In the latest decades, changes have been observed in the world food economy that have reflected changes in the population’s eating patterns(Reference Monteiro, Moubarac and Cannon1,2) . Such changes verified in the adult population(Reference Martins, Levy and Claro3,Reference Louzada, Martins and Canella4) are also already demonstrated in the young population, in which there is a substitution of foods and culinary preparations based on unprocessed or minimally processed foods by ready or precooked products for consumption, such as ultraprocessed food(Reference Moreno, Gottrand and Huybrechts5–Reference Neri, Martinez-Steele and Monteiro7).
In the NOVA food classification system, items are grouped according to the nature, purpose and degree of industrial processing, comprising four groups: unprocessed or minimally processed foods (e.g. rice, meat, fruits and vegetables), processed culinary ingredients (e.g. salt, sugar, and oils and fat), processed foods (e.g. bread and cheese) and ultraprocessed foods (e.g. soft drinks, ready frozen food and candies), the latter being rich in sugars, fats, Na, and low in fibre, micronutrients and proteins(Reference Kelly and Jacoby8).
Adolescence is a phase characterised by several biopsychosocial and behavioural changes, thus becoming a period in which adolescents may be more vulnerable to the presence of risky behaviours which have health implications, such as the emergence of non-communicable diseases(9,10) .
Studies conducted with adolescents have shown that ultraprocessed food consumption was associated with family income and parents’ education level(Reference Enes, Camargo and Justino6,Reference Maia, Silva and Santos11,Reference D’ávila and Kirsten12) ; physical inactivity(Reference Souza, Lima and Fernandes13); sedentary behaviour, especially in individuals with high screen time(Reference Costa, Flores and Wendt14); culminating in excess of weight(Reference Canella, Levy and Martins15–Reference Askari, Heshmati and Shahinfar18), metabolic(Reference Tavares, Fonseca and Garcia Rosa19) and blood pressure alterations(Reference Payab, Roya and Mostafa20). Although diseases appear mainly in adulthood, their precursors can still be present during childhood and adolescence(Reference Schommer, Barbiero and Cesa21,Reference Enes and Silva22) .
Considering the ongoing increase in the energy percentage (E %) of the diet from ultraprocessed foods(Reference Monteiro, Moubarac and Cannon1–Reference Martins, Levy and Claro3,23) and its implications for health(Reference Askari, Heshmati and Shahinfar18,Reference Chen, Zhang and Yang24) , this study contributes to the existing literature, showing the association with sociodemographic, anthropometric, biochemical, clinical and behavioural characteristics, in contrast to other studies that work with these variables separately(Reference Maia, Silva and Santos11,Reference Costa, Flores and Wendt14,Reference Payab, Roya and Mostafa20,Reference Martins, Jarret and Crawley25–Reference Martins, Watanabe and Silva27) . This study is relevant because it contributes effective strategies for promoting healthy eating habits in adolescence and is also important because it deals with the different realities of the Brazilian population, since Brazil is a country with continental dimensions.
Thus, the aim of this study was to evaluate the energetic contribution according to the degree of industrial food processing and its association with sociodemographic, anthropometric, biochemical, clinical and behavioural characteristics in Brazilian adolescents.
Methods
Data source, population and sampling
This is a cross-sectional study based on data from the Adolescent Lifestyle Study (Portuguese acronym EVA-JF Study), a representative survey of adolescents from public schools in Juiz de Fora, Minas Gerais, Brazil.
The study included adolescents, aged 14–19 years, of both sexes, regularly enrolled in schools located in the urban area of the municipality. The sample calculation (n 790) was performed based on the number of students enrolled in basic education in the years 2018 and 2019, referring to the morning classes of the 9th grade of elementary school and 1st, 2nd and 3rd year of high school, according to the Brazilian educational system, and the prevalence of obesity among adolescents of 8·0 %(10,Reference Bloch, Klein and Szklo28,29) , accuracy around 2·0 % of prevalence, se of 1·0 %, CI of 95 % and subject loss of 20 %. The participants were 835 adolescents. Considering the lack of information about food consumption data, the final sample consisted of 804 adolescents.
The selection of participants was carried out at random, stratified and proportional by administrative regions, schools, school years, classes and sex. Data collection was carried out by a team trained at the educational institutions themselves, in the morning, from May 2018 to May 2019. Further details on the sampling, selection of participants and methodology can be obtained in Neves et al. (Reference Neves, Fontes and Pereira30).
