ABSTRACT
Background:
Food labels are one of the most important and direct means of communicating information to consumers about food products.
Materials and Methods:
It was a mixed methods study conducted among 162 consumers in 15 supermarkets of Bathinda city using a pretested structured questionnaire regarding food label literacy and determinants of food choices of consumers of supermarkets. This information was triangulated with qualitative data obtained by conducting in-depth interviews with 12 participants. The suitability of the data for the application of factor analysis was confirmed, and nine factors were obtained that explained 79.51% of variance according to the inflection point of the scree plot.
Results:
It was observed that most of the consumers (often 39.5%, sometimes 35.8%) read nutrition facts labels when buying prepackaged food. 24.7% ‘rarely or never’ paid attention to food labels. Professionals and semiprofessionals were more frequent food-label readers than other occupational categories. This difference was statistically significant. Multivariate analysis revealed that unskilled workers were not able to comprehend the information about added sugar and sodium. Label reading increased with educational status, with the majority of professionals reading the food labels, and the difference was statistically significant.
Conclusion:
People found it difficult to comprehend nutrition information, so there is a need to create public awareness on various components of food labels so as to increase the utilization of information given on food labels and encourage healthy food consumption.
Keywords: Consumers, determinants, food label literacy
Introduction
India is going through an epidemiological transition, characterized by changes in the pattern of mortality and a shift in disease patterns from communicable to noncommunicable lifestyle diseases. These diseases are primarily attributed to four life-style-related risk factors, namely, unhealthy diet, smoking, physical inactivity, and stress. Major shifts in dietary patterns are occurring throughout the country, resulting in a change from a traditional complex whole-grain diet toward a “Western” diet rich in fats, sugars, meats, and highly processed foods that are low in dietary fiber.[1]
While this nutrition transition started in high-income countries in the late nineteenth century around the time of the Industrial Revolution, most low and middle-income countries have been increasingly witnessing this since the 1980s. This change in dietary pattern can be linked to various interlinked factors such as urbanization, increased per capita income, liberalized markets, food marketing, changed food policies, personal taste-based preferences, accessibility and availability of supermarkets, and reduced time available for cooking food due to changes in familial roles and job profiles of both husband and wife.[1] The number of supermarkets has swollen from 500 in 2006 to 435,000 in 2020 in India.[2]
Processed foods are defined as foods that are altered from their natural state before being available for purchase and range from minimally processed foods such as frozen/chilled milk, grains, fruits, vegetables, and fresh meats to ultraprocessed readymade foods such as ready meals, tinned food, bakery items, soft drinks, and packaged juices. These packages of processed foods are mandated to mention the nutritional profile as per regulations of each country and should ideally form the basis of food choices made by consumers.[2,3]
The food or nutrition label is an important and one of the direct means of disseminating information to consumers about food products. The term food label is defined as ‘any tag, brand, mark, pictorial or other descriptive matter, written, printed, stencilled, marked, embossed or impressed on, or attached to, a container of food or food product’.[3] However, the ability to read and interpret these food labels has not been studied in detail in developing countries such as India[4,5] and more so in the state of Punjab, which has undergone a faster nutrition transition and has reported a very high prevalence of risk factors of noncommunicable diseases.[5,6,7]
The present study is an attempt to assess food label literacy and factors that determine the choice of a particular food item among consumers of supermarkets in Bathinda City, which is the fifth largest district of Punjab state in India and is also a major trade center.
Materials and Methods
The present study was a mixed methods study conducted among consumers visiting supermarkets of Bathinda district which was selling any food product. Consumers purchasing food products that have food labels on them were included in the study. Fifteen supermarkets of Bathinda city situated within the periphery of 10 Km of this tertiary care institute were visited for collection of data. The study was conducted during the months of August–September 2022.
The sample size for the study was calculated using the following formula: n = z2* p*(1 - p)/e2, where n = required sample size, Z (0.05) =1.96, with a confidence level of 95%, P (proportion of consumers reviewing food label before purchase of food items) =72.4%, and e = margin of error (relative precision) =10% of P = 7.24%.[8] The sample size came out to be 147, and after considering 10% nonresponse, the final sample size calculated was 162.
