Since the mid-20th century, the world’s food environment shifted due to increased urbanisation and market globalisation, resulting in a growing trend for consuming food out of home(Reference Guthrie, Lin and Frazao1–Reference Kant and Graubard4). Demographic and socio-economic changes, such as longer working hours(Reference Celnik, Gillespie and Lean5,Reference Devine, Farrell and Blake6) , time pressure among working women(Reference French, Story and Jeffery7,Reference Welch, McNaughton and Hunter8) and higher availability of food service establishments(Reference Janssen, Davies and Richardson9), contributed to less time being spent preparing meals at home and increased reliance on out-of-home meals. In the USA, contribution of out-of-home foods to total energy intake (TEI) has risen from 18 % in 1977–1978 to 32 % in 1994–1996(Reference Nielsen, Siega-Riz and Popkin10). In Portugal, a survey from Nielsen company reported that, in 2016, about 23 % of the population ate at least one meal out of home, with 15 % ordering food out to eat at home(11). Recent data from the Eurostat (2019) regarding Portuguese families showed that 9·5 % of total expenses were spent in out-of-home meals(12).
Food environment, age group, area of residence and socio-economic indicators, such as education, income and occupation, have all been associated with eating out of home(Reference Janssen, Davies and Richardson9). This behaviour can be tied to special occasions or merely with routine meals bought at take-away and fast-food restaurants. Indeed, families point out different reasons to eat out, such as convenience, cost-effectiveness, variety and the enjoyment of ‘family time’(Reference McGuffin, Price and McCaffrey13). Diet quality and health status seem to weigh less when deciding to eat away from home. Previous studies have focused on the relation of at-home and out-of-home consumption or considered eating out within restaurants and fast-food establishments. Still, the institutional food services, particularly school and work canteens, are highly relevant provisioners of out-of-home meals(Reference Ni Mhurchu, Aston and Jebb14), as individuals spend more than half of their waking hours working or learning(Reference Lachat, Nago and Verstraeten15).
Previous research has pinpointed the nutritional shortcomings of out-of-home meals, namely large portion sizes and high energy density, combined with a small offer of healthy options and a lack of food skills and health literacy among some consumer segments, particularly the most vulnerable(Reference Lachat, Nago and Verstraeten15). A cross-sectional study from the HECTOR project identified men, young adults and those highly educated as the ones who eat more frequently out of home(Reference Orfanos, Naska and Trichopoulos3). The energy and nutrients intakes, as well as the types of food groups most consumed by those who often eat out, may differ from those who eat mostly at home. In Europe, out-of-home meals are an important part of dietary habits and have been linked to the increase in overweight and obesity(Reference Orfanos, Naska and Trichopoulou16–Reference Orfanos, Naska and Rodrigues18).
On the one hand, meals at European schools are not always be linked to a better dietary intake in children and adolescents(Reference Müller, Libuda and Diethelm19,Reference Briefel, Wilson and Gleason20) , whereas those at home seem to frequently be associated with adequate nutrient intake and low dietary energy density(Reference Müller, Libuda and Diethelm19,Reference Tyrrell, Greenhalgh and Hodgson21) . However, in a Portuguese study with preschool children(Reference Moreira, Severo and Oliveira22), eating meals at school was associated with higher dietary adequacy index scores, through greater consumption of fibre, fish, vegetables and fruit, and lower intake of total fat and meat, compared with other eating locations. Yet, consumption in restaurants, when compared with at school or home, was linked to lower dietary adequacy index scores, given the higher consumption of cakes, salty snacks, soft drinks and fruit juices. Similarly, a higher energy intake derived from ‘core foods’ at home and school, paired with a higher contribution of ‘non-core foods’ to the daily energy intake from leisure places and food outlets, was observed among children and adolescents in the UK(Reference Ziauddeen, Page and Penney23).
Identifying priority areas to promote healthy eating is dependent on the eating location profile, which differ from country to country and by age groups, according to extant research. Knowledge of how dietary choices and nutrient intake are linked to consumption settings should therefore be advanced. Very few studies have investigated these issues using data from national representative samples entailing all age groups. Given the above, and using data from the Portuguese National Food, Nutrition and Physical Activity Survey (IAN-AF 2015/2016), the present study aims to describe and compare the food consumption and the energy and macronutrient intakes of the Portuguese population, segmented by age group and across eating location.
Methods
Participants
The protocol and methodology from the IAN-AF 2015/2016 have been published earlier(Reference Lopes, Torres and Oliveira24,Reference Lopes, Torres and Oliveira25) . This survey collected nationwide and regional data on food consumption and its relation to health determinants from individuals aged between 3 months and 84 years. The study population was represented by a probabilistic sample obtained from the National Health registry, through multistage sampling: first, by the stratification of the seven statistical geographic units (including mainland and islands); second, by randomly selecting Primary Health Care Units in each region; and finally, individuals were randomly selected from each Health Care Unit according to sex and age. A sample of 6553 individuals participated in one face-to-face interview (response rate among eligible of 33·37 %); 5811 completed two dietary assessments 8–15 d apart (response rate among eligible of 29·60 %). Comparatively to individuals who participated, those who refuse to participate and who filled out a refusal questionnaire were older and less educated. Still, for variables representing dietary consumption, the differences were of a small magnitude. Only data from participants with two complete dietary assessments and aged 3–84 years (n 5005) were analysed in the present study to avoid the inclusion of children who were not totally introduced on the family’s diet (aged < 3 years). More detailed descriptions on sampling procedure and participants can be found in previous publications(Reference Lopes, Torres and Oliveira24,Reference Lopes, Torres and Oliveira25) .