Food consumption and classification
Food consumption data were obtained by applying two 24-h dietary recalls, with an interval of approximately 1 week, on alternate weekdays. Both recalls were applied in person by trained researchers using the multiple-pass method(Reference Conway, Ingwersen and Vinyard31). A food photo manual containing examples of portion sizes, serving utensils and household measures was used, helping to estimate the quantities consumed, as well as providing more accurate information on food consumption(Reference Zaboto32).
Energy intake (kcal) was estimated using a table of food consumption(33) and nutritional labels, when necessary, with foods classified according to the extent and purpose of industrial processing, according to the NOVA food classification system, proposed by Monteiro et al. (Reference Monteiro, Cannon and Levy34,Reference Monteiro, Cannon and Lawrence35) . Foods were divided into three groups: (a) unprocessed or minimally processed foods, culinary ingredients and preparations based on these foods (e.g. rice, beans, fruits, milk, salt and oils); (b) processed foods (e.g. breads and processed cheeses) and (c) ultraprocessed foods (e.g. soft drinks, salted crackers, filled cookies, sweeties, frozen foods). Culinary ingredients were clustered with unprocessed or minimally processed foods, since they are rarely used in isolation, but generally used in culinary preparations. In addition, all reported culinary preparations had their respective foods listed and later classified into the groups to which they belonged. Supplement 1 presents the main foods and/or food groups consumed by adolescents in the study.
Subsequently, the usual diet was estimated using the Multiple Source Method version 1.0.1 (German Department of Epidemiology of the Potsdam-Rehbrücke Institute of Human Nutrition, Germany), which can estimate the usual intake of each individual by means of adjustment based on 24-h dietary recalls, attenuating the intra-individual variance. The outcome variable was described using the energy percentage (E %) of each food group.
Sociodemographic and anthropometric variables
Information about age, sex, skin colour and educational level of the legal guardians was obtained through interviews using structured questionnaires. The socio-economic status was verified using the Brazilian Criterion of Economic Classification(36), defining the economic classes at A1, A2, B1, B2, C1, C2, D and E, in decreasing order of purchasing power.
For anthropometric analysis, weight and body fat were measured using a bipolar bioelectrical impedance scale (Tanita Ironman®, model BC-553, Tanita Corp.), with participants standing upright in the centre of platform, barefoot and wearing light clothing(Reference Lohman, Roche and Martorell37). Height was determined using a portable stadiometer (Alturexata®), with participants with their backs to the marker, in an upright position, barefoot and with their feet joined at the ankles(Reference Lohman, Roche and Martorell37). The BMI-for-age was defined by the Z-score, according to the WHO(Reference de Onis, Onyango and Borghi38) criteria. Neck circumference was measured using an inelastic tape (Sanny®, American Medical Ltda), in the middle cervical spine and the mid-anterior region, perpendicular to the longitudinal axis, at the level below the laryngeal prominence(Reference Ben-Noun and Laor39). Waist circumference was measured midway between the lowest rib and the iliac crest with inelastic tape(Reference McCarthy, Jarret and Crawley40,41) .
Biochemical, clinical and behavioural variables
For the evaluation of biochemical parameters, blood samples were collected after fasting for 8–12 h to measure total cholesterol, cholesterol associated with HDL, cholesterol associated with LDL and TAG, using the reference values of the Brazilian Society of Cardiology(Reference Simão, Precoma and Andrade42,Reference Faludi, Izar and Saraiva43) by age group, and fasting blood glucose, classified according to the criteria established by the American Diabetes Association(44), also according to age.
Blood pressure levels were measured using a digital oscillometric device (Omron® 705-IT, Omron Healthcare Inc.) with an adjustable cuff for the perimeter of the arm. The values of systolic blood pressure and diastolic blood pressure were classified by percentiles according to sex, age and height, according to the recommendations of the Brazilian Society of Cardiology(Reference Malachias, Souza and Plavnik45), and for those between 18 and 19 years of age, the classifications recommended for adults were used(Reference Malachias, Souza and Plavnik46).