There were approximately 61 supermarket/grocery stores in Bathinda District. We identified a total of 10 supermarkets/grocery shops within the range of 10 km with the help of Google Maps. After inspecting the area within the periphery of 10 km to the AIIMS Bathinda, five more supermarket stores were identified. Therefore, a total of 15 supermarkets were taken to recruit the consumers. Since the total sample size calculated was 162, 162/15 = 10-11 consumers from each of the selected supermarkets based on convenience sampling were approached to fill out the questionnaire. This information was triangulated with qualitative data by conducting in-depth interviews with 12 participants.
To obtain qualitative data, an in-depth interview was conducted among 12 participants who did not participate in the quantitative survey until saturation was obtained. Interviews were conducted in English, Hindi, or Punjabi in a public health setting at locations convenient to the participants after taking written informed consent, recorded on a mobile phone with the permission of the participant, and transcribed verbatim to text. Interviews conducted in Hindi/Punjabi were translated to English, and the English translation was back-translated to ensure appropriate translation.
Tools of data collection
Data were collected through a pretested structured questionnaire, and it consisted of four sections: Section 1: was the consent form which was in both English and vernacular language (Punjabi). Section 2: assessed the participants’ information including their sociodemographic profile. Section 3: Food label literacy was assessed by using the FDA Health and Diet Survey Tool. Understanding and using health claims on packaged food items was also done using the corresponding section from the same tool.[9] Section 4: A validated ‘Food Choice Questionnaire’ was used to assess the determinants of food choices of consumers of supermarkets.[10] Responses were recorded using a Likert scale with options ranging from ‘Often’ to ‘Never’, along with an option for ‘Refused’ and ‘Don’t know’ to determine the distribution of label-checking behavior among participants so as to capture a broad spectrum of consumer habits and preferences. The qualitative information was collected by conducting an in-depth interview with 12 participants [Figure 1].
Figure 1.
Distribution of checking of nutrition facts information from food labels among the participants
Statistical analysis
Quantitative data were analyzed using Microsoft Excel, SPSS Inc., IBM version 29 software. Ordinal logistic regression and factorial analysis were used. An objective test of the factorability of the correlation matrix is Bartlett’s test of sphericity, which statistically tests the hypothesis that the correlation matrix contains ones on the diagonal and zeros on the off-diagonals. Hence, it was generated by random data. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was used to reflect the extent to which correlations are a function of the variance shared across all variables rather than the variance shared by particular pairs of variables. KMO values range from 0.00 to 1.00 and were computed for the total correlation matrix as well as for each measured variable. Overall KMO values ≥0.70 are desired, but values less than 0.50 are generally considered unacceptable,[11] indicating that the correlation matrix is not factorable. (KMO values in the 0.90s, marvellous; in the 0.80s, meritorious; in the 0.70s, middling; in the 0.60s, mediocre; in the 0.50s, miserable; below 0.50, unacceptable). Great care was exercised in selecting the variables and participants so that the measured variables are sufficiently intercorrelated to justify factor analysis. A subjective method is to examine the correlation matrix. A sizable number of correlations exceeded ± 0.30.[12] A p-value of <0.05 was considered significant, and a P value of <0.001 was considered highly significant.
Reflexive thematic analysis was done to analyze the transcripts. Eight themes were identified: prepackaged foods that were usually bought by people, frequency of buying prepackaged foods, reasons for preference for prepackaged foods, considerations while buying prepackaged foods, checking of labels on prepackaged foods, aspects of labels checked and reasons for checking them, and suggestions for any changes in the labels.
Ethical consideration
The study was an ICMR-funded STS project (2022-09316). Ethical clearance for the study was obtained from the Institutional Ethical Committee (Ref. No.: IEC/AIIMS/BTI/202 dated 30/07/2022).
