Abstracts

Introduction

The study of eating occasions aims to describe some of the various aspects related to food or beverage consumption. Generally, eating occasions are categorized according to the amount and type of food consumed and to the timing of consumption ¹1. Barrington WE, Beresford SAA. Eating occasions, obesity and related behaviors in working adults: does it matter when you snack? Nutrients 2019; 11:2320.. Daily food consumption is usually characterized by three main meals - namely breakfast, lunch, and dinner - that can be interspersed with snacks ²2. Primary Health Care Department, Secretariat of Health Care, Ministry of Health of Brazil. Dietary guidelines for the Brazilian population. Brasilia: Ministry of Health of Brazil; 2015.^,33. St-Onge M-P, Ard J, Baskin ML, Chiuve SE, Johnson HM, Kris-Etherton P, et al. Meal timing and frequency: implications for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 2017; 135:e96-e121..

The interest in this topic is related to the role possibly played by the number and timing of meals on metabolism ⁴4. Titan SM, Bingham S, Welch A, Luben R, Oakes S, Day N, et al. Frequency of eating and concentrations of serum cholesterol in the Norfolk population of the European prospective investigation into cancer (EPIC-Norfolk): cross-sectional study. BMJ 2001; 323:1286-8.^,55. Marinac CR, Sears DD, Natarajan L, Gallo LC, Breen CI, Patterson RE. Frequency and circadian timing of eating may influence biomarkers of inflammation and insulin resistance associated with breast cancer risk. PLoS One 2015; 10:e0136240., weight gain ⁶6. van der Heijden AAWA, Hu FB, Rimm EB, van Dam RM. A prospective study of breakfast consumption and weight gain among U.S. men. Obesity (Silver Spring) 2007; 15:2463-9.^,77. Bandín C, Scheer FAJL, Luque AJ, Ávila-Gandía V, Zamora S, Madrid JA, et al. Meal timing affects glucose tolerance, substrate oxidation and circadian-related variables: a randomized, crossover trial. Int J Obes (Lond) 2015; 39:828-33., and insulin-related metabolic disorders ⁸8. Leech RM, Worsley A, Timperio A, McNaughton SA. Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality. Nutr Res Rev 2015; 28:1-21.^,99. Medin AC, Myhre JB, Diep LM, Andersen LF. Diet quality on days without breakfast or lunch - identifying targets to improve adolescents' diet. Appetite 2019; 135:123-30.. In addition, the habit of skipping main meals and replacing them with snacks has been associated with a low-quality diet ⁸8. Leech RM, Worsley A, Timperio A, McNaughton SA. Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality. Nutr Res Rev 2015; 28:1-21.^,99. Medin AC, Myhre JB, Diep LM, Andersen LF. Diet quality on days without breakfast or lunch - identifying targets to improve adolescents' diet. Appetite 2019; 135:123-30., body adiposity ¹1. Barrington WE, Beresford SAA. Eating occasions, obesity and related behaviors in working adults: does it matter when you snack? Nutrients 2019; 11:2320., cardiovascular risk ³3. St-Onge M-P, Ard J, Baskin ML, Chiuve SE, Johnson HM, Kris-Etherton P, et al. Meal timing and frequency: implications for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 2017; 135:e96-e121., noncommunicable chronic diseases ¹⁰10. Kim YJ, Yoon JH, Choi HS, Kim CS, Bae EH, Ma SK, et al. Meal frequency and skipping breakfast are associated with chronic kidney disease. Nutrients 2020; 12:331., and sleep disorders ¹¹11. Vernia F, Di Ruscio M, Ciccone A, Viscido A, Frieri G, Stefanelli G, et al. Sleep disorders related to nutrition and digestive diseases: a neglected clinical condition. Int J Med Sci 2021; 18:593-603.. The daily frequency of eating occasions has been inversely associated with concentrations of total cholesterol and low-density lipoprotein cholesterol ⁴4. Titan SM, Bingham S, Welch A, Luben R, Oakes S, Day N, et al. Frequency of eating and concentrations of serum cholesterol in the Norfolk population of the European prospective investigation into cancer (EPIC-Norfolk): cross-sectional study. BMJ 2001; 323:1286-8. and C-reactive protein ⁵5. Marinac CR, Sears DD, Natarajan L, Gallo LC, Breen CI, Patterson RE. Frequency and circadian timing of eating may influence biomarkers of inflammation and insulin resistance associated with breast cancer risk. PLoS One 2015; 10:e0136240.. Additionally, a 10-year follow-up study showed that breakfast consumption may help prevent weight gain ⁶6. van der Heijden AAWA, Hu FB, Rimm EB, van Dam RM. A prospective study of breakfast consumption and weight gain among U.S. men. Obesity (Silver Spring) 2007; 15:2463-9.. Furthermore, the habit of eating lunch late (i.e., at 4:30 p.m.) was associated with impaired free cortisol concentrations throughout the day and reduced resting energy expenditure, fasting carbohydrate oxidation, glucose tolerance, and in the thermic effect of food ⁷7. Bandín C, Scheer FAJL, Luque AJ, Ávila-Gandía V, Zamora S, Madrid JA, et al. Meal timing affects glucose tolerance, substrate oxidation and circadian-related variables: a randomized, crossover trial. Int J Obes (Lond) 2015; 39:828-33..