The practice of physical activity in the last 12 months was assessed using a questionnaire, in which the type of exercise, frequency and time spent in a regular week were reported(Reference Matsudo, Araújo and Matsudo47,Reference Guedes, Lopes and Guedes48) . Participants who reported practicing ≥420 min/week were classified as physically active(49). Furthermore, information was obtained regarding screen time (watching movies, soap operas, playing video games, and using a smartphone, tablet or computer); the information was obtained using the cut-off point of the American Academy of Pediatrics(50), which recommends 2 h as the maximum daily limit.
Statistical analysis
For statistical analysis, the normality of continuous variables was initially verified using the Kolmogorov–Smirnov test. The sociodemographic, anthropometric, biochemical, clinical and behavioural characteristics of the sample were described using measures of central tendency (mean and sd). For quantitative variables, absolute and relative frequencies (percentages and absolute numbers) were used.
An analysis was performed using linear regression models adjusted between the E % from the consumption of each food group, according to NOVA, and the sociodemographic, anthropometric, biochemical, clinical and behavioural variables. The level of education of the legal guardians was described by years of study, and screen time and regular physical activity using the daily average (in minutes), including weekdays and weekends. For the analysis of the association between sociodemographic and behavioural variables, the E % from the consumption of each food group was used as the dependent variable, whereas the analysis with anthropometric, biochemical and clinical variables, the E % from the consumption of each food group was used as an independent variable. Subsequently, the analyses were adjusted for sex, age and skin colour. For all analyses, a 95 % CI and a 5 % significance level were taken into account. The data were analysed using the Statistical Package for the Social Sciences (SPSS) version 20.0 statistical software.
Results
Population characteristics
The sample consisted of 804 adolescents with a mean age of 16·1 ± 1·2 years. Females comprised 57·7 % (n 462) of the evaluated students. Regarding skin colour, 62·4 % (n 497) declared themselves as black or brown and 46·9 % (n 377) presented low income (including classes C1, C2, D–E) from the Brazilian Criterion of Economic Classification. Regarding maternal schooling, 75·2 % (n 548) of the students reported that their mothers had more than 8 years of education.
Most of the sample showed normal weight (70·0 %, n 561) and normal blood pressure levels (84·4 %, n 677). The practice of regular physical activity in the last 12 months was reported by 55·3 % (n 445); however, only 16·5 % (n 133) engaged in exercise for ≥420 min/week. Regarding sedentary behaviour, 91·8 % (n 738) of the adolescents had a daily screen time of more than 2 h. Table 1 shows the sociodemographic, anthropometric, biochemical, clinical and behavioural characteristics of the sample.
Table 1 Sociodemographic, anthropometric, biochemical, clinical and behavioural characteristics, EVA-JF study, Brazil, 2018–2019
SBP, systolic blood pressure; DBP, diastolic blood pressure.
* Daily exposure time considering activities such as watching movies, soap operas, series, playing video games, and using smartphones, tablets or computers for other purposes.
† Regular practice of sports or physical exercise at home, on the street, at a square, park, club, gym or sports school considering the last 12 months.
Distribution of total energy by food group
The average energetic intake of the sample was 8949·95 ± 2005·39 kJ. Regarding the degree of industrial processing, the mean E % of the contribution of unprocessed or minimally processed foods and preparations based on these foods was 43·1 % (95 % CI 42·4, 43·8), of processed foods 11·0 % (95 % CI 10·7, 11·3) and 45·9 % (95 % CI 45·1, 46·7) for ultraprocessed foods.
Associations of sociodemographic and behavioural variables with food consumption
In linear regression models (Table 2), when comparing the E % of contribution with sociodemographic and behavioural characteristics, a negative association was observed between the classification score of the socio-economic stratum and unprocessed foods after adjustment for sex and skin colour (adjusted β = −0·093; P = 0·032) and positive with ultraprocessed foods, with and without adjustments (crude β = 0·089; P = 0·047; adjusted β = 0·118; P = 0·011), as well as screen time with all food groups, unprocessed or minimally processed foods, culinary ingredients and preparations based on these foods (crude β = −0·233; P = 0·047; adjusted β = −0·247; P = 0·036), processed foods (crude β = −0·114; P = 0·025; adjusted β = −0·128; P = 0·011) and ultraprocessed foods (crude β = 0·347; P = 0·006; adjusted β = 0·375; P = 0·003).