Results
Demographic profile of the consumers
A total of 162 individuals, who had purchased prepackaged foods from 15 supermarkets in Bathinda, participated in the study. It was observed that a higher proportion of women shopped for prepackaged foods than men (65% vs 35%). More than half (51%) of the participants were literate and professionals. 58% of consumers were married, and 65% belonged to nuclear families.
The findings of our study indicated that most of the consumers (often 39.5%, sometimes 35.8%,) read nutrition facts labels when buying prepackaged food, while 7.4% of participants reported that they had never checked or rarely checked (17.3%) nutrition facts food labels. Females (78.3% ‘often and sometimes’) were found to be slightly more frequent readers of food labels than males (69.6%); however, the difference was not statistically significant. Professionals and semiprofessionals were comparatively more frequent food-label readers than other occupational categories, and this difference was statistically significant (P < 0.001) [Table 1]. Income, marital status, and chronic disease had not shown any statistically significant association with frequency of reading food labels.
Table 1.
Association of sociodemographic profile with frequency of reading food labels among the participants
| Response | Often (n) | Sometimes (n) | Rarely (n) | Never (n) | Total | P-(X2 test) |
|---|---|---|---|---|---|---|
| Age (in completed years) | ||||||
| 18-30 | 29 (43.3) | 24 (35.8) | 11 (16.4) | 3 (4.5) | 67 (100) | 0.084 |
| 31-40 | 14 (50) | 9 (32.1) | 5 (17.9) | 0 (0) | 28 (100) | |
| 41-50 | 16 (37.2) | 17 (39.5) | 6 (14) | 4 (9.3) | 43 (100) | |
| 51-60 | 2 (13.3) | 7 (46.7) | 4 (26.7) | 2 (13.3) | 15 (100) | |
| >60 | 3 (33.3) | 1 (11.1) | 2 (22.2) | 3 (33.3) | 9 (100) | |
| Sex | ||||||
| Female | 45 (42.5) | 38 (35.8) | 16 (15.1) | 7 (6.6) | 106 (100) | 0.617 |
| Male | 19 (33.9) | 20 (35.7) | 12 (21.4) | 5 (8.9) | 56 (100) | |
| Education | ||||||
| High school pass | 2 (50) | 1 (25) | 1 (25) | 0 (0) | 4 (100) | 0.544 |
| Intermediate pass | 11 (31.4) | 14 (40) | 7 (20) | 3 (8.6) | 35 (100) | |
| Graduation/Diploma | 18 (33.3) | 19 (35.2) | 10 (18.5) | 7 (13) | 54 (100) | |
| Professional degree/Honors/MA and above | 33 (47.8) | 24 (34.8) | 10 (14.5) | 2 (2.9) | 69 (100) | |
| Occupation | ||||||
| Professional | 39 (47) | 33 (39.8) | 9 (10.8) | 2 (2.4) | 83 (100) | 0.001** |
| Semiprofessional | 10 (66.7) | 5 (33.3) | 0 (0) | 0 (0) | 15 (100) | |
| Clerk/Shopkeeper/Business/Farm owner | 5 (25) | 7 (35) | 4 (20) | 4 (20) | 20 (100) | |
| Semi-skilled worker | 0 (0) | 2 (66.7) | 0 (0) | 1 (33.3) | 3 (100) | |
| Skilled worker | 3 (25) | 5 (41.7) | 4 (33.3) | 0 (0) | 12 (100) | |
| Unemployed/Housewife | 4 (21.1) | 4 (21.1) | 8 (42.1) | 3 (15.8) | 19 (100) | |
| Unskilled worker | 3 (30) | 2 (20) | 3 (30) | 2 (20) | 10 (100) | |