Several studies have explored the habits related to specific meals ³3. St-Onge M-P, Ard J, Baskin ML, Chiuve SE, Johnson HM, Kris-Etherton P, et al. Meal timing and frequency: implications for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 2017; 135:e96-e121.^,88. Leech RM, Worsley A, Timperio A, McNaughton SA. Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality. Nutr Res Rev 2015; 28:1-21., but few have particularly focused on the assessment of meal patterns, i.e., the combination of different eating occasions throughout the day ¹²12. O'Hara C, Gibney ER. Meal pattern analysis in nutritional science: recent methods and findings. Adv Nutr 2021; 12:1365-78.. In Brazil, local surveys have assessed the meal patterns of adolescents ¹³13. Estima CCP, Costa RS, Sichieri R, Pereira RA, Veiga GV. Meal consumption patterns and anthropometric measurements in adolescents from a low socioeconomic neighborhood in the metropolitan area of Rio de Janeiro, Brazil. Appetite 2009; 52:735-9.^,1414. Leal GVS, Philippi S, Matsudo SMM, Toassa EC. Food intake and meal patterns of adolescents, São Paulo, Brazil. Rev Bras Epidemiol 2010; 13:457-67.^,1515. Araki EL, Philippi ST, Martinez MF, Estima CCP, Leal GVS, Alvarenga MS. Pattern of meals eaten by adolescents from technical schools of São Paulo, SP, Brazil. Rev Paul Pediatr 2011; 29:164-70.^,1616. Rodrigues PRM, Luiz RR, Monteiro LS, Ferreira MG, Gonçalves-Silva RMV, Pereira RA. Adolescents' unhealthy eating habits are associated with meals skipping. Nutrition 2017; 42:114-20.^,1717. De Cnop ML, Monteiro LS, Rodrigues PRM, Estima CCP, Veiga GV, Pereira RA. Meal habits and anthropometric indicators in adolescents from public and private schools of the metropolitan region of Rio de Janeiro. Rev Nutr 2018; 31:35-47.. To date, no study with a nationally representative sample has explored the meal and snack patterns of the Brazilian population, which are addressed in this study, based on an analysis of data from the 2017-2018 Brazilian National Dietary Survey (INA).

Methods and materials

Study design and sample

This study was based on data from the INA included in the 2017-2018 Brazilian Household Budget Survey (POF), carried out by the Brazilian Institute of Geography and Statistics (IBGE). The random sampling plan adopted in the POF is based on a master sample composed of a set of census sectors (primary sample units) stratified according to geographical area, household location (urban or rural), and household income. A subsample of households was selected by simple random sampling to form the INA sample, consisting of all residents aged 10 years or over. In the INA 2017-2018, data on food consumption were collected from 46,164 individuals from 20,112 households. The survey was carried out over a 12-month period, so that all geographic and socioeconomic strata were covered in the four quarters, allowing researchers to analyze the coverage of seasonal variation in the events ¹⁸18. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares 2017-2018: análise do consumo alimentar pessoal no Brasil. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101742 (accessed on 19/Oct/2023).
https://biblioteca.ibge.gov.br/index.php... .

Ethics statement

This study was deemed exempt by the Research Ethics Committee of the Institute of Social Medicine, State University of Rio de Janeiro (review n. 4,316,087), under the Brazilian National Health Council Resolution n. 46/2012 and Operational Act n. 001/2013, since the data used is de-identified and publicly available (http://www.ibge.gov.br).