Data collection
Dietary assessment
To capture season effects and daily variations on food consumption, data were collected from October 2015 to September 2016 by trained fieldworkers with background in Nutrition and Dietetics. Computer-assisted personal interviews were distributed over the four seasons and included all days of the week (randomly selected). In children under the age of 10 years, dietary intake was assessed in two non-consecutive 1-d food diaries which were filled by parents or caregivers on paper, followed by a computer-assisted personal interview in the day before to check for completeness and add details on food description and quantification. For the remaining participants, dietary intake was evaluated through two non-consecutive 24-h recalls conducted by computer-assisted personal interviews. For subjects aged from 10 to 14 years, it was mandatory to have the presence of one parent or caregiver during the assessment. Most of the procedures were adapted from the European Food Safety Authority (EFSA) guidance, taking in account the EU Menu methodology(26).
The ‘eAT24’ software, previously validated(Reference Goios, Severo and Lloyd27), integrates an automated multiple-pass method employing five steps(Reference Moshfegh, Rhodes and Baer28) and the classification system FoodEx2(29). This software was used to collect all dietary data and describe the food, recipes and supplements consumed during meals, including information on time and consumption location. The initial food list was based on the Portuguese food composition table(30), being expanded to a total of 2479 food items and 117 supplements for the purpose of the study. Furthermore, a total of 1696 recipes that reflect the Portuguese cuisine were included(Reference Lopes, Torres and Oliveira24,Reference Lopes, Torres and Oliveira31) . Food portion size was quantified through a food picture book(Reference Vilela, Lopes and Guiomar32), as well as predefined household measures, weight or volume methods, and standard unit methods. When the participant knew the weight or volume of the food consumed, the quantity was manually entered by the interviewer. A list of default mean portions was made available to participants who did not know how to estimate portion size for a food item. To ensure overall validity of dietary intake information: first, individual energy and macronutrient intake was controlled at the end of interview with outliers being signalised with an alert message allowing the interviewer to perform the corrections directly in the ‘eAt24’ software; additionally, the accuracy of this software was previously assessed by examined differences between estimates from dietary and urine measures(Reference Goios, Severo and Lloyd27); and misreporters were identified according to the Goldberg method and their exclusion had a small impact on energy and nutrient estimates(Reference Magalhães, Severo and Torres33).
The food groups considered in this study are described elsewhere(Reference Lopes, Torres and Oliveira31). Water was excluded from the non-alcoholic beverages for the purpose of the present analysis. Alcoholic beverage intake information was complemented by using a food propensity questionnaire, with a reference period of 12 months. In the present study, this intake was analysed by frequency of consumption for four categories – all alcoholic beverages, wine, beer and other alcoholic beverages – and for each eating location group.
Definition of eating location categories
The distribution of meals per each place of consumption recorded by ‘eAT24’ was used to group eating locations under Home, Homes of Relatives or Friends, School or Work, Restaurants or Other Out of Home Places. Based on Naska et al. definition of eating out (‘meals, beverages and snacks consumed out of home, irrespective of where the items had been prepared’)(Reference Naska, Katsoulis and Orfanos34), four eating location categories were then defined following a similar methodology of a previous study of the research group(Reference Moreira, Severo and Oliveira22), grouping participants under Home (at least 80 % of meals consumed at home), Other Homes (less than 80 % of meals consumed at home and the remaining ones mainly at the home of relatives or friends), School or Work (less than 80 % of meals consumed at home and the remaining ones mainly at school or work, including canteens) or Restaurants and Other Places (less than 80 % of meals consumed at home and the remaining ones mainly at restaurants, bars, coffee shops, pastry or snack bars, while travelling, outdoors or other public spaces). Among the elderly, the eating location patterns Home and Other Homes were concatenated, as well as School or Work and Restaurants and Other Places, due to low frequency of patterns Other Homes and School or Work (4·8 % and 2·5 %, respectively).
Healthy eating score
A healthy eating score (HES) was computed to assess the individual dietary adequacy of the meals consumed within each eating location category, according to an approach previously used to study diet quality among Portuguese children(Reference Vilela, Oliveira and Ramos35). This approach is based on the dietary recommendations proposed by the WHO(36) and considers nine food groups (rather than nutrients): (1) ‘fruit, vegetables and pulses’; (2) ‘dairy’ (milk, yogurt and cheese); (3) ‘cereals and starchy tubers’ (rice, pasta, potatoes, bread and other grains); (4) ‘white meat, fish and eggs’; (5) ‘red meat and processed meats’, (6) ‘salty snacks’ (chips, snacks, pizzas and commercial burgers); (7) ‘sweets’ (cakes, candies, sweet pastry, chocolate, biscuits and ice cream, breakfast cereals and cereals bars); (8) ‘sugar and honey’; and (9)‘sugar-sweetened beverages’ (soft drinks and nectars). Quartiles of consumption were calculated for each food group, by age, and a score ranging from 1 to 4 was assigned. For the first four groups, the lowest quartile of consumption was assigned a score of 1, intermediate quartiles were given the scores 2 and 3 and the highest quartile was given a score of 4. The remaining groups (from 5–9) were scored in the reverse direction with the highest quartile of consumption receiving the lowest score. The HES ranged between 9 and 36, with higher scores representing a more adequate diet.
