which 22 studies were excluded because the results reported in original papers were incomplete (e.g., missing standard deviations/SEs) and the authors were unable to provide the complete results after contacting via email. In total, 134 studies were eligible for quantitative synthesis (Fig 2). The details of included studies are presented in S1 Data.
Of the 156 studies eligible for systematic review, 154 were published in English, and 2 in Spanish. The majority of articles were published 2018 to present (64%), and the most common study populations were in the regions of Europe (31%), Latin America (28%), and North America (24%). Most studies were carried out in a laboratory setting (95%). Of the studies based in real-world settings (5%), half were conducted in the out-of-home sector and half in retail outlets. Most studies were RCTs (65%). None of the studies were industry funded.
The majority of studies assessed TLS (62%), while 40% studies investigated NW, 22% studied NS, and 17% evaluated HW (Fig 3). For comparators, 80% studies used a no-label condition. Most studies (78%) did not provide NFt along with the labels in the intervention group. Most studies used multiple types of foods and drinks to assess the effect of labels (56%), and 40% focused on a single type. Nearly all studies leveraged individual-level data (97%), and only 4 studies were based on sales data. Most studies had a population of primarily adults (88%) (Table 2).
The design of colour-coded labels (A, B) and warning labels (C, D). (A) UK traffic light label uses green, amber, and red to represent low, moderate, and high levels of fat, saturated fat, sugar, and sodium per 100 g or per 100 ml on the front of food packages, respectively, with addition of the % reference intake value for calorie and each nutrient. (B) NS includes a colour spectrum ranging from dark green to dark orange with letters from A to E. Products assigned an “A” were considered to have the best nutritional quality while “E” the poorest. (C) Chilean warning labels is a type of NW with a textual warning “high in [calorie/nutrient]” presented on the octagonal signs in a black-and-white design. (D) California safety warning is a type of HWs designed for sugar-sweetened beverages with ≥75 calories contributed by added sugar. HW, health warning; NS, Nutri-Score; NW, nutrient warning.
Table 2
| Study characteristics | Number of studies | Proportion (%) |
|---|---|---|
| Publication year | ||
| 2021 | 17 | 10.9 |
| 2020 | 26 | 16.67 |
| 2019 | 32 | 20.51 |
| 2018 | 25 | 16.03 |
| 2017 | 8 | 5.13 |
| 2016 | 9 | 5.77 |
| 2015 | 9 | 5.77 |
| 2014 | 8 | 5.13 |
| 2013 | 5 | 3.21 |
| 2012 | 4 | 2.56 |
| 2011 | 5 | 3.21 |
| 2010 and before | 8 | 5.13 |
| Language | ||
| English | 154 | 98.72 |
| 2 | 1.28 | |
| Country | ||
| US | 27 | 17.31 |
| Uruguay | 19 | 12.18 |
| France | 16 | 10.26 |
| Canada | 11 | 7.05 |
| Australia | 10 | 6.41 |
| UK | 9 | 5.77 |
| New Zealand | 7 | 4.49 |
| Chile | 7 | 4.49 |
| Brazil | 7 | 4.49 |
| Germany | 6 | 3.85 |
| Ecuador | 5 | 3.21 |
| Switzerland | 3 | 1.92 |
| Mexico | 3 | 1.92 |
| Other | 19 | 16.67 |
| Region | ||
| Europe | 49 | 31.41 |
| Latin America | 43 | 27.56 |
| North America | 38 | 24.36 |
| Oceania | 17 | 10.9 |
| Asia | 4 | 2.56 |
| Africa | 1 | 0.64 |
| Multiple | 4 | 2.56 |
| Setting | ||
| Laboratory | 148 | 94.87 |
| Real-world | 8 | 5.13 |
| Out-of-home sectors* | 4 | 2.56 |
| Retailer outlets | 4 | 2.56 |
| Study design | ||
| RCT | 101 | 64.74 |
| Quasi-experimental studies and cross-sectional survey | 55 | 35.26 |
| Intervention label | ||
| TLS | 97 | 62.18 |
| NW | 62 | 39.74 |
| NS | 35 | 22.44 |
| HW | 27 | 17.31 |
| Control condition | ||
| No label | 125 | 80.13 |
| NFt | 12 | 7.69 |
| Compared against each other colour-coded or warning labels | 19 | 12.18 |
| Types of foods and drinks tested | ||
| Multiple | 87 | 55.77 |
| Single | 62 | 39.74 |
| Not stated | 7 | 17.31 |
| Research data | ||
| Individual data | 152 | 97.44 |
| Sales data | 4 | 2.56 |
| Age group | ||
| Primarily adults | 134 | 88.16 |
| Primarily children or adolescents | 5 | 3.29 |
| Mixed population made up of adults, children, and adolescents | 5 | 3.29 |
| Not stated | 8 | 5.26 |
| Sex | ||
| Mixed | 85 | 55.92 |
| Mostly female | 60 | 39.47 |
| Mostly male | 1 | 0.66 |
| Not stated | 6 | 3.95 |
| Education # | ||
| High | 47 | 30.92 |
| Low | 45 | 29.61 |
| Not stated | 60 | 39.47 |
| Individual/Familial income | ||
| High | 1 | 0.66 |
| Low | 6 | 3.95 |
| Mixed | 48 | 31.58 |
| Not stated | 97 | 63.82 |
| Occupation | ||
| Undergraduate students | 6 | 3.95 |
| Primary school students | 1 | 0.66 |
| Mixed | 11 | 7.24 |
| Not stated | 134 | 88.16 |
| Presence of NFt along with intervention label | ||
| Yes | 34 | 21.79 |
| No | 122 | 78.21 |
| Risk of bias | ||
| High | 107 | 68.59 |
| Moderate/Some concerns | 35 | 22.44 |
| Low | 14 | 8.97 |
*Out-of-home sector includes any outlet where food or drink is prepared for immediate consumption by consumers, such as restaurants, cafes, and takeaways.
#Education was classified as “high” if >50% of study population completed university or college education, otherwise classified into “low.”
HW, health warning; NFt, Nutrition Facts table; NS, nutri-score; NW, nutrient warning; RCT, randomised controlled trial; TLS, traffic light labelling system.
The comparison-adjusted funnel showed that there was no publication bias detected for most outcomes (S4–S6 Figs). For a few outcomes (e.g., probability of selecting more healthful options, energy and sugar of purchased products, objective understanding and perceived effectiveness of FOPLs) presenting funnel asymmetry, we cannot simply predict them as publication biases either as controversy remains in the test accuracy as the appearance of the plot may be affected by the coding of outcomes and choice of measures [52].
Consumers’ attention, perception, and understanding of colour-coded and warning labels
Objective understanding
The objective understanding of labels was measured by 4 indexes that represent different aspects of label interpretation: (1) comparison or ranking; (2) recall; (3) classification; and (4) mathematical manipulation (estimation) of overall healthfulness and nutrient content. TLS was the only label observed to improve understanding of nutrition information in all 4 types of tasks (Tables (Tables33 and S5 and S2 Fig). NS was also observed to boost the participants’ ability to compare/rank products and estimate overall healthfulness, and NW was associated with improved ability to compare/rank and classify overall healthfulness. When colour-coded labels and warning labels were compared against each other, TLS was found to outperform NW in classifying sugar and saturated fat than NW, and NS was linked to better capacity in the comparison/ranking of overall healthfulness than NW. Comparisons between the 2 colour-coded labels suggested that NS might perform better than TLS in comparing/ranking overall healthfulness task.