Food consumption assessment

Food consumption was assessed using the 24-hour diet recall (24hR) on two non-consecutive days (84% of participants responded to the second 24hR) chosen over one week. Informers were asked to report all food and beverages (including water) they had consumed the day before the household interview, which was conducted based on the Automated Multiple-Pass Method ¹⁹19. Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumplerm WV, et al. The US Department of Agriculture automated multiple-pass method reduces bias in the collection of energy intakes. Am J Clin Nutr 2008; 88:324-32., using a computational tool designed specifically for this assessment. For each food and drink, information was requested on the amount consumed, place and time of consumption, and eating occasion (breakfast, lunch, dinner, or snack) ¹⁸18. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares 2017-2018: análise do consumo alimentar pessoal no Brasil. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101742 (accessed on 19/Oct/2023).
https://biblioteca.ibge.gov.br/index.php... .

In this analysis, “breakfast”, “lunch”, and “dinner” were considered main meals, while “snacks” were classified as “morning snack” (6:00 a.m.-12:00 a.m.), “afternoon snack” (1:00 p.m.-5:00 p.m.) or “evening snack” (6:00 p.m.-5:00 a.m,), based on the time of consumption ²⁰20. Monteiro LS, Rodrigues PRM, Vasconcelos TM, Sperandio N, Yokoo EM, Sichieri R, et al. Snacking habits of Brazilian adolescents: Brazilian National Dietary Survey, 2017-2018. Nutr Bull 2022; 47:449-60..

The consumption of each meal or snack was represented by a yes-no variable. Thus, using the exploratory data analysis (EDA) ²¹21. Tukey JW. Exploratory data analysis. London: Pearson; 1977., meal and snack patterns were identified by combining the six eating occasions. The pattern identification and the combination algorithm were implemented in the R programing language, version 3.5.3 (http://www.r-project.org).

Firstly, the individuals were classified according to whether they ate one, two or three main meals during the day. Then, the combination of the main meals with the morning, afternoon, and evening snacks resulted in the combinations of one, two, or three main meals with none, one, two, or three snacks. Of the 64 possible combinations, eight included three main meals, 24 included two main meals, 24 only included only one main meal, and eight did not include any main meals. The latter were disregarded, as their frequencies were negligible (0.1%). Box 1 shows the 56 possible combinations considered and used to identify the meal and snack patterns. These combinations were classified into broad categories considering the number of main meals and snacks consumed in one day. Then, the categories including one and two main meals were aggregated. The final eight meal patterns were defined based on their interpretability and the frequency with which they were reported by the population. The same process was applied to data collected during days 1 and 2 of the 24hR, and as no differences were found between the meal patterns identified during these two days (Supplementary Material: https://cadernos.ensp.fiocruz.br/static//arquivo/1678-4464-csp-40-02-EN009923-s.pdf), only data from the first 24hR were reported.

Statistical analyses

The proportion (and 95% confidence intervals [95%CI]) of individuals with each meal and snack pattern was estimated according to gender, age group (adolescents: 10-19 years old; adults: 20-59 years old; and older adults: ≥ 60 years old), and food consumption day (typical or atypical). This information was provided by the participants at the end of the 24hR. Non-overlapping 95%CI indicated differences in the proportions across the categories of explanatory variables. The analysis considered the sample weights and the effect of the study design using the Complex Sample module of the SPSS, version 19 (https://www.ibm.com/).

Results

In total, 52.1% of the study participants were women, 17.8% were adolescents, 64.6% were adults, and 17.6% were older adults. A total of 88.8% of the study population reported having a typical day of food consumption (Tables 1 and 2).

In total, eight meal and snack patterns were identified: (1) three main meals + one snack, reported by 25.1% of the individuals; (2) three main meals + two snacks (24.6%); (3) three main meals + three snacks (18.5%); (4) three main meals and no snacks (10.9%); (5) one or two main meals + two snacks (7.4%); (6) one or two main meals + one snack (6.9%); (7) one or two main meals + three snacks (4.2%); and (8) one or two main meals and no snacks (2.3%). Overall, 79.1% of the population reported having all three meals, with or without snacks, and 86.7% of participants were classified in patterns including at least one snack (Table 1).