Other variables
Among other variables, all participants reported on the following demographic and socio-economic characteristics, analysed in this study: sex, age, education and degree of urbanisation of area of residence (henceforth, degree of urbanisation). Adults also reported on completed education, with this variable being re-classified as ‘No education/primary’ (low education), ‘Secondary’ (middle education) and ‘Tertiary’ (high education). Children and adolescents were attributed the highest education registered for their parents. Regarding the degree of urbanisation, the Typology of Urban Areas (TIPAU) 2014, developed in Portugal, classifies the country’s territory into three categories based on urbanisation levels: Predominantly Urban Areas (APU), Moderately Urban Areas (AMU) and Predominantly Rural Areas (APR). This classification, replacing the 2009 version, utilises quantitative and qualitative criteria to distinguish areas, considering factors such as population density, land use and administrative boundaries.
Statistical analysis
The distribution of meals (%) per eating location was estimated for the total sample, and by sex, age group, degree of urbanisation and education. The mean contribution of meals to TEI (% kcal) per eating location was also estimated for the total sample. The distribution of individuals by the four eating location categories previously defined was performed according to the same sociodemographic variables.
Mean daily intakes of energy (in kcal), nutrients (as % of TEI or in weight (grams/ milligrams)) and food groups (in grams) were estimated per eating location category, stratified by age group. The existence of significant interactions between eating location categories and age groups was tested for each nutrient and food group; significant interactions were included in further analyses. The significance of differences in nutrient and food group intakes between categories and within age groups was tested using ANOVA.
To assess the degree of association between mean daily intakes of energy, nutrients, and food groups, and eating location categories, linear regression coefficients (β) and 95 % CI were estimated, controlling for sex, degree of urbanisation and education. The degree of association between the consumption of alcoholic beverages (overall and per type) in adults and in elderly and eating location categories was evaluated by estimating OR and 95 % CI with logistic regression models. Two models were fitted: a crude model (model 1) and a model adjusted for sex, degree of urbanisation and education (model 2).
The R software version 3.4.1 for Windows was used for the statistical analysis, and all estimates were weighted to the distribution of the Portuguese population. A significance level of 5 % was considered.
Results
Proportion of meals per eating location
Table 1 shows that the majority of meals (69·1 %) was consumed at individuals’ homes, and at-home meals were the main contributor to TEI (70·3 %). The second highest proportion of meals was eaten at schools or workplaces (11·7 %), followed closely by restaurants (10·9 %).
Table 1. Distribution of meals of the Portuguese population (3–84 years old) according to eating location by sociodemographic characteristics and its mean contribution to total energy intake, the IAN-AF 2015/2016
All significant values are bold.
* Education completed by parents for participants < 18 years of age.
Eating location categories
Table 2 shows that, overall, individuals were mostly classified into the Home (36·8 %) or the School or Work (31·1 %) eating location categories, followed by Restaurants and Other Places (24·4 %) and finally by Other Homes (7·7 %). Females were mostly classified in the Home category (42·1 %), followed by School or Work (32·3 %) and Restaurants and Other Places (17·9 %), whereas males were evenly distributed across these three categories (31·2 %, 29·8 % and 31·3 %, respectively). Children, adolescents and adults were predominantly classified into the School or Work category (54·4 %, 44·7 % and 35·0 %, respectively), but elderly mainly in the Home categories (72·2 %). About a third of individuals residing in predominantly rural areas were classified into the Home and Restaurants and Other Places categories and approximately a quarter in School or Work; roughly, the opposite was observed for the remainder. The majority of individuals with low education level were classified into the Home category (56·5 %) and represented the smallest proportion of those in the Restaurants and Other Places (21·1 %). Individuals with high education level, on the contrary, were predominantly found in the School or Work category (40·8 %) and then roughly evenly distributed between Restaurants and Other Places (26·7 %) and Home (23·8 %) categories.
Table 2. Distribution of individuals according to eating location patterns by sociodemographic characteristics, weighted for the distribution of the Portuguese population, the IAN-AF 2015/2016
All significant values are bold.
* Education completed by parents for participants < 18 years of age.
† Home: at least 80 % of meals consumed at home; Other Homes: less than 80 % of meals consumed at home and the remaining ones mainly at the home of relatives or friends; School or Work: less than 80 % of meals consumed at home and the remaining ones mainly at school or work, including canteens; Restaurants and Other Places: less than 80 % of meals consumed at home and the remaining ones mainly at restaurants, bars, coffee shops, pastry or snack bars, while travelling, outdoors or other public spaces.
Dietary intake and diet adequacy
Mean daily intakes of food groups and corresponding HES per eating location group, by age group, weighted for the distribution of the Portuguese population, are depicted in online Supplementary Table S1. Values show that all interactions between eating locations categories and age group were significant, except for ‘fruit, vegetables and pulses’.