Table 3
| Outcome | Comparison | Number of comparisons | Direct estimate (OR and 95% CI) | Indirect estimate (OR and 95% CI) | Network estimate (OR and 95% CI) | Direct evidence proportion |
|---|---|---|---|---|---|---|
| Comparison/Ranking of overall healthfulness and nutrient content | ||||||
| Overall healthfulness | NS vs. control | 14 | 4.84 (3.36, 6.98) | 3.52 (1.72, 7.19) | 4.53 (3.28, 6.28) | 0.79 |
| NS vs. NW | 7 | 1.62 (0.97, 2.72) | 1.93 (1.08, 3.44) | 1.75 (1.19, 2.57) | 0.56 | |
| NS vs. TLS | 8 | 1.38 (0.85, 2.24) | 1.87 (1.07, 3.26) | 1.57 (1.09, 2.27) | 0.57 | |
| NW vs. control | 14 | 2.22 (1.54, 3.2) | 3.92 (2.15, 7.17) | 2.59 (1.89, 3.54) | 0.73 | |
| NW vs. TLS | 10 | 0.91 (0.59, 1.41) | 0.88 (0.51, 1.53) | 0.9 (0.64, 1.26) | 0.61 | |
| TLS vs. control | 21 | 2.91 (2.15, 3.94) | 2.75 (1.44, 5.24) | 2.88 (2.19, 3.79) | 0.82 | |
| Energy | TLS vs. control | 2 | 3.1 (1.36, 7.08) | . | 3.1 (1.36, 7.08) | 1.00 |
| Sodium/Salt | TLS vs. control | 2 | 2.92 (1.06, 8.06) | . | 2.92 (1.06, 8.06) | 1.00 |
| Mathematical manipulation (estimation) of overall healthfulness and nutrient content | ||||||
| Overall healthfulness | NS vs. control | 0 | . | 2.57 (1.42, 4.65) | 2.57 (1.42, 4.65) | 0 |
| NS vs. TLS | 1 | 0.88 (0.69, 1.14) | . | 0.88 (0.69, 1.14) | 1.00 | |
| TLS vs. control | 1 | 2.91 (1.7, 4.98) | . | 2.91 (1.7, 4.98) | 1.00 | |
| Energy | TLS vs. control | 2 | 2.4 (0.39, 14.69) | . | 2.4 (0.39, 14.69) | 1.00 |
| Fat | TLS vs. control | 1 | 1.32 (0.87, 2) | . | 1.32 (0.87, 2) | 1.00 |
| Recall of overall healthfulness and nutrient content | ||||||
| Overall healthfulness | TLS vs. control | 1 | 1.34 (0.8, 2.22) | . | 1.34 (0.8, 2.22) | 1.00 |
| Energy | TLS vs. control | 1 | 1.55 (1.14, 2.11) | . | 1.55 (1.14, 2.11) | 1.00 |
| Classification of overall healthfulness and nutrient content | ||||||
| Overall healthfulness | NW vs. control | 4 | 2.63 (1.42, 4.88) | 6.46 (1.72, 24.18) | 3.09 (1.77, 5.41) | 0.82 |
| NW vs. TLS | 2 | 1.81 (0.75, 4.39) | 1.02 (0.4, 2.63) | 1.39 (0.73, 2.65) | 0.53 | |
| TLS vs. control | 5 | 2.45 (1.4, 4.27) | 1.25 (0.31, 4.98) | 2.23 (1.33, 3.73) | 0.86 | |
| Energy | TLS vs. control | 3 | 4.53 (0.63, 32.37) | . | 4.53 (0.63, 32.37) | 1.00 |
| Sodium/Salt | TLS vs. control | 4 | 3.97 (1.89, 8.32) | . | 3.97 (1.89, 8.32) | 1.00 |
| Sugar | NW vs. control | 1 | 1.31 (0.56, 3.06) | . | 1.31 (0.56, 3.06) | 1.00 |
| NW vs. TLS | 0 | . | 0.31 (0.11, 0.89) | 0.31 (0.11, 0.89) | 0 | |
| TLS vs. control | 2 | 4.28 (2.23, 8.18) | . | 4.28 (2.23, 8.18) | 1.00 | |
| Fat | TLS vs. control | 3 | 3.09 (1.26, 7.6) | . | 3.09 (1.26, 7.6) | 1.00 |
| Saturated fat | NW vs. control | 1 | 1.8 (0.96, 3.38) | . | 1.8 (0.96, 3.38) | 1.00 |
| NW vs. TLS | 0 | . | 0.4 (0.18, 0.88) | 0.4 (0.18, 0.88) | 0.00 | |
| TLS vs. control | 2 | 4.54 (2.78, 7.43) | . | 4.54 (2.78, 7.43) | 1.00 | |
CI, confidence interval; HW, health warning; NS, Nutri-Score; NW, nutrient warning; OR, odds ratio; TLS, traffic light labelling system.
Subjective understanding
We assessed different dimensions of subjective understanding of the labels in terms of ease of understanding (16 studies), elimination of confusion (5 studies), perceived provision of information (13 studies), and perceived workload (5 studies) in label processing (S5 Table and S3 Fig). TLS caused more confusion for consumers (OR and 95% CI for elimination of confusion: 0.66 [0.45, 0.97]) but was seen as providing sufficient information (3.08 [1.6, 5.93]). NS was considered easy to understand (1.84 [1.19, 2.85]) and providing sufficient information for consumers (2.44 [1.03, 5.78]). NW was evaluated as the easiest to understand (NW versus control: 2.82 [1.82, 4.36]; NW versus TLS: 2.03 [1.32, 3.12]).
Perceived effectiveness and credibility
Thirteen studies were included for the evaluation of perceived effectiveness, while 8 studies for the credibility assessment (S5 Table and S3 Fig). TLS, NS, and HW were all perceived effective by consumer surveys (OR and 95% CI: TLS, 3.2 [1.43, 7.19]; NS, 3.92 [1.42, 10.8]; HW: 4.75 [1.03, 21.88]). Comparing colour-coded labels against the warning labels suggested that NS was believed to be more effective than NW. None of the FOPLs were thought more credible than the control group.
Salience and visual attention
Several self-reported measures were applied to evaluate the salience of labels, including how conspicuous labels were (10 studies included using self-reported ratings of the question “to what degree to you think the label stands out”) and how frequently they were noticed (5 studies assessed the frequency of participants claiming that they noticed the labels) and recalled (3 studies evaluated if participants correctly recalled the labels). TLS, NW, NW, and HW were all noticed more frequently than the control group (OR and 95% CI: TLS, 2.58 [1.65, 4.02]; NS, 5.65 [2.84, 11.22]; NW, 3.04 [1.87, 4.95]; HW, 6.09 [3, 12.36]). Only NS was perceived to be conspicuous (7.53 [1.78, 31.89]) and was more likely to be recalled (3.46 [2.74, 4.37]) than the control. Comparing colour-coded labels and warning labels showed that NS and HW was noticed more often than other options. NS was correctly recalled more frequently than TLS (S5 Table and S3 Fig).
To measure visual attention more accurately and precisely, some studies utilised eye-tracking devices to record the eye movement, and a fixation was defined as low velocity of eye movement (S5 Table and S3 Fig). Only TLS and NS were evaluated in 5 eye-tracking studies. Our analysis indicated that TLS attracted fixations more frequently and quickly with longer duration. The use of TLS also delayed the fixation on NFt and reduced the visual attention on NFt. According to our findings, TLS was perceived to provide more nutrition information and thus required more time to interpret. In addition, the dependence on conventional NFt was lessened due to the salience and perceived provision of information of TLS. NS also captured visual attentions faster than the control condition, but total duration and number of fixations on the label were also reduced compared with NFt control, probably due to the nature of summarised label simplifying the interpretation of nutrition information.
Consumers’ perception and attitudes towards foods and drinks
To explore the mechanisms underpinning behavioural changes (formation of attitudes), we further assessed the influence of different colour-coded and warning labels on consumers’ perception and attitudes towards products, based on perceived healthfulness and risks, perceived recommended amount and frequency of consumption, self-reported product appeal, and willingness-to-pay (WTP).
The results indicated that TLS, NW, and HW all reduced the perception of healthfulness for less healthful products (RMD and 95% CI: TLS, −0.077 [−0.116, −0.038]; NW, −0.224 [−0.263, −0.186]; HW, −0.126 [−0.16, −0.092]) and reduced the perceived recommended amount to consume unhealthful foods (perceived recommended amount to consume: TLS, −0.05 [−0.054, −0.046]; NS, −0.08 [−0.082, −0.078]; NW, −0.47 [−0.509, −0.431]), but NW performed significantly better than TLS and HW (perceived healthfulness for unhealthful products: NW versus TLS, −0.147 [−0.195, −0.1]; NW versus NS, −0.188 [−0.301, −0.075]; perceived recommended amount of unhealthful products to consume: NW versus TLS, −0.42 [−0.459, −0.381]; NW versus NS, −0.39 [−0.429, −0.351]; perceived recommended frequency of unhealthful products to consume: NW versus TLS, −0.41 [−0.449, −0.371]; NW versus NS, −0.455 [−0.529, −0.381]) (Tables (Tables44 and S4 and S1 Fig). NW and HW also increased the perceived disease risk of unhealthful products, as well as reduced the appeal of unhealthful products (Tables (Tables55 and S4). On the other hand, however, TLS and NS promoted the perceived healthfulness for more healthful products better than NW (NW versus TLS, −0.145 [−0.282, −0.008]; NW versus NS, 0.201 [−0.399, −0.003]) (Tables (Tables44 and and55 and S4 and S1 Fig).