The comparison of the distribution of meal and snack patterns according to typical and atypical food consumption days showed that the patterns that included the three main meals were more frequent on typical days (81.3%) than on atypical days (60.8%). The opposite was observed for all the patterns that included one or two main meals, which were more frequent on atypical days (38.3%) than on typical days (18.6%). The most important differences between typical and atypical food consumption days were found for the patterns “one or two main meals + two snacks” (6.5 vs. 14.5%), “three main meals + three snacks” (19.2% vs. 12.3%), and “three main meals + one snack” (25.8% vs. 19.4%) (Table 1).

Patterns including three main meals were more frequent among men (80.5%) than among women (77.9%) and less reported by adolescents (75.1%) than by adults (79.8%) and older adults (80.9%). The meal and snack pattern most frequently reported by men, adolescents, and adults was “three meals + one snack”, and the most frequent pattern among women and older adults was “three meals + two snacks”. A meal consumption pattern including one or two main meals was reported by 24.9% of adolescents, a proportion greater than that found for adults (20.2%) and older adults (19%) (Table 2).

The greatest differences between men and women were found for the set of meal and snack patterns including at least one snack. These patterns were more frequently reported by women (88.2%) than by men (85%). The “three main meals” pattern and the remaining patterns including three main meals were both more frequent among men than women (12.4% vs. 9.6% and 80.6% vs. 77.8%, respectively) (Table 1).

The most important differences between age groups were observed for the set of patterns including one or two main meals, which were more frequently reported by adolescents (24.9%) than by adults (20.2%) and older adults (19%). On the other hand, the patterns including three main meals were more commonly reported by older adults (80.9%) and adults (79.8%) than by adolescents (75%). Lastly, it became evident that older adults had at least one snack between their daily meals more frequently than adults (88.6% vs. 85.7%) (Table 2).

Discussion

Eight patterns of daily meal and snack consumption were identified in the analysis of the INA 2017-2018. Generally, the three main meals - breakfast, lunch, and dinner - were part of the daily eating habits of approximately 80% of participants, and the consumption of at least one snack throughout the day was reported by 87% of the Brazilian population. In the present study, it was found that 80% of Brazilians had a meal pattern compatible with the recommendations on meal habits offered by the Dietary Guidelines for the Brazilian Population ². However, alternative meal patterns were also observed in the population: about one fifth of participants reported skipping at least one main meal per day, a proportion that almost doubled on atypical days and was more evident among women and adolescents.

In this study, the principle of parsimony ²²22. Mackay DJC. Information theory, inference and learning algorithms. Cambridge: Cambridge University Press; 2003. was followed to choose an objective approach - the EDA ²¹21. Tukey JW. Exploratory data analysis. London: Pearson; 1977. - to examine meal and snack patterns, which were derived from the combinations of meals and/or snacks according to their consumption by participants in specific days. This helped avoid arbitrariness, eliminating events such as the determination of the number of dimensions proper of the multiple correspondence analysis ²³23. Le Roux B, Rouanet H. Geometric data analysis: from correspondence analysis to structured data analysis. Dordrecht/Boston/London: Kluwer Academic Publishers; 2004. or the number of clusters generated by k-means clustering ²⁴24. Wong MA, Hartigan JA. Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Ser C Appl Stat 1979; 28:100-8.. Moreover, patterns generated by correspondence analysis or clustering can be difficult to interpret ²⁵25. Forgy EW. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 1965; 21:768-9.. Additionally, Leech et al. ²⁶26. Leech RM, Worsley A, Timperio A, McNaughton SA. Characterizing eating patterns: a comparison of eating occasion definitions. Am J Clin Nutr 2015; 102:1229-37. highlight that the techniques for identifying meal patterns guided by exploratory data analysis have advantages over approaches based on hypotheses that only consider the time of consumption of meals, as they allow for the observation of different eating habits in different segments of the population, including less conventional meal patterns.

A recent narrative review found that the principal component analysis, clustering, latent class analysis, and decision trees were the most commonly used statistical techniques to identify meal patterns in observational studies ¹²12. O'Hara C, Gibney ER. Meal pattern analysis in nutritional science: recent methods and findings. Adv Nutr 2021; 12:1365-78.. The authors pointed out that meal patterns can be grouped into three categories: (i) temporal patterns, referring to the timing and distribution of meals throughout the day; (ii) content patterns, referring to the combinations of foods within each meal and combinations of meals over the day; and (iii) context patterns, referring to external elements of the meal, such as location, activities done while eating, and the presence or absence of other people. O’Hara & Gibney ¹²12. O'Hara C, Gibney ER. Meal pattern analysis in nutritional science: recent methods and findings. Adv Nutr 2021; 12:1365-78. observed that the most common temporal meal patterns were the three meals per day pattern, the breakfast skipping pattern, and a grazing pattern consisting of smaller but more frequent meals. Likewise, in our analysis, temporal meal patterns were identified and the patterns including three main daily meals were found to be the most common.