Children and adolescents
Table 3A shows the associations between children and adolescents’ eating location categories and main food groups, using the Home category as reference category. For children and adolescents, there was a significant positive association between the School or Work category and the consumption of ‘fruit, vegetables and pulses’, and of ‘cereals and starchy tubers’. In the case of adolescents, there was furthermore a negative association for the consumption of ‘sugar-sweetened beverages’ (β = −59·7 g/d; 95 % CI = −105·4, −14·1).
Table 3A. Adjusted association of mean daily intakes of food groups and healthy eating score with eating location patterns in children and adolescents, weighted for the distribution of the Portuguese population, the IAN-AF 2015/2016
All significant values are bold.
β, standardised coefficient; SSB, sugar-sweetened beverages; HES, Healthy Eating Score.
Models adjusted for sex, degree of urbanisation and education level.
Children and adolescents classified in the patterns Other homes or Restaurants and Other Places consume significantly fewer ‘dairy’ and more ‘salty snacks’ (children: β = 18·6 g/d; 95 % CI = 3·0, 34·3; adolescents: β = 28·8 g/d; 95 % CI = 12·2, 45·4) and ‘sugar-sweetened beverages’ (children: β = 113·6 g/d; 95 % CI = 51·8, 175·4; adolescents: β = 130·5 g/d; 95 % CI = 69·3, 191·7). Adolescents in Restaurants and Other Places also consume significantly more ‘red and processed meats’ (β = 21·1 g/d; 95 % CI = 2·8, 39·4) and of ‘Sweets’ (β = 24·1 g/d; 95 % CI = 4·3, 43·9).
The association between being in the School or Work category and HES was positive, only significantly in children (β = 0·9; 95 % CI = 0·3, 1·5), and negative for those being in the Other Homes category, only significant in adolescents (β = −1·3; 95 % CI = −2·2, −0·4). Noticeably, negative associations between being classified in the Restaurants and Other Places category and HES was observed for both age groups (children: β = −1·0; 95 % CI = −2·0, −0·04; adolescents: β = −2·4; 95 % CI = −3·2, −1·5).
Adults and elderly
Table 3B shows the associations between adults and elderly’ eating location categories and main food groups, using the Home category as outcome reference. In the case of adults, the consumption of ‘red and processed meats’, ‘sugar-sweetened beverages’ and ‘sweets’ was significantly higher in any of the eating out location patterns, when compared with Home. Also, they consume more ‘salty snacks’ in Other homes, more ‘white meat, fish and eggs’ at School or work, and in Restaurants and Other Places category they eat significantly more ‘salty snacks’ and ‘sugar and honey’, and by the contrary less ‘fruit, vegetables and pulses’ and ‘Dairy’.
Table 3B. Adjusted association of mean daily intakes of food groups and healthy eating score with eating location patterns in adults and elderly, weighted for the distribution of the Portuguese population, the IAN-AF 2015/2016
All significant values are bold.
β, standardised coefficient; SSB, sugar-sweetened beverages; HES, Healthy Eating Score.
Models adjusted for sex, degree of urbanisation and education level.
In elderly, Table 3B shows that being classified in the School or Work and Restaurants and Other Places category was significantly positively associated with the consumption of ‘red and processed meats’, ‘salty snacks’, ‘sugar-sweetened beverages’ and ‘sugar and honey’.
For all eating out location patterns, when compared with Home category, a negative association was observed with HES in adults (School or Work (β = −0·3; 95 % CI = −0·6, −0·01), Other Homes (β = −0·9; 95 % CI = −1·3, −0·4) and Restaurants and Other Places (β = −1·3; 95 % CI = −1·6, −1·0)) and in the elderly (School or Work and Restaurants and Other Places v. Home and Other homes (β = −0·8; 95 % CI = −1·3, −0·3)).
Table 4 displays the associations between the frequency of consumption of alcoholic beverages and eating location groups for adults and elderly, using the Home category as outcome reference. In adults, there was a significant positive association between being classified in the Restaurants and Other Places category and the frequency of consumption of alcoholic beverages, altogether and per type of beverage (model 2 – all alcoholic beverages: OR = 1·74, 95 % CI = 1·17, 2·58; wine: OR = 1·42, 95 % CI = 1·05, 1·91; beer: OR = 3·02, 95 % CI = 2·00, 4·57). In particular, the frequency of consuming beer was positively linked to being classified in the School or Work category, in the case of adults (OR = 1·52, 95 % CI = 1·04, 2·29), and in the School or Work and Restaurants and Other Places one in the case of elderly (OR = 2·23, 95 % CI = 1·17, 4·23); after the adjustment for sex, degree of urbanisation and education level, however, the latter did not remain statistically significant.
Table 4. Association of consuming alcoholic beverages with eating location patterns in adults and elderly, weighted for the distribution of Portuguese population, the IAN-AF 2015/2016
All significant values are bold.
* Model 1: crude model.
† Model 2: model adjusted for sex, degree of urbanisation and education level.