Table 4
| Outcome | Comparison | Number of comparisons | Direct estimate (RMD and 95% CI) | Indirect estimate (RMD and 95% CI) | Network estimate (RMD and 95% CI) | Proportion of direct evidence |
|---|---|---|---|---|---|---|
| Less healthful products | HW vs. control | 10 | −0.125 (−0.159, −0.091) | −0.289 (−0.712, 0.133) | −0.126 (−0.16, −0.092) | 1.00 |
| HW vs. NS | 0 | . | −0.089 (−0.204, 0.025) | −0.089 (−0.204, 0.025) | 0 | |
| HW vs. NW | 1 | 0.099 (−0.061, 0.259) | 0.098 (0.045, 0.152) | 0.098 (0.048, 0.149) | 0 | |
| HW vs. TLS | 0 | . | −0.049 (−0.1, 0.003) | −0.049 (−0.1, 0.003) | 0 | |
| NS vs. control | 1 | −0.078 (−0.383, 0.227) | −0.031 (−0.148, 0.087) | −0.037 (−0.146, 0.073) | 0.05 | |
| NS vs. NW | 0 | . | 0.188 (0.075, 0.301) | 0.188 (0.075, 0.301) | 0 | |
| NS vs. TLS | 1 | 0.046 (−0.064, 0.156) | −0.001 (−0.309, 0.306) | 0.041 (−0.063, 0.144) | 0.95 | |
| NW vs. control | 6 | −0.215 (−0.255, −0.175) | −0.327 (−0.46, −0.194) | −0.224 (−0.263, −0.186) | 0.99 | |
| NW vs. TLS | 3 | −0.181 (−0.244, −0.118) | −0.102 (−0.175, −0.03) | −0.147 (−0.195, −0.1) | 0.96 | |
| TLS vs. control | 6 | −0.086 (−0.127, −0.046) | 0.014 (−0.114, 0.143) | −0.077 (−0.116, −0.038) | 0.98 | |
| Products of mixed healthfulness | NS vs. control | 2 | 0.066 (−0.026, 0.157) | . | 0.066 (−0.026, 0.157) | 1.00 |
| NS vs. TLS | 0 | . | 0.108 (−0.01, 0.226) | 0.108 (−0.01, 0.226) | 0 | |
| TLS vs. control | 2 | −0.043 (−0.118, 0.032) | . | −0.043 (−0.118, 0.032) | 1.00 | |
| More healthful products | HW vs. control | 2 | −0.016 (−0.128, 0.097) | . | −0.016 (−0.128, 0.097) | 1.00 |
| HW vs. NS | 0 | . | −0.137 (−0.319, 0.045) | −0.137 (−0.319, 0.045) | 0 | |
| HW vs. NW | 0 | . | 0.064 (−0.113, 0.241) | 0.064 (−0.113, 0.241) | 0 | |
| HW vs. TLS | 0 | . | −0.081 (−0.215, 0.053) | −0.081 (−0.215, 0.053) | 0 | |
| NS vs. control | 2 | 0.121 (−0.022, 0.264) | . | 0.121 (−0.022, 0.264) | 1.00 | |
| NS vs. NW | 0 | . | 0.201 (0.003, 0.399) | 0.201 (0.003, 0.399) | 0 | |
| NS vs. TLS | 0 | . | 0.056 (−0.104, 0.216) | 0.056 (−0.104, 0.216) | 0 | |
| NW vs. control | 1 | −0.069 (−0.222, 0.084) | −0.125 (−0.437, 0.186) | −0.08 (−0.217, 0.057) | 1.00 | |
| NW vs. TLS | 1 | −0.156 (−0.309, −0.003) | −0.1 (−0.411, 0.212) | −0.145 (−0.282, −0.008) | 1.00 | |
| TLS vs. control | 6 | 0.066 (−0.006, 0.139) | . | 0.065 (−0.007, 0.138) | 1.00 |
RMD refers to the percentage of change comparing intervention with control group (RMD = (x2 − x1) / x1; x1: mean of continuous outcome in the intervention group or after implementation of intervention, x2: mean of continuous outcome in the control group or before implementation of intervention).
CI, confidence interval; HW, health warning; NS, Nutri-Score; NW, nutrient warning; RMD, relative mean difference; TLS, traffic light labelling system.
Table 5
| Outcome | Comparison | Number of comparisons | Direct estimate (RMD and 95% CI) | Indirect estimate (RMD and 95% CI) | Network estimate (RMD and 95% CI) | Proportion of direct evidence |
|---|---|---|---|---|---|---|
| Less healthful products | HW vs. control* | 10 | 0.124 (0.053, 0.196) | 0.6 (0.153, 1.048) | 0.136 (0.065, 0.207) | 1.00 |
| HW vs. NW* | 2 | 0.063 (−0.09, 0.215) | −0.562 (−0.766, −0.357) | −0.16 (−0.283, −0.038) | 0.67 | |
| HW vs. NW + HW* | 1 | 0.03 (−0.181, 0.241) | −0.653 (−1.006, −0.3) | −0.15 (−0.331, 0.031) | 0.93 | |
| NW vs. control* | 3 | 0.43 (0.299, 0.561) | −0.304 (−0.582, −0.027) | 0.296 (0.178, 0.415) | 0.66 | |
| NW vs. NW + HW* | 1 | −0.11 (−0.324, 0.104) | 0.463 (0.048, 0.878) | 0.01 (−0.18, 0.2) | 0.61 | |
| NW + HW vs. control | 1 | 0.36 (0.146, 0.574) | 0.102 (−0.236, 0.439) | 0.286 (0.105, 0.467) | 0.23 | |
| More healthful products | HW vs. control | 2 | 0.024 (0.008, 0.04) | . | 0.024 (0.008, 0.04) | 1.00 |
RMD refers to the percentage of change comparing intervention with control group (RMD = (x2 − x1) / x1; x1: mean of continuous outcome in the intervention group or after implementation of intervention, x2: mean of continuous outcome in the control group or before implementation of intervention).
*The direct and indirect effects were observed significantly inconsistent (p < 0.05), and the network estimate may violate the assumption of consistency and transitivity of NMA, thus only direct evidence was used for interpretation.
CI, confidence interval; HW, health warning; NMA, network meta-analysis; NS, Nutri-Score; NW, nutrient warning; RMD, relative mean difference; TLS, traffic light labelling system.
WTP is an economic concept used to evaluate consumers’ demand for a product [53]. In our systematic review, 3 studies considered WTP of less healthful products, suggesting that NS significantly reduced the WTP for less healthful products by 16%, but no significant effect was observed for textual NW or HW. Another 3 studies investigated the WTP for more healthful products but did not find evidence for change in WTP when labelled with TLS, textual NW, or HW (S1 Data).
Changes in consumers’ purchasing and consuming behaviour or intentions
The NMA revealed that all colour-coded and warning labels were significantly associated with changes in purchasing behaviour. Warning labels also have a significant effect on purchasing intention (Fig 4 and S3 Table). NS and NW were both associated with an increasing overall healthfulness of products purchased. TLS, NS, and NW all were associated with purchasing lower energy, sodium/salt, total fat, or saturated fat (Figs (Figs55 and and66 and S3 Table). Only one study looked at the outcome of consumption (S3 Table), thus we did not include it in our meta-analysis. This study suggested that TLS was not significantly associated with change in energy consumption.
RMD refers to the percentage of change comparing intervention with control group (RMD = (x2 − x1) / x1; x1: mean of continuous outcome in the intervention group or after implementation of intervention, x2: mean of continuous outcome in the control group or before implementation of intervention). *The direct and indirect effects were observed significantly inconsistent (p < 0.05), and the network estimate may violate the assumption of consistency and transitivity of NMA, thus only direct evidence was used for interpretation. **Estimates with significant effect given α = 0.05 and β = 80%. CI, confidence interval; HW, health warning; NMA, network meta-analysis; NS, Nutri-Score; NW, nutrient warning; RMD, relative mean difference; OR, odds ratio; TLS, traffic light labelling system.
RMD refers to the percentage of change comparing intervention with control group (RMD = (x2 − x1) / x1; x1: mean of continuous outcome in the intervention group or after implementation of intervention, x2: mean of continuous outcome in the control group or before implementation of intervention). *The direct and indirect effects were observed significantly inconsistent (p < 0.05), and the network estimate may violate the assumption of consistency and transitivity of NMA, thus only direct evidence was used for interpretation. **Estimates with significant effect given α = 0.05 and β = 80%. CI, confidence interval; HW, health warning; NMA, network meta-analysis; NS, Nutri-Score; NW, nutrient warning; RMD, relative mean difference; TLS, traffic light labelling system.