Table 2 Linear regression models, association of food consumption according to degree of industrial processing and sociodemographic and behavioural characteristics, EVA-JF study, Brazil, 2018–2019
* Or minimally processed, culinary ingredients and preparations based on these foods.
† Adjusted for sex.
‡ Score obtained from the Brazilian Criterion of Economic Classification (BCEC).
§ Adjusted for sex and skin colour.
|| Or female legal guardian.
¶ Or male legal guardian.
** Time spent on sports or physical activities in a typical week.
†† Adjusted for sex and age.
‡‡ Daily exposure time considering activities such as watching movies, soap operas, television shows, playing video games, and using smartphone, tablet or computer for other purposes.
Associations of anthropometric, biochemical and clinical variables with food consumption
Regarding anthropometric characteristics (Table 3), a negative association was observed between BMI and consumption of ultraprocessed foods after adjustment for sex and age (adjusted β = −0·029; P = 0·025). Furthermore, there was a positive association between the neck circumference and the group of unprocessed or minimally processed foods, remaining significant after adjustment for sex and age (crude β = 0·023; P = 0·034; adjusted β = 0·017; P = 0·049), and a negative association with the consumption of ultraprocessed foods, in both models (crude β = −0·031; P = 0·002; adjusted β = −0·017; P = 0·028). No association was found with the other anthropometric variables.
Table 3 Linear regression models, association of food consumption according to degree of industrial processing and anthropometric characteristics, EVA-JF study, Brazil, 2018–2019
* Or minimally processed, culinary ingredients and preparations based on these foods.
† Adjusted for sex and age.
Regarding biochemical and clinical characteristics (Table 4), there was an inverse association between total cholesterol and unprocessed or minimally processed foods (crude β = −0·262; P = 0·019) and a positive one with ultraprocessed foods (crude β = 0·228; P = 0·029). However, after adjustments, they did not remain significant. An association was also observed in the same directions between the HDL-cholesterol fraction with the same food groups, with and without adjustments, for unprocessed foods (crude β = −0·178; P = 0·001; adjusted β = −0·156; P = 0·003) and ultraprocessed foods (crude β = 0·175; P = 0·000; adjusted β = 0·150; P = 0·002).
Table 4 Linear regression models, association of food consumption according to degree of industrial processing and biochemical and clinical characteristics, EVA-JF study, Brazil, 2018–2019
TC, total cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure.
* Or minimally processed, culinary ingredients and preparations based on these foods.
† Adjusted for sex and age.
Discussion
The results of the present study showed a high E % of contribution from ultraprocessed foods. Furthermore, an association was observed between food consumption, according to the degree of industrial processing, and socio-economic stratum, screen time, BMI, neck circumference and serum HDL-cholesterol levels.
The E % from ultraprocessed foods found in this study was similar to the one observed by D’ávila and Kirsten(Reference D’ávila and Kirsten12), in a Brazilian municipality, representing 49·2 % of the total daily energy value. Similarly, in another study conducted with adolescents in the Northeast of Brazil, a high E % of contribution from this group of foods (46·2 %)(Reference Melo, Costa and Santos26) was also demonstrated. Therefore, a high intake of ultraprocessed foods is observed in this age group.
When evaluating the association between food consumption and sociodemographic characteristics, an inverse association was found between the socio-economic stratum and unprocessed foods, demonstrating a higher consumption of these foods among those with less purchasing power. In contrast, a positive association was found with the consumption of ultraprocessed foods, with a higher E % of contribution of foods belonging to this group among those with a higher socio-economic stratum. A similar pattern was observed in a study conducted with Brazilian(Reference Enes, Camargo and Justino6) and Chilean(Reference Cediel, Reyes and Louzada51) adolescents; young individuals living in urban areas and with higher income had a higher intake of ultraprocessed foods, although inverse associations were also found(Reference D’ávila and Kirsten12).
Several factors can influence food choices, such as physiological, behavioural, social and economic, among others. Unlike what we find in relation to income, there was no association between food consumption and education level of parents or guardians. Sociodemographic factors such as higher income and education allow better access to information and a more varied diet; however, this condition is not always related to adequate food choices(Reference Enes, Camargo and Justino6,Reference Leal, Paiva and Sousa52) . According to data from the Household Budget Survey (POF 2017–2018), carried out in Brazil, the total energetic intake of ultraprocessed foods in the diet tended to increase with the increase in income, that is, families that had higher incomes, purchased twice the amount of ultraprocessed food compared to families with lower incomes (24·7 % of total energies v. 12·5 %, respectively)(23).