In the INA 2017-2018, the elevated presence of meal and snack patterns that did not include the three main meals may have resulted from the changes in eating habits owing to the nutrition transition process that had taken place over the last decades ²⁷27. Baker P, Machado P, Santos T, Sievert K, Backholer K, Hadjikakou M, et al. Ultra-processed foods and the nutrition transition: global, regional, and national trends, food systems transformations and political economy drivers. Obes Rev 2020; 21:e13126., which ultimately resulted in reduced time for cooking and eating at home and in increases in the consumption of packaged, ready-to-eat products as well as the habit of eating away from home ²⁸28. Devine CM. A life course perspective: understanding food choices in time, social location, and history. J Nutr Educ Behav 2005; 37:121-8.^,2929. Hess JM, Jonnalagadda SS, Slavin JL. What is a snack, why do we snack, and how can we choose better snacks? A review of the definitions of snacking, motivations to snack, contributions to dietary intake, and recommendations for improvement. Adv Nutr Int Rev J 2016; 7:466e75.. Additionally, meal and snack patterns including one or two main meals were found to be more frequent on atypical food consumption days. Atypical food consumption days are characterized by non-habitual consumption - days like these include weekends, celebrations, festive dates, sick days, and other events causing changes in the daily routine -, while typical food consumption days reflect the individual’s eating routine, i.e., in these days, consumption is closer to usual ³⁰30. Craig MR, Kristal AR, Cheney CL, Shattuck AL. The prevalence and impact of 'atypical' days in 4-day food records. J Am Diet Assoc 2000; 100:421-7.^,3131. An R. Weekend-weekday differences in diet among US adults, 2003-2012. Ann Epidemiol 2016; 26:57-65.^,3232. Monteiro LS, Hassan BK, Estima CCP, Souza AM, Verly Junior E, Sichieri R, et al. Food consumption according to the days of the week - National Food Survey, 2008-2009. Rev Saúde Pública 2017; 51:93..

Differences in the definition of eating occasions may affect how meal patterns are characterized and the direction and magnitude of associations with diet quality and health outcomes ²⁶26. Leech RM, Worsley A, Timperio A, McNaughton SA. Characterizing eating patterns: a comparison of eating occasion definitions. Am J Clin Nutr 2015; 102:1229-37.^,3333. Murakami K, Shinozaki N, Livingstone MBE, Fujiwara A, Asakura K, Masayasu S, et al. Meal and snack frequency in relation to diet quality in Japanese adults: a cross-sectional study using different definitions of meals and snacks. Br J Nutr 2020; 124:1219-28.. Leech et al. ²⁶26. Leech RM, Worsley A, Timperio A, McNaughton SA. Characterizing eating patterns: a comparison of eating occasion definitions. Am J Clin Nutr 2015; 102:1229-37. identified the eight most commonly used definitions to describe eating occasions, and the ones employed most frequently considered the time of day and the designation of the respondent or identified participant. Due to the fact that there are multiple ways to define eating occasions, studies on this subject usually detail the criteria used to classify consumption occasions ³⁴34. Hess JM, Slavin JL. The benefits of defining "snacks". Physiol Behav 2018; 193:284-7.. In this study, eating occasions were designated by participants when answering a 24hR, and snacks were classified as morning, afternoon, or evening snacks according to the time of day when they were consumed.