| Income (in Rs) | ||||||
| 9634-14462 | 2 (33.3) | 2 (33.3) | 1 (16.7) | 1 (16.7) | 6 (100) | 0.128 |
| 14463-19290 | 2 (16.7) | 7 (58.3) | 1 (8.3) | 2 (16.7) | 12 (100) | |
| 19291-38599 | 6 (28.6) | 5 (23.8) | 7 (33.3) | 3 (14.3) | 21 (100) | |
| >38600 | 54 (43.9) | 44 (35.8) | 19 (15.4) | 6 (4.9) | 123 (100) | |
| Marital status | ||||||
| Divorced/Widowed | 1 (16.7) | 2 (33.3) | 1 (16.7) | 2 (33.3) | 6 | 0.122 |
| Married (Staying apart) | 1 (25) | 3 (75) | 0 (0) | 0 (0) | 4 | |
| Married (Staying together) | 34 (36.2) | 33 (35.1) | 18 (19.1) | 9 (9.6) | 94 | |
| Unmarried | 28 (48.3) | 20 (34.5) | 9 (15.5) | 1 (1.7) | 58 | |
| Chronic disease | ||||||
| Anxiety | 1 (100) | 0 (0) | 0 (0) | 0 (0) | 1 | 0.087 |
| Diabetes Mellitus | 2 (25) | 3 (37.5) | 0 (0) | 3 (37.5) | 8 | |
| Hypertension | 9 (56.3) | 3 (18.8) | 3 (18.8) | 1 (6.3) | 16 | |
| Hyperthyroidism | 0 (0) | 0 (0) | 1 (100) | 0 (0) | 1 | |
| Hypothyroidism | 1 (25) | 3 (75) | 0 (0) | 0 (0) | 4 | |
| PCOD | 0 (0) | 1 (100) | 0 (0) | 0 (0) | 1 | |
| Rheumatoid Arthritis | 0 (0) | 0 (0) | 1 (100) | 0 (0) | 1 | |
| No | 51 (39.2) | 48 (36.9) | 23 (17.7) | 8 (6.2) | 130 | |
| Total | 64 (39.5) | 58 (35.8) | 28 (17.3) | 12 (7.4) | 162 (100) |
Figure 1 describes the frequency of checking nutrition facts information in food labels among the participants. It shows that 45.7% of participants checked the brand name ‘sometimes’, while 22.2% ‘never’ paid attention to the brand name. Furthermore, 40.7% ‘never’ considered the portion sizes given on the food labels, while 38.3% ‘never’ used food-label information to compare food items with each other. Food label was most commonly used to get a general idea about nutritional content of food by 32.7% participants, while 32.1% participants paid attention to specific nutrient levels like salt and calories mentioned on the food label.
Table 2 describes the association of sociodemographic factors with the use of sodium and antioxidant-related information displayed through food labels. Ordinal logistic regression was applied to determine possible associations between the factors that might influence the habit of using this information. It was observed that participants having higher education levels were more frequently using this information than those having lower education status. Similarly, occupation was also found to be an important factor for determining the use of information related to sodium and antioxidants as unskilled workers {aOR: 0.11 (0.02–0.54)} and clerk/shopkeeper/business/farm owner {aOR: 0.19 (0.06–0.63)}, who were found using this information less frequently. The rest of the factors like gender, age, marital status, income, and suffering from morbidity were not found to have a statistically significant impact on using food label information.
Table 2.