RMD refers to the percentage of change comparing intervention with control group (RMD = (x2 − x1) / x1; x1: mean of continuous outcome in the intervention group or after implementation of intervention, x2: mean of continuous outcome in the control group or before implementation of intervention). **Estimates with significant effect given α = 0.05 and β = 0.05. CI, confidence interval; HW, health warning; NS, Nutri-Score; NW, nutrient warning; RMD, relative mean difference; TLS, traffic light labelling system.
Eight studies conducted in real-world settings examined the association between FOPLs and sales of unhealthful or healthful products (S7 Table). NW was linked to reduced probability of purchasing/selecting of unhealthful items (OR and 95% CI: 0.50 [0.34, 0.73]), while TLS was associated with increased likelihood of purchasing/selecting more healthful products (1.32 [1.02, 1.72]).
When colour-coded labels and warning labels were compared against each other, we found that NW appeared to outperform TLS in discouraging purchasing unhealthful products (OR and 95% CI: 0.81 [0.67, 0.98]), reducing intended purchase of unhealthful products (RMD and 95% CI: −0.197 [−0.352, −0.042]), and lowering total amount of energy purchased (RMD and 95% CI: −0.064 [−0.125, −0.004]). NW was also observed to perform better than NS in improving overall healthfulness (RMD and 95% CI: 0.127 [0.029, 0.225]) and reduce total energy (RMD and 95% CI: −0.07 [−0.008, −0.131]) and saturated fat (RMD and 95% CI: −0.156 [−0.264, −0.049]) in shopping basket, but NS appeared to encourage the purchase of healthful products better than NW (OR and 95% CI: 1.51 [1.08, 2.11]) (S3 Table).
Comparisons within colour-coded labels or warning labels suggested moderate difference as well. NW reduced the purchase intention for unhealthful products more than HW (RMD and 95% CI: 0.178 [0.002, 0.355]), but HW performed better in boosting the intended purchase of healthful products (0.429 [0.058, 0.8]). TLS did not differ from NS much in terms of the behavioural changes. When comparing the results of different subtypes of TLS (summary versus nutrient-specific TLS), NW (textual NW versus nontextual NW), and HW (textual HW versus nontextual HW, negative message versus positive message), we did not find any difference on the behavioural changes.
Considering the evident heterogeneity and inconsistency for some outcome measures that might violate the consistency and transitivity assumptions required for an NMA (S3 Table), our findings should be interpreted with caution for these outcomes.
Discussion
This systematic review evaluated the impact of colour-coded and warning labels on consumers’ purchasing behaviour, the psychological mechanism underpinning purchasing modification, including consumers’ perception and liking for foods and drinks, as well as the understanding and evaluation of label attributes. We found that all colour-coded and warning labels appeared to have beneficial effects by encouraging the purchase of more healthful products, reducing the purchase of less healthful options, improving overall nutritional quality, and reducing the energy, sodium/salt, fat, and saturated fat content of processed foods and drinks purchased/chosen. Based on the health communication theory, the results suggested that colour-coded and warning labels successfully drew more of consumers’ attention than the control condition and improved consumers’ understanding of nutrition information. The labels also modified perceived healthfulness, recommended consumption amount, and frequency of consumption for products. These mechanisms can establish more healthful purchasing behaviour by improving both the nutritional quality and nutrient content purchased/chosen by consumers.
Despite the heterogeneity in label types, labelling formats, position, study population, study design, and experimental settings across studies [14,23–40], FOPLs were generally considered to have positive effects on guiding consumers in making more healthful food choices, especially in populations with low socioeconomic status and limited knowledge of nutrition labels [24,54]. The health communication theory suggested that attention to the FOPL is a prerequisite for establishing specific perception of the label itself and an understanding of the nutrition information. Our study, together with previous evidence [23,24,30], suggested that colour-coded labels and warning labels were able to ease the difficulty in processing nutrition information and improve the objective understanding of nutrition information with the use of eye-catching design, but the perception of labels might differ, especially between the 2 colour-coded labels (TLS and NS). Although TLS and NS both used colour scales to indicate healthfulness of foods, TLS scores the level of each target nutrient, while NS summarises the overall nutritional quality taking all preferable and detrimental nutrients into consideration. Therefore, according to our study, TLS was perceived to provide sufficient information, which was also reflected in the finding that TLS was associated with a better performance in complex understanding tasks (e.g., classification of sugar and saturated fat). However, too much information could also elicit confusion in consumers when reading TLS. NS, on the other hand, was perceived to be more salient and thus thought easier to understand for consumers.
According to the health communication theory and previous studies [22–26], mechanisms of motivating consumers’ behavioural change vary across FOPL types [26]. NW and HW, compared with the colour-coded counterparts, are more dependent on eliciting the perception of severe risk and negative emotions, which mediate the reduced selection of unhealthful products [26]. TLS and NS, on the other hand, rely more on enhancing the perception of healthfulness for more healthful options and thereby perform better at promoting purchase of healthful foods [27,28,31], which were supported by our findings (Tables (Tables44 and and5).5). Our study, together with previous evidence, explained the role of perception of healthfulness in mediating the effect of colour-coded labels and warning labels on consumers’ purchasing behaviour. Warning labels are more often associated with “danger” due to the use of symbols (e.g., octagon stopping sign), colour (black and white), and cautionary texts, thus elicit negative perception and emotions towards unhealthful food products marked with warnings on the front of packages. For healthful products, warnings are not displayed, but TLS is presented with green lights on, which is indicative of the concept of “health,” “nature,” and “sustainability,” thus associated with perception of better healthfulness for food products.
While our review found that FOPL can effectively change purchasing behaviour, mandatory FOPL policies are likely to be much more effective at changing consumption than voluntary policies. Voluntary FOPL systems have been adopted slowly in the marketplace, and consumers also perceived the products without FOPL as more healthful, albeit the nutritional quality might be worse [15]. Even though most FOPL policies are currently implemented on a voluntary basis, over the past 3 years mandatory FOPL systems have increasingly been favoured, of which warning labels and TLS were the most popular interpretative FOPLs worldwide. More countries have also proposed to make ongoing voluntary FOPL policies mandatory, which would better guide consumers’ food choice and stimulate reformulation in the food industry [7,8]. To ensure the effectiveness of mandatory FOPL policies, consumer education and monitoring systems for the market should be launched, and further studies should also be carried out in real-world settings to add to the evidence of mandatory FOPL policies in different populations and regional cultures.
Compared to previous meta-analyses that assessed the effectiveness of colour-coded labels and warning labels against a control group (mostly no labels) [25,31,41,46], we utilised the NMA method to explore the difference in the effectiveness and indicators of psychological mechanisms of one type of FOPL compared to another. This provided a deeper insight into the question of which is the optimal FOPL that can be applied to each country. Generally, our findings suggested that different FOPL types might dominate different outcome measures. NS performed better in nudging the purchase of more healthful products than NW, while NW had the advantage in discouraging unhealthful purchasing behaviour. The underlying psychological mechanisms also vary across labels. Warning labels reduced the perceived healthfulness of unhealthful products and remind consumers to eat less of unhealthful foods, while colour-coded labels enhanced the perception of healthfulness for more healthful products (Table 4 and S4 Table). Consumers’ perception towards labels differed across types as well. NW was recognised as easier to understand and improved the classification of nutrient content (e.g., sugar and saturated fat) compared to TLS. According to our NMA, TLS was considered to provide more sufficient information than NS, but the latter was recalled more frequently (see Results in the “Salience and visual attention” section). The performance of colour-coded labels and warning labels on multiple dimensions make it necessary for policy-makers to weigh pros and cons according to local context. For example, in countries with high levels of NCDs, which place a large burden on individuals and healthcare systems, and a food system dominated by ultraprocessed food and drinks, NW or HW could help lower supply-driven consumption as they are known to be easier to interpret by consumers, who can recognise that they are applied to unhealthful products high in sugar, salt, and saturated fat [55,56].
Our review included empirical evidence from multiple databases, providing the latest and most comprehensive evidence for different aspects of colour-coded and warning labels, including different subtypes: summary and nutrient-specific type, nontextual and textual types, and positive- and negative-framed types. We applied the NMA method to make full use of both direct and indirect evidence of the comparisons between intervention labels and the control condition. These results can support policy-makers to make decisions based on the performance of labelling in different dimensions. In addition to the effect on purchasing and consuming behaviour, we also investigated the underlying psychological outcomes quantitatively based on the health communication framework, which systematically suggested the mechanism underpinning the effect of consumers’ behaviour.