Nutrient profile
Mean daily intakes of energy and nutrients per eating location group, by age group, weighted for the distribution of the Portuguese population, are depicted in online Supplementary Table S2. Results showed that all interactions between eating location categories and age group were significant.
Children and adolescents
Table 5A shows the associations between children and adolescents’ eating location patterns and energy and nutrients intake, using the Home category as reference category. Children, being classified in the School or Work category, present lower intake of free sugars (β = –2·6 %TEI; 95 % CI = −3·9, −1·3), but higher of fibre (β = 1·8 g/d; 95 % CI = 0·7, 2·9) and Na (β = 190 mg/d; 95 % CI = 19, 362). Meanwhile, in the case of adolescents, positive associations were uncovered with the intakes of energy (β = 142 kcal/d; 95 % CI = 35, 250), fibre (β = 2·5 g/d; 95 % CI = 1·4, 3·5) and Na (β = 361 mg/d; 95 % CI = 165, 556) and negative with the intake of protein (β= –1·0 %TEI; 95 % CI = −1·7, −0·3).
Table 5A. Adjusted association of mean daily intakes of energy and nutrients with eating location patterns in children and adolescents, weighted for the distribution of the Portuguese population, the IAN-AF 2015/2016
All significant values are bold.
β, standardised coefficient; TEI, total energy intake.
Models adjusted for sex, degree of urbanisation and education level.
In the case of both children and adolescents, being classified in the Restaurants and Other Places category present higher intake of energy (β = 163 kcal/d; 95 % CI = 7, 318 and β = 249 kcal/d; 95 % CI = 105, 393, respectively), fat (β = 2·0 %TEI; 95 % CI = 0·1, 3·9 and β = 2·4 %TEI; 95 % CI = 1·0, 3·9, respectively), SFA (β = 1·2 %TEI; 95 % CI = 0·3, 2·0 and β = 1·3 %TEI; 95 % CI = 0·6, 2·0, respectively) and Na (β = 350 mg/d; 95 % CI = 69, 631 and β = 410 mg/d; 95 % CI = 148, 672, respectively) and lower intake of protein (β= –1·8 %TEI; 95 % CI = −2·9, −0·7 and β= –1·3 %TEI; 95 % CI = −2·3, −0·3, respectively). For adolescents in this group, there was also a positive association with the intake of free sugars (β = 2·4 %TEI; 95 % CI = 0·8, 4·0).
In the case of adolescents, being classified in the Other Homes category was positively linked to intakes of fat, SFA and Na and negatively associated with the intake of protein.
Adults and elderly
Table 5B shows the associations between adults and elderly’ eating location categories and energy and nutrients intake, using the Home as reference category. In adults, the intake of energy, free sugars and Na was higher in all eating out location patterns, when compared with Home pattern. Additionally, those being classified in the School or Work category presents higher fibre intake and SFA and those being classified in the Restaurants and Other Places category presents higher intakes of fat (β = 0·9 %TEI; 95 % CI = 0·3, 1·5) and SFA (β = 0·7 %TEI; 95 % CI = 0·4, 2·0), but lower intakes of protein (β = −0·5 %TEI; 95 % CI = −0·9, −0·1) and total carbohydrates (β = −1·8 %TEI; 95 % CI = −2·6, −1·0).
Table 5B. Adjusted association of mean daily intakes of energy and nutrients with eating location patterns in adults and elderly, weighted for the distribution of the Portuguese population, the IAN-AF 2015/2016
All significant values are bold.
β, standardised coefficient; TEI, total energy intake.
Models adjusted for sex, degree of urbanisation and education level.
In the case of the elderly, being classified in the School or Work and Restaurants and Other Places category was positively linked to the intake of free sugars (β = 1·6 %TEI; 95 % CI = 0·7, 2·4) and fat (β = 0·3 %TEI; 95 % CI = 1·0, 1·5).
Discussion
Based on our knowledge, this is the first study investigating associations between eating locations patterns, food intake and diet adequacy, involving a whole range of age groups(Reference Wellard-Cole, Davies and Allman-Farinelli37). Importantly, it is also one of a small number of reports on out-of-home food consumption that looks specifically at the relevance of the meals taken in non-commercial (i.e. mass catering) establishments, namely school and work food service facilities, to individuals’ diet and nutritional status(Reference Moreira, Severo and Oliveira22,Reference Ziauddeen, Page and Penney23,Reference Zang, Luo and Wang38,Reference Bezerra, Medeiros and de Moura Souza39) .
Home was the most prevalent pattern followed by School or work and Restaurants and Other Places, with close to a third (30 %) of the TEI deriving from out-of-home meals. Our results regarding this contribution are similar to those found in other European countries that used the same eating out definition as ours(Reference Orfanos, Naska and Rodrigues18). In adults, the contribution of out-of-home meals to TEI varied from 27·1 % in Germany, 27·7 % in the Netherlands and 29 % in Sweden, for men; in women, it varied from 22·1 % in Germany, 20·8 % in the Netherlands and 31 % in Sweden. Still in the European context, a study with adults by Naska et al. (Reference Naska, Orfanos and Trichopoulou40) showed that men ate more at restaurants or at workplace than women. This noticed tendency for women being less frequent out-of-home eaters is observed in our study, and it might relate to their role in meal preparation and consumption within households.