Similar to the present study, in the third National Health and Nutrition Examination Survey, Kerver et al. ³⁵35. Kerver JM, Yang EJ, Obayashi S, Bianchi L, Song WO. Meal and snack patterns are associated with dietary intake of energy and nutrients in US adults. J Am Diet Assoc 2006; 106:46-53. used participant-identified meal and snack patterns of U.S. adults based on meal and snack/beverage consumption occasions reported in a 24hR. The authors used the five most reported meal and snack combinations to identify the meal patterns, which were divided into the following categories: breakfast + lunch + dinner + two or more snacks (31.6%); breakfast + lunch + dinner + one snack (15.4%); breakfast + dinner + two or more snacks (13.1%); breakfast + lunch + dinner (8.3%); and lunch + dinner + two or more snacks (7.6%). The authors emphasized that meal patterns including the main meals may be associated with diets with better nutritional quality, since individuals who reported the patterns breakfast + lunch + dinner + two or more snacks and breakfast + lunch + dinner + one snack had the highest intakes of all micronutrients examined, except cholesterol, vitamin B₆, and sodium ³⁵35. Kerver JM, Yang EJ, Obayashi S, Bianchi L, Song WO. Meal and snack patterns are associated with dietary intake of energy and nutrients in US adults. J Am Diet Assoc 2006; 106:46-53..

In the Santé, Inégalités et Ruptures Sociales survey (SIRS - Health, Inequalities and Social Ruptures), conducted with the adult population of the metropolitan area of Paris (France), Lhuissier et al. ³⁶36. Lhuissier A, Tichit C, Caillavet F, Cardon P, Masullo A, Martin-Fernandez J, et al. Who still eats three meals a day? Findings from a quantitative survey in the Paris area. Appetite 2013; 63:59-69. identified meal patterns by asking three questions: (i) how many times do you eat during the day?; (ii) how many of these eating occasions do you consider as meals?; and (iii) what are your meal times during the day? The authors observed that, similar to the findings of this study, the three-meal pattern was as a strong cultural trait (65.9%) and was more frequent among older individuals (60 years old and over: 77.4%) compared to younger ones (18-29 years old: 54.9%). Furthermore, it appears that the practice of having three main meals a day is more common in Brazil than in Paris. Additionally, while in Brazil men were found to adopt the three-meal pattern more frequently than women, in Paris, the three-meal pattern was more common among women (70.5%) compared to men (60.7%).

In Brazil, meal and snack patterns have been evaluated in studies with adolescents, and most information on meals was obtained using self-administered, close-ended questionnaires about the usual frequency of meals; additionally, meal habits were classified as satisfactory or unsatisfactory depending on whether the main meals (breakfast, lunch, and dinner) were regularly or irregularly consumed ¹³13. Estima CCP, Costa RS, Sichieri R, Pereira RA, Veiga GV. Meal consumption patterns and anthropometric measurements in adolescents from a low socioeconomic neighborhood in the metropolitan area of Rio de Janeiro, Brazil. Appetite 2009; 52:735-9.^,1515. Araki EL, Philippi ST, Martinez MF, Estima CCP, Leal GVS, Alvarenga MS. Pattern of meals eaten by adolescents from technical schools of São Paulo, SP, Brazil. Rev Paul Pediatr 2011; 29:164-70.^,1616. Rodrigues PRM, Luiz RR, Monteiro LS, Ferreira MG, Gonçalves-Silva RMV, Pereira RA. Adolescents' unhealthy eating habits are associated with meals skipping. Nutrition 2017; 42:114-20.^,1717. De Cnop ML, Monteiro LS, Rodrigues PRM, Estima CCP, Veiga GV, Pereira RA. Meal habits and anthropometric indicators in adolescents from public and private schools of the metropolitan region of Rio de Janeiro. Rev Nutr 2018; 31:35-47.. The present study adds important contributions to the knowledge on this subject in Brazil, as it expands the identification of meal consumption patterns to other age groups, such as adults and older adults. Furthermore, in addition to relying on the respondent’s designation to characterize the meals, this analysis used an exploratory statistical approach to identify the possible daily combinations of meals and snacks and recognize the meal patterns that represent the eating occasions in Brazilians’ routines.

The possible limitations of this study are related to the fact that the 24hR was used to obtain information on meal and snack habits. The 24hR allows researchers to obtain detailed information on food consumption, including the composition and time of consumption of meals and snacks ²⁶26. Leech RM, Worsley A, Timperio A, McNaughton SA. Characterizing eating patterns: a comparison of eating occasion definitions. Am J Clin Nutr 2015; 102:1229-37.^,3535. Kerver JM, Yang EJ, Obayashi S, Bianchi L, Song WO. Meal and snack patterns are associated with dietary intake of energy and nutrients in US adults. J Am Diet Assoc 2006; 106:46-53., but it relies on the respondents’ memory and may be subject to underreporting. In the INA 2017-2018, strategies to reduce misreport included following the Automated Multiple-Pass Method ¹⁹19. Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumplerm WV, et al. The US Department of Agriculture automated multiple-pass method reduces bias in the collection of energy intakes. Am J Clin Nutr 2008; 88:324-32. to apply the 24hR with the support of computational resources. Moreover, as the 24hR is not associated with systematic errors, it is often used in extensive population-based surveys carried out worldwide ³⁷37. Keyzer W, Bracke T, McNaughton SA, Parnell W, Moshfegh AJ, Pereira RA, et al. Cross-continental comparison of national food consumption survey methods - a narrative review. Nutrients 2015; 7:3587-620.. Furthermore, each participant informed whether the reported food consumption corresponded to a typical or an atypical food consumption day. Lastly, the analyses showed that there were no significant differences between the meal and snack patterns identified in the first and the second 24hR.