Our study had some limitations. First, compared to the relatively large amount of evidence on the purchasing behaviour elicited by FOPLs, the data on food consumption were quite limited. Our findings suggested that both warning labels and colour-coded labels would reduce the perceived recommended consumption amount or frequency of unhealthful products, and warning labels might outperform colour-coded, which could be seen as an indirect evidence that FOPLs are able to change dietary consumption. However, the research gap between purchase and actual intake of different nutrients remains to be validated by future studies, which is crucial to inform decision-making on labelling policies. Second, most of the studies were laboratory experiments, and, thus, findings mainly indicate the immediate or short-term effect of colour-coded and warning label interventions. There were very few real-world studies assessing the effect of mandatory labelling policies on genuine “purchase.” However, considering the highly controlled condition in RCTs and quasi-experimental studies, our findings will need further demonstration by real-world evidence to build the evidence for the generalisation of labelling to other parts of the world. In our meta-analysis, we found that most of the existing real-world studies evaluated TLS and found it effectively increased the purchase/selection of more healthful products. For NS, NW, and other HW types, more research in real-world settings is needed to confirm their effectiveness. Third, more than half of the studies included had a high risk of bias. Considering the nature of nutrition labelling intervention studies, it was inevitable that participants would be aware of their assigned interventions, and, in turn, such awareness could influence the assessment of outcomes. Therefore, in the sensitivity analyses, we included only RCTs in the analyses of consumers’ behaviour, and the results were consistent with those of the primary analyses. Fourth, we only searched for peer-reviewed articles in 4 of the most commonly used databases and the bibliographic references of eligible articles to ensure that our search strategy could be easily replicated and repeated for the update. We believed that most relevant studies should have been covered in this way, though some relevant studies reported in other databases or in the grey literature may have been missed. Fifth, we did not conduct meta-regression to explore the heterogeneity and inconsistency between direct and indirect comparisons, due to the limited number of studies for most outcomes. Instead, we used a random-effect inconsistency model to accommodate the inconsistency and heterogeneity within and across comparisons. We also carried out a series of subgroup analyses by a range of effect modifiers based on previous studies, including age, sex, study setting, accessibility of NFt, and product types. Another sensitivity analysis was also performed excluding studies using NFt as control setting. The results of the sensitivity analyses did not differ much from our findings in the primary analysis, which suggested the biases generated from combining 2 control settings (NFt control and no-label control) might be relatively small. Sixth, we only considered energy and unfavourable nutrients (e.g., sugar, salt, fat, and saturated fat) that are common in various FOPLs and are considered the major risk factors of NCDs burden [55], but favourable nutrients (e.g., fibre, protein) are also components of interest in many FOPLs (e.g., NS and Health Star Rating) for their beneficial health effect [57,58]. So far, few studies have evaluated the effect of FOPLs on favourable nutrients [59], and there has been a disagreement in the inclusion of favourable nutrients in FOPLs as they might have a health halo effect to products that are high in salt, sugar, or fat [60,61]. For these reasons, we did not summarise the results on unfavourable nutrients in this systematic review, and further studies are needed to clarify the pros and cons of favourable nutrients on colour-coded and warning labels. Finally, the numbers of studies were limited for some comparisons (e.g., only one study provided direct evidence on the comparison between NS and HW for the probability of purchasing more healthful products), especially in the analysis of outcomes concerning the perception and understanding of FOPLs, and perception and attitudes towards food options. Our findings for these secondary outcomes need to be validated by more studies in the future.
In summary, our findings suggest that both colour-coded labels and warnings appeared effective in nudging consumers’ behaviour towards more healthful products by changing the healthfulness perception and eliciting negative emotions. Each type of label may have some different attributes, but the difference between different forms of labels remains to be demonstrated by further studies. Our study can support policy-makers to push forward mandatory FOPL policies to make use of the full potential of FOPL in directing consumers’ food choice and encouraging reformulation in the food industry.
Supporting information
S1 PRISMA Checklist
NMA checklist of items to include when reporting a systematic review involving a network meta-analysis.(DOCX)
S1 Fig
Forest plots of network estimates combining direct and indirect effects for consumers’ perception consumers’ perception and attitudes for foods and drinks (secondary outcomes).CI, confidence interval; HW, health warning; MD, mean difference; NS, Nutri-Score; NW, nutrient warning; OR, odds ratio; TLS, traffic light labelling system.
(TIFF)
S2 Fig
Forest plots of network estimates for consumers’ objective understanding of colour-coded and warning labels (secondary outcomes).CI, confidence interval; NS, Nutri-Score; NW, nutrient warning; OR, odds ratio; TLS, traffic light labelling system.
(TIFF)
S3 Fig
Forest plots of network estimates for consumers’ subjective understanding, attention, and perception of colour-coded and warning labels (secondary outcomes).CI, confidence interval; HW, health warning; MD, mean difference; NFt, Nutrition Facts table; NS, Nutri-Score; NW, nutrient warning; OR, odds ratio; TLS, traffic light labelling system.
(TIFF)
S4 Fig
Comparison-adjusted funnel plots for comparison of interventions with control condition for changes in consumers’ purchasing and consuming behaviour (primary outcomes).HW, health warning; MD, mean difference; NS, Nutri-Score; NW, nutrient warning; TLS, traffic light labelling system.
(TIFF)
S5 Fig
Comparison-adjusted funnel plots for changes in consumers’ perception and attitudes for foods and drinks (secondary outcomes).HW, health warning; MD, mean difference; NS, Nutri-Score; NW, nutrient warning; OR, odds ratio; TLS, traffic light labelling system.
(TIFF)
S6 Fig
Comparison-adjusted funnel plots for consumers’ attention, perception, and understanding of colour-coded and warning labels (secondary outcomes).HW, health warning; NS, Nutri-Score; NW, nutrient warning; OR, odds ratio; TLS, traffic light labelling system.
(TIFF)
S1 Table
Measurement and definition of outcomes in the systematic review of colour-coded and warning labels.(XLSX)
S2 Table
Characteristics of studies included in the systematic review.(PDF)
S3 Table
The netsplitting of network meta-analysis estimates for changes in consumers dietary behaviour into the contribution of direct and indirect evidence and test for local inconsistency.(XLSX)
S4 Table
The netsplitting of network meta-analysis estimates for changes in consumers’ perception and attitudes for products into the contribution of direct and indirect evidence and test for local inconsistency.(XLSX)
S5 Table
The netsplitting of network meta-analysis estimates for consumers’ attention, perception, and understanding towards labels into the contribution of direct and indirect evidence and test for local inconsistency.(XLSX)
S6 Table
The netsplitting of network meta-analysis estimates into the contribution of direct and indirect evidence and test for local inconsistency in the analysis of label subtypes.(XLSX)
S7 Table
Netsplitting of network meta-analysis estimates for the primary outcomes into the contribution of direct and indirect evidence and test for local inconsistency grouped by age, sex, SES, study setting, type of products, and NFt display.(XLSX)
S8 Table
The netsplitting of sensitivity network meta-analysis in studies estimates (including only randomised controlled trials) for changes in consumers dietary behaviour into the contribution of direct and indirect evidence and test for local inconsistency.(XLSX)
S9 Table
The netsplitting of sensitivity network meta-analysis estimates (removing studies using NFt control) for changes in consumers dietary behaviour into the contribution of direct and indirect evidence and test for local inconsistency.(XLSX)
S10 Table
Risk of bias of randomised controlled trials included in the systematic review using the revised Cochrane risk-of-bias tool for randomised trials (RoB 2).(XLSX)
S11 Table
Risk of bias of quasi-experiments included in the systematic review using Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I).(XLSX)
S12 Table
Risk of bias of cross-sectional studies included in the systematic review using NHLBI study quality assessment tools.(XLSX)
Abbreviations
| CI | confidence interval |
| FOPL | front-of-package nutrition labelling |
| HW | health warning |
| NCD | non communicable disease |
| NFt | Nutrition Facts table |
| NMA | network meta-analysis |
| NS | Nutri-Score |
| NW | nutrient warning |
| OR | odds ratio |
| PICOS | population, intervention, comparator, outcome, and study design |
| RCT | randomised controlled trial |
| RMD | relative mean difference |
| SE | standard error |
| SIDE | separate indirect from direct evidence |
| TLS | traffic light labelling system |
| WHO | World Health Organization |
| WTP | willingness-to-pay |
Funding Statement
The authors received no specific funding for this work.