A study with British children and adolescents further support our results given that asides home, school was the main eating location in individuals aged 1·5–18 years(Reference Ziauddeen, Page and Penney23). A systematic review by Lachat et al. (Reference Lachat, Nago and Verstraeten15) uncovered the existence of an age gradient in the contribution of out-of-home food consumption to energy intake, with a peak during childhood and young adulthood. The lower relevance of away-from-home meals among the elderly may be related to retiring from professional activities combined with less disposable income(Reference Muriel, Oldfield, Banks, Lessof, Nazroo, Rogers, Stafford and Steptoe41), declining health and mobility, and weaker out-of-home consumption habits(Reference Kant and Graubard4,Reference Adams, Goffe and Brown42) .
Education and income have been shown to be important to the contribution of foods eaten away from home to energy intake. In the UK, Ziauddeen et al. (Reference Ziauddeen, Page and Penney23,Reference Ziauddeen, Almiron-Roig and Penney43) found that both children/adolescents and adults/elderly from lower-income quintiles were more likely to eat their meals at home. In our study, less-educated individuals consumed more meals at home justified by the tendency to be less well-off and for that reason spend less in out-of-home meals than highly educated individuals. Out of home, they did it more frequently in restaurants than at school or work. The educational attainment of these individuals can influence the place to eat out of home, whether due to unemployment or nutritional literacy. Also, they are more prone to have poor nutritional knowledge and are less likely to believe in the relationship between diet and health; subsequently, they might have fewer healthy dietary intakes(Reference Miura and Turrell44), purchasing fast food and consuming more frequently take-away food(Reference Miura, Giskes and Turrell45,Reference Wolfson, Leung and Richardson46) , also attractive given its low price. A higher percentage of high-educated individuals belonged to the School or Work category. Working patterns can relate to meal sourcing since among employed parents and individuals with longer working hours or volatile schedules, alternatives to home-cooked meals are preferred due to time pressures(Reference Celnik, Gillespie and Lean5,Reference Devine, Farrell and Blake6) .
Our research also found that the percentage of at-home meals is similar between regions, although lower in the predominantly urban areas. Results from the latest national survey to the household expenses (IDEF 2015/2016)(47,Reference Moreira48) showed an inverse relation between the degree of urbanisation and at-home food consumption, and that expenses with out-of-home food consumption were higher in predominantly urban and mostly urban areas comparing with rural areas. There are some important age and education differences since urban areas are commonly populated by younger and higher educated individuals, unlike predominantly rural areas which are mainly occupied by the elderly. About 30 % of the Portuguese population live in rural areas are over 65 years old and about 9 % over 80 years old or more(Reference Moreira48). Simultaneously, there is a lower frequency of working individuals among the elderly. The lower proportion of individuals in the School or Work category in rural areas and higher in Restaurants and Other Places could be partially explained by the lack of companies or schools able to have functional food services and the availability of supermarkets or retail stores with foods ready to cook. So, professionally active individuals may have to resort to cafes, bakeries, minimarkets or small restaurants that serve meals at very low prices that come as an advantage when comparing to the cost of cooking at home.
Children and adolescents
Compared with the Home category, the School or Work category was characterised by a higher consumption of fruit, vegetables and pulses and cereals and starchy tubers, which explains the higher fibre intake in this pattern. Similar results were reported among preschool children in Portugal(Reference Moreira, Severo and Oliveira22) and children and adolescents in the UK(Reference Ziauddeen, Page and Penney23). Being in the School or Work category was also linked to higher Na intake in these population groups which can be explained through the consumption of soup and bread(Reference Moreira, Severo and Oliveira22,Reference Lopes, Torres and Oliveira49,Reference Araújo, Severo and Lopes50) , important salt contributors in the Portuguese population. Furthermore, adolescents, when compared with children, had higher daily intakes of sugar-sweetened beverages, sugar and honey and sweets at School, probably explaining why a negative association with intake of free sugars was observed in children only. As observed in other studies and in our sample, food environments in schools and their impacts on dietary choices may be different for younger and older children(Reference Ziauddeen, Page and Penney23,Reference Palla, Chapman and Beh51) . It might be more feasible for children to have a healthier diet at school than at home, a result further confirmed by the positive association found between being classified in the School or Work category and the HES (but not in adolescents). This is probably due to public policies, such as School Scheme, which distributes fruits and vegetables in preschools and first-cycle students(52). Adolescents are more autonomous in their food choices and are more vulnerable to the food environment they are in, which influences their dietary choices through differences in availability and access to foods(Reference Nelson, Nicholas and Suleiman53–Reference Patterson, Risby and Chan55). Also, healthy foods are more expensive, a factor that often weighs more than the nutritional value of foods at the moment of purchase(Reference Khan, Powell and Wada56). The sale of unhealthy foods outside school (e.g. ultra-processed foods) has been documented in previous studies(Reference Andrade, da Costa Louzada and Azeredo57,Reference Azeredo, de Rezende and Canella58) that suggested that an unhealthy school neighbourhood may contribute to poorer dietary choices.