Association of sociodemographic factors with the use of sodium and antioxidant-related information displayed through food labels
| Characteristics | Adj. Odds Ratio (95% C.I) | P | |
|---|---|---|---|
| Gender | Male | 1.25 (0.62-2.58) | 0.551 |
| Female | Reference | - | |
| Age group (in completed years) | 18-28 | 1.34 (0.13-14.28) | 0.807 |
| 29-38 | 2.79 (0.31-25.25) | 0.361 | |
| 39-48 | 1.46 (0.19-11.2) | 0.715 | |
| 49-58 | 1.13 (0.13-9.98) | 0.909 | |
| 59-68 | 0.15 (0.01-4.06) | 0.256 | |
| 69 and above | Reference | - | |
| Marital status | Unmarried | 1.34 (0.12-14.66) | 0.810 |
| Married (staying together) | 0.79 (0.1-6.27) | 0.822 | |
| Married (staying apart) | 0.74 (0.04-14.92) | 0.844 | |
| Divorced/Widowed | Reference | - | |
| Education status | High school | 0.26 (0.03-2.28) | 0.224 |
| Intermediate | 0.27 (0.10-0.77) | 0.014 | |
| Graduation/Diploma | 0.47 (0.18-1.24) | 0.126 | |
| Prof. degree/Honours/Post Graduate and above | Reference | - | |
| Occupation | Unemployed/housewife | 0.43 (0.12-1.59) | 0.205 |
| Unskilled worker | 0.11 (0.02-0.54) | 0.007 | |
| Skilled worker | 0.93 (0.22-3.9) | 0.921 | |
| Clerk/Shop keeper/Business/Farm owner | 0.19 (0.06-0.63) | 0.007 | |
| Semi-professional | 0.38 (0.12-1.26) | 0.115 | |
| Professional | Reference | - | |
| Income (in Rs) | 9634-14462 | 5.13 (0.89-29.55) | 0.067 |
| 14463-19290 | 1.56 (0.42-5.81) | 0.511 | |
| 19291-38599 | 1.3 (0.4-4.22) | 0.663 | |
| ≥38600 | Reference | - | |
| Morbidity | Yes | 0.76 (0.3-1.95) | 0.567 |
| No | Reference | - | |
Table 3 describes the association of sociodemographic factors with the use of information related to ‘added sugar’ displayed through food labels. As found in usage of sodium and antioxidant patterns, unskilled workers {aOR: 0.07 (0.01–0.34)} did not comprehend the information regarding added sugar. The rest of the factors had no association with it.
Table 3.
Association of sociodemographic factors with use of information related to ‘added sugar’ through food labels
| Variables | Odds Ratio (95% C.I) | P | |
|---|---|---|---|
| Gender | Male | 0.96 (0.46-1.97) | 0.906 |
| Female | Reference | - | |
| Education status | High school | 0.13 (0.01-1.3) | 0.082 |
| Intermediate | 0.55 (0.2-1.52) | 0.248 | |
| Graduation/Diploma | 0.56 (0.21-1.48) | 0.242 | |
| Prof. degree/Honours/Post Graduate and above | Reference | - | |
| Occupation | Unemployed/housewife | 0.6 (0.16-2.25) | 0.451 |
| Unskilled worker | 0.07 (0.01-0.34) | 0.001 | |
| Skilled worker | 0.86 (0.2-3.64) | 0.833 | |
| Clerk/Shop keeper/Business/Farm owner | 0.39 (0.12-1.3) | 0.126 | |
| Semiprofessional | 1.63 (0.49-5.44) | 0.430 | |
| Professional | Reference | - | |
| Income (in Rs) | 9634-14462 | 1.82 (0.32-10.23) | 0.495 |
| 14463-19290 | 0.91 (0.24-3.45) | 0.892 | |
| 19291-38599 | 0.64 (0.19-2.1) | 0.461 | |
| ≥38600 | Reference | - | |
| Morbidity | Yes | 1.44 (0.56-3.67) | 0.447 |
| No | Reference | - | |
| Age group (in completed years) | 18-28 | 2.3 (0.21-25.03) | 0.496 |
| 29-38 | 3.66 (0.39-34.09) | 0.254 | |
| 39-48 | 1.67 (0.21-13.11) | 0.627 | |
| 49-58 | 1.02 (0.11-9.03) | 0.988 | |
| 59-68 | 0.49 (0.03-7.52) | 0.607 | |
| 69 and above | Reference | - | |
| Marital status | Unmarried | 1.07 (0.11-10.75) | 0.952 |
| Married (staying together) | 0.63 (0.09-4.56) | 0.646 | |
| Married (staying apart) | 0.31 (0.02-6.09) | 0.443 | |
| Divorced/Widowed | Reference | - |
The suitability of the data for the application of factor analysis was confirmed since the value of KMO was 0.830, which corresponds to good on the scale, and the value of Bartlett’s test of sphericity was significant (P < 0.0001). We proceeded to carry out exploratory factor analysis using the principal component analysis method, with Varimax rotation. Nine factors were obtained that explained, in total, 79.51% of the variance according to the inflection point of the Scree Plot [Table 4 and Figure 2]. The themes were related to mood, accessibility, sensory appeal, health, convenience, natural contents, ethical concerns, and familiarity.