Sociocultural changes over the years have shown a decrease in the time spent on food preparation and the consequent increase in consumption of ultraprocessed foods. Some studies point out the lack of time due to work, family and other daily tasks as factors for such changes(53,Reference Campos54) . The present study investigates the food consumption of adolescents and potential negative influences on their consumption. Despite having autonomy over their own food choices, aspects related to family issues, such as the acquisition of food at home, the work situation of parents or guardians, the availability of time for preparing meals, among others, should be considered(Reference Villa, Silva and Santos55).
The daily exposure time to screens was also associated with the degree of industrial food processing, showing an inverse correlation with the consumption of unprocessed or minimally processed foods and processed foods, and a positive correlation with the consumption of ultraprocessed foods. Data from the latest National Adolescent School-based Health Survey, carried out in 2009 and 2015, showed high prevalence of sedentary behaviour (79·5 % and 68·1 %, respectively) among the evaluated students, with the latter showing that the higher the number of hours in front of screens, the higher the prevalence of daily consumption of ultraprocessed foods (P < 0·001)(Reference Costa, Flores and Wendt14,Reference Malta, Sardinha and Mendes56) . This behaviour can be attributed to the greater practicality of consuming ultraprocessed foods, since they do not require preparation time and can be consumed while watching television, using tablets and playing video games. Furthermore, these means of communication are vehicles for advertisements and advertising campaigns that tend to favour inappropriate food consumption(Reference Mallarino, Gómez and González-Zapata57,Reference Monteiro, Cannon and Moubarac58) .
Regarding anthropometric variables, only BMI and neck circumference were associated with food consumption by degree of industrial processing. There was a relationship between increase of unprocessed food consumption and the increase in these anthropometric parameters, and inverse relation with ultraprocessed food consumption. Although no association was found with weight and waist circumference, the same pattern of consumption can be observed. This inverse relationship of anthropometric parameters with ultraprocessed foods may have occurred due to the fact that the majority of the sample had appropriate weight. Unlike other studies that find inverse associations(Reference Louzada, Baraldi and Steele17,Reference Juul, Martinez-Steele and Parekh59) , the higher consumption of unprocessed foods and less processed foods in the present study can be attributed to a possible change in eating behaviour due to the presence of overweight in these individuals, called reverse causality. However, due to the characteristic of the design of the present study, it will not be possible to confirm this relationship.
Associations with biochemical parameters were also found in this study. There was a negative correlation for unprocessed or minimally processed foods and preparations based on these foods with total cholesterol, and a positive correlation with ultraprocessed foods, so that the serum levels decreased as the percentage of contribution of the first group increased and were directly proportional to the ultraprocessed foods. Corroborating our findings, other studies have also found positive associations(Reference Dishchekenian, Escrivão and Palma60,Reference Ferreira, Vasconcelos and Santos61) . However, after adjustments for sex and age, they did not remain significant.
The same pattern was also observed with the HDL-cholesterol fraction, showing associations with and without adjustments according to the degree of industrial processing. However, the results of this study differ from those found in other studies, in which the consumption of unprocessed foods favours the increase of HDL-cholesterol levels, while the consumption of ultraprocessed foods favours its reduction, the latter being a risk factor for the development of CVD in adulthood(Reference Dishchekenian, Escrivão and Palma60,Reference Ambrosini, Huang and Mori62,Reference Cunha, Costa and Oliveira63) . Only one study carried out in India, with women over 35 years old, also found a positive association between dietary pattern composed of saturated, hydrogenated fats, sweet condiments, fish and refined grains and serum levels of HDL-cholesterol(Reference Ganguli, Das and Saha64). The authors relate the finding to the greater presence of fats and lower carbohydrate content in this pattern, which may have favoured the increase in HDL-cholesterol.
In our sample, 97 % of adolescents presented adequate HDL-cholesterol levels and high mean values, different from other studies, where lower prevalence of adequate HDL-cholesterol levels was observed, such as in the Study of Cardiovascular Risks in Adolescents (ERICA), in 53·2 % of the sample(Reference Neto-Faria, Bento and Baena65) and in the study carried out by Martins et al. (Reference Martins, Watanabe and Silva27), in which 47·7 % of those evaluated had adequate serum HDL-cholesterol levels. A possible explanation for the association we found is that with an increase in the consumption of ultraprocessed foods, there is an increase in serum HDL-cholesterol levels, to a level similar to adolescents with lower consumption of ultraprocessed foods.