The Restaurant and Other Places pattern was associated with a poorer diet quality, supported with positive associations for intakes of salty snacks and sugar-sweetened beverages in both groups and of red and processed meats and sweets in adolescents. As a result, there were as well, positive associations with intakes of total energy, fat, SFA, trans-fatty acids, Na and free sugars. In Portuguese preschool children(Reference Moreira, Severo and Oliveira22) and by using a similar analytical approach, it was reported the lowest dietary adequacy score for children classified in the Other Out of Home pattern. A study with Irish children aged 5–12 years, that considered eating location as the place where food was prepared or obtained, showed that there was a higher percentage of energy from fat out of home, comparing with home(Reference Burke, McCarthy and O’Neill59). Authors observed that mothers are more lenient regarding their child’s food choices when eating out in restaurants(Reference Kasparian, Mann and Serrano60), suggesting the need for healthier menu options and educational strategies.
Both children and adolescents in the Other Homes category presented lower HES, but it was only significant in adolescents. Adolescents had higher fat, SFA and Na intake. Peer influence has been shown to contribute to higher consumption of fast food in adolescents of poor neighbourhoods(Reference Patterson, Risby and Chan55) and of snacks high in solid fats and added sugars at friend’s homes(Reference Cohen, Ghosh-Dastidar and Beckman61). Data on social facilitation, that is, eating more in the presence of others, would be an important factor to consider in adolescents, given the effects of peer pressure on their food choices(Reference Francis, Martin and Costa62).
Adults and elderly
Comparing to Home, both adults and elderly had a negative association with the HES regarding out-of-home eating patterns. A vast number of studies, mainly performed with adults, have previously confirmed a worse diet quality when eating at restaurants(Reference Wellard-Cole, Davies and Allman-Farinelli37,Reference Auchincloss, Li and Moore63) . European multicentre studies conducted on differences of dietary intake with food consumption locations among adults over 35 years of age also found beverages, sugar, desserts, sweet and savoury bakery products to be consumed more out of home than at home(Reference Orfanos, Naska and Trichopoulos3,Reference Naska, Katsoulis and Orfanos34) . Unhealthy dietary choices made out of home, particularly in commercial food service establishments, can be driven by several factors: celebrating special occasions, in which taste preferences or the pleasure of eating can often surpass health eating considerations, eating socially, which may translate into increases portion sizes, and variety-seeking, which may result both in unusual and more energy-dense food choices, as well as larger portion sizes(Reference Clendenen, Herman and Polivy64). Results of the present study further support previous findings about a higher consumption of alcohol taking place out of home(Reference Orfanos, Naska and Trichopoulos3,Reference Bezerra, Junior and Pereira65) . Despite wine being the most common alcoholic beverage in Portuguese adults(Reference Lopes, Torres and Oliveira49) and more frequently consumed in Restaurants and Other Places category than at Home, beer consumption was two to three times greater than in the Home. Bento et al.(Reference Bento, Gonçalves and Cordeiro66) reported a reversal of the relative availability of popular alcoholic beverages in Portugal since 1988, with beer gradually taking the place of wine.
In adults, the higher consumption of sugar-sweetened beverages and sweets probably explains the equally higher intake of free sugars in the School or Work category. In the UK, non-milk extrinsic sugars, also known as free sugars, were consumed more at work than at home, a trend that remained from young adults (19–23 years old) to older adults (50–64 years old)(Reference Ziauddeen, Almiron-Roig and Penney43). Tea and coffee have been previously documented as foods most eaten out of home among European adults, contributing both in quantity and energy, on account of the added sugar(Reference Orfanos, Naska and Trichopoulos3). The higher consumption of white meat, fish and eggs, red and processed meats and sweets possibly contributed to the association between higher intake of SFA and this category. Other studies consistently show that eating at work contributes to higher energy intakes through higher fat and carbohydrates intake(Reference Orfanos, Naska and Rodrigues18,Reference O’Dwyer, Gibney and Burke67) . Consumption at work v. at home can vary with sex, as found in a Norwegian study(Reference Myhre, Løken and Wandel68) in working adults, where the consumption of added sugars, meat and meat products, and sugar-sweetened beverages was higher in men than in women eating at work. However, we could not distinguish between the sources of consumption at work, so we cannot infer that this consumption came from workplace canteens. Still, eating at staff canteens should be promoted since this practice has been associated with higher compliance with nutrition guidelines, such as eating vegetables more than once daily(Reference Roos, Sarlio-Lähteenkorva and Lallukka69).
Information regarding consumption in other homes among adults remains scarce, and some authors choose to aggregate data in a single category such as ‘home’(Reference O’Dwyer, Gibney and Burke67,Reference Robson, Vadiveloo and Green70) .
Strengths and limitations
One main strength of the present study is the use of data from a representative sample of the Portuguese population and of a European harmonised and standardised methodology of dietary assessment(26). Despite non-participants being older and less educated, common in dietary surveys, these differences were not significant and even though the participation rate was low, results were similar to other national dietary surveys. Other additional strengths of this study are the inclusion of food and beverages and not only nutrients, the use of HES to characterise dietary adequacy and the assessment of a wide range of age groups, especially in children and adolescents which is relevant since many of the existent studies were performed with adults. Still, utilising data from the IAN-AF 2015/2016, which is approximately 7–8 years old, might not accurately depict the present circumstances in Portugal, particularly in light of the COVID-19 pandemic’s onset.