Table 4.
Distribution of various items within different factors
| Items | Components | Loading |
|---|---|---|
| 7. helps me cope with stress | Mood | 0.793 |
| 8. helps me to cope with life | 0.854 | |
| 9. helps me relax | 0.835 | |
| 10. keeps me awake/alert | 0.814 | |
| 11. It cheers me up | 0.763 | |
| 12. It makes me feel good | 0.749 | |
| 16. can be bought in shops close to where I live or work | Accessibility | 0.686 |
| 17. is easily available in shops and supermarkets | 0.619 | |
| 25. is not expensive | 0.808 | |
| 26. is cheap | 0.751 | |
| 27. is good value for money | 0.599 | |
| 18. smells nice | Sensory appeal | 0.732 |
| 19. looks nice | 0.881 | |
| 20. has a pleasant texture | 0.715 | |
| 21. tastes good | 0.871 | |
| 1. contains a lot of vitamins and minerals | Health | 0.637 |
| 2. keeps me healthy | 0.886 | |
| 3.is nutritious | 0.891 | |
| 4.is high in protein | 0.768 | |
| 6.is high in fibre and roughage | 0.636 | |
| 13.is easy to prepare | Convenience | 0.906 |
| 14.can be cooked very simply | 0.918 | |
| 15.takes no time to prepare | 0.872 | |
| 28.is low in calories | 0.834 | |
| 29. helps me control my weight | 0.841 | |
| 30. is low in fat | 0.886 | |
| 22. contains no additives | Natural contents | 0.839 |
| 23. contains natural ingredients | 0.768 | |
| 24. contains no artificial ingredients | 0.847 | |
| 34. comes from countries I approve of politically | Ethical concerns | 0.855 |
| 35. Has the country of origin clearly marked | 0.851 | |
| 36. is packaged in an environmentally friendly way | 0.581 | |
| 5. is good for my skin/teeth/hair/nails etc. | Familiarity | 0.451 |
| 31. is what I usually eat | 0.683 | |
| 32. is familiar | 0.732 | |
| 33. is like the food I ate when I was a child | 0.495 |
Figure 2.
Scree plot for factor analysis using PCA with varimax rotation
From the qualitative analysis, it was observed that the majority of people bought biscuits, snacks, milk packet, bread, breakfast cereals, maggie noodles, and cooking oil. However, the mother of an 8-month-old infant bought ‘cerelac’.
“I buy Cerelac baby cereal because I am a working office going lady and I have no time to prepare semisolid food and it helps baby to develop taste for normal foods” (IDI 007)
It was also seen that the majority of the population considered brand name while buying prepackaged foods followed by taste, expiry date of the product, mood, and cost.
“I always buy Amul butter and ghee since my mother used to use the same, it’s a tried and tested brand. So quality is assured.” (IDI 004)
People also preferred those prepackaged food which were comparatively easy to cook. This was especially seen if one was a working person staying in a hostel.
“Yes, since I am a hostel person, I don’t have access to all things, and prepackaged food is easy to make and consume” (IDI 003)
“Certain things which are difficult to make at home are readily available as packaged goods like coconut milk” ((IDI 009)
A few people also preferred prepackaged food as they find them to be of better quality and unadulterated.
“Packed items like wheat flour and rice are not adulterated and of better quality than the ones bought open from local kirana stores” (IDI 009)
However, it was also found that a few people were not in favour of buying prepackaged foods as they contained a high number of chemicals and preservatives which are harmful to health.