Another finding that draws attention in the study was the negative association between systolic blood pressure and consumption of ultraprocessed foods. However, after adjusting the model, it did not remain significant. Other studies carried out with children and adolescents show that the Western pattern, with the presence of processed foods, ready-made foods, sugary drinks, as they have high amounts of Na and energies from simple fats and carbohydrates, and low in proteins and fibres, favour the elevations in blood pressure levels(Reference Shang, Li and Liu66,Reference Chen, Zhu and Gutin67) . As it is a chronic change, changes in blood pressure levels may not yet exist in our population.
The study has some limitations. The use of only two 24-h recalls, both performed on weekdays, may not represent the individuals’ usual intake. However, the inclusion of a third recall was not possible and to minimise this existing intrapersonal variation, correction was then used, using the Multiple Source Method. Another limitation is that the sample is only representative of adolescents from public schools, which impedes generalising the results to Brazilian adolescents from private schools of the same age group. The cross-sectional study design may not be a limitation, since it does not allow inferences about causality; however, it helps in generating hypotheses about the causes of health problems. The study was carried out with a poorly studied population and with a representative sample and sought to analyse the relationship between the consumption of food according to its processing and several variables, which can contribute in the implementation of preventive interventions and the promotion of healthy habits in adolescence.
Conclusions
The present study found that in the diet of adolescents in Brazilian public schools, the E % contribution of ultraprocessed foods is high. Moreover, associations were found between socio-economic stratum, screen time, BMI, neck circumference and serum HDL-cholesterol levels and food consumption according to the degree of industrial food processing. Therefore, the importance is highlighted for food and nutrition education activities in the school environment to guide appropriate food choices, together with effective public policies that promote actions and interventions that make this age group aware of adopting healthy lifestyle habits, such as engaging in regular physical activity and reducing screen time. In addition, changes in food labels, making them clearer and more understandable to the population, as well as the implementation of strict inspection policies on ultraprocessed foods, are necessary and urgent measures.
Acknowledgements
Acknowledgements: The authors would like to thank the Minas Gerais Research Foundation – FAPEMIG and Coordination for the Improvement of Higher Education Personnel – CAPES for the financial support and granting of scholarships, to the master’s students involved in the operationalisation of data collection, to the technicians responsible for the Nutritional Surveillance and Assessment Laboratory and the Experimental Nutrition Laboratory of UFJF, to the scholarship students and volunteers, who were present in the field, the schools and the students who participated in the research. Financial support: This work was supported by the Minas Gerais Research Foundation – FAPEMIG (grant number for R.M.S.O.: APQ-02891-18; grant number for G.L.L.M.-C.: APQ-02643-15; and doctoral scholarship for F.S.N.); Coordination for the Improvement of Higher Education Personnel – CAPES (doctoral scholarship for V.S.F. and master’s scholarship for A.S.T.M.). Funders FAPEMIG and CAPES did not participate in the preparation, analysis or writing of this article. Conflict of interest: The authors have no conflicts of interest. Authorship: A.S.T.M. performed the statistical analysis and wrote the manuscript with contributions from all authors. A.P.B. and G.L.L.M.C. performed biochemical analyses. D.S.S. helped to estimate food consumption. F.S.N., E.R.F., M.P.N. and R.M.S.O. participated in the conception and design of this work and data analysis, as well as in the writing of the manuscript. V.S.F. conceived and designed the cross-sectional study and revised the article. A.P.C.C. conceived and designed the cross-sectional study, coordinated the project and revised the paper. All authors contributed critically to the discussion and interpretation of the data, reviewed and approved the final manuscript. Ethics of human subject participation: This study was conducted according to the guidelines established in the Declaration of Helsinki, and all procedures involving research participants were approved by the Human Research Ethics Committee of the Federal University of Juiz de Fora, under protocol number 3.412.539 (CAAE: 68601617.1.0000.5147). Written informed consent was obtained from all participants.
Supplementary material
For supplementary material accompanying this paper visit https://doi.org/10.1017/S1368980021000100