Data Availability
All relevant data are contained within the Supporting Information files.
References
Decision Letter 0
11 Dec 2020
Dear Dr He,
Thank you for submitting your manuscript entitled "Impact of color-coded and warning nutrition labelling schemes: a systematic review and network meta-analysis" for consideration by PLOS Medicine.
Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.
However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.
Please re-submit your manuscript within two working days, i.e. by .
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Feel free to email us at gro.solp@enicidemsolp if you have any queries relating to your submission.
Sincerely,
Richard Turner, PhD,
Senior editor, PLOS Medicine
Decision Letter 1
11 Jan 2021
Dear Dr. He,
Thank you very much for submitting your manuscript "Impact of color-coded and warning nutrition labelling schemes: a systematic review and network meta-analysis" (PMEDICINE-D-20-05962R1) for consideration at PLOS Medicine.
Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:
[LINK]
In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.
In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.
In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at gro.solp@enicideMSOLP.
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PLOS Medicine
On behalf of Richard Turner, PhD, Senior Editor,
PLOS Medicine
-----------------------------------------------------------
Requests from the editors:
*Please restructure the abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions - "Methods and Findings" is a single subsection).
*In the Abstract, following text, assume that the OR's given in the brackets reflects the range of OR's for the point estimates for those specific four forms of displaying nutrition information? This is a somewhat unusual way of representing statistics for this type of study, where generally you'd expect an OR for the point estimate followed by 95% CIs pertaining to that point estimate. Some readers might find this presentation a bit confusing, so if the authors can use a more conventional presentation that might be helpful.
"We found that the Traffic-light labelling system (TLS), Nutri-score (NS), Nutrient Warning (NW) and Health
Warning (HW) all increased the probability of selecting healthier products (OR: 1.71-2.83), and warning labels were particularly effective in reducing consumers’ probability of selecting less healthy products (odds ratio [OR]: 0.61-0.62)".
*In the abstract, for the following statement, is it possible to add information on what the outcome measure is for "healthfulness"? Also note that "healthy/unhealthy" is often seen as British English but "healthful" is more US English, would suggest the authors go for one or the other rather than a mix.
"TLS, NS and NW increased the overall healthfulness (TLS: +3%; NS: +5.4%; NW: +3.1%)"
*In the last sentence of the Abstract Methods and Findings section, please add a note about any key limitation(s) of the study's methodology.
*In the author summary, some of the bullets could be rephrased a bit to flow better:
- Evidence on the impact of each type of color-coded labels and warning labels on modifying consumers purchasing behaviours were mixed. - perhaps "Prior to this study, evidence on the impact of...... was mixed"
- The feasibility and effectiveness of each label type applied in different contexts were
unclear. - perhaps "Previously, the feasibility and likely effectiveness of using different label types in different contexts was unclear".
*Suggest also rephrase the following:
- We provided the most comprehensive evidence to guide policy-makers in choosing the optimal front-of-package labelling policies, which will also inspire the generalization of mandatory front-of-package labelling schemes and largely mitigate the noncommunicable chronic disease burden. - perhaps tone this down a bit - "We provide more comprehensive evidence to guide..... This evidence synthesis may inform further generalization of mandatory front-of-package labelling schemes and help to mitigate the burden of noncommunicable chronic disease"
*Suggest perhaps the following bullet could instead be replaced by something noting any limitations of the study which might affect interpretation/generalisation?
- In the future, we will expand our network meta-analysis work by summarizing on the impact of interpretative front-of-package labelling schemes on industrial reformulation and consumers’ actual consumption behaviours.
*Please reformat the citation style into PLOS Medicine's format (should be straight forward if using referencing software) - this should use callouts formatted as sequential numerals in square brackets (not superscript).
-----------------------------------------------------------
Comments from the reviewers:
Reviewer #1: The manuscript deals with an actual and highly relevant topic for policy making worldwide. The authors have done a tremendous amount of work summarizing and analyzing all the available information on the topic. However, some of the analyses are not clearly explained or presented, which makes it difficult to judge the conclusions reached by the authors. I recommend a major revision after major methodological issues are addressed. Based on the data report on the paper I cannot say whether results are valid or not. Please find below some detailed comments. When submitting a revised version of the manuscript, please include line number to facilitate the review.
Introduction
- The manuscript should include a logic model explaining how front-of-package nutrition labelling (FOPNL) is expected to influence health outcomes. References to the health communication theory should be provided on the Introduction.
- The authors state that "Observational studies found that the mandatory implementation of warning labels in Chile resulted in lower sales of beverages high in sugars, salt, saturated fat or energy". However, the Chilean law does not only include FOPNL, which makes impossible to disentangle the effects of the different components (FOPNL, marketing regulations and prohibition of products with warnings at schools). In addition, Study 21 only reports qualitative data from focus groups with a specific group of participants, so the authors should tune down their statements related to the mechanisms underlying the efficacy of the warnings.
Methods
- The authors should include detailed information on the criteria used for assessing risk of bias. This is particularly relevant for risk of bias for measurement of the outcome and confounding, given that most studies were categorized as high risk of bias. The authors refer to Table S22 but do not provide any specific explanation of how it was assessed or the reasons underlying the classification.
- Studies on FOPNL have been conducted using very different methodologies and study designs. Therefore, it is not clear how the authors calculated and analyzed the data to report ORs for all the variables. This is a major concern given that conclusions are reached based on these estimates. A detailed description on how these ORs were calculated should be provided in the manuscript.
- A similar comment applies to the network analysis, more detailed information should be provided to enable reviewers to actually judge the validity of the conclusions.
Results
- The authors have included a huge number of Tables and Figures, which makes it difficult to follow the manuscript. I recommend the authors to reduce the number of Tables and Figures to facilitate understanding.
- In Table 1, I recommend providing a more detailed split of the publication year of the articles. The authors grouped 2018-2020 and 1998-2018 without a clear rationale. Regarding countries, I think that summary information by continent or region of the world will be more informative than country income. For example, Chile and Uruguay are high income countries but are very different from European countries or the USA. Regarding SES, I do not agree with how the authors grouped studies as they may be using different criteria than the ones reported in the articles. They should refrain from re-categorizing variables reported in the studies. Finally, considering that most articles do not report race, I would remove this criteria.
- Results for the different outcomes should be presented following the order of a logic model, e.g. consumer perceptions and attitudes come before purchase intention.
- The authors should clarify how "less healthy" and "healthier" were defined. This is particularly relevant given that studies tend to use different criteria.
- Results from network analysis are not clearly presented.
- The sections consumer's perception and attitudes and consumers' understanding are actually very important but results are not presented in detail. The authors should choose between excluding these sections or providing additional information. This is particularly relevant given that these outcomes could define decisions to implement different FOPNL schemes. In their discussion section they say that the review evaluated the impact of color-coded and warning labels on "the psychological mechanism underpinning purchasing modification, including consumers' perceptions and liking for foods and drinks, as well as the understanding and evaluation of label attributes". However, detailed information is not provided.
Discussion
- The discussion section should be rewritten and improved to provide based on an in-depth discussion of the results reported in the manuscript. In the current version, the authors discuss results that are not presented in the manuscript, e.g. "Nutrition labels with numerical information and technical terms ... added to the difficulty in understanding nutrition information". In addition, it is not clear how the authors draw some of their conclusions based on their results, e.g. "TLS is considered to provide more sufficient information than NS, but the latter is recalled more frequently"
- For the interpretation of results, the psychological mechanisms underlying the different schemes have not been adequately discussed. In this sense, recent studies showing how framing effects influence the effect of FOPNL on consumers' choice or the potential role of heurisitics on consumers' perception of products with warnings are not included in the review nor discussed.
- Some of the comments are not entirely accurate, particularly for a review on FOPNL. For example, the authors state "In addition, ultraprocessed foods and drinks, which are usually high in free sugar, total fat, saturated fat and sodium, are generally labeled with a NW for at least one nutrient, but some of which may be labeled as healthy in TLS or NS (e.g., sugar sweetened beverages may have green lights on sodium and saturated fat contents when labeled with TLS)". I don't think it is accurate to say that the TLS would label sugar sweetened beverages as healthy, as it is not the purpose of the TLS. The problem is not the label per se, but how consumers may understand the information provided by the TLS. In this sense, the authors report one study showing this effect (Reference 61).