“No, I don’t prefer pre-packaged food much as it has high amount of salt and preservatives which are harmful for health” (IDI 006)
Participants also commented on the lack of important information on the food labels:
“They should mention fibre content in addition to other nutrient information” (IDI 001)
“There should be information about packaging material like its carcinogenicity, effect of temperature and microwaves on it” (IDI 011)
Another reason mentioned for not reading the food labels was that “the font size of the label was too small and information on the nutrition fact label is complicated and difficult to understand”. (IDI02)
Discussion
Nutrition information on food labels is regarded as an effective method of encouraging consumers to make healthier choices when buying food products, but it is underutilized by consumers.[13] Although consumers value nutrition-related knowledge when deciding which foods to buy, nutrition information on food labels is complex and is not always communicated effectively.[13,14,15]
As per the current regulation, the nutrient content display has been made mandatory on nearly all prepackaged foods and the labeling regulations are at par with those in advanced countries.[4] However, there are hardly any studies looking into the use of food labels in India, while elsewhere in the world, there are a few studies examining the use of nutrition information on labels by consumers for making healthy food choices.[16,17] The present study assessed not only the food labels literacy and its use but also the factors considered by the consumers before buying them.[18]
Food label reading trends
The current study reported that a total of 39.5% of participants ‘often’ engaged in reading food labels. This is notably lower than those reported in previous studies conducted in India. Vemula et al.[8] in urban India found that 72.4% of supermarket consumers read food labels regularly. This difference could be attributed to differences in sampling populations, urban exposure levels, or rising awareness about nutritional information over time.
Globally, similar studies in developed nations report varying trends. A systematic review by Cowburn and Stockley[17] highlighted that approximately 60–80% of consumers in high-income countries like the U.S. and Australia use food labels to guide their purchases. The review also revealed that while most consumers in developed nations recognize and understand front-of-pack labeling systems, the effectiveness of these labels varies depending on design, clarity, and cultural relevance. This disparity emphasizes the need for enhanced education and public health campaigns in regions like India to bridge the nutritional literacy gap. Adopting clear, visually engaging labelling formats, alongside targeted efforts to improve nutrition awareness, could encourage more informed food choices in LMICs.
Sociodemographic factors and label usage
The present study revealed that higher educational attainment significantly increased the likelihood of label reading. This finding is consistent with both Indian and international studies. A study by Ali and Kapoor[19] in India demonstrated that literate consumers were more inclined to engage with food labels compared to their less educated counterparts. Similarly, a study conducted in Sri Lanka by Prathiraja and Ariyawardan[16] observed that individuals with higher education levels were more likely to understand and utilize nutritional information.
Gender differences in label reading, with women showing greater engagement than men, were also observed in this study. This aligns with findings from a U.K-based study by Wardle et al.,[20] which noted that women are more likely to avoid high-fat foods and use labels to monitor calorie and sugar intake due to a greater concern for weight and health management. Such trends emphasize the importance of tailoring nutrition education campaigns to specific demographic groups.
Determinants of food choices
Brand loyalty emerged as a key factor influencing food purchases in this study. Approximately 90% of participants associated trusted brands with quality, a finding echoed in studies by Goyal and Deshmukh[7] and Vemula et al.,[8] where urban consumers displayed a strong preference for established brands. This tendency may stem from concerns about food adulteration and the perception that branded products are safer and of higher quality.
In contrast, studies in high-income countries often report health considerations as the primary determinant of food choices, with branding playing a secondary role. For example, research in Europe[21] found that consumers prioritized nutritional content over branding due to better awareness and regulatory enforcement on food safety.
Challenges in label comprehension
A recurring theme across various studies is the difficulty in interpreting food label information, particularly among populations with limited nutritional knowledge. In the current study, unskilled workers and older adults were less likely to comprehend technical details on labels, mirroring findings from a study in South India by Sudershan et al.[22] The latter identified a need for simpler, more visually appealing labels to enhance usability. International comparisons reveal similar challenges. Campos et al.[23] found that while consumers in the U.S. were generally familiar with food labels, many struggled with understanding technical terms or calculating serving sizes. This indicates a universal need for clearer, standardized labeling formats that cater to diverse literacy levels of consumers.