Comparisons with other studies might be challenged by the diversity of methodological approaches : evaluation of the whole food service sector or a particular element (take-away restaurants and fast-food restaurants); analysis of a full day or specific meals; different data collection methods or age groups; and different definitions of eating out. Describing food consumption according to eating location categories and not just outside/inside home provides a better comprehension of out-of-home food consumption. Most research regarding eating out practices considered foods prepared out of home irrespective of where the items were consumed and methods to classify instances of eating out are not homogeneous. In our study, eating location patterns were defined a priori and did not consider food that was prepared out of home and consumed at home, or the reverse. It also did not consider specific meals; but about 90 % of breakfast and dinner meals eaten by the Portuguese population were made at home and about 40 % of lunch and snacks meals were made out of home. The assessment of the source and/or preparation of the food instead of the place consumption would be an advantage as well as the analysis by specific meals. Despite the current study used the HECTOR consortium(Reference Orfanos, Naska and Gedrich71) core definition of ‘eating out’, including all meals, beverages and snacks consumed out of home, that was adopted in different multicentre European studies(Reference Orfanos, Naska and Rodrigues18), this can be masking the real prevalence of eating out as the place of preparation has a relevant role in the composition of the food independently of the place of consumption.
Additionally, we did not consider the number of people living in the household which could have a moderator effect specifically in differences between living alone or with someone. However, we did not consider this variable in our adjusted models because the expected correlation between living alone and age is high and could result in collinearity.
Conclusions
In the present study, eating location patterns apart from Home were strongly associated with higher energy intakes and specifically Restaurants and other places, with worse dietary adequacy. One important finding is that among children, it is possible to have a better diet adequacy within the school environment than at home, which demystifies the premise that eating out has necessarily a negative impact on the diet adequacy.
Given that children and adolescents do most of their out-of-home meals at school, food and nutrition policies regarding these groups should consider the role of the school food environment and its surroundings. Among adults, there is a growing acknowledgement that the workplace setting could have a significant impact on health given the contribution of meals to overall diet. Promoting healthy diets in the workplace can benefit individuals, employers and society since diet-related burdens such as obesity are related to increased sickness absence and absenteeism in employees, injuries at work and compensation claims.
The present analysis give light on the issues regarding out of consumption in Portugal, contributing to the current scientific evidence in different age groups, highlighting the need to improve the supply of health-promoting products in commercial food service establishments and ultimately providing inputs for policymakers and caterers to continue an efficient planning and execution of effective public health policies.
Acknowledgements
The authors gratefully acknowledge the participants enrolled in IAN-AF 2015/2016, all members of the research team and the institutional support from the General Directorate of Health (DGS), the Regional Health Administration Departments, the Central Administration of the Health System (ACSS) and the European Food Safety Authority.
The IAN-AF 2015/2016 received funding from the EEA Grants Program, Public Health Initiatives (grant number: PT06-000088SI3), and this study was supported by the Fundação Francisco Manuel dos Santos (https://www.ffms.pt/en) throughout ‘How We Eat What We Eat – A Portrait of Meal Consumption in Portugal’ Project (grant number: 1042382FCEEINV328). The funding sources had no involvement in study design, the collection, analysis and interpretation of data, the writing of the report and the decision to submit the article for publication.
The author contributions are as follows: M. S. (email: mcsilva2208@gmail.com; ORCID-ID: 0000-0003-2552-0320) contributed to the conception and design of the study, data analysis, interpretation of the findings and wrote the manuscript; D. M. C. (email: danielamc@med.up.pt; ORCID-ID: 0000-0001-8886-3211) contributed to the analysis of data and writing – review and editing of the manuscript; M. C. C. R. (email: marianarei@fcna.up.pt; ORCID-ID: 0000-0001-8945-3708), M. S. (email: milton@ispup.up.pt; ORCID-ID: 0000-0002-5787-4871), A. I. A. C. (email: anacosta@ucp.pt; ORCID-ID: 0000-0001-6443-8229) and D. P. M. T. (email: dupamato@fcna.up.pt; ORCID-ID: 0000-0001-8960-2160) contributed to the conception and design of the study and writing – review and editing of the manuscript; S. S. P. R. (email: saraspr@fcna.up.pt; ORCID-ID: 0000-0003-0647-5018) and C. M. M. L. (email: carlal@med.up.pt; ORCID-ID: 0000-0003-1524-852X) contributed to the conception and design of the study, interpretation of the findings and writing – review and editing of the manuscript. All authors have read and approved the final manuscript.
The authors declare none.
The IAN-AF 2015/2016 was developed under the guidelines present in the Declaration of Helsinki and the national legislation. All personal information was kept in the necessary confidentiality. Procedures were approved by the National Commission for Data Protection, the Ethical Committee of the Institute of Public Health of the University of Porto, and the Ethical Committee of each Regional Health Administration. Participants or legal caregivers (for children and adolescents aged less than 18 years) signed a written informed consent, and for adolescents aged 10–17 years, both caregivers and participants signed the written informed consent. Identifiable information was treated separately and introduced in an exclusive database. All researchers signed a declaration of confidentiality and good practices.
Supplementary material
For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114524000990
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