- The discussion of how TLS or NW may be adequate for countries with different socioeconomic status is based on experimental data. The authors state that warnings "may be more suitable for countries with high levels of citizens with low socioeconomic status, who may indicate a lack of nutrition knowledge and/or health consciousness, and who may be unable to pay for expensive healthier foods". This statement does not take into account the effect of the food environment on consumers' eating patterns. For example, the share of ultra-processed products consumed is higher in the USA or UK compared to Latin American countries. Therefore, I do not think that NW would not be effective in the USA or the UK, even if consumers may have "higher nutrition knowledge or ability to purchase healthier alternatives". The authors should revised their statements based on a food systems' approach. I recommend the review by Swinburn et al. published last year on the Lancet discussing the syndemic of obesity, undernutrition and climate change.
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Reviewer #2: Feng He and colleagues presented here a systematic review and network meta-analysis about the impact of color-coded and warning nutrition labelling schemes. The topic is particularly relevant as it is a timely public health issue with high stakes in the current international debates on food nutrition labels.
The study aims to assess the impact of the color-coded and warning labels, the most studied and promising labels, on changes in both intended and actual purchasing and consumption behaviours. The study aims too to gain insight into the underlying psychological mechanism based on health communication theory to explore the heterogeneity across label types
It is an excellent and very useful paper. The manuscript is clear and well-written. The description of the methods and resultsare all good. The literature review of peer-reviewed articles on the topic (118 articles) seems to me really exhaustive. The statistical methods used are appropriate.
The study addressed interesting and very important research questions. This is a fascinating piece of work using a strong methodology.
I have few comments.
1. Authors claimed they followed the flow chart of the PRISMA recommendations . However it is not identical to the PRISMA flow diagram described on the website http://prisma-statement.org/. Some information are missing : how many unique articled authord have found (excluding duplicates), or how many articles were obtained from other sources (for example, by reviewing the bibliographic references of the identified articles).
2. In their flow chart, authors indicate that 14,785 items are excluded without giving the reason: duplicates? Do not meet the inclusion criteria? If so, what criteria?
3. Two independent reviewers reviewed the articles. What was the percentage of discrepancy? Please precise.
4. The conclusions on the objective understanding of FOP Nutrition label are questionnable . Authors conclude on the effect of the TLS but not so much NS whereas many studies performed in France, Spain, Germany, UK, the Netherlands, Bulgaria, Switzeland, Italy, Singapore, USA, Canada,… have focused on this point and show well the superiority of the NS...it is visible in the additional figure S4 and the difference in outcome between Figure A and C is not understandable...
5. Sometimes discussion is not consistent with results. In some places, authors highlight the performances of TLS in the discussion while this is true also on the NS (and it is said in the results).
6. The proposed outcomes in term of nutrients include only unfavourable nutrients (I agree this is a major point) but they do not include favourable nutrients, while it is a major point of interest for NS (and other FOP nutrition labels in the world such as australian HSR) to have effects on positive components, especially +++ fiber. It would be worthwhile to point out that issue, since the effects of FOP nutrition labels must be able to take into account all nutrients which could have an effect on health.
7. Authors should include in their discussion the paper on serving sizes from Egnell et al (Egnell M, Kesse-Guyot E, Galan P, Touvier M, Rayner M, Jewell J, Breda J, Hercberg S, et Julia C. Impact of Front-of-Pack Nutrition Labels on Portion Size Selection : An Experimental Study in a French Cohort. Nutrients 10, no 9 (2018) : 1268) , especially when they talk about the 'gap in knowledge' between buying and consumption.
8. The study of Acton et al. on the NS should not be taken into considération for analysis of Nutri-Score since, as Acton et all indicated in their methodology, they used the graphic format of NS in their study, they did not used the correct algorithm for the NS (this is responsible of important modification for classification fo foods)
9. In tables 2 and 3, some limits of the interval are not accurate (either they are significant and should be bold, or they are not and should not be ( I think it is a rounding problem). For ex : NW vs TLS (-0.051 (-0.103,0) ; TLS vs control (-0.026 (-0.051,0))
10. Under Tables 2 to 4, the word intervention has been replaced by the word interaction in the footnote
11. I did not understand what the N column correspond to in tables 2 to 4. I thought it was the number of studies included in the aggregation of the statistical parameter, but if it was the case, there shouldn't be zeros…. Please clarify.
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Reviewer #3:
Impact of color-coded and warning nutrition labelling schemes: a systematic review and network meta-analysis.
Song et al
This well conducted systematic review and meta-analysis aimed to analyse the impact of color-coded interpretive labels and warning labels on consumer purchasing behaviour.
The results are potentially important for policy makers and the wider public health community,
as are the potential insights into the underlying psychological mechanisms of behavioral change.
ABSTRACT
Generally good.
Results, line 16
Here, and at various points beyond, please replace "showed" by "suggested".
(Best to be a little modest and circumspect, given the limitations of the data, and analyses).
INTRODUCTION
Line 6 "To mitigate the healthcare burden resulting from NCDs, providing clear information about the nutritional profile of products is a recognised method to nudge consumers to healthier food
and drink options 8."
Please also mention that nutrition labelling also puts pressure on manufacturers to reformulate. Currently that important concept is not mentioned until late in the manuscript (Page 19). "..stimulate reformulation in the food industry53,54."
Para 1, penultimate line "systems, such as the Guideline Daily Amount, convey nutritional content, allowing consumers …", "
Please say "systems, such as the Guideline Daily Amount, convey nutritional content as numbers rather than graphics, allowing consumers …",
METHODS
The protocol of this systematic review was registered on PROSPERO (CRD42020161877). - Good
Search Strategy. They searched four databases.
Did they do any hand-searching, or consultation of topic-experts?? If not, they likely missed some studies.
They sensibly identified one Primary Outcome:
changes in consumers' purchasing and consumption behaviours,
and relegated two other outcomes to Secondary status: a) consumer's perceptions and attitudes towards products, and b) consumers' understanding and perceptions of color-coded and warning labels.
P9. "Inclusion and exclusion, data extraction and risk of bias were first assessed by one reviewer
(J.S), independently reviewed by a second reviewer (M.B)". Good.
RESULTS
Generally good.
Most studies were carried out in a laboratory setting (94%). Obvious issues of generalisability to real world settings. This is picked up in the Discussion.
74% had a high risk of bias.
What % were industry funded?
Tables 2 & 3 are particularly informative, but complicated.
Might it be possible to present the key findings as a graphic, perhaps a Forrest Plot?
The language throughout the manuscript needs to be toned down, and made a bit less categorical
Eg Abstract
"Results. A total of 135 studies nested in 120 articles were incorporated into the systematic review, of which 118 studies in 105 articles were eligible for meta-analysis.
We found that the Traffic-light labelling system (TLS), Nutri-score (NS), Nutrient Warning (NW) and Health Warning (HW) all INCREASED the probability of selecting healthier products (OR: 1.71-2.83),…"
Given the limitations of the data, and analyses, it would be wiser to say:
"We found that the Traffic-light labelling system (TLS), Nutri-score (NS), Nutrient Warning (NW) and Health Warning (HW) APPARENTLY all increased the probability of selecting healthier products (OR: 1.71-2.83),…"
Likewise, for intance, in the main Results, Page 16, line 2:
"When color-coded labels and warning labels were compared against each other, we found that NS WAS more effective than NW in promoting the probability of purchasing healthier
products (OR and 95%CI: 1.64 [1.09, 2.48]),…"
Better to say:
"When color-coded labels and warning labels were compared against each other, we found that NS APPEARED more effective than NW in promoting the probability of purchasing healthier products (OR and 95%CI: 1.64 [1.09, 2.48]),…"
In general, almost every use of the term "showing" would be more honestly stated by using the term "suggesting".
That would not detract at all from the authority or main messages of this comprehensive analysis.
The results sections also needs at least one sentence summarising the results of the Funnel Plot analyses.
DISCUSSION
Generally good.
Page 18 states " our study found that FOPLs had a positive effect on guiding consumers in making healthier food choices,
especially in populations with low socio-economic status and limited knowledge of nutrition labels 8,52."
However, I cannot see any results text to support the statement about "especially in low SE status" ??
Limitations para.
Generally good.
But it would be nice to see an acknowledgement that some some studies were probably missed, however, that would be unlikely to alter the main messages.
Acknowledgements
It might be sensible to state how the various authors were funded (mainly to pre-empt subsequent unwarranted accusations of conflicts of interest).
Supplement
generally informative.