Preference for simplified food labels
The study participants expressed interest in simplified food labels highlighting sugar, fat, and salt content. This is consistent with evidence from international studies. A randomized controlled trial in Italy by Fialon et al.[24] demonstrated that front-of-pack labels, such as the Nutri-Score or traffic light system, significantly improved consumer understanding and influenced healthier choices. Similar preferences were observed in a study by Shine et al.[15] in Ireland.
In contrast, India’s labeling regulations, though comparable to global standards, have not yet mandated front-of-pack labels. Our study suggests that such measures could enhance label effectiveness and align India with best practices in public health nutrition.
A significant proportion of participants frequently checked labels for ‘brand’ name and determining portion sizes. This aligns with studies from high-income countries[17] where similar practices are common. The majority of participants rarely or never checked for nutrient-specific information (e.g., calories, salt levels) or used labels for meal planning. This is consistent with findings from LMICs, where nutritional literacy tends to be lower.[25] A notable proportion (51.1%) sometimes checked labels for advertising accuracy. This is in contrast with higher-income countries, where studies show that consumers place greater trust in food brands and rarely cross-check claims.[26] This could be due to different study settings and infrastructure in developed countries.
Dietary patterns and food safety concerns
The current study found that convenience and taste drove the purchase of prepackaged foods, with concerns about preservatives being secondary. This finding was in contrast from those of Subba Rao et al.,[27] who reported that South Indian mothers prioritized food safety over taste and convenience, thus avoiding products with additives. The regional and cultural differences in dietary preferences and perceptions of safety likely account for these variations. Similar studies in the U.S. and Europe emphasize health motivations as primary drivers, with convenience being a secondary consideration. This highlights the impact of socioeconomic and cultural contexts on consumer behavior related to food. This comparative analysis emphasizes the need for region-specific interventions. While global best practices, such as front-of-pack labeling, are highly relevant, they must be adapted to local literacy levels and cultural preferences.[28,29] Public health campaigns should prioritize simplifying label formats and educating consumers about the health implications of their dietary choices.[30]
Additionally, policymakers in India could consider implementing mandatory front-of-pack labels and graphics to improve label visibility and comprehension.[31] These measures, combined with targeted nutrition education for low-income and less educated populations, could bridge the gaps identified in the study.[32,33]
Strengths and limitations
The study uses both quantitative and qualitative methods, which allows for a comprehensive understanding of food label literacy and its determinants. This dual approach enhances the richness and depth of data collected, providing detailed insights. The study provides localized insights, which can guide public health interventions tailored to the specific population. Given the growing concern about the relationship of increasing consumption of packaged foods with the rising trend of noncommunicable diseases like hypertension, diabetes, obesity-related cancers, and nonalcoholic fatty liver disease, this study addresses a timely issue by examining how well people understand food labels as it can be a critical tool for making informed dietary choices. The study collects data at a single point in time, making it difficult to establish causality or changes in behavior with time. Data are based on self-reported behaviours or perceptions, which can introduce bias, such as over-reporting or under-reporting of food label usage.
Conclusion
In the current study, it was observed that a majority of people did not use information from the food labels as they found it difficult to comprehend the nutritional information. Therefore, there is a need to implement a two-pronged approach, that is, to create public awareness of basic nutrition, various components of food labels and experiment with newer ways of information display to make food labels consumer friendly. To enhance public health, policymakers should prioritize consumer education campaigns on interpreting food labels, particularly targeting lower-education and low-income groups. Mandatory, clear, and standardized labeling regulations can improve accessibility and understanding of nutritional information. Collaboration with government authorities, healthcare providers, and food industries is essential to implement effective interventions. These policies could help encourage healthier eating by empowering consumers to read and comprehend food labels, thereby making healthier food choices.
Conflict of interest
There are no conflicts of interest.
Acknowledgments
We would like to acknowledge the study participants for their cooperation and participation in the study.
Funding Statement
ICMR funded STS study (2022-09316) approved by ethical committee (IEC/AIIMS/BTI/202).
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