One strength of this study is its large, nationally representative sample, which included Brazilians aged 10 years and over and provided robust estimates. In addition, the eating occasions were identified objectively, based on participants’ designation and the time of consumption. Another strong point of this study is the fact that it took a holistic approach to meal and snack habits rather than focusing on isolated meals, which made it possible to assess participants’ compliance with dietary recommendations and to identify alternative meal and snack patterns. This approach allows future studies to explore the association between dietary habits and health outcomes, an aspect that has been highlighted by chrono-nutrition studies ³⁸38. Almoosawi S, Vingeliene S, Karagounis LG, Pot GK. Chrono-nutrition: a review of current evidence from observational studies on global trends in time-of-day of energy intake and its association with obesity. Proc Nutr Soc 2016; 75:487-500..

Traditionally, nutritional epidemiology research has focused on the assessment of dietary intake and patterns; however, the focus on dietary habits is still restricted ²⁶26. Leech RM, Worsley A, Timperio A, McNaughton SA. Characterizing eating patterns: a comparison of eating occasion definitions. Am J Clin Nutr 2015; 102:1229-37., especially in low- and middle-income countries such as Brazil. As pointed out by Popkin & Ng ³⁹39. Popkin BM, Ng SW. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra-processed foods is not inevitable. Obes Rev 2022; 23:e13366., the process of nutritional transition, which takes place more rapidly in middle and low-income countries than in developed ones, includes changes in eating habits, for example, an increase in the number of eating occasions and in the contribution of ultra-processed products to total energy intake. Various studies have indicated that the frequency, location, and nutritional composition of main meals and snacks are associated with diet quality and health ¹²12. O'Hara C, Gibney ER. Meal pattern analysis in nutritional science: recent methods and findings. Adv Nutr 2021; 12:1365-78.. Moreover, specific meal patterns may be associated with better diet quality and nutrient intake, considering that these patterns may reflect healthier lifestyle choices ³⁵35. Kerver JM, Yang EJ, Obayashi S, Bianchi L, Song WO. Meal and snack patterns are associated with dietary intake of energy and nutrients in US adults. J Am Diet Assoc 2006; 106:46-53.. In Brazil, Rodrigues et al. ¹⁶16. Rodrigues PRM, Luiz RR, Monteiro LS, Ferreira MG, Gonçalves-Silva RMV, Pereira RA. Adolescents' unhealthy eating habits are associated with meals skipping. Nutrition 2017; 42:114-20. carried out a cross-sectional school-based study to evaluate the quality of adolescents’ diets and observed that the habit of skipping meals was associated with diets of low nutritional quality, especially those with low consumption of fruits and vegetables and high consumption of solid fats, added sugar, and alcoholic beverages. Therefore, acknowledging meal consumption patterns is central for targeting and tailoring actions to promote healthy eating.

These findings may be an initial step towards the recognition of meal and snack habits in Brazil. Further studies should assess the socioeconomic and demographic characteristics associated with each meal and snack pattern, as well as the distribution of energy intake and other dietary features specific to these patterns.

Conclusion

Eight meal and snack patterns were identified among Brazilians aged over 10 years. It is noteworthy that a significant portion of the population studied consumed three main meals a day, while less than 20% did not report consuming snacks. Skipping at least one of the main meals on atypical food consumption days was almost twice as frequent as on typical days. Women and adolescents skipped at least one of the main meals to a greater extent than men, adults, and older adults. By evaluating a nationally representative sample, this study contributes to a better understanding of Brazilians’ eating habits.

Acknowledgments

To Brazilian National Research Council (CNPq, n. 443369/2016-0) for funding.

References

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