I would like to see the very useful Figure S5 promoted into the main manuscript;
Perhaps split into two figures, one covering the more "objective" measures of purchasing/consumption (calories, salt, fat etc),
the other the various psychological/behavioural intentions.
The PICOS Table (Table S1 PICOS criteria for inclusion and exclusion of studies) might also be usefully promoted into the main manuscript.
Nil else.
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Reviewer #4: See attachment
Michael Dewey
-----------------------------------------------------------
Any attachments provided with reviews can be seen via the following link:
[LINK]
Author response to Decision Letter 1
2 Feb 2021
Attachment
Submitted filename: Reviewers comments 2021.1.31_final.docx
Decision Letter 2
23 May 2021
Dear Dr. He,
Thank you very much for submitting your manuscript "Impact of color-coded and warning nutrition labelling schemes: a systematic review and network meta-analysis" (PMEDICINE-D-20-05962R2) for consideration at PLOS Medicine. We do apologize the delay in sending you a response.
Your paper was re-evaluated by our independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:
[LINK]
In light of these reviews, we will again be unable to accept the manuscript for publication in the journal in its current form, but we would like to invite you to submit a further revised version that addresses the reviewers' and editors' comments fully. You will appreciate that we cannot make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more reviewers.
In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.
In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at gro.solp@enicideMSOLP.
We hope to receive your revised manuscript by Jun 11 2021 11:59PM. Please email us (gro.solp@enicidemsolp) if you have any questions or concerns.
***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***
We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.
Please use the following link to submit the revised manuscript:
https://www.editorialmanager.com/pmedicine/
Your article can be found in the "Submissions Needing Revision" folder.
To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols
Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.
Please let me know if you have any questions, and we look forward to receiving your revised manuscript.
Sincerely,
Richard Turner, PhD
Senior Editor, PLOS Medicine
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Requests from the editors:
Please update the search to the end of March, 2021, say.
Please make that "quasi-experimental studies" in the abstract and elsewhere.
Please quote the numbers of randomized and non-randomized studies included, around line 11 of the abstract.
In the abstract and elsewhere in the paper, please indicate where you are quoting findings from randomized and non-randomized studies. Where data from the latter study designs are included, please adapt the language used so as not to imply causality, e.g., "... were associated with reduced probability of consumers purchasing ...".
Please adapt the final sentence of the "Methods and findings" subsection of your abstract so that it begins "Study limitations include ..." or similar, and quotes 2-3 of the study's main limitations.
Throughout the text, please adapt reference call-outs so that they are preceded by a space, and contain no spaces within the square brackets, e.g., "... nutrition labels [24,54].".
In the reference list, please use the journal name abbreviation "PLoS Med.".
Please break the PRISMA checklist out into a separate attached file, labelled "S1_PRISMA_Checklist" or similar and referred to as such in the text.
In the checklist, please refer to individual items by section (e.g., "Methods") and paragraph number, not by line or page numbers, as these generally change in the event of publication.
Comments from the reviewers:
*** Reviewer #1:
The authors have largely improved their manuscript according to the reviewers' suggestions. However, there are several major issues that have not been adequately addressed and deserve further consideration.
- As suggested, the authors have included a logic model to explain the effect of FOPL on consumer behavior. However, the model deserves revision. Attention is a pre-requisite for perception and understanding. Intention to purchase or consume is not the same as purchase or consumption. This should be clarified in the model, even if the authors grouped the two outcomes for analysis. Additional explanations should be included in the text. The authors refer to "negative perception of food products" as a requirement to modify food choice. This is not necessarily the case. FOPL can potentially modify choice by increasing healthiness perception (e.g. health logos).Something similar occurs with references to negative emotions (Line 126). I recommend the authors to more carefully examine the literature on the effects of FOPL on consumer perception and behavior. In Line 114, the authors refer to "confounding", which is not accurate. The characteristics of the schemes, product category and personal characteristics moderate the influence of FOPL on consumer behavior. This should be clearly explained in the manuscript.
- Throughout the manuscript, the authors refer to "colour coded" FOPL and merge traffic-light with Nutriscore. However, these two schemes are conceptually different. What about the differences between the two schemes? The rationale for grouping these two schemes should be better explained and their differences further discussed.
- Grouping of the outcomes should respond to the logic model. As I previously commented, salience and attention should not be grouped with understanding and perception. The term "recall" is used for expressing different outcomes. However, this is not clear in the manuscript. For example, Table 3 refers to recall of overall healthfulness (Supplementary Table 1) but the authors only refer to "recall"
- Some of the effects reported in the Tables were calculated based on 1 or 2 comparison. This should be acknowledged as a limitation of the analysis.
- The discussion section lacks accuracy in many sections. The authors should be more careful in their description of the findings. I include below a couple of examples
Line 504: "perception of severe risk" there is no evidence to state that the labels raised associations with "perceived risk"
Line 534: "remind consumers to eat less of unhealthful foods" what is the evidence for this?
- Lines 507-511: Evidence has shown that the traffic light system does not decrease healthfulness perception as much as warnings for products with low content of some nutrients. This should be further discussed.
- Differences between TFL and Nutriscore should be discussed in depth in the discussion, as they provide different types of information.
*** Reviewer #2:
Authors have satisfactorily taken into consideration my different comments
I think this paper is relevant and particularly important and deserves to be published.
*** Reviewer #4:
I still feel that combining no label and NFt is a mistake. The authors' rebuttal has not convinced me here. Their point 1 is that other people do it. That does not make it right. Their point 2 is about precision but I am concerned about bias. Their point 3 seems to suggest that they have done the wrong study. If the interest is in whether FOPLs increase the effectiveness of NFt then only those studies are relevant not the ones comparing FOPL with no label.
I am not sure where the authors got the idea that I advocate removing material from the supplement or elsewhere. My view is that most studies are under-reported and I would usually be suggesting adding more not taking away. But that is a minor point.
Michael Dewey
***
Any attachments provided with reviews can be seen via the following link:
[LINK]
Author response to Decision Letter 2
9 Jun 2021
Attachment
Submitted filename: Rebuttal letter 6.8.docx
Decision Letter 3
5 Aug 2021
Dear Dr. He,
Thank you very much for re-submitting your manuscript "Impact of color-coded and warning nutrition labelling schemes: a systematic review and network meta-analysis" (PMEDICINE-D-20-05962R3) for consideration at PLOS Medicine.
I have discussed the paper with editorial colleagues and it was also seen again by two reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are dealt with, we expect to be able to accept the paper for publication in the journal.
The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:
[LINK]
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Please let me know if you have any questions, and we look forward to receiving the revised manuscript.
Sincerely,
Richard Turner, PhD
Senior Editor, PLOS Medicine
------------------------------------------------------------
Requests from Editors:
At line 9, please add a sentence, say, to state what the primary outcomes were.
Around line 11, we ask you to note the number or proportion of laboratory studies included (alternatively, this could be quoted as a limitation).
At line 16, we think that "Nutri-score" (NS) needs to be spelt out at first use.
At line 24, we suggest "... content of purchases.".
At line 28, we suggest "... nudging consumers towards the purchase ..." or similar.
The sentence beginning at line 30 ("The difference could lie ...") is really a sentence of discussion, and we therefore ask you to remove it, and if you wish add a few words to this effect to the "Discussion" subsection of your abstract.
At line 34, we ask you to substitute "impact" in place of "effectiveness", bearing in mind that not all the evidence included is from randomized studies.
At line 96, please make that "...had implemented".
At line 158, should that be "2021" as in the abstract?
At line 492, please make that "Despite the heterogeneity ...".
Throughout the text, please add a space immediate preceding reference call-outs (e.g., "... their products [7,8].".
Please remove the quotation marks from the title for reference 21.
Please spell out the "L" in the author of reference 45.
Comments from Reviewers:
Reviewer #1:
[supportive report received]
Reviewer #4:
The authors have addressed my second round of comments.
Michael Dewey
***
Any attachments provided with reviews can be seen via the following link:
[LINK]
Author response to Decision Letter 3
9 Aug 2021
Attachment
Submitted filename: Rebuttal letter 2021.8.9.docx
Decision Letter 4
11 Aug 2021
Dear Dr He,
On behalf of my colleagues and the Academic Editor, Dr Ares, I am pleased to inform you that we have agreed to publish your manuscript "Impact of color-coded and warning nutrition labelling schemes: a systematic review and network meta-analysis" (PMEDICINE-D-20-05962R4) in PLOS Medicine.
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PRESS
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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols
Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.
Sincerely,
Richard Turner, PhD
Senior Editor, PLOS Medicine
